V1

Nouveau projet maman/Untitled.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 149,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "## Projet Maman\n",
+    "\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "donnees_init = pd.read_csv('Initial.csv').set_index(\"Question cachée pour enregistrer l'ID anonymisé des apprenants.\\xa0\")\n",
+    "donnees_fin = pd.read_csv('Final.csv').set_index(\"Question cachée pour enregistrer l'ID anonymisé des apprenants.\\xa0\")\n",
+    "donnees_init = donnees_init.dropna(subset=[\"Date de soumission\"])\n",
+    "donnees_fin = donnees_fin.dropna(subset=[\"Date de soumission\"])\n",
+    "donnees_init = donnees_init[~donnees_init.index.duplicated()]\n",
+    "donnees_index= donnees_fin.index.intersection(donnees_init.index)\n",
+    "donnees_columns=[]\n",
+    "for i in list(donnees_init.columns):\n",
+    "    if i not in list(donnees_fin.columns):\n",
+    "        donnees_columns+=[i]\n",
+    "donnees=pd.concat([donnees_init.loc[list(donnees_index),donnees_columns],donnees_fin.loc[list(donnees_index),:]],axis=1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 155,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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yDQAAAAMp0wAAADCQMg0AAAADKdMAAAAwkDINAAAAAynTAAAAMJAyDQAAAAMp0wAAADCQMg0AAAADKdMAAAAwkDINAAAAAynTAAAAMJAyDQAAAAMp0wAAADCQMg0AAAADKdMAAAAwkDINAAAAAynTAAAAMJAyDQAAAAMp0wAAADCQMg0AAAADKdMAAAAwkDINAAAAAynTAAAAMJAyDQAAAAMp0wAAADCQMg0AAAADKdMAAAAw0ILLdFWtqqrPVNUHphkIAAAAZt2QkekTk1w0rSAAAACwXCyoTFfVuiTHJHn9dOMAAADA7Nt1gde9Isnzk+y1rQuq6oQkJyTJ+vXrb3kygIU4ae+xE9wyJ10xdgIAAHbAvCPTVfWoJN/p7nO3d113n9LdG7t749q1axctIAAAAMyahUzzflCSY6vqkiRvS3JkVb1lqqkAAABghs1bprv7D7t7XXdvSPKEJGd295OnngwAAABmlOdMAwAAwEALXYAsSdLdZyc5eypJAAAAYJkwMg0AAAADKdMAAAAwkDINAAAAAynTAAAAMJAyDQAAAAMp0wAAADCQMg0AAAADKdMAAAAwkDINAAAAAynTAAAAMJAyDQAAAAMp0wAAADCQMg0AAAADKdMAAAAwkDINAAAAAynTAAAAMJAyDQAAAAMp0wAAADCQMg0AAAADKdMAAAAwkDINAAAAAynTAAAAMJAyDQAAAAMp0wAAADCQMg0AAAADKdMAAAAwkDINAAAAAynTAAAAMJAyDQAAAAMp0wAAADCQMg0AAAADKdMAAAAwkDINAAAAAynTAAAAMJAyDQAAAAMp0wAAADDQvGW6qtZU1TlVdX5Vfa6qTl6KYAAAADCrdl3ANT9OcmR3/7CqdkvyD1X14e7+lylnAwAAgJk0b5nu7k7yw8nubpNXTzMUAAAAzLIF3TNdVauq6rwk30lyRnf/63RjAQAAwOxayDTvdPcNSQ6uqtsmOb2qfrG7L9zymqo6IckJSbJ+/fpFDwoAwI7b8MIPjh3hFrlkzdgJAG5q0Gre3f0fSc5O8oitnDuluzd298a1a9cuUjwAAACYPQtZzXvtZEQ6VbV7kl9OcvG0gwEAAMCsWsg07zsn+ZuqWpW58v2O7v7AdGMBAADA7FrIat4XJDlkCbIAAADAsjDonmkAAABAmQYAAIDBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIGUaQAAABhImQYAAICBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIGUaQAAABhImQYAAICBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIGUaQAAABhImQYAAICBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIGUaQAAABhImQYAAICBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIGUaQAAABhImQYAAICBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIGUaQAAABho3jJdVT9fVWdV1UVV9bmqOnEpggEAAMCs2nUB11yf5L9296eraq8k51bVGd39+SlnAwAAgJk078h0d3+ruz892b4yyUVJ7jrtYAAAADCrFjIyfaOq2pDkkCT/upVzJyQ5IUnWr1+/CNGApbLhhR8cO8IOu2TN2AkAgFm1vL/HeeLYEW6Zk64YO8HULXgBsqq6dZK/TfLc7v7Bzc939yndvbG7N65du3YxMwIAAMBMWVCZrqrdMlek39rd755uJAAAAJhtC1nNu5K8IclF3f3y6UcCAACA2baQkekHJfnNJEdW1XmT169MORcAAADMrHkXIOvuf0hSS5AFAAAAloUFL0AGAAAAzFGmAQAAYCBlGgAAAAZSpgEAAGAgZRoAAAAGUqYBAABgIGUaAAAABlKmAQAAYCBlGgAAAAZSpgEAAGAgZRoAAAAGUqYBAABgIGUaAAAABlKmAQAAYCBlGgAAAAZSpgEAAGAgZRoAAAAGUqYBAABgIGUaAAAABlKmAQAAYCBlGgAAAAZSpgEAAGAgZRoAAAAGUqYBAABgIGUaAAAABlKmAQAAYCBlGgAAAAZSpgEAAGAgZRoAAAAGUqYBAABgIGUaAAAABlKmAQAAYCBlGgAAAAZSpgEAAGAgZRoAAAAGUqYBAABgoHnLdFX9dVV9p6ouXIpAAAAAMOsWMjL9piSPmHIOAAAAWDbmLdPd/ckk31uCLAAAALAs7LpYb1RVJyQ5IUnWr1+/WG/LECftPXaCW+akK8ZOAOwkNrzwg2NHuEUu+bNjxo4AADu9RVuArLtP6e6N3b1x7dq1i/W2AAAAMHOs5g0AAAADKdMAAAAw0EIejXVakn9OckBVXVpVT59+LAAAAJhd8y5A1t3HLUUQAAAAWC5M8wYAAICBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIGUaQAAABhImQYAAICBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIGUaQAAABhImQYAAICBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIGUaQAAABhImQYAAICBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIGUaQAAABhImQYAAICBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIGUaQAAABhImQYAAICBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIGUaQAAABhoQWW6qh5RVV+oqi9V1QunHQoAAABm2bxluqpWJXlNkkcmuXeS46rq3tMOBgAAALNqISPThyX5Und/pbuvTfK2JI+ebiwAAACYXQsp03dN8o0t9i+dHAMAAICdUnX39i+oelySo7v7GZP930xyWHc/+2bXnZDkhMnuAUm+sPhxmccdknx37BCwxHzu2Rn53LMz8rlnZ+MzP567dffa+S7adQFvdGmSn99if12Sb978ou4+JckpC47HoquqTd29cewcsJR87tkZ+dyzM/K5Z2fjMz/7FjLN+1NJ7lFV+1bVrZI8Icn7phsLAAAAZte8I9PdfX1V/ZckH0myKslfd/fnpp4MAAAAZtRCpnmnuz+U5ENTzsItZ5o9OyOfe3ZGPvfsjHzu2dn4zM+4eRcgAwAAAG5qIfdMAwAAAFtQpgEAAGCgBd0zDQAsvao6srvPrKrHbO18d797qTMBAHOMTK8AVfVLVfVbk+21VbXv2JkAWBQPmXz91a28HjVWKFgqVfXxhRyDlaCqfq6q3lBVH57s37uqnj52LrbNAmTLUFX9wubHk1XVi5NsTHJAd+9fVXdJ8s7uftCoIWFKqurnkvxpkrt09yOr6t5JHtjdbxg5GgCLpKrWJNkjyVlJjkhSk1O3SfLh7r7XSNFgUVXVk5N8rLv/fVKi35jkj7v7PlW1a5LPdPeB46ZkW4xML093q6o/m2z/WpJjk1yVJN39zSR7jRUMlsCbMvfc+7tM9v8tyXNHSwNLoKr2rqqXV9WmyesvqmrvsXPBFP12knOT3HPydfPrvUleM2IuWGxnJvkfk+07dPc7kvwkSbr7+iQ3jBWM+SnTy9Dkud9nTXav7bnpBZ0kVbXnaMFgafiHhp3RXye5MsnjJ68fZG70Alak7n5ld++b5A+6e7/u3nfyuk93/8+x88FimQyEPWuye1VV3T4//b7+AUmuGCsb87MA2TLV3R+ZbL6jql6X5LZV9cwkxyd5/XjJYOr8Q8PO6O7d/etb7J9cVeeNlgaWSHe/uqoOT7IhW3zf2t2njhYKFll3b/4+5veTvC/J3avqH5OsTfK40YIxL2V6mevul1XVwzI3SnFAkhd19xkjx4Jp2to/NI8dNxJM3dVV9Uvd/Q9JUlUPSnL1yJlg6qrqzUnunuS8/HQWUidRplmJPpe5hScPyNw6AV+ImcQzzQJky1xV/Xl3v2C+Y7ASVNUuSR6Q5Jxs8Q9Nd183ajCYsqo6OMnfJNk7c5/77yV5WnefP2owmLKquijJvds3rOwEqurT3X3f+Y4xO4xML38PS3Lz4vzIrRyDZa+7f1JVf9HdD8zcT29hp9Dd5yW5T1XdZrL/g5EjwVK5MMmdknxr7CAwLVV1pyR3TbJ7VR2Sm65ev8dowZiXMr1MVdXvJPnPSfarqgu2OLVXkn8cJxUsiY9W1a8nebeRCla6qvr9bRxPknT3y5c0ECy9OyT5fFWdk+THmw9297HjRYJFd3SSpyVZl2TLv9evTPJHYwRiYUzzXqYmj0S5XZL/luSFW5y6sru/N04qmL6qujLJnkmuT3JN5n562919m1GDwRRU1Yu3d767T16qLDCGqnrI1o539yeWOgtMW1X9enf/7dg5WDhlepmqqtt09w+qap+tnVeoAQBg9lXVk7v7LVX1XzN5WsmWzEKaXVaHW77+9+TruUk2Tb6eu8U+rEhV9fGFHIOVpKr2q6r3V9VlVfWdqnpvVe03di6Ytqp6QFV9qqp+WFXXVtUNVWXNAFaaPSdfb525WzZv/mJGGZkGloWqWpO5RTjOSnJEbro4x4e7+14jRYOpq6p/SfKaJKdNDj0hybO7+/7jpYLpq6pNmfu8vzPJxiRPSXKP7nYfKStOVa3t7svGzsHCWYBsBaiq2yW5R5I1m4919yfHSwRT8dtJnpvkLkk+vcXxH2SuZMBKVt395i3231JV/2W0NLCEuvtLVbWqu29I8saq+qexM8GU/FNVfTXJ2zO30Or3xw7E9hmZXuaq6hlJTszc6n/nZe4ZvP/c3UeOGgympKqe3d2vHjsHLKWq+rMk/5HkbZm7n+43kqzO5AdJ1slgpaqqTyb55SSvT/LvmXtE1tO6+z6jBoMpqarDMjcb4z8l+XySt3X3W8ZNxbYo08tcVX02yaFJ/qW7D66qeyY5ubt/Y+RoMBVV9ZStHe/uU5c6CyyVyUjFtnR3u3+aFamq7pbkO0l2S/J7SfZO8r+6+0ujBoMpq6o7ZO4xWU/q7lVj52HrTPNe/q7p7muqKlW1ursvrqoDxg4FU3ToFttrkhyVuWnfyjQrVnfvO3YGGEN3f22yeXUSj4JjRauq2yT5tcyNTN89yelJDhs1FNulTC9/l1bVbZO8J8kZVfX9JN8cORNMTXc/e8v9yTPX37yNy2FFqKpVSY5JsiFb/NvtcSmsVFX1ju5+/GQG3tYeFXTQCLFg2s7P3Pf0L+nufx47DPMzzXsFqaqHZG76099197Vj54GlUFW7Jflsd99z7CwwLVX1oSTXJPlskp9sPt7dRupYkarqzt39rck075+xxYg1rBhVVa2cLStGppexqtolyQXd/YtJ0t2fGDkSTF1VvT8/HaVYleReSd4xXiJYEuuMxLEz6e5vTb4qzax4VfWK7n5ukvdV1dZmYhw7QiwWQJlexrr7J1V1flWt7+6vj50HlsjLtti+PnPPmz5upCywVD5cVQ/v7o+OHQSWQlVdma1M796su2+zhHFg2jbfrvay7V7FzFGml787J/lcVZ2T5KrNB/0Ei5Wquz9RVQcneWKSxyf5apK/HTcVTN2/JDl9MiPpusz9EKkVClaq7t4rSarqJZl7JNabM/e5f1KSvUaMBouuu8+dbB7c3a/c8lxVnZjE7NMZ5Z7pZW5yn/TPMOWblaaq9s/c6pbHJbk8yduT/EF3b/V+OlhJquormXvm6GfdT8fOpKr+tbvvP98xWAmq6tPdfd+bHftMdx8yVia2z8j08vcr3f2CLQ9U1Z/HT7BYeS5O8vdJfnXz80Wr6vfGjQRL5otJLlSk2QndUFVPSvK2zE37Pi7JDeNGgsVVVcdlbsbdvlX1vi1O7ZW5AQRmlDK9/D0syQtuduyRWzkGy92vZ25k+qyq+rvMfWNV40aCJfOtJGdX1YeT/HjzQY/GYifwxCSvnLw6yT9OjsFK8k+Z+3v+Dkn+YovjVya5YJRELIhp3stUVf1Okv+cZL8kX97i1F5J/qm7nzRKMJiyqtozc9Ndj0tyZJK/SXK6hZlYyarqxVs77tFYACtHVe2X5Jvdfc1kf/ckP9fdl4wajG1Sppepqto7ye2S/LckL9zi1JXd/b1xUsHSqqp9kjwuyW9095Fj54Fpq6o9u/uq+a+ElaGq3pitrOrd3cePEAemqqo2JTm8u6+d7N8qyT9296HjJmNbdhk7ADumu6/o7ku6+7gkP5/kyMmzGHc0tp6AAAAL7klEQVSpqn1HjgdLoru/192vU6RZ6arqgVX1+SQXTfbvU1X/a+RYsBQ+kOSDk9fHk9wmyQ9HTQTTs+vmIp0kk+1bjZiHebhnepmbTP3bmOSAJG/M3P/h3pLkQWPmAmBRvSLJ0UnelyTdfX5VPXjcSDB93X2TRx9W1WlJPjZSHJi2y6rq2O5+X5JU1aOTfHfkTGyHMr38/VqSQ5J8Okm6+5tV5fmLACtMd3+j6iZr7lnRmJ3RPZKsHzsETMmzkry1qv5n5hZZ/UaSp4wbie1Rppe/a7u7q6qTGxdnAmBl+UZVHZ6kJ/fQPSeTKd+wklXVlbnpPdP/Hk8sYYXq7i8neUBV3Tpza1tdOXYmtk+ZXv7eUVWvS3LbqnpmkuOTvH7kTAAsrmdl7tFAd01yaZKPJvndURPBEuhus+3YqVTVMUl+IcmazbORuvslo4Zim6zmvQJU1cOSPDxz00E+0t1njBwJAOAWq6qPd/dR8x2DlaCqXptkjyQPzdzg2GOTnNPdTx81GNukTK8wVbUqyRO6+61jZwHglpksMtlJftjdLx87DyyVqlqTuVJxVpIjMjdgkMyt5v3h7r7XSNFgaqrqgu4+aIuvt07y7u5++NjZ2DrTvJepqrpN5qb43TVzq7ueMdl/XpLzkijTAMvfJZOvV48ZAkbw20mem+QumSyyOvGDJK8ZJRFM3+a/639UVXdJcnkSj7ydYUaml6mqem+S7yf55yRHJbld5h6LdWJ3nzdmNgCAxVBVz+7uV4+dA5ZCVf1Jkldn7nv712RuZtJfdfeLRg3GNinTy1RVfba7D5xsr8rcM+jWW/UPYOWoqvfnpisZ30R3H7uEcWDJVNXzu/v/m2w/rrvfucW5P+3uPxovHUxfVa1Osqa7rxg7C9u2y9gB2GHXbd7o7huSfFWRBlhxXpbkL5J8NXPT//5q8vphkgtHzAVTUVXrJptP2OLwH97sskcsURwYTXf/WJGefe6ZXr7uU1U/mGxXkt0n+5Wku/s240UDYDF09yeSpKr+n+5+8Ban3l9VnxwpFkzT66vqT292rObZBxiFMr1MdfeqsTMAsGTWVtV+3f2VJKmqfZOsHTkTTMMj87MLLt38Vgf3KAIzwT3TADDjquoRSU5J8pXJoQ1JTujuj44WCqaoqm5IclUms++S/GjzqczdR7rbWNlgmqrqrknuli0GPbvbTKQZpUwDwDIwWYzmnpPdi7v7x2PmAWBxVdWfJ/mNJJ9PcsPkcFtscnYp0wAAACOrqi8kOcgPS5cPq3kDAACM7ytJ3MKwjFiADAAAYHw/SnJeVX08yY2j0939nPEisT3KNADMuKqqJE9Ksl93v6Sq1ie5U3efM3I0ABbP+yYvlgn3TAPAjKuqv0zykyRHdve9qup2ST7a3YeOHA2ARVRVuydZ391fGDsL83PPNADMvvt39+8muSZJuvv7SW41biQAFlNV/WqS85L83WT/4KoyUj3DlGkAmH3XVdWqJJ0kVbU2cyPVAKwcJyU5LMl/JEl3n5dk3zEDsX3KNADMvlclOT3JHavqpUn+IcmfjhsJgEV2fXdfcbNj7smdYRYgA4AZ191vrapzkxyVpJL8p+6+aORYACyuC6vqiUlWVdU9kjwnyT+NnIntsAAZACwDk2neP5ctfhDe3V8fLxEAi6mq9kjyx0kePjn0kST/b3dfM14qtkeZBoAZV1XPTvLiJN9OckPmRqe7uw8aNRgA7MSUaQCYcVX1pcyt6H352FkAgDkWIAOA2feNJDdflAYAGJGRaQCYUVX1+5PNX0hyQJIPJvnx5vPd/fIxcgEARqYBYJbtNXl9PckZSW61xbG9RswFwCKrqv2r6uNVdeFk/6Cq+r/HzsW2GZkGAAAYQVU9K8nZ3X1xVX0iyfOSvK67D5mcv7C7f3HUkGyTkWkAmHFVdUZV3XaL/dtV1UfGzATAonhLkj+cbO/R3efc7Pz1S5yHAZRpAJh9a7v7PzbvdPf3k9xxxDwALILu/mGSZ0x2v1tVd0/SSVJVj03yrbGyMb9dxw4AAMzrhqpa391fT5Kqulsm32wBsLx193WTzd9NckqSe1bV/0ny1SRPHi0Y83LPNADMuKp6ROa+wfrE5NCDk5zQ3aZ6A6wwVbVnkl26+8qxs7B9yjQAzLCqqiTrklyd5AFJKsk/d/d3Rw0GwKKoqid391u2eBziTXgM4uwyzRsAZlh3d1W9p7vvl+QDY+cBYNHtOfnqkYfLjDINALPvX6rq0O7+1NhBAFhc3f26qlqV5Afd/T/GzsPCmeYNADOuqj6f5IAklyS5KnNTvbu7DxozFwCLp6rO6u6Hjp2DhVOmAWDGTVbv/hnd/bWlzgLAdFTVS5PsneTtmfvBaZKkuz89Wii2S5kGgGWgqn4pyT26+41VtTbJrbv7q2PnAmBxVNVZWznc3X3kkodhQZRpAJhxVfXiJBuTHNDd+1fVXZK8s7sfNHI0ANhpWYAMAGbfryU5JMmnk6S7v1lVVn0FWGGq6pgkv5BkzeZj3f2S8RKxPbuMHQAAmNe1PTeVrJOkqvac53oAlpmqem2S30jy7MwtNPm4JFtdM4PZoEwDwOx7R1W9Lsltq+qZST6W5K9GzgTA4jq8u5+S5PvdfXKSByb5+ZEzsR2meQPAjOvul1XVw5L8IHOPyHpRd58xciwAFtfVk68/mqyNcXmSfUfMwzyUaQBYBiblWYEGWLk+UFW3TfLfM7dGRid5/biR2B6reQPAjKuqxyT58yR3zNx9dJW5x6XcZtRgAExFVa1Osqa7rxg7C9umTAPAjKuqLyX51e6+aOwsACyuyQ9Mt6m7371UWRjGNG8AmH3fVqQBVqxf3c65TqJMzygj0wAwo7YYrXhIkjsleU+SH28+b7QCAMajTAPAjKqqN27ndHf38UsWBoCpq6pjkvxCkjWbj3X3S8ZLxPaY5g0AM6q7f2vsDAAsjap6bZI9kjw0c6t4PzbJOaOGYrt2GTsAALB9VfU3k8elbN6/XVX99ZiZAFh0h3f3U5J8v7tPTvLAJD8/cia2Q5kGgNl3UHf/x+ad7v5+kkNGzAPA4rt68vVHVXWXJNcl2XfEPMxDmQaA2bdLVd1u805V7RO3agGsNB+YzEL670k+neSSJKeNmojtsgAZAMy4qnpKkj9M8q7MPSbl8Ule2t1vHjUYAFNRVauTrOnuK8bOwrYp0wCwDFTVvZMcmaSSfLy7Pz9yJAAWQVUdmuQb3f3vk/2nJPn1JF9LclJ3f2/MfGybad4AsDzsk+Sq7n51ksuqyn10ACvD65JcmyRV9eAkf5bk1CRXJDllxFzMw8g0AMy4qnpxko1JDuju/ScL07yzux80cjQAbqGqOr+77zPZfk2Sy7r7pMn+ed198Jj52DYj0wAw+34tybFJrkqS7v5mkr1GTQTAYllVVZsXlTwqyZlbnLPY5AzzPw4AzL5ru7urqpOkqvYcOxAAi+a0JJ+oqu9m7vFYf58kVfV/ZW6qNzNKmQaA2feOqnpdkttW1TOTHJ/kr0bOBMAi6O6XVtXHk9w5yUf7p/fh7pLk2eMlYz7umQaAZaCqHpbk4Zlbzfsj3X3GyJEAYKemTAPADKuqVZkrz788dhYA4KcsQAYAM6y7b0jyo6rae+wsAMBPuWcaAGbfNUk+W1VnZLKid5J093PGiwQAOzdlGgBm3wcnLwBgRrhnGgAAAAZyzzQAAAAMpEwDAADAQMo0AAAADGQBMgCYcVW1f5LnJblbtvi3u7uPHC0UAOzkLEAGADOuqs5P8tok5ya5YfPx7j53tFAAsJNTpgFgxlXVud19v7FzAAA/pUwDwIyrqpOSfCfJ6Ul+vPl4d39vrEwAsLNTpgFgxlXVV7dyuLt7vyUPAwAkUaYBAABgMI/GAoAZVVXP32L7cTc796dLnwgA2EyZBoDZ9YQttv/wZucesZRBAICbUqYBYHbVNra3tg8ALCFlGgBmV29je2v7AMASsgAZAMyoqrohyVWZG4XePcmPNp9Ksqa7dxsrGwDs7JRpAAAAGMg0bwAAABhImQYAAICBlGkAAAAYSJkGAACAgZRpAAAAGEiZBgAAgIH+f/DIgNB7QYo6AAAAAElFTkSuQmCC\n",
+      "text/plain": [
+       "<Figure size 1224x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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>Féminin</th>\n",
+       "      <th>Masculin</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Retraité</th>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Autre</th>\n",
+       "      <td>2</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>En recherche d'emploi</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Étudiant</th>\n",
+       "      <td>3</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Salarié en activité</th>\n",
+       "      <td>2</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       Féminin  Masculin\n",
+       "Retraité                     0         1\n",
+       "Autre                        2         4\n",
+       "En recherche d'emploi        1         0\n",
+       "Étudiant                     3         8\n",
+       "Salarié en activité          2         2"
+      ]
+     },
+     "execution_count": 155,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def hist_double_situation_professionnelle(colonne):\n",
+    "    liste_situation=list(set(donnees.loc[:,\"Quelle est votre situation professionnelle ?\"].values))\n",
+    "    liste_colonne=list(set(donnees.loc[:,colonne].values))\n",
+    "    if np.nan in liste_situation:\n",
+    "        del liste_situation[liste_situation.index(np.nan)]\n",
+    "    if np.nan in liste_colonne:\n",
+    "        del liste_colonne[liste_colonne.index(np.nan)]\n",
+    "    liste_dico=[]\n",
+    "    for situation in liste_situation:\n",
+    "        dico={}\n",
+    "        for item_colonne in liste_colonne:\n",
+    "            dico[item_colonne]=donnees.loc[(donnees.loc[:,\"Quelle est votre situation professionnelle ?\"]==situation) & (donnees.loc[:,colonne]==item_colonne),colonne].count()\n",
+    "        liste_dico.append(dico)\n",
+    "    pd.DataFrame(liste_dico,index=liste_situation).plot(kind='bar',figsize=(17,7))\n",
+    "    plt.show()\n",
+    "    return pd.DataFrame(liste_dico,index=liste_situation)\n",
+    "\n",
+    "hist_double_situation_professionnelle(\"Indiquez votre genre :\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 156,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA90AAAHsCAYAAADPd86nAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvIxREBQAAHqlJREFUeJzt3X+w3XV95/HXmwQSxF8FwspyxSQuCLQE1EuoOC1RtgJCYco6LbD+RGTp1srubFsYu1Joh512dB3BWiOtLkUdaMsqAlKxFil1Ow5cVqBEQJCmmtJijF2klF+Bz/5xL/ESrtxLuJ8czrmPx8wdzvd7PjnnffNPeM73c76nWmsBAAAA5t8Ogx4AAAAARpXoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0MniQb3x7rvv3pYvXz6otwcAAIBtdtNNN32/tbZstnUDi+7ly5dnYmJiUG8PAAAA26yq/n4u62wvBwAAgE5ENwAAAHQiugEAAKCTgX2mGwAAgO3jsccey4YNG/Lwww8PepShs3Tp0oyNjWXHHXfcpj8vugEAAEbchg0b8qIXvSjLly9PVQ16nKHRWsumTZuyYcOGrFixYptew/ZyAACAEffwww9nt912E9zPUlVlt912e047BEQ3AADAAiC4t81z/XsT3QAAAAzcZz/72XznO98Z9Bjzzme6AQAAFpjlZ31xXl9v/e8eM+uaRYsW5cADD9xyfPnll2f58uVJkk9+8pN54IEHsvfeez+r9127dm1e8IIX5O1vf/uPXTMxMZGLL744F1xwwbN67fkiugEAAOhu5513zs033zzjc+9+97u36TVPP/30WdeMj49nfHx8m15/PtheDgAAwEA8/vjj+fVf//UccsghWbVqVT7xiU8kSa677rocfvjh+cVf/MXsu+++Oeuss/LZz342q1evzoEHHphvf/vbSZJzzjknH/rQh5Ika9asyZlnnpnVq1dn3333zV//9V9vea1jjz12y/pTTjkla9asycqVK7fL1W9XugEAAOjuoYceysEHH5wkWbFiRT7/+c/nk5/8ZF7ykpfkxhtvzCOPPJLXv/71edOb3pQkueWWW3L77bdn1113zcqVK3PqqafmhhtuyPnnn5+PfvSj+chHPvK099i8eXNuuOGGXH311Tn33HPzla985Wlr7rjjjnz1q1/NAw88kFe96lX55V/+5W3+Du65EN0AAAB0N9P28i9/+cu59dZbc9lllyVJ7r///tx1113Zaaedcsghh2TPPfdMkrzyla/cEuMHHnhgvvrVr874HieccEKS5LWvfW3Wr18/45pjjjkmS5YsyZIlS7LHHnvkvvvuy9jY2Hz8ijOaNbqr6lNJjk3yvdbaT83wfCU5P8mbk/xrkne21v7vfA8KAADAaGmt5aMf/WiOPPLIp5y/7rrrsmTJki3HO+yww5bjHXbYIZs3b57x9Z5cs2jRolnXzLZuvszlM90XJTnqGZ4/Osk+Uz+nJfn4cx8LAACAUXfkkUfm4x//eB577LEkybe+9a08+OCDA55qfs16pbu1dn1VLX+GJccnubi11pJ8vapeWlV7ttb+cZ5mBAAAYB7N5Su+todTTz0169evz2te85q01rJs2bJcfvnlgx5rXtVkK8+yaDK6r/ox28uvSvK7rbWvTR3/ZZIzW2sTz/Sa4+PjbWLiGZcAAAAwD26//fbsv//+gx5jaM3091dVN7XWZv0usvm4kVrNcG7Gkq+q0zK5Bf1Zf+k5MFjLz/rioEcAnqX1S08e9AjAs3HO/YOeAOhgPr6ne0OSl087Hkty70wLW2sXttbGW2vjy5Ytm4e3BgAAgOev+YjuK5K8vSb9dJL7fZ4bAAAA5vaVYZckWZNk96rakOS3kuyYJK21tUmuzuTXhd2dya8Me1evYQEAAGCYzOXu5SfN8nxL8ivzNhEAAACMiPnYXg4AAADMYD7uXg4AAMAwOecl8/x6s999v6ry1re+NZ/+9KeTJJs3b86ee+6ZQw89NFddddX8zrOV9evX59hjj81tt92WiYmJXHzxxbngggu6vueTRDcAAADd7bLLLrntttvy0EMPZeedd85f/MVfZK+99truc4yPj2d8fNav1543tpcDAACwXRx99NH54he/mCS55JJLctJJP7qF2A033JDDDjssr371q3PYYYflzjvvTJKsW7cuq1evzsEHH5xVq1blrrvuSpJcfPHFWbVqVQ466KC87W1vS5K8853vzGWXXbblNV/4whc+bYbrrrsuxx57bJLknHPOySmnnJI1a9Zk5cqVXa5+i24AAAC2ixNPPDGXXnppHn744dx666059NBDtzy333775frrr883vvGN/PZv/3be//73J0nWrl2bM844IzfffHMmJiYyNjaWdevW5bzzzsu1116bW265Jeeff/42z3THHXfkmmuuyQ033JBzzz03jz322HP+PaezvRwAAIDtYtWqVVm/fn0uueSSvPnNb37Kc/fff3/e8Y535K677kpVbYnf173udTnvvPOyYcOGnHDCCdlnn31y7bXX5i1veUt23333JMmuu+66zTMdc8wxWbJkSZYsWZI99tgj9913X8bGxrb9l9yKK90AAABsN8cdd1x+7dd+7Slby5PkAx/4QN7whjfktttuy5VXXpmHH344SXLyySfniiuuyM4775wjjzwy1157bVprqaqnvfbixYvzxBNPJElaa3n00UdnnWfJkiVbHi9atCibN29+Lr/e04huAAAAtptTTjklZ599dg488MCnnL///vu33Fjtoosu2nL+nnvuycqVK/O+970vxx13XG699dYcccQR+dM//dNs2rQpSfKDH/wgSbJ8+fLcdNNNSZIvfOEL875VfFvYXg4AALDQzOErvnoZGxvLGWec8bTzv/Ebv5F3vOMd+fCHP5w3vvGNW87/yZ/8ST7zmc9kxx13zMte9rKcffbZ2XXXXfObv/mbOfzww7No0aK8+tWvzkUXXZT3vOc9Of7447N69eocccQR2WWXXbbnrzajaq0N5I3Hx8fbxMTEQN4bePaWn/XFQY8APEvrl5486BGAZ2OAEcTou/3227P//vsPeoyhNdPfX1Xd1Fqb9bvHbC8HAACATkQ3AAAAdCK6AQAAFoBBfbR42D3XvzfRDQAAMOKWLl2aTZs2Ce9nqbWWTZs2ZenSpdv8Gu5eDgAAMOLGxsayYcOGbNy4cdCjDJ2lS5dmbGxsm/+86AYAABhxO+64Y1asWDHoMRYk28sBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBO5hTdVXVUVd1ZVXdX1VkzPP+Sqrqyqm6pqnVV9a75HxUAAACGy6zRXVWLknwsydFJDkhyUlUdsNWyX0nyzdbaQUnWJPmfVbXTPM8KAAAAQ2UuV7pXJ7m7tXZPa+3RJJcmOX6rNS3Ji6qqkrwwyQ+SbJ7XSQEAAGDIzCW690ry3WnHG6bOTff7SfZPcm+Sv01yRmvtiXmZEAAAAIbUXKK7ZjjXtjo+MsnNSf5tkoOT/H5VvfhpL1R1WlVNVNXExo0bn/WwAAAAMEzmEt0bkrx82vFYJq9oT/euJJ9rk+5O8ndJ9tv6hVprF7bWxltr48uWLdvWmQEAAGAozCW6b0yyT1WtmLo52olJrthqzXeSHJEkVfVvkrwqyT3zOSgAAAAMm8WzLWitba6q9ya5JsmiJJ9qra2rqtOnnl+b5HeSXFRVf5vJ7ehntta+33FuAAAAeN6bNbqTpLV2dZKrtzq3dtrje5O8aX5HAwAAgOE2l+3lAAAAwDYQ3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCeiGwAAADoR3QAAANCJ6AYAAIBORDcAAAB0IroBAACgE9ENAAAAnYhuAAAA6ER0AwAAQCdziu6qOqqq7qyqu6vqrB+zZk1V3VxV66rqr+Z3TAAAABg+i2dbUFWLknwsyc8l2ZDkxqq6orX2zWlrXprkD5Ic1Vr7TlXt0WtgAAAAGBZzudK9OsndrbV7WmuPJrk0yfFbrTk5yedaa99Jktba9+Z3TAAAABg+c4nuvZJ8d9rxhqlz0+2b5Ceq6rqquqmq3j7TC1XVaVU1UVUTGzdu3LaJAQAAYEjMJbprhnNtq+PFSV6b5JgkRyb5QFXt+7Q/1NqFrbXx1tr4smXLnvWwAAAAMExm/Ux3Jq9sv3za8ViSe2dY8/3W2oNJHqyq65MclORb8zIlAAAADKG5XOm+Mck+VbWiqnZKcmKSK7Za84UkP1NVi6vqBUkOTXL7/I4KAAAAw2XWK92ttc1V9d4k1yRZlORTrbV1VXX61PNrW2u3V9WXktya5Ikkf9Rau63n4AAAAPB8N5ft5WmtXZ3k6q3Ord3q+INJPjh/owEAAMBwm8v2cgAAAGAbiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQyp+iuqqOq6s6quruqznqGdYdU1eNV9Zb5GxEAAACG06zRXVWLknwsydFJDkhyUlUd8GPW/V6Sa+Z7SAAAABhGc7nSvTrJ3a21e1prjya5NMnxM6z71ST/O8n35nE+AAAAGFpzie69knx32vGGqXNbVNVeSX4hydr5Gw0AAACG21yiu2Y417Y6/kiSM1trjz/jC1WdVlUTVTWxcePGuc4IAAAAQ2nxHNZsSPLyacdjSe7das14kkurKkl2T/LmqtrcWrt8+qLW2oVJLkyS8fHxrcMdAAAARspcovvGJPtU1Yok/5DkxCQnT1/QWlvx5OOquijJVVsHNwAAACw0s0Z3a21zVb03k3clX5TkU621dVV1+tTzPscNAAAAM5jLle601q5OcvVW52aM7dbaO5/7WAAAADD85nIjNQAAAGAbiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKCTOUV3VR1VVXdW1d1VddYMz//Hqrp16udvquqg+R8VAAAAhsus0V1Vi5J8LMnRSQ5IclJVHbDVsr9LcnhrbVWS30ly4XwPCgAAAMNmLle6Vye5u7V2T2vt0SSXJjl++oLW2t+01v556vDrScbmd0wAAAAYPnOJ7r2SfHfa8Yapcz/Ou5P8+UxPVNVpVTVRVRMbN26c+5QAAAAwhOYS3TXDuTbjwqo3ZDK6z5zp+dbaha218dba+LJly+Y+JQAAAAyhxXNYsyHJy6cdjyW5d+tFVbUqyR8lObq1tml+xgMAAIDhNZcr3Tcm2aeqVlTVTklOTHLF9AVVtXeSzyV5W2vtW/M/JgAAAAyfWa90t9Y2V9V7k1yTZFGST7XW1lXV6VPPr01ydpLdkvxBVSXJ5tbaeL+xAQAA4PlvLtvL01q7OsnVW51bO+3xqUlOnd/RAAAAYLjNZXs5AAAAsA1ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOhHdAAAA0InoBgAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ6IbAAAAOplTdFfVUVV1Z1XdXVVnzfB8VdUFU8/fWlWvmf9RAQAAYLjMGt1VtSjJx5IcneSAJCdV1QFbLTs6yT5TP6cl+fg8zwkAAABDZy5Xulcnubu1dk9r7dEklyY5fqs1xye5uE36epKXVtWe8zwrAAAADJW5RPdeSb477XjD1LlnuwYAAAAWlMVzWFMznGvbsCZVdVomt58nyb9U1Z1zeH8AYBtUsnuS7w96DmCOzp3pf6mB57FXzGXRXKJ7Q5KXTzseS3LvNqxJa+3CJBfOZTAA4LmpqonW2vig5wCAhWwu28tvTLJPVa2oqp2SnJjkiq3WXJHk7VN3Mf/pJPe31v5xnmcFAACAoTLrle7W2uaqem+Sa5IsSvKp1tq6qjp96vm1Sa5O8uYkdyf51yTv6jcyAAAADIdq7WkfvQYARkBVnTb10S4AYEBENwAAAHQyl890AwAAANtAdAMAAEAnohsARkhVvaKq/v3U452r6kWDngkAFjLRDQAjoqrek+SyJJ+YOjWW5PLBTQQAiG4AGB2/kuT1SX6YJK21u5LsMdCJAGCBE90AMDoeaa09+uRBVS1O4mtKAGCARDcAjI6/qqr3J9m5qn4uyZ8luXLAMwHAguZ7ugFgRFTVDkneneRNSSrJNUn+qPnHHgAGRnQDwAioqkVJ/ri19tZBzwIA/Ijt5QAwAlprjydZVlU7DXoWAOBHFg96AABg3qxP8n+q6ookDz55srX24YFNBAALnOgGgNFx79TPDkleNOBZAID4TDcAAAB040o3AAy5qroyz/B93K2147bjOADANKIbAIbfh6b+e0KSlyX5zNTxSZn8nDcAMCC2lwPAiKiq61trPzvbOQBg+/GVYQAwOpZV1conD6pqRZJlA5wHABY828sBYHT81yTXVdU9U8fLk/ynwY0DANheDgAjpKqWJNlv6vCO1tojg5wHABY60Q0AI6SqfirJAUmWPnmutXbx4CYCgIVNdAPAiKiq30qyJpPRfXWSo5N8rbX2lkHOBQALmRupAcDoeEuSI5L8U2vtXUkOSrJksCMBwMImugFgdDzUWnsiyeaqenGS7yVZOcufAQA6cvdyABgdE1X10iR/mOSmJP+S5IbBjgQAC5vPdAPACKqq5Ule3Fq7dcCjAMCCZns5AAy5qjqyqp5ys7TW2vokB1bVzw1mKgAgcaUbAIZeVX09yc+31jZudf5lST7fWnvdYCYDAFzpBoDh94KtgztJWmv/lGSXAcwDAEwR3QAw/JZW1dNujlpVOybZeQDzAABTRDcADL/PJfnDqtpyVXvq8dqp5wCAARHdADD8/nuS+5L8fVXdVFU3JVmfZOPUcwDAgLiRGgCMiKraOcm/mzq8u7X20CDnAQBENwAAAHRjezkAAAB0IroBYMjNdOdyAOD5wfZyABhyVTWRZEOSLyX5Umtt/WAnAgCeJLoBYARU1SuSHJ3kqCR7Jflakj9P8lettUcGORsALGSiGwBGTFXtmORnMhnga5JsbK0dM9ChAGCBEt0AMOKqaq/W2j8Meg4AWIhENwAAAHTi7uUAAADQiegGgBFQVYuq6oODngMAeCrRDQAjoLX2eJLXVlUNehYA4EcWD3oAAGDefCPJF6rqz5I8+OTJ1trnBjcSACxsohsARseuSTYleeO0cy2J6AaAAXH3cgAAAOjElW4AGBFV9b8yeWX7KVprpwxgHAAgohsARslV0x4vTfILSe4d0CwAQGwvB4CRVVU7JPlKa+2Nsy4GALrwlWEAMLr2SbL3oIcAgIXM9nIAGBFV9UAmP9NdU//9pyRnDnQoAFjgbC8HAACATmwvB4ARUZPeWlUfmDreu6pWD3ouAFjIXOkGgBFRVR9P8kSSN7bW9q+qn0jy5dbaIQMeDQAWLJ/pBoDRcWhr7TVV9Y0kaa39c1XtNOihAGAhs70cAEbHY1W1KJM3UUtVLcvklW8AYEBENwCMjguSfD7JHlV1XpKvJfkfgx0JABY2n+kGgBFSVfslOSKTXxv2l6212wc8EgAsaKIbAIZcVb24tfbDqtp1pudbaz/Y3jMBAJNENwAMuaq6qrV2bFX9XSY/z13T/9taWznQAQFgARPdAAAA0ImvDAOAEVJVeyV5Rab9G99au35wEwHAwia6AWBEVNXvJfmlJN9M8vjU6ZZEdAPAgNheDgAjoqruTLKqtfbIoGcBACb5nm4AGB33JNlx0EMAAD9iezkAjI5/TXJzVf1lki1Xu1tr7xvcSACwsIluABgdV0z9AADPEz7TDQAjpKp2TrJ3a+3OQc8CAPhMNwAMtap6ybTHP5/k5iRfmjo+uKpc+QaAARLdADDcfqmq/sPU43OSrE7y/5KktXZzkhUDmgsAiOgGgKHWWrswyQFTh5tba/dvvWQ7jwQATONGagAw5FprvzP18LaqOjnJoqraJ8n7kvzN4CYDAFzpBoDR8atJfjKTXxd2SZIfJvkvA50IABY4dy8HAACATmwvB4ARUVVX5umf4b4/yUSST7TWHt7+UwHAwmZ7OQCMjnuS/EuSP5z6+WGS+5LsO3UMAGxntpcDwIioqutbaz8707mqWtda+8lBzQYAC5Ur3QAwOpZV1d5PHkw93n3q8NHBjAQAC5vPdAPA6PhvSb5WVd9OUklWJPnPVbVLkj8e6GQAsEDZXg4AI6SqliTZL5PRfYebpwHAYIluABghVXVYkuWZtputtXbxwAYCgAXO9nIAGBFV9ekkr0xyc5LHp063JKIbAAbElW4AGBFVdXuSA5p/3AHgecPdywFgdNyW5GWDHgIA+BHbywFgdOye5JtVdUOSR5482Vo7bnAjAcDCJroBYHScM+gBAICn8pluAAAA6MSVbgAYclX1QCbvUv60p5K01tqLt/NIAMAUV7oBAACgE3cvBwAAgE5ENwAAAHQiugEAAKAT0Q0AAACdiG4AAADoRHQDAABAJ/8filH3pT1SUV8AAAAASUVORK5CYII=\n",
+      "text/plain": [
+       "<Figure size 1224x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "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>Féminin</th>\n",
+       "      <th>Masculin</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>Ingénieur / Cadre</th>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                   Féminin  Masculin\n",
+       "Ingénieur / Cadre        1         1"
+      ]
+     },
+     "execution_count": 156,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def hist_double_fonction(colonne):\n",
+    "    liste_fonction=list(set(donnees.loc[:,\"Quelle est votre fonction ?\"].values))\n",
+    "    liste_colonne=list(set(donnees.loc[:,colonne].values))\n",
+    "    if np.nan in liste_fonction:\n",
+    "        del liste_fonction[liste_fonction.index(np.nan)]\n",
+    "    if np.nan in liste_colonne:\n",
+    "        del liste_colonne[liste_colonne.index(np.nan)]\n",
+    "    liste_dico=[]\n",
+    "    for fonction in liste_fonction:\n",
+    "        dico={}\n",
+    "        for item_colonne in liste_colonne:\n",
+    "            dico[item_colonne]=donnees.loc[(donnees.loc[:,\"Quelle est votre fonction ?\"]==fonction) & (donnees.loc[:,colonne]==item_colonne),colonne].count()\n",
+    "        liste_dico.append(dico)\n",
+    "    pd.DataFrame(liste_dico,index=liste_fonction).plot(kind='bar',figsize=(17,7))\n",
+    "    plt.show()\n",
+    "    return pd.DataFrame(liste_dico,index=liste_fonction)\n",
+    "\n",
+    "hist_double_fonction(\"Indiquez votre genre :\")"
+   ]
+  }
+ ],
+ "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}