exercice1_firstTry

module2/exo2/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.113000000000001\n",
+      "4.334094455301447\n",
+      "2.8\n",
+      "23.4\n",
+      "14.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "donnees = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
+    "\n",
+    "moyenne = np.mean(donnees, 0)\n",
+    "ecart_type = np.std(donnees, 0, ddof = 1)\n",
+    "minimum = np.min(donnees)\n",
+    "maximum = np.max(donnees)\n",
+    "mediane = np.median(donnees, 0)\n",
+    "print(moyenne)\n",
+    "print(ecart_type)\n",
+    "print(minimum)\n",
+    "print(maximum)\n",
+    "print(mediane)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +58,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module2/exo5/exo5_fr.ipynb

@@ -705,7 +705,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,

module3/exo1/analyse-syndrome-grippal.ipynb

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,15 +28,26 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [],
    "source": [
     "data_url = \"http://www.sentiweb.fr/datasets/incidence-PAY-3.csv\""
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import urllib\n",
+    "csv_file = \"fichier_symptomes_gripaux.csv\"\n",
+    "if not os.path.exists(csv_file):\n",
+    "    urllib.request.urlretrieve(data_url, csv_file)"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -65,7 +76,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "raw_data = pd.read_csv(data_url, skiprows=1)\n",
+    "csv_file = \"fichier_symptomes_gripaux.csv\"\n",
+    "if os.path.exist(csv_file):\n",
+    "raw_data = pd.read_csv(csv_file, skiprows=1) \n",
     "raw_data"
    ]
   },
@@ -153,9 +166,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "sorted_data = data.set_index('period').sort_index()"
@@ -253,9 +264,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "first_august_week = [pd.Period(pd.Timestamp(y, 8, 1), 'W')\n",
@@ -341,9 +350,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -364,7 +371,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.1"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,