{ "cells": [ { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "# titre" ] }, { "cell_type": "markdown", "metadata": { "hideCode": true, "hidePrompt": true }, "source": [ "Tout d'abord, il faut commencer par inclure les bibliothèques dont on aura besoin." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il faut ensuite charger et lire le fichier" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"Subject6_smoking.csv\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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SmokerStatusAge
0YesAlive21.0
1YesAlive19.3
2NoDead57.5
3NoAlive47.1
4YesAlive81.4
5NoAlive36.8
6NoAlive23.8
7YesDead57.5
8YesAlive24.8
9YesAlive49.5
10YesAlive30.0
11NoDead66.0
12YesAlive49.2
13NoAlive58.4
14NoDead60.6
15NoAlive25.1
16NoAlive43.5
17NoAlive27.1
18NoAlive58.3
19YesAlive65.7
20NoDead73.2
21YesAlive38.3
22NoAlive33.4
23YesDead62.3
24NoAlive18.0
25NoAlive56.2
26YesAlive59.2
27NoAlive25.8
28NoDead36.9
29NoAlive20.2
............
1284YesDead36.0
1285YesAlive48.3
1286NoAlive63.1
1287NoAlive60.8
1288YesDead39.3
1289NoAlive36.7
1290NoAlive63.8
1291NoDead71.3
1292NoAlive57.7
1293NoAlive63.2
1294NoAlive46.6
1295YesDead82.4
1296YesAlive38.3
1297YesAlive32.7
1298NoAlive39.7
1299YesDead60.0
1300NoDead71.0
1301NoAlive20.5
1302NoAlive44.4
1303YesAlive31.2
1304YesAlive47.8
1305YesAlive60.9
1306NoDead61.4
1307YesAlive43.0
1308NoAlive42.1
1309YesAlive35.9
1310NoAlive22.3
1311YesDead62.1
1312NoDead88.6
1313NoAlive39.1
\n", "

1314 rows × 3 columns

\n", "
" ], "text/plain": [ " Smoker Status Age\n", "0 Yes Alive 21.0\n", "1 Yes Alive 19.3\n", "2 No Dead 57.5\n", "3 No Alive 47.1\n", "4 Yes Alive 81.4\n", "5 No Alive 36.8\n", "6 No Alive 23.8\n", "7 Yes Dead 57.5\n", "8 Yes Alive 24.8\n", "9 Yes Alive 49.5\n", "10 Yes Alive 30.0\n", "11 No Dead 66.0\n", "12 Yes Alive 49.2\n", "13 No Alive 58.4\n", "14 No Dead 60.6\n", "15 No Alive 25.1\n", "16 No Alive 43.5\n", "17 No Alive 27.1\n", "18 No Alive 58.3\n", "19 Yes Alive 65.7\n", "20 No Dead 73.2\n", "21 Yes Alive 38.3\n", "22 No Alive 33.4\n", "23 Yes Dead 62.3\n", "24 No Alive 18.0\n", "25 No Alive 56.2\n", "26 Yes Alive 59.2\n", "27 No Alive 25.8\n", "28 No Dead 36.9\n", "29 No Alive 20.2\n", "... ... ... ...\n", "1284 Yes Dead 36.0\n", "1285 Yes Alive 48.3\n", "1286 No Alive 63.1\n", "1287 No Alive 60.8\n", "1288 Yes Dead 39.3\n", "1289 No Alive 36.7\n", "1290 No Alive 63.8\n", "1291 No Dead 71.3\n", "1292 No Alive 57.7\n", "1293 No Alive 63.2\n", "1294 No Alive 46.6\n", "1295 Yes Dead 82.4\n", "1296 Yes Alive 38.3\n", "1297 Yes Alive 32.7\n", "1298 No Alive 39.7\n", "1299 Yes Dead 60.0\n", "1300 No Dead 71.0\n", "1301 No Alive 20.5\n", "1302 No Alive 44.4\n", "1303 Yes Alive 31.2\n", "1304 Yes Alive 47.8\n", "1305 Yes Alive 60.9\n", "1306 No Dead 61.4\n", "1307 Yes Alive 43.0\n", "1308 No Alive 42.1\n", "1309 Yes Alive 35.9\n", "1310 No Alive 22.3\n", "1311 Yes Dead 62.1\n", "1312 No Dead 88.6\n", "1313 No Alive 39.1\n", "\n", "[1314 rows x 3 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Création de 2 tableaux à partir du contenu du fichier csv :\n", " nonFumeuses contient les données des personnes qui ne fument pas (qui ont \"No\" dans la colonne \"Smoker)\n", " fumeuses contient les données des personnes qui fument (qui ont \"Yes\" dans la colonne \"Smoker\")" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "#trier = raw_data.sort_values(by = [\"Smoker\"])\n", "masq = raw_data[\"Smoker\"] == \"Yes\"\n", "fumeuses = raw_data.loc[masq]\n", "nonFumeuses = raw_data.loc[raw_data[\"Smoker\"]==\"No\"]\n", "\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SmokerStatusAge
0YesAlive21.0
1YesAlive19.3
4YesAlive81.4
7YesDead57.5
8YesAlive24.8
9YesAlive49.5
10YesAlive30.0
12YesAlive49.2
19YesAlive65.7
21YesAlive38.3
23YesDead62.3
26YesAlive59.2
30YesAlive34.6
31YesAlive51.9
32YesAlive49.9
35YesAlive46.7
36YesAlive44.4
37YesAlive29.5
38YesDead33.0
39YesAlive35.6
40YesAlive39.1
42YesAlive35.7
46YesDead44.3
48YesAlive37.5
49YesAlive22.1
53YesAlive39.0
56YesAlive40.1
60YesAlive58.1
61YesAlive37.3
63YesDead36.3
............
1240YesAlive29.7
1243YesAlive40.1
1251YesAlive27.8
1252YesAlive52.4
1253YesAlive27.8
1254YesAlive41.0
1259YesAlive40.8
1260YesAlive20.4
1263YesAlive20.9
1264YesAlive45.5
1269YesAlive38.8
1270YesAlive55.5
1271YesAlive24.9
1273YesAlive55.7
1276YesAlive58.5
1278YesAlive43.7
1282YesAlive51.2
1284YesDead36.0
1285YesAlive48.3
1288YesDead39.3
1295YesDead82.4
1296YesAlive38.3
1297YesAlive32.7
1299YesDead60.0
1303YesAlive31.2
1304YesAlive47.8
1305YesAlive60.9
1307YesAlive43.0
1309YesAlive35.9
1311YesDead62.1
\n", "

582 rows × 3 columns

\n", "
" ], "text/plain": [ " Smoker Status Age\n", "0 Yes Alive 21.0\n", "1 Yes Alive 19.3\n", "4 Yes Alive 81.4\n", "7 Yes Dead 57.5\n", "8 Yes Alive 24.8\n", "9 Yes Alive 49.5\n", "10 Yes Alive 30.0\n", "12 Yes Alive 49.2\n", "19 Yes Alive 65.7\n", "21 Yes Alive 38.3\n", "23 Yes Dead 62.3\n", "26 Yes Alive 59.2\n", "30 Yes Alive 34.6\n", "31 Yes Alive 51.9\n", "32 Yes Alive 49.9\n", "35 Yes Alive 46.7\n", "36 Yes Alive 44.4\n", "37 Yes Alive 29.5\n", "38 Yes Dead 33.0\n", "39 Yes Alive 35.6\n", "40 Yes Alive 39.1\n", "42 Yes Alive 35.7\n", "46 Yes Dead 44.3\n", "48 Yes Alive 37.5\n", "49 Yes Alive 22.1\n", "53 Yes Alive 39.0\n", "56 Yes Alive 40.1\n", "60 Yes Alive 58.1\n", "61 Yes Alive 37.3\n", "63 Yes Dead 36.3\n", "... ... ... ...\n", "1240 Yes Alive 29.7\n", "1243 Yes Alive 40.1\n", "1251 Yes Alive 27.8\n", "1252 Yes Alive 52.4\n", "1253 Yes Alive 27.8\n", "1254 Yes Alive 41.0\n", "1259 Yes Alive 40.8\n", "1260 Yes Alive 20.4\n", "1263 Yes Alive 20.9\n", "1264 Yes Alive 45.5\n", "1269 Yes Alive 38.8\n", "1270 Yes Alive 55.5\n", "1271 Yes Alive 24.9\n", "1273 Yes Alive 55.7\n", "1276 Yes Alive 58.5\n", "1278 Yes Alive 43.7\n", "1282 Yes Alive 51.2\n", "1284 Yes Dead 36.0\n", "1285 Yes Alive 48.3\n", "1288 Yes Dead 39.3\n", "1295 Yes Dead 82.4\n", "1296 Yes Alive 38.3\n", "1297 Yes Alive 32.7\n", "1299 Yes Dead 60.0\n", "1303 Yes Alive 31.2\n", "1304 Yes Alive 47.8\n", "1305 Yes Alive 60.9\n", "1307 Yes Alive 43.0\n", "1309 Yes Alive 35.9\n", "1311 Yes Dead 62.1\n", "\n", "[582 rows x 3 columns]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fumeuses" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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SmokerStatusAge
2NoDead57.5
3NoAlive47.1
5NoAlive36.8
6NoAlive23.8
11NoDead66.0
13NoAlive58.4
14NoDead60.6
15NoAlive25.1
16NoAlive43.5
17NoAlive27.1
18NoAlive58.3
20NoDead73.2
22NoAlive33.4
24NoAlive18.0
25NoAlive56.2
27NoAlive25.8
28NoDead36.9
29NoAlive20.2
33NoAlive19.4
34NoAlive56.9
41NoDead69.7
43NoDead75.8
44NoAlive25.3
45NoDead83.0
47NoAlive18.5
50NoAlive82.8
51NoAlive45.0
52NoDead73.3
54NoAlive28.4
55NoDead73.7
............
1262NoAlive41.2
1265NoAlive26.7
1266NoAlive41.8
1267NoAlive33.7
1268NoAlive56.5
1272NoAlive33.0
1274NoAlive25.7
1275NoAlive19.5
1277NoAlive23.4
1279NoAlive34.4
1280NoDead83.9
1281NoAlive34.9
1283NoDead86.3
1286NoAlive63.1
1287NoAlive60.8
1289NoAlive36.7
1290NoAlive63.8
1291NoDead71.3
1292NoAlive57.7
1293NoAlive63.2
1294NoAlive46.6
1298NoAlive39.7
1300NoDead71.0
1301NoAlive20.5
1302NoAlive44.4
1306NoDead61.4
1308NoAlive42.1
1310NoAlive22.3
1312NoDead88.6
1313NoAlive39.1
\n", "

732 rows × 3 columns

\n", "
" ], "text/plain": [ " Smoker Status Age\n", "2 No Dead 57.5\n", "3 No Alive 47.1\n", "5 No Alive 36.8\n", "6 No Alive 23.8\n", "11 No Dead 66.0\n", "13 No Alive 58.4\n", "14 No Dead 60.6\n", "15 No Alive 25.1\n", "16 No Alive 43.5\n", "17 No Alive 27.1\n", "18 No Alive 58.3\n", "20 No Dead 73.2\n", "22 No Alive 33.4\n", "24 No Alive 18.0\n", "25 No Alive 56.2\n", "27 No Alive 25.8\n", "28 No Dead 36.9\n", "29 No Alive 20.2\n", "33 No Alive 19.4\n", "34 No Alive 56.9\n", "41 No Dead 69.7\n", "43 No Dead 75.8\n", "44 No Alive 25.3\n", "45 No Dead 83.0\n", "47 No Alive 18.5\n", "50 No Alive 82.8\n", "51 No Alive 45.0\n", "52 No Dead 73.3\n", "54 No Alive 28.4\n", "55 No Dead 73.7\n", "... ... ... ...\n", "1262 No Alive 41.2\n", "1265 No Alive 26.7\n", "1266 No Alive 41.8\n", "1267 No Alive 33.7\n", "1268 No Alive 56.5\n", "1272 No Alive 33.0\n", "1274 No Alive 25.7\n", "1275 No Alive 19.5\n", "1277 No Alive 23.4\n", "1279 No Alive 34.4\n", "1280 No Dead 83.9\n", "1281 No Alive 34.9\n", "1283 No Dead 86.3\n", "1286 No Alive 63.1\n", "1287 No Alive 60.8\n", "1289 No Alive 36.7\n", "1290 No Alive 63.8\n", "1291 No Dead 71.3\n", "1292 No Alive 57.7\n", "1293 No Alive 63.2\n", "1294 No Alive 46.6\n", "1298 No Alive 39.7\n", "1300 No Dead 71.0\n", "1301 No Alive 20.5\n", "1302 No Alive 44.4\n", "1306 No Dead 61.4\n", "1308 No Alive 42.1\n", "1310 No Alive 22.3\n", "1312 No Dead 88.6\n", "1313 No Alive 39.1\n", "\n", "[732 rows x 3 columns]" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nonFumeuses" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "732\n", "582\n" ] } ], "source": [ "nbTotalF = len(fumeuses.axes[0])\n", "nbTotalNF = len(nonFumeuses.axes[0])\n", "print(nbTotalF)\n", "print(nbTotalNF)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "139" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nbDecedeesF = len(fumeuses.loc[fumeuses[\"Status\"]==\"Dead\"])\n", "nbDecedeesF" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "230" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nbDecedeesNF = len(nonFumeuses.loc[nonFumeuses[\"Status\"]==\"Dead\"])\n", "nbDecedeesNF" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Sur la période donnée, il y a pour les fumeuses un taux de mortalité de : 18.989071038251364 %\n", "et il y a pour les non fumeuses un taux de mortalité de : 39.51890034364261 %\n" ] } ], "source": [ "tauxMortF = nbDecedeesF/nbTotalF\n", "tauxMortNF = nbDecedeesNF/nbTotalNF\n", "print(\"Sur la période donnée, il y a pour les fumeuses un taux de mortalité de : \", tauxMortF*100, \"%\")\n", "print(\"et il y a pour les non fumeuses un taux de mortalité de : \", tauxMortNF*100, \"%\")" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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FumeusestauxMortalite
0Fumeuses18.989071
1nonFumeuses39.518900
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" ], "text/plain": [ " Fumeuses tauxMortalite\n", "0 Fumeuses 18.989071\n", "1 nonFumeuses 39.518900" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = {\"tauxMortalite\" : [tauxMortF*100, tauxMortNF*100], \"Fumeuses\" : [\"Fumeuses\", \"nonFumeuses\"]}\n", "dt = pd.DataFrame(data = d)\n", "dt" ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]],\n", " dtype=object)" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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