Just want to try what this command does

module2/exo1/toy_notebook_fr.ipynb

 {
- "cells": [],
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# First Mooc document\n",
+    "\n",
+    "## What is up ? "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'AlessandroSergio'"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "##### Having some fun\n",
+    "x=\"Alessandro\"\n",
+    "y=\"Sergio\"\n",
+    "\n",
+    "x+y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "####Defining some variables\n",
+    "Some=\"variables\"\n",
+    "DaddyCool=12092020"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "12092020"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "DaddyCool"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##### Messing around with numpy\n",
+    "import numpy as np\n",
+    "mu, sigma= 15, 1 #Setting my parameters for a law\n",
+    "x= np.random.normal(loc=mu, scale=sigma, size=1000) #Storing on x the result of aleatoire tirage 1000 from the normal loa"
+   ]
+  },
+  {
+   "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": [
+    "##### Objective visualisation\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "plt.hist(x)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Here changing languages\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "##### Using R\n",
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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": [
+    "###Test de la completion des variables\n",
+    "DaddyCool\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Hola Chicos\n"
+     ]
+    }
+   ],
+   "source": [
+    "print (\"Hola Chicos\")"
+   ]
+  }
+ ],
  "metadata": {
   "kernelspec": {
    "display_name": "Python 3",
@@ -16,10 +181,9 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.4"
   }
  },
  "nbformat": 4,
  "nbformat_minor": 2
 }
-