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mooc-rr
Commit d7dc7074 authored by d3ffe1dda057aedb6d37daa14d4dce86's avatar d3ffe1dda057aedb6d37daa14d4dce86
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question 2

parent a83e3112
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Showing with 280 additions and 27 deletions
module3/exo3/exercice.ipynb
View file @ d7dc7074
...@@ -627,20 +627,31 @@ ...@@ -627,20 +627,31 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"total_smoker = 0\n",
"total_non_smoker = 0\n",
"total_alive = 0\n",
"total_dead = 0\n",
"\n",
"alive_and_smoker = 0\n", "alive_and_smoker = 0\n",
"alive_and_non_smoker = 0\n", "alive_and_non_smoker = 0\n",
"dead_and_smoker = 0\n", "dead_and_smoker = 0\n",
"dead_and_non_smoker = 0\n", "dead_and_non_smoker = 0\n",
"for i in range(len(raw_data)):\n", "for i in range(len(raw_data)):\n",
" if raw_data.iloc[i][0] == \"Yes\":\n", " if raw_data.iloc[i][0] == \"Yes\":\n",
" total_smoker += 1\n",
" if raw_data.iloc[i][1] == \"Alive\":\n", " if raw_data.iloc[i][1] == \"Alive\":\n",
" total_alive +=1\n",
" alive_and_smoker += 1\n", " alive_and_smoker += 1\n",
" else :\n", " else :\n",
" total_dead +=1\n",
" dead_and_smoker += 1\n", " dead_and_smoker += 1\n",
" else :\n", " else :\n",
" total_non_smoker += 1\n",
" if raw_data.iloc[i][1] == \"Alive\":\n", " if raw_data.iloc[i][1] == \"Alive\":\n",
" total_alive +=1\n",
" alive_and_non_smoker += 1\n", " alive_and_non_smoker += 1\n",
" else :\n", " else :\n",
" total_dead +=1\n",
" dead_and_non_smoker += 1" " dead_and_non_smoker += 1"
] ]
}, },
...@@ -679,6 +690,7 @@ ...@@ -679,6 +690,7 @@
" <th></th>\n", " <th></th>\n",
" <th>Smoker</th>\n", " <th>Smoker</th>\n",
" <th>Non-Smoker</th>\n", " <th>Non-Smoker</th>\n",
" <th>Total</th>\n",
" </tr>\n", " </tr>\n",
" </thead>\n", " </thead>\n",
" <tbody>\n", " <tbody>\n",
...@@ -686,20 +698,29 @@ ...@@ -686,20 +698,29 @@
" <th>Alive</th>\n", " <th>Alive</th>\n",
" <td>443</td>\n", " <td>443</td>\n",
" <td>502</td>\n", " <td>502</td>\n",
" <td>945</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
" <th>Dead</th>\n", " <th>Dead</th>\n",
" <td>139</td>\n", " <td>139</td>\n",
" <td>230</td>\n", " <td>230</td>\n",
" <td>369</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Total</th>\n",
" <td>582</td>\n",
" <td>732</td>\n",
" <td>1314</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
"</div>" "</div>"
], ],
"text/plain": [ "text/plain": [
" Smoker Non-Smoker\n", " Smoker Non-Smoker Total\n",
"Alive 443 502\n", "Alive 443 502 945\n",
"Dead 139 230" "Dead 139 230 369\n",
"Total 582 732 1314"
] ]
}, },
"execution_count": 7, "execution_count": 7,
...@@ -708,9 +729,9 @@ ...@@ -708,9 +729,9 @@
} }
], ],
"source": [ "source": [
"data = [[alive_and_smoker,alive_and_non_smoker],[dead_and_smoker, dead_and_non_smoker]]\n", "data = [[alive_and_smoker,alive_and_non_smoker,total_alive],[dead_and_smoker, dead_and_non_smoker,total_dead], [total_smoker,total_non_smoker,(total_alive+total_dead)]]\n",
"\n", "\n",
"pd.DataFrame(data, columns=[\"Smoker\", \"Non-Smoker\"], index = [\"Alive\", \"Dead\"])" "pd.DataFrame(data, columns=[\"Smoker\", \"Non-Smoker\", \"Total\"], index = [\"Alive\", \"Dead\",\"Total\"])"
] ]
}, },
{ {
...@@ -724,7 +745,7 @@ ...@@ -724,7 +745,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 26, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -741,7 +762,7 @@ ...@@ -741,7 +762,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -750,13 +771,13 @@ ...@@ -750,13 +771,13 @@
"Text(0,0.5,'Mortality Rate')" "Text(0,0.5,'Mortality Rate')"
] ]
}, },
"execution_count": 9, "execution_count": 18,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
{ {
"data": { "data": {
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\n", "image/png": "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\n",
"text/plain": [ "text/plain": [
"<Figure size 432x288 with 1 Axes>" "<Figure size 432x288 with 1 Axes>"
] ]
...@@ -770,7 +791,7 @@ ...@@ -770,7 +791,7 @@
"source": [ "source": [
"mortality_rate = [mortality_rate_smoker,mortality_rate_non_smoker]\n", "mortality_rate = [mortality_rate_smoker,mortality_rate_non_smoker]\n",
"smoking = ['smoker', 'non-smoker']\n", "smoking = ['smoker', 'non-smoker']\n",
"plt.bar(smoking, mortality_rate,color=['blue', 'green'])\n", "plt.bar(smoking, mortality_rate,color=['#E69F00', '#56B4E9'],width = 0.5)\n",
"plt.xticks(smoking)\n", "plt.xticks(smoking)\n",
"plt.yticks(mortality_rate)\n", "plt.yticks(mortality_rate)\n",
"plt.xlabel('Smoking')\n", "plt.xlabel('Smoking')\n",
...@@ -800,7 +821,7 @@ ...@@ -800,7 +821,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 25, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -825,39 +846,271 @@ ...@@ -825,39 +846,271 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Nous allons reprendre les calculs d'effectif et de taux de mortalite calcules precedemment, mais nous allons les categoriser par tranche d'age. Les femmes ayant participe a ces etudes seront reparties dans quatre categories en fonction de leur age : 18-34 ans, 34-54 ans, 55-64 ans, plus de 65 ans." "Nous allons reprendre les calculs d'effectif et de taux de mortalite calcules precedemment, mais nous allons les categoriser par tranche d'age. Les femmes ayant participe a ces etudes seront reparties dans quatre categories en fonction de leur age : 18-35 ans, 35-55 ans, 55-64 ans, plus de 65 ans."
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"class_18_to_35 = []\n",
"class_35_to_55 = []\n",
"class_55_to_64 = []\n",
"class_over_65 = []\n",
"for i in range(len(raw_data)):\n",
" if raw_data.iloc[i][2] < 35:\n",
" class_18_to_35.append(raw_data.iloc[i])\n",
" elif 35 <= raw_data.iloc[i][2] < 55:\n",
" class_35_to_55.append(raw_data.iloc[i])\n",
" elif 55 <= raw_data.iloc[i][2] < 65 :\n",
" class_55_to_64.append(raw_data.iloc[i])\n",
" else :\n",
" class_over_65.append(raw_data.iloc[i])"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 12, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [],
"source": [
"alive_and_smoker_18to35 = 0\n",
"alive_and_non_smoker_18to35 = 0\n",
"dead_and_smoker_18to35 = 0\n",
"dead_and_non_smoker_18to35 = 0\n",
"for i in range(len(class_18_to_35)):\n",
" if class_18_to_35[i][0] == \"Yes\":\n",
" if class_18_to_35[i][1] == \"Alive\":\n",
" alive_and_smoker_18to35 += 1\n",
" else :\n",
" dead_and_smoker_18to35 += 1\n",
" else :\n",
" if class_18_to_35[i][1] == \"Alive\":\n",
" alive_and_non_smoker_18to35 += 1\n",
" else :\n",
" dead_and_non_smoker_18to35 += 1\n",
"\n",
"alive_and_smoker_35to55 = 0\n",
"alive_and_non_smoker_35to55 = 0\n",
"dead_and_smoker_35to55 = 0\n",
"dead_and_non_smoker_35to55 = 0\n",
"for i in range(len(class_35_to_55)):\n",
" if class_35_to_55[i][0] == \"Yes\":\n",
" if class_35_to_55[i][1] == \"Alive\":\n",
" alive_and_smoker_35to55 += 1\n",
" else :\n",
" dead_and_smoker_35to55 += 1\n",
" else :\n",
" if class_35_to_55[i][1] == \"Alive\":\n",
" alive_and_non_smoker_35to55 += 1\n",
" else :\n",
" dead_and_non_smoker_35to55 += 1\n",
"\n",
"alive_and_smoker_55to64 = 0\n",
"alive_and_non_smoker_55to64 = 0\n",
"dead_and_smoker_55to64 = 0\n",
"dead_and_non_smoker_55to64 = 0\n",
"for i in range(len(class_55_to_64)):\n",
" if class_55_to_64[i][0] == \"Yes\":\n",
" if class_55_to_64[i][1] == \"Alive\":\n",
" alive_and_smoker_55to64 += 1\n",
" else :\n",
" dead_and_smoker_55to64 += 1\n",
" else :\n",
" if class_55_to_64[i][1] == \"Alive\":\n",
" alive_and_non_smoker_55to64 += 1\n",
" else :\n",
" dead_and_non_smoker_55to64 += 1\n",
"\n",
"alive_and_smoker_over65 = 0\n",
"alive_and_non_smoker_over65 = 0\n",
"dead_and_smoker_over65 = 0\n",
"dead_and_non_smoker_over65 = 0\n",
"for i in range(len(class_over_65)):\n",
" if class_over_65[i][0] == \"Yes\":\n",
" if class_over_65[i][1] == \"Alive\":\n",
" alive_and_smoker_over65 += 1\n",
" else :\n",
" dead_and_smoker_over65 += 1\n",
" else :\n",
" if class_over_65[i][1] == \"Alive\":\n",
" alive_and_non_smoker_over65 += 1\n",
" else :\n",
" dead_and_non_smoker_over65 += 1"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Smoker [18-35]</th>\n",
" <th>Non-Smoker [18-35]</th>\n",
" <th>Smoker [35-55]</th>\n",
" <th>Non-Smoker [35-55]</th>\n",
" <th>Smoker [55-64]</th>\n",
" <th>Non-Smoker [55-64]</th>\n",
" <th>Smoker [65+]</th>\n",
" <th>Non-Smoker [65+]</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Alive</th>\n",
" <td>182</td>\n",
" <td>221</td>\n",
" <td>190</td>\n",
" <td>172</td>\n",
" <td>64</td>\n",
" <td>81</td>\n",
" <td>7</td>\n",
" <td>28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dead</th>\n",
" <td>7</td>\n",
" <td>6</td>\n",
" <td>39</td>\n",
" <td>19</td>\n",
" <td>51</td>\n",
" <td>40</td>\n",
" <td>42</td>\n",
" <td>165</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Smoker [18-35] Non-Smoker [18-35] Smoker [35-55] Non-Smoker [35-55] \\\n",
"Alive 182 221 190 172 \n",
"Dead 7 6 39 19 \n",
"\n",
" Smoker [55-64] Non-Smoker [55-64] Smoker [65+] Non-Smoker [65+] \n",
"Alive 64 81 7 28 \n",
"Dead 51 40 42 165 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data_18to35 = [[alive_and_smoker_18to35,alive_and_non_smoker_18to35],[dead_and_smoker_18to35, dead_and_non_smoker_18to35]]\n",
"data_35to55 = [[alive_and_smoker_35to55,alive_and_non_smoker_35to55],[dead_and_smoker_35to55, dead_and_non_smoker_35to55]]\n",
"data_55to64 = [[alive_and_smoker_55to64,alive_and_non_smoker_55to64],[dead_and_smoker_55to64, dead_and_non_smoker_55to64]]\n",
"data_over65 = [[alive_and_smoker_over65,alive_and_non_smoker_over65],[dead_and_smoker_over65, dead_and_non_smoker_over65]]\n",
"\n",
"df1 = pd.DataFrame(data_18to35, columns=[\"Smoker [18-35]\", \"Non-Smoker [18-35]\"], index = [\"Alive\", \"Dead\"])\n",
"df2 = pd.DataFrame(data_35to55, columns=[\"Smoker [35-55]\", \"Non-Smoker [35-55]\"], index = [\"Alive\", \"Dead\"])\n",
"df3 = pd.DataFrame(data_55to64, columns=[\"Smoker [55-64]\", \"Non-Smoker [55-64]\"], index = [\"Alive\", \"Dead\"])\n",
"df4 = pd.DataFrame(data_over65, columns=[\"Smoker [65+]\", \"Non-Smoker [65+]\"], index = [\"Alive\", \"Dead\"])\n",
"\n",
"df_total = pd.concat([df1,df2,df3,df4],axis=1)\n",
"df_total"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"398 438 236 242\n" "0.037037037037037035 0.02643171806167401\n",
"0.1703056768558952 0.09947643979057591\n",
"0.4434782608695652 0.3305785123966942\n",
"0.8571428571428571 0.8549222797927462\n"
] ]
} }
], ],
"source": [ "source": [
"class_18_to_34 = []\n", "mortality_rate_smoker_18to35 = dead_and_smoker_18to35/(alive_and_smoker_18to35 + dead_and_smoker_18to35)\n",
"class_34_to_54 = []\n", "mortality_rate_non_smoker_18to35 = dead_and_non_smoker_18to35/(alive_and_non_smoker_18to35 + dead_and_non_smoker_18to35)\n",
"class_55_to_64 = []\n", "\n",
"class_over_65 = []\n", "mortality_rate_smoker_35to55 = dead_and_smoker_35to55/(alive_and_smoker_35to55 + dead_and_smoker_35to55)\n",
"for i in range(len(raw_data)):\n", "mortality_rate_non_smoker_35to55 = dead_and_non_smoker_35to55/(alive_and_non_smoker_35to55 + dead_and_non_smoker_35to55)\n",
" if raw_data.iloc[i][2] < 34:\n", "\n",
" class_18_to_34.append(raw_data.iloc[i])\n", "mortality_rate_smoker_55to64 = dead_and_smoker_55to64/(alive_and_smoker_55to64 + dead_and_smoker_55to64)\n",
" elif 34 <= raw_data.iloc[i][2] < 55:\n", "mortality_rate_non_smoker_55to64 = dead_and_non_smoker_55to64/(alive_and_non_smoker_55to64 + dead_and_non_smoker_55to64)\n",
" class_34_to_54.append(raw_data.iloc[i])\n", "\n",
" elif 55 <= raw_data.iloc[i][2] < 65 :\n", "mortality_rate_smoker_over65 = dead_and_smoker_over65/(alive_and_smoker_over65 + dead_and_smoker_over65)\n",
" class_55_to_64.append(raw_data.iloc[i])\n", "mortality_rate_non_smoker_over65 = dead_and_non_smoker_over65/(alive_and_non_smoker_over65 + dead_and_non_smoker_over65)\n",
" else :\n", "\n",
" class_over_65.append(raw_data.iloc[i])\n", "print(mortality_rate_smoker_18to35,mortality_rate_non_smoker_18to35)\n",
"print(len(class_18_to_34),len(class_34_to_54),len(class_55_to_64),len(class_over_65))" "print(mortality_rate_smoker_35to55,mortality_rate_non_smoker_35to55)\n",
"print(mortality_rate_smoker_55to64,mortality_rate_non_smoker_55to64)\n",
"print(mortality_rate_smoker_over65,mortality_rate_non_smoker_over65)\n"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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mlkEOdzOzDEoV7pJGSForqU7StCL7h0naKqk2eX2r9KWamVlaLT6sQ1IF8ABwIblnpdZImh8RqwuaPh8R/6sdajQzs1ZKM3IfAtRFxLqI2AHMAUa1b1lmZrYn0oR7L2B93np9sq3QUEnLJf1S0ieLHUjSRElLJS1taGhoQ7lmZpZGmnBXkW1RsP4KcGxEnAR8H/h5sQNFxMyIqI6I6srKytZVamZmqaUJ93qgT956b2BDfoOIeCsitiXLC4H9JPUoWZVmZtYqacK9BugvqUrS/sBYYH5+A0lHS1KyPCQ57uZSF2tmZum0eLVMRDRKmgw8DVQAsyJilaRJyf4ZwL8A/y6pEfg7MDYiCk/dmJlZB2kx3KHpVMvCgm0z8pbvB+4vbWlmZtZWvkPVzCyDHO5mZhnkcDczyyCHu5lZBjnczcwyyOFuZpZBDnczswxyuJuZZVCqm5jMrGUbZxebY6/9HDPBN4Fb8zxyNzPLIIe7mVkGOdzNzDLI4W5mlkEOdzOzDHK4m5llUKpwlzRC0lpJdZKm7abdqZJ2SvqX0pVoZmat1WK4S6oAHgBGAgOBcZIGNtPuLnJPbDIzszJKM3IfAtRFxLqI2AHMAUYVaTcFeAJ4o4T1mZlZG6QJ917A+rz1+mRbE0m9gMuBGeyGpImSlkpa2tDQ0NpazcwspTThXuye6sL7nv8D+FpE7NzdgSJiZkRUR0R1ZWVl2hrNzKyV0swtUw/0yVvvDWwoaFMNzJEE0AO4WFJjRPy8JFWamVmrpAn3GqC/pCrgf4CxwL/mN4iIqg+WJc0GFjjYzczKp8Vwj4hGSZPJXQVTAcyKiFWSJiX7d3ue3czMOl6qKX8jYiGwsGBb0VCPiAl7XpaZme0J36FqZpZBDnczswxyuJuZZZDD3cwsgxzuZmYZ5HA3M8sgh7uZWQY53M3MMsjhbmaWQQ53M7MMcribmWVQqrllzKzzGfNkxz707PFLjuzQz7M945G7mVkGOdzNzDLI4W5mlkGpwl3SCElrJdVJmlZk/yhJKyTVJg/APrP0pZqZWVot/kFVUgXwAHAhueep1kiaHxGr85r9GpgfESFpMPBj4IT2KNjMzFqWZuQ+BKiLiHURsQOYA4zKbxAR2yIiktWDgMDMzMomTbj3Atbnrdcn2z5E0uWSXgWeBK4tdiBJE5PTNksbGhraUq+ZmaWQJtxVZNsuI/OI+FlEnABcBny72IEiYmZEVEdEdWVlZesqNTOz1NKEez3QJ2+9N7ChucYR8RzwMUk99rA2MzNrozThXgP0l1QlaX9gLDA/v4Gkj0tSsvwpYH9gc6mLNTOzdFq8WiYiGiVNBp4GKoBZEbFK0qRk/wzgn4Hxkt4D/g6MyfsDq5mZdbBUc8tExEJgYcG2GXnLdwF3lbY0MzNrK9+hamaWQQ53M7MMcribmWWQw93MLIMc7mZmGeRwNzPLIIe7mVkGOdzNzDLI4W5mlkEOdzOzDHK4m5llkMPdzCyDHO5mZhnkcDczyyCHu5lZBqUKd0kjJK2VVCdpWpH9V0pakbx+K+mk0pdqZmZptRjukiqAB4CRwEBgnKSBBc3+BJwTEYPJPRx7ZqkLNTOz9NKM3IcAdRGxLiJ2AHOAUfkNIuK3EfG3ZPVFcg/RNjOzMkkT7r2A9Xnr9cm25nwO+GWxHZImSloqaWlDQ0P6Ks3MrFXShLuKbCv68GtJ55IL968V2x8RMyOiOiKqKysr01dpZmatkuYB2fVAn7z13sCGwkaSBgM/AEZGxObSlGdmZm2RZuReA/SXVCVpf2AsMD+/gaS+wFzgqoj4Q+nLNDOz1mhx5B4RjZImA08DFcCsiFglaVKyfwbwLeAI4D8lATRGRHX7lW1mZruT5rQMEbEQWFiwbUbe8ueBz5e2NDMzayvfoWpmlkEOdzOzDEp1Wsaya+PsYle6tp9jJhS9itbMSswjdzOzDHK4m5llkMPdzCyDHO5mZhnkcDczyyCHu5lZBjnczcwyyOFuZpZBDnczswxyuJuZZZDD3cwsgxzuZmYZlCrcJY2QtFZSnaRpRfafIGmJpHclfbn0ZZqZWWu0OCukpArgAeBCcs9TrZE0PyJW5zV7E5gKXNYuVZqZWaukGbkPAeoiYl1E7ADmAKPyG0TEGxFRA7zXDjWamVkrpQn3XsD6vPX6ZJuZmXVSaR7WUexpDm164oKkicBEgL59+7blELaXG/PkGx36eY9fcmSHfp5ZZ5Fm5F4P9Mlb7w1saMuHRcTMiKiOiOrKysq2HMLMzFJIE+41QH9JVZL2B8YC89u3LDMz2xMtnpaJiEZJk4GngQpgVkSskjQp2T9D0tHAUqA78L6k64GBEfFWO9ZuZmbNSPWA7IhYCCws2DYjb/l1cqdrzMysE/AdqmZmGeRwNzPLIIe7mVkGOdzNzDLI4W5mlkEOdzOzDHK4m5llkMPdzCyDHO5mZhnkcDczy6BU0w90NhtnF5uFuP0cM6FNMxybmZWNR+5mZhm0V47cO5ofMGFmexuP3M3MMsjhbmaWQQ53M7MMShXukkZIWiupTtK0Ivsl6b5k/wpJnyp9qWZmllaL4S6pAngAGAkMBMZJGljQbCTQP3lNBP5Pies0M7NWSDNyHwLURcS6iNgBzAFGFbQZBfwwcl4EDpV0TIlrNTOzlNJcCtkLWJ+3Xg+clqJNL2BjfiNJE8mN7AG2SVrbqmrL5qi2vrEHsKm1b/pxWz9tr+C+LB33ZensVX15bJpGacK92O2ghbdspmlDRMwEZqb4zEyQtDQiqstdRxa4L0vHfVk6nbkv05yWqQf65K33Bja0oY2ZmXWQNOFeA/SXVCVpf2AsML+gzXxgfHLVzOnA1ojYWHggMzPrGC2elomIRkmTgaeBCmBWRKySNCnZPwNYCFwM1AHvANe0X8l7lX3mFFQHcF+WjvuydDptXyrCMx6amWWN71A1M8sgh7uZWQY53FOQNEvSG5JW5m07WdKLkmolLZU0pJn3fjuZkqFW0iJJPZPt/ST9PdleK2lGR32fcpLUTdLvJC2XtErSrcn2WyT9T15/XNzM+4u224f788+Sfv/B72GyLVVfJm2nJFOLrJL0nYJ9fSVtk/Tl9v4enZGkQyX9VNKrktZIGtqavi03n3NPQdLZwDZyd+GemGxbBNwbEb9MfsBfjYhhRd7bPSLeSpanAgMjYpKkfsCCD463r5Ak4KCI2CZpP2Ax8EVgBLAtIqa38P5birXbh/vzz0B1RGzK23YL6fryXOBm4JKIeFfSkRHxRt7+J4D3gZdaOlYWSXoEeD4ifpBcKXggcD0t9G3S/3+OiNkdUmgz/LCOFCLiuSQ8PrQZ6J4sH0Iz1/V/EOyJgyhyc9e+JHKjiW3J6n7Ja5/ukzL6d+DOiHgXoCDYLwPWAW+XqbayktQdOBuYAJBMvbIjNzbZO/i0TNtdD9wtaT0wHfh6cw0l3ZG0uxL4Vt6uKknLJP1G0lntW27nIalCUi3wBvDfEfFSsmtycgprlqTDdnOI5trti/0ZwCJJLyfTe3wgTV8OAM6S9FLSZ6cCSDoI+Bpwa/uW3qkdBzQADye/Uz9I+gXS/56WV0T4leIF9ANW5q3fB/xzsnwF8KsUx/g6cGuyfABwRLL8aXJz83Qv9/fs4D49FHgGOJHc5B4V5AYcd5C7n6LYe4q221f7E+iZ/PNIYDm50WbavlyZ/B6L3ASBf0qWpwNXJG1uAb5c7u9Zhn6tBhqB05L17wHf3s3v3yCgNnm9DryWt35EWb5DuTtxb3kVCfet/ONvFgLeSpYfTn6gC4sc49j8YxTse5bcudOyf9cO7tf/XRge+X3dQn/2c39+6DvvEsS760vgKWBYXtv/C1QCzwN/Tl5bgDeByeX+fh3cl0eTO2/+wfpZwJPN9W2Rn8OEcn8Hn3Nvuw3AOeRC5DzgjwAR8aG7cyX1j4g/JqufBV5NtlcCb0bETknHkZsLf13HlF4+yfd+LyK2SPoIcAFwl6Rj4h9TVlxOblRZrD+LttsX+zM5TdAlIv5fsnwRcFvavgR+Tu5391lJA4D9gU0R0XRKK++Ps/e377fpXCLidUnrJR0fEWuB84HVzfVtZ+RwT0HSfwHDgB6S6smNNv8N+J6krsB2/jGVcaE7JR1P7qqDvwCTku1nk/sXsRHYCUyKiDfb71t0GscAjyj3EJguwI8jYoGkH0k6mdw55D8D1zXz/u80025f7M+jgJ8lf+TrCjwWEU+1oi9nAbOUu8R3B3B1JENPA2AK8Ghypcw6ctOq3Jeyb8vOl0KamWWQr5YxM8sgh7uZWQY53M3MMsjhbmaWQQ53M7MMcribmWWQw93MLIP+P0fdiOEAK6slAAAAAElFTkSuQmCC\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
] ]
}, },
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"mortality_rate_smoker = (mortality_rate_smoker_18to35,mortality_rate_smoker_35to55,mortality_rate_smoker_55to64,mortality_rate_smoker_over65)\n",
"mortality_rate_non_smoker = (mortality_rate_non_smoker_18to35,mortality_rate_non_smoker_35to55,mortality_rate_non_smoker_55to64,mortality_rate_non_smoker_over65)\n",
"age = ['18-35','35-55','55-64','65+']\n",
"indices = range(len(mortality_rate_smoker))\n",
"width = np.min(np.diff(indices))/3.\n",
"\n",
"fig = plt.figure()\n",
"ax = fig.add_subplot(111)\n",
"ax.bar(indices-width/2.,mortality_rate_smoker,width,color='#E69F00',label='Smoker')\n",
"ax.bar(indices+width/2.,mortality_rate_non_smoker,width,color='#56B4E9',label='Non-Smoker')\n",
"#tiks = ax.get_xticks().tolist()\n",
"plt.xticks(indices + width / 2, age)\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
......
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