diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index e26db08fa3fb3b95663c9820809629993493e3e5..3e4d81cf645c49585116751e3cdfce422010c71d 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -2571,7 +2571,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3a6587f98>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f665172c7b8>"
       ]
      },
      "execution_count": 11,
@@ -2625,7 +2625,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3a62c09e8>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f665146a4a8>"
       ]
      },
      "execution_count": 12,
@@ -2669,7 +2669,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3a636dc88>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f66515226a0>"
       ]
      },
      "execution_count": 13,
@@ -2730,7 +2730,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fa3a86362b0>]"
+       "[<matplotlib.lines.Line2D at 0x7f66537e0e10>]"
       ]
      },
      "execution_count": 14,
@@ -2861,13 +2861,15 @@
      "output_type": "stream",
      "text": [
       " \n",
-      "-2.952e-05 x - 2.344\n"
+      "-2.952e-05 x - 2.344\n",
+      "l'oscillation saisonnière est de 5.76 ppm en 1958\n",
+      "l'oscillation saisonnière est de 7.05 ppm en 2019\n"
      ]
     },
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fa3a63138d0>]"
+       "[<matplotlib.lines.Line2D at 0x7f664c3ebac8>]"
       ]
      },
      "execution_count": 18,
@@ -2900,9 +2902,29 @@
     "poly_min = np.poly1d(np.polyfit(x_env_min, y_env_min, 1))\n",
     "print(poly_min)\n",
     "\n",
+    "\n",
+    "\n",
+    "amp1=y_env[0]-y_env_min[0]\n",
+    "x1=pd.Timestamp(year=1900,month=1,day=1)+pd.Timedelta(days=x_env[0])\n",
+    "y1=x1.year\n",
+    "print('l\\'oscillation saisonnière est de %.2f ppm en %i' %(amp1,y1))\n",
+    "amp2=y_env[-1]-y_env_min[-1]\n",
+    "x2=pd.Timestamp(year=1900,month=1,day=1)+pd.Timedelta(days=x_env[-1])\n",
+    "y2=x2.year\n",
+    "print('l\\'oscillation saisonnière est de %.2f ppm en %i' %(amp2,y2))\n",
+    "\n",
     "plt.plot(x, poly_min(x), c='r')"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Caractérisation de l'oscillation périodique**\n",
+    "\n",
+    "L'oscillation saisonnière est périodique avec une période d'environ un an et une variation comprise entre 5.76 ppm en 1958 et 7.05 ppm en 2019"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -2918,7 +2940,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3a0a46780>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f664c414828>"
       ]
      },
      "execution_count": 19,
@@ -2965,7 +2987,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7fa3a09e0cc0>]"
+       "[<matplotlib.lines.Line2D at 0x7f66516b55f8>]"
       ]
      },
      "execution_count": 20,
@@ -3093,7 +3115,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3a08c41d0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f664c2bf780>"
       ]
      },
      "execution_count": 24,
@@ -3125,7 +3147,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7fa3a08b0940>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7f664c24d438>"
       ]
      },
      "execution_count": 25,