diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 4bebfa5bc78a1c2e35fdc145a5eccae05f49a795..90e2fc4cce8e901c542b4e37cb1f1fcc230a1b8d 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -21,7 +21,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -484,7 +484,7 @@
        "52          53  1821   54.0    NaN"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -496,7 +496,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -559,7 +559,7 @@
        "52          53  1821   54.0    NaN"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -572,7 +572,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -1011,7 +1011,7 @@
        "49          50  1810   99.0  30.00"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1032,7 +1032,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -1041,7 +1041,7 @@
        "<BarContainer object of 50 artists>"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1071,16 +1071,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PolyCollection at 0x7f5bf1d585c0>"
+       "<matplotlib.collections.PolyCollection at 0x7fde5bb00dd8>"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1116,16 +1116,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.collections.PolyCollection at 0x7f5bf18dfe10>"
+       "<matplotlib.collections.PolyCollection at 0x7fde5ba46eb8>"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1153,10 +1153,71 @@
     "\n",
     "\n",
     "plt.fill_between(x, y2, color='#539ecd')\n",
-    "#plt.legend([p1, p2])\n",
-    "#plt.show()\n"
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Utlisaion de 2 axes d'ordonnées"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 432x288 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# create figure and axis objects\n",
+    "fig,ax = plt.subplots()\n",
+    "\n",
+    "# make a plot\n",
+    "ax.plot(my_data[\"Unnamed: 0\"], my_data[\"Wheat\"], color = \"red\", marker = \"o\")\n",
+    "\n",
+    "# set x-axis l# set x-axis label\n",
+    "ax.set_xlabel(\"Unnamed: 0\",fontsize = 14)\n",
+    "# set y-axis l# set x-axis label\n",
+    "ax.set_ylabel(\"Wheat\", color = \"red\")\n",
+    "\n",
+    "# twin object for two different y-axis on the sample plot\n",
+    "ax2 = ax.twinx()\n",
+    "\n",
+    "# make a plot with different y-axis using second axis object\n",
+    "ax2.plot(my_data[\"Unnamed: 0\"],my_data[\"Wages\"] ,color = \"blue\",marker = \"o\")\n",
+    "ax2.set_ylabel(\"Wages\",color = \"blue\", fontsize = 14)\n",
+    "\n",
+    "\n",
+    "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": null,