{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Tech - carte\n", "\n", "Faire une carte, c'est toujours compliqu\u00e9. C'est simple jusqu'\u00e0 ce qu'on s'aper\u00e7oive qu'on doit r\u00e9cup\u00e9rer la description des zones administratives d'un pays, fournies parfois dans des coordonn\u00e9es autres que longitude et latitude. Quelques modules utiles :\n", "\n", "* [cartopy](https://scitools.org.uk/cartopy/docs/latest/) : surcouche de matplotlib pour faire des dessins avec des coordonn\u00e9es g\u00e9ographiques\n", "* [bokeh](https://docs.bokeh.org/) : pour tracer des cartes interactives\n", "* [pyproj](https://github.com/pyproj4/pyproj) : conversion entre syst\u00e8mes de coordonn\u00e9es\n", "* [shapely](https://shapely.readthedocs.io/en/stable/manual.html) : manipuler des polygones g\u00e9ographiques (union, intersection, ...)\n", "* [pyshp](https://github.com/GeospatialPython/pyshp) : lire ou \u00e9crire des polygones g\u00e9ographiques\n", "* [geopandas](https://geopandas.org/) : manipulation de dataframe avec des coordonn\u00e9es g\u00e9ographiques\n", "\n", "Quelques notebooks int\u00e9ressants :\n", "* [Tracer une carte en Python avec bokeh](http://195-154-200-30.rev.poneytelecom.eu/app/papierstat/helpsphinx/notebooks/enedis_cartes_bokeh.html)\n", "* [Tracer une carte en Python](http://195-154-200-30.rev.poneytelecom.eu/app/papierstat/helpsphinx/notebooks/enedis_cartes.html)\n", "* [Donn\u00e9es carroy\u00e9es et OpenStreetMap](http://195-154-200-30.rev.poneytelecom.eu/app/papierstat/helpsphinx/notebooks/carte_carreau.html)\n", "* [Carte de France avec les d\u00e9partements](http://www.xavierdupre.fr/app/ensae_teaching_cs/helpsphinx/notebooks/td1a_cenonce_session_12_carte.html)\n", "* [Carte de France avec les d\u00e9partements (2)](http://www.xavierdupre.fr/app/actuariat_python/helpsphinx/notebooks/seance6_graphes_correction.html)"]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [{"data": {"text/html": ["<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n", "<script>\n", "function repeat_indent_string(n){\n", "    var a = \"\" ;\n", "    for ( ; n > 0 ; --n)\n", "        a += \"    \";\n", "    return a;\n", "}\n", "// look up into all sections and builds an automated menu //\n", "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item, begin_format, end_format) {\n", "    var anchors = document.getElementsByClassName(\"section\");\n", "    if (anchors.length == 0) {\n", "        anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n", "    }\n", "    var i,t;\n", "    var text_menu = begin;\n", "    var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n", "    var ind = \"\";\n", "    var memo_level = 1;\n", "    var href;\n", "    var tags = [];\n", "    var main_item = 0;\n", "    var format_open = 0;\n", "    for (i = 0; i <= llast; i++)\n", "        tags.push(\"h\" + i);\n", "\n", "    for (i = 0; i < anchors.length; i++) {\n", "        text_memo += \"**\" + anchors[i].id + \"--\\n\";\n", "\n", "        var child = null;\n", "        for(t = 0; t < tags.length; t++) {\n", "            var r = anchors[i].getElementsByTagName(tags[t]);\n", "            if (r.length > 0) {\n", "child = r[0];\n", "break;\n", "            }\n", "        }\n", "        if (child == null) {\n", "            text_memo += \"null\\n\";\n", "            continue;\n", "        }\n", "        if (anchors[i].hasAttribute(\"id\")) {\n", "            // when converted in RST\n", "            href = anchors[i].id;\n", "            text_memo += \"#1-\" + href;\n", "            // passer \u00e0 child suivant (le chercher)\n", "        }\n", "        else if (child.hasAttribute(\"id\")) {\n", "            // in a notebook\n", "            href = child.id;\n", "            text_memo += \"#2-\" + href;\n", "        }\n", "        else {\n", "            text_memo += \"#3-\" + \"*\" + \"\\n\";\n", "            continue;\n", "        }\n", "        var title = child.textContent;\n", "        var level = parseInt(child.tagName.substring(1,2));\n", "\n", "        text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n", "\n", "        if ((level < lfirst) || (level > llast)) {\n", "            continue ;\n", "        }\n", "        if (title.endsWith('\u00b6')) {\n", "            title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\")\n", "         .replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\");\n", "        }\n", "        if (title.length == 0) {\n", "            continue;\n", "        }\n", "\n", "        while (level < memo_level) {\n", "            text_menu += end_format + \"</ul>\\n\";\n", "            format_open -= 1;\n", "            memo_level -= 1;\n", "        }\n", "        if (level == lfirst) {\n", "            main_item += 1;\n", "        }\n", "        if (keep_item != -1 && main_item != keep_item + 1) {\n", "            // alert(main_item + \" - \" + level + \" - \" + keep_item);\n", "            continue;\n", "        }\n", "        while (level > memo_level) {\n", "            text_menu += \"<ul>\\n\";\n", "            memo_level += 1;\n", "        }\n", "        text_menu += repeat_indent_string(level-2);\n", "        text_menu += begin_format + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n", "        format_open += 1;\n", "    }\n", "    while (1 < memo_level) {\n", "        text_menu += end_format + \"</ul>\\n\";\n", "        memo_level -= 1;\n", "        format_open -= 1;\n", "    }\n", "    text_menu += send;\n", "    //text_menu += \"\\n\" + text_memo;\n", "\n", "    while (format_open > 0) {\n", "        text_menu += end_format;\n", "        format_open -= 1;\n", "    }\n", "    return text_menu;\n", "};\n", "var update_menu = function() {\n", "    var sbegin = \"\";\n", "    var sformat = '<a href=\"#__HREF__\">__TITLE__</a>';\n", "    var send = \"\";\n", "    var begin_format = '<li>';\n", "    var end_format = '</li>';\n", "    var keep_item = -1;\n", "    var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item,\n", "       begin_format, end_format);\n", "    var menu = document.getElementById(\"my_id_menu_nb\");\n", "    menu.innerHTML=text_menu;\n", "};\n", "window.setTimeout(update_menu,2000);\n", "            </script>"], "text/plain": ["<IPython.core.display.HTML object>"]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}], "source": ["from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": ["%matplotlib inline"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Expos\u00e9\n", "\n", "On t\u00e9l\u00e9charge des [donn\u00e9es hospitali\u00e8res par d\u00e9partements](https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/)."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Donn\u00e9es COVID"]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>dep</th>\n", "      <th>sexe</th>\n", "      <th>jour</th>\n", "      <th>hosp</th>\n", "      <th>rea</th>\n", "      <th>HospConv</th>\n", "      <th>SSR_USLD</th>\n", "      <th>autres</th>\n", "      <th>rad</th>\n", "      <th>dc</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>301945</th>\n", "      <td>976</td>\n", "      <td>0</td>\n", "      <td>2022-12-02</td>\n", "      <td>8</td>\n", "      <td>0</td>\n", "      <td>8.0</td>\n", "      <td>0.0</td>\n", "      <td>0.0</td>\n", "      <td>1703</td>\n", "      <td>163</td>\n", "    </tr>\n", "    <tr>\n", "      <th>301946</th>\n", "      <td>976</td>\n", "      <td>1</td>\n", "      <td>2022-12-02</td>\n", "      <td>5</td>\n", "      <td>0</td>\n", "      <td>5.0</td>\n", "      <td>0.0</td>\n", "      <td>0.0</td>\n", "      <td>709</td>\n", "      <td>100</td>\n", "    </tr>\n", "    <tr>\n", "      <th>301947</th>\n", "      <td>976</td>\n", "      <td>2</td>\n", "      <td>2022-12-02</td>\n", "      <td>3</td>\n", "      <td>0</td>\n", "      <td>3.0</td>\n", "      <td>0.0</td>\n", "      <td>0.0</td>\n", "      <td>969</td>\n", "      <td>61</td>\n", "    </tr>\n", "    <tr>\n", "      <th>301948</th>\n", "      <td>978</td>\n", "      <td>0</td>\n", "      <td>2022-12-02</td>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>0.0</td>\n", "      <td>0.0</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "      <td>0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>301949</th>\n", "      <td>978</td>\n", "      <td>1</td>\n", "      <td>2022-12-02</td>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>0.0</td>\n", "      <td>0.0</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "      <td>0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["        dep  sexe        jour  hosp  rea  HospConv  SSR_USLD  autres   rad  \\\n", "301945  976     0  2022-12-02     8    0       8.0       0.0     0.0  1703   \n", "301946  976     1  2022-12-02     5    0       5.0       0.0     0.0   709   \n", "301947  976     2  2022-12-02     3    0       3.0       0.0     0.0   969   \n", "301948  978     0  2022-12-02     0    0       0.0       0.0     0.0     0   \n", "301949  978     1  2022-12-02     0    0       0.0       0.0     0.0     0   \n", "\n", "         dc  \n", "301945  163  \n", "301946  100  \n", "301947   61  \n", "301948    0  \n", "301949    0  "]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["# https://www.data.gouv.fr/fr/datasets/donnees-hospitalieres-relatives-a-lepidemie-de-covid-19/\n", "from pandas import read_csv\n", "url = \"https://www.data.gouv.fr/fr/datasets/r/63352e38-d353-4b54-bfd1-f1b3ee1cabd7\"\n", "covid = read_csv(url, sep=\";\")\n", "covid.tail()"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"data": {"text/plain": ["'2022-12-02'"]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["last_day = covid.loc[covid.index[-1], \"jour\"]\n", "last_day"]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"text/plain": ["(102, 8)"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["last_data = covid[covid.jour == last_day].groupby(\"dep\").sum(numeric_only=True)\n", "last_data.shape"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>sexe</th>\n", "      <th>hosp</th>\n", "      <th>rea</th>\n", "      <th>HospConv</th>\n", "      <th>SSR_USLD</th>\n", "      <th>autres</th>\n", "      <th>rad</th>\n", "      <th>dc</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>count</th>\n", "      <td>102.000000</td>\n", "      <td>102.000000</td>\n", "      <td>102.000000</td>\n", "      <td>102.000000</td>\n", "      <td>102.000000</td>\n", "      <td>102.000000</td>\n", "      <td>102.000000</td>\n", "      <td>102.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>mean</th>\n", "      <td>2.980392</td>\n", "      <td>386.686275</td>\n", "      <td>21.754902</td>\n", "      <td>231.264706</td>\n", "      <td>121.470588</td>\n", "      <td>12.196078</td>\n", "      <td>15482.431373</td>\n", "      <td>2534.039216</td>\n", "    </tr>\n", "    <tr>\n", "      <th>std</th>\n", "      <td>0.198030</td>\n", "      <td>413.069001</td>\n", "      <td>29.729619</td>\n", "      <td>238.054654</td>\n", "      <td>156.744397</td>\n", "      <td>17.687070</td>\n", "      <td>15273.986786</td>\n", "      <td>2444.945989</td>\n", "    </tr>\n", "    <tr>\n", "      <th>min</th>\n", "      <td>1.000000</td>\n", "      <td>0.000000</td>\n", "      <td>0.000000</td>\n", "      <td>0.000000</td>\n", "      <td>0.000000</td>\n", "      <td>0.000000</td>\n", "      <td>0.000000</td>\n", "      <td>0.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>25%</th>\n", "      <td>3.000000</td>\n", "      <td>130.250000</td>\n", "      <td>4.000000</td>\n", "      <td>89.000000</td>\n", "      <td>24.000000</td>\n", "      <td>0.000000</td>\n", "      <td>5949.500000</td>\n", "      <td>932.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>50%</th>\n", "      <td>3.000000</td>\n", "      <td>246.500000</td>\n", "      <td>12.000000</td>\n", "      <td>153.000000</td>\n", "      <td>73.500000</td>\n", "      <td>4.000000</td>\n", "      <td>10183.000000</td>\n", "      <td>1644.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>75%</th>\n", "      <td>3.000000</td>\n", "      <td>448.000000</td>\n", "      <td>25.500000</td>\n", "      <td>277.250000</td>\n", "      <td>155.000000</td>\n", "      <td>18.000000</td>\n", "      <td>19457.000000</td>\n", "      <td>3084.000000</td>\n", "    </tr>\n", "    <tr>\n", "      <th>max</th>\n", "      <td>3.000000</td>\n", "      <td>2203.000000</td>\n", "      <td>162.000000</td>\n", "      <td>1430.000000</td>\n", "      <td>1055.000000</td>\n", "      <td>83.000000</td>\n", "      <td>88785.000000</td>\n", "      <td>12515.000000</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["             sexe         hosp         rea     HospConv     SSR_USLD  \\\n", "count  102.000000   102.000000  102.000000   102.000000   102.000000   \n", "mean     2.980392   386.686275   21.754902   231.264706   121.470588   \n", "std      0.198030   413.069001   29.729619   238.054654   156.744397   \n", "min      1.000000     0.000000    0.000000     0.000000     0.000000   \n", "25%      3.000000   130.250000    4.000000    89.000000    24.000000   \n", "50%      3.000000   246.500000   12.000000   153.000000    73.500000   \n", "75%      3.000000   448.000000   25.500000   277.250000   155.000000   \n", "max      3.000000  2203.000000  162.000000  1430.000000  1055.000000   \n", "\n", "           autres           rad            dc  \n", "count  102.000000    102.000000    102.000000  \n", "mean    12.196078  15482.431373   2534.039216  \n", "std     17.687070  15273.986786   2444.945989  \n", "min      0.000000      0.000000      0.000000  \n", "25%      0.000000   5949.500000    932.000000  \n", "50%      4.000000  10183.000000   1644.000000  \n", "75%     18.000000  19457.000000   3084.000000  \n", "max     83.000000  88785.000000  12515.000000  "]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["last_data.describe()"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>sexe</th>\n", "      <th>hosp</th>\n", "      <th>rea</th>\n", "      <th>HospConv</th>\n", "      <th>SSR_USLD</th>\n", "      <th>autres</th>\n", "      <th>rad</th>\n", "      <th>dc</th>\n", "    </tr>\n", "    <tr>\n", "      <th>dep</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>01</th>\n", "      <td>3</td>\n", "      <td>201</td>\n", "      <td>6</td>\n", "      <td>102.0</td>\n", "      <td>89.0</td>\n", "      <td>4.0</td>\n", "      <td>9021</td>\n", "      <td>1625</td>\n", "    </tr>\n", "    <tr>\n", "      <th>02</th>\n", "      <td>3</td>\n", "      <td>252</td>\n", "      <td>16</td>\n", "      <td>134.0</td>\n", "      <td>92.0</td>\n", "      <td>10.0</td>\n", "      <td>15490</td>\n", "      <td>3099</td>\n", "    </tr>\n", "    <tr>\n", "      <th>03</th>\n", "      <td>3</td>\n", "      <td>116</td>\n", "      <td>6</td>\n", "      <td>98.0</td>\n", "      <td>10.0</td>\n", "      <td>2.0</td>\n", "      <td>10824</td>\n", "      <td>1772</td>\n", "    </tr>\n", "    <tr>\n", "      <th>04</th>\n", "      <td>3</td>\n", "      <td>227</td>\n", "      <td>2</td>\n", "      <td>102.0</td>\n", "      <td>123.0</td>\n", "      <td>0.0</td>\n", "      <td>4820</td>\n", "      <td>748</td>\n", "    </tr>\n", "    <tr>\n", "      <th>05</th>\n", "      <td>3</td>\n", "      <td>98</td>\n", "      <td>0</td>\n", "      <td>24.0</td>\n", "      <td>74.0</td>\n", "      <td>0.0</td>\n", "      <td>5646</td>\n", "      <td>787</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     sexe  hosp  rea  HospConv  SSR_USLD  autres    rad    dc\n", "dep                                                          \n", "01      3   201    6     102.0      89.0     4.0   9021  1625\n", "02      3   252   16     134.0      92.0    10.0  15490  3099\n", "03      3   116    6      98.0      10.0     2.0  10824  1772\n", "04      3   227    2     102.0     123.0     0.0   4820   748\n", "05      3    98    0      24.0      74.0     0.0   5646   787"]}, "execution_count": 8, "metadata": {}, "output_type": "execute_result"}], "source": ["last_data.head()"]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>sexe</th>\n", "      <th>hosp</th>\n", "      <th>rea</th>\n", "      <th>HospConv</th>\n", "      <th>SSR_USLD</th>\n", "      <th>autres</th>\n", "      <th>rad</th>\n", "      <th>dc</th>\n", "    </tr>\n", "    <tr>\n", "      <th>dep</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>972</th>\n", "      <td>3</td>\n", "      <td>105</td>\n", "      <td>6</td>\n", "      <td>83.0</td>\n", "      <td>16.0</td>\n", "      <td>0.0</td>\n", "      <td>9000</td>\n", "      <td>2142</td>\n", "    </tr>\n", "    <tr>\n", "      <th>973</th>\n", "      <td>3</td>\n", "      <td>46</td>\n", "      <td>10</td>\n", "      <td>34.0</td>\n", "      <td>2.0</td>\n", "      <td>0.0</td>\n", "      <td>12343</td>\n", "      <td>802</td>\n", "    </tr>\n", "    <tr>\n", "      <th>974</th>\n", "      <td>3</td>\n", "      <td>200</td>\n", "      <td>8</td>\n", "      <td>47.0</td>\n", "      <td>145.0</td>\n", "      <td>0.0</td>\n", "      <td>16726</td>\n", "      <td>1882</td>\n", "    </tr>\n", "    <tr>\n", "      <th>976</th>\n", "      <td>3</td>\n", "      <td>16</td>\n", "      <td>0</td>\n", "      <td>16.0</td>\n", "      <td>0.0</td>\n", "      <td>0.0</td>\n", "      <td>3381</td>\n", "      <td>324</td>\n", "    </tr>\n", "    <tr>\n", "      <th>978</th>\n", "      <td>1</td>\n", "      <td>0</td>\n", "      <td>0</td>\n", "      <td>0.0</td>\n", "      <td>0.0</td>\n", "      <td>0.0</td>\n", "      <td>0</td>\n", "      <td>0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     sexe  hosp  rea  HospConv  SSR_USLD  autres    rad    dc\n", "dep                                                          \n", "972     3   105    6      83.0      16.0     0.0   9000  2142\n", "973     3    46   10      34.0       2.0     0.0  12343   802\n", "974     3   200    8      47.0     145.0     0.0  16726  1882\n", "976     3    16    0      16.0       0.0     0.0   3381   324\n", "978     1     0    0       0.0       0.0     0.0      0     0"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["last_data.tail()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Donn\u00e9es d\u00e9partements"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On r\u00e9cup\u00e8re ensuite la d\u00e9finition [g\u00e9ographique des d\u00e9partements](https://www.data.gouv.fr/en/datasets/contours-geographiques-des-departements/)."]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>code</th>\n", "      <th>nom</th>\n", "      <th>geometry</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>91</th>\n", "      <td>91</td>\n", "      <td>Essonne</td>\n", "      <td>POLYGON ((2.22656 48.77610, 2.22866 48.77451, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>92</th>\n", "      <td>92</td>\n", "      <td>Hauts-de-Seine</td>\n", "      <td>POLYGON ((2.29097 48.95097, 2.29162 48.95077, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>93</th>\n", "      <td>93</td>\n", "      <td>Seine-Saint-Denis</td>\n", "      <td>POLYGON ((2.55306 49.00982, 2.55814 49.01201, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>94</th>\n", "      <td>94</td>\n", "      <td>Val-de-Marne</td>\n", "      <td>POLYGON ((2.33190 48.81701, 2.33371 48.81677, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>95</th>\n", "      <td>95</td>\n", "      <td>Val-d'Oise</td>\n", "      <td>POLYGON ((2.59052 49.07965, 2.59013 49.07786, ...</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["   code                nom                                           geometry\n", "91   91            Essonne  POLYGON ((2.22656 48.77610, 2.22866 48.77451, ...\n", "92   92     Hauts-de-Seine  POLYGON ((2.29097 48.95097, 2.29162 48.95077, ...\n", "93   93  Seine-Saint-Denis  POLYGON ((2.55306 49.00982, 2.55814 49.01201, ...\n", "94   94       Val-de-Marne  POLYGON ((2.33190 48.81701, 2.33371 48.81677, ...\n", "95   95         Val-d'Oise  POLYGON ((2.59052 49.07965, 2.59013 49.07786, ..."]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}], "source": ["import geopandas\n", "# dernier lien de la page (format shapefiles)\n", "url = \"https://www.data.gouv.fr/fr/datasets/r/90b9341a-e1f7-4d75-a73c-bbc010c7feeb\"\n", "geo = geopandas.read_file(url)\n", "geo.tail()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Il faudrait aussi fusionner avec la population de chaque d\u00e9partement. Ce sera pour une autre fois. "]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Carte"]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 360x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "fig, ax = plt.subplots(1, 1, figsize=(5, 4))\n", "geo.plot(ax=ax, color='white', edgecolor='black');"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On enl\u00e8ve tous les d\u00e9partements \u00e0 trois chiffres. Les d\u00e9partements outre-mer sont pr\u00e9sents ou non selon le jeu de donn\u00e9es t\u00e9l\u00e9charg\u00e9."]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>code</th>\n", "      <th>nom</th>\n", "      <th>geometry</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>91</th>\n", "      <td>91</td>\n", "      <td>Essonne</td>\n", "      <td>POLYGON ((2.22656 48.77610, 2.22866 48.77451, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>92</th>\n", "      <td>92</td>\n", "      <td>Hauts-de-Seine</td>\n", "      <td>POLYGON ((2.29097 48.95097, 2.29162 48.95077, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>93</th>\n", "      <td>93</td>\n", "      <td>Seine-Saint-Denis</td>\n", "      <td>POLYGON ((2.55306 49.00982, 2.55814 49.01201, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>94</th>\n", "      <td>94</td>\n", "      <td>Val-de-Marne</td>\n", "      <td>POLYGON ((2.33190 48.81701, 2.33371 48.81677, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>95</th>\n", "      <td>95</td>\n", "      <td>Val-d'Oise</td>\n", "      <td>POLYGON ((2.59052 49.07965, 2.59013 49.07786, ...</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["   code                nom                                           geometry\n", "91   91            Essonne  POLYGON ((2.22656 48.77610, 2.22866 48.77451, ...\n", "92   92     Hauts-de-Seine  POLYGON ((2.29097 48.95097, 2.29162 48.95077, ...\n", "93   93  Seine-Saint-Denis  POLYGON ((2.55306 49.00982, 2.55814 49.01201, ...\n", "94   94       Val-de-Marne  POLYGON ((2.33190 48.81701, 2.33371 48.81677, ...\n", "95   95         Val-d'Oise  POLYGON ((2.59052 49.07965, 2.59013 49.07786, ..."]}, "execution_count": 12, "metadata": {}, "output_type": "execute_result"}], "source": ["codes = [_ for _ in set(geo.code) if len(_) < 3]\n", "metropole = geo[geo.code.isin(codes)]\n", "metropole.tail()"]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 360x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(1, 1, figsize=(5, 4))\n", "metropole.plot(ax=ax, color='white', edgecolor='black')\n", "ax.set_title(\"%s d\u00e9partements\" % metropole.shape[0]);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Carte COVID"]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"data": {"text/plain": ["(96, 12)"]}, "execution_count": 14, "metadata": {}, "output_type": "execute_result"}], "source": ["merged = last_data.reset_index(drop=False).merge(metropole, left_on=\"dep\", right_on=\"code\")\n", "merged.shape"]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>dep</th>\n", "      <th>sexe</th>\n", "      <th>hosp</th>\n", "      <th>rea</th>\n", "      <th>HospConv</th>\n", "      <th>SSR_USLD</th>\n", "      <th>autres</th>\n", "      <th>rad</th>\n", "      <th>dc</th>\n", "      <th>code</th>\n", "      <th>nom</th>\n", "      <th>geometry</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>91</th>\n", "      <td>91</td>\n", "      <td>3</td>\n", "      <td>715</td>\n", "      <td>40</td>\n", "      <td>337.0</td>\n", "      <td>308.0</td>\n", "      <td>30.0</td>\n", "      <td>33284</td>\n", "      <td>5225</td>\n", "      <td>91</td>\n", "      <td>Essonne</td>\n", "      <td>POLYGON ((2.22656 48.77610, 2.22866 48.77451, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>92</th>\n", "      <td>92</td>\n", "      <td>3</td>\n", "      <td>1509</td>\n", "      <td>123</td>\n", "      <td>730.0</td>\n", "      <td>603.0</td>\n", "      <td>53.0</td>\n", "      <td>47450</td>\n", "      <td>7156</td>\n", "      <td>92</td>\n", "      <td>Hauts-de-Seine</td>\n", "      <td>POLYGON ((2.29097 48.95097, 2.29162 48.95077, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>93</th>\n", "      <td>93</td>\n", "      <td>3</td>\n", "      <td>1921</td>\n", "      <td>91</td>\n", "      <td>714.0</td>\n", "      <td>1055.0</td>\n", "      <td>61.0</td>\n", "      <td>40699</td>\n", "      <td>6297</td>\n", "      <td>93</td>\n", "      <td>Seine-Saint-Denis</td>\n", "      <td>POLYGON ((2.55306 49.00982, 2.55814 49.01201, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>94</th>\n", "      <td>94</td>\n", "      <td>3</td>\n", "      <td>1196</td>\n", "      <td>48</td>\n", "      <td>685.0</td>\n", "      <td>400.0</td>\n", "      <td>63.0</td>\n", "      <td>45694</td>\n", "      <td>7898</td>\n", "      <td>94</td>\n", "      <td>Val-de-Marne</td>\n", "      <td>POLYGON ((2.33190 48.81701, 2.33371 48.81677, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>95</th>\n", "      <td>95</td>\n", "      <td>3</td>\n", "      <td>726</td>\n", "      <td>18</td>\n", "      <td>440.0</td>\n", "      <td>252.0</td>\n", "      <td>16.0</td>\n", "      <td>31208</td>\n", "      <td>5478</td>\n", "      <td>95</td>\n", "      <td>Val-d'Oise</td>\n", "      <td>POLYGON ((2.59052 49.07965, 2.59013 49.07786, ...</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["   dep  sexe  hosp  rea  HospConv  SSR_USLD  autres    rad    dc code  \\\n", "91  91     3   715   40     337.0     308.0    30.0  33284  5225   91   \n", "92  92     3  1509  123     730.0     603.0    53.0  47450  7156   92   \n", "93  93     3  1921   91     714.0    1055.0    61.0  40699  6297   93   \n", "94  94     3  1196   48     685.0     400.0    63.0  45694  7898   94   \n", "95  95     3   726   18     440.0     252.0    16.0  31208  5478   95   \n", "\n", "                  nom                                           geometry  \n", "91            Essonne  POLYGON ((2.22656 48.77610, 2.22866 48.77451, ...  \n", "92     Hauts-de-Seine  POLYGON ((2.29097 48.95097, 2.29162 48.95077, ...  \n", "93  Seine-Saint-Denis  POLYGON ((2.55306 49.00982, 2.55814 49.01201, ...  \n", "94       Val-de-Marne  POLYGON ((2.33190 48.81701, 2.33371 48.81677, ...  \n", "95         Val-d'Oise  POLYGON ((2.59052 49.07965, 2.59013 49.07786, ...  "]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}], "source": ["merged.tail()"]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [{"data": {"image/png": "iVBORw0KGgoAAAANSUhEUgAAATkAAAEICAYAAAAkx4P5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy89olMNAAAACXBIWXMAAAsTAAALEwEAmpwYAAAVtElEQVR4nO3de5BcZZ3G8e9juBMkYGA2JEiCIEpBEWVkodDdBFCRsIBVlMJGDAsU6q6IGpUA6uJld4MXWG+rRkEQkYARDUZREYLIrsJOEEggIAgBEiExcg2ySuC3f5x3sE11z3T3nDM9/fbzqeqa7nNOn9/7TjrPnNNnen6KCMzMcvWiTg/AzKxKDjkzy5pDzsyy5pAzs6w55Mwsaw45M8uaQ84AkPQVSR8paV8vlbRB0rj0+HpJp5Sx77S/qyXNKWt/lrfNOj0Aq56kVUAfsBF4DrgT+CawICKeB4iId7awr1Mi4meNtomIB4HxIxv1C/XOAfaIiLfV7P9NZezbeoOP5HrHP0TEdsBuwHzgDOCCsotIyuYH5+CRqHU3h1yPiYgnIuIq4K3AHEn7AEi6SNIn0/2JkpZIelzSo5J+IelFki4BXgr8IJ2OfkjSVEkh6WRJDwLX1SyrDbyXSbpZ0pOSFkvaMdWaIWl17RglrZJ0mKTDgbOAt6Z6t6X1L5z+pnF9WNIDktZJ+qak7dO6wXHMkfSgpPWSzm70vUnfgy9L+pGkp4GZknaR9F1Jv5d0v6T31Gx/gKRfpu/Tw5K+KGmLEf4TWckccj0qIm4GVgOvq7N6blq3E8Vp7lnFU+IE4EGKo8LxEfGpmuf8PfBK4I0NSr4dOAmYRHHa/Pkmxvhj4N+By1O9/epsdmK6zQR2pzhN/uIm27wW2As4FPiopFcOUfYfgX8DtgP+B/gBcBswOT3/vZIG5/gc8D5gInBQWv/Pw83LRpdDrrf9DtixzvJnKcJot4h4NiJ+EcN/yPmciHg6Ip5psP6SiFgREU8DHwHeUtLp4GzgvIi4LyI2AGcCx21yFPmxiHgmIm6jCKx6YTlocUT8d3qvcl9gp4j4eET8OSLuA74GHAcQEcsi4lcRsTEiVgFfpQh7G0Mccr1tMvBoneWfBu4FfirpPknzmtjXQy2sfwDYnOIIaKR2Sfur3fdmFEeggx6puf9Hhr4oUjvO3YBd0uno45Iepziq7QOQ9PJ0Wv+IpCcpjjrLmJOVyCHXoyS9hiLkbtx0XUQ8FRFzI2J34Cjg/ZIOHVzdYJfDHentWnP/pRRHi+uBp4FtasY1juI0udn9/o4ijGr3vRFYO8zzGqmt9xBwf0RMqLltFxFHpPVfBu4C9oyIF1MEoNqsaxVxyPUYSS+WdCSwEPhWRCyvs82RkvaQJOAJiveenk+r11K899Wqt0naW9I2wMeBRRHxHPAbYCtJsyRtDnwY2LLmeWuBqZIavVYvA94naZqk8fzlPbyNbYxxUzcDT0k6Q9LWksZJ2if9gIDifbsngQ2SXgG8q4SaVjKHXO/4gaSnKI5OzgbOA/6pwbZ7Aj8DNgC/BP4rIpamdf8BfDidvn2ghfqXABdRnDpuBbwHiqu9FG/Wfx1YQ3FkV3u19Tvp6x8k3VJnvxemfd8A3A/8H3BaC+NqKIXwkcD0tO/1aZzbp00+QHGh4imK9+ouL6OulUv+o5lmljMfyZlZ1hxyZpY1h5yZZc0hZ2ZZG9UPU0+cODGmTp3a0nOefvpptt1222oG5Npjsn6v1u50/W6uvWzZsvURsVPdlRExarf9998/WrV06dKWn1OWXq3d6fq9WrvT9bu5NjAQDXLHp6tmljWHnJllzSFnZllzyJlZ1hxyZpY1h5yZZc0hZ2ZZc8iZWdYccmaWtTHfI3P5mic4cd4Pm95+1fxZFY7GzLqNj+TMLGtNh1z6+/a/lrQkPZ4m6SZJ90q63E11zWwsauVI7nRgZc3jc4HzI2IP4DHg5DIHZmZWhqZCTtIUYBZFEw9SF6dDgEVpk4uBYyoYn5nZiDTVyEbSIoouTdtRdCg6EfhVOopD0q7A1RGxT53nngqcCtDX17f/woULWxrgukefYG2jnux17Dt5++E3atKGDRsYP36oPsTV6WTtTtfv1dqdrt/NtWfOnLksIvrrrRv26mrq0bkuIpZJmtFq8YhYACwA6O/vjxkzWtvFFy5dzGeXN38ReNXs1vY/lOuvv55Wx5tD7U7X79Xana6fa+1m0uNg4ChJR1D0y3wx8DlggqTNomjiO4WiZ6aZ2Zgy7HtyEXFmREyJiKnAccB1ETEbWAocmzabAyyubJRmZm0aye/JnQG8X9K9wEuAC8oZkplZeVr6xENEXA9cn+7fBxxQ/pDMzMrjTzyYWdYccmaWNYecmWXNIWdmWXPImVnWHHJmljWHnJllzSFnZllzyJlZ1hxyZpY1h5yZZc0hZ2ZZc8iZWdYccmaWNYecmWXNIWdmWRs25CRtJelmSbdJukPSx9LyiyTdL+nWdJte+WjNzFrUzF8G/hNwSERskLQ5cKOkq9O6D0bEoiGea2bWUcOGXBSNWTekh5un2/DNWs3MxoBmm0uPA5YBewBfiogzJF0EHERxpHctMC8i/lTnuW4u3WW1O12/V2t3un431x6quXRTIffCxtIE4HvAacAfgEeALSiaR/82Ij4+1PP7+/tjYGCg6XrQRnPp+bNa2v9Qcm22O9br92rtTtfv5tqSGoZcS1dXI+Jxin6rh0fEw1H4E/AN3LnLzMagZq6u7pSO4JC0NfB64C5Jk9IyAccAK6obpplZe5o5D5wEXJzel3sRcEVELJF0naSdAAG3Au+sbphmZu1p5urq7cCr6iw/pJIRmZmVyJ94MLOsOeTMLGsOOTPLmkPOzLLmkDOzrDnkzCxrDjkzy5pDzsyy5pAzs6w55Mwsaw45M8uaQ87MsuaQM7OsOeTMLGsOOTPLmkPOzLI2kubS0yTdJOleSZdL2qL64ZqZtaaZI7nB5tL7AdOBwyUdCJwLnB8RewCPASdXNkozszYNG3KpI1e95tKHAIvS8ospmtmYmY0pbTWXBj4N/CodxSFpV+DqiNinznPdXLrLane6fq/W7nT9bq49VHPppro2R8RzwPSa5tKvaLZ4RCygaD5Nf39/tNpAtuXm0rNb2/9QurnZbjfX79Xana6fa+12m0sfBEyQNJg+U4A15Q7NzGzk2m0uvZIi7I5Nm80BFlc0RjOzto2kufSdwEJJnwR+DVxQ4TjNzNoykubS9wEHVDEoM7Oy+BMPZpY1h5yZZc0hZ2ZZc8iZWdYccmaWNYecmWXNIWdmWXPImVnWHHJmljWHnJllzSFnZllzyJlZ1hxyZpY1h5yZZc0hZ2ZZc8iZWdaa+fPnu0paKunO1Fz69LT8HElrJN2abkdUP1wzs9Y08+fPNwJzI+IWSdsByyRdk9adHxGfqW54ZmYj08yfP38YeDjdf0rSSmBy1QMzMytDS+/JSZpK0e/hprTo3ZJul3ShpB3KHpyZ2UgpIprbUBoP/Bz4t4i4UlIfsB4I4BPApIg4qc7zTgVOBejr69t/4cKFLQ1w3aNPsPaZ5rffd/L2Le1/KN3cUbyb6/dq7U7X7+baM2fOXBYR/fXWNRVykjYHlgA/iYjz6qyfCiyJiH2G2k9/f38MDAw0NehBX7h0MZ9d3sxbh4VV82e1tP+h5NpRfKzX79Xana7fzbUlNQy5Zq6uiqKn6sragJM0qWazNwMr2h6hmVlFmjlEOhg4AVgu6da07CzgeEnTKU5XVwHvqGB8ZmYj0szV1RsB1Vn1o/KHY2ZWLn/iwcyy5pAzs6w55Mwsaw45M8uaQ87MsuaQM7OsOeTMLGsOOTPLmkPOzLLmkDOzrDnkzCxrDjkzy5pDzsyy5pAzs6w55Mwsaw45M8uaQ87MstZMj4ddJS2VdKekOySdnpbvKOkaSfekr25JaGZjTjNHchuBuRGxN3Ag8C+S9gbmAddGxJ7AtemxmdmYMmzIRcTDEXFLuv8UsBKYDBwNXJw2uxg4pqIxmpm1renm0vBCf9UbgH2AByNiQlou4LHBx5s8Z1SbS7ejUUPqbm622831e7V2p+t3c+2hmks33bVZ0njgu8B7I+LJItcKERGS6qZlRCwAFkDRXLrVBrKtNpdux6rZM+ou7+Zmu91cv1drd7p+rrWburoqaXOKgLs0Iq5Mi9cONphOX9dVMkIzsxFo5uqqgAuAlRFxXs2qq4A56f4cYHH5wzMzG5lmzgMPBk4Alku6NS07C5gPXCHpZOAB4C2VjNDMbASGDbmIuBFQg9WHljscM7Ny+RMPZpY1h5yZZc0hZ2ZZc8iZWdYccmaWNYecmWXNIWdmWXPImVnWHHJmljWHnJllzSFnZllzyJlZ1hxyZpY1h5yZZc0hZ2ZZc8iZWdaa+fPnF0paJ2lFzbJzJK2RdGu6HVHtMM3M2tPMkdxFwOF1lp8fEdPT7UflDsvMrBzNNJe+AXh0FMZiZla6pppLp6bSSyJin/T4HOBE4ElgAJgbEY81eK6bS7fBTY57r3an63dz7aGaS7cbcn3AeiCATwCTIuKk4fbT398fAwMDLQx9lJpLz59Vd3muzXbHev1erd3p+t1cW1LDkGvr6mpErI2I5yLieeBrwAFtj87MrEJthZykSTUP3wysaLStmVknDXseKOkyYAYwUdJq4F+BGZKmU5yurgLeUd0Qzcza10xz6ePrLL6ggrGYmZXOn3gws6w55Mwsaw45M8uaQ87MsuaQM7OsOeTMLGsOOTPLmkPOzLLmkDOzrDnkzCxrDjkzy5pDzsyy5pAzs6w55Mwsaw45M8uaQ87MstZuc+kdJV0j6Z70dYdqh2lm1p52m0vPA66NiD2Ba9NjM7Mxp93m0kcDF6f7FwPHlDssM7NytNt39fGImJDuC3hs8HGd57q5dBvc5Lj3ane6fjfXHqq59Ii7NkdESGqYlBGxAFgARXPpVhvIjkpz6dkz6i7v5ma73Vy/V2t3un6utdu9urp2sPdq+rquvCGZmZWn3ZC7CpiT7s8BFpczHDOzcjXzKySXAb8E9pK0WtLJwHzg9ZLuAQ5Lj83Mxpx2m0sDHFryWDpm6rwf1l0+d9+NnNhg3ar5s6ockpmVxJ94MLOsOeTMLGsOOTPLmkPOzLLmkDOzrDnkzCxrDjkzy5pDzsyy5pAzs6w55Mwsaw45M8uaQ87MsuaQM7OsOeTMLGvV/l1xe0GjP+fUyNx9NzKjmqGY9RQfyZlZ1kZ0JCdpFfAU8BywsVG3HDOzTinjdHVmRKwvYT9mZqXz6aqZZW2kIRfATyUtS02kzczGFEU07As9/JOlyRGxRtLOwDXAaRFxwybbnAqcCtDX17f/woULW6qx7tEnWPtM20Mckb6t6WjtnXfcvjPF6e5u6t1au9P1u7n2zJkzlzW6JjCikPurHUnnABsi4jONtunv74+BgYGW9vuFSxfz2eWd+U2Xuftu7Gjt02Yf3ZHakG839bFcu9P1u7m2pIYh1/bpqqRtJW03eB94A7Ci3f2ZmVVhJIcpfcD3JA3u59sR8eNSRmVmVpK2Qy4i7gP2K3EsZmal88e6xrBWPwoGsGr+rApGYta9/HtyZpY1h5yZZc0hZ2ZZc8iZWdZ84cGy4Qs1Vo+P5Mwsaw45M8uaQ87MsuaQM7Os+cJDZlp9873X33jf9Ps1d9+NnDjE97DXv1/dyEdyZpY1h5yZZc0hZ2ZZc8iZWdZ84aHHNbpQMdQb8L385vtofaoip09vNDOX2tdb2fPwkZyZZW1EISfpcEl3S7pX0ryyBmVmVpaRNLIZB3wJeBOwN3C8pL3LGpiZWRlGciR3AHBvRNwXEX8GFgKd66FnZlZH231XJR0LHB4Rp6THJwB/GxHv3mS7F5pLA3sBd7dYaiKwvq1Bjlyv1u50/V6t3en63Vx7t4jYqd6Kyq+uRsQCYEG7z5c00KhpbNV6tXan6/dq7U7Xz7X2SE5X1wC71jyekpaZmY0ZIwm5/wX2lDRN0hbAccBV5QzLzKwcI2kuvVHSu4GfAOOACyPijtJG9hdtn+q6dtfW79Xana6fZe22LzyYmXUDf+LBzLLmkDOzrI3ZkBvNj4xJ2lXSUkl3SrpD0ulp+Y6SrpF0T/q6Q4VjGCfp15KWpMfTJN2U5n95urhTVe0JkhZJukvSSkkHjdbcJb0vfc9XSLpM0lZVzl3ShZLWSVpRs6zuXFX4fBrH7ZJeXUHtT6fv++2SvidpQs26M1PtuyW9cSS1G9WvWTdXUkiamB5XPve0/LQ0/zskfapmeXlzj4gxd6O4kPFbYHdgC+A2YO8K600CXp3ubwf8huKjap8C5qXl84BzKxzD+4FvA0vS4yuA49L9rwDvqrD2xcAp6f4WwITRmDswGbgf2LpmzidWOXfg74BXAytqltWdK3AEcDUg4EDgpgpqvwHYLN0/t6b23ul1vyUwLf1/GFd2/bR8V4oLiA8AE0dx7jOBnwFbpsc7VzH3Sv7TlPBCPAj4Sc3jM4EzR7H+YuD1FJ/OmJSWTQLurqjeFOBa4BBgSXphra958f/V96Pk2tunoNEmyyufewq5h4AdKa70LwHeWPXcgamb/GerO1fgq8Dx9bYrq/Ym694MXJru/9VrPoXQQWXPPS1bBOwHrKoJucrnTvHD7LA625U697F6ujr44h+0Oi2rnKSpwKuAm4C+iHg4rXoE6Kuo7H8CHwKeT49fAjweERvT4yrnPw34PfCNdLr8dUnbMgpzj4g1wGeAB4GHgSeAZYze3Ac1mutovw5Pojh6GrXako4G1kTEbZusGo36Lwdel96a+Lmk11RRe6yGXEdIGg98F3hvRDxZuy6KHyml/76NpCOBdRGxrOx9N2kzitOIL0fEq4CnKU7ZXlDh3Heg+KMO04BdgG2Bw8uu04qq5jocSWcDG4FLR7HmNsBZwEdHq+YmNqM4ij8Q+CBwhSSVXWSshtyof2RM0uYUAXdpRFyZFq+VNCmtnwSsq6D0wcBRklZR/CWXQ4DPARMkDf6ydpXzXw2sjoib0uNFFKE3GnM/DLg/In4fEc8CV1J8P0Zr7oMazXVUXoeSTgSOBGankB2t2i+j+AFzW3r9TQFukfQ3o1R/NXBlFG6mOJOZWHbtsRpyo/qRsfTT4wJgZUScV7PqKmBOuj+H4r26UkXEmRExJSKmUszzuoiYDSwFjq2ydqr/CPCQpL3SokOBOxmFuVOcph4oaZv0bzBYe1TmXqPRXK8C3p6uNB4IPFFzWlsKSYdTvFVxVET8cZMxHSdpS0nTgD2Bm8usHRHLI2LniJiaXn+rKS7APcIozB34PsXFByS9nOKi13rKnvtI38is6kZxdec3FFdWzq641mspTlFuB25NtyMo3hu7FriH4irQjhWPYwZ/ubq6e/qHvRf4DukKVEV1pwMDaf7fB3YYrbkDHwPuAlYAl1BcUats7sBlFO//PUvxn/rkRnOluAD0pfQaXA70V1D7Xor3nwZfd1+p2f7sVPtu4E1VzH2T9av4y4WH0Zj7FsC30r/9LcAhVczdH+sys6yN1dNVM7NSOOTMLGsOOTPLmkPOzLLmkDOzrDnkzCxrDjkzy9r/A0jXxIPPMFuBAAAAAElFTkSuQmCC\n", "text/plain": ["<Figure size 360x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(1, 1, figsize=(5, 4))\n", "merged.hist('rea', bins=20, ax=ax)\n", "ax.set_title(\"Distribution rea\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Les r\u00e9gions les plus peupl\u00e9es ont sans doute la plus grande capacit\u00e9 hospitali\u00e8re. Il faudrait diviser par cette capacit\u00e9 pour avoir une carte qui ait un peu plus de sens. Comme l'id\u00e9e est ici de simplement tracer la carte, on ne calculera pas de ratio."]}, {"cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>dep</th>\n", "      <th>sexe</th>\n", "      <th>hosp</th>\n", "      <th>rea</th>\n", "      <th>HospConv</th>\n", "      <th>SSR_USLD</th>\n", "      <th>autres</th>\n", "      <th>rad</th>\n", "      <th>dc</th>\n", "      <th>code</th>\n", "      <th>nom</th>\n", "      <th>geometry</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>12</th>\n", "      <td>13</td>\n", "      <td>3</td>\n", "      <td>1551</td>\n", "      <td>98</td>\n", "      <td>864.0</td>\n", "      <td>579.0</td>\n", "      <td>10.0</td>\n", "      <td>88785</td>\n", "      <td>11852</td>\n", "      <td>13</td>\n", "      <td>Bouches-du-Rh\u00f4ne</td>\n", "      <td>MULTIPOLYGON (((5.39670 43.17300, 5.39314 43.1...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>33</th>\n", "      <td>33</td>\n", "      <td>3</td>\n", "      <td>970</td>\n", "      <td>102</td>\n", "      <td>702.0</td>\n", "      <td>152.0</td>\n", "      <td>14.0</td>\n", "      <td>27382</td>\n", "      <td>3831</td>\n", "      <td>33</td>\n", "      <td>Gironde</td>\n", "      <td>MULTIPOLYGON (((-1.23902 44.59278, -1.23583 44...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>59</th>\n", "      <td>59</td>\n", "      <td>3</td>\n", "      <td>1723</td>\n", "      <td>110</td>\n", "      <td>1075.0</td>\n", "      <td>466.0</td>\n", "      <td>72.0</td>\n", "      <td>70645</td>\n", "      <td>11328</td>\n", "      <td>59</td>\n", "      <td>Nord</td>\n", "      <td>MULTIPOLYGON (((3.04040 50.15971, 3.04599 50.1...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>92</th>\n", "      <td>92</td>\n", "      <td>3</td>\n", "      <td>1509</td>\n", "      <td>123</td>\n", "      <td>730.0</td>\n", "      <td>603.0</td>\n", "      <td>53.0</td>\n", "      <td>47450</td>\n", "      <td>7156</td>\n", "      <td>92</td>\n", "      <td>Hauts-de-Seine</td>\n", "      <td>POLYGON ((2.29097 48.95097, 2.29162 48.95077, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>75</th>\n", "      <td>75</td>\n", "      <td>3</td>\n", "      <td>2203</td>\n", "      <td>162</td>\n", "      <td>1430.0</td>\n", "      <td>593.0</td>\n", "      <td>18.0</td>\n", "      <td>73440</td>\n", "      <td>12515</td>\n", "      <td>75</td>\n", "      <td>Paris</td>\n", "      <td>POLYGON ((2.33190 48.81701, 2.33247 48.81825, ...</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["   dep  sexe  hosp  rea  HospConv  SSR_USLD  autres    rad     dc code  \\\n", "12  13     3  1551   98     864.0     579.0    10.0  88785  11852   13   \n", "33  33     3   970  102     702.0     152.0    14.0  27382   3831   33   \n", "59  59     3  1723  110    1075.0     466.0    72.0  70645  11328   59   \n", "92  92     3  1509  123     730.0     603.0    53.0  47450   7156   92   \n", "75  75     3  2203  162    1430.0     593.0    18.0  73440  12515   75   \n", "\n", "                 nom                                           geometry  \n", "12  Bouches-du-Rh\u00f4ne  MULTIPOLYGON (((5.39670 43.17300, 5.39314 43.1...  \n", "33           Gironde  MULTIPOLYGON (((-1.23902 44.59278, -1.23583 44...  \n", "59              Nord  MULTIPOLYGON (((3.04040 50.15971, 3.04599 50.1...  \n", "92    Hauts-de-Seine  POLYGON ((2.29097 48.95097, 2.29162 48.95077, ...  \n", "75             Paris  POLYGON ((2.33190 48.81701, 2.33247 48.81825, ...  "]}, "execution_count": 17, "metadata": {}, "output_type": "execute_result"}], "source": ["merged.sort_values('rea').tail()"]}, {"cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [], "source": ["geomerged = geopandas.GeoDataFrame(merged)"]}, {"cell_type": "code", "execution_count": 18, "metadata": {"scrolled": false}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "\n", "fig, ax = plt.subplots(1, 1)\n", "\n", "# ligne \u00e0 ajouter pour avoir une l\u00e9gende ajust\u00e9e \u00e0 la taille du graphe\n", "cax = make_axes_locatable(ax).append_axes(\"right\", size=\"5%\", pad=0.1)\n", "\n", "geomerged.plot(column=\"rea\", ax=ax, edgecolor='black', legend=True, cax=cax)\n", "ax.set_title(\"R\u00e9animations pour les %d d\u00e9partements\" % metropole.shape[0]);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["La cr\u00e9ation de carte a toujours \u00e9t\u00e9 plus ou moins compliqu\u00e9. Les premiers notebooks que j'ai cr\u00e9\u00e9s sur le sujet \u00e9taient beaucoup plus complexe. *geopandas* a simplifi\u00e9 les choses. Son d\u00e9veloppement a commenc\u00e9 entre [2013](https://github.com/geopandas/geopandas/graphs/contributors) et a bien \u00e9volu\u00e9 depuis. Et j'ai d\u00fb passer quelques heures \u00e0 r\u00e9cup\u00e9rer les contours des d\u00e9partements il y a cinq ans."]}, {"cell_type": "markdown", "metadata": {}, "source": ["On peut \u00e9galement r\u00e9cup\u00e9rer la capacit\u00e9 maximale de chaque d\u00e9partement en regardant sur le pass\u00e9."]}, {"cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>sexe</th>\n", "      <th>hosp</th>\n", "      <th>rea</th>\n", "      <th>HospConv</th>\n", "      <th>SSR_USLD</th>\n", "      <th>autres</th>\n", "      <th>rad</th>\n", "      <th>dc</th>\n", "    </tr>\n", "    <tr>\n", "      <th>dep</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>01</th>\n", "      <td>3</td>\n", "      <td>904</td>\n", "      <td>78</td>\n", "      <td>287.0</td>\n", "      <td>187.0</td>\n", "      <td>36.0</td>\n", "      <td>9021</td>\n", "      <td>1625</td>\n", "    </tr>\n", "    <tr>\n", "      <th>02</th>\n", "      <td>3</td>\n", "      <td>688</td>\n", "      <td>125</td>\n", "      <td>392.0</td>\n", "      <td>250.0</td>\n", "      <td>18.0</td>\n", "      <td>15490</td>\n", "      <td>3099</td>\n", "    </tr>\n", "    <tr>\n", "      <th>03</th>\n", "      <td>3</td>\n", "      <td>558</td>\n", "      <td>58</td>\n", "      <td>394.0</td>\n", "      <td>91.0</td>\n", "      <td>12.0</td>\n", "      <td>10824</td>\n", "      <td>1772</td>\n", "    </tr>\n", "    <tr>\n", "      <th>04</th>\n", "      <td>3</td>\n", "      <td>381</td>\n", "      <td>30</td>\n", "      <td>136.0</td>\n", "      <td>184.0</td>\n", "      <td>26.0</td>\n", "      <td>4820</td>\n", "      <td>748</td>\n", "    </tr>\n", "    <tr>\n", "      <th>05</th>\n", "      <td>3</td>\n", "      <td>380</td>\n", "      <td>50</td>\n", "      <td>134.0</td>\n", "      <td>178.0</td>\n", "      <td>20.0</td>\n", "      <td>5646</td>\n", "      <td>787</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["     sexe  hosp  rea  HospConv  SSR_USLD  autres    rad    dc\n", "dep                                                          \n", "01      3   904   78     287.0     187.0    36.0   9021  1625\n", "02      3   688  125     392.0     250.0    18.0  15490  3099\n", "03      3   558   58     394.0      91.0    12.0  10824  1772\n", "04      3   381   30     136.0     184.0    26.0   4820   748\n", "05      3   380   50     134.0     178.0    20.0   5646   787"]}, "execution_count": 20, "metadata": {}, "output_type": "execute_result"}], "source": ["capacite = covid.groupby([\"jour\", \"dep\"]).sum().groupby(\"dep\").max()\n", "capacite.head()"]}, {"cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [{"data": {"text/html": ["<div>\n", "<style scoped>\n", "    .dataframe tbody tr th:only-of-type {\n", "        vertical-align: middle;\n", "    }\n", "\n", "    .dataframe tbody tr th {\n", "        vertical-align: top;\n", "    }\n", "\n", "    .dataframe thead th {\n", "        text-align: right;\n", "    }\n", "</style>\n", "<table border=\"1\" class=\"dataframe\">\n", "  <thead>\n", "    <tr style=\"text-align: right;\">\n", "      <th></th>\n", "      <th>0</th>\n", "      <th>1</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>dep</th>\n", "      <td>01</td>\n", "      <td>02</td>\n", "    </tr>\n", "    <tr>\n", "      <th>sexe_x</th>\n", "      <td>3</td>\n", "      <td>3</td>\n", "    </tr>\n", "    <tr>\n", "      <th>hosp_x</th>\n", "      <td>201</td>\n", "      <td>252</td>\n", "    </tr>\n", "    <tr>\n", "      <th>rea_x</th>\n", "      <td>6</td>\n", "      <td>16</td>\n", "    </tr>\n", "    <tr>\n", "      <th>HospConv_x</th>\n", "      <td>102.0</td>\n", "      <td>134.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>SSR_USLD_x</th>\n", "      <td>89.0</td>\n", "      <td>92.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>autres_x</th>\n", "      <td>4.0</td>\n", "      <td>10.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>rad_x</th>\n", "      <td>9021</td>\n", "      <td>15490</td>\n", "    </tr>\n", "    <tr>\n", "      <th>dc_x</th>\n", "      <td>1625</td>\n", "      <td>3099</td>\n", "    </tr>\n", "    <tr>\n", "      <th>code</th>\n", "      <td>01</td>\n", "      <td>02</td>\n", "    </tr>\n", "    <tr>\n", "      <th>nom</th>\n", "      <td>Ain</td>\n", "      <td>Aisne</td>\n", "    </tr>\n", "    <tr>\n", "      <th>geometry</th>\n", "      <td>POLYGON ((4.78021 46.17668, 4.78024 46.18905, ...</td>\n", "      <td>POLYGON ((3.17296 50.01131, 3.17382 50.01186, ...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>sexe_y</th>\n", "      <td>3</td>\n", "      <td>3</td>\n", "    </tr>\n", "    <tr>\n", "      <th>hosp_y</th>\n", "      <td>904</td>\n", "      <td>688</td>\n", "    </tr>\n", "    <tr>\n", "      <th>rea_y</th>\n", "      <td>78</td>\n", "      <td>125</td>\n", "    </tr>\n", "    <tr>\n", "      <th>HospConv_y</th>\n", "      <td>287.0</td>\n", "      <td>392.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>SSR_USLD_y</th>\n", "      <td>187.0</td>\n", "      <td>250.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>autres_y</th>\n", "      <td>36.0</td>\n", "      <td>18.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>rad_y</th>\n", "      <td>9021</td>\n", "      <td>15490</td>\n", "    </tr>\n", "    <tr>\n", "      <th>dc_y</th>\n", "      <td>1625</td>\n", "      <td>3099</td>\n", "    </tr>\n", "    <tr>\n", "      <th>occupation</th>\n", "      <td>0.076923</td>\n", "      <td>0.128</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                                                            0  \\\n", "dep                                                        01   \n", "sexe_x                                                      3   \n", "hosp_x                                                    201   \n", "rea_x                                                       6   \n", "HospConv_x                                              102.0   \n", "SSR_USLD_x                                               89.0   \n", "autres_x                                                  4.0   \n", "rad_x                                                    9021   \n", "dc_x                                                     1625   \n", "code                                                       01   \n", "nom                                                       Ain   \n", "geometry    POLYGON ((4.78021 46.17668, 4.78024 46.18905, ...   \n", "sexe_y                                                      3   \n", "hosp_y                                                    904   \n", "rea_y                                                      78   \n", "HospConv_y                                              287.0   \n", "SSR_USLD_y                                              187.0   \n", "autres_y                                                 36.0   \n", "rad_y                                                    9021   \n", "dc_y                                                     1625   \n", "occupation                                           0.076923   \n", "\n", "                                                            1  \n", "dep                                                        02  \n", "sexe_x                                                      3  \n", "hosp_x                                                    252  \n", "rea_x                                                      16  \n", "HospConv_x                                              134.0  \n", "SSR_USLD_x                                               92.0  \n", "autres_x                                                 10.0  \n", "rad_x                                                   15490  \n", "dc_x                                                     3099  \n", "code                                                       02  \n", "nom                                                     Aisne  \n", "geometry    POLYGON ((3.17296 50.01131, 3.17382 50.01186, ...  \n", "sexe_y                                                      3  \n", "hosp_y                                                    688  \n", "rea_y                                                     125  \n", "HospConv_y                                              392.0  \n", "SSR_USLD_y                                              250.0  \n", "autres_y                                                 18.0  \n", "rad_y                                                   15490  \n", "dc_y                                                     3099  \n", "occupation                                              0.128  "]}, "execution_count": 21, "metadata": {}, "output_type": "execute_result"}], "source": ["capa_merged = merged.merge(capacite, left_on=\"dep\", right_on=\"dep\")\n", "capa_merged[\"occupation\"] = capa_merged[\"rea_x\"] / capa_merged[\"rea_y\"]\n", "capa_merged.head(n=2).T"]}, {"cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": ["geocapa = geopandas.GeoDataFrame(capa_merged)"]}, {"cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(1, 1)\n", "\n", "# ligne \u00e0 ajouter pour avoir une l\u00e9gende ajust\u00e9e \u00e0 la taille du graphe\n", "cax = make_axes_locatable(ax).append_axes(\"right\", size=\"5%\", pad=0.1)\n", "\n", "geocapa.plot(column=\"occupation\", ax=ax, edgecolor='black', legend=True, cax=cax)\n", "ax.set_title(\"Occupations en r\u00e9animations pour les %d d\u00e9partements\" % metropole.shape[0]);"]}, {"cell_type": "markdown", "metadata": {}, "source": []}, {"cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 3 (ipykernel)", "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": "3.10.5"}}, "nbformat": 4, "nbformat_minor": 2}