diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..4653117f9bcf6d43ab425fe7df21725b3450a3ab 100644
--- a/module2/exo1/toy_notebook_fr.ipynb
+++ b/module2/exo1/toy_notebook_fr.ipynb
@@ -1,5 +1,200 @@
 {
- "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": [
+    {
+     "ename": "TypeError",
+     "evalue": "'builtin_function_or_method' object is not subscriptable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-2-5d3b6fcacb88>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mTypeError\u001b[0m: 'builtin_function_or_method' object is not subscriptable"
+     ]
+    }
+   ],
+   "source": [
+    "x=10\n",
+    "print[x]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=10\n",
+    "print['x']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x=10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "20\n"
+     ]
+    }
+   ],
+   "source": [
+    "x = x+10\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# petit exemple de completion"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100, 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = np.random.normal(loc=mu, scale=sigma, size=10000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "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 +211,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-