Un premier essai avec jupyter

module2/exo1/toy_notebook_fr.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Titre du document "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "4"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "2+2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "10\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Petit exemple de completion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100, 15 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(loc=mu, scale=sigma, size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "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": [
+    "%matplotlib inline\n",
+    "plt.hist(x)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Utilisation d'autres langages"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "%%R \n",
+    "plot(cars)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +162,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-