module1/exo2/fichier-markdown.md

-test
+# Partie 1
\ No newline at end of file
+## Sous-partie 1 : texte
+Une phrase sans rien
+
+*Une phrase en italique*
+
+**Une phrase en gras**
+
+Un lien vers [fun-mooc.fr](https://www.fun-mooc.fr/)
+
+Une ligne de `code`
+
+## Sous-partie 2 : listes
+
+**Liste à puce**
+- item
+ - sous-item
+ - sous-item
+- item
+- item
+
+**Liste numérotée**
+
+1. item
+2. item
+3. item
+
+## Sous-partie 3 : code
+
+```
+# Extrait de code 
+```
\ No newline at end of file

module2/exo2/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data = [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]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.113000000000001\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "moyenne = np.mean(data)\n",
+    "print(moyenne)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "4.334094455301447\n"
+     ]
+    }
+   ],
+   "source": [
+    "ecartType = np.std(data, ddof=1)\n",
+    "print(ecartType)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.8\n"
+     ]
+    }
+   ],
+   "source": [
+    "mini = np.min(data)\n",
+    "print(mini)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "14.5\n"
+     ]
+    }
+   ],
+   "source": [
+    "mediane = np.median(data)\n",
+    "print(mediane)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "23.4\n"
+     ]
+    }
+   ],
+   "source": [
+    "maxi = np.max(data)\n",
+    "print(maxi)"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +117,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module2/exo4/exercice.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
    "metadata": {
+    "hideCode": true,
+    "hidePrompt": true
+   },
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "hide_code_all_hidden": true,
   "kernelspec": {
    "display_name": "Python 3",
    "language": "python",
@@ -16,10 +28,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-

module3/exo3/Wheat.csv

+"","Year","Wheat","Wages"
+"1",1565,41,5
+"2",1570,45,5.05
+"3",1575,42,5.08
+"4",1580,49,5.12
+"5",1585,41.5,5.15
+"6",1590,47,5.25
+"7",1595,64,5.54
+"8",1600,27,5.61
+"9",1605,33,5.69
+"10",1610,32,5.78
+"11",1615,33,5.94
+"12",1620,35,6.01
+"13",1625,33,6.12
+"14",1630,45,6.22
+"15",1635,33,6.3
+"16",1640,39,6.37
+"17",1645,53,6.45
+"18",1650,42,6.5
+"19",1655,40.5,6.6
+"20",1660,46.5,6.75
+"21",1665,32,6.8
+"22",1670,37,6.9
+"23",1675,43,7
+"24",1680,35,7.3
+"25",1685,27,7.6
+"26",1690,40,8
+"27",1695,50,8.5
+"28",1700,30,9
+"29",1705,32,10
+"30",1710,44,11
+"31",1715,33,11.75
+"32",1720,29,12.5
+"33",1725,39,13
+"34",1730,26,13.3
+"35",1735,32,13.6
+"36",1740,27,14
+"37",1745,27.5,14.5
+"38",1750,31,15
+"39",1755,35.5,15.7
+"40",1760,31,16.5
+"41",1765,43,17.6
+"42",1770,47,18.5
+"43",1775,44,19.5
+"44",1780,46,21
+"45",1785,42,23
+"46",1790,47.5,25.5
+"47",1795,76,27.5
+"48",1800,79,28.5
+"49",1805,81,29.5
+"50",1810,99,30
+"51",1815,78,NA
+"52",1820,54,NA
+"53",1821,54,NA