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d3ffe1dda057aedb6d37daa14d4dce86
mooc-rr
Commits
d4ec2e32
Commit
d4ec2e32
authored
Apr 27, 2020
by
d3ffe1dda057aedb6d37daa14d4dce86
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b8ea82a1
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exercice.ipynb
module3/exo3/exercice.ipynb
+133
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module3/exo3/exercice.ipynb
View file @
d4ec2e32
...
...
@@ -628,32 +628,21 @@
"metadata": {},
"outputs": [],
"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_non_smoker = 0\n",
"dead_and_smoker = 0\n",
"dead_and_non_smoker = 0\n",
"for i in range(len(raw_data)):\n",
" if raw_data.iloc[i][0] == \"Yes\":\n",
" total_smoker += 1\n",
" if raw_data.iloc[i][1] == \"Alive\":\n",
" total_alive +=1\n",
" alive_and_smoker += 1\n",
" else :\n",
" total_dead +=1\n",
" dead_and_smoker += 1\n",
" else :\n",
" total_non_smoker += 1\n",
" if raw_data.iloc[i][1] == \"Alive\":\n",
" total_alive +=1\n",
" alive_and_non_smoker += 1\n",
" else :\n",
" total_dead +=1\n",
" dead_and_non_smoker += 1"
" dead_and_non_smoker += 1\n"
]
},
{
...
...
@@ -730,7 +719,7 @@
}
],
"source": [
"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",
"data = [[alive_and_smoker,alive_and_non_smoker,
(alive_and_smoker+alive_and_non_smoker)],[dead_and_smoker, dead_and_non_smoker,(dead_and_non_smoker+dead_and_smoker)], [(dead_and_smoker+alive_and_smoker),(dead_and_non_smoker+alive_and_non_smoker),(alive_and_smoker+alive_and_non_smoker + dead_and_non_smoker+dead_and_smoker
)]]\n",
"\n",
"pd.DataFrame(data, columns=[\"Smoker\", \"Non-Smoker\", \"Total\"], index = [\"Alive\", \"Dead\",\"Total\"])"
]
...
...
@@ -854,21 +843,43 @@
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"582 732\n"
]
}
],
"source": [
"class_18_to_35 = []\n",
"class_35_to_55 = []\n",
"class_55_to_64 = []\n",
"class_over_65 = []\n",
"#class_18_to_35 = []\n",
"#class_35_to_55 = []\n",
"#class_55_to_64 = []\n",
"#class_over_65 = []\n",
"\n",
"smoker = []\n",
"non_smoker = []\n",
"\n",
"raw_data[\"Status\"].replace({\"Dead\": \"1\", \"Alive\": \"0\"}, inplace=True)\n",
"#raw_data[\"Age\"] = raw_data[\"Age\"].astype(str)\n",
"\n",
"#raw_data\n",
"\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",
" if raw_data.iloc[i][0] == \"Yes\":\n",
" smoker.append(raw_data.iloc[i])\n",
" else :\n",
" class_over_65.append(raw_data.iloc[i])"
" non_smoker.append(raw_data.iloc[i])\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])\n",
"print(len(smoker), len(non_smoker))"
]
},
{
...
...
@@ -878,68 +889,64 @@
"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_over65 = 0\n",
"dead_and_smoker_over65 = 0\n",
"\n",
"for i in range(len(smoker)):\n",
" if smoker[i][1] == \"0\" :\n",
" if smoker[i][2] < 35:\n",
" alive_and_smoker_18to35 += 1\n",
" elif 35 <= smoker[i][2] < 55:\n",
" alive_and_smoker_35to55 += 1\n",
" elif 55 <= smoker[i][2] < 65 :\n",
" alive_and_smoker_55to64 += 1\n",
" else :\n",
"
dead_and_smoker_55to64
+= 1\n",
"
alive_and_smoker_over65
+= 1\n",
" else :\n",
" if class_55_to_64[i][1] == \"Alive\":\n",
" alive_and_non_smoker_55to64 += 1\n",
" if smoker[i][2] < 35:\n",
" dead_and_smoker_18to35 += 1\n",
" elif 35 <= smoker[i][2] < 55:\n",
" dead_and_smoker_35to55 += 1\n",
" elif 55 <= smoker[i][2] < 65 :\n",
" dead_and_smoker_55to64 += 1\n",
" else :\n",
" dead_and_non_smoker_55to64 += 1\n",
"\n",
"alive_and_smoker_over65 = 0\n",
" dead_and_smoker_over65 += 1\n",
" \n",
"alive_and_non_smoker_18to35 = 0\n",
"dead_and_non_smoker_18to35 = 0\n",
"alive_and_non_smoker_35to55 = 0\n",
"dead_and_non_smoker_35to55 = 0\n",
"alive_and_non_smoker_55to64 = 0\n",
"dead_and_non_smoker_55to64 = 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",
" \n",
"for i in range(len(non_smoker)):\n",
" if non_smoker[i][1] == \"0\" :\n",
" if non_smoker[i][2] < 35:\n",
" alive_and_non_smoker_18to35 += 1\n",
" elif 35 <= non_smoker[i][2] < 55:\n",
" alive_and_non_smoker_35to55 += 1\n",
" elif 55 <= non_smoker[i][2] < 65 :\n",
" alive_and_non_smoker_55to64 += 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"
" if non_smoker[i][2] < 35:\n",
" dead_and_non_smoker_18to35 += 1\n",
" elif 35 <= non_smoker[i][2] < 55:\n",
" dead_and_non_smoker_35to55 += 1\n",
" elif 55 <= non_smoker[i][2] < 65 :\n",
" dead_and_non_smoker_55to64 += 1\n",
" else :\n",
" dead_and_non_smoker_over65 += 1\n",
" \n",
" \n"
]
},
{
...
...
@@ -1107,482 +1114,69 @@
},
{
"cell_type": "code",
"execution_count": 1
7
,
"execution_count": 1
6
,
"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</th>\n",
" <th>Status</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>21.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>19.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>57.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>47.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>81.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>36.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>23.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>57.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>24.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>49.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>30.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>66.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>49.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>58.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>60.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>25.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>43.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>27.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>58.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>65.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>73.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>38.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>33.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>62.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>18.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>56.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>59.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>25.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>36.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>20.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1284</th>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>36.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1285</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>48.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1286</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>63.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1287</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>60.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1288</th>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>39.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1289</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>36.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1290</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>63.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1291</th>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>71.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1292</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>57.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1293</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>63.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1294</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>46.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1295</th>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>82.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1296</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>38.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1297</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>32.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1298</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>39.7</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1299</th>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1300</th>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>71.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1301</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>20.5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1302</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>44.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1303</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>31.2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1304</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>47.8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1305</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>60.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1306</th>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>61.4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1307</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>43.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1308</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>42.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1309</th>\n",
" <td>Yes</td>\n",
" <td>0</td>\n",
" <td>35.9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1310</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>22.3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1311</th>\n",
" <td>Yes</td>\n",
" <td>1</td>\n",
" <td>62.1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1312</th>\n",
" <td>No</td>\n",
" <td>1</td>\n",
" <td>88.6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1313</th>\n",
" <td>No</td>\n",
" <td>0</td>\n",
" <td>39.1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>1314 rows × 3 columns</p>\n",
"</div>"
"name": "stdout",
"output_type": "stream",
"text": [
"Status Smoker\n",
"0 No 502\n",
" Yes 443\n",
"1 No 230\n",
" Yes 139\n",
"dtype: int64\n"
]
}
],
"text/plain": [
" Smoker Status Age\n",
"0 Yes 0 21.0\n",
"1 Yes 0 19.3\n",
"2 No 1 57.5\n",
"3 No 0 47.1\n",
"4 Yes 0 81.4\n",
"5 No 0 36.8\n",
"6 No 0 23.8\n",
"7 Yes 1 57.5\n",
"8 Yes 0 24.8\n",
"9 Yes 0 49.5\n",
"10 Yes 0 30.0\n",
"11 No 1 66.0\n",
"12 Yes 0 49.2\n",
"13 No 0 58.4\n",
"14 No 1 60.6\n",
"15 No 0 25.1\n",
"16 No 0 43.5\n",
"17 No 0 27.1\n",
"18 No 0 58.3\n",
"19 Yes 0 65.7\n",
"20 No 1 73.2\n",
"21 Yes 0 38.3\n",
"22 No 0 33.4\n",
"23 Yes 1 62.3\n",
"24 No 0 18.0\n",
"25 No 0 56.2\n",
"26 Yes 0 59.2\n",
"27 No 0 25.8\n",
"28 No 1 36.9\n",
"29 No 0 20.2\n",
"... ... ... ...\n",
"1284 Yes 1 36.0\n",
"1285 Yes 0 48.3\n",
"1286 No 0 63.1\n",
"1287 No 0 60.8\n",
"1288 Yes 1 39.3\n",
"1289 No 0 36.7\n",
"1290 No 0 63.8\n",
"1291 No 1 71.3\n",
"1292 No 0 57.7\n",
"1293 No 0 63.2\n",
"1294 No 0 46.6\n",
"1295 Yes 1 82.4\n",
"1296 Yes 0 38.3\n",
"1297 Yes 0 32.7\n",
"1298 No 0 39.7\n",
"1299 Yes 1 60.0\n",
"1300 No 1 71.0\n",
"1301 No 0 20.5\n",
"1302 No 0 44.4\n",
"1303 Yes 0 31.2\n",
"1304 Yes 0 47.8\n",
"1305 Yes 0 60.9\n",
"1306 No 1 61.4\n",
"1307 Yes 0 43.0\n",
"1308 No 0 42.1\n",
"1309 Yes 0 35.9\n",
"1310 No 0 22.3\n",
"1311 Yes 1 62.1\n",
"1312 No 1 88.6\n",
"1313 No 0 39.1\n",
"source": [
"#raw_data[\"Status\"].replace({\"Dead\": \"1\", \"Alive\": \"0\"}, inplace=True)\n",
"#raw_data\n",
"\n",
"[1314 rows x 3 columns]"
"count = raw_data.groupby(['Status', 'Smoker']).size() \n",
"print(count)"
]
},
"execution_count": 17,
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"raw_data[\"Status\"].replace({\"Dead\": \"1\", \"Alive\": \"0\"}, inplace=True)\n",
"raw_data"
"raw_data[\"Status\"] = raw_data[\"Status\"].astype(int)\n",
"\n",
"df_smoker = raw_data[raw_data['Smoker'] == 'Yes']\n",
"df_non_smoker = raw_data[raw_data['Smoker'] == 'No']\n",
" \n",
"df_smoker.plot(kind='scatter',x='Age',y='Status',color='#E69F00')\n",
"df_non_smoker.plot(kind='scatter',x='Age',y='Status',color='#56B4E9')\n",
"plt.show()"
]
},
{
...
...
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