a

Projet Maman 3/Untitled.ipynb

@@ -2,27 +2,34 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
     "import numpy as np\n",
     "import pandas as pd\n",
     "import matplotlib.pyplot as plt\n",
-    "from IPython.display import Markdown, display\n",
-    "\n",
-    "\n",
-    "donnees=pd.read_csv(\"results-survey669838(3).csv\")"
+    "from IPython.display import Markdown, display"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-0.17\n",
+      "0.83\n",
+      "1.83\n",
+      "2.83\n"
+     ]
+    },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -38,7 +45,42 @@
     "    tableau_colonne = donnees.loc[:,colonne].value_counts().sort_values(ascending=False)\n",
     "    tableau_colonne.plot(kind=\"bar\")\n",
     "    plt.title(colonne)\n",
-    "    plt.show()"
+    "    plt.show()\n",
+    "    \n",
+    "def histogramme2(colonne):\n",
+    "    tableau_colonne = donnees.loc[:,colonne].value_counts().sort_values(ascending=False)\n",
+    "    x = np.arange(len(tableau_colonne.values))\n",
+    "    fig,ax = plt.subplots()\n",
+    "    rects=ax.bar(x,tableau_colonne.values,0.34)\n",
+    "    for rect in rects:\n",
+    "        print(rect.get_x())\n",
+    "        height = rect.get_height()\n",
+    "        ax.annotate('{}'.format(height),\n",
+    "                    xy=(rect.get_x()+0.34/2, height),\n",
+    "                    xytext=(0, 3),\n",
+    "                    textcoords=\"offset points\",\n",
+    "                    ha='center', va='bottom')\n",
+    "    plt.show()\n",
+    "    \n",
+    "histogramme2(\"Dans quelle mesure avez-vous apprécié les éléments suivants du cours ?  (1= pas satisfait ; 4= très satisfait)  [Vidéos de cours]\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def autolabel(graphs):\n",
+    "    for rect in graphs:\n",
+    "        height = rect.get_height()\n",
+    "        ax.annotate('{}'.format(height),\n",
+    "                    xy=(rect.get_x(), height),\n",
+    "                    xytext=(0, 3),\n",
+    "                    textcoords=\"offset points\",\n",
+    "                    ha='center', va='bottom')\n",
+    "\n",
+    "donnees=pd.read_csv(\"results-survey669838(3).csv\")"
    ]
   },
   {