diff --git a/module2/exo1/Essai Jupyter.ipynb b/module2/exo1/Essai Jupyter.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..ef8fca1c29a4a5691cf08848673492d6f937f674
--- /dev/null
+++ b/module2/exo1/Essai Jupyter.ipynb	
@@ -0,0 +1,285 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Titre du fichier\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## _Comprendre Jupyter_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "8"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "3 + 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "8\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=8\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "12\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=x+4\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "24\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=x * 2\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "mon text en <sub>indice</sub>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Mon texte en <sup>exposant</sup>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## complétion"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**importer une biblioteque ex: numpy, utliser la touche <tab>**\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "mu, sigma = 100, 15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "100 15\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(mu,sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[116.15200082  96.9539339  104.49880532 ...  65.66002195  95.42413749\n",
+      "  77.83089386]\n"
+     ]
+    }
+   ],
+   "source": [
+    "x=np.random.normal(loc=mu,scale=sigma,size=10000)\n",
+    "print(x)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**matplotlib : biblioteque pour travailler les graphiques**\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**%matplotlib inline, pour inserer et conserver le graphique dans mon dcoument computationnel**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "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": [
+    "## Utiliser d'autres langages: R et Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext rpy2.ipython"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "celltoolbar": "Format de la Cellule Texte Brut",
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb
deleted file mode 100644
index 0bbbe371b01e359e381e43239412d77bf53fb1fb..0000000000000000000000000000000000000000
--- a/module2/exo1/toy_notebook_fr.ipynb
+++ /dev/null
@@ -1,25 +0,0 @@
-{
- "cells": [],
- "metadata": {
-  "kernelspec": {
-   "display_name": "Python 3",
-   "language": "python",
-   "name": "python3"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.6.3"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
-