elections cagepiketty creation.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "58a36a75-86e5-469f-80c3-fd6b7e9e600e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import polars as pl\n",
    "import pandas as pd\n",
    "import requests\n",
    "import os\n",
    "from glob import glob\n",
    "import zipfile\n",
    "from io import BytesIO\n",
    "import numpy as np\n",
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1107c09b-c9dc-4e65-9d47-65a2bc91d46c",
   "metadata": {},
   "outputs": [],
   "source": [
    "leg_year_list = [\"1848\",\"1849\",\"1871juil\",\"1871fev\",\"1876\",\"1881\",\"1885\",\"1889\",\"1893\",\"1898\",\"1902\",\"1906\",\"1910\",\"1914\",\"1919\",\"1924\",\"1928\",\"1932\",\"1936\",\"1945\",\"1946Juin\", \"1946Nov\",\"1951\",\"1956\",\"1958\",\"1962\",\"1967\",\"1968\",\"1973\",\"1978\",\"1981\",\"1986\",\"1988\",\"1993\",\"1997\",\"2002\",\"2007\",\"2012\",\"2017\",\"2022\"]\n",
    "\n",
    "pres_year_list = ['1848','1965','1969','1974','1981','1988','1995','2002','2007','2012','2017','2022']\n",
    "\n",
    "ref_year_list = [\"1793\",\"1795\",\"1946\",\"1992\",\"2005\"]\n",
    "\n",
    "\n",
    "leg_name_list = [\"https://conflit-politique-data.ams3.cdn.digitaloceanspaces.com/zip/{}_csv.zip\".format(\"leg\" + i) for i in leg_year_list]\n",
    "pres_name_list = [\"https://conflit-politique-data.ams3.cdn.digitaloceanspaces.com/zip/{}_csv.zip\".format(\"pres\" + i) for i in pres_year_list]\n",
    "ref_name_list = [\"https://conflit-politique-data.ams3.cdn.digitaloceanspaces.com/zip/{}_csv.zip\".format(\"ref\" + i) for i in ref_year_list]\n",
    "\n",
    "df_com = pl.read_csv(\"https://www.insee.fr/fr/statistiques/fichier/6800675/v_commune_2023.csv\",infer_schema_length=100000)[[\"COM\",\"DEP\",\"LIBELLE\"]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "10191a2c-6f08-44c9-9bb7-6cb6b0a1a63f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "downloaded leg1848_csv.zip, done\n",
      "downloaded leg1849_csv.zip, done\n",
      "downloaded leg1871juil_csv.zip, done\n",
      "downloaded leg1871fev_csv.zip, done\n",
      "downloaded leg1876_csv.zip, done\n",
      "downloaded leg1881_csv.zip, done\n",
      "downloaded leg1885_csv.zip, done\n",
      "downloaded leg1889_csv.zip, done\n",
      "downloaded leg1893_csv.zip, done\n",
      "downloaded leg1898_csv.zip, done\n",
      "downloaded leg1902_csv.zip, done\n",
      "downloaded leg1906_csv.zip, done\n",
      "downloaded leg1910_csv.zip, done\n",
      "downloaded leg1914_csv.zip, done\n",
      "downloaded leg1919_csv.zip, done\n",
      "downloaded leg1924_csv.zip, done\n",
      "downloaded leg1928_csv.zip, done\n",
      "downloaded leg1932_csv.zip, done\n",
      "downloaded leg1936_csv.zip, done\n",
      "downloaded leg1945_csv.zip, done\n",
      "downloaded leg1946Juin_csv.zip, done\n",
      "downloaded leg1946Nov_csv.zip, done\n",
      "downloaded leg1951_csv.zip, done\n",
      "downloaded leg1956_csv.zip, done\n",
      "downloaded leg1958_csv.zip, done\n",
      "downloaded leg1962_csv.zip, done\n",
      "downloaded leg1967_csv.zip, done\n",
      "downloaded leg1968_csv.zip, done\n",
      "downloaded leg1973_csv.zip, done\n",
      "downloaded leg1978_csv.zip, done\n",
      "downloaded leg1981_csv.zip, done\n",
      "downloaded leg1986_csv.zip, done\n",
      "downloaded leg1988_csv.zip, done\n",
      "downloaded leg1993_csv.zip, done\n",
      "downloaded leg1997_csv.zip, done\n",
      "downloaded leg2002_csv.zip, done\n",
      "downloaded leg2007_csv.zip, done\n",
      "downloaded leg2012_csv.zip, done\n",
      "downloaded leg2017_csv.zip, done\n",
      "downloaded leg2022_csv.zip, done\n",
      "downloaded pres1848_csv.zip, done\n",
      "downloaded pres1965_csv.zip, done\n",
      "downloaded pres1969_csv.zip, done\n",
      "downloaded pres1974_csv.zip, done\n",
      "downloaded pres1981_csv.zip, done\n",
      "downloaded pres1988_csv.zip, done\n",
      "downloaded pres1995_csv.zip, done\n",
      "downloaded pres2002_csv.zip, done\n",
      "downloaded pres2007_csv.zip, done\n",
      "downloaded pres2012_csv.zip, done\n",
      "downloaded pres2017_csv.zip, done\n",
      "downloaded pres2022_csv.zip, done\n",
      "downloaded ref1793_csv.zip, done\n",
      "downloaded ref1795_csv.zip, done\n",
      "downloaded ref1946_csv.zip, done\n",
      "downloaded ref1992_csv.zip, done\n",
      "downloaded ref2005_csv.zip, done\n"
     ]
    }
   ],
   "source": [
    "os.makedirs(\"data\")\n",
    "\n",
    "#downloading all files\n",
    "for link in (leg_name_list + pres_name_list + ref_name_list):\n",
    "    r = requests.get(link)\n",
    "    file_name = link.replace(\"https://conflit-politique-data.ams3.cdn.digitaloceanspaces.com/zip/\",\"\")\n",
    "    print(\"downloaded \" + file_name, end=\", \")\n",
    "    with open('data/' + file_name, 'wb') as f:\n",
    "        f.write(r.content)\n",
    "\n",
    "link = \"https://conflit-politique-data.ams3.cdn.digitaloceanspaces.com/zip/CagePiketty2023CodesInformatiques.zip\"\n",
    "r = requests.get(link)\n",
    "file_name = link.replace(\"https://conflit-politique-data.ams3.cdn.digitaloceanspaces.com/zip/\",\"\")\n",
    "print(\"downloaded \" + file_name, end=\", \")\n",
    "with open(file_name, 'wb') as f:\n",
    "    f.write(r.content)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "ccd7075b-c467-443c-8b22-5345977d4f35",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div><style>\n",
       ".dataframe > thead > tr,\n",
       ".dataframe > tbody > tr {\n",
       "  text-align: right;\n",
       "  white-space: pre-wrap;\n",
       "}\n",
       "</style>\n",
       "<small>shape: (606, 7)</small><table border=\"1\" class=\"dataframe\"><thead><tr><th>voix_cat</th><th>voteC</th><th>voteCD</th><th>voteCG</th><th>voteD</th><th>voteG</th><th>elec</th></tr><tr><td>str</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>f32</td><td>str</td></tr></thead><tbody><tr><td>&quot;CONS&quot;</td><td>0.0</td><td>0.3</td><td>0.0</td><td>0.7</td><td>0.0</td><td>&quot;leg1848&quot;</td></tr><tr><td>&quot;DEMSOC&quot;</td><td>0.0</td><td>0.0</td><td>0.3</td><td>0.0</td><td>0.7</td><td>&quot;leg1848&quot;</td></tr><tr><td>&quot;DIV&quot;</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>&quot;leg1848&quot;</td></tr><tr><td>&quot;REPMOD&quot;</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>&quot;leg1848&quot;</td></tr><tr><td>&quot;CONS&quot;</td><td>0.0</td><td>0.3</td><td>0.0</td><td>0.7</td><td>0.0</td><td>&quot;leg1849&quot;</td></tr><tr><td>&quot;DEMSOC&quot;</td><td>0.0</td><td>0.0</td><td>0.3</td><td>0.0</td><td>0.7</td><td>&quot;leg1849&quot;</td></tr><tr><td>&quot;DIV&quot;</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>&quot;leg1849&quot;</td></tr><tr><td>&quot;REPCONS&quot;</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>&quot;leg1849&quot;</td></tr><tr><td>&quot;BON&quot;</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>&quot;leg1871fev&quot;</td></tr><tr><td>&quot;CLE&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>&quot;leg1871fev&quot;</td></tr><tr><td>&quot;CONS&quot;</td><td>0.0</td><td>0.5</td><td>0.0</td><td>0.5</td><td>0.0</td><td>&quot;leg1871fev&quot;</td></tr><tr><td>&quot;DIV&quot;</td><td>0.7</td><td>0.0</td><td>0.3</td><td>0.0</td><td>0.0</td><td>&quot;leg1871fev&quot;</td></tr><tr><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td><td>&hellip;</td></tr><tr><td>&quot;ARTHAUD&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>&quot;pres2022&quot;</td></tr><tr><td>&quot;DUPONTAIGNAN&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>&quot;pres2022&quot;</td></tr><tr><td>&quot;HIDALGO&quot;</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>&quot;pres2022&quot;</td></tr><tr><td>&quot;JADOT&quot;</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>&quot;pres2022&quot;</td></tr><tr><td>&quot;LASSALLE&quot;</td><td>0.2</td><td>0.2</td><td>0.2</td><td>0.2</td><td>0.2</td><td>&quot;pres2022&quot;</td></tr><tr><td>&quot;MACRON&quot;</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>&quot;pres2022&quot;</td></tr><tr><td>&quot;MELENCHON&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>&quot;pres2022&quot;</td></tr><tr><td>&quot;MLEPEN&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>&quot;pres2022&quot;</td></tr><tr><td>&quot;PECRESSE&quot;</td><td>0.0</td><td>1.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>&quot;pres2022&quot;</td></tr><tr><td>&quot;POUTOU&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>&quot;pres2022&quot;</td></tr><tr><td>&quot;ROUSSEL&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>&quot;pres2022&quot;</td></tr><tr><td>&quot;ZEMMOUR&quot;</td><td>0.0</td><td>0.0</td><td>0.0</td><td>1.0</td><td>0.0</td><td>&quot;pres2022&quot;</td></tr></tbody></table></div>"
      ],
      "text/plain": [
       "shape: (606, 7)\n",
       "┌──────────┬───────┬────────┬────────┬───────┬───────┬──────────┐\n",
       "│ voix_cat ┆ voteC ┆ voteCD ┆ voteCG ┆ voteD ┆ voteG ┆ elec     │\n",
       "│ ---      ┆ ---   ┆ ---    ┆ ---    ┆ ---   ┆ ---   ┆ ---      │\n",
       "│ str      ┆ f32   ┆ f32    ┆ f32    ┆ f32   ┆ f32   ┆ str      │\n",
       "╞══════════╪═══════╪════════╪════════╪═══════╪═══════╪══════════╡\n",
       "│ CONS     ┆ 0.0   ┆ 0.3    ┆ 0.0    ┆ 0.7   ┆ 0.0   ┆ leg1848  │\n",
       "│ DEMSOC   ┆ 0.0   ┆ 0.0    ┆ 0.3    ┆ 0.0   ┆ 0.7   ┆ leg1848  │\n",
       "│ DIV      ┆ 1.0   ┆ 0.0    ┆ 0.0    ┆ 0.0   ┆ 0.0   ┆ leg1848  │\n",
       "│ REPMOD   ┆ 1.0   ┆ 0.0    ┆ 0.0    ┆ 0.0   ┆ 0.0   ┆ leg1848  │\n",
       "│ …        ┆ …     ┆ …      ┆ …      ┆ …     ┆ …     ┆ …        │\n",
       "│ PECRESSE ┆ 0.0   ┆ 1.0    ┆ 0.0    ┆ 0.0   ┆ 0.0   ┆ pres2022 │\n",
       "│ POUTOU   ┆ 0.0   ┆ 0.0    ┆ 0.0    ┆ 0.0   ┆ 1.0   ┆ pres2022 │\n",
       "│ ROUSSEL  ┆ 0.0   ┆ 0.0    ┆ 0.0    ┆ 0.0   ┆ 1.0   ┆ pres2022 │\n",
       "│ ZEMMOUR  ┆ 0.0   ┆ 0.0    ┆ 0.0    ┆ 1.0   ┆ 0.0   ┆ pres2022 │\n",
       "└──────────┴───────┴────────┴────────┴───────┴───────┴──────────┘"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#lecture des orientations politiques\n",
    "\n",
    "dic_replace  =  {'leg1871 (fevrier)' : \"leg1871fev\", 'leg1871 (juillet)' : \"leg1871juil\" ,\"leg1946 (juin)\" : \"leg1946Juin\", 'leg1946 (novembre)' : \"leg1946Nov\"}\n",
    "\n",
    "nuances_df_list = []\n",
    "#ouverture du fichier stata contenant le codage des nuances\n",
    "for elec_type in [\"leg\",\"pres\"]:\n",
    "    with zipfile.ZipFile(\"CagePiketty2023CodesInformatiques.zip\") as archive:\n",
    "        zfiledata = BytesIO(archive.read(\"CodesInformatiquesExploitationFichiersElectoraux.zip\"))\n",
    "        with zipfile.ZipFile(zfiledata) as zfile2:\n",
    "\n",
    "            if elec_type == \"leg\":\n",
    "                filename = \"doAnnexeA2nuancespolitiqueslegislatives.txt\"\n",
    "            else:\n",
    "                filename = \"doAnnexeA3nuancespolitiquespresidentielles.txt\"\n",
    "            a = zfile2.open(filename, mode=\"r\").read().decode(\"latin\")\n",
    "\n",
    "    #on split le fichier par élection\n",
    "    leg_text_list = re.split(r\"\\*\\*\\d{4}[^\\*]+\\*\\*|\\*\\*\\d{4}\\*\\*\", a, flags=re.MULTILINE)[1:]\n",
    "\n",
    "    #liste des noms des élections\n",
    "    elect_list = [i[0] if i[1] == \"\" else i[1] for i in re.findall(\"\\*\\*(\\d{4}[^\\*]+)\\*\\*|\\*\\*(\\d{4})\\*\\*\", a)]\n",
    "    \n",
    "    df_list = []\n",
    "    \n",
    "    for leg_nb in range(len(leg_text_list)):\n",
    "\n",
    "        #liste des nuances et des listes/candidats y appartenant avec leur coeffience d'appartenance\n",
    "        vote_list = [i.replace(\"gen \",'').split(\"=\") for i in re.findall(\"(gen vote[A-Z]+=.+);\" ,leg_text_list[leg_nb]) if not \"voteT\" in i and not \"voteGCG\" in i and not \"voteDCD\" in i ]\n",
    "        \n",
    "        for i in range(len(vote_list)):\n",
    "           \n",
    "            vote_list[i][1] = vote_list[i][1].split(\"+\")\n",
    "            for n in range(len(vote_list[i][1])):\n",
    "                #si le code ne comporte pas de coefficient, le coefficient est 1\n",
    "                if not '*' in vote_list[i][1][n]:\n",
    "                    vote_list[i][1][n] = \"1*\" + vote_list[i][1][n]\n",
    "                vote_list[i][1][n] = vote_list[i][1][n].split(\"*\")\n",
    "\n",
    "        #mise sous forme tabulaire des résultats\n",
    "        d = pd.Series(dict(zip([i[0] for i in vote_list], [i[1] for i in vote_list]))).explode()\n",
    "        de = pd.DataFrame(d.to_list(), columns=[\"coeff\",\"voix\"])\n",
    "        de.index = d.index\n",
    "        de = de.reset_index()\n",
    "        de.columns = [\"vote_cat\",\"coeff\",\"voix_cat\"]\n",
    "        de = de.pivot(index=\"voix_cat\", columns=\"vote_cat\",values=\"coeff\").replace(np.nan,0).reset_index()\n",
    "        de[\"elec\"] = (elec_type + elect_list[leg_nb])\n",
    "        de[\"elec\"] = de[\"elec\"].replace(dic_replace)\n",
    "        de[\"voix_cat\"] = de[\"voix_cat\"].str.replace(\"^voix\",\"\",regex=True)\n",
    "        df_list.append(de)\n",
    "    df_leg_nuances = pd.concat(df_list)\n",
    "    nuances_df_list.append(df_leg_nuances)\n",
    "df_all_nuances = pd.concat(nuances_df_list)\n",
    "\n",
    "convert_dic = {\"voix_cat\" : str, \"voteC\" : \"float32\", \"voteCD\" : \"float32\",\"voteCG\" : \"float32\",\"voteD\" : \"float32\", \"voteG\" : 'float32', \"elec\" : str}\n",
    "df_all_nuances = df_all_nuances.astype(convert_dic)\n",
    "df_all_nuances = pl.from_pandas(df_all_nuances)\n",
    "df_all_nuances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "38b9b2f5-0b49-4d2c-a229-bc276604788b",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pres1981, leg1871fev, leg2012, leg1848, pres1965, leg1906, leg1871juil, leg1910, pres2007, pres1848, leg1898, leg1968, pres2012, pres1969, leg1946Nov, leg1876, pres2022, pres2017, leg1928, leg1958, leg1986, ref1992, leg1956, leg1993, leg2022, leg1997, ref1793, leg1962, leg1951, leg1988, leg2002, leg1981, leg1889, leg1914, leg1849, ref1795, ref2005, leg1881, leg1945, ref1946, leg1885, pres1974, leg1919, leg1978, leg1893, leg2007, leg1902, pres2002, leg1924, pres1995, leg1973, leg1936, pres1988, leg1967, leg1932, leg2017, leg1946Juin, "
     ]
    }
   ],
   "source": [
    "\n",
    "df_list = []\n",
    "for file in glob(\"data/*\"):\n",
    "    name = file.replace(\"data/\",\"\").replace(\"_csv.zip\",\"\")\n",
    "    year = re.search(\"\\d{4}\", name)[0]\n",
    "    elec_type = re.search(\"([a-z]+)\\d{4}\", name)[1]\n",
    "    \n",
    "    print(name, end=\", \")\n",
    "    if name == \"leg1871juil\":\n",
    "        name1 = name\n",
    "        name2 = \"leg1872\"\n",
    "    elif name == \"leg1871fev\":\n",
    "        name1 = \"leg1871\"\n",
    "        name2 = \"leg1871\"\n",
    "    elif name == \"leg1946Nov\":\n",
    "        name1 = name\n",
    "        name2 = \"leg1947\"\n",
    "    elif name == \"leg1946Juin\":\n",
    "        name1 = name\n",
    "        name2 = \"leg1946\"\n",
    "    else:\n",
    "        name1 = name\n",
    "        name2 = name\n",
    "\n",
    "    if name == \"leg1902\":\n",
    "        filename_ = f'{name1}comm.csv'\n",
    "    else:\n",
    "        filename_ =  \"{}_csv/{}comm.csv\".format(name1,name2)\n",
    "    df = pl.read_csv(\n",
    "    zipfile.ZipFile(f\"data/{name}_csv.zip\").open(filename_, mode='r').read(), dtypes={\"codecommune\" : str, \"dep\" : str}, infer_schema_length=1000000\n",
    "    ).with_columns(pl.col(\"inscrits\").sub(pl.col(\"votants\")).alias(\"abstention\"),\n",
    "                pl.col(\"votants\").sub(pl.col(\"exprimes\")).alias(\"nuls\")\n",
    "               ).filter(\n",
    "                ~pl.col(\"codecommune\").is_in([\"75056\"] + [str(i) for i in range(13201,13217)] + [str(i) for i in range(69381,69390)])\n",
    "               ) #Pour Paris, Lyon, Marseille, je supprime les doublons en ne gardant que la série la plus complète (les arrrondissements pour Paris, la ville en entier pour Lyon Marseille)\n",
    "\n",
    "\n",
    "    \n",
    "    if any('votantsT2' in colname for colname in df.columns):\n",
    "        is_t2 = True\n",
    "        df = df.with_columns(\n",
    "            pl.col(\"inscritsT2\").sub(pl.col(\"votantsT2\")).alias(\"abstentionT2\"),\n",
    "            pl.col(\"votantsT2\").sub(pl.col(\"exprimesT2\")).alias(\"nulsT2\")\n",
    "        )\n",
    "    \n",
    "    keep_cols = ([\"codecommune\",'abstention',\"nuls\"] + [i for i in df.columns if i.startswith(\"voix\") and \"voixtot\" not in i]\n",
    "                )\n",
    "    if is_t2:\n",
    "        keep_cols += ['abstentionT2',\"nulsT2\"]\n",
    "    is_t2 = False\n",
    "    \n",
    "    df = df[keep_cols].melt(id_vars=[\"codecommune\"], variable_name=\"choix\", value_name=\"votes\").with_columns(pl.col(\"votes\").cast(pl.Float32))\n",
    "    \n",
    "    df = df.with_columns(\n",
    "        pl.lit(name).alias(\"elec_name\"),\n",
    "        pl.lit(year).alias(\"year\"),\n",
    "        pl.lit(elec_type).alias(\"elec_type\"),\n",
    "        pl.when(pl.col(\"choix\").str.contains(\"T2\")).then(pl.lit(\"T2\")).otherwise(pl.lit(\"T1\")).alias(\"tour\"),\n",
    "        pl.when(pl.col(\"choix\").is_in([\"abstention\",\"nuls\",\"abstentionT2\",\"nulsT2\"])).then(0).otherwise(1).alias(\"exprime_flag\"),\n",
    "        pl.col(\"choix\").str.replace(\"T2\",\"\").str.replace('^voix',\"\").alias(\"choix\")\n",
    "        \n",
    "    )\n",
    "    df = df.join(df_all_nuances,how=\"left\", left_on=[\"elec_name\",\"choix\"], right_on=[\"elec\",\"voix_cat\"])\n",
    "\n",
    "    df_list.append(df)\n",
    "    \n",
    "df = pl.concat(df_list)\n",
    "del df_list\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "1c823a81-3a38-4c19-9f9d-d3cd9bd0070c",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "df.columns =[\"codecommune\",\"choix\",\"voix\",\"nom_election\",\"annee\",\"type_election\",\"tour\",\"exprimeflag\",\"centre_coef\",\"centredroit_coef\",\"centregauche_coef\",\"droite_coef\",\"gauche_coef\"]\n",
    "\n",
    "ordre = [\"codecommune\",\"nom_election\",\"annee\",\"type_election\",\"tour\",\"choix\",\"exprimeflag\", \"gauche_coef\",\"centregauche_coef\",\"centre_coef\", \"centredroit_coef\",\"droite_coef\",\"voix\"]\n",
    "df = df[ordre]\n",
    "\n",
    "df.write_parquet(\"elections_cagepiketty.parquet\")"
   ]
  }
 ],
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