update

module3/exo3/exercice.ipynb

@@ -15,7 +15,8 @@
    "source": [
     "%matplotlib inline\n",
     "import matplotlib.pyplot as plt\n",
-    "import pandas as pd"
+    "import pandas as pd\n",
+    "import numpy as np"
    ]
   },
   {
@@ -762,7 +763,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -771,13 +772,13 @@
        "Text(0,0.5,'Mortality Rate')"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": "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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -791,7 +792,7 @@
    "source": [
     "mortality_rate = [mortality_rate_smoker,mortality_rate_non_smoker]\n",
     "smoking = ['smoker', 'non-smoker']\n",
-    "plt.bar(smoking, mortality_rate,color=['#E69F00', '#56B4E9'],width = 0.5)\n",
+    "plt.bar(smoking, mortality_rate,color=['#E69F00', '#56B4E9'],width = 0.25)\n",
     "plt.xticks(smoking)\n",
     "plt.yticks(mortality_rate)\n",
     "plt.xlabel('Smoking')\n",
@@ -1071,7 +1072,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 36,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {