962 lines
20 KiB
Plaintext
962 lines
20 KiB
Plaintext
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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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"# NumPy \n",
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"\n",
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"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",
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"\n",
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"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",
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"\n",
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"Nous n'apprendrons que les bases de NumPy, pour commencer nous avons besoin de l'installer!"
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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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"## Instructions d'installation\n",
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"\n",
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"### NumPy est déjà inclus dans votre environnement! Si vous utilisez l'environnement pyfinance, c'est bon pour vous!\n",
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"\n",
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"_____\n",
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"##### Pour ceux n'utilisant pas l'environnement fourni:\n",
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"\n",
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"**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",
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" \n",
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" conda install numpy\n",
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" \n",
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"**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",
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"\n",
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"_____"
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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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"## Utiliser NumPy\n",
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"\n",
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"Une fois que vous avez installé NumPy, vous pouvez l'importer sous forme de bibliothèque :"
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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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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import numpy as np"
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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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"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",
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"\n",
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"# Tableaux Numpy\n",
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"\n",
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"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",
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"\n",
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"Commençons notre introduction en explorant comment créer des tableaux NumPy.\n",
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"\n",
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"## Création de tableaux NumPy\n",
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"\n",
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"### Depuis une liste Python\n",
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"\n",
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"Nous pouvons créer un tableau en convertissant directement une liste ou une liste de listes:"
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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": 19,
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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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"[1, 2, 3]"
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]
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},
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"execution_count": 19,
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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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"my_list = [1,2,3]\n",
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"my_list"
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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": 16,
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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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"array([1, 2, 3])"
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]
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},
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"execution_count": 16,
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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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"np.array(my_list)"
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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": 20,
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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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"[[1, 2, 3], [4, 5, 6], [7, 8, 9]]"
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]
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},
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"execution_count": 20,
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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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"my_matrix = [[1,2,3],[4,5,6],[7,8,9]]\n",
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"my_matrix"
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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": 21,
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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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"array([[1, 2, 3],\n",
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" [4, 5, 6],\n",
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" [7, 8, 9]])"
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]
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},
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"execution_count": 21,
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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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"np.array(my_matrix)"
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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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"## Méthodes intégrées\n",
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"\n",
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"Il existe de nombreuses méthodes intégrées pour générer des tableaux"
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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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"### arange\n",
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"\n",
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"Retourne des valeurs uniformément espacées dans un intervalle donné."
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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": 22,
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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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"array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
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]
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},
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"execution_count": 22,
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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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"np.arange(0,10)"
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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": 23,
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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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"array([ 0, 2, 4, 6, 8, 10])"
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]
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},
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"execution_count": 23,
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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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"np.arange(0,11,2)"
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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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"### zeros et ones\n",
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"\n",
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"Génère des tableaux de zéros ou de uns"
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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": 24,
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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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"array([ 0., 0., 0.])"
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]
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},
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"execution_count": 24,
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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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"np.zeros(3)"
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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": 26,
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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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"array([[ 0., 0., 0., 0., 0.],\n",
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" [ 0., 0., 0., 0., 0.],\n",
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" [ 0., 0., 0., 0., 0.],\n",
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" [ 0., 0., 0., 0., 0.],\n",
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" [ 0., 0., 0., 0., 0.]])"
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]
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},
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"execution_count": 26,
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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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"np.zeros((5,5))"
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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": 27,
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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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"array([ 1., 1., 1.])"
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]
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},
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"execution_count": 27,
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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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"np.ones(3)"
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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": 28,
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"metadata": {},
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"outputs": [
|
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{
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||
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"data": {
|
||
|
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"text/plain": [
|
||
|
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"array([[ 1., 1., 1.],\n",
|
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" [ 1., 1., 1.],\n",
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" [ 1., 1., 1.]])"
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]
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},
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"execution_count": 28,
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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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"np.ones((3,3))"
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]
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},
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{
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"cell_type": "markdown",
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||
|
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"metadata": {},
|
||
|
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"source": [
|
||
|
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"### linspace\n",
|
||
|
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"Retourne les nombres uniformément espacés sur un intervalle spécifié."
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]
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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": 29,
|
||
|
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"metadata": {},
|
||
|
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"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
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"array([ 0., 5., 10.])"
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||
|
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]
|
||
|
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},
|
||
|
|
"execution_count": 29,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
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||
|
|
}
|
||
|
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],
|
||
|
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"source": [
|
||
|
|
"np.linspace(0,10,3)"
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|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "code",
|
||
|
|
"execution_count": 31,
|
||
|
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"metadata": {},
|
||
|
|
"outputs": [
|
||
|
|
{
|
||
|
|
"data": {
|
||
|
|
"text/plain": [
|
||
|
|
"array([ 0. , 0.20408163, 0.40816327, 0.6122449 ,\n",
|
||
|
|
" 0.81632653, 1.02040816, 1.2244898 , 1.42857143,\n",
|
||
|
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" 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",
|
||
|
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" 5.71428571, 5.91836735, 6.12244898, 6.32653061,\n",
|
||
|
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" 6.53061224, 6.73469388, 6.93877551, 7.14285714,\n",
|
||
|
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" 7.34693878, 7.55102041, 7.75510204, 7.95918367,\n",
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||
|
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" 8.16326531, 8.36734694, 8.57142857, 8.7755102 ,\n",
|
||
|
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" 8.97959184, 9.18367347, 9.3877551 , 9.59183673,\n",
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||
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" 9.79591837, 10. ])"
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||
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]
|
||
|
|
},
|
||
|
|
"execution_count": 31,
|
||
|
|
"metadata": {},
|
||
|
|
"output_type": "execute_result"
|
||
|
|
}
|
||
|
|
],
|
||
|
|
"source": [
|
||
|
|
"np.linspace(0,10,50)"
|
||
|
|
]
|
||
|
|
},
|
||
|
|
{
|
||
|
|
"cell_type": "markdown",
|
||
|
|
"metadata": {},
|
||
|
|
"source": [
|
||
|
|
"## eye\n",
|
||
|
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"\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
|
||
|
|
}
|