{ "cells": [ { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On va importer les données." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "data_url = \"Subject6_smoking.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On observe le jeu de données." ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
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": 46, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = pd.read_csv(data_url, encoding = 'iso-8859-1')\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il n'y a pas de donnée manquante dans le jeu de données. \n", "Il ne faut donc pas enlever de lignes. \n", "On cherche à comptabiliser le nombre de femmes fumeuses qui sont vivantes et mortes, de même pour les non-fumeuses. \n", "On crée un jeu de données en utilisant les fonctions prédéfinies dans la bibliothèque Pandas.\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "resultat = data.groupby([\"Smoker\",\"Status\"])[\"Age\"].nunique()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maintenant, on crée un jeu de données pour afficher les données sous-forme d'un tableau, en y ajoutant le taux de mortalité pour les femmes fumeuses (en divisant le nombre de femmes fumeuses décédées par le nombre de femmes fumeuses total) et pour les femmes non-fumeuses." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DécédéesVivantesTotalTaux de mortalité
Fumeuses1212743950.306
Non-fumeuses1843235070.363
\n", "
" ], "text/plain": [ " Décédées Vivantes Total Taux de mortalité\n", "Fumeuses 121 274 395 0.306\n", "Non-fumeuses 184 323 507 0.363" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "non_fum_vivante = resultat[0]\n", "non_fum_morte = resultat[1]\n", "fum_vivante = resultat[2]\n", "fum_morte = resultat[3]\n", "\n", "données=[[fum_morte,fum_vivante,fum_morte+fum_vivante,round(fum_morte/(fum_morte+fum_vivante),3)],[non_fum_morte,non_fum_vivante,non_fum_morte+non_fum_vivante,round(non_fum_morte/(non_fum_morte+non_fum_vivante),3)]]\n", "res = pd.DataFrame(données, index = [\"Fumeuses\", \"Non-fumeuses\"] , columns = [\"Décédées\", \"Vivantes\",\"Total\",\"Taux de mortalité\"])\n", "res\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On observe les données finales dans un graphe." ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 49, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.title(\"Taux de mortalité en fonction de l'usage ou non du tabac\")\n", "plt.ylabel(\"Taux de mortalité\")\n", "plt.bar(res.index,res[\"Taux de mortalité\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On observe que le taux de mortalité est plus important chez les non-fumeuses (0.363) que chez les fumeuses (0.306).\n", "C'est un résultat surprenant : on aurait attendu l'inverse. \n", "On va chercher à utiliser les valeurs selon l'âge. On trie donc les valeurs selon celui-ci." ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
SmokerStatusAge
654YesAlive18.0
1132YesAlive18.0
832YesAlive18.0
220YesAlive18.0
24NoAlive18.0
922YesAlive18.1
206YesAlive18.1
449YesAlive18.1
91NoAlive18.3
168NoAlive18.3
1107NoAlive18.3
47NoAlive18.5
701YesAlive18.5
282NoAlive18.5
98YesAlive18.6
685NoAlive18.6
94YesAlive18.6
774YesAlive18.7
1138NoAlive18.7
788NoAlive18.8
1075NoAlive18.8
79NoAlive18.9
539NoAlive18.9
195NoAlive19.0
688NoAlive19.0
951NoAlive19.1
1128NoAlive19.1
615NoAlive19.2
929NoAlive19.2
1YesAlive19.3
............
468YesDead86.8
150NoDead86.8
483NoAlive86.9
183NoDead87.0
512NoAlive87.4
196NoDead87.6
883NoAlive87.6
823NoDead87.6
278NoDead87.7
129YesDead87.8
536YesDead87.9
1222YesDead87.9
1225NoDead87.9
1152NoDead88.0
545NoDead88.1
657YesDead88.3
932NoDead88.4
131NoDead88.4
507NoDead88.5
1080YesDead88.6
1312NoDead88.6
525YesDead88.7
393NoDead88.8
201YesDead89.2
1246NoDead89.2
114NoDead89.3
1126NoDead89.5
163NoDead89.7
369NoAlive89.7
1108NoDead89.9
\n", "

1314 rows × 3 columns

\n", "
" ], "text/plain": [ " Smoker Status Age\n", "654 Yes Alive 18.0\n", "1132 Yes Alive 18.0\n", "832 Yes Alive 18.0\n", "220 Yes Alive 18.0\n", "24 No Alive 18.0\n", "922 Yes Alive 18.1\n", "206 Yes Alive 18.1\n", "449 Yes Alive 18.1\n", "91 No Alive 18.3\n", "168 No Alive 18.3\n", "1107 No Alive 18.3\n", "47 No Alive 18.5\n", "701 Yes Alive 18.5\n", "282 No Alive 18.5\n", "98 Yes Alive 18.6\n", "685 No Alive 18.6\n", "94 Yes Alive 18.6\n", "774 Yes Alive 18.7\n", "1138 No Alive 18.7\n", "788 No Alive 18.8\n", "1075 No Alive 18.8\n", "79 No Alive 18.9\n", "539 No Alive 18.9\n", "195 No Alive 19.0\n", "688 No Alive 19.0\n", "951 No Alive 19.1\n", "1128 No Alive 19.1\n", "615 No Alive 19.2\n", "929 No Alive 19.2\n", "1 Yes Alive 19.3\n", "... ... ... ...\n", "468 Yes Dead 86.8\n", "150 No Dead 86.8\n", "483 No Alive 86.9\n", "183 No Dead 87.0\n", "512 No Alive 87.4\n", "196 No Dead 87.6\n", "883 No Alive 87.6\n", "823 No Dead 87.6\n", "278 No Dead 87.7\n", "129 Yes Dead 87.8\n", "536 Yes Dead 87.9\n", "1222 Yes Dead 87.9\n", "1225 No Dead 87.9\n", "1152 No Dead 88.0\n", "545 No Dead 88.1\n", "657 Yes Dead 88.3\n", "932 No Dead 88.4\n", "131 No Dead 88.4\n", "507 No Dead 88.5\n", "1080 Yes Dead 88.6\n", "1312 No Dead 88.6\n", "525 Yes Dead 88.7\n", "393 No Dead 88.8\n", "201 Yes Dead 89.2\n", "1246 No Dead 89.2\n", "114 No Dead 89.3\n", "1126 No Dead 89.5\n", "163 No Dead 89.7\n", "369 No Alive 89.7\n", "1108 No Dead 89.9\n", "\n", "[1314 rows x 3 columns]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_sorted = data.sort_values(by=\"Age\")\n", "data_sorted" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On regroupe les données dans quatre classes :\n", " - la première pour les individus de 18 ans compris à 34 ans non-compris.\n", " - la deuxième pour ceux de 34 ans compris à 55 ans non-compris.\n", " - la troisième pour les individus 55 ans compris à 65 ans non-compris.\n", " - la quatrième pour les individus de plus de 65 ans." ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "classe1 = data_sorted[(data_sorted.Age>=18) & (data_sorted.Age<34)]\n", "classe2 = data_sorted[(data_sorted.Age>=34) & (data_sorted.Age<55)]\n", "classe3 = data_sorted[(data_sorted.Age>=55) & (data_sorted.Age<65)]\n", "classe4 = data_sorted[(data_sorted.Age>=65)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On réalise sur chaque classe la même chose qu'on a effectué sur la série entière. On crée donc une fonction pour pouvoir la réutiliser sur les quatre classes." ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "def regroupement(data):\n", " resultat = data.groupby([\"Smoker\",\"Status\"])[\"Age\"].nunique()\n", " non_fum_vivante = resultat[0]\n", " non_fum_morte = resultat[1]\n", " fum_vivante = resultat[2]\n", " fum_morte = resultat[3]\n", "\n", " données=[[fum_morte,fum_vivante,fum_morte+fum_vivante,round(fum_morte/(fum_morte+fum_vivante),3)],[non_fum_morte,non_fum_vivante,non_fum_morte+non_fum_vivante,round(non_fum_morte/(non_fum_morte+non_fum_vivante),3)]]\n", " sortie = pd.DataFrame(données, index = [\"Fumeuses\", \"Non-fumeuses\"] , columns = [\"Décédées\", \"Vivantes\",\"Total\",\"Taux de mortalité\"])\n", " return sortie\n", "\n", "plt.bar([\"F18-34\",\"NF18-34\",\"F34-55\",\"NF34-55\",\"F55-65\",\"NF55-65\",\"F+65\",\"NF+65\"],concatenation[\"Taux de mortalité\"])\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On appelle la fonction avec chaque classe." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [], "source": [ "d1=regroupement(classe1)\n", "d2=regroupement(classe2)\n", "d3=regroupement(classe3)\n", "d4=regroupement(classe4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On concatène toutes les classes étant donné qu'elles ont les mêmes colums." ] }, { "cell_type": "code", "execution_count": 54, "metadata": {}, "outputs": [], "source": [ "concatenation=pd.concat([d1,d2,d3,d4])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On affiche toutes les données dans un graphe." ] }, { "cell_type": "code", "execution_count": 55, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.bar([\"F18-34\",\"NF18-34\",\"F34-55\",\"NF34-55\",\"F55-65\",\"NF55-65\",\"F+65\",\"NF+65\"],concatenation[\"Taux de mortalité\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "D'après le graphique précédent, on observe que pour la tranche 18-34 ans, le taux de mortalité entre les fumeurs et les non-fumeurs est presque semblable. \n", "Au contraire, on observe que sur les trois autres tranches d'âge, le taux de mortalité pour les fumeurs est plus élevé que le taux de mortalité des non-fumeurs. \n", "Ce qui est contraire aux résultats trouvés précédemment pour l'ensemble de la série de données. \n", "Cela peut s'expliquer par le fait que l'échantillon dans chaque sous-groupe n'est pas le même (le nombre de fumeurs décédés ou vivants, et le nombre de non-fumeurs décédés et vivants n'est pas le même)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il m'est impossible d'effectuer une régression logistique." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 4 }