{"cells": [{"cell_type": "markdown", "id": "9c563a13", "metadata": {}, "source": ["# Geopandas\n", "\n", "[geopandas](https://geopandas.org/en/stable/) est devenu le point d'entr\u00e9e \u00e0 tout traitement li\u00e9 aux coordonn\u00e9es g\u00e9ographiques."]}, {"cell_type": "code", "execution_count": 1, "id": "42456ac5", "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": "markdown", "id": "316903b9", "metadata": {}, "source": ["## Coordonn\u00e9es g\u00e9ographiques, et geocoding"]}, {"cell_type": "code", "execution_count": 2, "id": "a03b8ad9", "metadata": {}, "outputs": [], "source": ["import geopandas\n", "import pandas"]}, {"cell_type": "code", "execution_count": 3, "id": "4a65fab2", "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>name</th>\n", "      <th>address</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>A</td>\n", "      <td>50 rue du moulin 08000 Charleville-M\u00e9zi\u00e8res</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>B</td>\n", "      <td>50 rue dumoulin 08000 Charleville-M\u00e9zi\u00e8res</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>C</td>\n", "      <td>50 rue du moulin 08000 Charleville</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>D</td>\n", "      <td>29 rue Ordener 75018 Paris</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>E</td>\n", "      <td>Gare Lille</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["  name                                      address\n", "0    A  50 rue du moulin 08000 Charleville-M\u00e9zi\u00e8res\n", "1    B   50 rue dumoulin 08000 Charleville-M\u00e9zi\u00e8res\n", "2    C           50 rue du moulin 08000 Charleville\n", "3    D                   29 rue Ordener 75018 Paris\n", "4    E                                   Gare Lille"]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["df = pandas.DataFrame(dict(name=[\"A\", \"B\", \"C\", \"D\", \"E\"],\n", "                           address=[\"50 rue du moulin 08000 Charleville-M\u00e9zi\u00e8res\",\n", "                                    \"50 rue dumoulin 08000 Charleville-M\u00e9zi\u00e8res\",\n", "                                    \"50 rue du moulin 08000 Charleville\",\n", "                                    \"29 rue Ordener 75018 Paris\",\n", "                                    \"Gare Lille\"]))\n", "df"]}, {"cell_type": "code", "execution_count": 4, "id": "8445f7c3", "metadata": {}, "outputs": [], "source": ["from geopy.geocoders import Nominatim"]}, {"cell_type": "code", "execution_count": 5, "id": "896b42f2", "metadata": {}, "outputs": [{"data": {"text/plain": ["Location(50, Rue du Moulin, Charleville-M\u00e9zi\u00e8res, Ardennes, Grand Est, France m\u00e9tropolitaine, 08000, France, (49.775247, 4.721863, 0.0))"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["geolocator = Nominatim(timeout=10, user_agent = \"myGeolocator\")\n", "res = geolocator.geocode(\"50 rue du moulin 08000 Charleville-M\u00e9zi\u00e8res\")\n", "res"]}, {"cell_type": "code", "execution_count": 6, "id": "eccf0885", "metadata": {}, "outputs": [{"data": {"text/plain": ["(49.775247, 4.721863, Point(49.775247, 4.721863, 0.0))"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["res.latitude, res.longitude, res.point"]}, {"cell_type": "code", "execution_count": 7, "id": "6411883c", "metadata": {}, "outputs": [], "source": ["coordinates = df['address'].apply(geolocator.geocode)"]}, {"cell_type": "code", "execution_count": 8, "id": "324e9ae3", "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>name</th>\n", "      <th>address</th>\n", "      <th>position</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>A</td>\n", "      <td>50 rue du moulin 08000 Charleville-M\u00e9zi\u00e8res</td>\n", "      <td>POINT (4.721863 49.775247)</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>B</td>\n", "      <td>50 rue dumoulin 08000 Charleville-M\u00e9zi\u00e8res</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>C</td>\n", "      <td>50 rue du moulin 08000 Charleville</td>\n", "      <td>POINT (4.721863 49.775247)</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>D</td>\n", "      <td>29 rue Ordener 75018 Paris</td>\n", "      <td>POINT (2.3539607 48.8906617)</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>E</td>\n", "      <td>Gare Lille</td>\n", "      <td>POINT (3.162718387293441 50.69456630000001)</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["  name                                      address  \\\n", "0    A  50 rue du moulin 08000 Charleville-M\u00e9zi\u00e8res   \n", "1    B   50 rue dumoulin 08000 Charleville-M\u00e9zi\u00e8res   \n", "2    C           50 rue du moulin 08000 Charleville   \n", "3    D                   29 rue Ordener 75018 Paris   \n", "4    E                                   Gare Lille   \n", "\n", "                                      position  \n", "0                   POINT (4.721863 49.775247)  \n", "1                                          NaN  \n", "2                   POINT (4.721863 49.775247)  \n", "3                 POINT (2.3539607 48.8906617)  \n", "4  POINT (3.162718387293441 50.69456630000001)  "]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["import numpy\n", "from shapely.geometry import Point\n", "\n", "df['position'] = [numpy.nan if c is None else \n", "                  Point(c.point.longitude, c.point.latitude) for c in coordinates]\n", "df"]}, {"cell_type": "code", "execution_count": 9, "id": "9c749e05", "metadata": {}, "outputs": [], "source": ["geodf = geopandas.GeoDataFrame(df)"]}, {"cell_type": "code", "execution_count": 10, "id": "859dccc5", "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>name</th>\n", "      <th>address</th>\n", "      <th>position</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>A</td>\n", "      <td>50 rue du moulin 08000 Charleville-M\u00e9zi\u00e8res</td>\n", "      <td>POINT (4.721863 49.775247)</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>B</td>\n", "      <td>50 rue dumoulin 08000 Charleville-M\u00e9zi\u00e8res</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>C</td>\n", "      <td>50 rue du moulin 08000 Charleville</td>\n", "      <td>POINT (4.721863 49.775247)</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>D</td>\n", "      <td>29 rue Ordener 75018 Paris</td>\n", "      <td>POINT (2.3539607 48.8906617)</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>E</td>\n", "      <td>Gare Lille</td>\n", "      <td>POINT (3.162718387293441 50.69456630000001)</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["  name                                      address  \\\n", "0    A  50 rue du moulin 08000 Charleville-M\u00e9zi\u00e8res   \n", "1    B   50 rue dumoulin 08000 Charleville-M\u00e9zi\u00e8res   \n", "2    C           50 rue du moulin 08000 Charleville   \n", "3    D                   29 rue Ordener 75018 Paris   \n", "4    E                                   Gare Lille   \n", "\n", "                                      position  \n", "0                   POINT (4.721863 49.775247)  \n", "1                                          NaN  \n", "2                   POINT (4.721863 49.775247)  \n", "3                 POINT (2.3539607 48.8906617)  \n", "4  POINT (3.162718387293441 50.69456630000001)  "]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["geodf"]}, {"cell_type": "code", "execution_count": 11, "id": "b94db356", "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>name</th>\n", "      <th>address</th>\n", "      <th>position</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>A</td>\n", "      <td>50 rue du moulin 08000 Charleville-M\u00e9zi\u00e8res</td>\n", "      <td>POINT (4.721863 49.775247)</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>C</td>\n", "      <td>50 rue du moulin 08000 Charleville</td>\n", "      <td>POINT (4.721863 49.775247)</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>D</td>\n", "      <td>29 rue Ordener 75018 Paris</td>\n", "      <td>POINT (2.3539607 48.8906617)</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>E</td>\n", "      <td>Gare Lille</td>\n", "      <td>POINT (3.162718387293441 50.69456630000001)</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["  name                                      address  \\\n", "0    A  50 rue du moulin 08000 Charleville-M\u00e9zi\u00e8res   \n", "2    C           50 rue du moulin 08000 Charleville   \n", "3    D                   29 rue Ordener 75018 Paris   \n", "4    E                                   Gare Lille   \n", "\n", "                                      position  \n", "0                   POINT (4.721863 49.775247)  \n", "2                   POINT (4.721863 49.775247)  \n", "3                 POINT (2.3539607 48.8906617)  \n", "4  POINT (3.162718387293441 50.69456630000001)  "]}, "execution_count": 12, "metadata": {}, "output_type": "execute_result"}], "source": ["geodf.dropna()"]}, {"cell_type": "code", "execution_count": 12, "id": "3b0db32b", "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>name</th>\n", "      <th>address</th>\n", "      <th>position</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>A</td>\n", "      <td>50 rue du moulin 08000 Charleville-M\u00e9zi\u00e8res</td>\n", "      <td>POINT (4.72186 49.77525)</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>B</td>\n", "      <td>50 rue dumoulin 08000 Charleville-M\u00e9zi\u00e8res</td>\n", "      <td>None</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>C</td>\n", "      <td>50 rue du moulin 08000 Charleville</td>\n", "      <td>POINT (4.72186 49.77525)</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>D</td>\n", "      <td>29 rue Ordener 75018 Paris</td>\n", "      <td>POINT (2.35396 48.89066)</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>E</td>\n", "      <td>Gare Lille</td>\n", "      <td>POINT (3.16272 50.69457)</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["  name                                      address                  position\n", "0    A  50 rue du moulin 08000 Charleville-M\u00e9zi\u00e8res  POINT (4.72186 49.77525)\n", "1    B   50 rue dumoulin 08000 Charleville-M\u00e9zi\u00e8res                      None\n", "2    C           50 rue du moulin 08000 Charleville  POINT (4.72186 49.77525)\n", "3    D                   29 rue Ordener 75018 Paris  POINT (2.35396 48.89066)\n", "4    E                                   Gare Lille  POINT (3.16272 50.69457)"]}, "execution_count": 13, "metadata": {}, "output_type": "execute_result"}], "source": ["gdf = geodf.set_geometry('position')\n", "gdf"]}, {"cell_type": "markdown", "id": "697627b9", "metadata": {}, "source": ["## Carte"]}, {"cell_type": "code", "execution_count": 13, "id": "33c071cc", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["gdf.plot();"]}, {"cell_type": "code", "execution_count": 14, "id": "737b9fbe", "metadata": {}, "outputs": [], "source": ["import cartopy.crs as ccrs\n", "import cartopy.feature as cfeature\n", "import matplotlib.pyplot as plt"]}, {"cell_type": "code", "execution_count": 15, "id": "39987d8e", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 504x504 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig = plt.figure(figsize=(7,7))\n", "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", "ax.set_extent([-5, 10, 42, 52])\n", "\n", "ax.add_feature(cfeature.OCEAN)\n", "ax.add_feature(cfeature.COASTLINE)\n", "ax.add_feature(cfeature.RIVERS)\n", "ax.add_feature(cfeature.LAKES)\n", "ax.add_feature(cfeature.LAND)\n", "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", "ax.plot([2.35, 2.20], [48.85, 48.71], '.')\n", "ax.text(2.35, 48.85, \"Paris\")\n", "ax.text(2.20, 48.71, \"Saclay\", ha=\"right\")\n", "ax.set_title('France');"]}, {"cell_type": "code", "execution_count": 16, "id": "0bcc3466", "metadata": {}, "outputs": [], "source": []}, {"cell_type": "code", "execution_count": 17, "id": "0175ec83", "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 504x504 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig = plt.figure(figsize=(7,7))\n", "ax = fig.add_subplot(1, 1, 1, projection=ccrs.PlateCarree())\n", "ax = gdf.plot(ax=ax)\n", "ax.add_feature(cfeature.OCEAN)\n", "ax.add_feature(cfeature.COASTLINE)\n", "ax.add_feature(cfeature.RIVERS)\n", "ax.add_feature(cfeature.LAKES)\n", "ax.add_feature(cfeature.LAND)\n", "ax.add_feature(cfeature.BORDERS, linestyle=':')\n", "for i in range(gdf.shape[0]):\n", "    if gdf.geometry[i] is None:\n", "        continue\n", "    ax.text(gdf.geometry[i].x, gdf.geometry[i].y, gdf.name[i])\n", "ax;"]}, {"cell_type": "markdown", "id": "e01687a7", "metadata": {}, "source": ["## R\u00e9gions administratives"]}, {"cell_type": "code", "execution_count": 18, "id": "98ce460b", "metadata": {}, "outputs": [], "source": []}], "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": "3.9.5"}}, "nbformat": 4, "nbformat_minor": 5}