diff --git a/module2/exo1/toy_notebook_fr.ipynb b/module2/exo1/toy_notebook_fr.ipynb
index d348d0fce9c96bfd5bb1afe8dc3da97e9ec826f7..335cdd4a773780a303e5685caab7b0779535286a 100644
--- a/module2/exo1/toy_notebook_fr.ipynb
+++ b/module2/exo1/toy_notebook_fr.ipynb
@@ -18,26 +18,26 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## À propos du calcul de $\\pi$ "
+    "# À propos du calcul de $\\pi$ "
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### En demandant à la lib maths"
+    "## En demandant à la lib maths"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Mon ordinateur m’indique que $\\pi$ vaut approximativement"
+    "Mon ordinateur m’indique que $\\pi$ vaut *approximativement*"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -49,7 +49,7 @@
     }
    ],
    "source": [
-    "from math import * \n",
+    "from math import *\n",
     "print(pi)"
    ]
   },
@@ -57,7 +57,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### En utilisant la méthode des aiguilles de Buffon "
+    "## En utilisant la méthode des aiguilles de Buffon "
    ]
   },
   {
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 10,
    "metadata": {
     "scrolled": true
    },
@@ -80,17 +80,17 @@
        "3.128911138923655"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "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",
-    "theta = np.random.uniform(size=N, low=0, high=pi/2) \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",
+    "theta = np.random.uniform(size=N, low=0, high=pi/2)\n",
     "2/(sum((x+np.sin(theta))>1)/N)"
    ]
   },
@@ -98,7 +98,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Avec un argument \"fréquentiel\" de surface "
+    "## Avec un argument \"fréquentiel\" de surface "
    ]
   },
   {
@@ -113,7 +113,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
     {
@@ -130,19 +130,19 @@
     }
    ],
    "source": [
-    "%matplotlib inline \n",
+    "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
     "\n",
-    "np.random.seed(seed=42) \n",
-    "N = 1000 \n",
-    "x = np.random.uniform(size=N, low=0, high=1) \n",
+    "np.random.seed(seed=42)\n",
+    "N = 1000\n",
+    "x = np.random.uniform(size=N, low=0, high=1)\n",
     "y = np.random.uniform(size=N, low=0, high=1)\n",
-    "accept = (x*x+y*y) <= 1 \n",
+    "accept = (x*x+y*y) <= 1\n",
     "reject = np.logical_not(accept)\n",
     "\n",
     "fig, ax = plt.subplots(1) \n",
-    "ax.scatter(x[accept], y[accept], c='b', alpha=0.2, edgecolor=None) \n",
-    "ax.scatter(x[reject], y[reject], c='r', alpha=0.2, edgecolor=None) \n",
+    "ax.scatter(x[accept], y[accept], c='b', alpha=0.2, edgecolor=None)\n",
+    "ax.scatter(x[reject], y[reject], c='r', alpha=0.2, edgecolor=None)\n",
     "ax.set_aspect('equal')"
    ]
   },
@@ -156,7 +156,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 12,
    "metadata": {
     "scrolled": true
    },
@@ -167,7 +167,7 @@
        "3.112"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }