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Commit a3f83dd0 authored by 264af2e9a1e4e844f861df089a5604e3's avatar 264af2e9a1e4e844f861df089a5604e3
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module3/exo2/exerciceTabac.ipynb
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......@@ -1643,7 +1643,7 @@
},
{
"cell_type": "code",
"execution_count": 10,
"execution_count": 35,
"metadata": {},
"outputs": [
{
......@@ -1656,10 +1656,10 @@
}
],
"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, \"%\")"
"tauxMortF = nbDecedeesF/nbTotalF*100\n",
"tauxMortNF = nbDecedeesNF/nbTotalNF*100\n",
"print(\"Sur la période donnée, il y a pour les fumeuses un taux de mortalité de : \", tauxMortF, \"%\")\n",
"print(\"et il y a pour les non fumeuses un taux de mortalité de : \", tauxMortNF, \"%\")"
]
},
{
......@@ -1671,7 +1671,7 @@
},
{
"cell_type": "code",
"execution_count": 11,
"execution_count": 36,
"metadata": {},
"outputs": [
{
......@@ -1720,13 +1720,13 @@
"1 nonFumeuses 31.420765"
]
},
"execution_count": 11,
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"d = {\"tauxMortalite\" : [tauxMortF*100, tauxMortNF*100], \"Statut\" : [\"Fumeuses\", \"nonFumeuses\"]}\n",
"d = {\"tauxMortalite\" : [tauxMortF, tauxMortNF], \"Statut\" : [\"Fumeuses\", \"nonFumeuses\"]}\n",
"dt = pd.DataFrame(data = d)\n",
"dt"
]
......@@ -1740,7 +1740,7 @@
},
{
"cell_type": "code",
"execution_count": 15,
"execution_count": 37,
"metadata": {},
"outputs": [
{
......@@ -1783,37 +1783,251 @@
"# Etape 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Première tentative pour calculer le nombre total de fumeuses et de non fumeuses ayant entre 18 et 34 ans"
]
},
{
"cell_type": "code",
"execution_count": 25,
"execution_count": 65,
"metadata": {},
"outputs": [],
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"179 219\n"
]
}
],
"source": [
"nb18_34F = len(fumeuses.loc[fumeuses[\"Age\"]<=34]) - len(fumeuses.loc[fumeuses[\"Age\"]<18])\n",
"nb18_34NF = len(nonFumeuses.loc[nonFumeuses[\"Age\"]<=34]) - len(nonFumeuses.loc[nonFumeuses[\"Age\"]<18])"
"nb18_34F = len(fumeuses.loc[fumeuses[\"Age\"]<34]) - len(fumeuses.loc[fumeuses[\"Age\"]<18])\n",
"nb18_34NF = len(nonFumeuses.loc[nonFumeuses[\"Age\"]<34]) - len(nonFumeuses.loc[nonFumeuses[\"Age\"]<18])\n",
"print(nb18_34F, nb18_34NF)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calcul avec une autre méthode du nombre de fumeuses entre 18 et 34 ans et calcul du nombre de fumeuses de appartenant à cet intervalle d'âge qui sont mortes."
]
},
{
"cell_type": "code",
"execution_count": 34,
"execution_count": 63,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"179\n",
"5 fumeuses ayant entre 18 et 34 ans lors du premier sondage sont décédées durant la période de 20 ans\n"
]
}
],
"source": [
"test = fumeuses.loc[fumeuses[\"Age\"]<=34]\n",
"t2 = test.loc[test[\"Age\"]>18]\n",
"\n",
"test = fumeuses.loc[fumeuses[\"Age\"]<34]\n",
"t2 = test.loc[test[\"Age\"]>=18]\n",
"print(len(t2))\n",
"nbDecedees18_34F = len(t2.loc[t2[\"Status\"]==\"Dead\"])\n",
"print(nbDecedees18_34F, \"fumeuses ayant entre 18 et 34 ans lors du premier sondage sont décédées durant la période de 20 ans\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Calcul du taux de mortalité pour les fumeuses entre 18 et 34 ans."
]
},
{
"cell_type": "code",
"execution_count": 64,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.793296089385475"
]
},
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tauxMort18_34F = nbDecedees18_34F/nb18_34F*100\n",
"tauxMort18_34F"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Une fois les calculs trouvés et testés sur le premier intervalle d'âge \\[18, 34[ , il vaut mieux créer une fonction qui calcule le taux de mortalité pour un intervalle et une DataFrame donnés."
]
},
{
"cell_type": "code",
"execution_count": 69,
"metadata": {},
"outputs": [],
"source": [
"def calculTMparClAge(borneInf, borneSup, data): # la borne supérieure de l'intervalle n'est pas comprise :\n",
" t1 = data.loc[data[\"Age\"]<borneSup] # [borneInf, borneSup[\n",
" t2 = t1.loc[t1[\"Age\"]>=borneInf]\n",
" nb = len(t2)\n",
" #print(nb)\n",
" nbMort = len(t2.loc[t2[\"Status\"]==\"Dead\"])\n",
" #print(nbMort)\n",
" tauxM = nbMort/nb*100\n",
" return tauxM\n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Le taux de mortalité des fumeuses pour la classe d'âge 18-34 est de : 2.793296089385475 %\n",
"Le taux de mortalité des non fumeuses pour la classe d'âge 18-34 est de : 2.73972602739726\n"
]
}
],
"source": [
"tauxMort18_34Fv2 = calculTMparClAge(18, 34, fumeuses)\n",
"print(\"Le taux de mortalité des fumeuses pour la classe d'âge 18-34 est de :\", tauxMort18_34Fv2, \"%\")\n",
"\n",
"tauxMort18_34NF = calculTMparClAge(18, 34, nonFumeuses)\n",
"print(\"Le taux de mortalité des non fumeuses pour la classe d'âge 18-34 est de :\", tauxMort18_34NF)"
]
},
{
"cell_type": "code",
"execution_count": 82,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Le taux de mortalité des fumeuses pour la classe d'âge 34-54 est de : 17.154811715481173 %\n",
"Le taux de mortalité des non fumeuses pour la classe d'âge 34-54 est de : 9.547738693467336 %\n"
]
}
],
"source": [
"tauxMort34_54F = calculTMparClAge(34, 54, fumeuses)\n",
"print(\"Le taux de mortalité des fumeuses pour la classe d'âge 34-54 est de :\", tauxMort34_54F, \"%\")\n",
"\n",
"tauxMort34_54NF = calculTMparClAge(34, 54, nonFumeuses)\n",
"print(\"Le taux de mortalité des non fumeuses pour la classe d'âge 34-54 est de :\", tauxMort34_54NF, \"%\")"
]
},
{
"cell_type": "code",
"execution_count": 84,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Le taux de mortalité des fumeuses pour la classe d'âge 54-64 est de : 44.34782608695652 %\n",
"Le taux de mortalité des non fumeuses pour la classe d'âge 54-64 est de : 32.773109243697476 %\n"
]
}
],
"source": [
"tauxMort54_64F = calculTMparClAge(54, 64, fumeuses)\n",
"print(\"Le taux de mortalité des fumeuses pour la classe d'âge 54-64 est de :\", tauxMort54_64F, \"%\")\n",
"\n",
"tauxMort54_64NF = calculTMparClAge(54, 64, nonFumeuses)\n",
"print(\"Le taux de mortalité des non fumeuses pour la classe d'âge 54-64 est de :\", tauxMort54_64NF, \"%\")"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Le taux de mortalité des fumeuses de la classe d'âge 64-150 est de : 85.71428571428571\n",
"Le taux de mortalité des fumeuses de la classe d'âge 64-150 est de : 85.12820512820512\n"
]
}
],
"source": [
"tauxMort64_150F = calculTMparClAge(64, 150, fumeuses)\n",
"print(\"Le taux de mortalité des fumeuses de la classe d'âge 64-150 est de :\", tauxMort64_150F)\n",
"\n",
"tauxMort64_150NF = calculTMparClAge(64, 150, nonFumeuses)\n",
"print(\"Le taux de mortalité des fumeuses de la classe d'âge 64-150 est de :\", tauxMort64_150NF)"
]
},
{
"cell_type": "code",
"execution_count": 100,
"metadata": {},
"outputs": [],
"source": [
"d2 = {\"classeAge\" : [\"18-34F\", \"18-34NF\", \"34-54F\", \"34-54NF\", \"54-64F\", \"54-64NF\", \"64+F\", \"64+NF\"],\n",
" \"tauxMortalite\" : [tauxMort18_34Fv2, tauxMort18_34NF, tauxMort34_54F, tauxMort34_54NF, tauxMort54_64F, tauxMort54_64NF, tauxMort64_150F, tauxMort64_150NF]}\n",
"dt2 = pd.DataFrame(data = d2)"
]
},
{
"cell_type": "code",
"execution_count": 101,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 576x360 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"%matplotlib inline\n",
"plt.figure(figsize=(8, 5))\n",
"plt.bar(dt2[\"classeAge\"], dt2[\"tauxMortalite\"], color=['salmon', 'skyblue'])\n",
"\n",
"plt.title(\"Taux de mortalité par classe d'âge\")\n",
"plt.xlabel(\"Classe d'âge (F -> fumeuses et NF -> non fumeuses)\")\n",
"plt.ylabel(\"Taux de mortalité (%)\")\n",
"\n",
"plt.show()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
......
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