{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Ce billet a été écrit à l'aide d'un notebook Jupyter. Son contenu est sous licence BSD. Une vue statique de ce notebook peut être consultée et téléchargée ici : [20170508_ElectionsTour1.ipynb](http://nbviewer.ipython.org/urls/raw.github.com/flothesof/posts/master/20170508_ElectionsTour2.ipynb).*"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Dans ce billet, nous allons continuer l'exercice du [billet précédent](http://flothesof.github.io/cartes-premier-tour-presidentielle-2017.html) et dessiner quelques cartes en utilisant les données du deuxième tour de l'élection présidentielle 2017. \n",
"\n",
"En plus de cartes géographiques, nous allons faire des cartes simplifiées qui permettent de mieux appréhender les données de l'élection, inspiré par [cet article de Libération](http://www.liberation.fr/elections-presidentielle-legislatives-2017/2017/04/25/pourquoi-la-simple-carte-n-est-pas-toujours-la-meilleure-forme-pour-raconter-une-election_1565064)."
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"# Obtention des données depuis le site du ministère de l'intérieur et analyse sommaire "
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Comme dans mon billet précédent, nous allons directement utiliser les données officielles, disponibles ici: . Nous utilisons la stratégie précédente, qui consiste à utiliser les liens affichés sur la carte pour obtenir les résultats par département. Dans un premier temps, nous récupérons les liens vers les pages individuelles des départements :"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"from bs4 import BeautifulSoup\n",
"import requests"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"soup = BeautifulSoup(requests.get('http://elections.interieur.gouv.fr/presidentielle-2017/').text, 'html.parser')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"map_tag = soup.find('map')"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"area_tags = map_tag.find_all('area')"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"links = [tag.attrs['href'] for tag in area_tags]"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"unique_links = set(links)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Dans un deuxième temps, j'ai modifié la fonction du billet précédent pour également permettre d'obtenir les données concernant les abstentions, les votes nuls et les votes blancs."
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"def extract_candidate_votes(url):\n",
" \"Extracts votes for all candidates from a given url.\"\n",
" r = requests.get(url)\n",
" soup = BeautifulSoup(r.text, 'html.parser')\n",
" # département name\n",
" dept = soup.find(class_='span12 pub-fil-ariane').find_all('a')[-1].text\n",
" # candidate votes\n",
" table = soup.find('tbody')\n",
" results = {}\n",
" for row in table.find_all('tr'):\n",
" results[row.td.text] = int(row.td.next_sibling.next_sibling.text.replace(' ', ''))\n",
" # other data: non-voters, nil, invalid\n",
" second_table = soup.find_all('tbody')[1]\n",
" for row in second_table.find_all('tr'):\n",
" if row.td.text in ['Blancs', 'Nuls', 'Abstentions']:\n",
" results[row.td.text] = int(row.td.next_sibling.next_sibling.text.replace(' ', ''))\n",
" return dept, results"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Testons cette fonction sur le département des Vosges."
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"url = \"http://elections.interieur.gouv.fr/presidentielle-2017/\" + list(unique_links)[0]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/plain": [
"('Haute-Garonne (31)',\n",
" {'Abstentions': 220547,\n",
" 'Blancs': 60546,\n",
" 'M. Emmanuel MACRON': 436941,\n",
" 'Mme Marine LE PEN': 166668,\n",
" 'Nuls': 22130})"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"extract_candidate_votes(url)"
]
},
{
"cell_type": "markdown",
"metadata": {
"deletable": true,
"editable": true
},
"source": [
"Cela fonctionne : appliquons ceci à chacun des départements pour construire une grande table de données."
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"depts = []\n",
"results = []\n",
"for link in unique_links:\n",
" dept, result = extract_candidate_votes(\"http://elections.interieur.gouv.fr/presidentielle-2017/\" + link) \n",
" depts.append(dept)\n",
" results.append(result)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"collapsed": true,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [],
"source": [
"df_votes = pd.DataFrame(results, index=depts)"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"collapsed": false,
"deletable": true,
"editable": true
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"
\n",
" \n",
" \n",
" | \n",
" Abstentions | \n",
" Blancs | \n",
" M. Emmanuel MACRON | \n",
" Mme Marine LE PEN | \n",
" Nuls | \n",
"
\n",
" \n",
" \n",
" \n",
" | Haute-Garonne (31) | \n",
" 220547 | \n",
" 60546 | \n",
" 436941 | \n",
" 166668 | \n",
" 22130 | \n",
"
\n",
" \n",
" | Loiret (45) | \n",
" 103643 | \n",
" 30731 | \n",
" 195004 | \n",
" 113735 | \n",
" 9508 | \n",
"
\n",
" \n",
" | Aveyron (12) | \n",
" 43933 | \n",
" 16665 | \n",
" 109340 | \n",
" 40838 | \n",
" 7403 | \n",
"
\n",
" \n",
" | Haute-Marne (52) | \n",
" 32083 | \n",
" 9057 | \n",
" 45192 | \n",
" 44331 | \n",
" 3456 | \n",
"
\n",
" \n",
" | Territoire de Belfort (90) | \n",
" 23202 | \n",
" 6743 | \n",
" 36340 | \n",
" 26121 | \n",
" 2873 | \n",
"
\n",
" \n",
" | Haute-Savoie (74) | \n",
" 127886 | \n",
" 37148 | \n",
" 249127 | \n",
" 113718 | \n",
" 10282 | \n",
"
\n",
" \n",
" | Val-d'Oise (95) | \n",
" 204202 | \n",
" 42933 | \n",
" 342000 | \n",
" 129536 | \n",
" 11664 | \n",
"
\n",
" \n",
" | Orne (61) | \n",
" 45577 | \n",
" 14762 | \n",
" 88484 | \n",
" 55070 | \n",
" 4798 | \n",
"
\n",
" \n",
" | Bouches-du-Rhône (13) | \n",
" 364023 | \n",
" 81855 | \n",
" 519335 | \n",
" 378455 | \n",
" 26391 | \n",
"
\n",
" \n",
" | Wallis et Futuna (986) | \n",
" 2369 | \n",
" 76 | \n",
" 4715 | \n",
" 1243 | \n",
" 61 | \n",
"
\n",
" \n",
"
\n",
"
\n",
"
\n",
" \n",
" \n",
" | \n",
" Somme des votes | \n",
"
\n",
" \n",
" \n",
" \n",
" | Abstentions | \n",
" 12101416 | \n",
"
\n",
" \n",
" | Blancs | \n",
" 3019724 | \n",
"
\n",
" \n",
" | M. Emmanuel MACRON | \n",
" 20753798 | \n",
"
\n",
" \n",
" | Mme Marine LE PEN | \n",
" 10644118 | \n",
"
\n",
" \n",
" | Nuls | \n",
" 1049532 | \n",
"
\n",
" \n",
"
\n",
"