diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb
index 4251654efda3dc7f50a10960e1935253bc0626fa..5f1331e59d9be855f1ad23173b37526d32057f77 100644
--- a/module2/exo1/toy_notebook_fr.ipynb
+++ b/module2/exo1/toy_notebook_fr.ipynb
@@ -26,7 +26,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -38,7 +38,7 @@
     }
    ],
    "source": [
-    "In [1]: from math import *\n",
+    "from math import *\n",
     "print(pi)"
    ]
   },
@@ -57,16 +57,18 @@
    "metadata": {},
    "outputs": [
     {
-     "ename": "SyntaxError",
-     "evalue": "invalid syntax (<ipython-input-2-f24264f0d041>, line 5)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-2-f24264f0d041>\"\u001b[0;36m, line \u001b[0;32m5\u001b[0m\n\u001b[0;31m    theta = np.random.uniform(size=N, low=0, high=%pi/2)\u001b[0m\n\u001b[0m                                                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
-     ]
+     "data": {
+      "text/plain": [
+       "3.128911138923655"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "In [2]: import numpy as np\n",
+    "import numpy as np\n",
     "np.random.seed(seed=42)\n",
     "N = 10000\n",
     "x = np.random.uniform(size=N, low=0, high=1) \n",
@@ -85,7 +87,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -102,7 +104,7 @@
     }
    ],
    "source": [
-    "In [3]: %matplotlib inline\n",
+    "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "    \n",
     "np.random.seed(seed=42)\n",
@@ -127,19 +129,25 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
+   "execution_count": 4,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "3.112"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "In [4] : 4*np.mean(accept)"
+    "4*np.mean(accept)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {