788 lines
19 KiB
Plaintext
788 lines
19 KiB
Plaintext
{
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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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"# Pandas Exercices - Solutions"
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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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"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",
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"\n",
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"#### 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 ! "
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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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"##### Importer pandas et lire le fichier banklist.csv dans un dataframe nommé banks. "
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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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"import pandas as pd"
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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": {},
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"outputs": [],
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"source": [
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"banks = pd.read_csv('banklist.csv')"
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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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"##### Afficher les 5 premières lignes du dataframe"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"# CODE ICI"
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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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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Bank Name</th>\n",
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" <th>City</th>\n",
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" <th>ST</th>\n",
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" <th>CERT</th>\n",
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" <th>Acquiring Institution</th>\n",
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" <th>Closing Date</th>\n",
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" <th>Updated Date</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>Fayette County Bank</td>\n",
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" <td>Saint Elmo</td>\n",
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" <td>IL</td>\n",
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" <td>1802</td>\n",
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" <td>United Fidelity Bank, fsb</td>\n",
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" <td>26-May-17</td>\n",
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" <td>1-Jun-17</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>Guaranty Bank, (d/b/a BestBank in Georgia & Mi...</td>\n",
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" <td>Milwaukee</td>\n",
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" <td>WI</td>\n",
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" <td>30003</td>\n",
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" <td>First-Citizens Bank & Trust Company</td>\n",
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" <td>5-May-17</td>\n",
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" <td>1-Jun-17</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>First NBC Bank</td>\n",
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" <td>New Orleans</td>\n",
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" <td>LA</td>\n",
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" <td>58302</td>\n",
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" <td>Whitney Bank</td>\n",
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" <td>28-Apr-17</td>\n",
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" <td>23-May-17</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>Proficio Bank</td>\n",
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" <td>Cottonwood Heights</td>\n",
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" <td>UT</td>\n",
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" <td>35495</td>\n",
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" <td>Cache Valley Bank</td>\n",
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" <td>3-Mar-17</td>\n",
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" <td>18-May-17</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Seaway Bank and Trust Company</td>\n",
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" <td>Chicago</td>\n",
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" <td>IL</td>\n",
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" <td>19328</td>\n",
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" <td>State Bank of Texas</td>\n",
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" <td>27-Jan-17</td>\n",
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" <td>18-May-17</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Bank Name City ST \\\n",
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"0 Fayette County Bank Saint Elmo IL \n",
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"1 Guaranty Bank, (d/b/a BestBank in Georgia & Mi... Milwaukee WI \n",
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"2 First NBC Bank New Orleans LA \n",
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"3 Proficio Bank Cottonwood Heights UT \n",
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"4 Seaway Bank and Trust Company Chicago IL \n",
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"\n",
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" CERT Acquiring Institution Closing Date Updated Date \n",
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"0 1802 United Fidelity Bank, fsb 26-May-17 1-Jun-17 \n",
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"1 30003 First-Citizens Bank & Trust Company 5-May-17 1-Jun-17 \n",
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"2 58302 Whitney Bank 28-Apr-17 23-May-17 \n",
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"3 35495 Cache Valley Bank 3-Mar-17 18-May-17 \n",
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"4 19328 State Bank of Texas 27-Jan-17 18-May-17 "
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]
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},
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"execution_count": 4,
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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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"banks.head()"
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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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"##### Quels sont les noms de colonne?"
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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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"source": [
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"# CODE ICI"
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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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{
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"data": {
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"text/plain": [
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"Index(['Bank Name', 'City', 'ST', 'CERT', 'Acquiring Institution',\n",
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" 'Closing Date', 'Updated Date'],\n",
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" dtype='object')"
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]
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},
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"execution_count": 6,
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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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"banks.columns"
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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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"##### Combien d'états (ST) sont représentés dans cet ensemble de données?"
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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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"source": [
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"# CODE ICI"
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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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{
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"data": {
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"text/plain": [
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"44"
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]
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},
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"execution_count": 8,
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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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"banks['ST'].nunique()"
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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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"##### Obtenir une liste ou tableau de tous les états du dataset."
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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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"source": [
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"# CODE ICI"
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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": 10,
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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(['IL', 'WI', 'LA', 'UT', 'NJ', 'AR', 'GA', 'PA', 'TN', 'WA', 'CO',\n",
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" 'PR', 'FL', 'MN', 'CA', 'MD', 'OK', 'OH', 'SC', 'VA', 'ID', 'TX',\n",
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" 'CT', 'AZ', 'NV', 'NC', 'KY', 'MO', 'KS', 'AL', 'MI', 'IN', 'IA',\n",
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" 'NE', 'MS', 'NM', 'OR', 'NY', 'MA', 'SD', 'WY', 'WV', 'NH', 'HI'],\n",
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" dtype=object)"
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]
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},
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"execution_count": 10,
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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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"banks['ST'].unique()"
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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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"##### Quels sont les 5 états avec le plus de banques en faillite?"
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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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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# CODE ICI"
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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": 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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"ST\n",
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"GA 93\n",
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"FL 75\n",
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"IL 67\n",
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"CA 41\n",
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"MN 23\n",
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"Name: Bank Name, dtype: int64"
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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": [
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"banks.groupby(\"ST\").count().sort_values('Bank Name',ascending=False).iloc[:5]['Bank Name']"
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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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"##### Quels sont les 5 principaux établissements acquéreurs?"
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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": {},
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"outputs": [],
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"source": [
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"# CODE ICI"
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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": 14,
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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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"No Acquirer 31\n",
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"State Bank and Trust Company 12\n",
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"First-Citizens Bank & Trust Company 11\n",
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"Ameris Bank 10\n",
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"U.S. Bank N.A. 9\n",
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"Name: Acquiring Institution, dtype: int64"
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]
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},
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"execution_count": 14,
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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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"banks['Acquiring Institution'].value_counts().iloc[:5]"
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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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"##### Combien de banques State Bank of Texas a-t-elle acquises ? Combien d'entre elles étaient au Texas ?"
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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": 15,
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"metadata": {},
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"outputs": [],
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"source": [
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"# CODE ICI"
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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/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>Bank Name</th>\n",
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" <th>City</th>\n",
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" <th>ST</th>\n",
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" <th>CERT</th>\n",
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" <th>Acquiring Institution</th>\n",
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" <th>Closing Date</th>\n",
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" <th>Updated Date</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>Seaway Bank and Trust Company</td>\n",
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" <td>Chicago</td>\n",
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" <td>IL</td>\n",
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" <td>19328</td>\n",
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" <td>State Bank of Texas</td>\n",
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" <td>27-Jan-17</td>\n",
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" <td>18-May-17</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>21</th>\n",
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" <td>The National Republic Bank of Chicago</td>\n",
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" <td>Chicago</td>\n",
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" <td>IL</td>\n",
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" <td>916</td>\n",
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" <td>State Bank of Texas</td>\n",
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" <td>24-Oct-14</td>\n",
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" <td>6-Jan-16</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>450</th>\n",
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" <td>Millennium State Bank of Texas</td>\n",
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" <td>Dallas</td>\n",
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" <td>TX</td>\n",
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" <td>57667</td>\n",
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" <td>State Bank of Texas</td>\n",
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" <td>2-Jul-09</td>\n",
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" <td>26-Oct-12</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" Bank Name City ST CERT \\\n",
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"4 Seaway Bank and Trust Company Chicago IL 19328 \n",
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"21 The National Republic Bank of Chicago Chicago IL 916 \n",
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"450 Millennium State Bank of Texas Dallas TX 57667 \n",
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"\n",
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" Acquiring Institution Closing Date Updated Date \n",
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"4 State Bank of Texas 27-Jan-17 18-May-17 \n",
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"21 State Bank of Texas 24-Oct-14 6-Jan-16 \n",
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"450 State Bank of Texas 2-Jul-09 26-Oct-12 "
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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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"banks[banks['Acquiring Institution']=='State Bank of Texas']"
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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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"##### Quelle est la ville la plus fréquente en Californie pour qu'une banque fasse faillite ?"
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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": 17,
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|
"metadata": {},
|
|
"outputs": [],
|
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"source": [
|
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"# CODE ICI"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 18,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
|
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" <th></th>\n",
|
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" <th>Bank Name</th>\n",
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" <th>ST</th>\n",
|
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" <th>CERT</th>\n",
|
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" <th>Acquiring Institution</th>\n",
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" <th>Closing Date</th>\n",
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" <th>Updated Date</th>\n",
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" </tr>\n",
|
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" <tr>\n",
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" <th>City</th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" <th></th>\n",
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" </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
|
|
}
|