{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# 2A.i - Table de mortalit\u00e9 dans plusieurs containers\n", "\n", "Pas de calcul d'esp\u00e9rence de vie, seulement diff\u00e9rentes fa\u00e7ons de lire les donn\u00e9es d'une table de mortalit\u00e9."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": ["%matplotlib inline\n", "import matplotlib.pyplot as plt"]}, {"cell_type": "code", "execution_count": 2, "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": 3, "metadata": {}, "output_type": "execute_result"}], "source": ["from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## R\u00e9cup\u00e9ration des donn\u00e9es\n", "\n", "Les donn\u00e9es sont recens\u00e9es sur [Data Publica](http://www.data-publica.com/) : [Table de mortalit\u00e9](http://www.data-publica.com/opendata/7098--population-et-conditions-sociales-table-de-mortalite-de-1960-a-2010) qui les a r\u00e9cup\u00e9r\u00e9 depuis le site d'Eurostat via le listing suivant : [listing](http://epp.eurostat.ec.europa.eu/NavTree_prod/everybody/BulkDownloadListing?sort=1&dir=data). Pour faire court, le lien est le suivant : [demo_mlifetable.tsv.gz](http://epp.eurostat.ec.europa.eu/NavTree_prod/everybody/BulkDownloadListing?file=data/demo_mlifetable.tsv.gz). Le fichier est compress\u00e9 au format [gzip](http://fr.wikipedia.org/wiki/Gzip). On le t\u00e9l\u00e9charge, on le d\u00e9compresse."]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": ["url = \"http://ec.europa.eu/eurostat/estat-navtree-portlet-prod/BulkDownloadListing?file=data/\"\n", "file = \"demo_mlifetable.tsv.gz\"\n", "import pyensae.datasource\n", "local = pyensae.datasource.download_data(\"demo_mlifetable.tsv.gz\", url=url)\n", "local = local[0]+\".gz\"\n", "import gzip\n", "with gzip.open(local, 'rb') as f: \n", "    file_content = f.read()\n", "content = str(file_content, encoding=\"utf8\")\n", "with open(\"mortalite.txt\", \"w\", encoding=\"utf8\") as f:\n", "    f.write(content)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Puis on le charge sous forme de dataframe :"]}, {"cell_type": "code", "execution_count": 4, "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>indic_de,sex,age,geo\\time</th>\n", "      <th>2019</th>\n", "      <th>2018</th>\n", "      <th>2017</th>\n", "      <th>2016</th>\n", "      <th>2015</th>\n", "      <th>2014</th>\n", "      <th>2013</th>\n", "      <th>2012</th>\n", "      <th>2011</th>\n", "      <th>...</th>\n", "      <th>1969</th>\n", "      <th>1968</th>\n", "      <th>1967</th>\n", "      <th>1966</th>\n", "      <th>1965</th>\n", "      <th>1964</th>\n", "      <th>1963</th>\n", "      <th>1962</th>\n", "      <th>1961</th>\n", "      <th>1960</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>DEATHRATE,F,Y1,AL</td>\n", "      <td>0.00021</td>\n", "      <td>0.00067</td>\n", "      <td>0.00046</td>\n", "      <td>0.00043</td>\n", "      <td>0.00042</td>\n", "      <td>0.00042</td>\n", "      <td>0.00110</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>...</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>DEATHRATE,F,Y1,AM</td>\n", "      <td>0.00051</td>\n", "      <td>0.00043</td>\n", "      <td>0.00052</td>\n", "      <td>0.00035</td>\n", "      <td>0.00035</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>...</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>DEATHRATE,F,Y1,AT</td>\n", "      <td>0.00026</td>\n", "      <td>0.00021</td>\n", "      <td>0.00017</td>\n", "      <td>0.00027</td>\n", "      <td>0.00017</td>\n", "      <td>0.00015</td>\n", "      <td>0.00016</td>\n", "      <td>0.00018</td>\n", "      <td>0.00018</td>\n", "      <td>...</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>DEATHRATE,F,Y1,AZ</td>\n", "      <td>0.00072</td>\n", "      <td>0.00041</td>\n", "      <td>0.00081</td>\n", "      <td>:</td>\n", "      <td>0.00090</td>\n", "      <td>0.00092</td>\n", "      <td>0.00070</td>\n", "      <td>0.00132</td>\n", "      <td>:</td>\n", "      <td>...</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "      <td>:</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>DEATHRATE,F,Y1,BE</td>\n", "      <td>0.00024</td>\n", "      <td>0.00022</td>\n", "      <td>0.00028</td>\n", "      <td>0.00025</td>\n", "      <td>0.00034</td>\n", "      <td>0.00035</td>\n", "      <td>0.00036</td>\n", "      <td>0.00025</td>\n", "      <td>0.00030</td>\n", "      <td>...</td>\n", "      <td>0.00141</td>\n", "      <td>0.00155</td>\n", "      <td>0.00132</td>\n", "      <td>0.00129</td>\n", "      <td>0.00153</td>\n", "      <td>0.00137</td>\n", "      <td>0.00170</td>\n", "      <td>0.00161</td>\n", "      <td>0.00168</td>\n", "      <td>0.00159</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>5 rows \u00d7 61 columns</p>\n", "</div>"], "text/plain": ["  indic_de,sex,age,geo\\time     2019      2018      2017      2016      2015   \\\n", "0         DEATHRATE,F,Y1,AL  0.00021   0.00067   0.00046   0.00043   0.00042    \n", "1         DEATHRATE,F,Y1,AM  0.00051   0.00043   0.00052   0.00035   0.00035    \n", "2         DEATHRATE,F,Y1,AT  0.00026   0.00021   0.00017   0.00027   0.00017    \n", "3         DEATHRATE,F,Y1,AZ  0.00072   0.00041   0.00081         :   0.00090    \n", "4         DEATHRATE,F,Y1,BE  0.00024   0.00022   0.00028   0.00025   0.00034    \n", "\n", "      2014      2013      2012      2011   ...     1969      1968      1967   \\\n", "0  0.00042   0.00110         :         :   ...        :         :         :    \n", "1        :         :         :         :   ...        :         :         :    \n", "2  0.00015   0.00016   0.00018   0.00018   ...        :         :         :    \n", "3  0.00092   0.00070   0.00132         :   ...        :         :         :    \n", "4  0.00035   0.00036   0.00025   0.00030   ...  0.00141   0.00155   0.00132    \n", "\n", "      1966      1965      1964      1963      1962      1961      1960   \n", "0        :         :         :         :         :         :         :   \n", "1        :         :         :         :         :         :         :   \n", "2        :         :         :         :         :         :         :   \n", "3        :         :         :         :         :         :         :   \n", "4  0.00129   0.00153   0.00137   0.00170   0.00161   0.00168   0.00159   \n", "\n", "[5 rows x 61 columns]"]}, "execution_count": 5, "metadata": {}, "output_type": "execute_result"}], "source": ["import pandas\n", "dff = pandas.read_csv(\"mortalite.txt\", sep=\"\\t\", encoding=\"utf8\")\n", "dff.head()"]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"text/plain": ["(101136, 61)"]}, "execution_count": 6, "metadata": {}, "output_type": "execute_result"}], "source": ["dff.shape"]}, {"cell_type": "markdown", "metadata": {}, "source": ["La premi\u00e8re colonne contient une aggr\u00e9gation de champs. On souhaite transformer cette table de telle sorte qu'on ait un nombre r\u00e9duit de colonnes :\n", "\n", "- indicateur\n", "- genre\n", "- age\n", "- pays (ou ensemble de pays)\n", "- annee\n", "- valeur\n", "\n", "L'\u00e2ge est repr\u00e9sent\u00e9 sous forme de cha\u00eene de caract\u00e8res pour pouvoir \u00e9crire ``Y_LT1`` (moins d'un an), ``Y_GE85`` (plus de 85 ans). On change un peu le format pour pouvoir les trier par ordre croissant (en effet ``Y2`` est apr\u00e8s ``Y10``). On sauve le tout dans un fichier pour ne pas avoir \u00e0 recommencer ult\u00e9rieurement. Malgr\u00e9 tout, le code ci-dessous est tr\u00e8s lent pour la table compl\u00e8te qui contiendra au final pr\u00e8s de 5 millions de lignes. On supprime les valeurs manquantes."]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["\u00e9tape 1 (101136, 61)\n", "\u00e9tape 2 (6068160, 1)\n", "\u00e9tape 3 (3254823, 3)\n", "\u00e9tape 4\n"]}, {"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>annee</th>\n", "      <th>valeur</th>\n", "      <th>age</th>\n", "      <th>indicateur</th>\n", "      <th>genre</th>\n", "      <th>pays</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>2019</td>\n", "      <td>0.00021</td>\n", "      <td>Y01</td>\n", "      <td>DEATHRATE</td>\n", "      <td>F</td>\n", "      <td>AL</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>2018</td>\n", "      <td>0.00067</td>\n", "      <td>Y01</td>\n", "      <td>DEATHRATE</td>\n", "      <td>F</td>\n", "      <td>AL</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>2017</td>\n", "      <td>0.00046</td>\n", "      <td>Y01</td>\n", "      <td>DEATHRATE</td>\n", "      <td>F</td>\n", "      <td>AL</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>2016</td>\n", "      <td>0.00043</td>\n", "      <td>Y01</td>\n", "      <td>DEATHRATE</td>\n", "      <td>F</td>\n", "      <td>AL</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>2015</td>\n", "      <td>0.00042</td>\n", "      <td>Y01</td>\n", "      <td>DEATHRATE</td>\n", "      <td>F</td>\n", "      <td>AL</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["   annee   valeur  age indicateur genre pays\n", "0  2019   0.00021  Y01  DEATHRATE     F   AL\n", "1  2018   0.00067  Y01  DEATHRATE     F   AL\n", "2  2017   0.00046  Y01  DEATHRATE     F   AL\n", "3  2016   0.00043  Y01  DEATHRATE     F   AL\n", "4  2015   0.00042  Y01  DEATHRATE     F   AL"]}, "execution_count": 7, "metadata": {}, "output_type": "execute_result"}], "source": ["def format_age(s):\n", "    if s.startswith(\"Y_\") : \n", "        if s.startswith(\"Y_LT\"): s = \"Y00_LT\" + s[4:]\n", "        elif s.startswith(\"Y_GE\"): s = \"Y85_GE\" + s[4:]\n", "        else: raise FormatError(s)\n", "    else:\n", "        i = int(s.strip(\"Y\"))\n", "        return \"Y%02d\" % i\n", "    \n", "def format_value(s):\n", "    if s.strip() == \":\" : return -1\n", "    else : return float(s.strip(\" ebp\"))\n", "    \n", "if False:  # sur les donn\u00e9es compl\u00e8tes, c'est plut\u00f4t long, r\u00e9duire la taille pour essayer\n", "    dfsmall = dff.head(n = 1000) # on r\u00e9duit la taille pour \n", "    df = dfsmall # impl\u00e9menter la transformation\n", "else:\n", "    df = dff\n", "\n", "print(\"\u00e9tape 1\", df.shape)\n", "dfi = df.reset_index().set_index(\"indic_de,sex,age,geo\\\\time\")\n", "dfi = dfi.drop('index', axis=1)\n", "dfs = dfi.stack()\n", "dfs = pandas.DataFrame({\"valeur\": dfs } )\n", "\n", "print(\"\u00e9tape 2\", dfs.shape)\n", "dfs[\"valeur\"] = dfs[\"valeur\"].astype(str)\n", "dfs[\"valeur\"] = dfs[\"valeur\"].apply( format_value )\n", "dfs = dfs[ dfs.valeur >= 0 ].copy()\n", "dfs = dfs.reset_index()\n", "dfs.columns = [\"index\", \"annee\", \"valeur\"]\n", "\n", "print(\"\u00e9tape 3\", dfs.shape)\n", "dfs[\"age\"] = dfs[\"index\"].apply ( lambda i : format_age(i.split(\",\")[2]))\n", "dfs[\"indicateur\"] = dfs[\"index\"].apply ( lambda i : i.split(\",\")[0])\n", "dfs[\"genre\"] = dfs[\"index\"].apply ( lambda i : i.split(\",\")[1])\n", "dfs[\"pays\"] = dfs[\"index\"].apply ( lambda i : i.split(\",\")[3])\n", "\n", "print(\"\u00e9tape 4\")\n", "dfy = dfs.drop('index', axis=1)\n", "dfy.to_csv(\"mortalite_5column.txt\", sep=\"\\t\", encoding=\"utf8\", index=False)\n", "dfy.head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Graphe d'une coupe de la table de mortalit\u00e9 :"]}, {"cell_type": "code", "execution_count": 7, "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": ["view = dfs [    (dfs.pays==\"FR\") &\n", "                (dfs.age == \"Y80\") &\n", "                (dfs.indicateur == \"DEATHRATE\") &\n", "                (dfs.genre == \"T\")  ]\n", "view = view.sort_values(\"annee\")\n", "view.plot(x=\"annee\", y=\"valeur\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### SQLite\n", "\n", "[SQLite](http://www.sqlite.org/) est un outils de gestion de base de donn\u00e9es locales. Int\u00e9gr\u00e9 \u00e0 Python, il ne n\u00e9cessite aucune installation. Il est tr\u00e8s utile lorsque [Microsoft Excel](http://fr.wikipedia.org/wiki/Microsoft_Excel) ne peut pas contenir toutes les donn\u00e9es qu'on souhaite consulter. Plus de deux millions de lignes dans le cas de cette table."]}, {"cell_type": "markdown", "metadata": {}, "source": ["### version 1 : pandas to SQLite\n", "\n", "On utilise pour la m\u00e9thode [to_sql](http://pandas.pydata.org/pandas-docs/dev/generated/pandas.DataFrame.to_sql.html) et le module [sqlite3](https://docs.python.org/3.4/library/sqlite3.html). Ca prend un peu de temps (deux \u00e0 trois minutes)."]}, {"cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": ["import sqlite3\n", "con = sqlite3.connect(\"mortalite_sqlite3_y2.db3\")\n", "dfy.to_sql(\"table_mortalite\",con)\n", "con.close()  # il faut fermer la base qui sinon reste ouverte tant que le notebook\n", "             # n'est pas ferm\u00e9 --> elle n'est pas modifiable pas d'autre que ce notebook"]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [{"data": {"text/plain": ["['mortalite_sqlite3_y2.db3']"]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}], "source": ["import os\n", "[ _ for _ in os.listdir(\".\") if \"sqlite3\" in _ ]"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On utilise une requ\u00eate SQL pour r\u00e9cup\u00e9rer les donn\u00e9es \u00e9quivalent au code pandas cit\u00e9 ci-dessous :"]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": ["con = sqlite3.connect(\"mortalite_sqlite3_y2.db3\")\n", "view = pandas.read_sql(\"\"\"SELECT * FROM table_mortalite WHERE pays==\"FR\" \n", "            AND age == \"Y80\" \n", "            AND indicateur == \"DEATHRATE\"\n", "            AND genre == \"T\"\n", "            ORDER BY annee\"\"\", con)\n", "con.close()"]}, {"cell_type": "code", "execution_count": 11, "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": ["view.plot(x=\"annee\", y=\"valeur\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### version 2 : pyensae\n", "\n", "[import_flatfile_into_database](http://www.xavierdupre.fr/app/pyensae/helpsphinx/pyensae/sql/database_helper.html) est une fonction \u00e0 utiliser lorsqu'on ne sait pas toujours quel est le s\u00e9parateur de colonnes dans le fichier \u00e0 importer. La fonction le devine pour vous ainsi que le type de chaque colonne (quand c'est possible). L'autre aspect int\u00e9ressant est qu'elle affiche son \u00e9tat d'avancement. On rep\u00e8re plus rapidement que quelque chose se passe mal. Enfin, pour les gros fichiers, la fonction ne charge pas tout le fichier en m\u00e9moire. Cela permet de placer dans une base SQLite des milliards de lignes (cela peut prendre plus d'une heure). Ce n'est pas le cas ici, c'est juste \u00e0 titre d'exemple. On stocke l'ensemble des donn\u00e9es au format SQLite 3 de fa\u00e7on \u00e0 pouvoir les consulter plus facilement."]}, {"cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["  TextFile: opening file  mortalite_5column.txt\n", "  TextFile.guess_columns: processing file  mortalite_5column.txt\n", "  TextFile: opening file  mortalite_5column.txt\n", "  TextFile.guess_columns: using  101  lines\n", "  TextFile: closing file  mortalite_5column.txt\n", "  TextFile.guess_columns: sep  '\\t' nb cols 6  bestnb  101  more  {('\\t', 5): 101, (' ', 1): 100}\n", "  TextFile.guess_columns: header  True  columns  {0: ('annee', <class 'int'>), 1: ('valeur', <class 'float'>), 2: ('age', <class 'str'>), 3: ('indicateur', <class 'str'>), 4: ('genre', <class 'str'>), 5: ('pays', <class 'str'>)}\n", "  compiling ^(?P<annee>([-]?[1-9][0-9]*?)|(0?))\\t(?P<valeur>[-]?[0-9]*?([.][0-9]*?)?([eE][-]?[0-9]{0,4})?)\\t(?P<age>.*)\\t(?P<indicateur>.*)\\t(?P<genre>.*)\\t(?P<pays>.*)$\n", "  TextFile.guess_columns: regex  ^(?P<annee>([-]?[1-9][0-9]*?)|(0?))\\t(?P<valeur>[-]?[0-9]*?([.][0-9]*?)?([eE][-]?[0-9]{0,4})?)\\t(?P<age>.*)\\t(?P<indicateur>.*)\\t(?P<genre>.*)\\t(?P<pays>.*)$\n", "  TextFile.guess_columns: header  True  columns  {0: ('annee', <class 'int'>), 1: ('valeur', <class 'float'>), 2: ('age', (<class 'str'>, 6)), 3: ('indicateur', (<class 'str'>, 18)), 4: ('genre', (<class 'str'>, 2)), 5: ('pays', (<class 'str'>, 4))}\n", "  [_guess_columns] sep=['\\t']\n", "  TextFile: closing file  mortalite_5column.txt\n", "  [_guess_columns] columns_name=None\n", "   guess with  1001 lines\n", "   count_types  {0: {<class 'int'>: 1000}, 1: {<class 'float'>: 1000}, 2: {<class 'str'>: 1000}, 3: {<class 'str'>: 1000}, 4: {<class 'str'>: 1000}, 5: {<class 'str'>: 1000}}\n", "   columns  {0: ('annee', <class 'int'>), 1: ('valeur', <class 'float'>), 2: ('age', <class 'str'>), 3: ('indicateur', <class 'str'>), 4: ('genre', <class 'str'>), 5: ('pays', <class 'str'>)}\n", "   guess {0: ('annee', <class 'int'>), 1: ('valeur', <class 'float'>), 2: ('age', (<class 'str'>, 6)), 3: ('indicateur', (<class 'str'>, 18)), 4: ('genre', (<class 'str'>, 2)), 5: ('pays', (<class 'str'>, 20))}\n", "SQL  'CREATE TABLE mortalite_5column(annee INTEGER,'\n", "'       valeur FLOAT,'\n", "'       age TEXT,'\n", "'       indicateur TEXT,'\n", "'       genre TEXT,'\n", "'       pays TEXT);'\n", "   column_has_space False ['annee', 'valeur', 'age', 'indicateur', 'genre', 'pays']\n", "   changes {}\n", "    TextFileColumns (2): regex:  {0: ('annee', <class 'int'>), 1: ('valeur', <class 'float'>), 2: ('age', (<class 'str'>, 6)), 3: ('indicateur', (<class 'str'>, 18)), 4: ('genre', (<class 'str'>, 2)), 5: ('pays', (<class 'str'>, 20))}\n", "  TextFile.guess_columns: processing file  mortalite_5column.txt\n", "  TextFile: opening file  mortalite_5column.txt\n", "  TextFile.guess_columns: using  101  lines\n", "  TextFile: closing file  mortalite_5column.txt\n", "  TextFile.guess_columns: sep  '\\t' nb cols 6  bestnb  101  more  {('\\t', 5): 101, (' ', 1): 100}\n", "  TextFile.guess_columns: header  True  columns  {0: ('annee', <class 'int'>), 1: ('valeur', <class 'float'>), 2: ('age', <class 'str'>), 3: ('indicateur', <class 'str'>), 4: ('genre', <class 'str'>), 5: ('pays', <class 'str'>)}\n", "  compiling ^(?P<annee>([-]?[1-9][0-9]*?)|(0?))\\t(?P<valeur>[-]?[0-9]*?([.][0-9]*?)?([eE][-]?[0-9]{0,4})?)\\t(?P<age>.*)\\t(?P<indicateur>.*)\\t(?P<genre>.*)\\t(?P<pays>.*)$\n", "  TextFile.guess_columns: regex  ^(?P<annee>([-]?[1-9][0-9]*?)|(0?))\\t(?P<valeur>[-]?[0-9]*?([.][0-9]*?)?([eE][-]?[0-9]{0,4})?)\\t(?P<age>.*)\\t(?P<indicateur>.*)\\t(?P<genre>.*)\\t(?P<pays>.*)$\n", "  TextFile.guess_columns: header  True  columns  {0: ('annee', <class 'int'>), 1: ('valeur', <class 'float'>), 2: ('age', (<class 'str'>, 6)), 3: ('indicateur', (<class 'str'>, 18)), 4: ('genre', (<class 'str'>, 2)), 5: ('pays', (<class 'str'>, 4))}\n", "  TextFile: opening file  mortalite_5column.txt\n", "adding 100000 lines into table mortalite_5column\n", "adding 200000 lines into table mortalite_5column\n", "adding 300000 lines into table mortalite_5column\n", "adding 400000 lines into table mortalite_5column\n", "adding 500000 lines into table mortalite_5column\n", "adding 600000 lines into table mortalite_5column\n", "adding 700000 lines into table mortalite_5column\n", "adding 800000 lines into table mortalite_5column\n", "adding 900000 lines into table mortalite_5column\n", "adding 1000000 lines into table mortalite_5column\n", "adding 1100000 lines into table mortalite_5column\n", "adding 1200000 lines into table mortalite_5column\n", "adding 1300000 lines into table mortalite_5column\n", "adding 1400000 lines into table mortalite_5column\n", "adding 1500000 lines into table mortalite_5column\n", "adding 1600000 lines into table mortalite_5column\n", "adding 1700000 lines into table mortalite_5column\n", "adding 1800000 lines into table mortalite_5column\n", "adding 1900000 lines into table mortalite_5column\n", "adding 2000000 lines into table mortalite_5column\n", "adding 2100000 lines into table mortalite_5column\n", "  processing line  2124918  read bytes  67108864  sel  0  ratio 61.77 %\n", "adding 2200000 lines into table mortalite_5column\n", "adding 2300000 lines into table mortalite_5column\n", "adding 2400000 lines into table mortalite_5column\n", "adding 2500000 lines into table mortalite_5column\n", "adding 2600000 lines into table mortalite_5column\n", "adding 2700000 lines into table mortalite_5column\n", "adding 2800000 lines into table mortalite_5column\n", "adding 2900000 lines into table mortalite_5column\n", "adding 3000000 lines into table mortalite_5column\n", "adding 3100000 lines into table mortalite_5column\n", "adding 3200000 lines into table mortalite_5column\n", "^(?P<annee>([-]?[1-9][0-9]*?)|(0?))\\t(?P<valeur>[-]?[0-9]*?([.][0-9]*?)?([eE][-]?[0-9]{0,4})?)\\t(?P<age>.*)\\t(?P<indicateur>.*)\\t(?P<genre>.*)\\t(?P<pays>.*)$\n", "error regex 0 unable to interpret line  3254824 :  ''\n", "  TextFile: closing file  mortalite_5column.txt\n", "3254823  lines imported\n"]}, {"data": {"text/plain": ["'mortalite_5column'"]}, "execution_count": 13, "metadata": {}, "output_type": "execute_result"}], "source": ["from pyensae.sql import import_flatfile_into_database\n", "import_flatfile_into_database(\"mortalite.db3\", \"mortalite_5column.txt\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Ensuite, on peut facilement consulter les donn\u00e9es avec le logiciel (sous Windows) [SQLiteSpy](http://www.yunqa.de/delphi/doku.php/products/sqlitespy/index) ou l'extension [sqlite-manager](https://addons.mozilla.org/fr/firefox/addon/sqlite-manager/) pour Firefox sous toutes les plates-formes. Pour cet exercice, on ex\u00e9cute :"]}, {"cell_type": "code", "execution_count": 13, "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>annee</th>\n", "      <th>valeur</th>\n", "      <th>age</th>\n", "      <th>indicateur</th>\n", "      <th>genre</th>\n", "      <th>pays</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>1998</td>\n", "      <td>0.05303</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>1999</td>\n", "      <td>0.04811</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2</th>\n", "      <td>2000</td>\n", "      <td>0.05344</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>3</th>\n", "      <td>2001</td>\n", "      <td>0.05016</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>4</th>\n", "      <td>2002</td>\n", "      <td>0.04915</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>5</th>\n", "      <td>2003</td>\n", "      <td>0.04946</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>6</th>\n", "      <td>2004</td>\n", "      <td>0.04507</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>7</th>\n", "      <td>2005</td>\n", "      <td>0.04542</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>8</th>\n", "      <td>2006</td>\n", "      <td>0.04294</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>9</th>\n", "      <td>2007</td>\n", "      <td>0.04258</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>10</th>\n", "      <td>2008</td>\n", "      <td>0.04180</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>11</th>\n", "      <td>2009</td>\n", "      <td>0.04041</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>12</th>\n", "      <td>2010</td>\n", "      <td>0.03992</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>13</th>\n", "      <td>2011</td>\n", "      <td>0.03807</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>14</th>\n", "      <td>2012</td>\n", "      <td>0.03823</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>15</th>\n", "      <td>2013</td>\n", "      <td>0.03732</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>16</th>\n", "      <td>2014</td>\n", "      <td>0.03540</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>17</th>\n", "      <td>2015</td>\n", "      <td>0.03663</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>18</th>\n", "      <td>2016</td>\n", "      <td>0.03510</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>19</th>\n", "      <td>2017</td>\n", "      <td>0.03455</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>20</th>\n", "      <td>2018</td>\n", "      <td>0.03415</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "    <tr>\n", "      <th>21</th>\n", "      <td>2019</td>\n", "      <td>0.03418</td>\n", "      <td>Y80</td>\n", "      <td>DEATHRATE</td>\n", "      <td>T</td>\n", "      <td>FR</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["    annee   valeur  age indicateur genre pays\n", "0    1998  0.05303  Y80  DEATHRATE     T   FR\n", "1    1999  0.04811  Y80  DEATHRATE     T   FR\n", "2    2000  0.05344  Y80  DEATHRATE     T   FR\n", "3    2001  0.05016  Y80  DEATHRATE     T   FR\n", "4    2002  0.04915  Y80  DEATHRATE     T   FR\n", "5    2003  0.04946  Y80  DEATHRATE     T   FR\n", "6    2004  0.04507  Y80  DEATHRATE     T   FR\n", "7    2005  0.04542  Y80  DEATHRATE     T   FR\n", "8    2006  0.04294  Y80  DEATHRATE     T   FR\n", "9    2007  0.04258  Y80  DEATHRATE     T   FR\n", "10   2008  0.04180  Y80  DEATHRATE     T   FR\n", "11   2009  0.04041  Y80  DEATHRATE     T   FR\n", "12   2010  0.03992  Y80  DEATHRATE     T   FR\n", "13   2011  0.03807  Y80  DEATHRATE     T   FR\n", "14   2012  0.03823  Y80  DEATHRATE     T   FR\n", "15   2013  0.03732  Y80  DEATHRATE     T   FR\n", "16   2014  0.03540  Y80  DEATHRATE     T   FR\n", "17   2015  0.03663  Y80  DEATHRATE     T   FR\n", "18   2016  0.03510  Y80  DEATHRATE     T   FR\n", "19   2017  0.03455  Y80  DEATHRATE     T   FR\n", "20   2018  0.03415  Y80  DEATHRATE     T   FR\n", "21   2019  0.03418  Y80  DEATHRATE     T   FR"]}, "execution_count": 14, "metadata": {}, "output_type": "execute_result"}], "source": ["sql = \"\"\"SELECT * FROM mortalite_5column WHERE pays==\"FR\" \n", "            AND age == \"Y80\" \n", "            AND indicateur == \"DEATHRATE\"\n", "            AND genre == \"T\"\n", "            ORDER BY annee\"\"\"\n", "\n", "from pyensae.sql import Database\n", "db = Database(\"mortalite.db3\", LOG = lambda *l : None)\n", "db.connect()\n", "view = db.to_df(sql)\n", "view"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Visuellement, cela donne :"]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [{"data": {"text/plain": ["<AxesSubplot:xlabel='annee'>"]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}, {"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": ["view.plot(x=\"annee\", y=\"valeur\")"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Cube de donn\u00e9es\n", "\n", "On utilise l'expression *cube de donn\u00e9es* pour d\u00e9signer \u00e0 tableaux \u00e0 plusieurs dimensions. On le repr\u00e9sente souvent par une liste ``coordonn\u00e9es, valeurs``. C'est souvent beaucoup de donn\u00e9es et pas forc\u00e9ment de moyen pratique de les manipuler. On utilise le module [xarray](http://xarray.pydata.org/en/stable/). [pandas](https://pandas.pydata.org/pandas-docs/stable/) propose automatiquement d'exporter les donn\u00e9es vers ce module avec [to_xarray](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.to_xarray.html#pandas-dataframe-to-xarray)."]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [{"data": {"text/plain": ["(3254823, 6)"]}, "execution_count": 16, "metadata": {}, "output_type": "execute_result"}], "source": ["import pandas \n", "df = pandas.read_csv(\"mortalite_5column.txt\", sep=\"\\t\", encoding=\"utf8\")\n", "df.shape"]}, {"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>annee</th>\n", "      <th>valeur</th>\n", "      <th>age</th>\n", "      <th>indicateur</th>\n", "      <th>genre</th>\n", "      <th>pays</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>2019</td>\n", "      <td>0.00021</td>\n", "      <td>Y01</td>\n", "      <td>DEATHRATE</td>\n", "      <td>F</td>\n", "      <td>AL</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>2018</td>\n", "      <td>0.00067</td>\n", "      <td>Y01</td>\n", "      <td>DEATHRATE</td>\n", "      <td>F</td>\n", "      <td>AL</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["   annee   valeur  age indicateur genre pays\n", "0   2019  0.00021  Y01  DEATHRATE     F   AL\n", "1   2018  0.00067  Y01  DEATHRATE     F   AL"]}, "execution_count": 17, "metadata": {}, "output_type": "execute_result"}], "source": ["df.head(n=2)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On passe du c\u00f4t\u00e9 *index* toutes les colonnes except\u00e9 *valeur*."]}, {"cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [{"data": {"text/plain": ["['annee', 'age', 'indicateur', 'genre', 'pays']"]}, "execution_count": 18, "metadata": {}, "output_type": "execute_result"}], "source": ["cols = [_ for _ in df.columns if _ != \"valeur\"]\n", "cols"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On laisse tomber les valeurs manquantes."]}, {"cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [{"data": {"text/plain": ["(3254823, 6)"]}, "execution_count": 19, "metadata": {}, "output_type": "execute_result"}], "source": ["df.shape"]}, {"cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [{"data": {"text/plain": ["(3179097, 6)"]}, "execution_count": 20, "metadata": {}, "output_type": "execute_result"}], "source": ["df = df.dropna()\n", "df.shape"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On v\u00e9rifie qu'il n'y a pas de doublons car la conversion en *cube* ne fonctionne pas dans ce cas puisque deux valeurs seraient index\u00e9es avec les m\u00eames coordonn\u00e9es."]}, {"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></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th>valeur</th>\n", "    </tr>\n", "    <tr>\n", "      <th>annee</th>\n", "      <th>age</th>\n", "      <th>indicateur</th>\n", "      <th>genre</th>\n", "      <th>pays</th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["Empty DataFrame\n", "Columns: [valeur]\n", "Index: []"]}, "execution_count": 21, "metadata": {}, "output_type": "execute_result"}], "source": ["dup = df.groupby(cols).count().sort_values(\"valeur\", ascending=False)\n", "dup = dup[dup.valeur > 1]\n", "dup.head(n=2)"]}, {"cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [{"data": {"text/plain": ["(0, 1)"]}, "execution_count": 22, "metadata": {}, "output_type": "execute_result"}], "source": ["dup.shape"]}, {"cell_type": "code", "execution_count": 22, "metadata": {"scrolled": false}, "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></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th>valeur</th>\n", "    </tr>\n", "    <tr>\n", "      <th>annee</th>\n", "      <th>age</th>\n", "      <th>indicateur</th>\n", "      <th>genre</th>\n", "      <th>pays</th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2019</th>\n", "      <th>Y01</th>\n", "      <th>DEATHRATE</th>\n", "      <th>F</th>\n", "      <th>AL</th>\n", "      <td>0.00021</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2018</th>\n", "      <th>Y01</th>\n", "      <th>DEATHRATE</th>\n", "      <th>F</th>\n", "      <th>AL</th>\n", "      <td>0.00067</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                                  valeur\n", "annee age indicateur genre pays         \n", "2019  Y01 DEATHRATE  F     AL    0.00021\n", "2018  Y01 DEATHRATE  F     AL    0.00067"]}, "execution_count": 23, "metadata": {}, "output_type": "execute_result"}], "source": ["dfi = df.set_index(cols, verify_integrity=True)\n", "dfi.head(n=2)"]}, {"cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [{"data": {"text/plain": ["pandas.core.indexes.multi.MultiIndex"]}, "execution_count": 24, "metadata": {}, "output_type": "execute_result"}], "source": ["type(dfi.index)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On v\u00e9rifie que [xarray](http://xarray.pydata.org/en/stable/) est install\u00e9."]}, {"cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": ["import xarray"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Et on convertit en cube."]}, {"cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [], "source": ["cube = xarray.Dataset.from_dataframe(dfi) # ou dfi.to_xarray()"]}, {"cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [{"data": {"text/html": ["<div><svg style=\"position: absolute; width: 0; height: 0; overflow: hidden\">\n", "<defs>\n", "<symbol id=\"icon-database\" viewBox=\"0 0 32 32\">\n", "<path d=\"M16 0c-8.837 0-16 2.239-16 5v4c0 2.761 7.163 5 16 5s16-2.239 16-5v-4c0-2.761-7.163-5-16-5z\"></path>\n", "<path d=\"M16 17c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "<path d=\"M16 26c-8.837 0-16-2.239-16-5v6c0 2.761 7.163 5 16 5s16-2.239 16-5v-6c0 2.761-7.163 5-16 5z\"></path>\n", "</symbol>\n", "<symbol id=\"icon-file-text2\" viewBox=\"0 0 32 32\">\n", "<path d=\"M28.681 7.159c-0.694-0.947-1.662-2.053-2.724-3.116s-2.169-2.030-3.116-2.724c-1.612-1.182-2.393-1.319-2.841-1.319h-15.5c-1.378 0-2.5 1.121-2.5 2.5v27c0 1.378 1.122 2.5 2.5 2.5h23c1.378 0 2.5-1.122 2.5-2.5v-19.5c0-0.448-0.137-1.23-1.319-2.841zM24.543 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"\n", ".xr-var-data > table {\n", "  float: right;\n", "}\n", "\n", ".xr-var-name span,\n", ".xr-var-data,\n", ".xr-attrs {\n", "  padding-left: 25px !important;\n", "}\n", "\n", ".xr-attrs,\n", ".xr-var-attrs,\n", ".xr-var-data {\n", "  grid-column: 1 / -1;\n", "}\n", "\n", "dl.xr-attrs {\n", "  padding: 0;\n", "  margin: 0;\n", "  display: grid;\n", "  grid-template-columns: 125px auto;\n", "}\n", "\n", ".xr-attrs dt,\n", ".xr-attrs dd {\n", "  padding: 0;\n", "  margin: 0;\n", "  float: left;\n", "  padding-right: 10px;\n", "  width: auto;\n", "}\n", "\n", ".xr-attrs dt {\n", "  font-weight: normal;\n", "  grid-column: 1;\n", "}\n", "\n", ".xr-attrs dt:hover span {\n", "  display: inline-block;\n", "  background: var(--xr-background-color);\n", "  padding-right: 10px;\n", "}\n", "\n", ".xr-attrs dd {\n", "  grid-column: 2;\n", "  white-space: pre-wrap;\n", "  word-break: break-all;\n", "}\n", "\n", ".xr-icon-database,\n", ".xr-icon-file-text2 {\n", "  display: inline-block;\n", "  vertical-align: middle;\n", "  width: 1em;\n", "  height: 1.5em !important;\n", "  stroke-width: 0;\n", "  stroke: currentColor;\n", "  fill: currentColor;\n", "}\n", "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n", "Dimensions:     (annee: 60, age: 84, indicateur: 7, genre: 3, pays: 56)\n", "Coordinates:\n", "  * annee       (annee) int64 1960 1961 1962 1963 1964 ... 2016 2017 2018 2019\n", "  * age         (age) object &#x27;Y01&#x27; &#x27;Y02&#x27; &#x27;Y03&#x27; &#x27;Y04&#x27; ... &#x27;Y81&#x27; &#x27;Y82&#x27; &#x27;Y83&#x27; &#x27;Y84&#x27;\n", "  * indicateur  (indicateur) object &#x27;DEATHRATE&#x27; &#x27;LIFEXP&#x27; ... &#x27;TOTPYLIVED&#x27;\n", "  * genre       (genre) object &#x27;F&#x27; &#x27;M&#x27; &#x27;T&#x27;\n", "  * pays        (pays) object &#x27;AL&#x27; &#x27;AM&#x27; &#x27;AT&#x27; &#x27;AZ&#x27; &#x27;BE&#x27; ... &#x27;TR&#x27; &#x27;UA&#x27; &#x27;UK&#x27; &#x27;XK&#x27;\n", "Data variables:\n", "    valeur      (annee, age, indicateur, genre, pays) float64 nan nan ... nan</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-bba0c942-358a-4a6d-9f77-d16664b09cb5' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-bba0c942-358a-4a6d-9f77-d16664b09cb5' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>annee</span>: 60</li><li><span class='xr-has-index'>age</span>: 84</li><li><span class='xr-has-index'>indicateur</span>: 7</li><li><span class='xr-has-index'>genre</span>: 3</li><li><span class='xr-has-index'>pays</span>: 56</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-ae410174-937a-4f7c-8331-a1638db4882f' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ae410174-937a-4f7c-8331-a1638db4882f' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>annee</span></div><div class='xr-var-dims'>(annee)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>1960 1961 1962 ... 2017 2018 2019</div><input id='attrs-f313cc91-a39c-4561-ab00-e86d05ef1303' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-f313cc91-a39c-4561-ab00-e86d05ef1303' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-bd4b363e-8c38-403d-9374-28dce7e6e1dd' class='xr-var-data-in' type='checkbox'><label for='data-bd4b363e-8c38-403d-9374-28dce7e6e1dd' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971,\n", "       1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983,\n", "       1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995,\n", "       1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007,\n", "       2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019],\n", "      dtype=int64)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>age</span></div><div class='xr-var-dims'>(age)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;Y01&#x27; &#x27;Y02&#x27; &#x27;Y03&#x27; ... &#x27;Y83&#x27; &#x27;Y84&#x27;</div><input id='attrs-6db8a4e5-d01d-4ee0-9c45-b8c0fd734473' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-6db8a4e5-d01d-4ee0-9c45-b8c0fd734473' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-fbb564b9-eb10-4475-8f42-cba7046b8a1c' class='xr-var-data-in' type='checkbox'><label for='data-fbb564b9-eb10-4475-8f42-cba7046b8a1c' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;Y01&#x27;, &#x27;Y02&#x27;, &#x27;Y03&#x27;, &#x27;Y04&#x27;, &#x27;Y05&#x27;, &#x27;Y06&#x27;, &#x27;Y07&#x27;, &#x27;Y08&#x27;, &#x27;Y09&#x27;, &#x27;Y10&#x27;,\n", "       &#x27;Y11&#x27;, &#x27;Y12&#x27;, &#x27;Y13&#x27;, &#x27;Y14&#x27;, &#x27;Y15&#x27;, &#x27;Y16&#x27;, &#x27;Y17&#x27;, &#x27;Y18&#x27;, &#x27;Y19&#x27;, &#x27;Y20&#x27;,\n", "       &#x27;Y21&#x27;, &#x27;Y22&#x27;, &#x27;Y23&#x27;, &#x27;Y24&#x27;, &#x27;Y25&#x27;, &#x27;Y26&#x27;, &#x27;Y27&#x27;, &#x27;Y28&#x27;, &#x27;Y29&#x27;, &#x27;Y30&#x27;,\n", "       &#x27;Y31&#x27;, &#x27;Y32&#x27;, &#x27;Y33&#x27;, &#x27;Y34&#x27;, &#x27;Y35&#x27;, &#x27;Y36&#x27;, &#x27;Y37&#x27;, &#x27;Y38&#x27;, &#x27;Y39&#x27;, &#x27;Y40&#x27;,\n", "       &#x27;Y41&#x27;, &#x27;Y42&#x27;, &#x27;Y43&#x27;, &#x27;Y44&#x27;, &#x27;Y45&#x27;, &#x27;Y46&#x27;, &#x27;Y47&#x27;, &#x27;Y48&#x27;, &#x27;Y49&#x27;, &#x27;Y50&#x27;,\n", "       &#x27;Y51&#x27;, &#x27;Y52&#x27;, &#x27;Y53&#x27;, &#x27;Y54&#x27;, &#x27;Y55&#x27;, &#x27;Y56&#x27;, &#x27;Y57&#x27;, &#x27;Y58&#x27;, &#x27;Y59&#x27;, &#x27;Y60&#x27;,\n", "       &#x27;Y61&#x27;, &#x27;Y62&#x27;, &#x27;Y63&#x27;, &#x27;Y64&#x27;, &#x27;Y65&#x27;, &#x27;Y66&#x27;, &#x27;Y67&#x27;, &#x27;Y68&#x27;, &#x27;Y69&#x27;, &#x27;Y70&#x27;,\n", "       &#x27;Y71&#x27;, &#x27;Y72&#x27;, &#x27;Y73&#x27;, &#x27;Y74&#x27;, &#x27;Y75&#x27;, &#x27;Y76&#x27;, &#x27;Y77&#x27;, &#x27;Y78&#x27;, &#x27;Y79&#x27;, &#x27;Y80&#x27;,\n", "       &#x27;Y81&#x27;, &#x27;Y82&#x27;, &#x27;Y83&#x27;, &#x27;Y84&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>indicateur</span></div><div class='xr-var-dims'>(indicateur)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;DEATHRATE&#x27; ... &#x27;TOTPYLIVED&#x27;</div><input id='attrs-4a37840c-d77d-4a0d-a8a4-d2350bcc28c6' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4a37840c-d77d-4a0d-a8a4-d2350bcc28c6' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-d9141f10-0f1c-47da-9d87-50d8b7d0a774' class='xr-var-data-in' type='checkbox'><label for='data-d9141f10-0f1c-47da-9d87-50d8b7d0a774' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;DEATHRATE&#x27;, &#x27;LIFEXP&#x27;, &#x27;PROBDEATH&#x27;, &#x27;PROBSURV&#x27;, &#x27;PYLIVED&#x27;, &#x27;SURVIVORS&#x27;,\n", "       &#x27;TOTPYLIVED&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>genre</span></div><div class='xr-var-dims'>(genre)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;F&#x27; &#x27;M&#x27; &#x27;T&#x27;</div><input id='attrs-a64e098c-d170-4644-bfd6-994a06e4807f' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a64e098c-d170-4644-bfd6-994a06e4807f' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-76256a03-02cc-44de-95e5-503d430a9049' class='xr-var-data-in' type='checkbox'><label for='data-76256a03-02cc-44de-95e5-503d430a9049' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;F&#x27;, &#x27;M&#x27;, &#x27;T&#x27;], dtype=object)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>pays</span></div><div class='xr-var-dims'>(pays)</div><div class='xr-var-dtype'>object</div><div class='xr-var-preview xr-preview'>&#x27;AL&#x27; &#x27;AM&#x27; &#x27;AT&#x27; ... &#x27;UA&#x27; &#x27;UK&#x27; &#x27;XK&#x27;</div><input id='attrs-78607221-3feb-46af-abda-3c9fde423bf1' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-78607221-3feb-46af-abda-3c9fde423bf1' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-4bd83b33-54e4-4b64-9079-c5d05ef7aa06' class='xr-var-data-in' type='checkbox'><label for='data-4bd83b33-54e4-4b64-9079-c5d05ef7aa06' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;AL&#x27;, &#x27;AM&#x27;, &#x27;AT&#x27;, &#x27;AZ&#x27;, &#x27;BE&#x27;, &#x27;BG&#x27;, &#x27;BY&#x27;, &#x27;CH&#x27;, &#x27;CY&#x27;, &#x27;CZ&#x27;, &#x27;DE&#x27;,\n", "       &#x27;DE_TOT&#x27;, &#x27;DK&#x27;, &#x27;EA18&#x27;, &#x27;EA19&#x27;, &#x27;EE&#x27;, &#x27;EEA30_2007&#x27;, &#x27;EEA31&#x27;, &#x27;EFTA&#x27;,\n", "       &#x27;EL&#x27;, &#x27;ES&#x27;, &#x27;EU27_2007&#x27;, &#x27;EU27_2020&#x27;, &#x27;EU28&#x27;, &#x27;FI&#x27;, &#x27;FR&#x27;, &#x27;FX&#x27;, &#x27;GE&#x27;,\n", "       &#x27;HR&#x27;, &#x27;HU&#x27;, &#x27;IE&#x27;, &#x27;IS&#x27;, &#x27;IT&#x27;, &#x27;LI&#x27;, &#x27;LT&#x27;, &#x27;LU&#x27;, &#x27;LV&#x27;, &#x27;MD&#x27;, &#x27;ME&#x27;, &#x27;MK&#x27;,\n", "       &#x27;MT&#x27;, &#x27;NL&#x27;, &#x27;NO&#x27;, &#x27;PL&#x27;, &#x27;PT&#x27;, &#x27;RO&#x27;, &#x27;RS&#x27;, &#x27;RU&#x27;, &#x27;SE&#x27;, &#x27;SI&#x27;, &#x27;SK&#x27;, &#x27;SM&#x27;,\n", "       &#x27;TR&#x27;, &#x27;UA&#x27;, &#x27;UK&#x27;, &#x27;XK&#x27;], dtype=object)</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3acb2123-df0a-4fe5-8e7d-dbfa5c8df865' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3acb2123-df0a-4fe5-8e7d-dbfa5c8df865' class='xr-section-summary' >Data variables: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>valeur</span></div><div class='xr-var-dims'>(annee, age, indicateur, genre, pays)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>nan nan nan ... 1.746e+05 nan nan</div><input id='attrs-776090ab-3176-4ba7-aadf-7e2164c8d30c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-776090ab-3176-4ba7-aadf-7e2164c8d30c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-b73c1e19-8c50-417c-8166-c5e07222f918' class='xr-var-data-in' type='checkbox'><label for='data-b73c1e19-8c50-417c-8166-c5e07222f918' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([[[[[         nan,          nan,          nan, ...,\n", "                    nan,          nan,          nan],\n", "          [         nan,          nan,          nan, ...,\n", "                    nan,          nan,          nan],\n", "          [         nan,          nan,          nan, ...,\n", "                    nan,          nan,          nan]],\n", "\n", "         [[         nan,          nan,          nan, ...,\n", "                    nan,          nan,          nan],\n", "          [         nan,          nan,          nan, ...,\n", "                    nan,          nan,          nan],\n", "          [         nan,          nan,          nan, ...,\n", "                    nan,          nan,          nan]],\n", "\n", "         [[         nan,          nan,          nan, ...,\n", "                    nan,          nan,          nan],\n", "          [         nan,          nan,          nan, ...,\n", "                    nan,          nan,          nan],\n", "          [         nan,          nan,          nan, ...,\n", "                    nan,          nan,          nan]],\n", "...\n", "         [[4.856300e+04, 4.157700e+04, 6.111400e+04, ...,\n", "           3.774800e+04,          nan,          nan],\n", "          [3.602800e+04, 2.388900e+04, 4.374900e+04, ...,\n", "           1.503700e+04,          nan,          nan],\n", "          [4.202600e+04, 3.319400e+04, 5.262000e+04, ...,\n", "           2.689100e+04,          nan,          nan]],\n", "\n", "         [[5.109200e+04, 4.339000e+04, 6.300800e+04, ...,\n", "           4.003000e+04,          nan,          nan],\n", "          [3.838600e+04, 2.496600e+04, 4.574400e+04, ...,\n", "           1.621600e+04,          nan,          nan],\n", "          [4.447700e+04, 3.466000e+04, 5.454800e+04, ...,\n", "           2.865400e+04,          nan,          nan]],\n", "\n", "         [[2.284910e+05, 2.466920e+05, 4.630230e+05, ...,\n", "           2.435110e+05,          nan,          nan],\n", "          [2.185270e+05, 1.630810e+05, 2.968560e+05, ...,\n", "           9.955300e+04,          nan,          nan],\n", "          [2.198340e+05, 2.070100e+05, 3.837450e+05, ...,\n", "           1.745640e+05,          nan,          nan]]]]])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-5d5c21ef-ff19-4ba6-9a0a-1006d9026361' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-5d5c21ef-ff19-4ba6-9a0a-1006d9026361' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"], "text/plain": ["<xarray.Dataset>\n", "Dimensions:     (annee: 60, age: 84, indicateur: 7, genre: 3, pays: 56)\n", "Coordinates:\n", "  * annee       (annee) int64 1960 1961 1962 1963 1964 ... 2016 2017 2018 2019\n", "  * age         (age) object 'Y01' 'Y02' 'Y03' 'Y04' ... 'Y81' 'Y82' 'Y83' 'Y84'\n", "  * indicateur  (indicateur) object 'DEATHRATE' 'LIFEXP' ... 'TOTPYLIVED'\n", "  * genre       (genre) object 'F' 'M' 'T'\n", "  * pays        (pays) object 'AL' 'AM' 'AT' 'AZ' 'BE' ... 'TR' 'UA' 'UK' 'XK'\n", "Data variables:\n", "    valeur      (annee, age, indicateur, genre, pays) float64 nan nan ... nan"]}, "execution_count": 27, "metadata": {}, "output_type": "execute_result"}], "source": ["cube"]}, {"cell_type": "code", "execution_count": 27, "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>valeur</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>0</th>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>1</th>\n", "      <td>NaN</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["   valeur\n", "0     NaN\n", "1     NaN"]}, "execution_count": 28, "metadata": {}, "output_type": "execute_result"}], "source": ["back_to_pandas = cube.to_dataframe().reset_index(drop=True)\n", "back_to_pandas.head(n=2)"]}, {"cell_type": "markdown", "metadata": {}, "source": ["Et on prend le maximum par *indicateur* et *genre*."]}, {"cell_type": "code", "execution_count": 28, "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>genre</th>\n", "      <th>F</th>\n", "      <th>M</th>\n", "      <th>T</th>\n", "    </tr>\n", "    <tr>\n", "      <th>indicateur</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>DEATHRATE</th>\n", "      <td>9.978600e-01</td>\n", "      <td>1.130040e+00</td>\n", "      <td>1.040580e+00</td>\n", "    </tr>\n", "    <tr>\n", "      <th>LIFEXP</th>\n", "      <td>8.590000e+01</td>\n", "      <td>8.310000e+01</td>\n", "      <td>8.460000e+01</td>\n", "    </tr>\n", "    <tr>\n", "      <th>PROBDEATH</th>\n", "      <td>6.657100e-01</td>\n", "      <td>7.220600e-01</td>\n", "      <td>6.844600e-01</td>\n", "    </tr>\n", "    <tr>\n", "      <th>PROBSURV</th>\n", "      <td>1.000000e+00</td>\n", "      <td>1.000000e+00</td>\n", "      <td>1.000000e+00</td>\n", "    </tr>\n", "    <tr>\n", "      <th>PYLIVED</th>\n", "      <td>1.000000e+05</td>\n", "      <td>1.000000e+05</td>\n", "      <td>1.000000e+05</td>\n", "    </tr>\n", "    <tr>\n", "      <th>SURVIVORS</th>\n", "      <td>1.000000e+05</td>\n", "      <td>1.000000e+05</td>\n", "      <td>1.000000e+05</td>\n", "    </tr>\n", "    <tr>\n", "      <th>TOTPYLIVED</th>\n", "      <td>8.583361e+06</td>\n", "      <td>8.307177e+06</td>\n", "      <td>8.456903e+06</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["genre                  F             M             T\n", "indicateur                                          \n", "DEATHRATE   9.978600e-01  1.130040e+00  1.040580e+00\n", "LIFEXP      8.590000e+01  8.310000e+01  8.460000e+01\n", "PROBDEATH   6.657100e-01  7.220600e-01  6.844600e-01\n", "PROBSURV    1.000000e+00  1.000000e+00  1.000000e+00\n", "PYLIVED     1.000000e+05  1.000000e+05  1.000000e+05\n", "SURVIVORS   1.000000e+05  1.000000e+05  1.000000e+05\n", "TOTPYLIVED  8.583361e+06  8.307177e+06  8.456903e+06"]}, "execution_count": 29, "metadata": {}, "output_type": "execute_result"}], "source": ["cube.max(dim=[\"age\", \"annee\", \"pays\"]).to_dataframe().reset_index().pivot(\"indicateur\", \"genre\", \"valeur\")"]}, {"cell_type": "code", "execution_count": 29, "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>valeur</th>\n", "    </tr>\n", "    <tr>\n", "      <th>indicateur</th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>DEATHRATE</th>\n", "      <td>1.130040e+00</td>\n", "    </tr>\n", "    <tr>\n", "      <th>LIFEXP</th>\n", "      <td>8.590000e+01</td>\n", "    </tr>\n", "    <tr>\n", "      <th>PROBDEATH</th>\n", "      <td>7.220600e-01</td>\n", "    </tr>\n", "    <tr>\n", "      <th>PROBSURV</th>\n", "      <td>1.000000e+00</td>\n", "    </tr>\n", "    <tr>\n", "      <th>PYLIVED</th>\n", "      <td>1.000000e+05</td>\n", "    </tr>\n", "    <tr>\n", "      <th>SURVIVORS</th>\n", "      <td>1.000000e+05</td>\n", "    </tr>\n", "    <tr>\n", "      <th>TOTPYLIVED</th>\n", "      <td>8.583361e+06</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                  valeur\n", "indicateur              \n", "DEATHRATE   1.130040e+00\n", "LIFEXP      8.590000e+01\n", "PROBDEATH   7.220600e-01\n", "PROBSURV    1.000000e+00\n", "PYLIVED     1.000000e+05\n", "SURVIVORS   1.000000e+05\n", "TOTPYLIVED  8.583361e+06"]}, "execution_count": 30, "metadata": {}, "output_type": "execute_result"}], "source": ["cube.to_dataframe().groupby('indicateur').max()"]}, {"cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["cannot reduce over dimensions ['indicateur']. expected either '...' to reduce over all dimensions or one or more of Frozen({'annee': 60, 'age': 84, 'genre': 3, 'pays': 56}).\n"]}], "source": ["try:\n", "    cube.groupby(\"indicateur\").max().to_dataframe().head()\n", "except ValueError as e:\n", "    # It used to be working in 0.12 but not in 0.13...\n", "    print(e)"]}, {"cell_type": "code", "execution_count": 31, "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></th>\n", "      <th></th>\n", "      <th>indicateur</th>\n", "      <th>genre</th>\n", "      <th>valeur</th>\n", "    </tr>\n", "    <tr>\n", "      <th>annee</th>\n", "      <th>age</th>\n", "      <th>pays</th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th rowspan=\"5\" valign=\"top\">1980</th>\n", "      <th rowspan=\"5\" valign=\"top\">Y01</th>\n", "      <th>AL</th>\n", "      <td>LIFEXP</td>\n", "      <td>F</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>AM</th>\n", "      <td>LIFEXP</td>\n", "      <td>F</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>AT</th>\n", "      <td>LIFEXP</td>\n", "      <td>F</td>\n", "      <td>76.1</td>\n", "    </tr>\n", "    <tr>\n", "      <th>AZ</th>\n", "      <td>LIFEXP</td>\n", "      <td>F</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>BE</th>\n", "      <td>LIFEXP</td>\n", "      <td>F</td>\n", "      <td>76.5</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["               indicateur genre  valeur\n", "annee age pays                         \n", "1980  Y01 AL       LIFEXP     F     NaN\n", "          AM       LIFEXP     F     NaN\n", "          AT       LIFEXP     F    76.1\n", "          AZ       LIFEXP     F     NaN\n", "          BE       LIFEXP     F    76.5"]}, "execution_count": 32, "metadata": {}, "output_type": "execute_result"}], "source": ["cube[\"valeur\"].sel(indicateur=\"LIFEXP\", genre=\"F\", annee=slice(1980, 1985)).to_dataframe().head()"]}, {"cell_type": "code", "execution_count": 32, "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></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th>valeur</th>\n", "    </tr>\n", "    <tr>\n", "      <th>annee</th>\n", "      <th>indicateur</th>\n", "      <th>genre</th>\n", "      <th>pays</th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th rowspan=\"5\" valign=\"top\">1960</th>\n", "      <th rowspan=\"5\" valign=\"top\">DEATHRATE</th>\n", "      <th rowspan=\"5\" valign=\"top\">F</th>\n", "      <th>AL</th>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>AM</th>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>AT</th>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>AZ</th>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>BE</th>\n", "      <td>0.15967</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                              valeur\n", "annee indicateur genre pays         \n", "1960  DEATHRATE  F     AL        NaN\n", "                       AM        NaN\n", "                       AT        NaN\n", "                       AZ        NaN\n", "                       BE    0.15967"]}, "execution_count": 33, "metadata": {}, "output_type": "execute_result"}], "source": ["cube[\"valeur\"].max(dim=[\"age\"]).to_dataframe().head()"]}, {"cell_type": "markdown", "metadata": {}, "source": ["On ajoute une colonne avec un ratio o\u00f9 on divise par le maximum sur une classe d'\u00e2ge."]}, {"cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": ["cube[\"max_valeur\"] = cube[\"valeur\"] / cube[\"valeur\"].max(dim=[\"age\"])"]}, {"cell_type": "code", "execution_count": 34, "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></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th></th>\n", "      <th>valeur</th>\n", "      <th>max_valeur</th>\n", "    </tr>\n", "    <tr>\n", "      <th>annee</th>\n", "      <th>age</th>\n", "      <th>indicateur</th>\n", "      <th>genre</th>\n", "      <th>pays</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th rowspan=\"5\" valign=\"top\">1960</th>\n", "      <th rowspan=\"5\" valign=\"top\">Y01</th>\n", "      <th rowspan=\"5\" valign=\"top\">DEATHRATE</th>\n", "      <th rowspan=\"5\" valign=\"top\">F</th>\n", "      <th>AL</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>AM</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>AT</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>AZ</th>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "    </tr>\n", "    <tr>\n", "      <th>BE</th>\n", "      <td>0.00159</td>\n", "      <td>0.009958</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["                                  valeur  max_valeur\n", "annee age indicateur genre pays                     \n", "1960  Y01 DEATHRATE  F     AL        NaN         NaN\n", "                           AM        NaN         NaN\n", "                           AT        NaN         NaN\n", "                           AZ        NaN         NaN\n", "                           BE    0.00159    0.009958"]}, "execution_count": 35, "metadata": {}, "output_type": "execute_result"}], "source": ["cube.to_dataframe().head()"]}, {"cell_type": "code", "execution_count": 35, "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": 2}