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commit
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{
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"python.pythonPath": "C:\\Users\\Main-user\\anaconda3\\envs\\pyfinance\\python.exe"
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}
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@ -0,0 +1,444 @@
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# Python Cours Accéléré - Exercices \n",
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"\n",
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||||
"Cet exercice est facultatif, il permet de tester votre niveau Python. Les questions traitent au maximum le thème financier, mais ne vous attardez pas sur ces tâches, beaucoup d’entre elles n’ont aucune signification et n’ont pas de sens. Si vous trouvez cela extrêmement difficile, vous n'êtes probablement pas encore prêt pour le reste de ce cours et n'avez pas assez d'expérience en programmation pour continuer. Je vous suggère de prendre un autre cours plus orienté vers les débutants complets, tels que [Formation complète Python](https://www.udemy.com/course/python-datascience/?referralCode=9DB381B321631A0703C2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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||||
"## Exercices\n",
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||||
"\n",
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||||
"Répondez aux questions ou effectuez les exercices en gras ci-dessous, utilisez la méthode spécifique décrite le cas échéant."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Exercice #1\n",
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"\n",
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"Soit un prix donné price = 300, utilisez python pour trouver la raciné carrée du prix."
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]
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},
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||||
{
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||||
"cell_type": "code",
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"price = 300"
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]
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},
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{
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||||
"cell_type": "code",
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"execution_count": 2,
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||||
"metadata": {},
|
||||
"outputs": [
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||||
{
|
||||
"data": {
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||||
"text/plain": [
|
||||
"17.320508075688775"
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||||
]
|
||||
},
|
||||
"execution_count": 2,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
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||||
}
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||||
],
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"source": []
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},
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{
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||||
"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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||||
"outputs": [
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{
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"data": {
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"text/plain": [
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"17.320508075688775"
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||||
]
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},
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"execution_count": 3,
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"metadata": {},
|
||||
"output_type": "execute_result"
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}
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],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Exercice #2\n",
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"\n",
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"Soit la chaîne de caractères:\n",
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"\n",
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" stock_index = \"SP500\"\n",
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" \n",
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"Extraire '500' de la chaîne de caractères à l'aide de la méthode d'indexation."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"stock_index = \"SP500\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'500'"
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||||
]
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},
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"execution_count": 5,
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"metadata": {},
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||||
"output_type": "execute_result"
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||||
}
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||||
],
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||||
"source": []
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||||
},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Exercice #3\n",
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"\n",
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"** Soient les variables:**\n",
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"\n",
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" stock_index = \"SP500\"\n",
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" price = 300\n",
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"\n",
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||||
"** Utilisez .format() pour afficher la phrase suivante: **\n",
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||||
"\n",
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" Le SP500 est à 300 aujourd'hui."
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]
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||||
},
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||||
{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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||||
"outputs": [],
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"source": [
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"stock_index = \"SP500\"\n",
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"price = 300"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
|
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"Le SP500 est à 300 aujourd'hui\n"
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]
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}
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],
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"source": []
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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||||
"### Exercice #4\n",
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"\n",
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"** Soit la variable d'un dictionnaire imbriqué avec les listes imbriquées suivantes: **\n",
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"\n",
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" stock_info = {'sp500':{'today':300,'yesterday': 250}, 'info':['Time',[24,7,365]]}\n",
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" \n",
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"** Utilisez l'indexation et les clés correspondantes pour extraire les éléments suivants:**\n",
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"\n",
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||||
"* Le prix du SP500 d'hier (250)\n",
|
||||
"* Le nombre 365 imbriqué à l'intérieur d'une liste imnbriquée au niveau de la clé 'info'."
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]
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},
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{
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"cell_type": "code",
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||||
"execution_count": 8,
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": [
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||||
"stock_info = {'sp500':{'today':300,'yesterday': 250}, 'info':['Time',[24,7,365]]}"
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||||
]
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},
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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||||
"outputs": [
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||||
{
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||||
"data": {
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||||
"text/plain": [
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||||
"250"
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||||
]
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||||
},
|
||||
"execution_count": 9,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
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||||
}
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||||
],
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"source": []
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||||
},
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{
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||||
"cell_type": "code",
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||||
"execution_count": 10,
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||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
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||||
"data": {
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||||
"text/plain": [
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||||
"365"
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||||
]
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},
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"execution_count": 10,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
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||||
}
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||||
],
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||||
"source": []
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||||
},
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||||
{
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||||
"cell_type": "markdown",
|
||||
"metadata": {},
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||||
"source": [
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||||
"### Exercice #5\n",
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"\n",
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"** Soient des chaînes de caractères données avec cette forme où la dernière valeur source est toujours séparée par 2 tirets -- **\n",
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"\n",
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" \"PRICE:345.324:SOURCE--QUANDL\"\n",
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||||
" \n",
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||||
"**Créer une fonction nommée source_finder() qui retourne la source. Par exemple, la chaîne de caractères ci-dessus passée en argument dans la fonction devrait retourner \"QUANDL\"**"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 11,
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||||
"metadata": {},
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||||
"outputs": [],
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||||
"source": []
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 12,
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||||
"metadata": {},
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||||
"outputs": [
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||||
{
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||||
"data": {
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||||
"text/plain": [
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||||
"'QUANDL'"
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]
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||||
},
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||||
"execution_count": 12,
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||||
"metadata": {},
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||||
"output_type": "execute_result"
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||||
}
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||||
],
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||||
"source": [
|
||||
"source_finder(\"PRICE:345.324:SOURCE--QUANDL\")"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "markdown",
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||||
"metadata": {},
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||||
"source": [
|
||||
"### Exercice #6\n",
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||||
"\n",
|
||||
"** Créer une fonction nommée price_finder() qui retourne True si le mot 'prix' est dans la chaîne de caractères. Votre fonction doit fonctionner même si 'Prix' commence par une majuscule ou suivi directement d'une ponctuation ('prix!') **"
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||||
]
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||||
},
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||||
{
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||||
"cell_type": "code",
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||||
"execution_count": 13,
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||||
"metadata": {},
|
||||
"outputs": [],
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||||
"source": []
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||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_finder(\"Quel est le prix?\")"
|
||||
]
|
||||
},
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||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
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||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_finder(\"MEC, QUEL EST LE PRIX!!!\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
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||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_finder(\"Le prix est 300\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #7\n",
|
||||
"\n",
|
||||
"** Créer une fonction nommée count_price() qui compte le nombre de fois que le mot \"prix\" apparaît dans la chaîne de caractères. Prendre en compte la majuscule et si le mot \"prix\" est proche d'une ponctuation. **"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"s = \"Wow quel bon prix, très très bon Prix! J'ai dit prix 3 fois.\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"3"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"count_price(s)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #8\n",
|
||||
"\n",
|
||||
"**Créer une fonction nommée avg_price qui prend en paramètre une liste de nombres (prix d'action) et en calcule la moyenne (somme de tous les nombres divisée par le nombre d'éléments de la liste). Cela doit retourner un décimal float. **"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"4.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"avg_price([3,4,5])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon Travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,507 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Python Cours Accéléré - Solutions\n",
|
||||
"\n",
|
||||
"Cet exercice est facultatif, il permet de tester votre niveau Python. Les questions traitent au maximum le thème financier, mais ne vous attardez pas sur ces tâches, beaucoup d’entre elles n’ont aucune signification et n’ont pas de sens. Si vous trouvez cela extrêmement difficile, vous n'êtes probablement pas encore prêt pour le reste de ce cours et n'avez pas assez d'expérience en programmation pour continuer. Je vous suggère de prendre un autre cours plus orienté vers les débutants complets, tels que [Formation complète Python](https://www.udemy.com/course/python-datascience/?referralCode=9DB381B321631A0703C2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Exercices\n",
|
||||
"\n",
|
||||
"Répondez aux questions ou effectuez les exercices en gras ci-dessous, utilisez la méthode spécifique décrite le cas échéant."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #1\n",
|
||||
"\n",
|
||||
"Soit un prix donné price = 300, utilisez python pour trouver la raciné carrée du prix."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"price = 300"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"17.320508075688775"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price**(0.5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"17.320508075688775"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import math\n",
|
||||
"math.sqrt(price)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #2\n",
|
||||
"\n",
|
||||
"Soit la chaîne de caractères:\n",
|
||||
"\n",
|
||||
" stock_index = \"SP500\"\n",
|
||||
" \n",
|
||||
"Extraire '500' de la chaîne de caractères à l'aide de la méthode d'indexation."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"stock_index = \"SP500\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'500'"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stock_index[2:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #3\n",
|
||||
"\n",
|
||||
"** Soient les variables:**\n",
|
||||
"\n",
|
||||
" stock_index = \"SP500\"\n",
|
||||
" price = 300\n",
|
||||
"\n",
|
||||
"** Utilisez .format() pour afficher la phrase suivante: **\n",
|
||||
"\n",
|
||||
" Le SP500 est à 300 aujourd'hui."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"stock_index = \"SP500\"\n",
|
||||
"price = 300"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Le SP500 est à 300 aujourd'hui\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(\"Le {} est à {} aujourd'hui\".format(stock_index,price))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #4\n",
|
||||
"\n",
|
||||
"** Soit la variable d'un dictionnaire imbriqué avec les listes imbriquées suivantes: **\n",
|
||||
"\n",
|
||||
" stock_info = {'sp500':{'today':300,'yesterday': 250}, 'info':['Time',[24,7,365]]}\n",
|
||||
" \n",
|
||||
"** Utilisez l'indexation et les clés correspondantes pour extraire les éléments suivants:**\n",
|
||||
"\n",
|
||||
"* Le prix du SP500 d'hier (250)\n",
|
||||
"* Le nombre 365 imbriqué à l'intérieur d'une liste imnbriquée au niveau de la clé 'info'."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"stock_info = {'sp500':{'today':300,'yesterday': 250}, 'info':['Time',[24,7,365]]}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"dict_keys(['sp500', 'info'])"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stock_info.keys()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"250"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stock_info['sp500']['yesterday']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"365"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stock_info['info'][1][2]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #5\n",
|
||||
"\n",
|
||||
"** Soient des chaînes de caractères données avec cette forme où la dernière valeur source est toujours séparée par 2 tirets -- **\n",
|
||||
"\n",
|
||||
" \"PRICE:345.324:SOURCE--QUANDL\"\n",
|
||||
" \n",
|
||||
"**Créer une fonction nommée source_finder() qui retourne la source. Par exemple, la chaîne de caractères ci-dessus passée en argument dans la fonction devrait retourner \"QUANDL\"**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def source_finder(s):\n",
|
||||
" return s.split('--')[-1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'QUANDL'"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"source_finder(\"PRICE:345.324:SOURCE--QUANDL\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #6\n",
|
||||
"\n",
|
||||
"** Créer une fonction nommée price_finder() qui retourne True si le mot 'prix' est dans la chaîne de caractères. Votre fonction doit fonctionner même si 'Prix' commence par une majuscule ou suivi directement d'une ponctuation ('prix!') **"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def price_finder(s):\n",
|
||||
" return 'prix' in s.lower()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_finder(\"Quel est le prix?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_finder(\"MEC, QUEL EST LE PRIX!!!\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_finder(\"Le prix est 300\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #7\n",
|
||||
"\n",
|
||||
"** Créer une fonction nommée count_price() qui compte le nombre de fois que le mot \"prix\" apparaît dans la chaîne de caractères. Prendre en compte la majuscule et si le mot \"prix\" est proche d'une ponctuation. **"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def count_price(s):\n",
|
||||
" count = 0\n",
|
||||
" \n",
|
||||
" for word in s.lower().split():\n",
|
||||
" # Besoin d'utiliser in, == ne fonctionne pas avec la ponctuation\n",
|
||||
" if 'prix' in word:\n",
|
||||
" count = count + 1\n",
|
||||
" \n",
|
||||
" # Attention à l'indentation!\n",
|
||||
" return count"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def count_price(s):\n",
|
||||
" return s.lower().count('prix')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"s = \"Wow quel bon prix, très très bon Prix! J'ai dit prix 3 fois.\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"3"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"count_price(s)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #8\n",
|
||||
"\n",
|
||||
"**Créer une fonction nommée avg_price qui prend en paramètre une liste de nombres (prix d'action) et en calcule la moyenne (somme de tous les nombres divisée par le nombre d'éléments de la liste). Cela doit retourner un décimal float. **"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def avg_price(stocks):\n",
|
||||
" return sum(stocks)/len(stocks)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"4.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"avg_price([3,4,5])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon Travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,507 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Python Cours Accéléré - Solutions\n",
|
||||
"\n",
|
||||
"Cet exercice est facultatif, il permet de tester votre niveau Python. Les questions traitent au maximum le thème financier, mais ne vous attardez pas sur ces tâches, beaucoup d’entre elles n’ont aucune signification et n’ont pas de sens. Si vous trouvez cela extrêmement difficile, vous n'êtes probablement pas encore prêt pour le reste de ce cours et n'avez pas assez d'expérience en programmation pour continuer. Je vous suggère de prendre un autre cours plus orienté vers les débutants complets, tels que [Formation complète Python](https://www.udemy.com/course/python-datascience/?referralCode=9DB381B321631A0703C2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Exercices\n",
|
||||
"\n",
|
||||
"Répondez aux questions ou effectuez les exercices en gras ci-dessous, utilisez la méthode spécifique décrite le cas échéant."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #1\n",
|
||||
"\n",
|
||||
"Soit un prix donné price = 300, utilisez python pour trouver la raciné carrée du prix."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"price = 300"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"17.320508075688775"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price**(0.5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"17.320508075688775"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import math\n",
|
||||
"math.sqrt(price)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #2\n",
|
||||
"\n",
|
||||
"Soit la chaîne de caractères :\n",
|
||||
"\n",
|
||||
" stock_index = \"SP500\"\n",
|
||||
" \n",
|
||||
"Extraire '500' de la chaîne de caractères à l'aide de la méthode d'indexation."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"stock_index = \"SP500\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'500'"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stock_index[2:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #3\n",
|
||||
"\n",
|
||||
"**Soient les variables :**\n",
|
||||
"\n",
|
||||
" stock_index = \"SP500\"\n",
|
||||
" price = 300\n",
|
||||
"\n",
|
||||
"**Utilisez .format() pour afficher la phrase suivante :**\n",
|
||||
"\n",
|
||||
" Le SP500 est à 300 aujourd'hui."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"stock_index = \"SP500\"\n",
|
||||
"price = 300"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Le SP500 est à 300 aujourd'hui\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(\"Le {} est à {} aujourd'hui\".format(stock_index,price))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #4\n",
|
||||
"\n",
|
||||
"**Soit la variable d'un dictionnaire imbriqué avec les listes imbriquées suivantes :**\n",
|
||||
"\n",
|
||||
" stock_info = {'sp500':{'today':300,'yesterday': 250}, 'info':['Time',[24,7,365]]}\n",
|
||||
" \n",
|
||||
"**Utilisez l'indexation et les clés correspondantes pour extraire les éléments suivants :**\n",
|
||||
"\n",
|
||||
"* Le prix du SP500 d'hier (250)\n",
|
||||
"* Le nombre 365 imbriqué à l'intérieur d'une liste imnbriquée au niveau de la clé 'info'."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"stock_info = {'sp500':{'today':300,'yesterday': 250}, 'info':['Time',[24,7,365]]}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"dict_keys(['sp500', 'info'])"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stock_info.keys()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"250"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stock_info['sp500']['yesterday']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"365"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"stock_info['info'][1][2]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #5\n",
|
||||
"\n",
|
||||
"**Soient des chaînes de caractères données avec cette forme où la dernière valeur source est toujours séparée par 2 tirets --**\n",
|
||||
"\n",
|
||||
" \"PRICE:345.324:SOURCE--QUANDL\"\n",
|
||||
" \n",
|
||||
"**Créer une fonction nommée source_finder() qui retourne la source. Par exemple, la chaîne de caractères ci-dessus passée en argument dans la fonction devrait retourner \"QUANDL\"**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def source_finder(s):\n",
|
||||
" return s.split('--')[-1]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'QUANDL'"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"source_finder(\"PRICE:345.324:SOURCE--QUANDL\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #6\n",
|
||||
"\n",
|
||||
"**Créer une fonction nommée price_finder() qui retourne True si le mot 'prix' est dans la chaîne de caractères. Votre fonction doit fonctionner même si 'Prix' commence par une majuscule ou suivi directement d'une ponctuation ('prix!')**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def price_finder(s):\n",
|
||||
" return 'prix' in s.lower()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_finder(\"Quel est le prix?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_finder(\"MEC, QUEL EST LE PRIX!!!\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_finder(\"Le prix est 300\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #7\n",
|
||||
"\n",
|
||||
"**Créer une fonction nommée count_price() qui compte le nombre de fois que le mot \"prix\" apparaît dans la chaîne de caractères. Prendre en compte la majuscule et si le mot \"prix\" est proche d'une ponctuation.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def count_price(s):\n",
|
||||
" count = 0\n",
|
||||
" \n",
|
||||
" for word in s.lower().split():\n",
|
||||
" # Besoin d'utiliser in, == ne fonctionne pas avec la ponctuation\n",
|
||||
" if 'prix' in word:\n",
|
||||
" count = count + 1\n",
|
||||
" \n",
|
||||
" # Attention à l'indentation!\n",
|
||||
" return count"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def count_price(s):\n",
|
||||
" return s.lower().count('prix')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"s = \"Wow quel bon prix, très très bon Prix! J'ai dit prix 3 fois.\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"3"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"count_price(s)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #8\n",
|
||||
"\n",
|
||||
"**Créer une fonction nommée avg_price qui prend en paramètre une liste de nombres (prix d'action) et en calcule la moyenne (somme de tous les nombres divisée par le nombre d'éléments de la liste). Cela doit retourner un décimal float.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def avg_price(stocks):\n",
|
||||
" return sum(stocks)/len(stocks)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"4.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"avg_price([3,4,5])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon Travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,444 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Python Cours Accéléré - Exercices\n",
|
||||
"\n",
|
||||
"Cet exercice est facultatif, il permet de tester votre niveau Python. Les questions traitent au maximum le thème financier, mais ne vous attardez pas sur ces tâches, beaucoup d’entre elles n’ont aucune signification et n’ont pas de sens. Si vous trouvez cela extrêmement difficile, vous n'êtes probablement pas encore prêt pour le reste de ce cours et n'avez pas assez d'expérience en programmation pour continuer. Je vous suggère de prendre un autre cours plus orienté vers les débutants complets, tels que [Formation complète Python](https://www.udemy.com/course/python-datascience/?referralCode=9DB381B321631A0703C2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Exercices\n",
|
||||
"\n",
|
||||
"Répondez aux questions ou effectuez les exercices en gras ci-dessous, utilisez la méthode spécifique décrite le cas échéant."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #1\n",
|
||||
"\n",
|
||||
"Soit un prix donné price = 300, utilisez python pour trouver la raciné carrée du prix."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"price = 300"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"17.320508075688775"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"17.320508075688775"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #2\n",
|
||||
"\n",
|
||||
"Soit la chaîne de caractères :\n",
|
||||
"\n",
|
||||
" stock_index = \"SP500\"\n",
|
||||
" \n",
|
||||
"Extraire '500' de la chaîne de caractères à l'aide de la méthode d'indexation."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"stock_index = \"SP500\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'500'"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #3\n",
|
||||
"\n",
|
||||
"**Soient les variables :**\n",
|
||||
"\n",
|
||||
" stock_index = \"SP500\"\n",
|
||||
" price = 300\n",
|
||||
"\n",
|
||||
"**Utilisez .format() pour afficher la phrase suivante :**\n",
|
||||
"\n",
|
||||
" Le SP500 est à 300 aujourd'hui."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"stock_index = \"SP500\"\n",
|
||||
"price = 300"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Le SP500 est à 300 aujourd'hui\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #4\n",
|
||||
"\n",
|
||||
"**Soit la variable d'un dictionnaire imbriqué avec les listes imbriquées suivantes :**\n",
|
||||
"\n",
|
||||
" stock_info = {'sp500':{'today':300,'yesterday': 250}, 'info':['Time',[24,7,365]]}\n",
|
||||
" \n",
|
||||
"**Utilisez l'indexation et les clés correspondantes pour extraire les éléments suivants :**\n",
|
||||
"\n",
|
||||
"* Le prix du SP500 d'hier (250)\n",
|
||||
"* Le nombre 365 imbriqué à l'intérieur d'une liste imnbriquée au niveau de la clé 'info'."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"stock_info = {'sp500':{'today':300,'yesterday': 250}, 'info':['Time',[24,7,365]]}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"250"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"365"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #5\n",
|
||||
"\n",
|
||||
"**Soient des chaînes de caractères données avec cette forme où la dernière valeur source est toujours séparée par 2 tirets --**\n",
|
||||
"\n",
|
||||
" \"PRICE:345.324:SOURCE--QUANDL\"\n",
|
||||
" \n",
|
||||
"**Créer une fonction nommée source_finder() qui retourne la source. Par exemple, la chaîne de caractères ci-dessus passée en argument dans la fonction devrait retourner \"QUANDL\"**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"'QUANDL'"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"source_finder(\"PRICE:345.324:SOURCE--QUANDL\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #6\n",
|
||||
"\n",
|
||||
"**Créer une fonction nommée price_finder() qui retourne True si le mot 'prix' est dans la chaîne de caractères. Votre fonction doit fonctionner même si 'Prix' commence par une majuscule ou suivi directement d'une ponctuation ('prix!')**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_finder(\"Quel est le prix?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_finder(\"MEC, QUEL EST LE PRIX!!!\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"True"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"price_finder(\"Le prix est 300\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #7\n",
|
||||
"\n",
|
||||
"**Créer une fonction nommée count_price() qui compte le nombre de fois que le mot \"prix\" apparaît dans la chaîne de caractères. Prendre en compte la majuscule et si le mot \"prix\" est proche d'une ponctuation.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"s = \"Wow quel bon prix, très très bon Prix! J'ai dit prix 3 fois.\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"3"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"count_price(s)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exercice #8\n",
|
||||
"\n",
|
||||
"**Créer une fonction nommée avg_price qui prend en paramètre une liste de nombres (prix d'action) et en calcule la moyenne (somme de tous les nombres divisée par le nombre d'éléments de la liste). Cela doit retourner un décimal float.**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"4.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"avg_price([3,4,5])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon Travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,973 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"___\n",
|
||||
"\n",
|
||||
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
|
||||
"___\n",
|
||||
"<center>*Copyright Pierian Data 2017*</center>\n",
|
||||
"<center>*For more information, visit us at www.pieriandata.com*</center>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# NumPy \n",
|
||||
"\n",
|
||||
"NumPy (or Numpy) is a Linear Algebra Library for Python, the reason it is so important for Finance with Python is that almost all of the libraries in the PyData Ecosystem rely on NumPy as one of their main building blocks. Plus we will use it to generate data for our analysis examples later on!\n",
|
||||
"\n",
|
||||
"Numpy is also incredibly fast, as it has bindings to C libraries. For more info on why you would want to use Arrays instead of lists, check out this great [StackOverflow post](http://stackoverflow.com/questions/993984/why-numpy-instead-of-python-lists).\n",
|
||||
"\n",
|
||||
"We will only learn the basics of NumPy, to get started we need to install it!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Installation Instructions\n",
|
||||
"\n",
|
||||
"### NumPy is already included in your environment! You are good to go if you are using pyfinance env!\n",
|
||||
"\n",
|
||||
"_____\n",
|
||||
"##### For those not using the provided environment:\n",
|
||||
"\n",
|
||||
"**It is highly recommended you install Python using the Anaconda distribution to make sure all underlying dependencies (such as Linear Algebra libraries) all sync up with the use of a conda install. If you have Anaconda, install NumPy by going to your terminal or command prompt and typing:**\n",
|
||||
" \n",
|
||||
" conda install numpy\n",
|
||||
" \n",
|
||||
"**If you do not have Anaconda and can not install it, please refer to [Numpy's official documentation on various installation instructions.](http://docs.scipy.org/doc/numpy-1.10.1/user/install.html)**\n",
|
||||
"\n",
|
||||
"_____"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Using NumPy\n",
|
||||
"\n",
|
||||
"Once you've installed NumPy you can import it as a library:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Numpy has many built-in functions and capabilities. We won't cover them all but instead we will focus on some of the most important aspects of Numpy: vectors,arrays,matrices, and number generation. Let's start by discussing arrays.\n",
|
||||
"\n",
|
||||
"# Numpy Arrays\n",
|
||||
"\n",
|
||||
"NumPy arrays are the main way we will use Numpy throughout the course. Numpy arrays essentially come in two flavors: vectors and matrices. Vectors are strictly 1-d arrays and matrices are 2-d (but you should note a matrix can still have only one row or one column).\n",
|
||||
"\n",
|
||||
"Let's begin our introduction by exploring how to create NumPy arrays.\n",
|
||||
"\n",
|
||||
"## Creating NumPy Arrays\n",
|
||||
"\n",
|
||||
"### From a Python List\n",
|
||||
"\n",
|
||||
"We can create an array by directly converting a list or list of lists:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[1, 2, 3]"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"my_list = [1,2,3]\n",
|
||||
"my_list"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([1, 2, 3])"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.array(my_list)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"my_matrix = [[1,2,3],[4,5,6],[7,8,9]]\n",
|
||||
"my_matrix"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[1, 2, 3],\n",
|
||||
" [4, 5, 6],\n",
|
||||
" [7, 8, 9]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.array(my_matrix)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Built-in Methods\n",
|
||||
"\n",
|
||||
"There are lots of built-in ways to generate Arrays"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### arange\n",
|
||||
"\n",
|
||||
"Return evenly spaced values within a given interval."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.arange(0,10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 2, 4, 6, 8, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.arange(0,11,2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### zeros and ones\n",
|
||||
"\n",
|
||||
"Generate arrays of zeros or ones"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0., 0., 0.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.zeros(3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0., 0., 0., 0., 0.],\n",
|
||||
" [ 0., 0., 0., 0., 0.],\n",
|
||||
" [ 0., 0., 0., 0., 0.],\n",
|
||||
" [ 0., 0., 0., 0., 0.],\n",
|
||||
" [ 0., 0., 0., 0., 0.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.zeros((5,5))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1., 1., 1.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.ones(3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 1., 1., 1.],\n",
|
||||
" [ 1., 1., 1.],\n",
|
||||
" [ 1., 1., 1.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.ones((3,3))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### linspace\n",
|
||||
"Return evenly spaced numbers over a specified interval."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0., 5., 10.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.linspace(0,10,3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0. , 0.20408163, 0.40816327, 0.6122449 ,\n",
|
||||
" 0.81632653, 1.02040816, 1.2244898 , 1.42857143,\n",
|
||||
" 1.63265306, 1.83673469, 2.04081633, 2.24489796,\n",
|
||||
" 2.44897959, 2.65306122, 2.85714286, 3.06122449,\n",
|
||||
" 3.26530612, 3.46938776, 3.67346939, 3.87755102,\n",
|
||||
" 4.08163265, 4.28571429, 4.48979592, 4.69387755,\n",
|
||||
" 4.89795918, 5.10204082, 5.30612245, 5.51020408,\n",
|
||||
" 5.71428571, 5.91836735, 6.12244898, 6.32653061,\n",
|
||||
" 6.53061224, 6.73469388, 6.93877551, 7.14285714,\n",
|
||||
" 7.34693878, 7.55102041, 7.75510204, 7.95918367,\n",
|
||||
" 8.16326531, 8.36734694, 8.57142857, 8.7755102 ,\n",
|
||||
" 8.97959184, 9.18367347, 9.3877551 , 9.59183673,\n",
|
||||
" 9.79591837, 10. ])"
|
||||
]
|
||||
},
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.linspace(0,10,50)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## eye\n",
|
||||
"\n",
|
||||
"Creates an identity matrix"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 1., 0., 0., 0.],\n",
|
||||
" [ 0., 1., 0., 0.],\n",
|
||||
" [ 0., 0., 1., 0.],\n",
|
||||
" [ 0., 0., 0., 1.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.eye(4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Random \n",
|
||||
"\n",
|
||||
"Numpy also has lots of ways to create random number arrays:\n",
|
||||
"\n",
|
||||
"### rand\n",
|
||||
"Create an array of the given shape and populate it with\n",
|
||||
"random samples from a uniform distribution\n",
|
||||
"over ``[0, 1)``."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0.11570539, 0.35279769])"
|
||||
]
|
||||
},
|
||||
"execution_count": 47,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.rand(2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0.66660768, 0.87589888, 0.12421056, 0.65074126, 0.60260888],\n",
|
||||
" [ 0.70027668, 0.85572434, 0.8464595 , 0.2735416 , 0.10955384],\n",
|
||||
" [ 0.0670566 , 0.83267738, 0.9082729 , 0.58249129, 0.12305748],\n",
|
||||
" [ 0.27948423, 0.66422017, 0.95639833, 0.34238788, 0.9578872 ],\n",
|
||||
" [ 0.72155386, 0.3035422 , 0.85249683, 0.30414307, 0.79718816]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.rand(5,5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### randn\n",
|
||||
"\n",
|
||||
"Return a sample (or samples) from the \"standard normal\" distribution. Unlike rand which is uniform:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([-0.27954018, 0.90078368])"
|
||||
]
|
||||
},
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.randn(2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0.70154515, 0.22441999, 1.33563186, 0.82872577, -0.28247509],\n",
|
||||
" [ 0.64489788, 0.61815094, -0.81693168, -0.30102424, -0.29030574],\n",
|
||||
" [ 0.8695976 , 0.413755 , 2.20047208, 0.17955692, -0.82159344],\n",
|
||||
" [ 0.59264235, 1.29869894, -1.18870241, 0.11590888, -0.09181687],\n",
|
||||
" [-0.96924265, -1.62888685, -2.05787102, -0.29705576, 0.68915542]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 45,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.randn(5,5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### randint\n",
|
||||
"Return random integers from `low` (inclusive) to `high` (exclusive)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"44"
|
||||
]
|
||||
},
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.randint(1,100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([13, 64, 27, 63, 46, 68, 92, 10, 58, 24])"
|
||||
]
|
||||
},
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.randint(1,100,10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Array Attributes and Methods\n",
|
||||
"\n",
|
||||
"Let's discuss some useful attributes and methods or an array:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"arr = np.arange(25)\n",
|
||||
"ranarr = np.random.randint(0,50,10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
|
||||
" 17, 18, 19, 20, 21, 22, 23, 24])"
|
||||
]
|
||||
},
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([10, 12, 41, 17, 49, 2, 46, 3, 19, 39])"
|
||||
]
|
||||
},
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ranarr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Reshape\n",
|
||||
"Returns an array containing the same data with a new shape."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0, 1, 2, 3, 4],\n",
|
||||
" [ 5, 6, 7, 8, 9],\n",
|
||||
" [10, 11, 12, 13, 14],\n",
|
||||
" [15, 16, 17, 18, 19],\n",
|
||||
" [20, 21, 22, 23, 24]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr.reshape(5,5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### max,min,argmax,argmin\n",
|
||||
"\n",
|
||||
"These are useful methods for finding max or min values. Or to find their index locations using argmin or argmax"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([10, 12, 41, 17, 49, 2, 46, 3, 19, 39])"
|
||||
]
|
||||
},
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ranarr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"49"
|
||||
]
|
||||
},
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ranarr.max()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"4"
|
||||
]
|
||||
},
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ranarr.argmax()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"2"
|
||||
]
|
||||
},
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ranarr.min()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"5"
|
||||
]
|
||||
},
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ranarr.argmin()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Shape\n",
|
||||
"\n",
|
||||
"Shape is an attribute that arrays have (not a method):"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(25,)"
|
||||
]
|
||||
},
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Vector\n",
|
||||
"arr.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 66,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
|
||||
" 17, 18, 19, 20, 21, 22, 23, 24]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 66,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Notice the two sets of brackets\n",
|
||||
"arr.reshape(1,25)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(1, 25)"
|
||||
]
|
||||
},
|
||||
"execution_count": 69,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr.reshape(1,25).shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 70,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0],\n",
|
||||
" [ 1],\n",
|
||||
" [ 2],\n",
|
||||
" [ 3],\n",
|
||||
" [ 4],\n",
|
||||
" [ 5],\n",
|
||||
" [ 6],\n",
|
||||
" [ 7],\n",
|
||||
" [ 8],\n",
|
||||
" [ 9],\n",
|
||||
" [10],\n",
|
||||
" [11],\n",
|
||||
" [12],\n",
|
||||
" [13],\n",
|
||||
" [14],\n",
|
||||
" [15],\n",
|
||||
" [16],\n",
|
||||
" [17],\n",
|
||||
" [18],\n",
|
||||
" [19],\n",
|
||||
" [20],\n",
|
||||
" [21],\n",
|
||||
" [22],\n",
|
||||
" [23],\n",
|
||||
" [24]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 70,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr.reshape(25,1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 76,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(25, 1)"
|
||||
]
|
||||
},
|
||||
"execution_count": 76,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr.reshape(25,1).shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### dtype\n",
|
||||
"\n",
|
||||
"You can also grab the data type of the object in the array:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 75,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"dtype('int64')"
|
||||
]
|
||||
},
|
||||
"execution_count": 75,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr.dtype"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Great Job!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,329 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Opérations NumPy"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Arithmétique\n",
|
||||
"\n",
|
||||
"Vous pouvez facilement effectuer un tableau avec l'arithmétique tableau, ou scalaire avec l'arithmétique tableau. Voyons quelques exemples :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"arr = np.arange(0,10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18])"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr + arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81])"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr * arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr - arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/Users/marci/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
" if __name__ == '__main__':\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ nan, 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Avertissement en cas de division par zéro, mais pas une erreur !\n",
|
||||
"# Seulement remplacé par nan\n",
|
||||
"arr/arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/Users/marci/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in true_divide\n",
|
||||
" if __name__ == '__main__':\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ inf, 1. , 0.5 , 0.33333333, 0.25 ,\n",
|
||||
" 0.2 , 0.16666667, 0.14285714, 0.125 , 0.11111111])"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Avertissement aussi, mais pas une erreur, remplacé par l'infini\n",
|
||||
"1/arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729])"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr**3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Fonctions universelles de tableau\n",
|
||||
"\n",
|
||||
"Numpy vient avec beaucoup de [fonctions universelles de tableau](http://docs.scipy.org/doc/numpy/reference/ufuncs.html), qui sont essentiellement juste des opérations mathématiques que vous pouvez utiliser pour effectuer l'opération à travers le tableau. En voici quelques unes :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0. , 1. , 1.41421356, 1.73205081, 2. ,\n",
|
||||
" 2.23606798, 2.44948974, 2.64575131, 2.82842712, 3. ])"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Prendre la racine carrée\n",
|
||||
"np.sqrt(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1.00000000e+00, 2.71828183e+00, 7.38905610e+00,\n",
|
||||
" 2.00855369e+01, 5.45981500e+01, 1.48413159e+02,\n",
|
||||
" 4.03428793e+02, 1.09663316e+03, 2.98095799e+03,\n",
|
||||
" 8.10308393e+03])"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Calcul exponentiel (e^)\n",
|
||||
"np.exp(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"9"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.max(arr) # comme arr.max()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0. , 0.84147098, 0.90929743, 0.14112001, -0.7568025 ,\n",
|
||||
" -0.95892427, -0.2794155 , 0.6569866 , 0.98935825, 0.41211849])"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.sin(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/Users/marci/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log\n",
|
||||
" if __name__ == '__main__':\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ -inf, 0. , 0.69314718, 1.09861229, 1.38629436,\n",
|
||||
" 1.60943791, 1.79175947, 1.94591015, 2.07944154, 2.19722458])"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.log(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!\n",
|
||||
"\n",
|
||||
"C'est tout ce que nous avons besoin de savoir pour l'instant !"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,646 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"___\n",
|
||||
"\n",
|
||||
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
|
||||
"___\n",
|
||||
"<center>*Copyright Pierian Data 2017*</center>\n",
|
||||
"<center>*For more information, visit us at www.pieriandata.com*</center>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# NumPy Indexing and Selection\n",
|
||||
"\n",
|
||||
"In this lecture we will discuss how to select elements or groups of elements from an array."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#Creating sample array\n",
|
||||
"arr = np.arange(0,11)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Show\n",
|
||||
"arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Bracket Indexing and Selection\n",
|
||||
"The simplest way to pick one or some elements of an array looks very similar to python lists:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"8"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Get a value at an index\n",
|
||||
"arr[8]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([1, 2, 3, 4])"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Get values in a range\n",
|
||||
"arr[1:5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([0, 1, 2, 3, 4])"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Get values in a range\n",
|
||||
"arr[0:5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Broadcasting\n",
|
||||
"\n",
|
||||
"Numpy arrays differ from a normal Python list because of their ability to broadcast:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([100, 100, 100, 100, 100, 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Setting a value with index range (Broadcasting)\n",
|
||||
"arr[0:5]=100\n",
|
||||
"\n",
|
||||
"#Show\n",
|
||||
"arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Reset array, we'll see why I had to reset in a moment\n",
|
||||
"arr = np.arange(0,11)\n",
|
||||
"\n",
|
||||
"#Show\n",
|
||||
"arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([0, 1, 2, 3, 4, 5])"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Important notes on Slices\n",
|
||||
"slice_of_arr = arr[0:6]\n",
|
||||
"\n",
|
||||
"#Show slice\n",
|
||||
"slice_of_arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([99, 99, 99, 99, 99, 99])"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Change Slice\n",
|
||||
"slice_of_arr[:]=99\n",
|
||||
"\n",
|
||||
"#Show Slice again\n",
|
||||
"slice_of_arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Now note the changes also occur in our original array!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([99, 99, 99, 99, 99, 99, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Data is not copied, it's a view of the original array! This avoids memory problems!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([99, 99, 99, 99, 99, 99, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#To get a copy, need to be explicit\n",
|
||||
"arr_copy = arr.copy()\n",
|
||||
"\n",
|
||||
"arr_copy"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Indexing a 2D array (matrices)\n",
|
||||
"\n",
|
||||
"The general format is **arr_2d[row][col]** or **arr_2d[row,col]**. I recommend usually using the comma notation for clarity."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 5, 10, 15],\n",
|
||||
" [20, 25, 30],\n",
|
||||
" [35, 40, 45]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr_2d = np.array(([5,10,15],[20,25,30],[35,40,45]))\n",
|
||||
"\n",
|
||||
"#Show\n",
|
||||
"arr_2d"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([20, 25, 30])"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Indexing row\n",
|
||||
"arr_2d[1]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"20"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Format is arr_2d[row][col] or arr_2d[row,col]\n",
|
||||
"\n",
|
||||
"# Getting individual element value\n",
|
||||
"arr_2d[1][0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"20"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Getting individual element value\n",
|
||||
"arr_2d[1,0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[10, 15],\n",
|
||||
" [25, 30]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# 2D array slicing\n",
|
||||
"\n",
|
||||
"#Shape (2,2) from top right corner\n",
|
||||
"arr_2d[:2,1:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([35, 40, 45])"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Shape bottom row\n",
|
||||
"arr_2d[2]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([35, 40, 45])"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"#Shape bottom row\n",
|
||||
"arr_2d[2,:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## More Indexing Help\n",
|
||||
"Indexing a 2d matrix can be a bit confusing at first, especially when you start to add in step size. Try google image searching NumPy indexing to fins useful images, like this one:\n",
|
||||
"\n",
|
||||
"<img src= 'http://memory.osu.edu/classes/python/_images/numpy_indexing.png' width=500/>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Conditional Selection\n",
|
||||
"\n",
|
||||
"This is a very fundamental concept that will directly translate to pandas later on, make sure you understand this part!\n",
|
||||
"\n",
|
||||
"Let's briefly go over how to use brackets for selection based off of comparison operators."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr = np.arange(1,11)\n",
|
||||
"arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([False, False, False, False, True, True, True, True, True, True], dtype=bool)"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr > 4"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"bool_arr = arr>4"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([False, False, False, False, True, True, True, True, True, True], dtype=bool)"
|
||||
]
|
||||
},
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"bool_arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr[bool_arr]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 3, 4, 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr[arr>2]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 3, 4, 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x = 2\n",
|
||||
"arr[arr>x]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Great Job!\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,817 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"___\n",
|
||||
"\n",
|
||||
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
|
||||
"___\n",
|
||||
"<center>*Copyright Pierian Data 2017*</center>\n",
|
||||
"<center>*For more information, visit us at www.pieriandata.com*</center>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# NumPy Exercises\n",
|
||||
"Now that we've learned about NumPy let's test your knowledge. We'll start off with a few simple tasks and then you'll be asked some more complicated questions.\n",
|
||||
"\n",
|
||||
"** IMPORTANT NOTE! Make sure you don't run the cells directly above the example output shown, otherwise you will end up writing over the example output! **"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Import NumPy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create an array of 10 zeros "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE HERE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create an array of 10 ones"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE HERE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create an array of 10 fives"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE HERE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create an array of the integers from 10 to 50"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE HERE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
|
||||
" 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
|
||||
" 44, 45, 46, 47, 48, 49, 50])"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create an array of all the even integers from 10 to 50"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE HERE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
|
||||
" 44, 46, 48, 50])"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create a 3x3 matrix with values ranging from 0 to 8"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE HERE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[0, 1, 2],\n",
|
||||
" [3, 4, 5],\n",
|
||||
" [6, 7, 8]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create a 3x3 identity matrix"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE HERE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 1., 0., 0.],\n",
|
||||
" [ 0., 1., 0.],\n",
|
||||
" [ 0., 0., 1.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Use NumPy to generate a random number between 0 and 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE HERE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0.42829726])"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Use NumPy to generate an array of 25 random numbers sampled from a standard normal distribution"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE HERE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1.32031013, 1.6798602 , -0.42985892, -1.53116655, 0.85753232,\n",
|
||||
" 0.87339938, 0.35668636, -1.47491157, 0.15349697, 0.99530727,\n",
|
||||
" -0.94865451, -1.69174783, 1.57525349, -0.70615234, 0.10991879,\n",
|
||||
" -0.49478947, 1.08279872, 0.76488333, -2.3039931 , 0.35401124,\n",
|
||||
" -0.45454399, -0.64754649, -0.29391671, 0.02339861, 0.38272124])"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create the following matrix:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
|
||||
" [ 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
|
||||
" [ 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
|
||||
" [ 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
|
||||
" [ 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
|
||||
" [ 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
|
||||
" [ 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
|
||||
" [ 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
|
||||
" [ 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
|
||||
" [ 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Create an array of 20 linearly spaced points between 0 and 1:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
|
||||
" 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
|
||||
" 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
|
||||
" 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])"
|
||||
]
|
||||
},
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Numpy Indexing and Selection\n",
|
||||
"\n",
|
||||
"Now you will be given a few matrices, and be asked to replicate the resulting matrix outputs:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 1, 2, 3, 4, 5],\n",
|
||||
" [ 6, 7, 8, 9, 10],\n",
|
||||
" [11, 12, 13, 14, 15],\n",
|
||||
" [16, 17, 18, 19, 20],\n",
|
||||
" [21, 22, 23, 24, 25]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# HERE IS THE GIVEN MATRIX CALLED MAT\n",
|
||||
"# USE IT FOR THE FOLLOWING TASKS\n",
|
||||
"mat = np.arange(1,26).reshape(5,5)\n",
|
||||
"mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
|
||||
"# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
|
||||
"# BE ABLE TO SEE THE OUTPUT ANY MORE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[12, 13, 14, 15],\n",
|
||||
" [17, 18, 19, 20],\n",
|
||||
" [22, 23, 24, 25]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
|
||||
"# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
|
||||
"# BE ABLE TO SEE THE OUTPUT ANY MORE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"20"
|
||||
]
|
||||
},
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
|
||||
"# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
|
||||
"# BE ABLE TO SEE THE OUTPUT ANY MORE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 2],\n",
|
||||
" [ 7],\n",
|
||||
" [12]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
|
||||
"# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
|
||||
"# BE ABLE TO SEE THE OUTPUT ANY MORE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([21, 22, 23, 24, 25])"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# WRITE CODE HERE THAT REPRODUCES THE OUTPUT OF THE CELL BELOW\n",
|
||||
"# BE CAREFUL NOT TO RUN THE CELL BELOW, OTHERWISE YOU WON'T\n",
|
||||
"# BE ABLE TO SEE THE OUTPUT ANY MORE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[16, 17, 18, 19, 20],\n",
|
||||
" [21, 22, 23, 24, 25]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Now do the following"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Get the sum of all the values in mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE HERE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"325"
|
||||
]
|
||||
},
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Get the standard deviation of the values in mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE HERE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"7.2111025509279782"
|
||||
]
|
||||
},
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Get the sum of all the columns in mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE HERE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([55, 60, 65, 70, 75])"
|
||||
]
|
||||
},
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Bonus Question\n",
|
||||
"\n",
|
||||
"We worked a lot with random data with numpy, but is there a way we can insure that we always get the same random numbers? [Click Here for a Hint](https://www.google.com/search?q=numpy+random+seed&rlz=1C1CHBF_enUS747US747&oq=numpy+random+seed&aqs=chrome..69i57j69i60j0l4.2087j0j7&sourceid=chrome&ie=UTF-8)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Great Job!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,800 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Exercices NumPy\n",
|
||||
"Maintenant que nous avons découvert NumPy, testons vos connaissances. Nous commencerons par quelques tâches simples, puis des questions plus complexes.\n",
|
||||
"\n",
|
||||
"** REMARQUE IMPORTANTE : Veillez à ne pas exécuter les cellules directement au-dessus de la sortie de résultat attendu, sinon vous risquez de finir par écrire par dessus ! **"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Import NumPy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de 10 zéros "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de 10 uns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de 10 cinq"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau d'entiers de 10 à 50"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
|
||||
" 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
|
||||
" 44, 45, 46, 47, 48, 49, 50])"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de tous les entiers pairs entre 10 et 50"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
|
||||
" 44, 46, 48, 50])"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer une matrice 3x3 avec les valeurs de 0 à 8"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[0, 1, 2],\n",
|
||||
" [3, 4, 5],\n",
|
||||
" [6, 7, 8]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer une matrice identité 3x3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 1., 0., 0.],\n",
|
||||
" [ 0., 1., 0.],\n",
|
||||
" [ 0., 0., 1.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Utiliser NumPy pour générer un nombre aléatoire entre 0 et 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0.42829726])"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Utiliser NumPy pour générer un tableau de 25 nombres aléatoires échantillonnés à partir d'une distribution normale standard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1.32031013, 1.6798602 , -0.42985892, -1.53116655, 0.85753232,\n",
|
||||
" 0.87339938, 0.35668636, -1.47491157, 0.15349697, 0.99530727,\n",
|
||||
" -0.94865451, -1.69174783, 1.57525349, -0.70615234, 0.10991879,\n",
|
||||
" -0.49478947, 1.08279872, 0.76488333, -2.3039931 , 0.35401124,\n",
|
||||
" -0.45454399, -0.64754649, -0.29391671, 0.02339861, 0.38272124])"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer la matrice suivante:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
|
||||
" [ 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
|
||||
" [ 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
|
||||
" [ 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
|
||||
" [ 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
|
||||
" [ 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
|
||||
" [ 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
|
||||
" [ 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
|
||||
" [ 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
|
||||
" [ 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de 20 points espacés linéairement entre 0 et 1:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
|
||||
" 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
|
||||
" 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
|
||||
" 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])"
|
||||
]
|
||||
},
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Indexation et sélection Numpy\n",
|
||||
"\n",
|
||||
"Maintenant vous trouverez quelques matrices, il faut les reproduire:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 1, 2, 3, 4, 5],\n",
|
||||
" [ 6, 7, 8, 9, 10],\n",
|
||||
" [11, 12, 13, 14, 15],\n",
|
||||
" [16, 17, 18, 19, 20],\n",
|
||||
" [21, 22, 23, 24, 25]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# VOICI LA MATRICE MAT\n",
|
||||
"# A UTILISER POUR LES PROCHAINES TACHES\n",
|
||||
"import numpy as np\n",
|
||||
"mat = np.arange(1,26).reshape(5,5)\n",
|
||||
"mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ECRIRE LE CODE ICI QUI REPRODUIT LA SORTIE CI-DESSOUS\n",
|
||||
"# ATTENTION A NE PAS EXECUTER LA CELLULE CI-DESSOUS, SINON VOUS NE\n",
|
||||
"# POURREZ PLUS VOIR LA SORTIE ATTENDUE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[12, 13, 14, 15],\n",
|
||||
" [17, 18, 19, 20],\n",
|
||||
" [22, 23, 24, 25]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ECRIRE LE CODE ICI QUI REPRODUIT LA SORTIE CI-DESSOUS\n",
|
||||
"# ATTENTION A NE PAS EXECUTER LA CELLULE CI-DESSOUS, SINON VOUS NE\n",
|
||||
"# POURREZ PLUS VOIR LA SORTIE ATTENDUE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"20"
|
||||
]
|
||||
},
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ECRIRE LE CODE ICI QUI REPRODUIT LA SORTIE CI-DESSOUS\n",
|
||||
"# ATTENTION A NE PAS EXECUTER LA CELLULE CI-DESSOUS, SINON VOUS NE\n",
|
||||
"# POURREZ PLUS VOIR LA SORTIE ATTENDUE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 2],\n",
|
||||
" [ 7],\n",
|
||||
" [12]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ECRIRE LE CODE ICI QUI REPRODUIT LA SORTIE CI-DESSOUS\n",
|
||||
"# ATTENTION A NE PAS EXECUTER LA CELLULE CI-DESSOUS, SINON VOUS NE\n",
|
||||
"# POURREZ PLUS VOIR LA SORTIE ATTENDUE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([21, 22, 23, 24, 25])"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ECRIRE LE CODE ICI QUI REPRODUIT LA SORTIE CI-DESSOUS\n",
|
||||
"# ATTENTION A NE PAS EXECUTER LA CELLULE CI-DESSOUS, SINON VOUS NE\n",
|
||||
"# POURREZ PLUS VOIR LA SORTIE ATTENDUE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[16, 17, 18, 19, 20],\n",
|
||||
" [21, 22, 23, 24, 25]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Faire les tâches suivantes"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Obtenir la somme de toutes les valeurs de la matrice mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"325"
|
||||
]
|
||||
},
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Obtenir l'écart-type des valeurs de la matrice mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"7.2111025509279782"
|
||||
]
|
||||
},
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Obtenir la somme de toutes les colonnes de la matrice mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([55, 60, 65, 70, 75])"
|
||||
]
|
||||
},
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Question Bonus\n",
|
||||
"\n",
|
||||
"Nous avons beaucoup travaillé avec des données aléatoires avec numpy, mais y a-t-il un moyen de nous assurer que nous obtenons toujours les mêmes nombres aléatoires ? [Cliquez ici pour un indice](https://www.google.fr/search?q=numpy+random+seed&rlz=1C1CHBF_enUS747US747&oq=numpy+random+seed&aqs=chrome..69i57j69i60j0l4.2087j0j7&sourceid=chrome&ie=UTF-8)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,961 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# NumPy \n",
|
||||
"\n",
|
||||
"NumPy (ou Numpy) est une bibliothèque d'Algèbre Linéaire pour Python, la raison de son importance pour la Finance avec Python, c'est que preque toutes les bibliothèques de l'écosystème Data Science Python sont construites sur un bloc NumPy. De plus nous l'utiliserons pour générer des données pour nos exemples d'analyse plus tard!\n",
|
||||
"\n",
|
||||
"Numpy est aussi extrêmement rapide, de part ses liens avec le langage C. Pour pour d'informations sur l'utilisation des tableaux plutôt que des listes, regardez ce post [StackOverflow](http://stackoverflow.com/questions/993984/why-numpy-instead-of-python-lists).\n",
|
||||
"\n",
|
||||
"Nous n'apprendrons que les bases de NumPy, pour commencer nous avons besoin de l'installer!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Instructions d'installation\n",
|
||||
"\n",
|
||||
"### NumPy est déjà inclus dans votre environnement! Si vous utilisez l'environnement pyfinance, c'est bon pour vous!\n",
|
||||
"\n",
|
||||
"_____\n",
|
||||
"##### Pour ceux n'utilisant pas l'environnement fourni:\n",
|
||||
"\n",
|
||||
"**Il est fortement recommandé d'installer Python à l'aide de la distribution Anaconda pour vous assurer que toutes les dépendances sous-jacentes (comme les bibliothèques Linear Algebra) sont synchronisées avec l'utilisation d'une installation conda. Si vous avez Anaconda, installez NumPy en vous rendant à votre terminal ou à l'invite de commande et en tapant:**\n",
|
||||
" \n",
|
||||
" conda install numpy\n",
|
||||
" \n",
|
||||
"**Si vous n'avez pas Anaconda et ne pouvez pas l'installer, veuillez vous référer à [Documentation officielle de Numpy sur les différentes instructions d'installation.](http://docs.scipy.org/doc/numpy-1.10.1/user/install.html)**\n",
|
||||
"\n",
|
||||
"_____"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Utiliser NumPy\n",
|
||||
"\n",
|
||||
"Une fois que vous avez installé NumPy, vous pouvez l'importer sous forme de bibliothèque :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Numpy a de nombreuses fonctions et capacités intégrées. Nous ne les couvrirons pas toutes, mais nous nous concentrerons plutôt sur certains des aspects les plus importants de Numpy: vecteurs, tableaux, matrices et génération de nombres. Commençons par discuter des tableaux.\n",
|
||||
"\n",
|
||||
"# Tableaux Numpy\n",
|
||||
"\n",
|
||||
"Les tableaux NumPy sont la principale façon dont nous utiliserons Numpy tout au long du cours. Les tableaux Numpy sont essentiellement de deux types: les vecteurs et les matrices. Les vecteurs sont des tableaux 1-d et les matrices sont 2-d (mais vous devez noter qu'une matrice peut toujours avoir seulement une ligne ou une colonne).\n",
|
||||
"\n",
|
||||
"Commençons notre introduction en explorant comment créer des tableaux NumPy.\n",
|
||||
"\n",
|
||||
"## Création de tableaux NumPy\n",
|
||||
"\n",
|
||||
"### Depuis une liste Python\n",
|
||||
"\n",
|
||||
"Nous pouvons créer un tableau en convertissant directement une liste ou une liste de listes:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[1, 2, 3]"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"my_list = [1,2,3]\n",
|
||||
"my_list"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([1, 2, 3])"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.array(my_list)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"my_matrix = [[1,2,3],[4,5,6],[7,8,9]]\n",
|
||||
"my_matrix"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[1, 2, 3],\n",
|
||||
" [4, 5, 6],\n",
|
||||
" [7, 8, 9]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.array(my_matrix)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Méthodes intégrées\n",
|
||||
"\n",
|
||||
"Il existe de nombreuses méthodes intégrées pour générer des tableaux"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### arange\n",
|
||||
"\n",
|
||||
"Retourne des valeurs uniformément espacées dans un intervalle donné."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.arange(0,10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 2, 4, 6, 8, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.arange(0,11,2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### zeros et ones\n",
|
||||
"\n",
|
||||
"Génère des tableaux de zéros ou de uns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0., 0., 0.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.zeros(3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0., 0., 0., 0., 0.],\n",
|
||||
" [ 0., 0., 0., 0., 0.],\n",
|
||||
" [ 0., 0., 0., 0., 0.],\n",
|
||||
" [ 0., 0., 0., 0., 0.],\n",
|
||||
" [ 0., 0., 0., 0., 0.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.zeros((5,5))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1., 1., 1.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.ones(3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 1., 1., 1.],\n",
|
||||
" [ 1., 1., 1.],\n",
|
||||
" [ 1., 1., 1.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.ones((3,3))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### linspace\n",
|
||||
"Retourne les nombres uniformément espacés sur un intervalle spécifié."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0., 5., 10.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.linspace(0,10,3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0. , 0.20408163, 0.40816327, 0.6122449 ,\n",
|
||||
" 0.81632653, 1.02040816, 1.2244898 , 1.42857143,\n",
|
||||
" 1.63265306, 1.83673469, 2.04081633, 2.24489796,\n",
|
||||
" 2.44897959, 2.65306122, 2.85714286, 3.06122449,\n",
|
||||
" 3.26530612, 3.46938776, 3.67346939, 3.87755102,\n",
|
||||
" 4.08163265, 4.28571429, 4.48979592, 4.69387755,\n",
|
||||
" 4.89795918, 5.10204082, 5.30612245, 5.51020408,\n",
|
||||
" 5.71428571, 5.91836735, 6.12244898, 6.32653061,\n",
|
||||
" 6.53061224, 6.73469388, 6.93877551, 7.14285714,\n",
|
||||
" 7.34693878, 7.55102041, 7.75510204, 7.95918367,\n",
|
||||
" 8.16326531, 8.36734694, 8.57142857, 8.7755102 ,\n",
|
||||
" 8.97959184, 9.18367347, 9.3877551 , 9.59183673,\n",
|
||||
" 9.79591837, 10. ])"
|
||||
]
|
||||
},
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.linspace(0,10,50)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## eye\n",
|
||||
"\n",
|
||||
"Crée une matrice d'identité"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 1., 0., 0., 0.],\n",
|
||||
" [ 0., 1., 0., 0.],\n",
|
||||
" [ 0., 0., 1., 0.],\n",
|
||||
" [ 0., 0., 0., 1.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.eye(4)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Random \n",
|
||||
"\n",
|
||||
"Numpy a aussi beaucoup de façons de créer des tableaux de nombres aléatoires:\n",
|
||||
"\n",
|
||||
"### rand\n",
|
||||
"Créez un tableau de la forme donnée et remplissez-le avec\n",
|
||||
"des échantillons aléatoires provenant d'une distribution uniforme\n",
|
||||
"sur ``[0, 1)``."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 47,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0.11570539, 0.35279769])"
|
||||
]
|
||||
},
|
||||
"execution_count": 47,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.rand(2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0.66660768, 0.87589888, 0.12421056, 0.65074126, 0.60260888],\n",
|
||||
" [ 0.70027668, 0.85572434, 0.8464595 , 0.2735416 , 0.10955384],\n",
|
||||
" [ 0.0670566 , 0.83267738, 0.9082729 , 0.58249129, 0.12305748],\n",
|
||||
" [ 0.27948423, 0.66422017, 0.95639833, 0.34238788, 0.9578872 ],\n",
|
||||
" [ 0.72155386, 0.3035422 , 0.85249683, 0.30414307, 0.79718816]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.rand(5,5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### randn\n",
|
||||
"\n",
|
||||
"Retourne un échantillon (ou des échantillons) de la distribution \"normale standard\". Contrairement à rand qui est uniforme:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([-0.27954018, 0.90078368])"
|
||||
]
|
||||
},
|
||||
"execution_count": 48,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.randn(2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 45,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0.70154515, 0.22441999, 1.33563186, 0.82872577, -0.28247509],\n",
|
||||
" [ 0.64489788, 0.61815094, -0.81693168, -0.30102424, -0.29030574],\n",
|
||||
" [ 0.8695976 , 0.413755 , 2.20047208, 0.17955692, -0.82159344],\n",
|
||||
" [ 0.59264235, 1.29869894, -1.18870241, 0.11590888, -0.09181687],\n",
|
||||
" [-0.96924265, -1.62888685, -2.05787102, -0.29705576, 0.68915542]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 45,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.randn(5,5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### randint\n",
|
||||
"Retourne les nombres entiers aléatoires de `low` (inclus) à `high` (exclusif)."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"44"
|
||||
]
|
||||
},
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.randint(1,100)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([13, 64, 27, 63, 46, 68, 92, 10, 58, 24])"
|
||||
]
|
||||
},
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.randint(1,100,10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Attributs et méthodes des tableaux\n",
|
||||
"\n",
|
||||
"Discutons de quelques attributs et méthodes utiles pour un tableau :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"arr = np.arange(25)\n",
|
||||
"ranarr = np.random.randint(0,50,10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
|
||||
" 17, 18, 19, 20, 21, 22, 23, 24])"
|
||||
]
|
||||
},
|
||||
"execution_count": 56,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([10, 12, 41, 17, 49, 2, 46, 3, 19, 39])"
|
||||
]
|
||||
},
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ranarr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Reshape\n",
|
||||
"Retourne un tableau contenant les mêmes données avec une nouvelle forme."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0, 1, 2, 3, 4],\n",
|
||||
" [ 5, 6, 7, 8, 9],\n",
|
||||
" [10, 11, 12, 13, 14],\n",
|
||||
" [15, 16, 17, 18, 19],\n",
|
||||
" [20, 21, 22, 23, 24]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr.reshape(5,5)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### max, min, argmax, argmin\n",
|
||||
"\n",
|
||||
"Ce sont des méthodes utiles pour trouver des valeurs maximales ou minimales. Ou pour trouver l'emplacement de leur index à l'aide des méthodes argmin ou argmax"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([10, 12, 41, 17, 49, 2, 46, 3, 19, 39])"
|
||||
]
|
||||
},
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ranarr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"49"
|
||||
]
|
||||
},
|
||||
"execution_count": 61,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ranarr.max()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"4"
|
||||
]
|
||||
},
|
||||
"execution_count": 62,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ranarr.argmax()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"2"
|
||||
]
|
||||
},
|
||||
"execution_count": 63,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ranarr.min()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"5"
|
||||
]
|
||||
},
|
||||
"execution_count": 60,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ranarr.argmin()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Shape\n",
|
||||
"\n",
|
||||
"Shape est un attribut des tableaux (pas une méthode):"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(25,)"
|
||||
]
|
||||
},
|
||||
"execution_count": 65,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Vecteur\n",
|
||||
"arr.shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 66,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,\n",
|
||||
" 17, 18, 19, 20, 21, 22, 23, 24]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 66,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Remarquez les doubles crochets\n",
|
||||
"arr.reshape(1,25)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 69,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(1, 25)"
|
||||
]
|
||||
},
|
||||
"execution_count": 69,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr.reshape(1,25).shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 70,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0],\n",
|
||||
" [ 1],\n",
|
||||
" [ 2],\n",
|
||||
" [ 3],\n",
|
||||
" [ 4],\n",
|
||||
" [ 5],\n",
|
||||
" [ 6],\n",
|
||||
" [ 7],\n",
|
||||
" [ 8],\n",
|
||||
" [ 9],\n",
|
||||
" [10],\n",
|
||||
" [11],\n",
|
||||
" [12],\n",
|
||||
" [13],\n",
|
||||
" [14],\n",
|
||||
" [15],\n",
|
||||
" [16],\n",
|
||||
" [17],\n",
|
||||
" [18],\n",
|
||||
" [19],\n",
|
||||
" [20],\n",
|
||||
" [21],\n",
|
||||
" [22],\n",
|
||||
" [23],\n",
|
||||
" [24]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 70,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr.reshape(25,1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 76,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"(25, 1)"
|
||||
]
|
||||
},
|
||||
"execution_count": 76,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr.reshape(25,1).shape"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### dtype\n",
|
||||
"\n",
|
||||
"Vous pouvez également saisir le type de données de l'objet dans le tableau:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 75,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"dtype('int64')"
|
||||
]
|
||||
},
|
||||
"execution_count": 75,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr.dtype"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,329 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Opérations NumPy"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Arithmétique\n",
|
||||
"\n",
|
||||
"Vous pouvez facilement effectuer un tableau avec l'arithmétique tableau, ou scalaire avec l'arithmétique tableau. Voyons quelques exemples :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"arr = np.arange(0,10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18])"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr + arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 1, 4, 9, 16, 25, 36, 49, 64, 81])"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr * arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr - arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/Users/marci/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: invalid value encountered in true_divide\n",
|
||||
" if __name__ == '__main__':\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ nan, 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Avertissement en cas de division par zéro, mais pas une erreur !\n",
|
||||
"# Seulement remplacé par nan\n",
|
||||
"arr/arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/Users/marci/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in true_divide\n",
|
||||
" if __name__ == '__main__':\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ inf, 1. , 0.5 , 0.33333333, 0.25 ,\n",
|
||||
" 0.2 , 0.16666667, 0.14285714, 0.125 , 0.11111111])"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Avertissement aussi, mais pas une erreur, remplacé par l'infini\n",
|
||||
"1/arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 1, 8, 27, 64, 125, 216, 343, 512, 729])"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr**3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Fonctions universelles de tableau\n",
|
||||
"\n",
|
||||
"Numpy vient avec beaucoup de [fonctions universelles de tableau](http://docs.scipy.org/doc/numpy/reference/ufuncs.html), qui sont essentiellement juste des opérations mathématiques que vous pouvez utiliser pour effectuer l'opération à travers le tableau. En voici quelques unes :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0. , 1. , 1.41421356, 1.73205081, 2. ,\n",
|
||||
" 2.23606798, 2.44948974, 2.64575131, 2.82842712, 3. ])"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Prendre la racine carrée\n",
|
||||
"np.sqrt(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1.00000000e+00, 2.71828183e+00, 7.38905610e+00,\n",
|
||||
" 2.00855369e+01, 5.45981500e+01, 1.48413159e+02,\n",
|
||||
" 4.03428793e+02, 1.09663316e+03, 2.98095799e+03,\n",
|
||||
" 8.10308393e+03])"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Calcul exponentiel (e^)\n",
|
||||
"np.exp(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"9"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.max(arr) # comme arr.max()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0. , 0.84147098, 0.90929743, 0.14112001, -0.7568025 ,\n",
|
||||
" -0.95892427, -0.2794155 , 0.6569866 , 0.98935825, 0.41211849])"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.sin(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"/Users/marci/anaconda/lib/python3.5/site-packages/ipykernel/__main__.py:1: RuntimeWarning: divide by zero encountered in log\n",
|
||||
" if __name__ == '__main__':\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ -inf, 0. , 0.69314718, 1.09861229, 1.38629436,\n",
|
||||
" 1.60943791, 1.79175947, 1.94591015, 2.07944154, 2.19722458])"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.log(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!\n",
|
||||
"\n",
|
||||
"C'est tout ce que nous avons besoin de savoir pour l'instant !"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,635 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# NumPy Indexing et Selection\n",
|
||||
"\n",
|
||||
"Dans cette session, nous discuterons de la façon de sélectionner des éléments ou des groupes d'éléments à partir d'un tableau."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Création d'un tableau simple\n",
|
||||
"arr = np.arange(0,11)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# afficher\n",
|
||||
"arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Indexation et sélection\n",
|
||||
"La façon la plus simple de choisir un ou plusieurs éléments d'un tableau ressemble beaucoup aux listes python :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"8"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Obtenir une valeur à un index\n",
|
||||
"arr[8]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([1, 2, 3, 4])"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Obtenir des valeurs dans une plage d'entiers\n",
|
||||
"arr[1:5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([0, 1, 2, 3, 4])"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Obtenir des valeurs dans une plage d'entiers\n",
|
||||
"arr[0:5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Broadcasting\n",
|
||||
"\n",
|
||||
"Les tableaux Numpy diffèrent d'une liste Python normale en raison de leur capacité à diffuser :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([100, 100, 100, 100, 100, 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Réglage d'une valeur avec plage d'indice (Broadcasting)\n",
|
||||
"arr[0:5]=100\n",
|
||||
"\n",
|
||||
"# afficher\n",
|
||||
"arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Réinitialiser le tableau, nous verrons pourquoi \n",
|
||||
"# j'ai dû le réinitialiser plus bas.\n",
|
||||
"arr = np.arange(0,11)\n",
|
||||
"\n",
|
||||
"# afficher\n",
|
||||
"arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([0, 1, 2, 3, 4, 5])"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Remarques importantes sur les tranches (slices)\n",
|
||||
"slice_of_arr = arr[0:6]\n",
|
||||
"\n",
|
||||
"# afficher tranche\n",
|
||||
"slice_of_arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([99, 99, 99, 99, 99, 99])"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Changer de tranche\n",
|
||||
"slice_of_arr[:] = 99\n",
|
||||
"\n",
|
||||
"# Afficher de nouveau\n",
|
||||
"slice_of_arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Notez maintenant que les changements se produisent aussi dans notre tableau d'origine !"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([99, 99, 99, 99, 99, 99, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Les données ne sont pas copiées, c'est une vue du tableau d'origine ! Cela permet d'éviter les problèmes de mémoire !"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([99, 99, 99, 99, 99, 99, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Pour en obtenir une copie, il faut être explicite\n",
|
||||
"arr_copy = arr.copy()\n",
|
||||
"\n",
|
||||
"arr_copy"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Indexation d'un tableau 2D (matrices)\n",
|
||||
"\n",
|
||||
"Le format général est le suivant **arr_2d[row][col]** ou **arr_2d[row,col]**. Je recommande habituellement d'utiliser la notation par virgule pour plus de clarté."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 5, 10, 15],\n",
|
||||
" [20, 25, 30],\n",
|
||||
" [35, 40, 45]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr_2d = np.array(([5,10,15],[20,25,30],[35,40,45]))\n",
|
||||
"\n",
|
||||
"# afficher\n",
|
||||
"arr_2d"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([20, 25, 30])"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Indexer une ligne\n",
|
||||
"arr_2d[1]\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"20"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Le format est arr_2d[row][col] ou arr_2d[row,col]\n",
|
||||
"\n",
|
||||
"# Obtenir la valeur d'un élément individuel\n",
|
||||
"arr_2d[1][0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"20"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Obtenir la valeur d'un élément individuel\n",
|
||||
"arr_2d[1,0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[10, 15],\n",
|
||||
" [25, 30]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# découpage en tranches d'un tableau 2D\n",
|
||||
"\n",
|
||||
"# Forme (2,2) du coin supérieur droit\n",
|
||||
"arr_2d[:2,1:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([35, 40, 45])"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Forme de la rangée du bas\n",
|
||||
"arr_2d[2]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([35, 40, 45])"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Forme de la rangée du bas\n",
|
||||
"arr_2d[2,:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Plus d'aide pour l'indexation\n",
|
||||
"L'indexation d'une matrice 2d peut être un peu confuse au début, surtout lorsque vous commencez à ajouter une taille de pas. Essayez la recherche d'images sur google \"NumPy indexing\" pour trouver des images utiles, comme celle-ci :\n",
|
||||
"\n",
|
||||
"<img src= 'https://s2.qwant.com/thumbr/0x380/e/f/c01bff98594466ebc0010d2898208f123a92d43849fb809db543c82f2038c9/Numpy1.jpg?u=https%3A%2F%2Fmedia.geeksforgeeks.org%2Fwp-content%2Fuploads%2FNumpy1.jpg&q=0&b=1&p=0&a=1' width=500/>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Sélection conditionnelle\n",
|
||||
"\n",
|
||||
"C'est un concept très fondamental qui se réglera directement par pandas plus tard, assurez-vous de bien comprendre cette partie !\n",
|
||||
"\n",
|
||||
"Passons brièvement en revue la façon d'utiliser les parenthèses pour la sélection basée sur des opérateurs de comparaison."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr = np.arange(1,11)\n",
|
||||
"arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([False, False, False, False, True, True, True, True, True, True], dtype=bool)"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr > 4"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"bool_arr = arr>4"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([False, False, False, False, True, True, True, True, True, True], dtype=bool)"
|
||||
]
|
||||
},
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"bool_arr"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr[bool_arr]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 3, 4, 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"arr[arr>2]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 3, 4, 5, 6, 7, 8, 9, 10])"
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"x = 2\n",
|
||||
"arr[arr>x]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,629 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Exercices NumPy - Solutions\n",
|
||||
"Maintenant que nous avons découvert NumPy, testons vos connaissances. Nous commencerons par quelques tâches simples, puis des questions plus complexes.\n",
|
||||
"\n",
|
||||
"** REMARQUE IMPORTANTE : Veillez à ne pas exécuter les cellules directement au-dessus de la sortie de résultat attendu, sinon vous risquez de finir par écrire par dessus ! **"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Import NumPy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de 10 zéros "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.zeros(10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de 10 uns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.ones(10)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de 10 cinq"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.ones(10) * 5"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau d'entiers de 10 à 50"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
|
||||
" 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
|
||||
" 44, 45, 46, 47, 48, 49, 50])"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.arange(10,51)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de tous les entiers pairs entre 10 et 50"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
|
||||
" 44, 46, 48, 50])"
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.arange(10,51,2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer une matrice 3x3 avec les valeurs de 0 à 8"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[0, 1, 2],\n",
|
||||
" [3, 4, 5],\n",
|
||||
" [6, 7, 8]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.arange(0,9).reshape(3,3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer une matrice identité 3x3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[1., 0., 0.],\n",
|
||||
" [0., 1., 0.],\n",
|
||||
" [0., 0., 1.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.eye(3)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Utiliser NumPy pour générer un nombre aléatoire entre 0 et 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([0.0774109])"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.rand(1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Utiliser NumPy pour générer un tableau de 25 nombres aléatoires échantillonnés à partir d'une distribution normale standard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0.01471837, -0.54119397, 0.61856113, 0.75337696, 0.54470058,\n",
|
||||
" 0.21793039, 1.12443308, 0.77995286, -0.62594303, -0.61483113,\n",
|
||||
" 0.27331136, 0.69654147, 0.56232085, -0.05882004, -0.73255901,\n",
|
||||
" -0.31370757, 1.94487633, -1.27965513, -1.36078982, -0.39200937,\n",
|
||||
" -0.96238637, 0.47766402, -0.84683412, -1.79296081, -0.62813396])"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.random.randn(25)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer la matrice suivante:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
|
||||
" [0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
|
||||
" [0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
|
||||
" [0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
|
||||
" [0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
|
||||
" [0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
|
||||
" [0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
|
||||
" [0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
|
||||
" [0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
|
||||
" [0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.arange(1,101).reshape(10,10)/100"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de 20 points espacés linéairement entre 0 et 1:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
|
||||
" 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
|
||||
" 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
|
||||
" 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"np.linspace(0,1,20)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Indexation et sélection Numpy\n",
|
||||
"\n",
|
||||
"Maintenant vous trouverez quelques matrices, il faut les reproduire:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 1, 2, 3, 4, 5],\n",
|
||||
" [ 6, 7, 8, 9, 10],\n",
|
||||
" [11, 12, 13, 14, 15],\n",
|
||||
" [16, 17, 18, 19, 20],\n",
|
||||
" [21, 22, 23, 24, 25]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# VOICI LA MATRICE MAT\n",
|
||||
"# A UTILISER POUR LES PROCHAINES TACHES\n",
|
||||
"import numpy as np\n",
|
||||
"mat = np.arange(1,26).reshape(5,5)\n",
|
||||
"mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[12, 13, 14, 15],\n",
|
||||
" [17, 18, 19, 20],\n",
|
||||
" [22, 23, 24, 25]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mat[2:,1:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"20"
|
||||
]
|
||||
},
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mat[3,4]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 2],\n",
|
||||
" [ 7],\n",
|
||||
" [12]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mat[:3,1:2]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([21, 22, 23, 24, 25])"
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mat[4,:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[16, 17, 18, 19, 20],\n",
|
||||
" [21, 22, 23, 24, 25]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mat[3:,:]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Faire les tâches suivantes"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Obtenir la somme de toutes les valeurs de la matrice mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"325"
|
||||
]
|
||||
},
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mat.sum()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Obtenir l'écart-type des valeurs de la matrice mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"7.211102550927978"
|
||||
]
|
||||
},
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mat.std()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Obtenir la somme de toutes les colonnes de la matrice mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([55, 60, 65, 70, 75])"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"mat.sum(axis=0)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Question Bonus\n",
|
||||
"\n",
|
||||
"Nous avons beaucoup travaillé avec des données aléatoires avec numpy, mais y a-t-il un moyen de nous assurer que nous obtenons toujours les mêmes nombres aléatoires ? [Cliquez ici pour un indice](https://www.google.fr/search?q=numpy+random+seed&rlz=1C1CHBF_enUS747US747&oq=numpy+random+seed&aqs=chrome..69i57j69i60j0l4.2087j0j7&sourceid=chrome&ie=UTF-8)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 57,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"np.random.seed(101)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.7"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,800 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Exercices NumPy\n",
|
||||
"Maintenant que nous avons découvert NumPy, testons vos connaissances. Nous commencerons par quelques tâches simples, puis des questions plus complexes.\n",
|
||||
"\n",
|
||||
"**REMARQUE IMPORTANTE : Veillez à ne pas exécuter les cellules directement au-dessus de la sortie de résultat attendu, sinon vous risquez de finir par écrire par dessus !**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Import NumPy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de 10 zéros "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de 10 uns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1., 1., 1., 1., 1., 1., 1., 1., 1., 1.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de 10 cinq"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 5., 5., 5., 5., 5., 5., 5., 5., 5., 5.])"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau d'entiers de 10 à 50"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26,\n",
|
||||
" 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43,\n",
|
||||
" 44, 45, 46, 47, 48, 49, 50])"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de tous les entiers pairs entre 10 et 50"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42,\n",
|
||||
" 44, 46, 48, 50])"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer une matrice 3x3 avec les valeurs de 0 à 8"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[0, 1, 2],\n",
|
||||
" [3, 4, 5],\n",
|
||||
" [6, 7, 8]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer une matrice identité 3x3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 1., 0., 0.],\n",
|
||||
" [ 0., 1., 0.],\n",
|
||||
" [ 0., 0., 1.]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Utiliser NumPy pour générer un nombre aléatoire entre 0 et 1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0.42829726])"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Utiliser NumPy pour générer un tableau de 25 nombres aléatoires échantillonnés à partir d'une distribution normale standard"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 1.32031013, 1.6798602 , -0.42985892, -1.53116655, 0.85753232,\n",
|
||||
" 0.87339938, 0.35668636, -1.47491157, 0.15349697, 0.99530727,\n",
|
||||
" -0.94865451, -1.69174783, 1.57525349, -0.70615234, 0.10991879,\n",
|
||||
" -0.49478947, 1.08279872, 0.76488333, -2.3039931 , 0.35401124,\n",
|
||||
" -0.45454399, -0.64754649, -0.29391671, 0.02339861, 0.38272124])"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer la matrice suivante:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.1 ],\n",
|
||||
" [ 0.11, 0.12, 0.13, 0.14, 0.15, 0.16, 0.17, 0.18, 0.19, 0.2 ],\n",
|
||||
" [ 0.21, 0.22, 0.23, 0.24, 0.25, 0.26, 0.27, 0.28, 0.29, 0.3 ],\n",
|
||||
" [ 0.31, 0.32, 0.33, 0.34, 0.35, 0.36, 0.37, 0.38, 0.39, 0.4 ],\n",
|
||||
" [ 0.41, 0.42, 0.43, 0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5 ],\n",
|
||||
" [ 0.51, 0.52, 0.53, 0.54, 0.55, 0.56, 0.57, 0.58, 0.59, 0.6 ],\n",
|
||||
" [ 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7 ],\n",
|
||||
" [ 0.71, 0.72, 0.73, 0.74, 0.75, 0.76, 0.77, 0.78, 0.79, 0.8 ],\n",
|
||||
" [ 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9 ],\n",
|
||||
" [ 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1. ]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Créer un tableau de 20 points espacés linéairement entre 0 et 1:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([ 0. , 0.05263158, 0.10526316, 0.15789474, 0.21052632,\n",
|
||||
" 0.26315789, 0.31578947, 0.36842105, 0.42105263, 0.47368421,\n",
|
||||
" 0.52631579, 0.57894737, 0.63157895, 0.68421053, 0.73684211,\n",
|
||||
" 0.78947368, 0.84210526, 0.89473684, 0.94736842, 1. ])"
|
||||
]
|
||||
},
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Indexation et sélection Numpy\n",
|
||||
"\n",
|
||||
"Maintenant vous trouverez quelques matrices, il faut les reproduire:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 1, 2, 3, 4, 5],\n",
|
||||
" [ 6, 7, 8, 9, 10],\n",
|
||||
" [11, 12, 13, 14, 15],\n",
|
||||
" [16, 17, 18, 19, 20],\n",
|
||||
" [21, 22, 23, 24, 25]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# VOICI LA MATRICE MAT\n",
|
||||
"# A UTILISER POUR LES PROCHAINES TACHES\n",
|
||||
"import numpy as np\n",
|
||||
"mat = np.arange(1,26).reshape(5,5)\n",
|
||||
"mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ECRIRE LE CODE ICI QUI REPRODUIT LA SORTIE CI-DESSOUS\n",
|
||||
"# ATTENTION A NE PAS EXECUTER LA CELLULE CI-DESSOUS, SINON VOUS NE\n",
|
||||
"# POURREZ PLUS VOIR LA SORTIE ATTENDUE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[12, 13, 14, 15],\n",
|
||||
" [17, 18, 19, 20],\n",
|
||||
" [22, 23, 24, 25]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ECRIRE LE CODE ICI QUI REPRODUIT LA SORTIE CI-DESSOUS\n",
|
||||
"# ATTENTION A NE PAS EXECUTER LA CELLULE CI-DESSOUS, SINON VOUS NE\n",
|
||||
"# POURREZ PLUS VOIR LA SORTIE ATTENDUE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"20"
|
||||
]
|
||||
},
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ECRIRE LE CODE ICI QUI REPRODUIT LA SORTIE CI-DESSOUS\n",
|
||||
"# ATTENTION A NE PAS EXECUTER LA CELLULE CI-DESSOUS, SINON VOUS NE\n",
|
||||
"# POURREZ PLUS VOIR LA SORTIE ATTENDUE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[ 2],\n",
|
||||
" [ 7],\n",
|
||||
" [12]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ECRIRE LE CODE ICI QUI REPRODUIT LA SORTIE CI-DESSOUS\n",
|
||||
"# ATTENTION A NE PAS EXECUTER LA CELLULE CI-DESSOUS, SINON VOUS NE\n",
|
||||
"# POURREZ PLUS VOIR LA SORTIE ATTENDUE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([21, 22, 23, 24, 25])"
|
||||
]
|
||||
},
|
||||
"execution_count": 46,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# ECRIRE LE CODE ICI QUI REPRODUIT LA SORTIE CI-DESSOUS\n",
|
||||
"# ATTENTION A NE PAS EXECUTER LA CELLULE CI-DESSOUS, SINON VOUS NE\n",
|
||||
"# POURREZ PLUS VOIR LA SORTIE ATTENDUE"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([[16, 17, 18, 19, 20],\n",
|
||||
" [21, 22, 23, 24, 25]])"
|
||||
]
|
||||
},
|
||||
"execution_count": 49,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Faire les tâches suivantes"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Obtenir la somme de toutes les valeurs de la matrice mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"325"
|
||||
]
|
||||
},
|
||||
"execution_count": 50,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Obtenir l'écart-type des valeurs de la matrice mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"7.2111025509279782"
|
||||
]
|
||||
},
|
||||
"execution_count": 51,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Obtenir la somme de toutes les colonnes de la matrice mat"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([55, 60, 65, 70, 75])"
|
||||
]
|
||||
},
|
||||
"execution_count": 53,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Question Bonus\n",
|
||||
"\n",
|
||||
"Nous avons beaucoup travaillé avec des données aléatoires avec numpy, mais y a-t-il un moyen de nous assurer que nous obtenons toujours les mêmes nombres aléatoires ? [Cliquez ici pour un indice](https://www.google.fr/search?q=numpy+random+seed&rlz=1C1CHBF_enUS747US747&oq=numpy+random+seed&aqs=chrome..69i57j69i60j0l4.2087j0j7&sourceid=chrome&ie=UTF-8)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,62 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"___\n",
|
||||
"\n",
|
||||
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
|
||||
"___"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Introduction to Pandas\n",
|
||||
"\n",
|
||||
"In this section of the course we will learn how to use pandas for data analysis. You can think of pandas as an extremely powerful version of Excel, with a lot more features. In this section of the course, you should go through the notebooks in this order:\n",
|
||||
"\n",
|
||||
"* Introduction to Pandas\n",
|
||||
"* Series\n",
|
||||
"* DataFrames\n",
|
||||
"* Missing Data\n",
|
||||
"* GroupBy\n",
|
||||
"* Merging,Joining,and Concatenating\n",
|
||||
"* Operations\n",
|
||||
"* Data Input and Output"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"___"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
|
|
@ -0,0 +1,464 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"___\n",
|
||||
"\n",
|
||||
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
|
||||
"___\n",
|
||||
"# Series"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"The first main data type we will learn about for pandas is the Series data type. Let's import Pandas and explore the Series object.\n",
|
||||
"\n",
|
||||
"A Series is very similar to a NumPy array (in fact it is built on top of the NumPy array object). What differentiates the NumPy array from a Series, is that a Series can have axis labels, meaning it can be indexed by a label, instead of just a number location. It also doesn't need to hold numeric data, it can hold any arbitrary Python Object.\n",
|
||||
"\n",
|
||||
"Let's explore this concept through some examples:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Creating a Series\n",
|
||||
"\n",
|
||||
"You can convert a list,numpy array, or dictionary to a Series:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"labels = ['a','b','c']\n",
|
||||
"my_list = [10,20,30]\n",
|
||||
"arr = np.array([10,20,30])\n",
|
||||
"d = {'a':10,'b':20,'c':30}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"** Using Lists**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0 10\n",
|
||||
"1 20\n",
|
||||
"2 30\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(data=my_list)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"a 10\n",
|
||||
"b 20\n",
|
||||
"c 30\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(data=my_list,index=labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"a 10\n",
|
||||
"b 20\n",
|
||||
"c 30\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(my_list,labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"** NumPy Arrays **"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0 10\n",
|
||||
"1 20\n",
|
||||
"2 30\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"a 10\n",
|
||||
"b 20\n",
|
||||
"c 30\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(arr,labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"** Dictionary**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"a 10\n",
|
||||
"b 20\n",
|
||||
"c 30\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(d)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Data in a Series\n",
|
||||
"\n",
|
||||
"A pandas Series can hold a variety of object types:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0 a\n",
|
||||
"1 b\n",
|
||||
"2 c\n",
|
||||
"dtype: object"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(data=labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0 <built-in function sum>\n",
|
||||
"1 <built-in function print>\n",
|
||||
"2 <built-in function len>\n",
|
||||
"dtype: object"
|
||||
]
|
||||
},
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Even functions (although unlikely that you will use this)\n",
|
||||
"pd.Series([sum,print,len])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Using an Index\n",
|
||||
"\n",
|
||||
"The key to using a Series is understanding its index. Pandas makes use of these index names or numbers by allowing for fast look ups of information (works like a hash table or dictionary).\n",
|
||||
"\n",
|
||||
"Let's see some examples of how to grab information from a Series. Let us create two sereis, ser1 and ser2:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ser1 = pd.Series([1,2,3,4],index = ['USA', 'Germany','USSR', 'Japan']) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"USA 1\n",
|
||||
"Germany 2\n",
|
||||
"USSR 3\n",
|
||||
"Japan 4\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ser1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ser2 = pd.Series([1,2,5,4],index = ['USA', 'Germany','Italy', 'Japan']) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"USA 1\n",
|
||||
"Germany 2\n",
|
||||
"Italy 5\n",
|
||||
"Japan 4\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ser2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1"
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ser1['USA']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"source": [
|
||||
"Operations are then also done based off of index:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Germany 4.0\n",
|
||||
"Italy NaN\n",
|
||||
"Japan 8.0\n",
|
||||
"USA 2.0\n",
|
||||
"USSR NaN\n",
|
||||
"dtype: float64"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ser1 + ser2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Let's stop here for now and move on to DataFrames, which will expand on the concept of Series!\n",
|
||||
"# Great Job!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,367 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"___\n",
|
||||
"\n",
|
||||
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
|
||||
"___"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Missing Data\n",
|
||||
"\n",
|
||||
"Let's show a few convenient methods to deal with Missing Data in pandas:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame({'A':[1,2,np.nan],\n",
|
||||
" 'B':[5,np.nan,np.nan],\n",
|
||||
" 'C':[1,2,3]})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
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|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>A</th>\n",
|
||||
" <th>B</th>\n",
|
||||
" <th>C</th>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
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|
||||
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|
||||
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|
||||
" A B C\n",
|
||||
"0 1.0 5.0 1\n",
|
||||
"1 2.0 NaN 2\n",
|
||||
"2 NaN NaN 3"
|
||||
]
|
||||
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|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
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|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
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|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
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|
||||
" <th>B</th>\n",
|
||||
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|
||||
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|
||||
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|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"0 1.0 5.0 1"
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"output_type": "execute_result"
|
||||
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|
||||
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|
||||
"source": [
|
||||
"df.dropna()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>C</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
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|
||||
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|
||||
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|
||||
" <tr>\n",
|
||||
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|
||||
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|
||||
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|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>3</td>\n",
|
||||
" </tr>\n",
|
||||
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|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" C\n",
|
||||
"0 1\n",
|
||||
"1 2\n",
|
||||
"2 3"
|
||||
]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
}
|
||||
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|
||||
"source": [
|
||||
"df.dropna(axis=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>A</th>\n",
|
||||
" <th>B</th>\n",
|
||||
" <th>C</th>\n",
|
||||
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|
||||
" </thead>\n",
|
||||
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|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>1.0</td>\n",
|
||||
" <td>5.0</td>\n",
|
||||
" <td>1</td>\n",
|
||||
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|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2.0</td>\n",
|
||||
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|
||||
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|
||||
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|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
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|
||||
"text/plain": [
|
||||
" A B C\n",
|
||||
"0 1.0 5.0 1\n",
|
||||
"1 2.0 NaN 2"
|
||||
]
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"df.dropna(thresh=2)"
|
||||
]
|
||||
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|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>A</th>\n",
|
||||
" <th>B</th>\n",
|
||||
" <th>C</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>1</td>\n",
|
||||
" <td>5</td>\n",
|
||||
" <td>1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>FILL VALUE</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>FILL VALUE</td>\n",
|
||||
" <td>FILL VALUE</td>\n",
|
||||
" <td>3</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" A B C\n",
|
||||
"0 1 5 1\n",
|
||||
"1 2 FILL VALUE 2\n",
|
||||
"2 FILL VALUE FILL VALUE 3"
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.fillna(value='FILL VALUE')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0 1.0\n",
|
||||
"1 2.0\n",
|
||||
"2 1.5\n",
|
||||
"Name: A, dtype: float64"
|
||||
]
|
||||
},
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df['A'].fillna(value=df['A'].mean())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Great Job!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
|
|
@ -0,0 +1,891 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"___\n",
|
||||
"\n",
|
||||
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
|
||||
"___"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Groupby\n",
|
||||
"\n",
|
||||
"The groupby method allows you to group rows of data together and call aggregate functions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"# Create dataframe\n",
|
||||
"data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],\n",
|
||||
" 'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],\n",
|
||||
" 'Sales':[200,120,340,124,243,350]}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(data)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Company</th>\n",
|
||||
" <th>Person</th>\n",
|
||||
" <th>Sales</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>GOOG</td>\n",
|
||||
" <td>Sam</td>\n",
|
||||
" <td>200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>GOOG</td>\n",
|
||||
" <td>Charlie</td>\n",
|
||||
" <td>120</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>MSFT</td>\n",
|
||||
" <td>Amy</td>\n",
|
||||
" <td>340</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>MSFT</td>\n",
|
||||
" <td>Vanessa</td>\n",
|
||||
" <td>124</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>FB</td>\n",
|
||||
" <td>Carl</td>\n",
|
||||
" <td>243</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>5</th>\n",
|
||||
" <td>FB</td>\n",
|
||||
" <td>Sarah</td>\n",
|
||||
" <td>350</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Company Person Sales\n",
|
||||
"0 GOOG Sam 200\n",
|
||||
"1 GOOG Charlie 120\n",
|
||||
"2 MSFT Amy 340\n",
|
||||
"3 MSFT Vanessa 124\n",
|
||||
"4 FB Carl 243\n",
|
||||
"5 FB Sarah 350"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"** Now you can use the .groupby() method to group rows together based off of a column name. For instance let's group based off of Company. This will create a DataFrameGroupBy object:**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<pandas.core.groupby.DataFrameGroupBy object at 0x113014128>"
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.groupby('Company')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can save this object as a new variable:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"by_comp = df.groupby(\"Company\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"And then call aggregate methods off the object:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Sales</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Company</th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>FB</th>\n",
|
||||
" <td>296.5</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>GOOG</th>\n",
|
||||
" <td>160.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>MSFT</th>\n",
|
||||
" <td>232.0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Sales\n",
|
||||
"Company \n",
|
||||
"FB 296.5\n",
|
||||
"GOOG 160.0\n",
|
||||
"MSFT 232.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.mean()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Sales</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Company</th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>FB</th>\n",
|
||||
" <td>296.5</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>GOOG</th>\n",
|
||||
" <td>160.0</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>MSFT</th>\n",
|
||||
" <td>232.0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Sales\n",
|
||||
"Company \n",
|
||||
"FB 296.5\n",
|
||||
"GOOG 160.0\n",
|
||||
"MSFT 232.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.groupby('Company').mean()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"More examples of aggregate methods:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Sales</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Company</th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>FB</th>\n",
|
||||
" <td>75.660426</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>GOOG</th>\n",
|
||||
" <td>56.568542</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>MSFT</th>\n",
|
||||
" <td>152.735065</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Sales\n",
|
||||
"Company \n",
|
||||
"FB 75.660426\n",
|
||||
"GOOG 56.568542\n",
|
||||
"MSFT 152.735065"
|
||||
]
|
||||
},
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.std()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 39,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Person</th>\n",
|
||||
" <th>Sales</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Company</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>FB</th>\n",
|
||||
" <td>Carl</td>\n",
|
||||
" <td>243</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>GOOG</th>\n",
|
||||
" <td>Charlie</td>\n",
|
||||
" <td>120</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>MSFT</th>\n",
|
||||
" <td>Amy</td>\n",
|
||||
" <td>124</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Person Sales\n",
|
||||
"Company \n",
|
||||
"FB Carl 243\n",
|
||||
"GOOG Charlie 120\n",
|
||||
"MSFT Amy 124"
|
||||
]
|
||||
},
|
||||
"execution_count": 39,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.min()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 40,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Person</th>\n",
|
||||
" <th>Sales</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Company</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>FB</th>\n",
|
||||
" <td>Sarah</td>\n",
|
||||
" <td>350</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>GOOG</th>\n",
|
||||
" <td>Sam</td>\n",
|
||||
" <td>200</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>MSFT</th>\n",
|
||||
" <td>Vanessa</td>\n",
|
||||
" <td>340</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Person Sales\n",
|
||||
"Company \n",
|
||||
"FB Sarah 350\n",
|
||||
"GOOG Sam 200\n",
|
||||
"MSFT Vanessa 340"
|
||||
]
|
||||
},
|
||||
"execution_count": 40,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.max()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 41,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Person</th>\n",
|
||||
" <th>Sales</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Company</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>FB</th>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>GOOG</th>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>MSFT</th>\n",
|
||||
" <td>2</td>\n",
|
||||
" <td>2</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Person Sales\n",
|
||||
"Company \n",
|
||||
"FB 2 2\n",
|
||||
"GOOG 2 2\n",
|
||||
"MSFT 2 2"
|
||||
]
|
||||
},
|
||||
"execution_count": 41,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.count()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 42,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th>Sales</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Company</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th rowspan=\"8\" valign=\"top\">FB</th>\n",
|
||||
" <th>count</th>\n",
|
||||
" <td>2.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>mean</th>\n",
|
||||
" <td>296.500000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>std</th>\n",
|
||||
" <td>75.660426</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>min</th>\n",
|
||||
" <td>243.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>25%</th>\n",
|
||||
" <td>269.750000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>50%</th>\n",
|
||||
" <td>296.500000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>75%</th>\n",
|
||||
" <td>323.250000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>max</th>\n",
|
||||
" <td>350.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th rowspan=\"8\" valign=\"top\">GOOG</th>\n",
|
||||
" <th>count</th>\n",
|
||||
" <td>2.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>mean</th>\n",
|
||||
" <td>160.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>std</th>\n",
|
||||
" <td>56.568542</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>min</th>\n",
|
||||
" <td>120.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>25%</th>\n",
|
||||
" <td>140.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>50%</th>\n",
|
||||
" <td>160.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>75%</th>\n",
|
||||
" <td>180.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>max</th>\n",
|
||||
" <td>200.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th rowspan=\"8\" valign=\"top\">MSFT</th>\n",
|
||||
" <th>count</th>\n",
|
||||
" <td>2.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>mean</th>\n",
|
||||
" <td>232.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>std</th>\n",
|
||||
" <td>152.735065</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>min</th>\n",
|
||||
" <td>124.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>25%</th>\n",
|
||||
" <td>178.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>50%</th>\n",
|
||||
" <td>232.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>75%</th>\n",
|
||||
" <td>286.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>max</th>\n",
|
||||
" <td>340.000000</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Sales\n",
|
||||
"Company \n",
|
||||
"FB count 2.000000\n",
|
||||
" mean 296.500000\n",
|
||||
" std 75.660426\n",
|
||||
" min 243.000000\n",
|
||||
" 25% 269.750000\n",
|
||||
" 50% 296.500000\n",
|
||||
" 75% 323.250000\n",
|
||||
" max 350.000000\n",
|
||||
"GOOG count 2.000000\n",
|
||||
" mean 160.000000\n",
|
||||
" std 56.568542\n",
|
||||
" min 120.000000\n",
|
||||
" 25% 140.000000\n",
|
||||
" 50% 160.000000\n",
|
||||
" 75% 180.000000\n",
|
||||
" max 200.000000\n",
|
||||
"MSFT count 2.000000\n",
|
||||
" mean 232.000000\n",
|
||||
" std 152.735065\n",
|
||||
" min 124.000000\n",
|
||||
" 25% 178.000000\n",
|
||||
" 50% 232.000000\n",
|
||||
" 75% 286.000000\n",
|
||||
" max 340.000000"
|
||||
]
|
||||
},
|
||||
"execution_count": 42,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.describe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr>\n",
|
||||
" <th>Company</th>\n",
|
||||
" <th colspan=\"8\" halign=\"left\">FB</th>\n",
|
||||
" <th colspan=\"5\" halign=\"left\">GOOG</th>\n",
|
||||
" <th colspan=\"8\" halign=\"left\">MSFT</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th></th>\n",
|
||||
" <th>count</th>\n",
|
||||
" <th>mean</th>\n",
|
||||
" <th>std</th>\n",
|
||||
" <th>min</th>\n",
|
||||
" <th>25%</th>\n",
|
||||
" <th>50%</th>\n",
|
||||
" <th>75%</th>\n",
|
||||
" <th>max</th>\n",
|
||||
" <th>count</th>\n",
|
||||
" <th>mean</th>\n",
|
||||
" <th>...</th>\n",
|
||||
" <th>75%</th>\n",
|
||||
" <th>max</th>\n",
|
||||
" <th>count</th>\n",
|
||||
" <th>mean</th>\n",
|
||||
" <th>std</th>\n",
|
||||
" <th>min</th>\n",
|
||||
" <th>25%</th>\n",
|
||||
" <th>50%</th>\n",
|
||||
" <th>75%</th>\n",
|
||||
" <th>max</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>Sales</th>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>296.5</td>\n",
|
||||
" <td>75.660426</td>\n",
|
||||
" <td>243.0</td>\n",
|
||||
" <td>269.75</td>\n",
|
||||
" <td>296.5</td>\n",
|
||||
" <td>323.25</td>\n",
|
||||
" <td>350.0</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>160.0</td>\n",
|
||||
" <td>...</td>\n",
|
||||
" <td>180.0</td>\n",
|
||||
" <td>200.0</td>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>232.0</td>\n",
|
||||
" <td>152.735065</td>\n",
|
||||
" <td>124.0</td>\n",
|
||||
" <td>178.0</td>\n",
|
||||
" <td>232.0</td>\n",
|
||||
" <td>286.0</td>\n",
|
||||
" <td>340.0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"<p>1 rows × 24 columns</p>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
"Company FB GOOG \\\n",
|
||||
" count mean std min 25% 50% 75% max count \n",
|
||||
"Sales 2.0 296.5 75.660426 243.0 269.75 296.5 323.25 350.0 2.0 \n",
|
||||
"\n",
|
||||
"Company ... MSFT \\\n",
|
||||
" mean ... 75% max count mean std min 25% \n",
|
||||
"Sales 160.0 ... 180.0 200.0 2.0 232.0 152.735065 124.0 178.0 \n",
|
||||
"\n",
|
||||
"Company \n",
|
||||
" 50% 75% max \n",
|
||||
"Sales 232.0 286.0 340.0 \n",
|
||||
"\n",
|
||||
"[1 rows x 24 columns]"
|
||||
]
|
||||
},
|
||||
"execution_count": 43,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.describe().transpose()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 44,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>count</th>\n",
|
||||
" <th>mean</th>\n",
|
||||
" <th>std</th>\n",
|
||||
" <th>min</th>\n",
|
||||
" <th>25%</th>\n",
|
||||
" <th>50%</th>\n",
|
||||
" <th>75%</th>\n",
|
||||
" <th>max</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>Sales</th>\n",
|
||||
" <td>2.0</td>\n",
|
||||
" <td>160.0</td>\n",
|
||||
" <td>56.568542</td>\n",
|
||||
" <td>120.0</td>\n",
|
||||
" <td>140.0</td>\n",
|
||||
" <td>160.0</td>\n",
|
||||
" <td>180.0</td>\n",
|
||||
" <td>200.0</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" count mean std min 25% 50% 75% max\n",
|
||||
"Sales 2.0 160.0 56.568542 120.0 140.0 160.0 180.0 200.0"
|
||||
]
|
||||
},
|
||||
"execution_count": 44,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.describe().transpose()['GOOG']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Great Job!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,45 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Introduction à Pandas\n",
|
||||
"\n",
|
||||
"Dans cette section du cours, nous apprendrons comment utiliser pandas pour l'analyse des données. Vous pouvez considérer pandas comme une version extrêmement puissante d'Excel, avec beaucoup plus de fonctionnalités. Dans cette section du cours, vous devriez passer en revue les notebooks dans cet ordre :\n",
|
||||
"\n",
|
||||
"* Introduction à Pandas\n",
|
||||
"* Series\n",
|
||||
"* DataFrames\n",
|
||||
"* Données manquantes\n",
|
||||
"* GroupBy\n",
|
||||
"* Merge, Join et Concat\n",
|
||||
"* Opérations\n",
|
||||
"* Data Input et Output"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,430 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Series"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Le premier grand type de données que nous apprendrons sur pandas est le type de données de la série. Importez Pandas et explorez l'objet Série.\n",
|
||||
"\n",
|
||||
"Une série est très similaire à un tableau NumPy (en fait, elle est construite sur l'objet tableau NumPy). Ce qui différencie le tableau NumPy d'une série, c'est qu'une série peut avoir des étiquettes d'axes, ce qui signifie qu'elle peut être indexée par une étiquette, au lieu d'un simple emplacement de numéro. Il n'a pas non plus besoin de contenir des données numériques, il peut contenir n'importe quel objet Python arbitraire.\n",
|
||||
"\n",
|
||||
"Examinons ce concept à travers quelques exemples :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Création d'une d'un objet Series\n",
|
||||
"\n",
|
||||
"Vous pouvez convertir une liste, un tableau numpy ou un dictionnaire en série :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"labels = ['a','b','c']\n",
|
||||
"my_list = [10,20,30]\n",
|
||||
"arr = np.array([10,20,30])\n",
|
||||
"d = {'a':10,'b':20,'c':30}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**en utilisant des listes**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0 10\n",
|
||||
"1 20\n",
|
||||
"2 30\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 3
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(data=my_list)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"a 10\n",
|
||||
"b 20\n",
|
||||
"c 30\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 4
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(data=my_list,index=labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"a 10\n",
|
||||
"b 20\n",
|
||||
"c 30\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 5
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(my_list,labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**en utilisant des tableaux NumPy**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0 10\n",
|
||||
"1 20\n",
|
||||
"2 30\n",
|
||||
"dtype: int32"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 6
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(arr)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"a 10\n",
|
||||
"b 20\n",
|
||||
"c 30\n",
|
||||
"dtype: int32"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 7
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(arr,labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**en utilisant des dictionnaires**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"a 10\n",
|
||||
"b 20\n",
|
||||
"c 30\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 8
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(d)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Données dans une Série\n",
|
||||
"\n",
|
||||
"Une série pandas peut contenir une variété de types d'objets :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0 a\n",
|
||||
"1 b\n",
|
||||
"2 c\n",
|
||||
"dtype: object"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 9
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.Series(data=labels)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0 <built-in function sum>\n",
|
||||
"1 <built-in function print>\n",
|
||||
"2 <built-in function len>\n",
|
||||
"dtype: object"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 10
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Même es fonctions (bien qu'il soit peu probable que vous l'utilisiez)\n",
|
||||
"pd.Series([sum,print,len])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## En utilisant un Index\n",
|
||||
"\n",
|
||||
"La clé de l'utilisation d'une série est la compréhension de son index. Pandas utilise ces noms ou numéros d'index en permettant une recherche rapide de l'information (fonctionne comme une table de hachage ou un dictionnaire).\n",
|
||||
"\n",
|
||||
"Voyons quelques exemples de la façon d'extraire des informations d'une série. Créons deux séries ser1 et ser2 :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ser1 = pd.Series([1,2,3,4],index = ['USA', 'Germany','France', 'Japan']) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"USA 1\n",
|
||||
"Germany 2\n",
|
||||
"France 3\n",
|
||||
"Japan 4\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 12
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ser1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"ser2 = pd.Series([1,2,5,4],index = ['USA', 'Germany','Italy', 'Japan']) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"USA 1\n",
|
||||
"Germany 2\n",
|
||||
"Italy 5\n",
|
||||
"Japan 4\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 14
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ser2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 15
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ser1['USA']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Les opérations sont alors également effectuées sur la base de l'index :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"France NaN\n",
|
||||
"Germany 4.0\n",
|
||||
"Italy NaN\n",
|
||||
"Japan 8.0\n",
|
||||
"USA 2.0\n",
|
||||
"dtype: float64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 16
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"ser1 + ser2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Arrêtons-nous ici pour l'instant et passons aux DataFrames, qui vont développer le concept de Série !\n",
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "e89404a230d8800c54ad520c7b67d1bd9bb833a07b37dd3e521a178a3dc34904"
|
||||
}
|
||||
}
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.9-final"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
File diff suppressed because it is too large
Load Diff
|
|
@ -0,0 +1,206 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Données manquantes\n",
|
||||
"\n",
|
||||
"Montrons quelques méthodes pratiques pour traiter les données manquantes dans pandas :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame({'A':[1,2,np.nan],\n",
|
||||
" 'B':[5,np.nan,np.nan],\n",
|
||||
" 'C':[1,2,3]})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B C\n",
|
||||
"0 1.0 5.0 1\n",
|
||||
"1 2.0 NaN 2\n",
|
||||
"2 NaN NaN 3"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1.0</td>\n <td>5.0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2.0</td>\n <td>NaN</td>\n <td>2</td>\n </tr>\n <tr>\n <th>2</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>3</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 3
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B C\n",
|
||||
"0 1.0 5.0 1"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1.0</td>\n <td>5.0</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 4
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.dropna()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" C\n",
|
||||
"0 1\n",
|
||||
"1 2\n",
|
||||
"2 3"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>C</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 5
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.dropna(axis=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B C\n",
|
||||
"0 1.0 5.0 1\n",
|
||||
"1 2.0 NaN 2"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1.0</td>\n <td>5.0</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2.0</td>\n <td>NaN</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 6
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.dropna(thresh=2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B C\n",
|
||||
"0 1 5 1\n",
|
||||
"1 2 Valeur de remplacement 2\n",
|
||||
"2 Valeur de remplacement Valeur de remplacement 3"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>5</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>Valeur de remplacement</td>\n <td>2</td>\n </tr>\n <tr>\n <th>2</th>\n <td>Valeur de remplacement</td>\n <td>Valeur de remplacement</td>\n <td>3</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 7
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.fillna(value='Valeur de remplacement')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0 1.0\n",
|
||||
"1 2.0\n",
|
||||
"2 1.5\n",
|
||||
"Name: A, dtype: float64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 8
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df['A'].fillna(value=df['A'].mean())"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "e89404a230d8800c54ad520c7b67d1bd9bb833a07b37dd3e521a178a3dc34904"
|
||||
}
|
||||
}
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.9-final"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,390 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Groupby\n",
|
||||
"\n",
|
||||
"La méthode de regroupement par groupe vous permet de regrouper des lignes de données et d'appeler des fonctions d'agrégation."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"# Création dataframe\n",
|
||||
"data = {'Company':['GOOG','GOOG','MSFT','MSFT','FB','FB'],\n",
|
||||
" 'Person':['Sam','Charlie','Amy','Vanessa','Carl','Sarah'],\n",
|
||||
" 'Sales':[200,120,340,124,243,350]}"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(data)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Company Person Sales\n",
|
||||
"0 GOOG Sam 200\n",
|
||||
"1 GOOG Charlie 120\n",
|
||||
"2 MSFT Amy 340\n",
|
||||
"3 MSFT Vanessa 124\n",
|
||||
"4 FB Carl 243\n",
|
||||
"5 FB Sarah 350"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Company</th>\n <th>Person</th>\n <th>Sales</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>GOOG</td>\n <td>Sam</td>\n <td>200</td>\n </tr>\n <tr>\n <th>1</th>\n <td>GOOG</td>\n <td>Charlie</td>\n <td>120</td>\n </tr>\n <tr>\n <th>2</th>\n <td>MSFT</td>\n <td>Amy</td>\n <td>340</td>\n </tr>\n <tr>\n <th>3</th>\n <td>MSFT</td>\n <td>Vanessa</td>\n <td>124</td>\n </tr>\n <tr>\n <th>4</th>\n <td>FB</td>\n <td>Carl</td>\n <td>243</td>\n </tr>\n <tr>\n <th>5</th>\n <td>FB</td>\n <td>Sarah</td>\n <td>350</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 3
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Maintenant vous pouvez utiliser la méthode .groupby() pour regrouper les lignes en fonction d'un nom de colonne. Par exemple, groupons les personnes par société. Ceci créera un objet DataFrameGroupBy :**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<pandas.core.groupby.generic.DataFrameGroupBy object at 0x00000166211DA848>"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 4
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.groupby('Company')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Vous pouvez sauvegarder cet objet comme nouvelle variable :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"by_comp = df.groupby(\"Company\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Et ensuite, appelez les méthodes d'agrégation sur l'objet :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Sales\n",
|
||||
"Company \n",
|
||||
"FB 296.5\n",
|
||||
"GOOG 160.0\n",
|
||||
"MSFT 232.0"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Sales</th>\n </tr>\n <tr>\n <th>Company</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>FB</th>\n <td>296.5</td>\n </tr>\n <tr>\n <th>GOOG</th>\n <td>160.0</td>\n </tr>\n <tr>\n <th>MSFT</th>\n <td>232.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 6
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.mean()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Sales\n",
|
||||
"Company \n",
|
||||
"FB 296.5\n",
|
||||
"GOOG 160.0\n",
|
||||
"MSFT 232.0"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Sales</th>\n </tr>\n <tr>\n <th>Company</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>FB</th>\n <td>296.5</td>\n </tr>\n <tr>\n <th>GOOG</th>\n <td>160.0</td>\n </tr>\n <tr>\n <th>MSFT</th>\n <td>232.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 7
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.groupby('Company').mean()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Autres exemples de méthodes d'agrégation :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Sales\n",
|
||||
"Company \n",
|
||||
"FB 75.660426\n",
|
||||
"GOOG 56.568542\n",
|
||||
"MSFT 152.735065"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Sales</th>\n </tr>\n <tr>\n <th>Company</th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>FB</th>\n <td>75.660426</td>\n </tr>\n <tr>\n <th>GOOG</th>\n <td>56.568542</td>\n </tr>\n <tr>\n <th>MSFT</th>\n <td>152.735065</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 8
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.std()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Person Sales\n",
|
||||
"Company \n",
|
||||
"FB Carl 243\n",
|
||||
"GOOG Charlie 120\n",
|
||||
"MSFT Amy 124"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Person</th>\n <th>Sales</th>\n </tr>\n <tr>\n <th>Company</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>FB</th>\n <td>Carl</td>\n <td>243</td>\n </tr>\n <tr>\n <th>GOOG</th>\n <td>Charlie</td>\n <td>120</td>\n </tr>\n <tr>\n <th>MSFT</th>\n <td>Amy</td>\n <td>124</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 9
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.min()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Person Sales\n",
|
||||
"Company \n",
|
||||
"FB Sarah 350\n",
|
||||
"GOOG Sam 200\n",
|
||||
"MSFT Vanessa 340"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Person</th>\n <th>Sales</th>\n </tr>\n <tr>\n <th>Company</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>FB</th>\n <td>Sarah</td>\n <td>350</td>\n </tr>\n <tr>\n <th>GOOG</th>\n <td>Sam</td>\n <td>200</td>\n </tr>\n <tr>\n <th>MSFT</th>\n <td>Vanessa</td>\n <td>340</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 10
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.max()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Person Sales\n",
|
||||
"Company \n",
|
||||
"FB 2 2\n",
|
||||
"GOOG 2 2\n",
|
||||
"MSFT 2 2"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Person</th>\n <th>Sales</th>\n </tr>\n <tr>\n <th>Company</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>FB</th>\n <td>2</td>\n <td>2</td>\n </tr>\n <tr>\n <th>GOOG</th>\n <td>2</td>\n <td>2</td>\n </tr>\n <tr>\n <th>MSFT</th>\n <td>2</td>\n <td>2</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 11
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.count()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Sales \n",
|
||||
" count mean std min 25% 50% 75% max\n",
|
||||
"Company \n",
|
||||
"FB 2.0 296.5 75.660426 243.0 269.75 296.5 323.25 350.0\n",
|
||||
"GOOG 2.0 160.0 56.568542 120.0 140.00 160.0 180.00 200.0\n",
|
||||
"MSFT 2.0 232.0 152.735065 124.0 178.00 232.0 286.00 340.0"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead tr th {\n text-align: left;\n }\n\n .dataframe thead tr:last-of-type th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr>\n <th></th>\n <th colspan=\"8\" halign=\"left\">Sales</th>\n </tr>\n <tr>\n <th></th>\n <th>count</th>\n <th>mean</th>\n <th>std</th>\n <th>min</th>\n <th>25%</th>\n <th>50%</th>\n <th>75%</th>\n <th>max</th>\n </tr>\n <tr>\n <th>Company</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>FB</th>\n <td>2.0</td>\n <td>296.5</td>\n <td>75.660426</td>\n <td>243.0</td>\n <td>269.75</td>\n <td>296.5</td>\n <td>323.25</td>\n <td>350.0</td>\n </tr>\n <tr>\n <th>GOOG</th>\n <td>2.0</td>\n <td>160.0</td>\n <td>56.568542</td>\n <td>120.0</td>\n <td>140.00</td>\n <td>160.0</td>\n <td>180.00</td>\n <td>200.0</td>\n </tr>\n <tr>\n <th>MSFT</th>\n <td>2.0</td>\n <td>232.0</td>\n <td>152.735065</td>\n <td>124.0</td>\n <td>178.00</td>\n <td>232.0</td>\n <td>286.00</td>\n <td>340.0</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 12
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.describe()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Company FB GOOG MSFT\n",
|
||||
"Sales count 2.000000 2.000000 2.000000\n",
|
||||
" mean 296.500000 160.000000 232.000000\n",
|
||||
" std 75.660426 56.568542 152.735065\n",
|
||||
" min 243.000000 120.000000 124.000000\n",
|
||||
" 25% 269.750000 140.000000 178.000000\n",
|
||||
" 50% 296.500000 160.000000 232.000000\n",
|
||||
" 75% 323.250000 180.000000 286.000000\n",
|
||||
" max 350.000000 200.000000 340.000000"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Company</th>\n <th>FB</th>\n <th>GOOG</th>\n <th>MSFT</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th rowspan=\"8\" valign=\"top\">Sales</th>\n <th>count</th>\n <td>2.000000</td>\n <td>2.000000</td>\n <td>2.000000</td>\n </tr>\n <tr>\n <th>mean</th>\n <td>296.500000</td>\n <td>160.000000</td>\n <td>232.000000</td>\n </tr>\n <tr>\n <th>std</th>\n <td>75.660426</td>\n <td>56.568542</td>\n <td>152.735065</td>\n </tr>\n <tr>\n <th>min</th>\n <td>243.000000</td>\n <td>120.000000</td>\n <td>124.000000</td>\n </tr>\n <tr>\n <th>25%</th>\n <td>269.750000</td>\n <td>140.000000</td>\n <td>178.000000</td>\n </tr>\n <tr>\n <th>50%</th>\n <td>296.500000</td>\n <td>160.000000</td>\n <td>232.000000</td>\n </tr>\n <tr>\n <th>75%</th>\n <td>323.250000</td>\n <td>180.000000</td>\n <td>286.000000</td>\n </tr>\n <tr>\n <th>max</th>\n <td>350.000000</td>\n <td>200.000000</td>\n <td>340.000000</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 13
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.describe().transpose()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Sales count 2.000000\n",
|
||||
" mean 160.000000\n",
|
||||
" std 56.568542\n",
|
||||
" min 120.000000\n",
|
||||
" 25% 140.000000\n",
|
||||
" 50% 160.000000\n",
|
||||
" 75% 180.000000\n",
|
||||
" max 200.000000\n",
|
||||
"Name: GOOG, dtype: float64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 14
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"by_comp.describe().transpose()['GOOG']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "e89404a230d8800c54ad520c7b67d1bd9bb833a07b37dd3e521a178a3dc34904"
|
||||
}
|
||||
}
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.9-final"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,576 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Fusionner, Joindre et Concaténer\n",
|
||||
"\n",
|
||||
"Il y a 3 façons principales de combiner des DataFrames ensemble : Fusion, assemblage et concaténation. Dans ce notebook, nous discuterons de ces 3 méthodes avec des exemples.\n",
|
||||
"\n",
|
||||
"____"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Exemple DataFrames"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
|
||||
" 'B': ['B0', 'B1', 'B2', 'B3'],\n",
|
||||
" 'C': ['C0', 'C1', 'C2', 'C3'],\n",
|
||||
" 'D': ['D0', 'D1', 'D2', 'D3']},\n",
|
||||
" index=[0, 1, 2, 3])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
|
||||
" 'B': ['B4', 'B5', 'B6', 'B7'],\n",
|
||||
" 'C': ['C4', 'C5', 'C6', 'C7'],\n",
|
||||
" 'D': ['D4', 'D5', 'D6', 'D7']},\n",
|
||||
" index=[4, 5, 6, 7]) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],\n",
|
||||
" 'B': ['B8', 'B9', 'B10', 'B11'],\n",
|
||||
" 'C': ['C8', 'C9', 'C10', 'C11'],\n",
|
||||
" 'D': ['D8', 'D9', 'D10', 'D11']},\n",
|
||||
" index=[8, 9, 10, 11])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B C D\n",
|
||||
"0 A0 B0 C0 D0\n",
|
||||
"1 A1 B1 C1 D1\n",
|
||||
"2 A2 B2 C2 D2\n",
|
||||
"3 A3 B3 C3 D3"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>A0</td>\n <td>B0</td>\n <td>C0</td>\n <td>D0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>A1</td>\n <td>B1</td>\n <td>C1</td>\n <td>D1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>A2</td>\n <td>B2</td>\n <td>C2</td>\n <td>D2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>A3</td>\n <td>B3</td>\n <td>C3</td>\n <td>D3</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 5
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df1"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B C D\n",
|
||||
"4 A4 B4 C4 D4\n",
|
||||
"5 A5 B5 C5 D5\n",
|
||||
"6 A6 B6 C6 D6\n",
|
||||
"7 A7 B7 C7 D7"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>4</th>\n <td>A4</td>\n <td>B4</td>\n <td>C4</td>\n <td>D4</td>\n </tr>\n <tr>\n <th>5</th>\n <td>A5</td>\n <td>B5</td>\n <td>C5</td>\n <td>D5</td>\n </tr>\n <tr>\n <th>6</th>\n <td>A6</td>\n <td>B6</td>\n <td>C6</td>\n <td>D6</td>\n </tr>\n <tr>\n <th>7</th>\n <td>A7</td>\n <td>B7</td>\n <td>C7</td>\n <td>D7</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 6
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B C D\n",
|
||||
"8 A8 B8 C8 D8\n",
|
||||
"9 A9 B9 C9 D9\n",
|
||||
"10 A10 B10 C10 D10\n",
|
||||
"11 A11 B11 C11 D11"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>8</th>\n <td>A8</td>\n <td>B8</td>\n <td>C8</td>\n <td>D8</td>\n </tr>\n <tr>\n <th>9</th>\n <td>A9</td>\n <td>B9</td>\n <td>C9</td>\n <td>D9</td>\n </tr>\n <tr>\n <th>10</th>\n <td>A10</td>\n <td>B10</td>\n <td>C10</td>\n <td>D10</td>\n </tr>\n <tr>\n <th>11</th>\n <td>A11</td>\n <td>B11</td>\n <td>C11</td>\n <td>D11</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 7
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df3"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Concaténation\n",
|
||||
"\n",
|
||||
"La concaténation colle fondamentalement les DataFrames ensemble. Gardez à l'esprit que les dimensions doivent correspondre à l'axe sur lequel vous concaténez. Vous pouvez utiliser **pd.concat** et passer dans une liste de DataFrames à concaténer ensemble :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B C D\n",
|
||||
"0 A0 B0 C0 D0\n",
|
||||
"1 A1 B1 C1 D1\n",
|
||||
"2 A2 B2 C2 D2\n",
|
||||
"3 A3 B3 C3 D3\n",
|
||||
"4 A4 B4 C4 D4\n",
|
||||
"5 A5 B5 C5 D5\n",
|
||||
"6 A6 B6 C6 D6\n",
|
||||
"7 A7 B7 C7 D7\n",
|
||||
"8 A8 B8 C8 D8\n",
|
||||
"9 A9 B9 C9 D9\n",
|
||||
"10 A10 B10 C10 D10\n",
|
||||
"11 A11 B11 C11 D11"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>A0</td>\n <td>B0</td>\n <td>C0</td>\n <td>D0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>A1</td>\n <td>B1</td>\n <td>C1</td>\n <td>D1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>A2</td>\n <td>B2</td>\n <td>C2</td>\n <td>D2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>A3</td>\n <td>B3</td>\n <td>C3</td>\n <td>D3</td>\n </tr>\n <tr>\n <th>4</th>\n <td>A4</td>\n <td>B4</td>\n <td>C4</td>\n <td>D4</td>\n </tr>\n <tr>\n <th>5</th>\n <td>A5</td>\n <td>B5</td>\n <td>C5</td>\n <td>D5</td>\n </tr>\n <tr>\n <th>6</th>\n <td>A6</td>\n <td>B6</td>\n <td>C6</td>\n <td>D6</td>\n </tr>\n <tr>\n <th>7</th>\n <td>A7</td>\n <td>B7</td>\n <td>C7</td>\n <td>D7</td>\n </tr>\n <tr>\n <th>8</th>\n <td>A8</td>\n <td>B8</td>\n <td>C8</td>\n <td>D8</td>\n </tr>\n <tr>\n <th>9</th>\n <td>A9</td>\n <td>B9</td>\n <td>C9</td>\n <td>D9</td>\n </tr>\n <tr>\n <th>10</th>\n <td>A10</td>\n <td>B10</td>\n <td>C10</td>\n <td>D10</td>\n </tr>\n <tr>\n <th>11</th>\n <td>A11</td>\n <td>B11</td>\n <td>C11</td>\n <td>D11</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 8
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.concat([df1,df2,df3])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B C D A B C D A B C D\n",
|
||||
"0 A0 B0 C0 D0 NaN NaN NaN NaN NaN NaN NaN NaN\n",
|
||||
"1 A1 B1 C1 D1 NaN NaN NaN NaN NaN NaN NaN NaN\n",
|
||||
"2 A2 B2 C2 D2 NaN NaN NaN NaN NaN NaN NaN NaN\n",
|
||||
"3 A3 B3 C3 D3 NaN NaN NaN NaN NaN NaN NaN NaN\n",
|
||||
"4 NaN NaN NaN NaN A4 B4 C4 D4 NaN NaN NaN NaN\n",
|
||||
"5 NaN NaN NaN NaN A5 B5 C5 D5 NaN NaN NaN NaN\n",
|
||||
"6 NaN NaN NaN NaN A6 B6 C6 D6 NaN NaN NaN NaN\n",
|
||||
"7 NaN NaN NaN NaN A7 B7 C7 D7 NaN NaN NaN NaN\n",
|
||||
"8 NaN NaN NaN NaN NaN NaN NaN NaN A8 B8 C8 D8\n",
|
||||
"9 NaN NaN NaN NaN NaN NaN NaN NaN A9 B9 C9 D9\n",
|
||||
"10 NaN NaN NaN NaN NaN NaN NaN NaN A10 B10 C10 D10\n",
|
||||
"11 NaN NaN NaN NaN NaN NaN NaN NaN A11 B11 C11 D11"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>A0</td>\n <td>B0</td>\n <td>C0</td>\n <td>D0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>1</th>\n <td>A1</td>\n <td>B1</td>\n <td>C1</td>\n <td>D1</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>A2</td>\n <td>B2</td>\n <td>C2</td>\n <td>D2</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>3</th>\n <td>A3</td>\n <td>B3</td>\n <td>C3</td>\n <td>D3</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>4</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>A4</td>\n <td>B4</td>\n <td>C4</td>\n <td>D4</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>5</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>A5</td>\n <td>B5</td>\n <td>C5</td>\n <td>D5</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>6</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>A6</td>\n <td>B6</td>\n <td>C6</td>\n <td>D6</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>7</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>A7</td>\n <td>B7</td>\n <td>C7</td>\n <td>D7</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>8</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>A8</td>\n <td>B8</td>\n <td>C8</td>\n <td>D8</td>\n </tr>\n <tr>\n <th>9</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>A9</td>\n <td>B9</td>\n <td>C9</td>\n <td>D9</td>\n </tr>\n <tr>\n <th>10</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>A10</td>\n <td>B10</td>\n <td>C10</td>\n <td>D10</td>\n </tr>\n <tr>\n <th>11</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>A11</td>\n <td>B11</td>\n <td>C11</td>\n <td>D11</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 9
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.concat([df1,df2,df3],axis=1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"_____\n",
|
||||
"## Exemple DataFrames"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"left = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
|
||||
" 'A': ['A0', 'A1', 'A2', 'A3'],\n",
|
||||
" 'B': ['B0', 'B1', 'B2', 'B3']})\n",
|
||||
" \n",
|
||||
"right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
|
||||
" 'C': ['C0', 'C1', 'C2', 'C3'],\n",
|
||||
" 'D': ['D0', 'D1', 'D2', 'D3']}) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" key A B\n",
|
||||
"0 K0 A0 B0\n",
|
||||
"1 K1 A1 B1\n",
|
||||
"2 K2 A2 B2\n",
|
||||
"3 K3 A3 B3"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>key</th>\n <th>A</th>\n <th>B</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>K0</td>\n <td>A0</td>\n <td>B0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>K1</td>\n <td>A1</td>\n <td>B1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>K2</td>\n <td>A2</td>\n <td>B2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>K3</td>\n <td>A3</td>\n <td>B3</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 11
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"left"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" key C D\n",
|
||||
"0 K0 C0 D0\n",
|
||||
"1 K1 C1 D1\n",
|
||||
"2 K2 C2 D2\n",
|
||||
"3 K3 C3 D3"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>key</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>K0</td>\n <td>C0</td>\n <td>D0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>K1</td>\n <td>C1</td>\n <td>D1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>K2</td>\n <td>C2</td>\n <td>D2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>K3</td>\n <td>C3</td>\n <td>D3</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 12
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"right"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"___"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Fusion\n",
|
||||
"\n",
|
||||
"La fonction **merge** vous permet de fusionner des DataFrames ensemble en utilisant une logique similaire à celle de la fusion de Tables SQL. Par exemple :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" key A B C D\n",
|
||||
"0 K0 A0 B0 C0 D0\n",
|
||||
"1 K1 A1 B1 C1 D1\n",
|
||||
"2 K2 A2 B2 C2 D2\n",
|
||||
"3 K3 A3 B3 C3 D3"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>key</th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>K0</td>\n <td>A0</td>\n <td>B0</td>\n <td>C0</td>\n <td>D0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>K1</td>\n <td>A1</td>\n <td>B1</td>\n <td>C1</td>\n <td>D1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>K2</td>\n <td>A2</td>\n <td>B2</td>\n <td>C2</td>\n <td>D2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>K3</td>\n <td>A3</td>\n <td>B3</td>\n <td>C3</td>\n <td>D3</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 13
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.merge(left,right,how='inner',on='key')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Exemple plus compliqué :"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],\n",
|
||||
" 'key2': ['K0', 'K1', 'K0', 'K1'],\n",
|
||||
" 'A': ['A0', 'A1', 'A2', 'A3'],\n",
|
||||
" 'B': ['B0', 'B1', 'B2', 'B3']})\n",
|
||||
" \n",
|
||||
"right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],\n",
|
||||
" 'key2': ['K0', 'K0', 'K0', 'K0'],\n",
|
||||
" 'C': ['C0', 'C1', 'C2', 'C3'],\n",
|
||||
" 'D': ['D0', 'D1', 'D2', 'D3']})"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" key1 key2 A B C D\n",
|
||||
"0 K0 K0 A0 B0 C0 D0\n",
|
||||
"1 K1 K0 A2 B2 C1 D1\n",
|
||||
"2 K1 K0 A2 B2 C2 D2"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>key1</th>\n <th>key2</th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>K0</td>\n <td>K0</td>\n <td>A0</td>\n <td>B0</td>\n <td>C0</td>\n <td>D0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>K1</td>\n <td>K0</td>\n <td>A2</td>\n <td>B2</td>\n <td>C1</td>\n <td>D1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>K1</td>\n <td>K0</td>\n <td>A2</td>\n <td>B2</td>\n <td>C2</td>\n <td>D2</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 15
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.merge(left, right, on=['key1', 'key2'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" key1 key2 A B C D\n",
|
||||
"0 K0 K0 A0 B0 C0 D0\n",
|
||||
"1 K0 K1 A1 B1 NaN NaN\n",
|
||||
"2 K1 K0 A2 B2 C1 D1\n",
|
||||
"3 K1 K0 A2 B2 C2 D2\n",
|
||||
"4 K2 K1 A3 B3 NaN NaN\n",
|
||||
"5 K2 K0 NaN NaN C3 D3"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>key1</th>\n <th>key2</th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>K0</td>\n <td>K0</td>\n <td>A0</td>\n <td>B0</td>\n <td>C0</td>\n <td>D0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>K0</td>\n <td>K1</td>\n <td>A1</td>\n <td>B1</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>K1</td>\n <td>K0</td>\n <td>A2</td>\n <td>B2</td>\n <td>C1</td>\n <td>D1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>K1</td>\n <td>K0</td>\n <td>A2</td>\n <td>B2</td>\n <td>C2</td>\n <td>D2</td>\n </tr>\n <tr>\n <th>4</th>\n <td>K2</td>\n <td>K1</td>\n <td>A3</td>\n <td>B3</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>5</th>\n <td>K2</td>\n <td>K0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>C3</td>\n <td>D3</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 16
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.merge(left, right, how='outer', on=['key1', 'key2'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" key1 key2 A B C D\n",
|
||||
"0 K0 K0 A0 B0 C0 D0\n",
|
||||
"1 K1 K0 A2 B2 C1 D1\n",
|
||||
"2 K1 K0 A2 B2 C2 D2\n",
|
||||
"3 K2 K0 NaN NaN C3 D3"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>key1</th>\n <th>key2</th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>K0</td>\n <td>K0</td>\n <td>A0</td>\n <td>B0</td>\n <td>C0</td>\n <td>D0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>K1</td>\n <td>K0</td>\n <td>A2</td>\n <td>B2</td>\n <td>C1</td>\n <td>D1</td>\n </tr>\n <tr>\n <th>2</th>\n <td>K1</td>\n <td>K0</td>\n <td>A2</td>\n <td>B2</td>\n <td>C2</td>\n <td>D2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>K2</td>\n <td>K0</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>C3</td>\n <td>D3</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 17
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.merge(left, right, how='right', on=['key1', 'key2'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" key1 key2 A B C D\n",
|
||||
"0 K0 K0 A0 B0 C0 D0\n",
|
||||
"1 K0 K1 A1 B1 NaN NaN\n",
|
||||
"2 K1 K0 A2 B2 C1 D1\n",
|
||||
"3 K1 K0 A2 B2 C2 D2\n",
|
||||
"4 K2 K1 A3 B3 NaN NaN"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>key1</th>\n <th>key2</th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>K0</td>\n <td>K0</td>\n <td>A0</td>\n <td>B0</td>\n <td>C0</td>\n <td>D0</td>\n </tr>\n <tr>\n <th>1</th>\n <td>K0</td>\n <td>K1</td>\n <td>A1</td>\n <td>B1</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2</th>\n <td>K1</td>\n <td>K0</td>\n <td>A2</td>\n <td>B2</td>\n <td>C1</td>\n <td>D1</td>\n </tr>\n <tr>\n <th>3</th>\n <td>K1</td>\n <td>K0</td>\n <td>A2</td>\n <td>B2</td>\n <td>C2</td>\n <td>D2</td>\n </tr>\n <tr>\n <th>4</th>\n <td>K2</td>\n <td>K1</td>\n <td>A3</td>\n <td>B3</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 18
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.merge(left, right, how='left', on=['key1', 'key2'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Assemblage\n",
|
||||
"Join est une méthode pratique pour combiner les colonnes de deux DataFrames potentiellement indexées différemment résultant en un seul DataFrame."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],\n",
|
||||
" 'B': ['B0', 'B1', 'B2']},\n",
|
||||
" index=['K0', 'K1', 'K2']) \n",
|
||||
"\n",
|
||||
"right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],\n",
|
||||
" 'D': ['D0', 'D2', 'D3']},\n",
|
||||
" index=['K0', 'K2', 'K3'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B C D\n",
|
||||
"K0 A0 B0 C0 D0\n",
|
||||
"K1 A1 B1 NaN NaN\n",
|
||||
"K2 A2 B2 C2 D2"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>K0</th>\n <td>A0</td>\n <td>B0</td>\n <td>C0</td>\n <td>D0</td>\n </tr>\n <tr>\n <th>K1</th>\n <td>A1</td>\n <td>B1</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>K2</th>\n <td>A2</td>\n <td>B2</td>\n <td>C2</td>\n <td>D2</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 20
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"left.join(right)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B C D\n",
|
||||
"K0 A0 B0 C0 D0\n",
|
||||
"K1 A1 B1 NaN NaN\n",
|
||||
"K2 A2 B2 C2 D2\n",
|
||||
"K3 NaN NaN C3 D3"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>K0</th>\n <td>A0</td>\n <td>B0</td>\n <td>C0</td>\n <td>D0</td>\n </tr>\n <tr>\n <th>K1</th>\n <td>A1</td>\n <td>B1</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>K2</th>\n <td>A2</td>\n <td>B2</td>\n <td>C2</td>\n <td>D2</td>\n </tr>\n <tr>\n <th>K3</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>C3</td>\n <td>D3</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 21
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"left.join(right, how='outer')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "e89404a230d8800c54ad520c7b67d1bd9bb833a07b37dd3e521a178a3dc34904"
|
||||
}
|
||||
}
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.9-final"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,614 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"# Opérations\n",
|
||||
"\n",
|
||||
"Il y a beaucoup d'opérations avec pandas qui vous seront vraiment utiles, mais elles n'entraient dans aucune case. Montrons-les ici dans ce notebook:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" col1 col2 col3\n",
|
||||
"0 1 444 abc\n",
|
||||
"1 2 555 def\n",
|
||||
"2 3 666 ghi\n",
|
||||
"3 4 444 xyz"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>col1</th>\n <th>col2</th>\n <th>col3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>444</td>\n <td>abc</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>555</td>\n <td>def</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>666</td>\n <td>ghi</td>\n </tr>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>444</td>\n <td>xyz</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 1
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"df = pd.DataFrame({'col1':[1,2,3,4],'col2':[444,555,666,444],'col3':['abc','def','ghi','xyz']})\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Informations sur les valeurs uniques"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array([444, 555, 666], dtype=int64)"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 2
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df['col2'].unique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"3"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 3
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df['col2'].nunique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"444 2\n",
|
||||
"555 1\n",
|
||||
"666 1\n",
|
||||
"Name: col2, dtype: int64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 4
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df['col2'].value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Sélection des données"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# Sélection à partir d'un DataFrame en utilisant des critères \n",
|
||||
"# de plusieurs colonnes\n",
|
||||
"newdf = df[(df['col1']>2) & (df['col2']==444)]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" col1 col2 col3\n",
|
||||
"3 4 444 xyz"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>col1</th>\n <th>col2</th>\n <th>col3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>3</th>\n <td>4</td>\n <td>444</td>\n <td>xyz</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 6
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"newdf"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Appliquer des Fonctions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def times2(x):\n",
|
||||
" return x*2"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0 2\n",
|
||||
"1 4\n",
|
||||
"2 6\n",
|
||||
"3 8\n",
|
||||
"Name: col1, dtype: int64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 8
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df['col1'].apply(times2)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0 3\n",
|
||||
"1 3\n",
|
||||
"2 3\n",
|
||||
"3 3\n",
|
||||
"Name: col3, dtype: int64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 9
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df['col3'].apply(len)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"10"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 10
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df['col1'].sum()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Suppression définitive d'une colonne**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"del df['col1']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" col2 col3\n",
|
||||
"0 444 abc\n",
|
||||
"1 555 def\n",
|
||||
"2 666 ghi\n",
|
||||
"3 444 xyz"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>col2</th>\n <th>col3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>444</td>\n <td>abc</td>\n </tr>\n <tr>\n <th>1</th>\n <td>555</td>\n <td>def</td>\n </tr>\n <tr>\n <th>2</th>\n <td>666</td>\n <td>ghi</td>\n </tr>\n <tr>\n <th>3</th>\n <td>444</td>\n <td>xyz</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 12
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Obtenir les noms des colonnes et des index :**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Index(['col2', 'col3'], dtype='object')"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 13
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.columns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"RangeIndex(start=0, stop=4, step=1)"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 14
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Trier un DataFrame :**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" col2 col3\n",
|
||||
"0 444 abc\n",
|
||||
"1 555 def\n",
|
||||
"2 666 ghi\n",
|
||||
"3 444 xyz"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>col2</th>\n <th>col3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>444</td>\n <td>abc</td>\n </tr>\n <tr>\n <th>1</th>\n <td>555</td>\n <td>def</td>\n </tr>\n <tr>\n <th>2</th>\n <td>666</td>\n <td>ghi</td>\n </tr>\n <tr>\n <th>3</th>\n <td>444</td>\n <td>xyz</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 15
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" col2 col3\n",
|
||||
"0 444 abc\n",
|
||||
"3 444 xyz\n",
|
||||
"1 555 def\n",
|
||||
"2 666 ghi"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>col2</th>\n <th>col3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>444</td>\n <td>abc</td>\n </tr>\n <tr>\n <th>3</th>\n <td>444</td>\n <td>xyz</td>\n </tr>\n <tr>\n <th>1</th>\n <td>555</td>\n <td>def</td>\n </tr>\n <tr>\n <th>2</th>\n <td>666</td>\n <td>ghi</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 16
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.sort_values(by='col2') #inplace=False par défaut"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Recherche de valeurs nulles ou vérification des valeurs nulles**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" col2 col3\n",
|
||||
"0 False False\n",
|
||||
"1 False False\n",
|
||||
"2 False False\n",
|
||||
"3 False False"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>col2</th>\n <th>col3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>1</th>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>2</th>\n <td>False</td>\n <td>False</td>\n </tr>\n <tr>\n <th>3</th>\n <td>False</td>\n <td>False</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 17
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.isnull()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" col2 col3\n",
|
||||
"0 444 abc\n",
|
||||
"1 555 def\n",
|
||||
"2 666 ghi\n",
|
||||
"3 444 xyz"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>col2</th>\n <th>col3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>444</td>\n <td>abc</td>\n </tr>\n <tr>\n <th>1</th>\n <td>555</td>\n <td>def</td>\n </tr>\n <tr>\n <th>2</th>\n <td>666</td>\n <td>ghi</td>\n </tr>\n <tr>\n <th>3</th>\n <td>444</td>\n <td>xyz</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 18
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Supprimer les lignes contenant des valeurs NaN\n",
|
||||
"df.dropna()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Remplir les valeurs NaN avec autre chose :**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" col1 col2 col3\n",
|
||||
"0 1.0 NaN abc\n",
|
||||
"1 2.0 555.0 def\n",
|
||||
"2 3.0 666.0 ghi\n",
|
||||
"3 NaN 444.0 xyz"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>col1</th>\n <th>col2</th>\n <th>col3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1.0</td>\n <td>NaN</td>\n <td>abc</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2.0</td>\n <td>555.0</td>\n <td>def</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3.0</td>\n <td>666.0</td>\n <td>ghi</td>\n </tr>\n <tr>\n <th>3</th>\n <td>NaN</td>\n <td>444.0</td>\n <td>xyz</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 20
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.DataFrame({'col1':[1,2,3,np.nan],\n",
|
||||
" 'col2':[np.nan,555,666,444],\n",
|
||||
" 'col3':['abc','def','ghi','xyz']})\n",
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" col1 col2 col3\n",
|
||||
"0 1 FILL abc\n",
|
||||
"1 2 555 def\n",
|
||||
"2 3 666 ghi\n",
|
||||
"3 FILL 444 xyz"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>col1</th>\n <th>col2</th>\n <th>col3</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>1</td>\n <td>FILL</td>\n <td>abc</td>\n </tr>\n <tr>\n <th>1</th>\n <td>2</td>\n <td>555</td>\n <td>def</td>\n </tr>\n <tr>\n <th>2</th>\n <td>3</td>\n <td>666</td>\n <td>ghi</td>\n </tr>\n <tr>\n <th>3</th>\n <td>FILL</td>\n <td>444</td>\n <td>xyz</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 21
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.fillna('FILL')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data = {'A':['foo','foo','foo','bar','bar','bar'],\n",
|
||||
" 'B':['one','one','two','two','one','one'],\n",
|
||||
" 'C':['x','y','x','y','x','y'],\n",
|
||||
" 'D':[1,3,2,5,4,1]}\n",
|
||||
"\n",
|
||||
"df = pd.DataFrame(data)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B C D\n",
|
||||
"0 foo one x 1\n",
|
||||
"1 foo one y 3\n",
|
||||
"2 foo two x 2\n",
|
||||
"3 bar two y 5\n",
|
||||
"4 bar one x 4\n",
|
||||
"5 bar one y 1"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n <th>C</th>\n <th>D</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>foo</td>\n <td>one</td>\n <td>x</td>\n <td>1</td>\n </tr>\n <tr>\n <th>1</th>\n <td>foo</td>\n <td>one</td>\n <td>y</td>\n <td>3</td>\n </tr>\n <tr>\n <th>2</th>\n <td>foo</td>\n <td>two</td>\n <td>x</td>\n <td>2</td>\n </tr>\n <tr>\n <th>3</th>\n <td>bar</td>\n <td>two</td>\n <td>y</td>\n <td>5</td>\n </tr>\n <tr>\n <th>4</th>\n <td>bar</td>\n <td>one</td>\n <td>x</td>\n <td>4</td>\n </tr>\n <tr>\n <th>5</th>\n <td>bar</td>\n <td>one</td>\n <td>y</td>\n <td>1</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 23
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"C x y\n",
|
||||
"A B \n",
|
||||
"bar one 4.0 1.0\n",
|
||||
" two NaN 5.0\n",
|
||||
"foo one 1.0 3.0\n",
|
||||
" two 2.0 NaN"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>C</th>\n <th>x</th>\n <th>y</th>\n </tr>\n <tr>\n <th>A</th>\n <th>B</th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th rowspan=\"2\" valign=\"top\">bar</th>\n <th>one</th>\n <td>4.0</td>\n <td>1.0</td>\n </tr>\n <tr>\n <th>two</th>\n <td>NaN</td>\n <td>5.0</td>\n </tr>\n <tr>\n <th rowspan=\"2\" valign=\"top\">foo</th>\n <th>one</th>\n <td>1.0</td>\n <td>3.0</td>\n </tr>\n <tr>\n <th>two</th>\n <td>2.0</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 24
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.pivot_table(values='D',index=['A', 'B'],columns=['C'])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "e89404a230d8800c54ad520c7b67d1bd9bb833a07b37dd3e521a178a3dc34904"
|
||||
}
|
||||
}
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.9-final"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,261 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"**Nous nous contenterons soit de lire les fichiers csv directement, soit d'utiliser pandas-datareader ou quandl. Considérez ce notebook comme un bref aperçu de ce qui est possible de faire avec pandas (nous ne travaillerons pas avec des fichiers SQL ou excel dans ce cours).**"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Data Input et Output\n",
|
||||
"\n",
|
||||
"Ce notebook est le code de référence pour obtenir des entrées et des sorties, pandas peut lire une variété de types de fichiers en utilisant ses méthodes pd.read_. Jetons un coup d'oeil aux types de données les plus courants:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## CSV\n",
|
||||
"\n",
|
||||
"### CSV Input"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" a b c d\n",
|
||||
"0 0 1 2 3\n",
|
||||
"1 4 5 6 7\n",
|
||||
"2 8 9 10 11\n",
|
||||
"3 12 13 14 15"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>a</th>\n <th>b</th>\n <th>c</th>\n <th>d</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>1</td>\n <td>2</td>\n <td>3</td>\n </tr>\n <tr>\n <th>1</th>\n <td>4</td>\n <td>5</td>\n <td>6</td>\n <td>7</td>\n </tr>\n <tr>\n <th>2</th>\n <td>8</td>\n <td>9</td>\n <td>10</td>\n <td>11</td>\n </tr>\n <tr>\n <th>3</th>\n <td>12</td>\n <td>13</td>\n <td>14</td>\n <td>15</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 2
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df = pd.read_csv('example.csv')\n",
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### CSV Output"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.to_csv('example',index=False)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Excel\n",
|
||||
"Pandas peut lire et écrire des fichiers excel, gardez à l'esprit que ceci n'importe que des données. Pas de formules ni d'images, le fait d'avoir des images ou des macros peut provoquer le crash de cette méthode read_excel. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Excel Input"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Unnamed: 0 a b c d\n",
|
||||
"0 0 0 1 2 3\n",
|
||||
"1 1 4 5 6 7\n",
|
||||
"2 2 8 9 10 11\n",
|
||||
"3 3 12 13 14 15"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Unnamed: 0</th>\n <th>a</th>\n <th>b</th>\n <th>c</th>\n <th>d</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>0</td>\n <td>0</td>\n <td>1</td>\n <td>2</td>\n <td>3</td>\n </tr>\n <tr>\n <th>1</th>\n <td>1</td>\n <td>4</td>\n <td>5</td>\n <td>6</td>\n <td>7</td>\n </tr>\n <tr>\n <th>2</th>\n <td>2</td>\n <td>8</td>\n <td>9</td>\n <td>10</td>\n <td>11</td>\n </tr>\n <tr>\n <th>3</th>\n <td>3</td>\n <td>12</td>\n <td>13</td>\n <td>14</td>\n <td>15</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 4
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"pd.read_excel('Excel_Sample.xlsx',sheet_name='Sheet1')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Excel Output"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df.to_excel('Excel_Sample.xlsx',sheet_name='Sheet1')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## HTML\n",
|
||||
"\n",
|
||||
"Vous aurez besoin d'installer htmllib5, lxml et BeautifulSoup4. Dans votre terminal/command prompt tapez:\n",
|
||||
"\n",
|
||||
" conda install lxml\n",
|
||||
" conda install html5lib\n",
|
||||
" conda install BeautifulSoup4\n",
|
||||
"\n",
|
||||
"Puis redémarrez Jupyter Notebook.\n",
|
||||
"(ou utilisez pip install si vous n'utilisez pas la distribution Anaconda)\n",
|
||||
"\n",
|
||||
"Pandas peut lire du HTML. Par exemple:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### HTML Input\n",
|
||||
"\n",
|
||||
"La fonction pandas read_html lit les balises table d'une page web et retourne une liste d'objets DataFrame:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.read_html('http://www.fdic.gov/bank/individual/failed/banklist.html')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Bank Name City ST CERT \\\n",
|
||||
"0 Almena State Bank Almena KS 15426 \n",
|
||||
"1 First City Bank of Florida Fort Walton Beach FL 16748 \n",
|
||||
"2 The First State Bank Barboursville WV 14361 \n",
|
||||
"3 Ericson State Bank Ericson NE 18265 \n",
|
||||
"4 City National Bank of New Jersey Newark NJ 21111 \n",
|
||||
".. ... ... .. ... \n",
|
||||
"558 Superior Bank, FSB Hinsdale IL 32646 \n",
|
||||
"559 Malta National Bank Malta OH 6629 \n",
|
||||
"560 First Alliance Bank & Trust Co. Manchester NH 34264 \n",
|
||||
"561 National State Bank of Metropolis Metropolis IL 3815 \n",
|
||||
"562 Bank of Honolulu Honolulu HI 21029 \n",
|
||||
"\n",
|
||||
" Acquiring Institution Closing Date \n",
|
||||
"0 Equity Bank October 23, 2020 \n",
|
||||
"1 United Fidelity Bank, fsb October 16, 2020 \n",
|
||||
"2 MVB Bank, Inc. April 3, 2020 \n",
|
||||
"3 Farmers and Merchants Bank February 14, 2020 \n",
|
||||
"4 Industrial Bank November 1, 2019 \n",
|
||||
".. ... ... \n",
|
||||
"558 Superior Federal, FSB July 27, 2001 \n",
|
||||
"559 North Valley Bank May 3, 2001 \n",
|
||||
"560 Southern New Hampshire Bank & Trust February 2, 2001 \n",
|
||||
"561 Banterra Bank of Marion December 14, 2000 \n",
|
||||
"562 Bank of the Orient October 13, 2000 \n",
|
||||
"\n",
|
||||
"[563 rows x 6 columns]"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Bank Name</th>\n <th>City</th>\n <th>ST</th>\n <th>CERT</th>\n <th>Acquiring Institution</th>\n <th>Closing Date</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Almena State Bank</td>\n <td>Almena</td>\n <td>KS</td>\n <td>15426</td>\n <td>Equity Bank</td>\n <td>October 23, 2020</td>\n </tr>\n <tr>\n <th>1</th>\n <td>First City Bank of Florida</td>\n <td>Fort Walton Beach</td>\n <td>FL</td>\n <td>16748</td>\n <td>United Fidelity Bank, fsb</td>\n <td>October 16, 2020</td>\n </tr>\n <tr>\n <th>2</th>\n <td>The First State Bank</td>\n <td>Barboursville</td>\n <td>WV</td>\n <td>14361</td>\n <td>MVB Bank, Inc.</td>\n <td>April 3, 2020</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Ericson State Bank</td>\n <td>Ericson</td>\n <td>NE</td>\n <td>18265</td>\n <td>Farmers and Merchants Bank</td>\n <td>February 14, 2020</td>\n </tr>\n <tr>\n <th>4</th>\n <td>City National Bank of New Jersey</td>\n <td>Newark</td>\n <td>NJ</td>\n <td>21111</td>\n <td>Industrial Bank</td>\n <td>November 1, 2019</td>\n </tr>\n <tr>\n <th>...</th>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n <td>...</td>\n </tr>\n <tr>\n <th>558</th>\n <td>Superior Bank, FSB</td>\n <td>Hinsdale</td>\n <td>IL</td>\n <td>32646</td>\n <td>Superior Federal, FSB</td>\n <td>July 27, 2001</td>\n </tr>\n <tr>\n <th>559</th>\n <td>Malta National Bank</td>\n <td>Malta</td>\n <td>OH</td>\n <td>6629</td>\n <td>North Valley Bank</td>\n <td>May 3, 2001</td>\n </tr>\n <tr>\n <th>560</th>\n <td>First Alliance Bank & Trust Co.</td>\n <td>Manchester</td>\n <td>NH</td>\n <td>34264</td>\n <td>Southern New Hampshire Bank & Trust</td>\n <td>February 2, 2001</td>\n </tr>\n <tr>\n <th>561</th>\n <td>National State Bank of Metropolis</td>\n <td>Metropolis</td>\n <td>IL</td>\n <td>3815</td>\n <td>Banterra Bank of Marion</td>\n <td>December 14, 2000</td>\n </tr>\n <tr>\n <th>562</th>\n <td>Bank of Honolulu</td>\n <td>Honolulu</td>\n <td>HI</td>\n <td>21029</td>\n <td>Bank of the Orient</td>\n <td>October 13, 2000</td>\n </tr>\n </tbody>\n</table>\n<p>563 rows × 6 columns</p>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 7
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df[0]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"source": [
|
||||
"____"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "e89404a230d8800c54ad520c7b67d1bd9bb833a07b37dd3e521a178a3dc34904"
|
||||
}
|
||||
}
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.9-final"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
Binary file not shown.
Binary file not shown.
|
|
@ -0,0 +1,5 @@
|
|||
a,b,c,d
|
||||
0,1,2,3
|
||||
4,5,6,7
|
||||
8,9,10,11
|
||||
12,13,14,15
|
||||
|
|
@ -0,0 +1,780 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Pandas Exercices"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Il est temps de tester vos nouvelles compétences en pandas ! Utilisez le fichier csv de ce dossier pour effectuer les tâches en gras ci-dessous!\n",
|
||||
"\n",
|
||||
"#### NOTE : TOUTES LES TÂCHES DOIVENT ÊTRE EFFECTUÉES SUR UNE SEULE LIGNE AVEC DU CODE PANDAS. BLOQUé(e) ? PAS DE PROBLÈME ! CONSULTEZ LE NOTEBOOK AVEC LES SOLUTIONS ! "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Importer pandas et lire le fichier banklist.csv dans un dataframe nommé banks. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"banks = pd.read_csv('banklist.csv')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Afficher les 5 premières lignes du dataframe"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style>\n",
|
||||
" .dataframe thead tr:only-child th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: left;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Bank Name</th>\n",
|
||||
" <th>City</th>\n",
|
||||
" <th>ST</th>\n",
|
||||
" <th>CERT</th>\n",
|
||||
" <th>Acquiring Institution</th>\n",
|
||||
" <th>Closing Date</th>\n",
|
||||
" <th>Updated Date</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>Fayette County Bank</td>\n",
|
||||
" <td>Saint Elmo</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>1802</td>\n",
|
||||
" <td>United Fidelity Bank, fsb</td>\n",
|
||||
" <td>26-May-17</td>\n",
|
||||
" <td>1-Jun-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>Guaranty Bank, (d/b/a BestBank in Georgia & Mi...</td>\n",
|
||||
" <td>Milwaukee</td>\n",
|
||||
" <td>WI</td>\n",
|
||||
" <td>30003</td>\n",
|
||||
" <td>First-Citizens Bank & Trust Company</td>\n",
|
||||
" <td>5-May-17</td>\n",
|
||||
" <td>1-Jun-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>First NBC Bank</td>\n",
|
||||
" <td>New Orleans</td>\n",
|
||||
" <td>LA</td>\n",
|
||||
" <td>58302</td>\n",
|
||||
" <td>Whitney Bank</td>\n",
|
||||
" <td>28-Apr-17</td>\n",
|
||||
" <td>23-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>Proficio Bank</td>\n",
|
||||
" <td>Cottonwood Heights</td>\n",
|
||||
" <td>UT</td>\n",
|
||||
" <td>35495</td>\n",
|
||||
" <td>Cache Valley Bank</td>\n",
|
||||
" <td>3-Mar-17</td>\n",
|
||||
" <td>18-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>Seaway Bank and Trust Company</td>\n",
|
||||
" <td>Chicago</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>19328</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>27-Jan-17</td>\n",
|
||||
" <td>18-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Bank Name City ST \\\n",
|
||||
"0 Fayette County Bank Saint Elmo IL \n",
|
||||
"1 Guaranty Bank, (d/b/a BestBank in Georgia & Mi... Milwaukee WI \n",
|
||||
"2 First NBC Bank New Orleans LA \n",
|
||||
"3 Proficio Bank Cottonwood Heights UT \n",
|
||||
"4 Seaway Bank and Trust Company Chicago IL \n",
|
||||
"\n",
|
||||
" CERT Acquiring Institution Closing Date Updated Date \n",
|
||||
"0 1802 United Fidelity Bank, fsb 26-May-17 1-Jun-17 \n",
|
||||
"1 30003 First-Citizens Bank & Trust Company 5-May-17 1-Jun-17 \n",
|
||||
"2 58302 Whitney Bank 28-Apr-17 23-May-17 \n",
|
||||
"3 35495 Cache Valley Bank 3-Mar-17 18-May-17 \n",
|
||||
"4 19328 State Bank of Texas 27-Jan-17 18-May-17 "
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quels sont les noms de colonne?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Index(['Bank Name', 'City', 'ST', 'CERT', 'Acquiring Institution',\n",
|
||||
" 'Closing Date', 'Updated Date'],\n",
|
||||
" dtype='object')"
|
||||
]
|
||||
},
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien d'états (ST) sont représentés dans cet ensemble de données?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"44"
|
||||
]
|
||||
},
|
||||
"execution_count": 33,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Obtenir une liste ou tableau de tous les états du dataset."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array(['IL', 'WI', 'LA', 'UT', 'NJ', 'AR', 'GA', 'PA', 'TN', 'WA', 'CO',\n",
|
||||
" 'PR', 'FL', 'MN', 'CA', 'MD', 'OK', 'OH', 'SC', 'VA', 'ID', 'TX',\n",
|
||||
" 'CT', 'AZ', 'NV', 'NC', 'KY', 'MO', 'KS', 'AL', 'MI', 'IN', 'IA',\n",
|
||||
" 'NE', 'MS', 'NM', 'OR', 'NY', 'MA', 'SD', 'WY', 'WV', 'NH', 'HI'], dtype=object)"
|
||||
]
|
||||
},
|
||||
"execution_count": 32,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quels sont les 5 états avec le plus de banques en faillite?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true,
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"ST\n",
|
||||
"GA 93\n",
|
||||
"FL 75\n",
|
||||
"IL 67\n",
|
||||
"CA 41\n",
|
||||
"MN 23\n",
|
||||
"Name: Bank Name, dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quels sont les 5 principaux établissements acquéreurs?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"No Acquirer 31\n",
|
||||
"State Bank and Trust Company 12\n",
|
||||
"First-Citizens Bank & Trust Company 11\n",
|
||||
"Ameris Bank 10\n",
|
||||
"U.S. Bank N.A. 9\n",
|
||||
"Name: Acquiring Institution, dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de banques State Bank of Texas a-t-elle acquises ? Combien d'entre elles étaient au Texas ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style>\n",
|
||||
" .dataframe thead tr:only-child th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: left;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Bank Name</th>\n",
|
||||
" <th>City</th>\n",
|
||||
" <th>ST</th>\n",
|
||||
" <th>CERT</th>\n",
|
||||
" <th>Acquiring Institution</th>\n",
|
||||
" <th>Closing Date</th>\n",
|
||||
" <th>Updated Date</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>Seaway Bank and Trust Company</td>\n",
|
||||
" <td>Chicago</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>19328</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>27-Jan-17</td>\n",
|
||||
" <td>18-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>21</th>\n",
|
||||
" <td>The National Republic Bank of Chicago</td>\n",
|
||||
" <td>Chicago</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>916</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>24-Oct-14</td>\n",
|
||||
" <td>6-Jan-16</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>450</th>\n",
|
||||
" <td>Millennium State Bank of Texas</td>\n",
|
||||
" <td>Dallas</td>\n",
|
||||
" <td>TX</td>\n",
|
||||
" <td>57667</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>2-Jul-09</td>\n",
|
||||
" <td>26-Oct-12</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Bank Name City ST CERT \\\n",
|
||||
"4 Seaway Bank and Trust Company Chicago IL 19328 \n",
|
||||
"21 The National Republic Bank of Chicago Chicago IL 916 \n",
|
||||
"450 Millennium State Bank of Texas Dallas TX 57667 \n",
|
||||
"\n",
|
||||
" Acquiring Institution Closing Date Updated Date \n",
|
||||
"4 State Bank of Texas 27-Jan-17 18-May-17 \n",
|
||||
"21 State Bank of Texas 24-Oct-14 6-Jan-16 \n",
|
||||
"450 State Bank of Texas 2-Jul-09 26-Oct-12 "
|
||||
]
|
||||
},
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quelle est la ville la plus fréquente en Californie pour qu'une banque fasse faillite ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style>\n",
|
||||
" .dataframe thead tr:only-child th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: left;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Bank Name</th>\n",
|
||||
" <th>ST</th>\n",
|
||||
" <th>CERT</th>\n",
|
||||
" <th>Acquiring Institution</th>\n",
|
||||
" <th>Closing Date</th>\n",
|
||||
" <th>Updated Date</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>City</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>Los Angeles</th>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Bank Name ST CERT Acquiring Institution Closing Date \\\n",
|
||||
"City \n",
|
||||
"Los Angeles 4 4 4 4 4 \n",
|
||||
"\n",
|
||||
" Updated Date \n",
|
||||
"City \n",
|
||||
"Los Angeles 4 "
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de banques en faillite n'ont pas le mot \"Bank\" dans leur nom?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"14"
|
||||
]
|
||||
},
|
||||
"execution_count": 55,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de noms de banques commencent par la lettre 's' ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 58,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"53"
|
||||
]
|
||||
},
|
||||
"execution_count": 58,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de valeurs CERT sont supérieures à 20000 ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 61,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"417"
|
||||
]
|
||||
},
|
||||
"execution_count": 64,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de noms de banques se composent de seulement deux mots ? (par exemple \"First Bank\" et \"Bank Georgia\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 65,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"114"
|
||||
]
|
||||
},
|
||||
"execution_count": 67,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Bonus: Combien de banques ont fermé en 2008 ? (c'est difficile parce que techniquement nous n'avons pas encore vu les séries temporelles avec pandas ! N'hésitez pas à passer !"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"25"
|
||||
]
|
||||
},
|
||||
"execution_count": 54,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -0,0 +1,787 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Pandas Exercices - Solutions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Il est temps de tester vos nouvelles compétences en pandas ! Utilisez le fichier csv de ce dossier pour effectuer les tâches en gras ci-dessous!\n",
|
||||
"\n",
|
||||
"#### NOTE : TOUTES LES TÂCHES DOIVENT ÊTRE EFFECTUÉES SUR UNE SEULE LIGNE AVEC DU CODE PANDAS. BLOQUé(e) ? PAS DE PROBLÈME ! CONSULTEZ LE NOTEBOOK AVEC LES SOLUTIONS ! "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Importer pandas et lire le fichier banklist.csv dans un dataframe nommé banks. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"banks = pd.read_csv('banklist.csv')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Afficher les 5 premières lignes du dataframe"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Bank Name</th>\n",
|
||||
" <th>City</th>\n",
|
||||
" <th>ST</th>\n",
|
||||
" <th>CERT</th>\n",
|
||||
" <th>Acquiring Institution</th>\n",
|
||||
" <th>Closing Date</th>\n",
|
||||
" <th>Updated Date</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>Fayette County Bank</td>\n",
|
||||
" <td>Saint Elmo</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>1802</td>\n",
|
||||
" <td>United Fidelity Bank, fsb</td>\n",
|
||||
" <td>26-May-17</td>\n",
|
||||
" <td>1-Jun-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>Guaranty Bank, (d/b/a BestBank in Georgia & Mi...</td>\n",
|
||||
" <td>Milwaukee</td>\n",
|
||||
" <td>WI</td>\n",
|
||||
" <td>30003</td>\n",
|
||||
" <td>First-Citizens Bank & Trust Company</td>\n",
|
||||
" <td>5-May-17</td>\n",
|
||||
" <td>1-Jun-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>First NBC Bank</td>\n",
|
||||
" <td>New Orleans</td>\n",
|
||||
" <td>LA</td>\n",
|
||||
" <td>58302</td>\n",
|
||||
" <td>Whitney Bank</td>\n",
|
||||
" <td>28-Apr-17</td>\n",
|
||||
" <td>23-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>Proficio Bank</td>\n",
|
||||
" <td>Cottonwood Heights</td>\n",
|
||||
" <td>UT</td>\n",
|
||||
" <td>35495</td>\n",
|
||||
" <td>Cache Valley Bank</td>\n",
|
||||
" <td>3-Mar-17</td>\n",
|
||||
" <td>18-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>Seaway Bank and Trust Company</td>\n",
|
||||
" <td>Chicago</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>19328</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>27-Jan-17</td>\n",
|
||||
" <td>18-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Bank Name City ST \\\n",
|
||||
"0 Fayette County Bank Saint Elmo IL \n",
|
||||
"1 Guaranty Bank, (d/b/a BestBank in Georgia & Mi... Milwaukee WI \n",
|
||||
"2 First NBC Bank New Orleans LA \n",
|
||||
"3 Proficio Bank Cottonwood Heights UT \n",
|
||||
"4 Seaway Bank and Trust Company Chicago IL \n",
|
||||
"\n",
|
||||
" CERT Acquiring Institution Closing Date Updated Date \n",
|
||||
"0 1802 United Fidelity Bank, fsb 26-May-17 1-Jun-17 \n",
|
||||
"1 30003 First-Citizens Bank & Trust Company 5-May-17 1-Jun-17 \n",
|
||||
"2 58302 Whitney Bank 28-Apr-17 23-May-17 \n",
|
||||
"3 35495 Cache Valley Bank 3-Mar-17 18-May-17 \n",
|
||||
"4 19328 State Bank of Texas 27-Jan-17 18-May-17 "
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quels sont les noms de colonne?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Index(['Bank Name', 'City', 'ST', 'CERT', 'Acquiring Institution',\n",
|
||||
" 'Closing Date', 'Updated Date'],\n",
|
||||
" dtype='object')"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks.columns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien d'états (ST) sont représentés dans cet ensemble de données?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"44"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks['ST'].nunique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Obtenir une liste ou tableau de tous les états du dataset."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array(['IL', 'WI', 'LA', 'UT', 'NJ', 'AR', 'GA', 'PA', 'TN', 'WA', 'CO',\n",
|
||||
" 'PR', 'FL', 'MN', 'CA', 'MD', 'OK', 'OH', 'SC', 'VA', 'ID', 'TX',\n",
|
||||
" 'CT', 'AZ', 'NV', 'NC', 'KY', 'MO', 'KS', 'AL', 'MI', 'IN', 'IA',\n",
|
||||
" 'NE', 'MS', 'NM', 'OR', 'NY', 'MA', 'SD', 'WY', 'WV', 'NH', 'HI'],\n",
|
||||
" dtype=object)"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks['ST'].unique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quels sont les 5 états avec le plus de banques en faillite?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"ST\n",
|
||||
"GA 93\n",
|
||||
"FL 75\n",
|
||||
"IL 67\n",
|
||||
"CA 41\n",
|
||||
"MN 23\n",
|
||||
"Name: Bank Name, dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks.groupby(\"ST\").count().sort_values('Bank Name',ascending=False).iloc[:5]['Bank Name']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quels sont les 5 principaux établissements acquéreurs?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"No Acquirer 31\n",
|
||||
"State Bank and Trust Company 12\n",
|
||||
"First-Citizens Bank & Trust Company 11\n",
|
||||
"Ameris Bank 10\n",
|
||||
"U.S. Bank N.A. 9\n",
|
||||
"Name: Acquiring Institution, dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks['Acquiring Institution'].value_counts().iloc[:5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de banques State Bank of Texas a-t-elle acquises ? Combien d'entre elles étaient au Texas ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Bank Name</th>\n",
|
||||
" <th>City</th>\n",
|
||||
" <th>ST</th>\n",
|
||||
" <th>CERT</th>\n",
|
||||
" <th>Acquiring Institution</th>\n",
|
||||
" <th>Closing Date</th>\n",
|
||||
" <th>Updated Date</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>Seaway Bank and Trust Company</td>\n",
|
||||
" <td>Chicago</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>19328</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>27-Jan-17</td>\n",
|
||||
" <td>18-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>21</th>\n",
|
||||
" <td>The National Republic Bank of Chicago</td>\n",
|
||||
" <td>Chicago</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>916</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>24-Oct-14</td>\n",
|
||||
" <td>6-Jan-16</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>450</th>\n",
|
||||
" <td>Millennium State Bank of Texas</td>\n",
|
||||
" <td>Dallas</td>\n",
|
||||
" <td>TX</td>\n",
|
||||
" <td>57667</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>2-Jul-09</td>\n",
|
||||
" <td>26-Oct-12</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Bank Name City ST CERT \\\n",
|
||||
"4 Seaway Bank and Trust Company Chicago IL 19328 \n",
|
||||
"21 The National Republic Bank of Chicago Chicago IL 916 \n",
|
||||
"450 Millennium State Bank of Texas Dallas TX 57667 \n",
|
||||
"\n",
|
||||
" Acquiring Institution Closing Date Updated Date \n",
|
||||
"4 State Bank of Texas 27-Jan-17 18-May-17 \n",
|
||||
"21 State Bank of Texas 24-Oct-14 6-Jan-16 \n",
|
||||
"450 State Bank of Texas 2-Jul-09 26-Oct-12 "
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks[banks['Acquiring Institution']=='State Bank of Texas']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quelle est la ville la plus fréquente en Californie pour qu'une banque fasse faillite ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Bank Name</th>\n",
|
||||
" <th>ST</th>\n",
|
||||
" <th>CERT</th>\n",
|
||||
" <th>Acquiring Institution</th>\n",
|
||||
" <th>Closing Date</th>\n",
|
||||
" <th>Updated Date</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>City</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>Los Angeles</th>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Bank Name ST CERT Acquiring Institution Closing Date \\\n",
|
||||
"City \n",
|
||||
"Los Angeles 4 4 4 4 4 \n",
|
||||
"\n",
|
||||
" Updated Date \n",
|
||||
"City \n",
|
||||
"Los Angeles 4 "
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks[banks['ST']=='CA'].groupby('City').count().sort_values('Bank Name',ascending=False).head(1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de banques en faillite n'ont pas le mot \"Bank\" dans leur nom?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"14"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# banks['Bank Name'].apply(lambda name: 'Bank' not in name).value_counts()\n",
|
||||
"sum(banks['Bank Name'].apply(lambda name: 'Bank' not in name))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de noms de banques commencent par la lettre 's' ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"53"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sum(banks['Bank Name'].apply(lambda name:name[0].upper() =='S'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de valeurs CERT sont supérieures à 20000 ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"417"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sum(banks['CERT']>20000)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de noms de banques se composent de seulement deux mots ? (par exemple \"First Bank\" et \"Bank Georgia\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"114"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sum(banks['Bank Name'].apply(lambda name: len(name.split())==2))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Bonus: Combien de banques ont fermé en 2008 ? (c'est difficile parce que techniquement nous n'avons pas encore vu les séries temporelles avec pandas ! N'hésitez pas à passer !"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"25"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# ON APPRENDRA UN MEILLEUR MOYEN POUR FAIRE CELA BIENTOT!\n",
|
||||
"sum(banks['Closing Date'].apply(lambda date: date[-2:]) == '08')\n",
|
||||
"\n",
|
||||
"# Meilleure solution\n",
|
||||
"# sum(pd.to_datetime(banks['Closing Date']).apply(lambda date: date.year) == 2008)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -0,0 +1,787 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Pandas Exercices - Solutions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Il est temps de tester vos nouvelles compétences en pandas ! Utilisez le fichier csv de ce dossier pour effectuer les tâches en gras ci-dessous!\n",
|
||||
"\n",
|
||||
"#### NOTE : TOUTES LES TÂCHES DOIVENT ÊTRE EFFECTUÉES SUR UNE SEULE LIGNE AVEC DU CODE PANDAS. BLOQUé(e) ? PAS DE PROBLÈME ! CONSULTEZ LE NOTEBOOK AVEC LES SOLUTIONS ! "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Importer pandas et lire le fichier banklist.csv dans un dataframe nommé banks. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"banks = pd.read_csv('banklist.csv')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Afficher les 5 premières lignes du dataframe"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Bank Name</th>\n",
|
||||
" <th>City</th>\n",
|
||||
" <th>ST</th>\n",
|
||||
" <th>CERT</th>\n",
|
||||
" <th>Acquiring Institution</th>\n",
|
||||
" <th>Closing Date</th>\n",
|
||||
" <th>Updated Date</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>Fayette County Bank</td>\n",
|
||||
" <td>Saint Elmo</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>1802</td>\n",
|
||||
" <td>United Fidelity Bank, fsb</td>\n",
|
||||
" <td>26-May-17</td>\n",
|
||||
" <td>1-Jun-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>Guaranty Bank, (d/b/a BestBank in Georgia & Mi...</td>\n",
|
||||
" <td>Milwaukee</td>\n",
|
||||
" <td>WI</td>\n",
|
||||
" <td>30003</td>\n",
|
||||
" <td>First-Citizens Bank & Trust Company</td>\n",
|
||||
" <td>5-May-17</td>\n",
|
||||
" <td>1-Jun-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>First NBC Bank</td>\n",
|
||||
" <td>New Orleans</td>\n",
|
||||
" <td>LA</td>\n",
|
||||
" <td>58302</td>\n",
|
||||
" <td>Whitney Bank</td>\n",
|
||||
" <td>28-Apr-17</td>\n",
|
||||
" <td>23-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>Proficio Bank</td>\n",
|
||||
" <td>Cottonwood Heights</td>\n",
|
||||
" <td>UT</td>\n",
|
||||
" <td>35495</td>\n",
|
||||
" <td>Cache Valley Bank</td>\n",
|
||||
" <td>3-Mar-17</td>\n",
|
||||
" <td>18-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>Seaway Bank and Trust Company</td>\n",
|
||||
" <td>Chicago</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>19328</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>27-Jan-17</td>\n",
|
||||
" <td>18-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Bank Name City ST \\\n",
|
||||
"0 Fayette County Bank Saint Elmo IL \n",
|
||||
"1 Guaranty Bank, (d/b/a BestBank in Georgia & Mi... Milwaukee WI \n",
|
||||
"2 First NBC Bank New Orleans LA \n",
|
||||
"3 Proficio Bank Cottonwood Heights UT \n",
|
||||
"4 Seaway Bank and Trust Company Chicago IL \n",
|
||||
"\n",
|
||||
" CERT Acquiring Institution Closing Date Updated Date \n",
|
||||
"0 1802 United Fidelity Bank, fsb 26-May-17 1-Jun-17 \n",
|
||||
"1 30003 First-Citizens Bank & Trust Company 5-May-17 1-Jun-17 \n",
|
||||
"2 58302 Whitney Bank 28-Apr-17 23-May-17 \n",
|
||||
"3 35495 Cache Valley Bank 3-Mar-17 18-May-17 \n",
|
||||
"4 19328 State Bank of Texas 27-Jan-17 18-May-17 "
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quels sont les noms de colonne?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Index(['Bank Name', 'City', 'ST', 'CERT', 'Acquiring Institution',\n",
|
||||
" 'Closing Date', 'Updated Date'],\n",
|
||||
" dtype='object')"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks.columns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien d'états (ST) sont représentés dans cet ensemble de données?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"44"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks['ST'].nunique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Obtenir une liste ou tableau de tous les états du dataset."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array(['IL', 'WI', 'LA', 'UT', 'NJ', 'AR', 'GA', 'PA', 'TN', 'WA', 'CO',\n",
|
||||
" 'PR', 'FL', 'MN', 'CA', 'MD', 'OK', 'OH', 'SC', 'VA', 'ID', 'TX',\n",
|
||||
" 'CT', 'AZ', 'NV', 'NC', 'KY', 'MO', 'KS', 'AL', 'MI', 'IN', 'IA',\n",
|
||||
" 'NE', 'MS', 'NM', 'OR', 'NY', 'MA', 'SD', 'WY', 'WV', 'NH', 'HI'],\n",
|
||||
" dtype=object)"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks['ST'].unique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quels sont les 5 états avec le plus de banques en faillite?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"ST\n",
|
||||
"GA 93\n",
|
||||
"FL 75\n",
|
||||
"IL 67\n",
|
||||
"CA 41\n",
|
||||
"MN 23\n",
|
||||
"Name: Bank Name, dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks.groupby(\"ST\").count().sort_values('Bank Name',ascending=False).iloc[:5]['Bank Name']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quels sont les 5 principaux établissements acquéreurs?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"No Acquirer 31\n",
|
||||
"State Bank and Trust Company 12\n",
|
||||
"First-Citizens Bank & Trust Company 11\n",
|
||||
"Ameris Bank 10\n",
|
||||
"U.S. Bank N.A. 9\n",
|
||||
"Name: Acquiring Institution, dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks['Acquiring Institution'].value_counts().iloc[:5]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de banques State Bank of Texas a-t-elle acquises ? Combien d'entre elles étaient au Texas ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Bank Name</th>\n",
|
||||
" <th>City</th>\n",
|
||||
" <th>ST</th>\n",
|
||||
" <th>CERT</th>\n",
|
||||
" <th>Acquiring Institution</th>\n",
|
||||
" <th>Closing Date</th>\n",
|
||||
" <th>Updated Date</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>Seaway Bank and Trust Company</td>\n",
|
||||
" <td>Chicago</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>19328</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>27-Jan-17</td>\n",
|
||||
" <td>18-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>21</th>\n",
|
||||
" <td>The National Republic Bank of Chicago</td>\n",
|
||||
" <td>Chicago</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>916</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>24-Oct-14</td>\n",
|
||||
" <td>6-Jan-16</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>450</th>\n",
|
||||
" <td>Millennium State Bank of Texas</td>\n",
|
||||
" <td>Dallas</td>\n",
|
||||
" <td>TX</td>\n",
|
||||
" <td>57667</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>2-Jul-09</td>\n",
|
||||
" <td>26-Oct-12</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Bank Name City ST CERT \\\n",
|
||||
"4 Seaway Bank and Trust Company Chicago IL 19328 \n",
|
||||
"21 The National Republic Bank of Chicago Chicago IL 916 \n",
|
||||
"450 Millennium State Bank of Texas Dallas TX 57667 \n",
|
||||
"\n",
|
||||
" Acquiring Institution Closing Date Updated Date \n",
|
||||
"4 State Bank of Texas 27-Jan-17 18-May-17 \n",
|
||||
"21 State Bank of Texas 24-Oct-14 6-Jan-16 \n",
|
||||
"450 State Bank of Texas 2-Jul-09 26-Oct-12 "
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks[banks['Acquiring Institution']=='State Bank of Texas']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quelle est la ville la plus fréquente en Californie pour qu'une banque fasse faillite ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Bank Name</th>\n",
|
||||
" <th>ST</th>\n",
|
||||
" <th>CERT</th>\n",
|
||||
" <th>Acquiring Institution</th>\n",
|
||||
" <th>Closing Date</th>\n",
|
||||
" <th>Updated Date</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>City</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>Los Angeles</th>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Bank Name ST CERT Acquiring Institution Closing Date \\\n",
|
||||
"City \n",
|
||||
"Los Angeles 4 4 4 4 4 \n",
|
||||
"\n",
|
||||
" Updated Date \n",
|
||||
"City \n",
|
||||
"Los Angeles 4 "
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"banks[banks['ST']=='CA'].groupby('City').count().sort_values('Bank Name',ascending=False).head(1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de banques en faillite n'ont pas le mot \"Bank\" dans leur nom?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"14"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# banks['Bank Name'].apply(lambda name: 'Bank' not in name).value_counts()\n",
|
||||
"sum(banks['Bank Name'].apply(lambda name: 'Bank' not in name))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de noms de banques commencent par la lettre 's' ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"53"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sum(banks['Bank Name'].apply(lambda name:name[0].upper() =='S'))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de valeurs CERT sont supérieures à 20000 ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"417"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sum(banks['CERT']>20000)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de noms de banques se composent de seulement deux mots ? (par exemple \"First Bank\" et \"Bank Georgia\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"114"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"sum(banks['Bank Name'].apply(lambda name: len(name.split())==2))"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Bonus: Combien de banques ont fermé en 2008 ? (c'est difficile parce que techniquement nous n'avons pas encore vu les séries temporelles avec pandas ! N'hésitez pas à passer !"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"25"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# ON APPRENDRA UN MEILLEUR MOYEN POUR FAIRE CELA BIENTOT!\n",
|
||||
"sum(banks['Closing Date'].apply(lambda date: date[-2:]) == '08')\n",
|
||||
"\n",
|
||||
"# Meilleure solution\n",
|
||||
"# sum(pd.to_datetime(banks['Closing Date']).apply(lambda date: date.year) == 2008)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -0,0 +1,907 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Pandas Exercices - Solutions"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Il est temps de tester vos nouvelles compétences en pandas ! Utilisez le fichier csv de ce dossier pour effectuer les tâches en gras ci-dessous!\n",
|
||||
"\n",
|
||||
"#### NOTE : TOUTES LES TÂCHES DOIVENT ÊTRE EFFECTUÉES SUR UNE SEULE LIGNE AVEC DU CODE PANDAS. BLOQUé(e) ? PAS DE PROBLÈME ! CONSULTEZ LE NOTEBOOK AVEC LES SOLUTIONS ! "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Importer pandas et lire le fichier banklist.csv dans un dataframe nommé banks. "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df=pd.read_csv(\"banklist.csv\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Afficher les 5 premières lignes du dataframe"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Bank Name City ST \\\n",
|
||||
"0 Fayette County Bank Saint Elmo IL \n",
|
||||
"1 Guaranty Bank, (d/b/a BestBank in Georgia & Mi... Milwaukee WI \n",
|
||||
"2 First NBC Bank New Orleans LA \n",
|
||||
"3 Proficio Bank Cottonwood Heights UT \n",
|
||||
"4 Seaway Bank and Trust Company Chicago IL \n",
|
||||
"\n",
|
||||
" CERT Acquiring Institution Closing Date Updated Date \n",
|
||||
"0 1802 United Fidelity Bank, fsb 26-May-17 1-Jun-17 \n",
|
||||
"1 30003 First-Citizens Bank & Trust Company 5-May-17 1-Jun-17 \n",
|
||||
"2 58302 Whitney Bank 28-Apr-17 23-May-17 \n",
|
||||
"3 35495 Cache Valley Bank 3-Mar-17 18-May-17 \n",
|
||||
"4 19328 State Bank of Texas 27-Jan-17 18-May-17 "
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Bank Name</th>\n <th>City</th>\n <th>ST</th>\n <th>CERT</th>\n <th>Acquiring Institution</th>\n <th>Closing Date</th>\n <th>Updated Date</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>0</th>\n <td>Fayette County Bank</td>\n <td>Saint Elmo</td>\n <td>IL</td>\n <td>1802</td>\n <td>United Fidelity Bank, fsb</td>\n <td>26-May-17</td>\n <td>1-Jun-17</td>\n </tr>\n <tr>\n <th>1</th>\n <td>Guaranty Bank, (d/b/a BestBank in Georgia & Mi...</td>\n <td>Milwaukee</td>\n <td>WI</td>\n <td>30003</td>\n <td>First-Citizens Bank & Trust Company</td>\n <td>5-May-17</td>\n <td>1-Jun-17</td>\n </tr>\n <tr>\n <th>2</th>\n <td>First NBC Bank</td>\n <td>New Orleans</td>\n <td>LA</td>\n <td>58302</td>\n <td>Whitney Bank</td>\n <td>28-Apr-17</td>\n <td>23-May-17</td>\n </tr>\n <tr>\n <th>3</th>\n <td>Proficio Bank</td>\n <td>Cottonwood Heights</td>\n <td>UT</td>\n <td>35495</td>\n <td>Cache Valley Bank</td>\n <td>3-Mar-17</td>\n <td>18-May-17</td>\n </tr>\n <tr>\n <th>4</th>\n <td>Seaway Bank and Trust Company</td>\n <td>Chicago</td>\n <td>IL</td>\n <td>19328</td>\n <td>State Bank of Texas</td>\n <td>27-Jan-17</td>\n <td>18-May-17</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 11
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
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|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
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|
||||
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|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Bank Name</th>\n",
|
||||
" <th>City</th>\n",
|
||||
" <th>ST</th>\n",
|
||||
" <th>CERT</th>\n",
|
||||
" <th>Acquiring Institution</th>\n",
|
||||
" <th>Closing Date</th>\n",
|
||||
" <th>Updated Date</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>0</th>\n",
|
||||
" <td>Fayette County Bank</td>\n",
|
||||
" <td>Saint Elmo</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>1802</td>\n",
|
||||
" <td>United Fidelity Bank, fsb</td>\n",
|
||||
" <td>26-May-17</td>\n",
|
||||
" <td>1-Jun-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>1</th>\n",
|
||||
" <td>Guaranty Bank, (d/b/a BestBank in Georgia & Mi...</td>\n",
|
||||
" <td>Milwaukee</td>\n",
|
||||
" <td>WI</td>\n",
|
||||
" <td>30003</td>\n",
|
||||
" <td>First-Citizens Bank & Trust Company</td>\n",
|
||||
" <td>5-May-17</td>\n",
|
||||
" <td>1-Jun-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2</th>\n",
|
||||
" <td>First NBC Bank</td>\n",
|
||||
" <td>New Orleans</td>\n",
|
||||
" <td>LA</td>\n",
|
||||
" <td>58302</td>\n",
|
||||
" <td>Whitney Bank</td>\n",
|
||||
" <td>28-Apr-17</td>\n",
|
||||
" <td>23-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>3</th>\n",
|
||||
" <td>Proficio Bank</td>\n",
|
||||
" <td>Cottonwood Heights</td>\n",
|
||||
" <td>UT</td>\n",
|
||||
" <td>35495</td>\n",
|
||||
" <td>Cache Valley Bank</td>\n",
|
||||
" <td>3-Mar-17</td>\n",
|
||||
" <td>18-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>Seaway Bank and Trust Company</td>\n",
|
||||
" <td>Chicago</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>19328</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>27-Jan-17</td>\n",
|
||||
" <td>18-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Bank Name City ST \\\n",
|
||||
"0 Fayette County Bank Saint Elmo IL \n",
|
||||
"1 Guaranty Bank, (d/b/a BestBank in Georgia & Mi... Milwaukee WI \n",
|
||||
"2 First NBC Bank New Orleans LA \n",
|
||||
"3 Proficio Bank Cottonwood Heights UT \n",
|
||||
"4 Seaway Bank and Trust Company Chicago IL \n",
|
||||
"\n",
|
||||
" CERT Acquiring Institution Closing Date Updated Date \n",
|
||||
"0 1802 United Fidelity Bank, fsb 26-May-17 1-Jun-17 \n",
|
||||
"1 30003 First-Citizens Bank & Trust Company 5-May-17 1-Jun-17 \n",
|
||||
"2 58302 Whitney Bank 28-Apr-17 23-May-17 \n",
|
||||
"3 35495 Cache Valley Bank 3-Mar-17 18-May-17 \n",
|
||||
"4 19328 State Bank of Texas 27-Jan-17 18-May-17 "
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quels sont les noms de colonne?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Index(['Bank Name', 'City', 'ST', 'CERT', 'Acquiring Institution',\n",
|
||||
" 'Closing Date', 'Updated Date'],\n",
|
||||
" dtype='object')"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 13
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.columns"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Index(['Bank Name', 'City', 'ST', 'CERT', 'Acquiring Institution',\n",
|
||||
" 'Closing Date', 'Updated Date'],\n",
|
||||
" dtype='object')"
|
||||
]
|
||||
},
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien d'états (ST) sont représentés dans cet ensemble de données?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"44"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 18
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df[\"ST\"].nunique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"44"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Obtenir une liste ou tableau de tous les états du dataset."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array(['IL', 'WI', 'LA', 'UT', 'NJ', 'AR', 'GA', 'PA', 'TN', 'WA', 'CO',\n",
|
||||
" 'PR', 'FL', 'MN', 'CA', 'MD', 'OK', 'OH', 'SC', 'VA', 'ID', 'TX',\n",
|
||||
" 'CT', 'AZ', 'NV', 'NC', 'KY', 'MO', 'KS', 'AL', 'MI', 'IN', 'IA',\n",
|
||||
" 'NE', 'MS', 'NM', 'OR', 'NY', 'MA', 'SD', 'WY', 'WV', 'NH', 'HI'],\n",
|
||||
" dtype=object)"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 19
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df[\"ST\"].unique()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"array(['IL', 'WI', 'LA', 'UT', 'NJ', 'AR', 'GA', 'PA', 'TN', 'WA', 'CO',\n",
|
||||
" 'PR', 'FL', 'MN', 'CA', 'MD', 'OK', 'OH', 'SC', 'VA', 'ID', 'TX',\n",
|
||||
" 'CT', 'AZ', 'NV', 'NC', 'KY', 'MO', 'KS', 'AL', 'MI', 'IN', 'IA',\n",
|
||||
" 'NE', 'MS', 'NM', 'OR', 'NY', 'MA', 'SD', 'WY', 'WV', 'NH', 'HI'],\n",
|
||||
" dtype=object)"
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quels sont les 5 états avec le plus de banques en faillite?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {
|
||||
"scrolled": true
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"ST\n",
|
||||
"GA 93\n",
|
||||
"FL 75\n",
|
||||
"IL 67\n",
|
||||
"CA 41\n",
|
||||
"MN 23\n",
|
||||
"dtype: int64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 27
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.value_counts(\"ST\").sort_values(ascending=False).head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"ST\n",
|
||||
"GA 93\n",
|
||||
"FL 75\n",
|
||||
"IL 67\n",
|
||||
"CA 41\n",
|
||||
"MN 23\n",
|
||||
"Name: Bank Name, dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quels sont les 5 principaux établissements acquéreurs?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"No Acquirer 31\n",
|
||||
"State Bank and Trust Company 12\n",
|
||||
"First-Citizens Bank & Trust Company 11\n",
|
||||
"Ameris Bank 10\n",
|
||||
"U.S. Bank N.A. 9\n",
|
||||
"Name: Acquiring Institution, dtype: int64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 29
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df[\"Acquiring Institution\"].value_counts().sort_values(ascending=False).head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"No Acquirer 31\n",
|
||||
"State Bank and Trust Company 12\n",
|
||||
"First-Citizens Bank & Trust Company 11\n",
|
||||
"Ameris Bank 10\n",
|
||||
"U.S. Bank N.A. 9\n",
|
||||
"Name: Acquiring Institution, dtype: int64"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de banques State Bank of Texas a-t-elle acquises ? Combien d'entre elles étaient au Texas ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Bank Name City ST CERT \\\n",
|
||||
"4 Seaway Bank and Trust Company Chicago IL 19328 \n",
|
||||
"21 The National Republic Bank of Chicago Chicago IL 916 \n",
|
||||
"450 Millennium State Bank of Texas Dallas TX 57667 \n",
|
||||
"\n",
|
||||
" Acquiring Institution Closing Date Updated Date \n",
|
||||
"4 State Bank of Texas 27-Jan-17 18-May-17 \n",
|
||||
"21 State Bank of Texas 24-Oct-14 6-Jan-16 \n",
|
||||
"450 State Bank of Texas 2-Jul-09 26-Oct-12 "
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Bank Name</th>\n <th>City</th>\n <th>ST</th>\n <th>CERT</th>\n <th>Acquiring Institution</th>\n <th>Closing Date</th>\n <th>Updated Date</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>4</th>\n <td>Seaway Bank and Trust Company</td>\n <td>Chicago</td>\n <td>IL</td>\n <td>19328</td>\n <td>State Bank of Texas</td>\n <td>27-Jan-17</td>\n <td>18-May-17</td>\n </tr>\n <tr>\n <th>21</th>\n <td>The National Republic Bank of Chicago</td>\n <td>Chicago</td>\n <td>IL</td>\n <td>916</td>\n <td>State Bank of Texas</td>\n <td>24-Oct-14</td>\n <td>6-Jan-16</td>\n </tr>\n <tr>\n <th>450</th>\n <td>Millennium State Bank of Texas</td>\n <td>Dallas</td>\n <td>TX</td>\n <td>57667</td>\n <td>State Bank of Texas</td>\n <td>2-Jul-09</td>\n <td>26-Oct-12</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 30
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df[(df[\"Acquiring Institution\"]==\"State Bank of Texas\")]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Bank Name</th>\n",
|
||||
" <th>City</th>\n",
|
||||
" <th>ST</th>\n",
|
||||
" <th>CERT</th>\n",
|
||||
" <th>Acquiring Institution</th>\n",
|
||||
" <th>Closing Date</th>\n",
|
||||
" <th>Updated Date</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>4</th>\n",
|
||||
" <td>Seaway Bank and Trust Company</td>\n",
|
||||
" <td>Chicago</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>19328</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>27-Jan-17</td>\n",
|
||||
" <td>18-May-17</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>21</th>\n",
|
||||
" <td>The National Republic Bank of Chicago</td>\n",
|
||||
" <td>Chicago</td>\n",
|
||||
" <td>IL</td>\n",
|
||||
" <td>916</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>24-Oct-14</td>\n",
|
||||
" <td>6-Jan-16</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>450</th>\n",
|
||||
" <td>Millennium State Bank of Texas</td>\n",
|
||||
" <td>Dallas</td>\n",
|
||||
" <td>TX</td>\n",
|
||||
" <td>57667</td>\n",
|
||||
" <td>State Bank of Texas</td>\n",
|
||||
" <td>2-Jul-09</td>\n",
|
||||
" <td>26-Oct-12</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Bank Name City ST CERT \\\n",
|
||||
"4 Seaway Bank and Trust Company Chicago IL 19328 \n",
|
||||
"21 The National Republic Bank of Chicago Chicago IL 916 \n",
|
||||
"450 Millennium State Bank of Texas Dallas TX 57667 \n",
|
||||
"\n",
|
||||
" Acquiring Institution Closing Date Updated Date \n",
|
||||
"4 State Bank of Texas 27-Jan-17 18-May-17 \n",
|
||||
"21 State Bank of Texas 24-Oct-14 6-Jan-16 \n",
|
||||
"450 State Bank of Texas 2-Jul-09 26-Oct-12 "
|
||||
]
|
||||
},
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Quelle est la ville la plus fréquente en Californie pour qu'une banque fasse faillite ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 36,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Bank Name ST CERT Acquiring Institution Closing Date \\\n",
|
||||
"City \n",
|
||||
"Los Angeles 4 4 4 4 4 \n",
|
||||
"\n",
|
||||
" Updated Date \n",
|
||||
"City \n",
|
||||
"Los Angeles 4 "
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Bank Name</th>\n <th>ST</th>\n <th>CERT</th>\n <th>Acquiring Institution</th>\n <th>Closing Date</th>\n <th>Updated Date</th>\n </tr>\n <tr>\n <th>City</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>Los Angeles</th>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n <td>4</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 36
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df[df['ST']=='CA'].groupby('City').count().sort_values('Bank Name',ascending=False).head(1)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Bank Name</th>\n",
|
||||
" <th>ST</th>\n",
|
||||
" <th>CERT</th>\n",
|
||||
" <th>Acquiring Institution</th>\n",
|
||||
" <th>Closing Date</th>\n",
|
||||
" <th>Updated Date</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>City</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>Los Angeles</th>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" <td>4</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Bank Name ST CERT Acquiring Institution Closing Date \\\n",
|
||||
"City \n",
|
||||
"Los Angeles 4 4 4 4 4 \n",
|
||||
"\n",
|
||||
" Updated Date \n",
|
||||
"City \n",
|
||||
"Los Angeles 4 "
|
||||
]
|
||||
},
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de banques en faillite n'ont pas le mot \"Bank\" dans leur nom?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 43,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"False 537\n",
|
||||
"True 14\n",
|
||||
"Name: Bank Name, dtype: int64"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 43
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df['Bank Name'].apply(lambda name: 'Bank' not in name).value_counts()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"14"
|
||||
]
|
||||
},
|
||||
"execution_count": 20,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de noms de banques commencent par la lettre 's' ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"53"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de valeurs CERT sont supérieures à 20000 ?"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"417"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Combien de noms de banques se composent de seulement deux mots ? (par exemple \"First Bank\" et \"Bank Georgia\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"114"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Bonus: Combien de banques ont fermé en 2008 ? (c'est difficile parce que techniquement nous n'avons pas encore vu les séries temporelles avec pandas ! N'hésitez pas à passer !"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 27,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# CODE ICI"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"25"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bon travail!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "e89404a230d8800c54ad520c7b67d1bd9bb833a07b37dd3e521a178a3dc34904"
|
||||
}
|
||||
}
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.9-final"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
|
|
@ -0,0 +1,552 @@
|
|||
Bank Name,City,ST,CERT,Acquiring Institution,Closing Date,Updated Date
|
||||
Fayette County Bank,Saint Elmo,IL,1802,"United Fidelity Bank, fsb",26-May-17,1-Jun-17
|
||||
"Guaranty Bank, (d/b/a BestBank in Georgia & Michigan) ",Milwaukee,WI,30003,First-Citizens Bank & Trust Company,5-May-17,1-Jun-17
|
||||
First NBC Bank,New Orleans,LA,58302,Whitney Bank,28-Apr-17,23-May-17
|
||||
Proficio Bank,Cottonwood Heights,UT,35495,Cache Valley Bank,3-Mar-17,18-May-17
|
||||
Seaway Bank and Trust Company,Chicago,IL,19328,State Bank of Texas,27-Jan-17,18-May-17
|
||||
Harvest Community Bank,Pennsville,NJ,34951,First-Citizens Bank & Trust Company,13-Jan-17,18-May-17
|
||||
Allied Bank,Mulberry,AR,91,Today's Bank,23-Sep-16,17-Nov-16
|
||||
The Woodbury Banking Company,Woodbury,GA,11297,United Bank,19-Aug-16,1-Jun-17
|
||||
First CornerStone Bank,King of Prussia,PA,35312,First-Citizens Bank & Trust Company,6-May-16,6-Sep-16
|
||||
Trust Company Bank,Memphis,TN,9956,The Bank of Fayette County,29-Apr-16,6-Sep-16
|
||||
North Milwaukee State Bank,Milwaukee,WI,20364,First-Citizens Bank & Trust Company,11-Mar-16,13-Mar-17
|
||||
Hometown National Bank,Longview,WA,35156,Twin City Bank,2-Oct-15,13-Apr-16
|
||||
The Bank of Georgia,Peachtree City,GA,35259,Fidelity Bank,2-Oct-15,24-Oct-16
|
||||
Premier Bank,Denver,CO,34112,"United Fidelity Bank, fsb",10-Jul-15,17-Aug-16
|
||||
Edgebrook Bank,Chicago,IL,57772,Republic Bank of Chicago,8-May-15,12-Jul-16
|
||||
Doral Bank,San Juan,PR,32102,Banco Popular de Puerto Rico,27-Feb-15,13-May-15
|
||||
Capitol City Bank & Trust Company,Atlanta,GA,33938,First-Citizens Bank & Trust Company,13-Feb-15,21-Apr-15
|
||||
Highland Community Bank,Chicago,IL,20290,"United Fidelity Bank, fsb",23-Jan-15,21-Apr-15
|
||||
First National Bank of Crestview ,Crestview,FL,17557,First NBC Bank,16-Jan-15,8-May-17
|
||||
Northern Star Bank,Mankato,MN,34983,BankVista,19-Dec-14,6-Jan-16
|
||||
"Frontier Bank, FSB D/B/A El Paseo Bank",Palm Desert,CA,34738,"Bank of Southern California, N.A.",7-Nov-14,10-Nov-16
|
||||
The National Republic Bank of Chicago,Chicago,IL,916,State Bank of Texas,24-Oct-14,6-Jan-16
|
||||
NBRS Financial,Rising Sun,MD,4862,Howard Bank,17-Oct-14,26-Mar-15
|
||||
"GreenChoice Bank, fsb",Chicago,IL,28462,"Providence Bank, LLC",25-Jul-14,12-Dec-16
|
||||
Eastside Commercial Bank,Conyers,GA,58125,Community & Southern Bank,18-Jul-14,11-Jul-16
|
||||
The Freedom State Bank ,Freedom,OK,12483,Alva State Bank & Trust Company,27-Jun-14,25-Mar-16
|
||||
Valley Bank,Fort Lauderdale,FL,21793,"Landmark Bank, National Association",20-Jun-14,29-Jun-15
|
||||
Valley Bank,Moline,IL,10450,Great Southern Bank,20-Jun-14,26-Jun-15
|
||||
Slavie Federal Savings Bank,Bel Air,MD,32368,"Bay Bank, FSB",30-May-14,12-Dec-16
|
||||
Columbia Savings Bank,Cincinnati,OH,32284,"United Fidelity Bank, fsb",23-May-14,10-Nov-16
|
||||
AztecAmerica Bank ,Berwyn,IL,57866,Republic Bank of Chicago,16-May-14,20-Oct-16
|
||||
Allendale County Bank,Fairfax,SC,15062,Palmetto State Bank,25-Apr-14,18-Jul-14
|
||||
Vantage Point Bank,Horsham,PA,58531,First Choice Bank,28-Feb-14,3-Mar-15
|
||||
"Millennium Bank, National Association ",Sterling,VA,35096,WashingtonFirst Bank,28-Feb-14,3-Mar-15
|
||||
Syringa Bank,Boise,ID,34296,Sunwest Bank,31-Jan-14,12-Apr-16
|
||||
The Bank of Union,El Reno,OK,17967,BancFirst,24-Jan-14,25-Mar-16
|
||||
DuPage National Bank,West Chicago,IL,5732,Republic Bank of Chicago,17-Jan-14,20-Oct-16
|
||||
"Texas Community Bank, National Association",The Woodlands,TX,57431,"Spirit of Texas Bank, SSB",13-Dec-13,29-Dec-14
|
||||
Bank of Jackson County,Graceville,FL,14794,First Federal Bank of Florida,30-Oct-13,20-Oct-16
|
||||
First National Bank also operating as The National Bank of El Paso,Edinburg,TX,14318,PlainsCapital Bank,13-Sep-13,27-May-15
|
||||
The Community's Bank,Bridgeport,CT,57041,No Acquirer,13-Sep-13,7-Dec-15
|
||||
Sunrise Bank of Arizona,Phoenix,AZ,34707,"First Fidelity Bank, National Association",23-Aug-13,3-May-17
|
||||
Community South Bank,Parsons,TN,19849,"CB&S Bank, Inc.",23-Aug-13,12-Sep-14
|
||||
Bank of Wausau,Wausau,WI,35016,Nicolet National Bank,9-Aug-13,24-Oct-13
|
||||
First Community Bank of Southwest Florida (also operating as Community Bank of Cape Coral),Fort Myers,FL,34943,C1 Bank,2-Aug-13,9-Feb-17
|
||||
Mountain National Bank,Sevierville,TN,34789,"First Tennessee Bank, National Association",7-Jun-13,4-Feb-16
|
||||
1st Commerce Bank,North Las Vegas,NV,58358,Plaza Bank,6-Jun-13,12-Jul-13
|
||||
Banks of Wisconsin d/b/a Bank of Kenosha,Kenosha,WI,35386,"North Shore Bank, FSB",31-May-13,5-May-17
|
||||
Central Arizona Bank,Scottsdale,AZ,34527,Western State Bank,14-May-13,7-Dec-15
|
||||
Sunrise Bank,Valdosta,GA,58185,Synovus Bank,10-May-13,26-Jun-14
|
||||
Pisgah Community Bank,Asheville,NC,58701,"Capital Bank, N.A.",10-May-13,8-Aug-16
|
||||
Douglas County Bank,Douglasville,GA,21649,Hamilton State Bank,26-Apr-13,25-Apr-14
|
||||
Parkway Bank,Lenoir,NC,57158,"CertusBank, National Association",26-Apr-13,20-Oct-16
|
||||
Chipola Community Bank,Marianna,FL,58034,First Federal Bank of Florida,19-Apr-13,21-Sep-15
|
||||
Heritage Bank of North Florida,Orange Park,FL,26680,FirstAtlantic Bank,19-Apr-13,8-Aug-16
|
||||
First Federal Bank,Lexington,KY,29594,Your Community Bank,19-Apr-13,12-Dec-16
|
||||
Gold Canyon Bank,Gold Canyon,AZ,58066,"First Scottsdale Bank, National Association",5-Apr-13,7-Oct-15
|
||||
Frontier Bank,LaGrange,GA,16431,HeritageBank of the South,8-Mar-13,2-Mar-16
|
||||
Covenant Bank,Chicago,IL,22476,Liberty Bank and Trust Company,15-Feb-13,21-Sep-15
|
||||
1st Regents Bank,Andover,MN,57157,First Minnesota Bank,18-Jan-13,12-Jul-16
|
||||
Westside Community Bank,University Place,WA,33997,Sunwest Bank,11-Jan-13,8-Aug-16
|
||||
Community Bank of the Ozarks,Sunrise Beach,MO,27331,Bank of Sullivan,14-Dec-12,4-Apr-14
|
||||
Hometown Community Bank,Braselton,GA,57928,"CertusBank, National Association",16-Nov-12,6-Jan-16
|
||||
Citizens First National Bank,Princeton,IL,3731,Heartland Bank and Trust Company,2-Nov-12,22-Nov-13
|
||||
Heritage Bank of Florida,Lutz,FL,35009,Centennial Bank,2-Nov-12,21-Mar-14
|
||||
NOVA Bank,Berwyn,PA,27148,No Acquirer,26-Oct-12,24-Jan-13
|
||||
Excel Bank,Sedalia,MO,19189,Simmons First National Bank,19-Oct-12,22-Oct-13
|
||||
First East Side Savings Bank,Tamarac,FL,28144,Stearns Bank N.A.,19-Oct-12,6-Jan-16
|
||||
GulfSouth Private Bank,Destin,FL,58073,SmartBank,19-Oct-12,21-Mar-14
|
||||
First United Bank,Crete,IL,20685,"Old Plank Trail Community Bank, National Association",28-Sep-12,1-Nov-13
|
||||
Truman Bank,St. Louis,MO,27316,Simmons First National Bank,14-Sep-12,17-Dec-12
|
||||
First Commercial Bank,Bloomington,MN,35246,Republic Bank & Trust Company,7-Sep-12,17-Dec-12
|
||||
Waukegan Savings Bank,Waukegan,IL,28243,First Midwest Bank,3-Aug-12,7-Dec-15
|
||||
Jasper Banking Company,Jasper,GA,16240,Stearns Bank N.A.,27-Jul-12,21-Mar-14
|
||||
Second Federal Savings and Loan Association of Chicago,Chicago,IL,27986,Hinsdale Bank & Trust Company,20-Jul-12,14-Jan-13
|
||||
Heartland Bank,Leawood,KS,1361,Metcalf Bank,20-Jul-12,30-Jul-13
|
||||
First Cherokee State Bank,Woodstock,GA,32711,Community & Southern Bank,20-Jul-12,6-Jun-16
|
||||
Georgia Trust Bank,Buford,GA,57847,Community & Southern Bank,20-Jul-12,21-Mar-14
|
||||
The Royal Palm Bank of Florida,Naples,FL,57096,First National Bank of the Gulf Coast,20-Jul-12,21-Mar-14
|
||||
Glasgow Savings Bank,Glasgow,MO,1056,Regional Missouri Bank,13-Jul-12,19-Aug-14
|
||||
Montgomery Bank & Trust,Ailey,GA,19498,Ameris Bank,6-Jul-12,21-Mar-14
|
||||
The Farmers Bank of Lynchburg,Lynchburg,TN,1690,Clayton Bank and Trust,15-Jun-12,8-Aug-16
|
||||
Security Exchange Bank,Marietta,GA,35299,Fidelity Bank,15-Jun-12,21-Mar-14
|
||||
Putnam State Bank,Palatka,FL,27405,Harbor Community Bank,15-Jun-12,21-Mar-14
|
||||
Waccamaw Bank,Whiteville,NC,34515,First Community Bank,8-Jun-12,21-Mar-14
|
||||
Farmers' and Traders' State Bank,Shabbona,IL,9257,First State Bank,8-Jun-12,10-Oct-12
|
||||
Carolina Federal Savings Bank,Charleston,SC,35372,Bank of North Carolina,8-Jun-12,21-Mar-14
|
||||
First Capital Bank,Kingfisher,OK,416,F & M Bank,8-Jun-12,5-Feb-15
|
||||
"Alabama Trust Bank, National Association",Sylacauga,AL,35224,Southern States Bank,18-May-12,21-Sep-15
|
||||
"Security Bank, National Association",North Lauderdale,FL,23156,Banesco USA,4-May-12,12-Apr-16
|
||||
Palm Desert National Bank,Palm Desert,CA,23632,Pacific Premier Bank,27-Apr-12,7-Dec-15
|
||||
Plantation Federal Bank,Pawleys Island,SC,32503,First Federal Bank,27-Apr-12,10-Nov-16
|
||||
"Inter Savings Bank, fsb D/B/A InterBank, fsb",Maple Grove,MN,31495,Great Southern Bank,27-Apr-12,17-May-13
|
||||
HarVest Bank of Maryland,Gaithersburg,MD,57766,Sonabank,27-Apr-12,21-Sep-15
|
||||
Bank of the Eastern Shore,Cambridge,MD,26759,No Acquirer,27-Apr-12,17-Oct-12
|
||||
"Fort Lee Federal Savings Bank, FSB",Fort Lee,NJ,35527,Alma Bank,20-Apr-12,17-May-13
|
||||
Fidelity Bank,Dearborn,MI,33883,The Huntington National Bank,30-Mar-12,4-Feb-14
|
||||
Premier Bank,Wilmette,IL,35419,International Bank of Chicago,23-Mar-12,17-Oct-12
|
||||
Covenant Bank & Trust,Rock Spring,GA,58068,"Stearns Bank, N.A.",23-Mar-12,21-Mar-14
|
||||
New City Bank,Chicago,IL,57597,No Acquirer,9-Mar-12,29-Oct-12
|
||||
Global Commerce Bank,Doraville,GA,34046,Metro City Bank,2-Mar-12,26-Jun-14
|
||||
Home Savings of America,Little Falls,MN,29178,No Acquirer,24-Feb-12,17-Dec-12
|
||||
Central Bank of Georgia,Ellaville,GA,5687,Ameris Bank,24-Feb-12,21-Mar-14
|
||||
SCB Bank,Shelbyville,IN,29761,"First Merchants Bank, National Association",10-Feb-12,19-Feb-15
|
||||
Charter National Bank and Trust,Hoffman Estates,IL,23187,"Barrington Bank & Trust Company, National Association",10-Feb-12,25-Mar-13
|
||||
BankEast,Knoxville,TN,19869,"U.S. Bank, N.A.",27-Jan-12,7-Dec-15
|
||||
Patriot Bank Minnesota,Forest Lake,MN,34823,First Resource Bank,27-Jan-12,12-Sep-12
|
||||
Tennessee Commerce Bank,Franklin,TN,35296,Republic Bank & Trust Company,27-Jan-12,21-Mar-14
|
||||
First Guaranty Bank and Trust Company of Jacksonville,Jacksonville,FL,16579,"CenterState Bank of Florida, N.A.",27-Jan-12,11-Jul-16
|
||||
American Eagle Savings Bank,Boothwyn,PA,31581,"Capital Bank, N.A.",20-Jan-12,25-Jan-13
|
||||
The First State Bank,Stockbridge,GA,19252,Hamilton State Bank,20-Jan-12,21-Mar-14
|
||||
Central Florida State Bank,Belleview,FL,57186,"CenterState Bank of Florida, N.A.",20-Jan-12,6-Jun-16
|
||||
Western National Bank,Phoenix,AZ,57917,Washington Federal,16-Dec-11,5-Feb-15
|
||||
Premier Community Bank of the Emerald Coast,Crestview,FL,58343,Summit Bank,16-Dec-11,21-Mar-14
|
||||
Central Progressive Bank,Lacombe,LA,19657,First NBC Bank,18-Nov-11,5-Feb-15
|
||||
Polk County Bank,Johnston,IA,14194,Grinnell State Bank,18-Nov-11,15-Aug-12
|
||||
Community Bank of Rockmart,Rockmart,GA,57860,Century Bank of Georgia,10-Nov-11,21-Mar-14
|
||||
SunFirst Bank,Saint George,UT,57087,Cache Valley Bank,4-Nov-11,16-Nov-12
|
||||
"Mid City Bank, Inc.",Omaha,NE,19397,Premier Bank,4-Nov-11,15-Aug-12
|
||||
All American Bank,Des Plaines,IL,57759,International Bank of Chicago,28-Oct-11,15-Aug-12
|
||||
Community Banks of Colorado,Greenwood Village,CO,21132,"Bank Midwest, N.A.",21-Oct-11,2-Jan-13
|
||||
Community Capital Bank,Jonesboro,GA,57036,State Bank and Trust Company,21-Oct-11,6-Jan-16
|
||||
Decatur First Bank,Decatur,GA,34392,Fidelity Bank,21-Oct-11,21-Mar-14
|
||||
Old Harbor Bank,Clearwater,FL,57537,1st United Bank,21-Oct-11,21-Mar-14
|
||||
Country Bank,Aledo,IL,35395,Blackhawk Bank & Trust,14-Oct-11,13-Apr-17
|
||||
First State Bank,Cranford,NJ,58046,Northfield Bank,14-Oct-11,8-Nov-12
|
||||
"Blue Ridge Savings Bank, Inc.",Asheville,NC,32347,Bank of North Carolina,14-Oct-11,21-Mar-14
|
||||
Piedmont Community Bank,Gray,GA,57256,State Bank and Trust Company,14-Oct-11,21-Mar-14
|
||||
Sun Security Bank,Ellington,MO,20115,Great Southern Bank,7-Oct-11,8-May-17
|
||||
The RiverBank,Wyoming,MN,10216,Central Bank,7-Oct-11,7-Nov-12
|
||||
First International Bank,Plano,TX,33513,American First National Bank,30-Sep-11,5-Feb-15
|
||||
Citizens Bank of Northern California,Nevada City,CA,33983,Tri Counties Bank,23-Sep-11,9-Oct-12
|
||||
Bank of the Commonwealth,Norfolk,VA,20408,Southern Bank and Trust Company,23-Sep-11,9-Oct-12
|
||||
The First National Bank of Florida,Milton,FL,25155,CharterBank,9-Sep-11,21-Mar-14
|
||||
CreekSide Bank,Woodstock,GA,58226,Georgia Commerce Bank,2-Sep-11,21-Mar-14
|
||||
Patriot Bank of Georgia,Cumming,GA,58273,Georgia Commerce Bank,2-Sep-11,21-Mar-14
|
||||
First Choice Bank,Geneva,IL,57212,Inland Bank & Trust,19-Aug-11,5-Feb-15
|
||||
First Southern National Bank,Statesboro,GA,57239,Heritage Bank of the South,19-Aug-11,2-Mar-16
|
||||
Lydian Private Bank,Palm Beach,FL,35356,"Sabadell United Bank, N.A.",19-Aug-11,21-Mar-14
|
||||
Public Savings Bank,Huntingdon Valley,PA,34130,"Capital Bank, N.A.",18-Aug-11,15-Aug-12
|
||||
The First National Bank of Olathe,Olathe,KS,4744,Enterprise Bank & Trust,12-Aug-11,23-Aug-12
|
||||
Bank of Whitman,Colfax,WA,22528,Columbia State Bank,5-Aug-11,16-Aug-12
|
||||
Bank of Shorewood,Shorewood,IL,22637,Heartland Bank and Trust Company,5-Aug-11,20-Oct-16
|
||||
Integra Bank National Association,Evansville,IN,4392,Old National Bank,29-Jul-11,16-Aug-12
|
||||
"BankMeridian, N.A.",Columbia,SC,58222,SCBT National Association,29-Jul-11,21-Mar-14
|
||||
Virginia Business Bank,Richmond,VA,58283,Xenith Bank,29-Jul-11,9-Oct-12
|
||||
Bank of Choice,Greeley,CO,2994,"Bank Midwest, N.A.",22-Jul-11,12-Sep-12
|
||||
LandMark Bank of Florida,Sarasota,FL,35244,American Momentum Bank,22-Jul-11,21-Mar-14
|
||||
Southshore Community Bank,Apollo Beach,FL,58056,American Momentum Bank,22-Jul-11,5-Feb-15
|
||||
Summit Bank,Prescott,AZ,57442,The Foothills Bank,15-Jul-11,19-Aug-14
|
||||
First Peoples Bank,Port St. Lucie,FL,34870,"Premier American Bank, N.A.",15-Jul-11,12-Jul-16
|
||||
High Trust Bank,Stockbridge,GA,19554,Ameris Bank,15-Jul-11,21-Mar-14
|
||||
One Georgia Bank,Atlanta,GA,58238,Ameris Bank,15-Jul-11,21-Mar-14
|
||||
Signature Bank,Windsor,CO,57835,Points West Community Bank,8-Jul-11,26-Oct-12
|
||||
Colorado Capital Bank,Castle Rock,CO,34522,First-Citizens Bank & Trust Company,8-Jul-11,10-Nov-16
|
||||
First Chicago Bank & Trust,Chicago,IL,27935,Northbrook Bank & Trust Company,8-Jul-11,9-Sep-12
|
||||
Mountain Heritage Bank,Clayton,GA,57593,First American Bank and Trust Company,24-Jun-11,9-Feb-17
|
||||
First Commercial Bank of Tampa Bay,Tampa,FL,27583,Stonegate Bank,17-Jun-11,12-Sep-16
|
||||
McIntosh State Bank,Jackson,GA,19237,Hamilton State Bank,17-Jun-11,21-Mar-14
|
||||
Atlantic Bank and Trust,Charleston,SC,58420,"First Citizens Bank and Trust Company, Inc.",3-Jun-11,21-Mar-14
|
||||
First Heritage Bank,Snohomish,WA,23626,Columbia State Bank,27-May-11,28-Jan-13
|
||||
Summit Bank,Burlington,WA,513,Columbia State Bank,20-May-11,22-Jan-13
|
||||
First Georgia Banking Company,Franklin,GA,57647,"CertusBank, National Association",20-May-11,21-Mar-14
|
||||
Atlantic Southern Bank,Macon,GA,57213,"CertusBank, National Association",20-May-11,21-Mar-14
|
||||
Coastal Bank,Cocoa Beach,FL,34898,"Florida Community Bank, a division of Premier American Bank, N.A.",6-May-11,21-Sep-15
|
||||
Community Central Bank,Mount Clemens,MI,34234,Talmer Bank & Trust,29-Apr-11,13-Apr-17
|
||||
The Park Avenue Bank,Valdosta,GA,19797,Bank of the Ozarks,29-Apr-11,21-Mar-14
|
||||
First Choice Community Bank,Dallas,GA,58539,Bank of the Ozarks,29-Apr-11,21-Sep-15
|
||||
Cortez Community Bank,Brooksville,FL,57625,"Florida Community Bank, a division of Premier American Bank, N.A.",29-Apr-11,8-Aug-16
|
||||
First National Bank of Central Florida,Winter Park,FL,26297,"Florida Community Bank, a division of Premier American Bank, N.A.",29-Apr-11,4-May-16
|
||||
Heritage Banking Group,Carthage,MS,14273,Trustmark National Bank,15-Apr-11,21-Mar-14
|
||||
Rosemount National Bank,Rosemount,MN,24099,Central Bank,15-Apr-11,7-Dec-15
|
||||
Superior Bank,Birmingham,AL,17750,"Superior Bank, National Association",15-Apr-11,21-Mar-14
|
||||
Nexity Bank,Birmingham,AL,19794,AloStar Bank of Commerce,15-Apr-11,21-Mar-14
|
||||
New Horizons Bank,East Ellijay,GA,57705,Citizens South Bank,15-Apr-11,21-Mar-14
|
||||
Bartow County Bank,Cartersville,GA,21495,Hamilton State Bank,15-Apr-11,21-Mar-14
|
||||
Nevada Commerce Bank,Las Vegas,NV,35418,City National Bank,8-Apr-11,9-Sep-12
|
||||
Western Springs National Bank and Trust,Western Springs,IL,10086,Heartland Bank and Trust Company,8-Apr-11,22-Jan-13
|
||||
The Bank of Commerce,Wood Dale,IL,34292,Advantage National Bank Group,25-Mar-11,22-Jan-13
|
||||
Legacy Bank,Milwaukee,WI,34818,Seaway Bank and Trust Company,11-Mar-11,12-Sep-12
|
||||
First National Bank of Davis,Davis,OK,4077,The Pauls Valley National Bank,11-Mar-11,13-Apr-17
|
||||
Valley Community Bank,St. Charles,IL,34187,First State Bank,25-Feb-11,5-Feb-15
|
||||
"San Luis Trust Bank, FSB",San Luis Obispo,CA,34783,First California Bank,18-Feb-11,12-Sep-16
|
||||
Charter Oak Bank,Napa,CA,57855,Bank of Marin,18-Feb-11,12-Sep-12
|
||||
Citizens Bank of Effingham,Springfield,GA,34601,Heritage Bank of the South,18-Feb-11,21-Mar-14
|
||||
Habersham Bank,Clarkesville,GA,151,SCBT National Association,18-Feb-11,21-Mar-14
|
||||
Canyon National Bank,Palm Springs,CA,34692,Pacific Premier Bank,11-Feb-11,19-Aug-14
|
||||
Badger State Bank,Cassville,WI,13272,Royal Bank,11-Feb-11,12-Sep-12
|
||||
Peoples State Bank,Hamtramck,MI,14939,First Michigan Bank,11-Feb-11,22-Jan-13
|
||||
Sunshine State Community Bank,Port Orange,FL,35478,"Premier American Bank, N.A.",11-Feb-11,8-Aug-16
|
||||
Community First Bank Chicago,Chicago,IL,57948,Northbrook Bank & Trust Company,4-Feb-11,20-Aug-12
|
||||
North Georgia Bank,Watkinsville,GA,35242,BankSouth,4-Feb-11,21-Mar-14
|
||||
American Trust Bank,Roswell,GA,57432,Renasant Bank,4-Feb-11,21-Mar-14
|
||||
First Community Bank,Taos,NM,12261,"U.S. Bank, N.A.",28-Jan-11,12-Sep-12
|
||||
FirsTier Bank,Louisville,CO,57646,No Acquirer,28-Jan-11,12-Sep-12
|
||||
Evergreen State Bank,Stoughton,WI,5328,McFarland State Bank,28-Jan-11,12-Sep-12
|
||||
The First State Bank,Camargo,OK,2303,Bank 7,28-Jan-11,12-Sep-12
|
||||
United Western Bank,Denver,CO,31293,First-Citizens Bank & Trust Company,21-Jan-11,12-Sep-12
|
||||
The Bank of Asheville,Asheville,NC,34516,First Bank,21-Jan-11,21-Mar-14
|
||||
CommunitySouth Bank & Trust,Easley,SC,57868,"CertusBank, National Association",21-Jan-11,6-Jun-16
|
||||
Enterprise Banking Company,McDonough,GA,19758,No Acquirer,21-Jan-11,21-Mar-14
|
||||
Oglethorpe Bank,Brunswick,GA,57440,Bank of the Ozarks,14-Jan-11,21-Mar-14
|
||||
Legacy Bank,Scottsdale,AZ,57820,Enterprise Bank & Trust,7-Jan-11,12-Apr-16
|
||||
First Commercial Bank of Florida,Orlando,FL,34965,First Southern Bank,7-Jan-11,6-Jun-16
|
||||
Community National Bank,Lino Lakes,MN,23306,Farmers & Merchants Savings Bank,17-Dec-10,10-Nov-16
|
||||
First Southern Bank,Batesville,AR,58052,Southern Bank,17-Dec-10,20-Aug-12
|
||||
"United Americas Bank, N.A.",Atlanta,GA,35065,State Bank and Trust Company,17-Dec-10,17-Oct-15
|
||||
"Appalachian Community Bank, FSB",McCaysville,GA,58495,Peoples Bank of East Tennessee,17-Dec-10,21-Mar-14
|
||||
Chestatee State Bank,Dawsonville,GA,34578,Bank of the Ozarks,17-Dec-10,21-Sep-15
|
||||
"The Bank of Miami,N.A.",Coral Gables,FL,19040,1st United Bank,17-Dec-10,21-Mar-14
|
||||
Earthstar Bank,Southampton,PA,35561,Polonia Bank,10-Dec-10,20-Aug-12
|
||||
Paramount Bank,Farmington Hills,MI,34673,Level One Bank,10-Dec-10,21-Sep-15
|
||||
First Banking Center,Burlington,WI,5287,First Michigan Bank,19-Nov-10,20-Aug-12
|
||||
Allegiance Bank of North America,Bala Cynwyd,PA,35078,VIST Bank,19-Nov-10,20-Aug-12
|
||||
Gulf State Community Bank,Carrabelle,FL,20340,Centennial Bank,19-Nov-10,12-Dec-16
|
||||
Copper Star Bank,Scottsdale,AZ,35463,"Stearns Bank, N.A.",12-Nov-10,20-Aug-12
|
||||
Darby Bank & Trust Co.,Vidalia,GA,14580,Ameris Bank,12-Nov-10,21-Mar-14
|
||||
Tifton Banking Company,Tifton,GA,57831,Ameris Bank,12-Nov-10,21-Mar-14
|
||||
First Vietnamese American Bank,Westminster,CA,57885,Grandpoint Bank,5-Nov-10,12-Sep-12
|
||||
Pierce Commercial Bank,Tacoma,WA,34411,Heritage Bank,5-Nov-10,19-Oct-15
|
||||
Western Commercial Bank,Woodland Hills,CA,58087,First California Bank,5-Nov-10,12-Sep-16
|
||||
K Bank,Randallstown,MD,31263,Manufacturers and Traders Trust Company (M&T Bank),5-Nov-10,20-Aug-12
|
||||
"First Arizona Savings, A FSB",Scottsdale,AZ,32582,No Acquirer,22-Oct-10,20-Aug-12
|
||||
Hillcrest Bank,Overland Park,KS,22173,"Hillcrest Bank, N.A.",22-Oct-10,20-Aug-12
|
||||
First Suburban National Bank,Maywood,IL,16089,Seaway Bank and Trust Company,22-Oct-10,20-Aug-12
|
||||
The First National Bank of Barnesville,Barnesville,GA,2119,United Bank,22-Oct-10,17-Oct-15
|
||||
The Gordon Bank,Gordon,GA,33904,Morris Bank,22-Oct-10,21-Mar-14
|
||||
Progress Bank of Florida,Tampa,FL,32251,Bay Cities Bank,22-Oct-10,4-Feb-16
|
||||
First Bank of Jacksonville,Jacksonville,FL,27573,Ameris Bank,22-Oct-10,21-Mar-14
|
||||
Premier Bank,Jefferson City,MO,34016,Providence Bank,15-Oct-10,20-Aug-12
|
||||
WestBridge Bank and Trust Company,Chesterfield,MO,58205,Midland States Bank,15-Oct-10,9-Feb-17
|
||||
"Security Savings Bank, F.S.B.",Olathe,KS,30898,Simmons First National Bank,15-Oct-10,20-Aug-12
|
||||
Shoreline Bank,Shoreline,WA,35250,GBC International Bank,1-Oct-10,20-Aug-12
|
||||
Wakulla Bank,Crawfordville,FL,21777,Centennial Bank,1-Oct-10,21-Mar-14
|
||||
North County Bank,Arlington,WA,35053,Whidbey Island Bank,24-Sep-10,12-Apr-16
|
||||
Haven Trust Bank Florida,Ponte Vedra Beach,FL,58308,First Southern Bank,24-Sep-10,11-Jul-16
|
||||
Maritime Savings Bank,West Allis,WI,28612,"North Shore Bank, FSB",17-Sep-10,20-Aug-12
|
||||
Bramble Savings Bank,Milford,OH,27808,Foundation Bank,17-Sep-10,20-Aug-12
|
||||
The Peoples Bank,Winder,GA,182,Community & Southern Bank,17-Sep-10,4-May-16
|
||||
First Commerce Community Bank,Douglasville,GA,57448,Community & Southern Bank,17-Sep-10,4-May-16
|
||||
Bank of Ellijay,Ellijay,GA,58197,Community & Southern Bank,17-Sep-10,6-Jun-16
|
||||
ISN Bank,Cherry Hill,NJ,57107,Customers Bank,17-Sep-10,22-Aug-12
|
||||
Horizon Bank,Bradenton,FL,35061,Bank of the Ozarks,10-Sep-10,21-Mar-14
|
||||
Sonoma Valley Bank,Sonoma,CA,27259,Westamerica Bank,20-Aug-10,12-Sep-12
|
||||
Los Padres Bank,Solvang,CA,32165,Pacific Western Bank,20-Aug-10,12-Sep-12
|
||||
Butte Community Bank,Chico,CA,33219,"Rabobank, N.A.",20-Aug-10,12-Sep-12
|
||||
Pacific State Bank,Stockton,CA,27090,"Rabobank, N.A.",20-Aug-10,12-Sep-12
|
||||
ShoreBank,Chicago,IL,15640,Urban Partnership Bank,20-Aug-10,16-May-13
|
||||
Imperial Savings and Loan Association,Martinsville,VA,31623,"River Community Bank, N.A.",20-Aug-10,24-Aug-12
|
||||
Independent National Bank,Ocala,FL,27344,"CenterState Bank of Florida, N.A.",20-Aug-10,20-Oct-16
|
||||
Community National Bank at Bartow,Bartow,FL,25266,"CenterState Bank of Florida, N.A.",20-Aug-10,11-Jul-16
|
||||
Palos Bank and Trust Company,Palos Heights,IL,17599,First Midwest Bank,13-Aug-10,22-Aug-12
|
||||
Ravenswood Bank,Chicago,IL,34231,Northbrook Bank & Trust Company,6-Aug-10,22-Aug-12
|
||||
LibertyBank,Eugene,OR,31964,Home Federal Bank,30-Jul-10,22-Aug-12
|
||||
The Cowlitz Bank,Longview,WA,22643,Heritage Bank,30-Jul-10,22-Aug-12
|
||||
Coastal Community Bank,Panama City Beach,FL,9619,Centennial Bank,30-Jul-10,9-Feb-17
|
||||
Bayside Savings Bank,Port Saint Joe,FL,57669,Centennial Bank,30-Jul-10,12-Dec-16
|
||||
Northwest Bank & Trust,Acworth,GA,57658,State Bank and Trust Company,30-Jul-10,21-Sep-15
|
||||
Home Valley Bank,Cave Junction,OR,23181,South Valley Bank & Trust,23-Jul-10,12-Sep-12
|
||||
SouthwestUSA Bank,Las Vegas,NV,35434,Plaza Bank,23-Jul-10,22-Aug-12
|
||||
Community Security Bank,New Prague,MN,34486,Roundbank,23-Jul-10,4-Feb-16
|
||||
Thunder Bank,Sylvan Grove,KS,10506,The Bennington State Bank,23-Jul-10,13-Sep-12
|
||||
Williamsburg First National Bank,Kingstree,SC,17837,"First Citizens Bank and Trust Company, Inc.",23-Jul-10,20-Oct-16
|
||||
Crescent Bank and Trust Company,Jasper,GA,27559,Renasant Bank,23-Jul-10,21-Mar-14
|
||||
Sterling Bank,Lantana,FL,32536,IBERIABANK,23-Jul-10,21-Mar-14
|
||||
"Mainstreet Savings Bank, FSB",Hastings,MI,28136,Commercial Bank,16-Jul-10,13-Sep-12
|
||||
Olde Cypress Community Bank,Clewiston,FL,28864,"CenterState Bank of Florida, N.A.",16-Jul-10,11-Jul-16
|
||||
Turnberry Bank,Aventura,FL,32280,NAFH National Bank,16-Jul-10,12-Sep-16
|
||||
Metro Bank of Dade County,Miami,FL,25172,NAFH National Bank,16-Jul-10,8-Aug-16
|
||||
First National Bank of the South,Spartanburg,SC,35383,NAFH National Bank,16-Jul-10,21-Mar-14
|
||||
Woodlands Bank,Bluffton,SC,32571,Bank of the Ozarks,16-Jul-10,21-Sep-15
|
||||
Home National Bank,Blackwell,OK,11636,RCB Bank,9-Jul-10,12-Dec-16
|
||||
USA Bank,Port Chester,NY,58072,New Century Bank,9-Jul-10,14-Sep-12
|
||||
Ideal Federal Savings Bank,Baltimore,MD,32456,No Acquirer,9-Jul-10,8-May-15
|
||||
Bay National Bank,Baltimore,MD,35462,"Bay Bank, FSB",9-Jul-10,15-Jan-13
|
||||
High Desert State Bank,Albuquerque,NM,35279,First American Bank,25-Jun-10,14-Sep-12
|
||||
First National Bank,Savannah,GA,34152,"The Savannah Bank, N.A.",25-Jun-10,21-Mar-14
|
||||
Peninsula Bank,Englewood,FL,26563,"Premier American Bank, N.A.",25-Jun-10,21-Mar-14
|
||||
Nevada Security Bank,Reno,NV,57110,Umpqua Bank,18-Jun-10,23-Aug-12
|
||||
Washington First International Bank,Seattle,WA,32955,East West Bank,11-Jun-10,14-Sep-12
|
||||
TierOne Bank,Lincoln,NE,29341,Great Western Bank,4-Jun-10,14-Sep-12
|
||||
Arcola Homestead Savings Bank,Arcola,IL,31813,No Acquirer,4-Jun-10,8-May-15
|
||||
First National Bank,Rosedale,MS,15814,The Jefferson Bank,4-Jun-10,21-Mar-14
|
||||
Sun West Bank,Las Vegas,NV,34785,City National Bank,28-May-10,14-Sep-12
|
||||
"Granite Community Bank, NA",Granite Bay,CA,57315,Tri Counties Bank,28-May-10,14-Sep-12
|
||||
Bank of Florida - Tampa,Tampa,FL,57814,EverBank,28-May-10,8-Aug-16
|
||||
Bank of Florida - Southwest,Naples,FL,35106,EverBank,28-May-10,8-Aug-16
|
||||
Bank of Florida - Southeast,Fort Lauderdale,FL,57360,EverBank,28-May-10,8-Aug-16
|
||||
Pinehurst Bank,Saint Paul,MN,57735,Coulee Bank,21-May-10,26-Oct-12
|
||||
Midwest Bank and Trust Company,Elmwood Park,IL,18117,"FirstMerit Bank, N.A.",14-May-10,23-Aug-12
|
||||
Southwest Community Bank,Springfield,MO,34255,Simmons First National Bank,14-May-10,4-Feb-16
|
||||
New Liberty Bank,Plymouth,MI,35586,Bank of Ann Arbor,14-May-10,23-Aug-12
|
||||
Satilla Community Bank,Saint Marys,GA,35114,Ameris Bank,14-May-10,21-Mar-14
|
||||
1st Pacific Bank of California,San Diego,CA,35517,City National Bank,7-May-10,13-Dec-12
|
||||
Towne Bank of Arizona,Mesa,AZ,57697,Commerce Bank of Arizona,7-May-10,23-Aug-12
|
||||
Access Bank,Champlin,MN,16476,PrinsBank,7-May-10,5-Feb-15
|
||||
The Bank of Bonifay,Bonifay,FL,14246,First Federal Bank of Florida,7-May-10,5-Nov-12
|
||||
Frontier Bank,Everett,WA,22710,"Union Bank, N.A.",30-Apr-10,15-Jan-13
|
||||
BC National Banks,Butler,MO,17792,Community First Bank,30-Apr-10,23-Aug-12
|
||||
Champion Bank,Creve Coeur,MO,58362,BankLiberty,30-Apr-10,6-Jun-16
|
||||
CF Bancorp,Port Huron,MI,30005,First Michigan Bank,30-Apr-10,13-Apr-16
|
||||
Westernbank Puerto Rico,Mayaguez,PR,31027,Banco Popular de Puerto Rico,30-Apr-10,21-Mar-14
|
||||
R-G Premier Bank of Puerto Rico,Hato Rey,PR,32185,Scotiabank de Puerto Rico,30-Apr-10,5-Aug-15
|
||||
Eurobank,San Juan,PR,27150,Oriental Bank and Trust,30-Apr-10,21-Mar-14
|
||||
Wheatland Bank,Naperville,IL,58429,Wheaton Bank & Trust,23-Apr-10,23-Aug-12
|
||||
Peotone Bank and Trust Company,Peotone,IL,10888,First Midwest Bank,23-Apr-10,23-Aug-12
|
||||
Lincoln Park Savings Bank,Chicago,IL,30600,Northbrook Bank & Trust Company,23-Apr-10,23-Aug-12
|
||||
New Century Bank,Chicago,IL,34821,"MB Financial Bank, N.A.",23-Apr-10,23-Aug-12
|
||||
Citizens Bank and Trust Company of Chicago,Chicago,IL,34658,Republic Bank of Chicago,23-Apr-10,5-Jun-14
|
||||
Broadway Bank,Chicago,IL,22853,"MB Financial Bank, N.A.",23-Apr-10,23-Aug-12
|
||||
"Amcore Bank, National Association",Rockford,IL,3735,Harris N.A.,23-Apr-10,23-Aug-12
|
||||
City Bank,Lynnwood,WA,21521,Whidbey Island Bank,16-Apr-10,14-Sep-12
|
||||
Tamalpais Bank,San Rafael,CA,33493,"Union Bank, N.A.",16-Apr-10,23-Aug-12
|
||||
Innovative Bank,Oakland,CA,23876,Center Bank,16-Apr-10,23-Aug-12
|
||||
Butler Bank,Lowell,MA,26619,People's United Bank,16-Apr-10,23-Aug-12
|
||||
Riverside National Bank of Florida,Fort Pierce,FL,24067,"TD Bank, N.A.",16-Apr-10,21-Mar-14
|
||||
AmericanFirst Bank,Clermont,FL,57724,"TD Bank, N.A.",16-Apr-10,21-Mar-14
|
||||
First Federal Bank of North Florida,Palatka,FL,28886,"TD Bank, N.A.",16-Apr-10,21-Mar-14
|
||||
Lakeside Community Bank,Sterling Heights,MI,34878,No Acquirer,16-Apr-10,23-Aug-12
|
||||
Beach First National Bank,Myrtle Beach,SC,34242,Bank of North Carolina,9-Apr-10,21-Mar-14
|
||||
Desert Hills Bank,Phoenix,AZ,57060,New York Community Bank,26-Mar-10,23-Aug-12
|
||||
Unity National Bank,Cartersville,GA,34678,Bank of the Ozarks,26-Mar-10,21-Sep-15
|
||||
Key West Bank,Key West,FL,34684,Centennial Bank,26-Mar-10,9-Feb-17
|
||||
McIntosh Commercial Bank,Carrollton,GA,57399,CharterBank,26-Mar-10,11-Jul-16
|
||||
State Bank of Aurora,Aurora,MN,8221,Northern State Bank,19-Mar-10,8-Aug-16
|
||||
First Lowndes Bank,Fort Deposit,AL,24957,First Citizens Bank,19-Mar-10,21-Mar-14
|
||||
Bank of Hiawassee,Hiawassee,GA,10054,Citizens South Bank,19-Mar-10,21-Mar-14
|
||||
Appalachian Community Bank,Ellijay,GA,33989,Community & Southern Bank,19-Mar-10,21-Mar-14
|
||||
Advanta Bank Corp.,Draper,UT,33535,No Acquirer,19-Mar-10,31-Mar-16
|
||||
Century Security Bank,Duluth,GA,58104,Bank of Upson,19-Mar-10,13-Apr-16
|
||||
American National Bank,Parma,OH,18806,The National Bank and Trust Company,19-Mar-10,21-Sep-15
|
||||
Statewide Bank,Covington,LA,29561,Home Bank,12-Mar-10,23-Aug-12
|
||||
Old Southern Bank,Orlando,FL,58182,Centennial Bank,12-Mar-10,9-Feb-17
|
||||
The Park Avenue Bank,New York,NY,27096,Valley National Bank,12-Mar-10,23-Aug-12
|
||||
LibertyPointe Bank,New York,NY,58071,Valley National Bank,11-Mar-10,23-Aug-12
|
||||
Centennial Bank,Ogden,UT,34430,No Acquirer,5-Mar-10,14-Sep-12
|
||||
Waterfield Bank,Germantown,MD,34976,No Acquirer,5-Mar-10,23-Aug-12
|
||||
Bank of Illinois,Normal,IL,9268,Heartland Bank and Trust Company,5-Mar-10,23-Aug-12
|
||||
Sun American Bank,Boca Raton,FL,27126,First-Citizens Bank & Trust Company,5-Mar-10,21-Mar-14
|
||||
Rainier Pacific Bank,Tacoma,WA,38129,Umpqua Bank,26-Feb-10,23-Aug-12
|
||||
Carson River Community Bank,Carson City,NV,58352,Heritage Bank of Nevada,26-Feb-10,15-Jan-13
|
||||
"La Jolla Bank, FSB",La Jolla,CA,32423,"OneWest Bank, FSB",19-Feb-10,24-Aug-12
|
||||
George Washington Savings Bank,Orland Park,IL,29952,"FirstMerit Bank, N.A.",19-Feb-10,24-Aug-12
|
||||
The La Coste National Bank,La Coste,TX,3287,Community National Bank,19-Feb-10,19-Aug-14
|
||||
Marco Community Bank,Marco Island,FL,57586,Mutual of Omaha Bank,19-Feb-10,21-Mar-14
|
||||
1st American State Bank of Minnesota,Hancock,MN,15448,"Community Development Bank, FSB",5-Feb-10,1-Nov-13
|
||||
American Marine Bank,Bainbridge Island,WA,16730,Columbia State Bank,29-Jan-10,24-Aug-12
|
||||
First Regional Bank,Los Angeles,CA,23011,First-Citizens Bank & Trust Company,29-Jan-10,4-Mar-16
|
||||
Community Bank and Trust,Cornelia,GA,5702,SCBT National Association,29-Jan-10,13-Apr-16
|
||||
"Marshall Bank, N.A.",Hallock,MN,16133,United Valley Bank,29-Jan-10,23-Aug-12
|
||||
Florida Community Bank,Immokalee,FL,5672,"Premier American Bank, N.A.",29-Jan-10,21-Mar-14
|
||||
First National Bank of Georgia,Carrollton,GA,16480,Community & Southern Bank,29-Jan-10,21-Mar-14
|
||||
Columbia River Bank,The Dalles,OR,22469,Columbia State Bank,22-Jan-10,14-Sep-12
|
||||
Evergreen Bank,Seattle,WA,20501,Umpqua Bank,22-Jan-10,15-Jan-13
|
||||
Charter Bank,Santa Fe,NM,32498,Charter Bank,22-Jan-10,23-Aug-12
|
||||
Bank of Leeton,Leeton,MO,8265,"Sunflower Bank, N.A.",22-Jan-10,15-Jan-13
|
||||
Premier American Bank,Miami,FL,57147,"Premier American Bank, N.A.",22-Jan-10,21-Sep-15
|
||||
Barnes Banking Company,Kaysville,UT,1252,No Acquirer,15-Jan-10,23-Aug-12
|
||||
St. Stephen State Bank,St. Stephen,MN,17522,First State Bank of St. Joseph,15-Jan-10,8-Aug-16
|
||||
Town Community Bank & Trust,Antioch,IL,34705,First American Bank,15-Jan-10,23-Aug-12
|
||||
Horizon Bank,Bellingham,WA,22977,Washington Federal Savings and Loan Association,8-Jan-10,23-Aug-12
|
||||
"First Federal Bank of California, F.S.B.",Santa Monica,CA,28536,"OneWest Bank, FSB",18-Dec-09,23-Aug-12
|
||||
Imperial Capital Bank,La Jolla,CA,26348,City National Bank,18-Dec-09,5-Sep-12
|
||||
Independent Bankers' Bank,Springfield,IL,26820,The Independent BankersBank (TIB),18-Dec-09,23-Aug-12
|
||||
New South Federal Savings Bank,Irondale,AL,32276,Beal Bank,18-Dec-09,23-Aug-12
|
||||
Citizens State Bank,New Baltimore,MI,1006,No Acquirer,18-Dec-09,21-Mar-14
|
||||
Peoples First Community Bank,Panama City,FL,32167,Hancock Bank,18-Dec-09,5-Nov-12
|
||||
RockBridge Commercial Bank,Atlanta,GA,58315,No Acquirer,18-Dec-09,21-Mar-14
|
||||
SolutionsBank,Overland Park,KS,4731,Arvest Bank,11-Dec-09,19-Jul-16
|
||||
"Valley Capital Bank, N.A.",Mesa,AZ,58399,Enterprise Bank & Trust,11-Dec-09,20-Oct-16
|
||||
"Republic Federal Bank, N.A.",Miami,FL,22846,1st United Bank,11-Dec-09,21-Mar-14
|
||||
Greater Atlantic Bank,Reston,VA,32583,Sonabank,4-Dec-09,21-Mar-14
|
||||
Benchmark Bank,Aurora,IL,10440,"MB Financial Bank, N.A.",4-Dec-09,23-Aug-12
|
||||
AmTrust Bank,Cleveland,OH,29776,New York Community Bank,4-Dec-09,21-Mar-14
|
||||
The Tattnall Bank,Reidsville,GA,12080,Heritage Bank of the South,4-Dec-09,21-Mar-14
|
||||
First Security National Bank,Norcross,GA,26290,State Bank and Trust Company,4-Dec-09,17-Oct-15
|
||||
The Buckhead Community Bank,Atlanta,GA,34663,State Bank and Trust Company,4-Dec-09,21-Mar-14
|
||||
Commerce Bank of Southwest Florida,Fort Myers,FL,58016,Central Bank,20-Nov-09,21-Mar-14
|
||||
Pacific Coast National Bank,San Clemente,CA,57914,Sunwest Bank,13-Nov-09,10-Apr-17
|
||||
Orion Bank,Naples,FL,22427,IBERIABANK,13-Nov-09,21-Mar-14
|
||||
"Century Bank, F.S.B.",Sarasota,FL,32267,IBERIABANK,13-Nov-09,21-Mar-14
|
||||
United Commercial Bank,San Francisco,CA,32469,East West Bank,6-Nov-09,5-Nov-12
|
||||
Gateway Bank of St. Louis,St. Louis,MO,19450,Central Bank of Kansas City,6-Nov-09,22-Aug-12
|
||||
Prosperan Bank,Oakdale,MN,35074,"Alerus Financial, N.A.",6-Nov-09,22-Aug-12
|
||||
Home Federal Savings Bank,Detroit,MI,30329,Liberty Bank and Trust Company,6-Nov-09,22-Aug-12
|
||||
United Security Bank,Sparta,GA,22286,Ameris Bank,6-Nov-09,21-Mar-14
|
||||
North Houston Bank,Houston,TX,18776,U.S. Bank N.A.,30-Oct-09,22-Aug-12
|
||||
Madisonville State Bank,Madisonville,TX,33782,U.S. Bank N.A.,30-Oct-09,22-Aug-12
|
||||
Citizens National Bank,Teague,TX,25222,U.S. Bank N.A.,30-Oct-09,22-Aug-12
|
||||
Park National Bank,Chicago,IL,11677,U.S. Bank N.A.,30-Oct-09,22-Aug-12
|
||||
Pacific National Bank,San Francisco,CA,30006,U.S. Bank N.A.,30-Oct-09,22-Aug-12
|
||||
California National Bank,Los Angeles,CA,34659,U.S. Bank N.A.,30-Oct-09,5-Sep-12
|
||||
San Diego National Bank,San Diego,CA,23594,U.S. Bank N.A.,30-Oct-09,22-Aug-12
|
||||
Community Bank of Lemont,Lemont,IL,35291,U.S. Bank N.A.,30-Oct-09,15-Jan-13
|
||||
"Bank USA, N.A.",Phoenix,AZ,32218,U.S. Bank N.A.,30-Oct-09,22-Aug-12
|
||||
First DuPage Bank,Westmont,IL,35038,First Midwest Bank,23-Oct-09,22-Aug-12
|
||||
Riverview Community Bank,Otsego,MN,57525,Central Bank,23-Oct-09,22-Aug-12
|
||||
Bank of Elmwood,Racine,WI,18321,Tri City National Bank,23-Oct-09,22-Aug-12
|
||||
Flagship National Bank,Bradenton,FL,35044,First Federal Bank of Florida,23-Oct-09,5-Feb-15
|
||||
Hillcrest Bank Florida,Naples,FL,58336,Stonegate Bank,23-Oct-09,22-Aug-12
|
||||
American United Bank,Lawrenceville,GA,57794,Ameris Bank,23-Oct-09,5-Sep-12
|
||||
Partners Bank,Naples,FL,57959,Stonegate Bank,23-Oct-09,5-Feb-15
|
||||
San Joaquin Bank,Bakersfield,CA,23266,Citizens Business Bank,16-Oct-09,22-Aug-12
|
||||
Southern Colorado National Bank,Pueblo,CO,57263,Legacy Bank,2-Oct-09,12-Dec-16
|
||||
Jennings State Bank,Spring Grove,MN,11416,Central Bank,2-Oct-09,21-Aug-12
|
||||
Warren Bank,Warren,MI,34824,The Huntington National Bank,2-Oct-09,21-Aug-12
|
||||
Georgian Bank,Atlanta,GA,57151,"First Citizens Bank and Trust Company, Inc.",25-Sep-09,21-Aug-12
|
||||
"Irwin Union Bank, F.S.B.",Louisville,KY,57068,"First Financial Bank, N.A.",18-Sep-09,5-Sep-12
|
||||
Irwin Union Bank and Trust Company,Columbus,IN,10100,"First Financial Bank, N.A.",18-Sep-09,21-Aug-12
|
||||
Venture Bank,Lacey,WA,22868,First-Citizens Bank & Trust Company,11-Sep-09,21-Aug-12
|
||||
Brickwell Community Bank,Woodbury,MN,57736,CorTrust Bank N.A.,11-Sep-09,20-Oct-16
|
||||
"Corus Bank, N.A.",Chicago,IL,13693,"MB Financial Bank, N.A.",11-Sep-09,21-Aug-12
|
||||
First State Bank,Flagstaff,AZ,34875,Sunwest Bank,4-Sep-09,5-Feb-15
|
||||
Platinum Community Bank,Rolling Meadows,IL,35030,No Acquirer,4-Sep-09,21-Aug-12
|
||||
Vantus Bank,Sioux City,IA,27732,Great Southern Bank,4-Sep-09,21-Aug-12
|
||||
InBank,Oak Forest,IL,20203,"MB Financial Bank, N.A.",4-Sep-09,17-Oct-15
|
||||
First Bank of Kansas City,Kansas City,MO,25231,Great American Bank,4-Sep-09,21-Aug-12
|
||||
Affinity Bank,Ventura,CA,27197,Pacific Western Bank,28-Aug-09,21-Aug-12
|
||||
Mainstreet Bank,Forest Lake,MN,1909,Central Bank,28-Aug-09,21-Aug-12
|
||||
Bradford Bank,Baltimore,MD,28312,Manufacturers and Traders Trust Company (M&T Bank),28-Aug-09,15-Jan-13
|
||||
Guaranty Bank,Austin,TX,32618,BBVA Compass,21-Aug-09,21-Aug-12
|
||||
CapitalSouth Bank,Birmingham,AL,22130,IBERIABANK,21-Aug-09,15-Jan-13
|
||||
First Coweta Bank,Newnan,GA,57702,United Bank,21-Aug-09,7-Dec-15
|
||||
ebank,Atlanta,GA,34682,"Stearns Bank, N.A.",21-Aug-09,21-Aug-12
|
||||
Community Bank of Nevada,Las Vegas,NV,34043,No Acquirer,14-Aug-09,21-Aug-12
|
||||
Community Bank of Arizona,Phoenix,AZ,57645,MidFirst Bank,14-Aug-09,21-Aug-12
|
||||
"Union Bank, National Association",Gilbert,AZ,34485,MidFirst Bank,14-Aug-09,21-Aug-12
|
||||
Colonial Bank,Montgomery,AL,9609,"Branch Banking & Trust Company, (BB&T)",14-Aug-09,12-Jun-14
|
||||
Dwelling House Savings and Loan Association,Pittsburgh,PA,31559,"PNC Bank, N.A.",14-Aug-09,15-Jan-13
|
||||
Community First Bank,Prineville,OR,23268,Home Federal Bank,7-Aug-09,15-Jan-13
|
||||
Community National Bank of Sarasota County,Venice,FL,27183,"Stearns Bank, N.A.",7-Aug-09,20-Aug-12
|
||||
First State Bank,Sarasota,FL,27364,"Stearns Bank, N.A.",7-Aug-09,20-Aug-12
|
||||
Mutual Bank,Harvey,IL,18659,United Central Bank,31-Jul-09,20-Aug-12
|
||||
First BankAmericano,Elizabeth,NJ,34270,Crown Bank,31-Jul-09,20-Aug-12
|
||||
Peoples Community Bank,West Chester,OH,32288,"First Financial Bank, N.A.",31-Jul-09,20-Aug-12
|
||||
Integrity Bank,Jupiter,FL,57604,Stonegate Bank,31-Jul-09,20-Aug-12
|
||||
First State Bank of Altus,Altus,OK,9873,Herring Bank,31-Jul-09,20-Aug-12
|
||||
Security Bank of Jones County,Gray,GA,8486,State Bank and Trust Company,24-Jul-09,20-Aug-12
|
||||
Security Bank of Houston County,Perry,GA,27048,State Bank and Trust Company,24-Jul-09,17-Oct-15
|
||||
Security Bank of Bibb County,Macon,GA,27367,State Bank and Trust Company,24-Jul-09,20-Aug-12
|
||||
Security Bank of North Metro,Woodstock,GA,57105,State Bank and Trust Company,24-Jul-09,6-Jan-16
|
||||
Security Bank of North Fulton,Alpharetta,GA,57430,State Bank and Trust Company,24-Jul-09,20-Aug-12
|
||||
Security Bank of Gwinnett County,Suwanee,GA,57346,State Bank and Trust Company,24-Jul-09,20-Aug-12
|
||||
Waterford Village Bank,Williamsville,NY,58065,"Evans Bank, N.A.",24-Jul-09,1-Nov-13
|
||||
Temecula Valley Bank,Temecula,CA,34341,First-Citizens Bank & Trust Company,17-Jul-09,20-Oct-16
|
||||
Vineyard Bank,Rancho Cucamonga,CA,23556,California Bank & Trust,17-Jul-09,20-Aug-12
|
||||
BankFirst,Sioux Falls,SD,34103,"Alerus Financial, N.A.",17-Jul-09,20-Aug-12
|
||||
First Piedmont Bank,Winder,GA,34594,First American Bank and Trust Company,17-Jul-09,8-Aug-16
|
||||
Bank of Wyoming,Thermopolis,WY,22754,Central Bank & Trust,10-Jul-09,20-Aug-12
|
||||
Founders Bank,Worth,IL,18390,The PrivateBank and Trust Company,2-Jul-09,20-Aug-12
|
||||
Millennium State Bank of Texas,Dallas,TX,57667,State Bank of Texas,2-Jul-09,26-Oct-12
|
||||
First National Bank of Danville,Danville,IL,3644,"First Financial Bank, N.A.",2-Jul-09,20-Aug-12
|
||||
Elizabeth State Bank,Elizabeth,IL,9262,Galena State Bank and Trust Company,2-Jul-09,20-Aug-12
|
||||
Rock River Bank,Oregon,IL,15302,The Harvard State Bank,2-Jul-09,20-Aug-12
|
||||
First State Bank of Winchester,Winchester,IL,11710,The First National Bank of Beardstown,2-Jul-09,20-Aug-12
|
||||
John Warner Bank,Clinton,IL,12093,State Bank of Lincoln,2-Jul-09,20-Aug-12
|
||||
Mirae Bank,Los Angeles,CA,57332,Wilshire State Bank,26-Jun-09,20-Aug-12
|
||||
MetroPacific Bank,Irvine,CA,57893,Sunwest Bank,26-Jun-09,5-Feb-15
|
||||
Horizon Bank,Pine City,MN,9744,"Stearns Bank, N.A.",26-Jun-09,5-Feb-15
|
||||
Neighborhood Community Bank,Newnan,GA,35285,CharterBank,26-Jun-09,7-Dec-15
|
||||
Community Bank of West Georgia,Villa Rica,GA,57436,No Acquirer,26-Jun-09,17-Aug-12
|
||||
First National Bank of Anthony,Anthony,KS,4614,Bank of Kansas,19-Jun-09,21-Sep-15
|
||||
Cooperative Bank,Wilmington,NC,27837,First Bank,19-Jun-09,17-Aug-12
|
||||
Southern Community Bank,Fayetteville,GA,35251,United Community Bank,19-Jun-09,21-Sep-15
|
||||
Bank of Lincolnwood,Lincolnwood,IL,17309,Republic Bank of Chicago,5-Jun-09,17-Aug-12
|
||||
Citizens National Bank,Macomb,IL,5757,Morton Community Bank,22-May-09,4-Sep-12
|
||||
Strategic Capital Bank,Champaign,IL,35175,Midland States Bank,22-May-09,4-Sep-12
|
||||
"BankUnited, FSB",Coral Gables,FL,32247,BankUnited,21-May-09,17-Aug-12
|
||||
Westsound Bank,Bremerton,WA,34843,Kitsap Bank,8-May-09,4-Sep-12
|
||||
America West Bank,Layton,UT,35461,Cache Valley Bank,1-May-09,17-Aug-12
|
||||
Citizens Community Bank,Ridgewood,NJ,57563,North Jersey Community Bank,1-May-09,4-Sep-12
|
||||
"Silverton Bank, NA",Atlanta,GA,26535,No Acquirer,1-May-09,17-Aug-12
|
||||
First Bank of Idaho,Ketchum,ID,34396,"U.S. Bank, N.A.",24-Apr-09,17-Aug-12
|
||||
First Bank of Beverly Hills,Calabasas,CA,32069,No Acquirer,24-Apr-09,4-Sep-12
|
||||
Michigan Heritage Bank,Farmington Hills,MI,34369,Level One Bank,24-Apr-09,17-Aug-12
|
||||
American Southern Bank,Kennesaw,GA,57943,Bank of North Georgia,24-Apr-09,17-Aug-12
|
||||
Great Basin Bank of Nevada,Elko,NV,33824,Nevada State Bank,17-Apr-09,4-Sep-12
|
||||
American Sterling Bank,Sugar Creek,MO,8266,Metcalf Bank,17-Apr-09,31-Aug-12
|
||||
New Frontier Bank,Greeley,CO,34881,No Acquirer,10-Apr-09,4-Sep-12
|
||||
Cape Fear Bank,Wilmington,NC,34639,First Federal Savings and Loan Association,10-Apr-09,10-Apr-17
|
||||
Omni National Bank,Atlanta,GA,22238,No Acquirer,27-Mar-09,17-Aug-12
|
||||
"TeamBank, NA",Paola,KS,4754,Great Southern Bank,20-Mar-09,12-Sep-16
|
||||
Colorado National Bank,Colorado Springs,CO,18896,Herring Bank,20-Mar-09,12-Dec-16
|
||||
FirstCity Bank,Stockbridge,GA,18243,No Acquirer,20-Mar-09,17-Aug-12
|
||||
Freedom Bank of Georgia,Commerce,GA,57558,Northeast Georgia Bank,6-Mar-09,17-Aug-12
|
||||
Security Savings Bank,Henderson,NV,34820,Bank of Nevada,27-Feb-09,7-Sep-12
|
||||
Heritage Community Bank,Glenwood,IL,20078,"MB Financial Bank, N.A.",27-Feb-09,17-Aug-12
|
||||
Silver Falls Bank,Silverton,OR,35399,Citizens Bank,20-Feb-09,17-Aug-12
|
||||
Pinnacle Bank of Oregon,Beaverton,OR,57342,Washington Trust Bank of Spokane,13-Feb-09,7-Dec-15
|
||||
Corn Belt Bank & Trust Co.,Pittsfield,IL,16500,The Carlinville National Bank,13-Feb-09,17-Aug-12
|
||||
Riverside Bank of the Gulf Coast,Cape Coral,FL,34563,TIB Bank,13-Feb-09,17-Aug-12
|
||||
Sherman County Bank,Loup City,NE,5431,Heritage Bank,13-Feb-09,17-Aug-12
|
||||
County Bank,Merced,CA,22574,Westamerica Bank,6-Feb-09,4-Sep-12
|
||||
Alliance Bank,Culver City,CA,23124,California Bank & Trust,6-Feb-09,16-Aug-12
|
||||
FirstBank Financial Services,McDonough,GA,57017,Regions Bank,6-Feb-09,16-Aug-12
|
||||
Ocala National Bank,Ocala,FL,26538,"CenterState Bank of Florida, N.A.",30-Jan-09,4-Sep-12
|
||||
Suburban FSB,Crofton,MD,30763,Bank of Essex,30-Jan-09,4-Feb-16
|
||||
MagnetBank,Salt Lake City,UT,58001,No Acquirer,30-Jan-09,16-Aug-12
|
||||
1st Centennial Bank,Redlands,CA,33025,First California Bank,23-Jan-09,13-Apr-16
|
||||
Bank of Clark County,Vancouver,WA,34959,Umpqua Bank,16-Jan-09,16-Aug-12
|
||||
National Bank of Commerce,Berkeley,IL,19733,Republic Bank of Chicago,16-Jan-09,16-Aug-12
|
||||
Sanderson State Bank,Sanderson,TX,11568,The Pecos County State Bank,12-Dec-08,25-Oct-13
|
||||
Haven Trust Bank,Duluth,GA,35379,"Branch Banking & Trust Company, (BB&T)",12-Dec-08,16-Aug-12
|
||||
First Georgia Community Bank,Jackson,GA,34301,United Bank,5-Dec-08,16-Aug-12
|
||||
PFF Bank & Trust,Pomona,CA,28344,"U.S. Bank, N.A.",21-Nov-08,4-Jan-13
|
||||
Downey Savings & Loan,Newport Beach,CA,30968,"U.S. Bank, N.A.",21-Nov-08,4-Jan-13
|
||||
Community Bank,Loganville,GA,16490,Bank of Essex,21-Nov-08,4-Sep-12
|
||||
Security Pacific Bank,Los Angeles,CA,23595,Pacific Western Bank,7-Nov-08,28-Aug-12
|
||||
"Franklin Bank, SSB",Houston,TX,26870,Prosperity Bank,7-Nov-08,16-Aug-12
|
||||
Freedom Bank,Bradenton,FL,57930,Fifth Third Bank,31-Oct-08,16-Aug-12
|
||||
Alpha Bank & Trust,Alpharetta,GA,58241,"Stearns Bank, N.A.",24-Oct-08,16-Aug-12
|
||||
Meridian Bank,Eldred,IL,13789,National Bank,10-Oct-08,31-May-12
|
||||
Main Street Bank,Northville,MI,57654,Monroe Bank & Trust,10-Oct-08,1-Aug-13
|
||||
Washington Mutual Bank (Including its subsidiary Washington Mutual Bank FSB),Henderson,NV,32633,JP Morgan Chase Bank,25-Sep-08,4-Aug-15
|
||||
Ameribank,Northfork,WV,6782,"The Citizens Savings Bank, Pioneer Community Bank, Inc.",19-Sep-08,21-Sep-15
|
||||
Silver State Bank,Henderson,NV,34194,Nevada State Bank,5-Sep-08,25-Oct-13
|
||||
Integrity Bank,Alpharetta,GA,35469,Regions Bank,29-Aug-08,16-Aug-12
|
||||
Columbian Bank & Trust,Topeka,KS,22728,Citizens Bank & Trust,22-Aug-08,26-Mar-15
|
||||
First Priority Bank,Bradenton,FL,57523,SunTrust Bank,1-Aug-08,12-Dec-16
|
||||
"First Heritage Bank, NA",Newport Beach,CA,57961,Mutual of Omaha Bank,25-Jul-08,12-Sep-16
|
||||
First National Bank of Nevada,Reno,NV,27011,Mutual of Omaha Bank,25-Jul-08,28-Aug-12
|
||||
IndyMac Bank,Pasadena,CA,29730,"OneWest Bank, FSB",11-Jul-08,22-Apr-15
|
||||
"First Integrity Bank, NA",Staples,MN,12736,First International Bank and Trust,30-May-08,20-Oct-16
|
||||
"ANB Financial, NA",Bentonville,AR,33901,Pulaski Bank and Trust Company,9-May-08,28-Aug-12
|
||||
Hume Bank,Hume,MO,1971,Security Bank,7-Mar-08,28-Aug-12
|
||||
Douglass National Bank,Kansas City,MO,24660,Liberty Bank and Trust Company,25-Jan-08,26-Oct-12
|
||||
Miami Valley Bank,Lakeview,OH,16848,The Citizens Banking Company,4-Oct-07,12-Sep-16
|
||||
NetBank,Alpharetta,GA,32575,ING DIRECT,28-Sep-07,28-Aug-12
|
||||
Metropolitan Savings Bank,Pittsburgh,PA,35353,Allegheny Valley Bank of Pittsburgh,2-Feb-07,27-Oct-10
|
||||
Bank of Ephraim,Ephraim,UT,1249,Far West Bank,25-Jun-04,9-Apr-08
|
||||
Reliance Bank,White Plains,NY,26778,Union State Bank,19-Mar-04,9-Apr-08
|
||||
Guaranty National Bank of Tallahassee,Tallahassee,FL,26838,Hancock Bank of Florida,12-Mar-04,5-Jun-12
|
||||
Dollar Savings Bank,Newark,NJ,31330,No Acquirer,14-Feb-04,9-Apr-08
|
||||
Pulaski Savings Bank,Philadelphia,PA,27203,Earthstar Bank,14-Nov-03,22-Jul-05
|
||||
First National Bank of Blanchardville,Blanchardville,WI,11639,The Park Bank,9-May-03,5-Jun-12
|
||||
Southern Pacific Bank,Torrance,CA,27094,Beal Bank,7-Feb-03,20-Oct-08
|
||||
Farmers Bank of Cheneyville,Cheneyville,LA,16445,Sabine State Bank & Trust,17-Dec-02,20-Oct-04
|
||||
Bank of Alamo,Alamo,TN,9961,No Acquirer,8-Nov-02,18-Mar-05
|
||||
AmTrade International Bank,Atlanta,GA,33784,No Acquirer,30-Sep-02,11-Sep-06
|
||||
Connecticut Bank of Commerce,Stamford,CT,19183,Hudson United Bank,26-Jun-02,14-Feb-12
|
||||
New Century Bank,Shelby Township,MI,34979,No Acquirer,28-Mar-02,18-Mar-05
|
||||
Net 1st National Bank,Boca Raton,FL,26652,Bank Leumi USA,1-Mar-02,9-Apr-08
|
||||
"NextBank, NA",Phoenix,AZ,22314,No Acquirer,7-Feb-02,5-Feb-15
|
||||
Oakwood Deposit Bank Co.,Oakwood,OH,8966,The State Bank & Trust Company,1-Feb-02,25-Oct-12
|
||||
Bank of Sierra Blanca,Sierra Blanca,TX,22002,The Security State Bank of Pecos,18-Jan-02,6-Nov-03
|
||||
"Hamilton Bank, NA",Miami,FL,24382,Israel Discount Bank of New York,11-Jan-02,21-Sep-15
|
||||
"Superior Bank, FSB",Hinsdale,IL,32646,"Superior Federal, FSB",27-Jul-01,19-Aug-14
|
||||
Malta National Bank,Malta,OH,6629,North Valley Bank,3-May-01,18-Nov-02
|
||||
First Alliance Bank & Trust Co.,Manchester,NH,34264,Southern New Hampshire Bank & Trust,2-Feb-01,18-Feb-03
|
||||
National State Bank of Metropolis,Metropolis,IL,3815,Banterra Bank of Marion,14-Dec-00,17-Mar-05
|
||||
Bank of Honolulu,Honolulu,HI,21029,Bank of the Orient,13-Oct-00,17-Mar-05
|
||||
|
|
|
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|
|||
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|
||||
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||||
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|
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|
||||
|
|
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|
|||
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|
||||
0,1,2,3
|
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||||
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||||
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|
||||
|
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a,b,c,d
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0.33627233637218457,0.3250110687231613,0.0010196408377848298,0.40140189720154196
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Load Diff
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@ -0,0 +1,502 @@
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|||
{
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||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Pandas Datareader\n",
|
||||
"\n",
|
||||
"** NOTE: Not every geographical location works well with pandas datareader, your firewall may also block it!**\n",
|
||||
"\n",
|
||||
"Functions from pandas_datareader.data and pandas_datareader.wb extract data from various Internet sources into a pandas DataFrame. Currently the following sources are supported:\n",
|
||||
"\n",
|
||||
"* Yahoo! Finance\n",
|
||||
"* Google Finance\n",
|
||||
"* Enigma\n",
|
||||
"* St.Louis FED (FRED)\n",
|
||||
"* Kenneth French’s data library\n",
|
||||
"* World Bank\n",
|
||||
"* OECD\n",
|
||||
"* Eurostat\n",
|
||||
"* Thrift Savings Plan\n",
|
||||
"* Oanda currency historical rate\n",
|
||||
"* Nasdaq Trader symbol definitions (remote_data.nasdaq_symbols)\n",
|
||||
"\n",
|
||||
"It should be noted, that various sources support different kinds of data, so not all sources implement the same methods and the data elements returned might also differ."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas_datareader.data as web\n",
|
||||
"\n",
|
||||
"import datetime\n",
|
||||
"\n",
|
||||
"start = datetime.datetime(2015, 1, 1)\n",
|
||||
"\n",
|
||||
"end = datetime.datetime(2017, 1, 1)\n",
|
||||
"\n",
|
||||
"facebook = web.DataReader(\"FB\", 'google', start, end)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style>\n",
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2015-01-02</th>\n",
|
||||
" <td>78.58</td>\n",
|
||||
" <td>78.93</td>\n",
|
||||
" <td>77.70</td>\n",
|
||||
" <td>78.45</td>\n",
|
||||
" <td>18177475</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2015-01-05</th>\n",
|
||||
" <td>77.98</td>\n",
|
||||
" <td>79.25</td>\n",
|
||||
" <td>76.86</td>\n",
|
||||
" <td>77.19</td>\n",
|
||||
" <td>26452191</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2015-01-06</th>\n",
|
||||
" <td>77.23</td>\n",
|
||||
" <td>77.59</td>\n",
|
||||
" <td>75.36</td>\n",
|
||||
" <td>76.15</td>\n",
|
||||
" <td>27399288</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2015-01-07</th>\n",
|
||||
" <td>76.76</td>\n",
|
||||
" <td>77.36</td>\n",
|
||||
" <td>75.82</td>\n",
|
||||
" <td>76.15</td>\n",
|
||||
" <td>22045333</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2015-01-08</th>\n",
|
||||
" <td>76.74</td>\n",
|
||||
" <td>78.23</td>\n",
|
||||
" <td>76.08</td>\n",
|
||||
" <td>78.18</td>\n",
|
||||
" <td>23960953</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume\n",
|
||||
"Date \n",
|
||||
"2015-01-02 78.58 78.93 77.70 78.45 18177475\n",
|
||||
"2015-01-05 77.98 79.25 76.86 77.19 26452191\n",
|
||||
"2015-01-06 77.23 77.59 75.36 76.15 27399288\n",
|
||||
"2015-01-07 76.76 77.36 75.82 76.15 22045333\n",
|
||||
"2015-01-08 76.74 78.23 76.08 78.18 23960953"
|
||||
]
|
||||
},
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"facebook.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Experimental Options\n",
|
||||
"\n",
|
||||
"The Options class allows the download of options data from Google Finance.\n",
|
||||
"\n",
|
||||
"The get_options_data method downloads options data for specified expiry date and provides a formatted DataFrame with a hierarchical index, so its easy to get to the specific option you want.\n",
|
||||
"\n",
|
||||
"Available expiry dates can be accessed from the expiry_dates property."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from pandas_datareader.data import Options\n",
|
||||
"\n",
|
||||
"fb_options = Options('FB', 'google')"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"data = fb_options.get_options_data(expiry=fb_options.expiry_dates[0])"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
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||||
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||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th>Last</th>\n",
|
||||
" <th>Bid</th>\n",
|
||||
" <th>Ask</th>\n",
|
||||
" <th>Chg</th>\n",
|
||||
" <th>PctChg</th>\n",
|
||||
" <th>Vol</th>\n",
|
||||
" <th>Open_Int</th>\n",
|
||||
" <th>Root</th>\n",
|
||||
" <th>Underlying_Price</th>\n",
|
||||
" <th>Quote_Time</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Strike</th>\n",
|
||||
" <th>Expiry</th>\n",
|
||||
" <th>Type</th>\n",
|
||||
" <th>Symbol</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th rowspan=\"2\" valign=\"top\">5.0</th>\n",
|
||||
" <th rowspan=\"2\" valign=\"top\">2018-01-19</th>\n",
|
||||
" <th>call</th>\n",
|
||||
" <th>FB180119C00005000</th>\n",
|
||||
" <td>149.10</td>\n",
|
||||
" <td>150.00</td>\n",
|
||||
" <td>150.65</td>\n",
|
||||
" <td>0.3</td>\n",
|
||||
" <td>0.20</td>\n",
|
||||
" <td>50.0</td>\n",
|
||||
" <td>9528.0</td>\n",
|
||||
" <td>FB</td>\n",
|
||||
" <td>155.27</td>\n",
|
||||
" <td>2017-07-11 23:35:24.466303</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>put</th>\n",
|
||||
" <th>FB180119P00005000</th>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>0.03</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>3053.0</td>\n",
|
||||
" <td>FB</td>\n",
|
||||
" <td>155.27</td>\n",
|
||||
" <td>2017-07-11 23:35:24.466303</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th rowspan=\"2\" valign=\"top\">10.0</th>\n",
|
||||
" <th rowspan=\"2\" valign=\"top\">2018-01-19</th>\n",
|
||||
" <th>call</th>\n",
|
||||
" <th>FB180119C00010000</th>\n",
|
||||
" <td>143.20</td>\n",
|
||||
" <td>145.05</td>\n",
|
||||
" <td>145.75</td>\n",
|
||||
" <td>-0.5</td>\n",
|
||||
" <td>-0.35</td>\n",
|
||||
" <td>40.0</td>\n",
|
||||
" <td>2855.0</td>\n",
|
||||
" <td>FB</td>\n",
|
||||
" <td>155.27</td>\n",
|
||||
" <td>2017-07-11 23:35:24.466303</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>put</th>\n",
|
||||
" <th>FB180119P00010000</th>\n",
|
||||
" <td>0.01</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>0.02</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>2056.0</td>\n",
|
||||
" <td>FB</td>\n",
|
||||
" <td>155.27</td>\n",
|
||||
" <td>2017-07-11 23:35:24.466303</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>15.0</th>\n",
|
||||
" <th>2018-01-19</th>\n",
|
||||
" <th>call</th>\n",
|
||||
" <th>FB180119C00015000</th>\n",
|
||||
" <td>135.80</td>\n",
|
||||
" <td>140.10</td>\n",
|
||||
" <td>140.80</td>\n",
|
||||
" <td>0.0</td>\n",
|
||||
" <td>0.00</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>240.0</td>\n",
|
||||
" <td>FB</td>\n",
|
||||
" <td>155.27</td>\n",
|
||||
" <td>2017-07-11 23:35:24.466303</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Last Bid Ask Chg PctChg \\\n",
|
||||
"Strike Expiry Type Symbol \n",
|
||||
"5.0 2018-01-19 call FB180119C00005000 149.10 150.00 150.65 0.3 0.20 \n",
|
||||
" put FB180119P00005000 0.01 NaN 0.03 0.0 0.00 \n",
|
||||
"10.0 2018-01-19 call FB180119C00010000 143.20 145.05 145.75 -0.5 -0.35 \n",
|
||||
" put FB180119P00010000 0.01 NaN 0.02 0.0 0.00 \n",
|
||||
"15.0 2018-01-19 call FB180119C00015000 135.80 140.10 140.80 0.0 0.00 \n",
|
||||
"\n",
|
||||
" Vol Open_Int Root \\\n",
|
||||
"Strike Expiry Type Symbol \n",
|
||||
"5.0 2018-01-19 call FB180119C00005000 50.0 9528.0 FB \n",
|
||||
" put FB180119P00005000 NaN 3053.0 FB \n",
|
||||
"10.0 2018-01-19 call FB180119C00010000 40.0 2855.0 FB \n",
|
||||
" put FB180119P00010000 NaN 2056.0 FB \n",
|
||||
"15.0 2018-01-19 call FB180119C00015000 NaN 240.0 FB \n",
|
||||
"\n",
|
||||
" Underlying_Price \\\n",
|
||||
"Strike Expiry Type Symbol \n",
|
||||
"5.0 2018-01-19 call FB180119C00005000 155.27 \n",
|
||||
" put FB180119P00005000 155.27 \n",
|
||||
"10.0 2018-01-19 call FB180119C00010000 155.27 \n",
|
||||
" put FB180119P00010000 155.27 \n",
|
||||
"15.0 2018-01-19 call FB180119C00015000 155.27 \n",
|
||||
"\n",
|
||||
" Quote_Time \n",
|
||||
"Strike Expiry Type Symbol \n",
|
||||
"5.0 2018-01-19 call FB180119C00005000 2017-07-11 23:35:24.466303 \n",
|
||||
" put FB180119P00005000 2017-07-11 23:35:24.466303 \n",
|
||||
"10.0 2018-01-19 call FB180119C00010000 2017-07-11 23:35:24.466303 \n",
|
||||
" put FB180119P00010000 2017-07-11 23:35:24.466303 \n",
|
||||
"15.0 2018-01-19 call FB180119C00015000 2017-07-11 23:35:24.466303 "
|
||||
]
|
||||
},
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"data.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# FRED"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas_datareader.data as web\n",
|
||||
"\n",
|
||||
"import datetime\n",
|
||||
"\n",
|
||||
"start = datetime.datetime(2010, 1, 1)\n",
|
||||
"\n",
|
||||
"end = datetime.datetime(2017, 1, 1)\n",
|
||||
"\n",
|
||||
"gdp = web.DataReader(\"GDP\", \"fred\", start, end)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style>\n",
|
||||
" .dataframe thead tr:only-child th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: left;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>GDP</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>DATE</th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2010-01-01</th>\n",
|
||||
" <td>14681.1</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2010-04-01</th>\n",
|
||||
" <td>14888.6</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2010-07-01</th>\n",
|
||||
" <td>15057.7</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2010-10-01</th>\n",
|
||||
" <td>15230.2</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2011-01-01</th>\n",
|
||||
" <td>15238.4</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" GDP\n",
|
||||
"DATE \n",
|
||||
"2010-01-01 14681.1\n",
|
||||
"2010-04-01 14888.6\n",
|
||||
"2010-07-01 15057.7\n",
|
||||
"2010-10-01 15230.2\n",
|
||||
"2011-01-01 15238.4"
|
||||
]
|
||||
},
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"gdp.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": []
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.6.1"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,279 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Pandas Datareader\n",
|
||||
"\n",
|
||||
"**EMARQUE : Tous les emplacements géographiques ne fonctionnent pas bien avec le lecteur de données pandas, votre pare-feu peut aussi le bloquer !**\n",
|
||||
"\n",
|
||||
"Les fonctions de pandas_datareader.data et pandas_datareader.wb extraient les données de diverses sources Internet dans un DataFrame pandas. Actuellement, les sources suivantes sont supportées :\n",
|
||||
"\n",
|
||||
"* Yahoo! Finance\n",
|
||||
"* Enigma\n",
|
||||
"* St.Louis FED (FRED)\n",
|
||||
"* Kenneth French’s data library\n",
|
||||
"* World Bank\n",
|
||||
"* OECD\n",
|
||||
"* Eurostat\n",
|
||||
"* Thrift Savings Plan\n",
|
||||
"* Oanda currency historical rate\n",
|
||||
"* Nasdaq Trader symbol definitions (remote_data.nasdaq_symbols)\n",
|
||||
"\n",
|
||||
"Il convient de noter que diverses sources soutiennent différents types de données, de sorte que toutes les sources n'appliquent pas les mêmes méthodes et que les éléments de données retournés peuvent également différer."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas_datareader.data as web\n",
|
||||
"\n",
|
||||
"import datetime\n",
|
||||
"\n",
|
||||
"start = datetime.datetime(2015, 1, 1)\n",
|
||||
"\n",
|
||||
"end = datetime.datetime(2017, 1, 1)\n",
|
||||
"\n",
|
||||
"facebook = web.DataReader(\"FB\", 'yahoo', start, end)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2015-01-02</th>\n",
|
||||
" <td>78.930000</td>\n",
|
||||
" <td>77.699997</td>\n",
|
||||
" <td>78.580002</td>\n",
|
||||
" <td>78.449997</td>\n",
|
||||
" <td>18177500</td>\n",
|
||||
" <td>78.449997</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2015-01-05</th>\n",
|
||||
" <td>79.250000</td>\n",
|
||||
" <td>76.860001</td>\n",
|
||||
" <td>77.980003</td>\n",
|
||||
" <td>77.190002</td>\n",
|
||||
" <td>26452200</td>\n",
|
||||
" <td>77.190002</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2015-01-06</th>\n",
|
||||
" <td>77.589996</td>\n",
|
||||
" <td>75.360001</td>\n",
|
||||
" <td>77.230003</td>\n",
|
||||
" <td>76.150002</td>\n",
|
||||
" <td>27399300</td>\n",
|
||||
" <td>76.150002</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2015-01-07</th>\n",
|
||||
" <td>77.360001</td>\n",
|
||||
" <td>75.820000</td>\n",
|
||||
" <td>76.760002</td>\n",
|
||||
" <td>76.150002</td>\n",
|
||||
" <td>22045300</td>\n",
|
||||
" <td>76.150002</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2015-01-08</th>\n",
|
||||
" <td>78.230003</td>\n",
|
||||
" <td>76.080002</td>\n",
|
||||
" <td>76.739998</td>\n",
|
||||
" <td>78.180000</td>\n",
|
||||
" <td>23961000</td>\n",
|
||||
" <td>78.180000</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" High Low Open Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2015-01-02 78.930000 77.699997 78.580002 78.449997 18177500 78.449997\n",
|
||||
"2015-01-05 79.250000 76.860001 77.980003 77.190002 26452200 77.190002\n",
|
||||
"2015-01-06 77.589996 75.360001 77.230003 76.150002 27399300 76.150002\n",
|
||||
"2015-01-07 77.360001 75.820000 76.760002 76.150002 22045300 76.150002\n",
|
||||
"2015-01-08 78.230003 76.080002 76.739998 78.180000 23961000 78.180000"
|
||||
]
|
||||
},
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"facebook.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# FRED"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas_datareader.data as web\n",
|
||||
"\n",
|
||||
"import datetime\n",
|
||||
"\n",
|
||||
"start = datetime.datetime(2010, 1, 1)\n",
|
||||
"\n",
|
||||
"end = datetime.datetime(2017, 1, 1)\n",
|
||||
"\n",
|
||||
"gdp = web.DataReader(\"GDP\", \"fred\", start, end)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<style scoped>\n",
|
||||
" .dataframe tbody tr th:only-of-type {\n",
|
||||
" vertical-align: middle;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe tbody tr th {\n",
|
||||
" vertical-align: top;\n",
|
||||
" }\n",
|
||||
"\n",
|
||||
" .dataframe thead th {\n",
|
||||
" text-align: right;\n",
|
||||
" }\n",
|
||||
"</style>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>GDP</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>DATE</th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2010-01-01</th>\n",
|
||||
" <td>14721.350</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2010-04-01</th>\n",
|
||||
" <td>14926.098</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2010-07-01</th>\n",
|
||||
" <td>15079.917</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2010-10-01</th>\n",
|
||||
" <td>15240.843</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2011-01-01</th>\n",
|
||||
" <td>15285.828</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" GDP\n",
|
||||
"DATE \n",
|
||||
"2010-01-01 14721.350\n",
|
||||
"2010-04-01 14926.098\n",
|
||||
"2010-07-01 15079.917\n",
|
||||
"2010-10-01 15240.843\n",
|
||||
"2011-01-01 15285.828"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"gdp.head()"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.5"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,467 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"___\n",
|
||||
"\n",
|
||||
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
|
||||
"___\n",
|
||||
"<center>*Copyright Pierian Data 2017*</center>\n",
|
||||
"<center>*For more information, visit us at www.pieriandata.com*</center>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Introduction to Time Series with Pandas\n",
|
||||
"\n",
|
||||
"A lot of our financial data will have a datatime index, so let's learn how to deal with this sort of data with pandas!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from datetime import datetime"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# To illustrate the order of arguments\n",
|
||||
"my_year = 2017\n",
|
||||
"my_month = 1\n",
|
||||
"my_day = 2\n",
|
||||
"my_hour = 13\n",
|
||||
"my_minute = 30\n",
|
||||
"my_second = 15"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# January 2nd, 2017\n",
|
||||
"my_date = datetime(my_year,my_month,my_day)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"datetime.datetime(2017, 1, 2, 0, 0)"
|
||||
]
|
||||
},
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Defaults to 0:00\n",
|
||||
"my_date "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# January 2nd, 2017 at 13:30:15\n",
|
||||
"my_date_time = datetime(my_year,my_month,my_day,my_hour,my_minute,my_second)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"datetime.datetime(2017, 1, 2, 13, 30, 15)"
|
||||
]
|
||||
},
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"my_date_time"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"You can grab any part of the datetime object you want"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"2"
|
||||
]
|
||||
},
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"my_date.day"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"13"
|
||||
]
|
||||
},
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"my_date_time.hour"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Pandas with Datetime Index\n",
|
||||
"\n",
|
||||
"You'll usually deal with time series as an index when working with pandas dataframes obtained from some sort of financial API. Fortunately pandas has a lot of functions and methods to work with time series!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[datetime.datetime(2016, 1, 1, 0, 0), datetime.datetime(2016, 1, 2, 0, 0)]"
|
||||
]
|
||||
},
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Create an example datetime list/array\n",
|
||||
"first_two = [datetime(2016, 1, 1), datetime(2016, 1, 2)]\n",
|
||||
"first_two"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"DatetimeIndex(['2016-01-01', '2016-01-02'], dtype='datetime64[ns]', freq=None)"
|
||||
]
|
||||
},
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Converted to an index\n",
|
||||
"dt_ind = pd.DatetimeIndex(first_two)\n",
|
||||
"dt_ind"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 29,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"[[-1.58270607 0.47766839]\n",
|
||||
" [ 0.34171008 0.5889566 ]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Attached to some random data\n",
|
||||
"data = np.random.randn(2,2)\n",
|
||||
"print(data)\n",
|
||||
"cols = ['A','B']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(data,dt_ind,cols)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 30,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>A</th>\n",
|
||||
" <th>B</th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-01-01</th>\n",
|
||||
" <td>0.165224</td>\n",
|
||||
" <td>-0.767629</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-01-02</th>\n",
|
||||
" <td>-0.482305</td>\n",
|
||||
" <td>0.307934</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" A B\n",
|
||||
"2016-01-01 0.165224 -0.767629\n",
|
||||
"2016-01-02 -0.482305 0.307934"
|
||||
]
|
||||
},
|
||||
"execution_count": 30,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 31,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"DatetimeIndex(['2016-01-01', '2016-01-02'], dtype='datetime64[ns]', freq=None)"
|
||||
]
|
||||
},
|
||||
"execution_count": 31,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 34,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1"
|
||||
]
|
||||
},
|
||||
"execution_count": 34,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Latest Date Location\n",
|
||||
"df.index.argmax()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 35,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Timestamp('2016-01-02 00:00:00')"
|
||||
]
|
||||
},
|
||||
"execution_count": 35,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.index.max()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 37,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0"
|
||||
]
|
||||
},
|
||||
"execution_count": 37,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Earliest Date Index Location\n",
|
||||
"df.index.argmin()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 38,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Timestamp('2016-01-01 00:00:00')"
|
||||
]
|
||||
},
|
||||
"execution_count": 38,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.index.min()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Great, let's move on!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python [conda root]",
|
||||
"language": "python",
|
||||
"name": "conda-root-py"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
|
|
@ -0,0 +1,811 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"___\n",
|
||||
"\n",
|
||||
"<a href='http://www.pieriandata.com'> <img src='../Pierian_Data_Logo.png' /></a>\n",
|
||||
"___\n",
|
||||
"<center>*Copyright Pierian Data 2017*</center>\n",
|
||||
"<center>*For more information, visit us at www.pieriandata.com*</center>"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Time Shifting\n",
|
||||
"\n",
|
||||
"Sometimes you may need to shift all your data up or down along the time series index, in fact, a lot of pandas built-in methods do this under the hood. This isn't something we won't do often in the course, but its definitely good to know about this anyways!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {
|
||||
"collapsed": true
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"%matplotlib inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 20,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.read_csv('time_data/walmart_stock.csv',index_col='Date')\n",
|
||||
"df.index = pd.to_datetime(df.index)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 21,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-03</th>\n",
|
||||
" <td>59.970001</td>\n",
|
||||
" <td>61.060001</td>\n",
|
||||
" <td>59.869999</td>\n",
|
||||
" <td>60.330002</td>\n",
|
||||
" <td>12668800</td>\n",
|
||||
" <td>52.619235</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-04</th>\n",
|
||||
" <td>60.209999</td>\n",
|
||||
" <td>60.349998</td>\n",
|
||||
" <td>59.470001</td>\n",
|
||||
" <td>59.709999</td>\n",
|
||||
" <td>9593300</td>\n",
|
||||
" <td>52.078475</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-05</th>\n",
|
||||
" <td>59.349998</td>\n",
|
||||
" <td>59.619999</td>\n",
|
||||
" <td>58.369999</td>\n",
|
||||
" <td>59.419998</td>\n",
|
||||
" <td>12768200</td>\n",
|
||||
" <td>51.825539</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-06</th>\n",
|
||||
" <td>59.419998</td>\n",
|
||||
" <td>59.450001</td>\n",
|
||||
" <td>58.869999</td>\n",
|
||||
" <td>59.000000</td>\n",
|
||||
" <td>8069400</td>\n",
|
||||
" <td>51.459220</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-09</th>\n",
|
||||
" <td>59.029999</td>\n",
|
||||
" <td>59.549999</td>\n",
|
||||
" <td>58.919998</td>\n",
|
||||
" <td>59.180000</td>\n",
|
||||
" <td>6679300</td>\n",
|
||||
" <td>51.616215</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2012-01-03 59.970001 61.060001 59.869999 60.330002 12668800 52.619235\n",
|
||||
"2012-01-04 60.209999 60.349998 59.470001 59.709999 9593300 52.078475\n",
|
||||
"2012-01-05 59.349998 59.619999 58.369999 59.419998 12768200 51.825539\n",
|
||||
"2012-01-06 59.419998 59.450001 58.869999 59.000000 8069400 51.459220\n",
|
||||
"2012-01-09 59.029999 59.549999 58.919998 59.180000 6679300 51.616215"
|
||||
]
|
||||
},
|
||||
"execution_count": 21,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 22,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-23</th>\n",
|
||||
" <td>69.430000</td>\n",
|
||||
" <td>69.750000</td>\n",
|
||||
" <td>69.360001</td>\n",
|
||||
" <td>69.540001</td>\n",
|
||||
" <td>4803900</td>\n",
|
||||
" <td>69.032411</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-27</th>\n",
|
||||
" <td>69.300003</td>\n",
|
||||
" <td>69.820000</td>\n",
|
||||
" <td>69.250000</td>\n",
|
||||
" <td>69.699997</td>\n",
|
||||
" <td>4435700</td>\n",
|
||||
" <td>69.191240</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-28</th>\n",
|
||||
" <td>69.940002</td>\n",
|
||||
" <td>70.000000</td>\n",
|
||||
" <td>69.260002</td>\n",
|
||||
" <td>69.309998</td>\n",
|
||||
" <td>4875700</td>\n",
|
||||
" <td>68.804087</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-29</th>\n",
|
||||
" <td>69.209999</td>\n",
|
||||
" <td>69.519997</td>\n",
|
||||
" <td>69.120003</td>\n",
|
||||
" <td>69.260002</td>\n",
|
||||
" <td>4298400</td>\n",
|
||||
" <td>68.754456</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-30</th>\n",
|
||||
" <td>69.120003</td>\n",
|
||||
" <td>69.430000</td>\n",
|
||||
" <td>68.830002</td>\n",
|
||||
" <td>69.120003</td>\n",
|
||||
" <td>6889500</td>\n",
|
||||
" <td>68.615479</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2016-12-23 69.430000 69.750000 69.360001 69.540001 4803900 69.032411\n",
|
||||
"2016-12-27 69.300003 69.820000 69.250000 69.699997 4435700 69.191240\n",
|
||||
"2016-12-28 69.940002 70.000000 69.260002 69.309998 4875700 68.804087\n",
|
||||
"2016-12-29 69.209999 69.519997 69.120003 69.260002 4298400 68.754456\n",
|
||||
"2016-12-30 69.120003 69.430000 68.830002 69.120003 6889500 68.615479"
|
||||
]
|
||||
},
|
||||
"execution_count": 22,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## .shift() forward"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 23,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-03</th>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-04</th>\n",
|
||||
" <td>59.970001</td>\n",
|
||||
" <td>61.060001</td>\n",
|
||||
" <td>59.869999</td>\n",
|
||||
" <td>60.330002</td>\n",
|
||||
" <td>12668800.0</td>\n",
|
||||
" <td>52.619235</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-05</th>\n",
|
||||
" <td>60.209999</td>\n",
|
||||
" <td>60.349998</td>\n",
|
||||
" <td>59.470001</td>\n",
|
||||
" <td>59.709999</td>\n",
|
||||
" <td>9593300.0</td>\n",
|
||||
" <td>52.078475</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-06</th>\n",
|
||||
" <td>59.349998</td>\n",
|
||||
" <td>59.619999</td>\n",
|
||||
" <td>58.369999</td>\n",
|
||||
" <td>59.419998</td>\n",
|
||||
" <td>12768200.0</td>\n",
|
||||
" <td>51.825539</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-09</th>\n",
|
||||
" <td>59.419998</td>\n",
|
||||
" <td>59.450001</td>\n",
|
||||
" <td>58.869999</td>\n",
|
||||
" <td>59.000000</td>\n",
|
||||
" <td>8069400.0</td>\n",
|
||||
" <td>51.459220</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2012-01-03 NaN NaN NaN NaN NaN NaN\n",
|
||||
"2012-01-04 59.970001 61.060001 59.869999 60.330002 12668800.0 52.619235\n",
|
||||
"2012-01-05 60.209999 60.349998 59.470001 59.709999 9593300.0 52.078475\n",
|
||||
"2012-01-06 59.349998 59.619999 58.369999 59.419998 12768200.0 51.825539\n",
|
||||
"2012-01-09 59.419998 59.450001 58.869999 59.000000 8069400.0 51.459220"
|
||||
]
|
||||
},
|
||||
"execution_count": 23,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.shift(1).head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 24,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-23</th>\n",
|
||||
" <td>71.239998</td>\n",
|
||||
" <td>71.239998</td>\n",
|
||||
" <td>69.209999</td>\n",
|
||||
" <td>69.589996</td>\n",
|
||||
" <td>12106800.0</td>\n",
|
||||
" <td>69.082042</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-27</th>\n",
|
||||
" <td>69.430000</td>\n",
|
||||
" <td>69.750000</td>\n",
|
||||
" <td>69.360001</td>\n",
|
||||
" <td>69.540001</td>\n",
|
||||
" <td>4803900.0</td>\n",
|
||||
" <td>69.032411</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-28</th>\n",
|
||||
" <td>69.300003</td>\n",
|
||||
" <td>69.820000</td>\n",
|
||||
" <td>69.250000</td>\n",
|
||||
" <td>69.699997</td>\n",
|
||||
" <td>4435700.0</td>\n",
|
||||
" <td>69.191240</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-29</th>\n",
|
||||
" <td>69.940002</td>\n",
|
||||
" <td>70.000000</td>\n",
|
||||
" <td>69.260002</td>\n",
|
||||
" <td>69.309998</td>\n",
|
||||
" <td>4875700.0</td>\n",
|
||||
" <td>68.804087</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-30</th>\n",
|
||||
" <td>69.209999</td>\n",
|
||||
" <td>69.519997</td>\n",
|
||||
" <td>69.120003</td>\n",
|
||||
" <td>69.260002</td>\n",
|
||||
" <td>4298400.0</td>\n",
|
||||
" <td>68.754456</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2016-12-23 71.239998 71.239998 69.209999 69.589996 12106800.0 69.082042\n",
|
||||
"2016-12-27 69.430000 69.750000 69.360001 69.540001 4803900.0 69.032411\n",
|
||||
"2016-12-28 69.300003 69.820000 69.250000 69.699997 4435700.0 69.191240\n",
|
||||
"2016-12-29 69.940002 70.000000 69.260002 69.309998 4875700.0 68.804087\n",
|
||||
"2016-12-30 69.209999 69.519997 69.120003 69.260002 4298400.0 68.754456"
|
||||
]
|
||||
},
|
||||
"execution_count": 24,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# You will lose that last piece of data that no longer has an index!\n",
|
||||
"df.shift(1).tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## shift() backwards"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 25,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-03</th>\n",
|
||||
" <td>60.209999</td>\n",
|
||||
" <td>60.349998</td>\n",
|
||||
" <td>59.470001</td>\n",
|
||||
" <td>59.709999</td>\n",
|
||||
" <td>9593300.0</td>\n",
|
||||
" <td>52.078475</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-04</th>\n",
|
||||
" <td>59.349998</td>\n",
|
||||
" <td>59.619999</td>\n",
|
||||
" <td>58.369999</td>\n",
|
||||
" <td>59.419998</td>\n",
|
||||
" <td>12768200.0</td>\n",
|
||||
" <td>51.825539</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-05</th>\n",
|
||||
" <td>59.419998</td>\n",
|
||||
" <td>59.450001</td>\n",
|
||||
" <td>58.869999</td>\n",
|
||||
" <td>59.000000</td>\n",
|
||||
" <td>8069400.0</td>\n",
|
||||
" <td>51.459220</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-06</th>\n",
|
||||
" <td>59.029999</td>\n",
|
||||
" <td>59.549999</td>\n",
|
||||
" <td>58.919998</td>\n",
|
||||
" <td>59.180000</td>\n",
|
||||
" <td>6679300.0</td>\n",
|
||||
" <td>51.616215</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-09</th>\n",
|
||||
" <td>59.430000</td>\n",
|
||||
" <td>59.709999</td>\n",
|
||||
" <td>58.980000</td>\n",
|
||||
" <td>59.040001</td>\n",
|
||||
" <td>6907300.0</td>\n",
|
||||
" <td>51.494109</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2012-01-03 60.209999 60.349998 59.470001 59.709999 9593300.0 52.078475\n",
|
||||
"2012-01-04 59.349998 59.619999 58.369999 59.419998 12768200.0 51.825539\n",
|
||||
"2012-01-05 59.419998 59.450001 58.869999 59.000000 8069400.0 51.459220\n",
|
||||
"2012-01-06 59.029999 59.549999 58.919998 59.180000 6679300.0 51.616215\n",
|
||||
"2012-01-09 59.430000 59.709999 58.980000 59.040001 6907300.0 51.494109"
|
||||
]
|
||||
},
|
||||
"execution_count": 25,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.shift(-1).head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 26,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-23</th>\n",
|
||||
" <td>69.300003</td>\n",
|
||||
" <td>69.820000</td>\n",
|
||||
" <td>69.250000</td>\n",
|
||||
" <td>69.699997</td>\n",
|
||||
" <td>4435700.0</td>\n",
|
||||
" <td>69.191240</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-27</th>\n",
|
||||
" <td>69.940002</td>\n",
|
||||
" <td>70.000000</td>\n",
|
||||
" <td>69.260002</td>\n",
|
||||
" <td>69.309998</td>\n",
|
||||
" <td>4875700.0</td>\n",
|
||||
" <td>68.804087</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-28</th>\n",
|
||||
" <td>69.209999</td>\n",
|
||||
" <td>69.519997</td>\n",
|
||||
" <td>69.120003</td>\n",
|
||||
" <td>69.260002</td>\n",
|
||||
" <td>4298400.0</td>\n",
|
||||
" <td>68.754456</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-29</th>\n",
|
||||
" <td>69.120003</td>\n",
|
||||
" <td>69.430000</td>\n",
|
||||
" <td>68.830002</td>\n",
|
||||
" <td>69.120003</td>\n",
|
||||
" <td>6889500.0</td>\n",
|
||||
" <td>68.615479</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2016-12-30</th>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" <td>NaN</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2016-12-23 69.300003 69.820000 69.250000 69.699997 4435700.0 69.191240\n",
|
||||
"2016-12-27 69.940002 70.000000 69.260002 69.309998 4875700.0 68.804087\n",
|
||||
"2016-12-28 69.209999 69.519997 69.120003 69.260002 4298400.0 68.754456\n",
|
||||
"2016-12-29 69.120003 69.430000 68.830002 69.120003 6889500.0 68.615479\n",
|
||||
"2016-12-30 NaN NaN NaN NaN NaN NaN"
|
||||
]
|
||||
},
|
||||
"execution_count": 26,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.shift(-1).tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Shifting based off Time String Code \n",
|
||||
"### Using tshift()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 28,
|
||||
"metadata": {
|
||||
"collapsed": false
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"data": {
|
||||
"text/html": [
|
||||
"<div>\n",
|
||||
"<table border=\"1\" class=\"dataframe\">\n",
|
||||
" <thead>\n",
|
||||
" <tr style=\"text-align: right;\">\n",
|
||||
" <th></th>\n",
|
||||
" <th>Open</th>\n",
|
||||
" <th>High</th>\n",
|
||||
" <th>Low</th>\n",
|
||||
" <th>Close</th>\n",
|
||||
" <th>Volume</th>\n",
|
||||
" <th>Adj Close</th>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>Date</th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" <th></th>\n",
|
||||
" </tr>\n",
|
||||
" </thead>\n",
|
||||
" <tbody>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-31</th>\n",
|
||||
" <td>59.970001</td>\n",
|
||||
" <td>61.060001</td>\n",
|
||||
" <td>59.869999</td>\n",
|
||||
" <td>60.330002</td>\n",
|
||||
" <td>12668800</td>\n",
|
||||
" <td>52.619235</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-31</th>\n",
|
||||
" <td>60.209999</td>\n",
|
||||
" <td>60.349998</td>\n",
|
||||
" <td>59.470001</td>\n",
|
||||
" <td>59.709999</td>\n",
|
||||
" <td>9593300</td>\n",
|
||||
" <td>52.078475</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-31</th>\n",
|
||||
" <td>59.349998</td>\n",
|
||||
" <td>59.619999</td>\n",
|
||||
" <td>58.369999</td>\n",
|
||||
" <td>59.419998</td>\n",
|
||||
" <td>12768200</td>\n",
|
||||
" <td>51.825539</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-31</th>\n",
|
||||
" <td>59.419998</td>\n",
|
||||
" <td>59.450001</td>\n",
|
||||
" <td>58.869999</td>\n",
|
||||
" <td>59.000000</td>\n",
|
||||
" <td>8069400</td>\n",
|
||||
" <td>51.459220</td>\n",
|
||||
" </tr>\n",
|
||||
" <tr>\n",
|
||||
" <th>2012-01-31</th>\n",
|
||||
" <td>59.029999</td>\n",
|
||||
" <td>59.549999</td>\n",
|
||||
" <td>58.919998</td>\n",
|
||||
" <td>59.180000</td>\n",
|
||||
" <td>6679300</td>\n",
|
||||
" <td>51.616215</td>\n",
|
||||
" </tr>\n",
|
||||
" </tbody>\n",
|
||||
"</table>\n",
|
||||
"</div>"
|
||||
],
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2012-01-31 59.970001 61.060001 59.869999 60.330002 12668800 52.619235\n",
|
||||
"2012-01-31 60.209999 60.349998 59.470001 59.709999 9593300 52.078475\n",
|
||||
"2012-01-31 59.349998 59.619999 58.369999 59.419998 12768200 51.825539\n",
|
||||
"2012-01-31 59.419998 59.450001 58.869999 59.000000 8069400 51.459220\n",
|
||||
"2012-01-31 59.029999 59.549999 58.919998 59.180000 6679300 51.616215"
|
||||
]
|
||||
},
|
||||
"execution_count": 28,
|
||||
"metadata": {},
|
||||
"output_type": "execute_result"
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Shift everything forward one month\n",
|
||||
"df.tshift(periods=1,freq='M').head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"That is it for now!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"display_name": "Python [conda root]",
|
||||
"language": "python",
|
||||
"name": "conda-root-py"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.5.3"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,396 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Introduction aux Séries temporelles avec Pandas\n",
|
||||
"\n",
|
||||
"Beaucoup de nos données financières auront un index datatime, alors apprenons comment traiter ce genre de données avec pandas !"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import numpy as np\n",
|
||||
"import pandas as pd\n",
|
||||
"import matplotlib.pyplot as plt"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from datetime import datetime"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# pour illustrer l'ordre des arguments\n",
|
||||
"my_year = 2017\n",
|
||||
"my_month = 1\n",
|
||||
"my_day = 2\n",
|
||||
"my_hour = 13\n",
|
||||
"my_minute = 30\n",
|
||||
"my_second = 15"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# January 2nd, 2017\n",
|
||||
"my_date = datetime(my_year,my_month,my_day)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"datetime.datetime(2017, 1, 2, 0, 0)"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 5
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Par défaut 0:00\n",
|
||||
"my_date "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# January 2nd, 2017 à 13:30:15\n",
|
||||
"my_date_time = datetime(my_year,my_month,my_day,my_hour,my_minute,my_second)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"datetime.datetime(2017, 1, 2, 13, 30, 15)"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 7
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"my_date_time"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Vous pouvez saisir n'importe quelle partie de l'objet datetime:"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"2"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 8
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"my_date.day"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"13"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 9
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"my_date_time.hour"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"### Index dataetime avec Pandas\n",
|
||||
"\n",
|
||||
"Vous traiterez généralement les séries temporelles comme un index lorsque vous travaillez avec des dataframes pandas obtenues à partir d'APIs financières. Heureusement, pandas a beaucoup de fonctions et de méthodes pour travailler avec des séries temporelles !"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 10,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"[datetime.datetime(2016, 1, 1, 0, 0), datetime.datetime(2016, 1, 2, 0, 0)]"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 10
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Créer un exemple de liste/tableau datetime\n",
|
||||
"first_two = [datetime(2016, 1, 1), datetime(2016, 1, 2)]\n",
|
||||
"first_two"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"DatetimeIndex(['2016-01-01', '2016-01-02'], dtype='datetime64[ns]', freq=None)"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 11
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Converti en un index\n",
|
||||
"dt_ind = pd.DatetimeIndex(first_two)\n",
|
||||
"dt_ind"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "stream",
|
||||
"name": "stdout",
|
||||
"text": [
|
||||
"[[-1.05763777 -0.15818632]\n [-0.79985649 -0.93157171]]\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Quelques données alétaoires\n",
|
||||
"data = np.random.randn(2,2)\n",
|
||||
"print(data)\n",
|
||||
"cols = ['A','B']"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.DataFrame(data,dt_ind,cols)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 14,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" A B\n",
|
||||
"2016-01-01 -1.057638 -0.158186\n",
|
||||
"2016-01-02 -0.799856 -0.931572"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>A</th>\n <th>B</th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2016-01-01</th>\n <td>-1.057638</td>\n <td>-0.158186</td>\n </tr>\n <tr>\n <th>2016-01-02</th>\n <td>-0.799856</td>\n <td>-0.931572</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 14
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 15,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"DatetimeIndex(['2016-01-01', '2016-01-02'], dtype='datetime64[ns]', freq=None)"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 15
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.index"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 16,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"1"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 16
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Emplacement de la date la plus récente\n",
|
||||
"df.index.argmax()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 17,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Timestamp('2016-01-02 00:00:00')"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 17
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.index.max()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 18,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"0"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 18
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Emplacement de la date la plus ancienne\n",
|
||||
"df.index.argmin()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 19,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"Timestamp('2016-01-01 00:00:00')"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 19
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.index.min()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Top, passons à la suite!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "e89404a230d8800c54ad520c7b67d1bd9bb833a07b37dd3e521a178a3dc34904"
|
||||
}
|
||||
}
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.9-final"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
|
|
@ -0,0 +1,281 @@
|
|||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Décalage Temporel\n",
|
||||
"\n",
|
||||
"Parfois, vous pouvez avoir besoin de déplacer toutes vos données vers le haut ou vers le bas le long de l'index des séries temporelles, en fait, beaucoup de méthodes intégrées pandas le font. Ce n'est pas quelque chose que nous ferons souvent dans le cours, mais c'est très bon à connaître !"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import pandas as pd\n",
|
||||
"\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"%matplotlib inline"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"df = pd.read_csv('time_data/walmart_stock.csv',index_col='Date')\n",
|
||||
"df.index = pd.to_datetime(df.index)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2012-01-03 59.970001 61.060001 59.869999 60.330002 12668800 52.619235\n",
|
||||
"2012-01-04 60.209999 60.349998 59.470001 59.709999 9593300 52.078475\n",
|
||||
"2012-01-05 59.349998 59.619999 58.369999 59.419998 12768200 51.825539\n",
|
||||
"2012-01-06 59.419998 59.450001 58.869999 59.000000 8069400 51.459220\n",
|
||||
"2012-01-09 59.029999 59.549999 58.919998 59.180000 6679300 51.616215"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Open</th>\n <th>High</th>\n <th>Low</th>\n <th>Close</th>\n <th>Volume</th>\n <th>Adj Close</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-01-03</th>\n <td>59.970001</td>\n <td>61.060001</td>\n <td>59.869999</td>\n <td>60.330002</td>\n <td>12668800</td>\n <td>52.619235</td>\n </tr>\n <tr>\n <th>2012-01-04</th>\n <td>60.209999</td>\n <td>60.349998</td>\n <td>59.470001</td>\n <td>59.709999</td>\n <td>9593300</td>\n <td>52.078475</td>\n </tr>\n <tr>\n <th>2012-01-05</th>\n <td>59.349998</td>\n <td>59.619999</td>\n <td>58.369999</td>\n <td>59.419998</td>\n <td>12768200</td>\n <td>51.825539</td>\n </tr>\n <tr>\n <th>2012-01-06</th>\n <td>59.419998</td>\n <td>59.450001</td>\n <td>58.869999</td>\n <td>59.000000</td>\n <td>8069400</td>\n <td>51.459220</td>\n </tr>\n <tr>\n <th>2012-01-09</th>\n <td>59.029999</td>\n <td>59.549999</td>\n <td>58.919998</td>\n <td>59.180000</td>\n <td>6679300</td>\n <td>51.616215</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 3
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2016-12-23 69.430000 69.750000 69.360001 69.540001 4803900 69.032411\n",
|
||||
"2016-12-27 69.300003 69.820000 69.250000 69.699997 4435700 69.191240\n",
|
||||
"2016-12-28 69.940002 70.000000 69.260002 69.309998 4875700 68.804087\n",
|
||||
"2016-12-29 69.209999 69.519997 69.120003 69.260002 4298400 68.754456\n",
|
||||
"2016-12-30 69.120003 69.430000 68.830002 69.120003 6889500 68.615479"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Open</th>\n <th>High</th>\n <th>Low</th>\n <th>Close</th>\n <th>Volume</th>\n <th>Adj Close</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2016-12-23</th>\n <td>69.430000</td>\n <td>69.750000</td>\n <td>69.360001</td>\n <td>69.540001</td>\n <td>4803900</td>\n <td>69.032411</td>\n </tr>\n <tr>\n <th>2016-12-27</th>\n <td>69.300003</td>\n <td>69.820000</td>\n <td>69.250000</td>\n <td>69.699997</td>\n <td>4435700</td>\n <td>69.191240</td>\n </tr>\n <tr>\n <th>2016-12-28</th>\n <td>69.940002</td>\n <td>70.000000</td>\n <td>69.260002</td>\n <td>69.309998</td>\n <td>4875700</td>\n <td>68.804087</td>\n </tr>\n <tr>\n <th>2016-12-29</th>\n <td>69.209999</td>\n <td>69.519997</td>\n <td>69.120003</td>\n <td>69.260002</td>\n <td>4298400</td>\n <td>68.754456</td>\n </tr>\n <tr>\n <th>2016-12-30</th>\n <td>69.120003</td>\n <td>69.430000</td>\n <td>68.830002</td>\n <td>69.120003</td>\n <td>6889500</td>\n <td>68.615479</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 4
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## .shift() - décalage en avance"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2012-01-03 NaN NaN NaN NaN NaN NaN\n",
|
||||
"2012-01-04 59.970001 61.060001 59.869999 60.330002 12668800.0 52.619235\n",
|
||||
"2012-01-05 60.209999 60.349998 59.470001 59.709999 9593300.0 52.078475\n",
|
||||
"2012-01-06 59.349998 59.619999 58.369999 59.419998 12768200.0 51.825539\n",
|
||||
"2012-01-09 59.419998 59.450001 58.869999 59.000000 8069400.0 51.459220"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Open</th>\n <th>High</th>\n <th>Low</th>\n <th>Close</th>\n <th>Volume</th>\n <th>Adj Close</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-01-03</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n <tr>\n <th>2012-01-04</th>\n <td>59.970001</td>\n <td>61.060001</td>\n <td>59.869999</td>\n <td>60.330002</td>\n <td>12668800.0</td>\n <td>52.619235</td>\n </tr>\n <tr>\n <th>2012-01-05</th>\n <td>60.209999</td>\n <td>60.349998</td>\n <td>59.470001</td>\n <td>59.709999</td>\n <td>9593300.0</td>\n <td>52.078475</td>\n </tr>\n <tr>\n <th>2012-01-06</th>\n <td>59.349998</td>\n <td>59.619999</td>\n <td>58.369999</td>\n <td>59.419998</td>\n <td>12768200.0</td>\n <td>51.825539</td>\n </tr>\n <tr>\n <th>2012-01-09</th>\n <td>59.419998</td>\n <td>59.450001</td>\n <td>58.869999</td>\n <td>59.000000</td>\n <td>8069400.0</td>\n <td>51.459220</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 5
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.shift(1).head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2016-12-23 71.239998 71.239998 69.209999 69.589996 12106800.0 69.082042\n",
|
||||
"2016-12-27 69.430000 69.750000 69.360001 69.540001 4803900.0 69.032411\n",
|
||||
"2016-12-28 69.300003 69.820000 69.250000 69.699997 4435700.0 69.191240\n",
|
||||
"2016-12-29 69.940002 70.000000 69.260002 69.309998 4875700.0 68.804087\n",
|
||||
"2016-12-30 69.209999 69.519997 69.120003 69.260002 4298400.0 68.754456"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Open</th>\n <th>High</th>\n <th>Low</th>\n <th>Close</th>\n <th>Volume</th>\n <th>Adj Close</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2016-12-23</th>\n <td>71.239998</td>\n <td>71.239998</td>\n <td>69.209999</td>\n <td>69.589996</td>\n <td>12106800.0</td>\n <td>69.082042</td>\n </tr>\n <tr>\n <th>2016-12-27</th>\n <td>69.430000</td>\n <td>69.750000</td>\n <td>69.360001</td>\n <td>69.540001</td>\n <td>4803900.0</td>\n <td>69.032411</td>\n </tr>\n <tr>\n <th>2016-12-28</th>\n <td>69.300003</td>\n <td>69.820000</td>\n <td>69.250000</td>\n <td>69.699997</td>\n <td>4435700.0</td>\n <td>69.191240</td>\n </tr>\n <tr>\n <th>2016-12-29</th>\n <td>69.940002</td>\n <td>70.000000</td>\n <td>69.260002</td>\n <td>69.309998</td>\n <td>4875700.0</td>\n <td>68.804087</td>\n </tr>\n <tr>\n <th>2016-12-30</th>\n <td>69.209999</td>\n <td>69.519997</td>\n <td>69.120003</td>\n <td>69.260002</td>\n <td>4298400.0</td>\n <td>68.754456</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 6
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# You will lose that last piece of data that no longer has an index!\n",
|
||||
"df.shift(1).tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## shift() - décalage en arrière"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2012-01-03 60.209999 60.349998 59.470001 59.709999 9593300.0 52.078475\n",
|
||||
"2012-01-04 59.349998 59.619999 58.369999 59.419998 12768200.0 51.825539\n",
|
||||
"2012-01-05 59.419998 59.450001 58.869999 59.000000 8069400.0 51.459220\n",
|
||||
"2012-01-06 59.029999 59.549999 58.919998 59.180000 6679300.0 51.616215\n",
|
||||
"2012-01-09 59.430000 59.709999 58.980000 59.040001 6907300.0 51.494109"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Open</th>\n <th>High</th>\n <th>Low</th>\n <th>Close</th>\n <th>Volume</th>\n <th>Adj Close</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-01-03</th>\n <td>60.209999</td>\n <td>60.349998</td>\n <td>59.470001</td>\n <td>59.709999</td>\n <td>9593300.0</td>\n <td>52.078475</td>\n </tr>\n <tr>\n <th>2012-01-04</th>\n <td>59.349998</td>\n <td>59.619999</td>\n <td>58.369999</td>\n <td>59.419998</td>\n <td>12768200.0</td>\n <td>51.825539</td>\n </tr>\n <tr>\n <th>2012-01-05</th>\n <td>59.419998</td>\n <td>59.450001</td>\n <td>58.869999</td>\n <td>59.000000</td>\n <td>8069400.0</td>\n <td>51.459220</td>\n </tr>\n <tr>\n <th>2012-01-06</th>\n <td>59.029999</td>\n <td>59.549999</td>\n <td>58.919998</td>\n <td>59.180000</td>\n <td>6679300.0</td>\n <td>51.616215</td>\n </tr>\n <tr>\n <th>2012-01-09</th>\n <td>59.430000</td>\n <td>59.709999</td>\n <td>58.980000</td>\n <td>59.040001</td>\n <td>6907300.0</td>\n <td>51.494109</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 7
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.shift(-1).head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2016-12-23 69.300003 69.820000 69.250000 69.699997 4435700.0 69.191240\n",
|
||||
"2016-12-27 69.940002 70.000000 69.260002 69.309998 4875700.0 68.804087\n",
|
||||
"2016-12-28 69.209999 69.519997 69.120003 69.260002 4298400.0 68.754456\n",
|
||||
"2016-12-29 69.120003 69.430000 68.830002 69.120003 6889500.0 68.615479\n",
|
||||
"2016-12-30 NaN NaN NaN NaN NaN NaN"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Open</th>\n <th>High</th>\n <th>Low</th>\n <th>Close</th>\n <th>Volume</th>\n <th>Adj Close</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2016-12-23</th>\n <td>69.300003</td>\n <td>69.820000</td>\n <td>69.250000</td>\n <td>69.699997</td>\n <td>4435700.0</td>\n <td>69.191240</td>\n </tr>\n <tr>\n <th>2016-12-27</th>\n <td>69.940002</td>\n <td>70.000000</td>\n <td>69.260002</td>\n <td>69.309998</td>\n <td>4875700.0</td>\n <td>68.804087</td>\n </tr>\n <tr>\n <th>2016-12-28</th>\n <td>69.209999</td>\n <td>69.519997</td>\n <td>69.120003</td>\n <td>69.260002</td>\n <td>4298400.0</td>\n <td>68.754456</td>\n </tr>\n <tr>\n <th>2016-12-29</th>\n <td>69.120003</td>\n <td>69.430000</td>\n <td>68.830002</td>\n <td>69.120003</td>\n <td>6889500.0</td>\n <td>68.615479</td>\n </tr>\n <tr>\n <th>2016-12-30</th>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n <td>NaN</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 8
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"df.shift(-1).tail()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Décalage basé sur le Code Time String\n",
|
||||
"### Utilisation de tshift()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 9,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
" Open High Low Close Volume Adj Close\n",
|
||||
"Date \n",
|
||||
"2012-01-31 59.970001 61.060001 59.869999 60.330002 12668800 52.619235\n",
|
||||
"2012-01-31 60.209999 60.349998 59.470001 59.709999 9593300 52.078475\n",
|
||||
"2012-01-31 59.349998 59.619999 58.369999 59.419998 12768200 51.825539\n",
|
||||
"2012-01-31 59.419998 59.450001 58.869999 59.000000 8069400 51.459220\n",
|
||||
"2012-01-31 59.029999 59.549999 58.919998 59.180000 6679300 51.616215"
|
||||
],
|
||||
"text/html": "<div>\n<style scoped>\n .dataframe tbody tr th:only-of-type {\n vertical-align: middle;\n }\n\n .dataframe tbody tr th {\n vertical-align: top;\n }\n\n .dataframe thead th {\n text-align: right;\n }\n</style>\n<table border=\"1\" class=\"dataframe\">\n <thead>\n <tr style=\"text-align: right;\">\n <th></th>\n <th>Open</th>\n <th>High</th>\n <th>Low</th>\n <th>Close</th>\n <th>Volume</th>\n <th>Adj Close</th>\n </tr>\n <tr>\n <th>Date</th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n <th></th>\n </tr>\n </thead>\n <tbody>\n <tr>\n <th>2012-01-31</th>\n <td>59.970001</td>\n <td>61.060001</td>\n <td>59.869999</td>\n <td>60.330002</td>\n <td>12668800</td>\n <td>52.619235</td>\n </tr>\n <tr>\n <th>2012-01-31</th>\n <td>60.209999</td>\n <td>60.349998</td>\n <td>59.470001</td>\n <td>59.709999</td>\n <td>9593300</td>\n <td>52.078475</td>\n </tr>\n <tr>\n <th>2012-01-31</th>\n <td>59.349998</td>\n <td>59.619999</td>\n <td>58.369999</td>\n <td>59.419998</td>\n <td>12768200</td>\n <td>51.825539</td>\n </tr>\n <tr>\n <th>2012-01-31</th>\n <td>59.419998</td>\n <td>59.450001</td>\n <td>58.869999</td>\n <td>59.000000</td>\n <td>8069400</td>\n <td>51.459220</td>\n </tr>\n <tr>\n <th>2012-01-31</th>\n <td>59.029999</td>\n <td>59.549999</td>\n <td>58.919998</td>\n <td>59.180000</td>\n <td>6679300</td>\n <td>51.616215</td>\n </tr>\n </tbody>\n</table>\n</div>"
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 9
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"# Avancer tout d'un mois\n",
|
||||
"df.tshift(periods=1,freq='M').head()"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"C'est tout pour maintenant!"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"anaconda-cloud": {},
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3.7.9 64-bit ('pyfinance': conda)",
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "e89404a230d8800c54ad520c7b67d1bd9bb833a07b37dd3e521a178a3dc34904"
|
||||
}
|
||||
}
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.9-final"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 1
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
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File diff suppressed because one or more lines are too long
|
|
@ -0,0 +1,148 @@
|
|||
"Month","Thousands of Passengers"
|
||||
"1949-01",112
|
||||
"1949-02",118
|
||||
"1949-03",132
|
||||
"1949-04",129
|
||||
"1949-05",121
|
||||
"1949-06",135
|
||||
"1949-07",148
|
||||
"1949-08",148
|
||||
"1949-09",136
|
||||
"1949-10",119
|
||||
"1949-11",104
|
||||
"1949-12",118
|
||||
"1950-01",115
|
||||
"1950-02",126
|
||||
"1950-03",141
|
||||
"1950-04",135
|
||||
"1950-05",125
|
||||
"1950-06",149
|
||||
"1950-07",170
|
||||
"1950-08",170
|
||||
"1950-09",158
|
||||
"1950-10",133
|
||||
"1950-11",114
|
||||
"1950-12",140
|
||||
"1951-01",145
|
||||
"1951-02",150
|
||||
"1951-03",178
|
||||
"1951-04",163
|
||||
"1951-05",172
|
||||
"1951-06",178
|
||||
"1951-07",199
|
||||
"1951-08",199
|
||||
"1951-09",184
|
||||
"1951-10",162
|
||||
"1951-11",146
|
||||
"1951-12",166
|
||||
"1952-01",171
|
||||
"1952-02",180
|
||||
"1952-03",193
|
||||
"1952-04",181
|
||||
"1952-05",183
|
||||
"1952-06",218
|
||||
"1952-07",230
|
||||
"1952-08",242
|
||||
"1952-09",209
|
||||
"1952-10",191
|
||||
"1952-11",172
|
||||
"1952-12",194
|
||||
"1953-01",196
|
||||
"1953-02",196
|
||||
"1953-03",236
|
||||
"1953-04",235
|
||||
"1953-05",229
|
||||
"1953-06",243
|
||||
"1953-07",264
|
||||
"1953-08",272
|
||||
"1953-09",237
|
||||
"1953-10",211
|
||||
"1953-11",180
|
||||
"1953-12",201
|
||||
"1954-01",204
|
||||
"1954-02",188
|
||||
"1954-03",235
|
||||
"1954-04",227
|
||||
"1954-05",234
|
||||
"1954-06",264
|
||||
"1954-07",302
|
||||
"1954-08",293
|
||||
"1954-09",259
|
||||
"1954-10",229
|
||||
"1954-11",203
|
||||
"1954-12",229
|
||||
"1955-01",242
|
||||
"1955-02",233
|
||||
"1955-03",267
|
||||
"1955-04",269
|
||||
"1955-05",270
|
||||
"1955-06",315
|
||||
"1955-07",364
|
||||
"1955-08",347
|
||||
"1955-09",312
|
||||
"1955-10",274
|
||||
"1955-11",237
|
||||
"1955-12",278
|
||||
"1956-01",284
|
||||
"1956-02",277
|
||||
"1956-03",317
|
||||
"1956-04",313
|
||||
"1956-05",318
|
||||
"1956-06",374
|
||||
"1956-07",413
|
||||
"1956-08",405
|
||||
"1956-09",355
|
||||
"1956-10",306
|
||||
"1956-11",271
|
||||
"1956-12",306
|
||||
"1957-01",315
|
||||
"1957-02",301
|
||||
"1957-03",356
|
||||
"1957-04",348
|
||||
"1957-05",355
|
||||
"1957-06",422
|
||||
"1957-07",465
|
||||
"1957-08",467
|
||||
"1957-09",404
|
||||
"1957-10",347
|
||||
"1957-11",305
|
||||
"1957-12",336
|
||||
"1958-01",340
|
||||
"1958-02",318
|
||||
"1958-03",362
|
||||
"1958-04",348
|
||||
"1958-05",363
|
||||
"1958-06",435
|
||||
"1958-07",491
|
||||
"1958-08",505
|
||||
"1958-09",404
|
||||
"1958-10",359
|
||||
"1958-11",310
|
||||
"1958-12",337
|
||||
"1959-01",360
|
||||
"1959-02",342
|
||||
"1959-03",406
|
||||
"1959-04",396
|
||||
"1959-05",420
|
||||
"1959-06",472
|
||||
"1959-07",548
|
||||
"1959-08",559
|
||||
"1959-09",463
|
||||
"1959-10",407
|
||||
"1959-11",362
|
||||
"1959-12",405
|
||||
"1960-01",417
|
||||
"1960-02",391
|
||||
"1960-03",419
|
||||
"1960-04",461
|
||||
"1960-05",472
|
||||
"1960-06",535
|
||||
"1960-07",622
|
||||
"1960-08",606
|
||||
"1960-09",508
|
||||
"1960-10",461
|
||||
"1960-11",390
|
||||
"1960-12",432
|
||||
|
||||
International airline passengers: monthly totals in thousands. Jan 49 ? Dec 60
|
||||
|
||||
|
Can't render this file because it has a wrong number of fields in line 147.
|
|
|
@ -0,0 +1,172 @@
|
|||
"Month","Monthly milk production: pounds per cow. Jan 62 ? Dec 75"
|
||||
"1962-01",589
|
||||
"1962-02",561
|
||||
"1962-03",640
|
||||
"1962-04",656
|
||||
"1962-05",727
|
||||
"1962-06",697
|
||||
"1962-07",640
|
||||
"1962-08",599
|
||||
"1962-09",568
|
||||
"1962-10",577
|
||||
"1962-11",553
|
||||
"1962-12",582
|
||||
"1963-01",600
|
||||
"1963-02",566
|
||||
"1963-03",653
|
||||
"1963-04",673
|
||||
"1963-05",742
|
||||
"1963-06",716
|
||||
"1963-07",660
|
||||
"1963-08",617
|
||||
"1963-09",583
|
||||
"1963-10",587
|
||||
"1963-11",565
|
||||
"1963-12",598
|
||||
"1964-01",628
|
||||
"1964-02",618
|
||||
"1964-03",688
|
||||
"1964-04",705
|
||||
"1964-05",770
|
||||
"1964-06",736
|
||||
"1964-07",678
|
||||
"1964-08",639
|
||||
"1964-09",604
|
||||
"1964-10",611
|
||||
"1964-11",594
|
||||
"1964-12",634
|
||||
"1965-01",658
|
||||
"1965-02",622
|
||||
"1965-03",709
|
||||
"1965-04",722
|
||||
"1965-05",782
|
||||
"1965-06",756
|
||||
"1965-07",702
|
||||
"1965-08",653
|
||||
"1965-09",615
|
||||
"1965-10",621
|
||||
"1965-11",602
|
||||
"1965-12",635
|
||||
"1966-01",677
|
||||
"1966-02",635
|
||||
"1966-03",736
|
||||
"1966-04",755
|
||||
"1966-05",811
|
||||
"1966-06",798
|
||||
"1966-07",735
|
||||
"1966-08",697
|
||||
"1966-09",661
|
||||
"1966-10",667
|
||||
"1966-11",645
|
||||
"1966-12",688
|
||||
"1967-01",713
|
||||
"1967-02",667
|
||||
"1967-03",762
|
||||
"1967-04",784
|
||||
"1967-05",837
|
||||
"1967-06",817
|
||||
"1967-07",767
|
||||
"1967-08",722
|
||||
"1967-09",681
|
||||
"1967-10",687
|
||||
"1967-11",660
|
||||
"1967-12",698
|
||||
"1968-01",717
|
||||
"1968-02",696
|
||||
"1968-03",775
|
||||
"1968-04",796
|
||||
"1968-05",858
|
||||
"1968-06",826
|
||||
"1968-07",783
|
||||
"1968-08",740
|
||||
"1968-09",701
|
||||
"1968-10",706
|
||||
"1968-11",677
|
||||
"1968-12",711
|
||||
"1969-01",734
|
||||
"1969-02",690
|
||||
"1969-03",785
|
||||
"1969-04",805
|
||||
"1969-05",871
|
||||
"1969-06",845
|
||||
"1969-07",801
|
||||
"1969-08",764
|
||||
"1969-09",725
|
||||
"1969-10",723
|
||||
"1969-11",690
|
||||
"1969-12",734
|
||||
"1970-01",750
|
||||
"1970-02",707
|
||||
"1970-03",807
|
||||
"1970-04",824
|
||||
"1970-05",886
|
||||
"1970-06",859
|
||||
"1970-07",819
|
||||
"1970-08",783
|
||||
"1970-09",740
|
||||
"1970-10",747
|
||||
"1970-11",711
|
||||
"1970-12",751
|
||||
"1971-01",804
|
||||
"1971-02",756
|
||||
"1971-03",860
|
||||
"1971-04",878
|
||||
"1971-05",942
|
||||
"1971-06",913
|
||||
"1971-07",869
|
||||
"1971-08",834
|
||||
"1971-09",790
|
||||
"1971-10",800
|
||||
"1971-11",763
|
||||
"1971-12",800
|
||||
"1972-01",826
|
||||
"1972-02",799
|
||||
"1972-03",890
|
||||
"1972-04",900
|
||||
"1972-05",961
|
||||
"1972-06",935
|
||||
"1972-07",894
|
||||
"1972-08",855
|
||||
"1972-09",809
|
||||
"1972-10",810
|
||||
"1972-11",766
|
||||
"1972-12",805
|
||||
"1973-01",821
|
||||
"1973-02",773
|
||||
"1973-03",883
|
||||
"1973-04",898
|
||||
"1973-05",957
|
||||
"1973-06",924
|
||||
"1973-07",881
|
||||
"1973-08",837
|
||||
"1973-09",784
|
||||
"1973-10",791
|
||||
"1973-11",760
|
||||
"1973-12",802
|
||||
"1974-01",828
|
||||
"1974-02",778
|
||||
"1974-03",889
|
||||
"1974-04",902
|
||||
"1974-05",969
|
||||
"1974-06",947
|
||||
"1974-07",908
|
||||
"1974-08",867
|
||||
"1974-09",815
|
||||
"1974-10",812
|
||||
"1974-11",773
|
||||
"1974-12",813
|
||||
"1975-01",834
|
||||
"1975-02",782
|
||||
"1975-03",892
|
||||
"1975-04",903
|
||||
"1975-05",966
|
||||
"1975-06",937
|
||||
"1975-07",896
|
||||
"1975-08",858
|
||||
"1975-09",817
|
||||
"1975-10",827
|
||||
"1975-11",797
|
||||
"1975-12",843
|
||||
|
||||
Monthly milk production: pounds per cow. Jan 62 ? Dec 75
|
||||
|
||||
|
Can't render this file because it has a wrong number of fields in line 171.
|
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in New Issue