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module2/exo4/.ipynb

+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Efficacité aux quizz durant ce module\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Graphique des quizz en fonction des notes obtenus durant ce module "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "La note maximum est  1.0\n",
+      "La note minimum est  0.6666666666666666\n",
+      "L'écart type des notes est  0.14433756729740646\n",
+      "La moyenne des notes est  0.9166666666666665\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "numQuizz = [\n",
+    "\"1\",\n",
+    "\"2\",\n",
+    "\"3\",\n",
+    "\"4\",\n",
+    "\"5\",\n",
+    "\"6\",\n",
+    "\"7\",\n",
+    "\"8\",\n",
+    "\"9\",\n",
+    "\"10\",\n",
+    "\"11\",\n",
+    "\"P1\"\n",
+    "]\n",
+    "notesQuizz = [\n",
+    "4/4,\n",
+    "3/3,\n",
+    "3/3,\n",
+    "3/3,\n",
+    "4/4,\n",
+    "5/5,\n",
+    "3/3,\n",
+    "2/3,\n",
+    "2/2,\n",
+    "2/3,\n",
+    "2/3,\n",
+    "3/3\n",
+    "]\n",
+    "maxN = np.max(notesQuizz)\n",
+    "print(\"La note maximum est \",maxN)\n",
+    "minN = np.min(notesQuizz)\n",
+    "print(\"La note minimum est \",minN)\n",
+    "etN = np.std(notesQuizz)\n",
+    "print(\"L'écart type des notes est \",etN)\n",
+    "avgN = np.average(notesQuizz)\n",
+    "print(\"La moyenne des notes est \",avgN)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "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": [
+    "plt.plot(numQuizz,notesQuizz)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "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": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}