Modification finale.

module3/exo2/exercice.ipynb

@@ -395,7 +395,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f262c5cac88>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3ef2fc5c0>"
       ]
      },
      "execution_count": 10,
@@ -434,7 +434,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f262a3d0898>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3ed12a208>"
       ]
      },
      "execution_count": 11,
@@ -488,7 +488,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -510,7 +510,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -534,16 +534,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f262a363f98>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3ed0182e8>"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -573,7 +573,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -609,7 +609,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -633,7 +633,7 @@
     {
      "data": {
       "text/plain": [
-       "<matplotlib.axes._subplots.AxesSubplot at 0x7f262a3522b0>"
+       "<matplotlib.axes._subplots.AxesSubplot at 0x7ff3ecfe2ef0>"
       ]
      },
      "execution_count": 16,
@@ -642,7 +642,7 @@
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -656,13 +656,6 @@
    "source": [
     "yearly_incidence.hist(xrot=20)"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {