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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 @@ ...@@ -1643,7 +1643,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 35,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1656,10 +1656,10 @@ ...@@ -1656,10 +1656,10 @@
} }
], ],
"source": [ "source": [
"tauxMortF = nbDecedeesF/nbTotalF\n", "tauxMortF = nbDecedeesF/nbTotalF*100\n",
"tauxMortNF = nbDecedeesNF/nbTotalNF\n", "tauxMortNF = nbDecedeesNF/nbTotalNF*100\n",
"print(\"Sur la période donnée, il y a pour les fumeuses un taux de mortalité de : \", tauxMortF*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*100, \"%\")" "print(\"et il y a pour les non fumeuses un taux de mortalité de : \", tauxMortNF, \"%\")"
] ]
}, },
{ {
...@@ -1671,7 +1671,7 @@ ...@@ -1671,7 +1671,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 11, "execution_count": 36,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1720,13 +1720,13 @@ ...@@ -1720,13 +1720,13 @@
"1 nonFumeuses 31.420765" "1 nonFumeuses 31.420765"
] ]
}, },
"execution_count": 11, "execution_count": 36,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "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 = pd.DataFrame(data = d)\n",
"dt" "dt"
] ]
...@@ -1740,7 +1740,7 @@ ...@@ -1740,7 +1740,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 37,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1783,37 +1783,251 @@ ...@@ -1783,37 +1783,251 @@
"# Etape 2" "# 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", "cell_type": "code",
"execution_count": 25, "execution_count": 65,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"179 219\n"
]
}
],
"source": [ "source": [
"nb18_34F = len(fumeuses.loc[fumeuses[\"Age\"]<=34]) - len(fumeuses.loc[fumeuses[\"Age\"]<18])\n", "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_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", "cell_type": "code",
"execution_count": 34, "execution_count": 63,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"179\n",
"5 fumeuses ayant entre 18 et 34 ans lors du premier sondage sont décédées durant la période de 20 ans\n" "5 fumeuses ayant entre 18 et 34 ans lors du premier sondage sont décédées durant la période de 20 ans\n"
] ]
} }
], ],
"source": [ "source": [
"test = fumeuses.loc[fumeuses[\"Age\"]<=34]\n", "test = fumeuses.loc[fumeuses[\"Age\"]<34]\n",
"t2 = test.loc[test[\"Age\"]>18]\n", "t2 = test.loc[test[\"Age\"]>=18]\n",
"\n", "print(len(t2))\n",
"nbDecedees18_34F = len(t2.loc[t2[\"Status\"]==\"Dead\"])\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\")" "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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pNUm/lHSHpNslbS1pdUmXSpqb38csmV0xMzNb+rRy1/r3gJXy8K9aWOfiiNgUeDNwOzAVmBURE4FZedzMzMwGodnNbj+VtF5h0hrAacCMPNyUpFWBbYFTINXeI+IJYLdcBvl998EEbmZmZs1r5EcB35Y0TdIqwHHAb4DLgf8uUfYGQB9wmqQbJZ0saSVgfETMB8jv4+qtLGmKpNmSZvf19bWwS2ZmZkuPZtfI74qIvYCLgXNITePbR8RWEXF2ibJHAlsCP4yILYC/00IzekRMj4jeiOjt6ekpu5qZmdlSpVnT+mhJB5Nq1h8CngUukbRTybIfAB6IiGvy+C9JiX2BpAl5GxOAhYMN3szMbGnXrGn9V4CA1YCfRsSppOvb75Q04M1uEfEwcL+kTfKkScBfgAuAyXnaZOD8QcZuZma21Gv2O/Ie4HRgeeDfASLi78BXJa1dsvxPA2fk35vfDRxA+vIwU9KBwH3AHoML3czMzJol8q+Tend7Efiv4oyIeKBM4RFxE9BbZ9aksgGamZlZYw0TeUTMBGZ2MBYzMzNrUbOb3X4oadMG81aQ9FFJ+7QvNDMzMxtIs6b1k4Gj881qt5B+E748MBEYS7p+fmK7AzQzM7PGmjWtXw98MPfQ9jZgAvAM8L2ImNOh+MzMzKyJZjVyACLiKeCyDsRiZmZmLWrloSlmZmY2xDiRm5mZVVjpRC5puXYGYmZmZq2rm8gljSwMv03SrcDcPP5mSd/vUHxmZmbWRKMa+cGSts7DxwPvBx4FiIibgXd3IDYzMzMbQKNE/iNg3/5lIuLemvkvti8kMzMzK6vuz88i4gXgU3n0fklvA0LSCNKDUP7aofjMzMysiTI3u30COBRYF1gAbAUc0s6gzMzMrJwBO4QBNoiIvYsTJG1FvmZuZmZm3VOmRv6DOtPcx7qZmdkQ0LBGnq+Lbw30SPpMYdaqwKh2B2ZmZmYDa9a0vhLpKWcjgZ7C9KeBPdoZlJmZmZXT7OlnVwBXSDotIu7uYExmZmZWUrOm9WMj4jDgWElROz8iPtjWyMzMzGxAzZrWz87vJ3QiEDMzM2tds6b1a/P7rM6FY2ZmZq1o1rR+I/CaJvV+EbHlQIVLmke6Oe5F4IWI6JW0Oqm2vx4wD9gzIh5vKWozMzMDmjetf3gJbePdEfFIYXwqMCsipkmamse/tIS2ZWZmtlRp1rT+tzZtczdguzw8A7gSJ3IzM7NBGbBnN0lvlXS1pCcl/VPSs5KeKll+AJdIul7SlDxtfETMB8jv4xpsd4qk2ZJm9/X1ldycmZnZ0qVMX+s/APYDzgLeBuwPrFOy/HdGxEOSxgGXSrqjbGARMR2YDtDb29vwWr2ZmdnSrExf68tExJ3AyIh4PiJ+DOxQpvCIeCi/LwTOI30RWCBpAkB+XzioyM3MzKxUIv+7pGWBmyUdLenTwMoDrSRpJUmr9A8D7wFuAy4AJufFJgPnDypyMzMzK9W0vj8p4X8KOAyYCHyoxHrjgfMk9W/n5xFxsaTrgJmSDgTuw/22m5mZDVqZRL5zRJwA/BP4KoCkTwFzm62U+2d/c53pjwKTWg/VzMzMapVpWv9YnWkHLulAzMzMrHXNenbbC9gb2EDSuYVZqwJPtDswMzMzG1izpvVrgUeBtYETC9OfBm5sZ1BmZmZWTrOe3e6RdB/wmB+cYmZmNjQ1vUYeES8Cz0latUPxmJmZWQvK3LW+iPQb8kuAv/dPjIhD2xaVmZmZlVImkV+WX2ZmZjbEDJjII+IUSSOBjfKkuyLihfaGZWZmZmUMmMglbQP8FHgQEPA6SR+JiP9rd3BmZmbWXJmm9eNIvbv9BUDSZqTE3tvOwMzMzGxgZXp2W7Y/iQNExO3Asu0LyczMzMoqUyO/QdKPSLVwgH1xhzBmS63njzqs2yGUMuqIY7sdgllHlEnkhwCfAb5IukZ+FfD9dgZlZmZm5ZS5a/2fwHfyy8zMzIaQAa+RS9pJ0nWSFkp6TNLjkh7rRHBmZmbWXJmm9ROAPYFbgZfaG46ZmZm1okwifwC4KSKcxM3MzIaYMon8i8CFkq4Enu2fGBHHtysoMzMzK6dMIj8KeB5YDTetm5mZDSllEvm4iHhL2yMxMzOzlpXp2W2WpO3bHomZmZm1rEwi/zhwmaRFg/n5maQRkm6U9Os8vrqkSyXNze9jBhu8mZnZ0q5MIh8LjAJGAz15vKeFbXwWuL0wPhWYFRETgVl53MzMzAZhwEQeES/We5UpXNLawC7AyYXJuwEz8vAMYPdWgzYzM7OkTI18cfwv6edrxbvdx0fEfID8Pq7NMZiZmQ1bbUvkkt4PLIyI6we5/hRJsyXN7uvrW8LRmZmZDQ+lErmkrSR9NA+vIWndEqu9E9hV0jzgLGB7ST8DFkiakMuaACyst3JETI+I3ojo7elp5ZK8mZnZ0qPMQ1MOB44ADs+Tlgd+PtB6EfHliFg7ItYD9gYuj4j9gAuAyXmxycD5g4jbzMzMKFcj/zCwM/B3gIh4EFh1MbY5DdhR0lxgxzxuZmZmg1CmZ7dnIyIkBYCkFVvdSERcCVyZhx8FJrVahpmZmb1WmRr5uZJOBEZLOgC4BDi1vWGZmZlZGQPWyCPi25LeBzwHvBn4ZkT8tu2RmZmZ2YDKNK2TE7eTt5mZ2RDTMJFLehyIRvMjYvW2RGRmZmalNauRjwVE+ulZH/DTPL4v0PINb2ZmZrbkNUzk/f2pS3pPRLy9MOv7kq4Gvt3u4MzMzKy5Mneth6S9JAlA0l5tjsnMzMxKKpPI/x34KPCopEeAj5Ca183MzKzLyvz87G7So0jNzMxsiGn3Y0zNzMysjZzIzczMKsyJ3MzMrMLKPMb0NEmrFMbXlnRJe8MyMzOzMsrUyGcD10p6T35oyhXAD9sblpmZmZVR5q71EyXdTErgjwBbRsT8tkdmZmZmAyrTtL4P6bGlHwN+Blwo6U3tDszMzMwGVubpZ/sC20bEwwCStgbOID3S1Mys0qbd+Ei3Qyhl6hZjux2CDVFlmtbfXzP+Z0lvb7S8mZmZdc6AiVzS9AazpizhWMzMzKxFZZrWZxWGlwc+ANzfnnDMzMysFWWa1s8ujkv6KXBp2yIyMzOz0gbTs9v6wOsHWkjS8pKulXSzpDmSjsrTV5d0qaS5+X3MIGIwMzMzyv387HFJj+XXE6Ta+FdKlP0ssH1EvBnYHNhJ0lbAVGBWREwkNdtPHXz4ZmZmS7cy18iLv3l4KSKiTMF5uUV5dFR+BbAbsF2ePgO4EvhSmTLNzMzs1cpcI39R0mhgQ2B5Sf3T/zTQupJGANcDGwEnRsQ1ksb39wwXEfMljVucHTAzM1ualfn52YHAocBawK3AW4GreaVW3VBEvAhsLmk14LxWeoSTNIX8E7d111237GpmZmZLlTI3u30O6AXmRcQ2wFuAlvpaj4gnSE3oOwELJE0AyO8LG6wzPSJ6I6K3p6enlc2ZmZktNcok8n9GxDMAkpaNiDnApgOtJKkn18SRtAKwA3AHcAEwOS82GTh/MIGbmZlZk6Z1SSMj4gVgfk7IFwK/k/QYsKBE2ROAGfk6+TLAzIj4taQ/AzNzk/19wB6LvRdmZmZLqWbXyK8lPbJ01zz+VUmTgNHARQMVHBG3AFvUmf4oMGkQsZqZmVmNZolctRMiYla9Bc3MzKw7miXyHkmHNpoZEf/ThnjMzMysBc0S+QhgZerUzM3MzGxoaJbI50fE1zsWiZmZmbWs2c/PXBM3MzMb4polct9ZbmZmNsQ1TOQR8VgnAzEzM7PWDeZ55GZmZjZEOJGbmZlVmBO5mZlZhTmRm5mZVZgTuZmZWYU5kZuZmVWYE7mZmVmFOZGbmZlVmBO5mZlZhTmRm5mZVZgTuZmZWYU5kZuZmVWYE7mZmVmFOZGbmZlVmBO5mZlZhbUtkUtaR9IVkm6XNEfSZ/P01SVdKmlufh/TrhjMzMyGu3bWyF8ADouIzYCtgE9KegMwFZgVEROBWXnczMzMBqFtiTwi5kfEDXn4aeB2YC1gN2BGXmwGsHu7YjAzMxvuOnKNXNJ6wBbANcD4iJgPKdkD4xqsM0XSbEmz+/r6OhGmmZlZ5bQ9kUtaGTgH+FxEPFV2vYiYHhG9EdHb09PTvgDNzMwqrK2JXNIoUhI/IyLOzZMXSJqQ508AFrYzBjMzs+GsnXetCzgFuD0i/qcw6wJgch6eDJzfrhjMzMyGu5FtLPudwEeAWyXdlKd9BZgGzJR0IHAfsEcbYzAzMxvW2pbII+KPgBrMntSu7ZqZmS1N3LObmZlZhTmRm5mZVVg7r5GbDcrzRx3W7RBKGXXEsd0OwczMNXIzM7MqcyI3MzOrMCdyMzOzCnMiNzMzqzAncjMzswpzIjczM6swJ3IzM7MKcyI3MzOrMCdyMzOzCnMiNzMzqzAncjMzswpzIjczM6swJ3IzM7MKcyI3MzOrMCdyMzOzCnMiNzMzqzAncjMzswpzIjczM6uwtiVySadKWijptsK01SVdKmlufh/Tru2bmZktDdpZIz8d2Klm2lRgVkRMBGblcTMzMxuktiXyiLgKeKxm8m7AjDw8A9i9Xds3MzNbGnT6Gvn4iJgPkN/HNVpQ0hRJsyXN7uvr61iAZmZmVTKy2wE0EhHTgekAvb290eVwzAZt2o2PdDuEUqZuMbbbIZjZIHS6Rr5A0gSA/L6ww9s3MzMbVjqdyC8AJufhycD5Hd6+mZnZsNLOn5+dCfwZ2ETSA5IOBKYBO0qaC+yYx83MzGyQ2naNPCL2aTBrUru2aWZmtrRxz25mZmYV5kRuZmZWYU7kZmZmFeZEbmZmVmFO5GZmZhXmRG5mZlZhTuRmZmYV5kRuZmZWYU7kZmZmFeZEbmZmVmFO5GZmZhXmRG5mZlZhTuRmZmYV5kRuZmZWYU7kZmZmFeZEbmZmVmEjux1ANzx/1GHdDqGUUUccW2q5aTc+0uZIloypW4ztdghmZsOOa+RmZmYV5kRuZmZWYU7kZmZmFeZEbmZmVmFdSeSSdpJ0p6S7JE3tRgxmZmbDQccTuaQRwInA+4A3APtIekOn4zAzMxsOulEjfxtwV0TcHRHPAWcBu3UhDjMzs8pTRHR2g9KHgZ0i4qA8/hHg7RHxqZrlpgBT8ugmwJ0dDbR1Y4Fq/KC7HO/P0Ob9Gdq8P0NbFfbn9RHRU2bBbnQIozrTXvNtIiKmA9PbH86SIWl2RPR2O44lxfsztHl/hjbvz9A23PanG03rDwDrFMbXBh7qQhxmZmaV141Efh0wUdL6kpYF9gYu6EIcZmZmldfxpvWIeEHSp4DfASOAUyNiTqfjaIPKXAYoyfsztHl/hjbvz9A2rPan4ze7mZmZ2ZLjnt3MzMwqzInczMyswpzIM0mnSloo6bbCtM0lXS3pJkmzJb2twbr/LemWvNwlktasmb+upEWSvlCYNk/SrXmdmyS9o4Lxn1MY/7Ck0/Pw/pL6Cvv2k8Xct+UlXSvpZklzJB1VM/8LkkJS3QeeSzpS0oOFeHYuuX9L8vx0ax86co4K2+s/ZrNb2b+8zKdz181zJH2n5P4tyXPUjfg7dn7qxLuapF9KukPS7ZK2LswbcH/rlLdEz0eJ7bUj/q6dj8USEX6l+wS2BbYEbitMuwR4Xx7eGbiywbqrFoY/A5xUM/8c4BfAFwrT5gFjKx7/vcAb8/iHgdPz8P7ACUtw3wSsnIdHAdcAW+XxdUg3Tt7b6HgCRxZjrzO/E+enW/vQkXPU7JiV3L93A5cBy+XxcV04R92Iv2Pnp07MM4CD8vCywGpl9jd/Fvcve/wqFn/XzsfivFwjzyLiKuCx2snAqnl4NA1+7x4RTxVGV6LQwY2k3YG7gbbemd+l+I8BvjLIkEuLZFEeHZVf/TEeB3yROp0KldHB89OtfejIORpAmf37BDAtIp4FiIiF/TM6dY6aaGf8XTk/klYlffk/BSAinouIJ/Lsxfo8dkIb4x8Kfy8tcyJv7nPAdyXdTzrBX260oKRv5uX2Bb6Wp60EfAk4qsFqV+RmmmuWbNgva3f8M4EtJW1UZ95ehWaoAxZnJ3IsIyTdBCwELo2IayTtCjwYETeXKOJTSpcPTpU0JpfZ0fPTpX3o2Dki/eO8RNL1Sl0s08L+bQxsI+kaSb+h3u0tAAALZElEQVSX9Na8fifPUTfi7+T5KdoA6ANOk3SjpJMlrdTi57Gedv9P69eu+Lt1PhZPt5sEhtILWI9XN00fD3woD+8JXFaijC8DR+XhY4A945XmnLY1C3YrfuBjpG/FHWmGAlYDrgD+hdQ8PXqg4wmMJ/VZsAzwTVLfBR0/P93ah06dI2DN/D4OuJlUYyq7f7flz6tID1a6Jw937Bx1K/5O/w3l8nuBF0jPuQD4HvDdRvsL/D/gpvx6GLivML5Gu/9mOhl/N87HYh+PbgcwlF68NhE+ySu/tRfwVB4+LX8AflOnjNf3lwH8IX845gFPkJq+P1X7Iaty/KQm4rnAoR38J3QE8FVSzbY/vhfyH+frBti/l49Rp89Pt/ahS+foyFb2D7gY2K6w/t+Anm6do07G36Xz8zpgXmF8G2BWo/2tc2z2r1Nm285HJ+PvxvlY3Fc3HppSJQ8B/wpcCWxPOrlExKuaVSRNjIi5eXRX4I683DaFZY4EFkXECW2P+hVtjz8inpd0HDAVuLwdOyGpB3g+Ip6QtAKwA/DtiBhXWGYe0BsRjwC1+zchIubn0Q+Qak8dPT/d3IcOnaOVgGUi4uk8/B7g62X3D/gV6TN6paSNSTcvPdKpc9TN+DtxfmpFxMOS7pe0SUTcCUwCboiISYV45/HK/g4p7Yy/G+djcTmRZ5LOBLYDxkp6gFRj+jjwPUkjgX/yymNVa02TtAnwEumux0PaH/GrdTn+U4DDBxN3SROAGZL6m5ZnRsSvW1j/O5I2J10DnQccvORDHFC396Hd52g8cJ4kSP9Xfh4RF7ew/qnAqUo/n3wOmBy5KtQh3Y6/3eennk8DZyg98+JuXvvlZKhrZ/zdOB+D5i5azczMKsx3rZuZmVWYE7mZmVmFOZGbmZlVmBO5mZlZhTmR27AhaZ/80x8zs6WGE7m9hqTXSTpL0t8k/UXSbyRtLGk9FZ6u1qXY5ik/0Sj/TrR/+ibALsDXl/D2dpfU32Vt7RPIpi3hbW2ay71R0oZLsuyhROlJUms2mHd6PsbL5fGx/ec5f/6eKRz/m/JPj4Y0SctJuizHu1e34xkMScdI2r7bcVh9/h25vYrSD2nPA2ZExN552uak39ne383YBrAB8ElSP8njI2LBEir3i6ROcvodFxHHlF1Z0piIeLzk4rsD50fEEa0EWEH7kzq0qfsQH+BFUjeZP6wz728RsXnZDbV4/NtlC2BUK3EPQd8HfkxFOkhZ2rhGbrXeTeqB7KT+CRFxU0T8obhQrh39QdIN+fWOPH2CpKty7eM2SdsoPSzk9Dx+q6TP52U3lHSx0kMq/iBp09pgJK2h9Iz0GyX9iNTVbL++vMzKwBdI/ZcfD2xVWP+rSs8rvlTSmcrPgy657Y2BZxezZ6vZkn4uafv8JakupeeLfw44SNIVta0fSs9XPjIPXynpuHycb5f0VknnSpor6RuFdfZTegb6TZJ+lDujQdKiwjLFZy7vkc/RzZKuytNGSPqupOuUHthycJ7+mvNcZ5/eovQAkesl/S6v82FSP9ln5HVXqHM4/hf4vFJHRovrP/MxOFjpiVkN5ZaCc/PnYq4KzxRXumxza97XbxemL1J64NDNkq6WNL6mzHHAz4DN8/5uqFe3KvVKujIPHylpRv68z5P0QUnfydu9WNKovNxrjmuefqWk3jxcbMl4Y+FzcIukiXn6az4favC3GhH3AmtIet1inQ1rj273EevX0HqRnkd+XIN56/FKH98rAsvn4YnA7Dx8GPBfeXgEsArwFtLTvvrL6X9u8CxgYh5+O3B5nW0eD3wtD+9C6tlsbM0yI8nPVCf1lXwXKeH3kvrDXiHHMZf80IqS2z4AOLYwfiTwIK88bOG9JY7nCOD9wLnA7aRHJK7ZYNkjC/G9fKzz+BeAI/PwlaTuXQE+S6rZTgCWAx4A1gA2Ay4k1QQBfgB8NA8vKpRbfDDErcBaNedoCnB4Hl4OmA2sX+881+zLKOBPQE8e34tXHvRyJanrzHrH4PQc06n5+I8l96mdj8kzheN/YsnP9CbAt/Pn4nTgXQ2W25/UQ9hoYHlSL4frAGuS+uzuIX3WLgd2z+sE8G95+Dv9x6qm3O2AXxfG5/HKwzx6gSsL5/+P+di9GfgH8L487zxSi02p41pz3L4P7JuHlyX9PdT9fNDgbzUP/5j8ECa/htbLTes2WKOAE5Sa3V8kPcYR4DpSV5WjgF9FxE2S7gY2kPR94CLSoyJXBt4B/KJQUV2uzna2BT4IEBEXSarXTCrgaEnbkrqZXYt0KeBdpKbqZwAkXZjfy257ArnWX9BS03pEvAj8Gvi1Un/r3wLuk/SOiLi2bDl1XJDfbwXmRO6HPR/rdUj7/hbguryPK5AeKNHM/wGnS5pJ+uIBqc/xf8k1aUhJbiJ1znNNWZsAbwIuzdsfAcynvKPzPl5UM72lpnWASH1xf0nSV4C9SefiJxHxmTqLz4qIJwEk/YX0EKE1SMm2vwXoDNLn8lek7lj7u9q9Htixldjq+G2kvr5vJR2z/m5ibyV9kRnMcf0z8F+S1gbOjYi5kiZR//NxITV/q4VyFpK+1NgQ40RuteaQakQD+TywgFRzWIbUlzsRcVVOqLsAP5X03Yj4iaQ3A+8lXcfek9SM/ETJf8oD9SO8L6m29Jb8T3AeqUbVqCl7mZLbfoaUuEqRtA7pHyHASZEvT0gaTao5HQA8DxwI3DJAcS/w6ktfy9fMfza/v1QY7h8fSdr3GRFR7xn0xeP5crkRcYikt5PO3U35S5qAT0fE72oLqXeei7NJXzC2brKPDUXEXUrPbt+zlfUknUa6Jv1QROycp4l0yegAUuvLCcDJDYooHssXeeVYNvJ8RETN8gMpntu65zUiXpJULLt4Xhsd17rlRsTPlZ4PvgvwO0kH0eTzUedv9WOFMp8psX/WYb5GbrUuB5aT9PH+CUrXYP+1ZrnRwPyIeAn4CKlmgKTXAwsj4sekBw9sma8HLhMR55AeDbllRDwF3CNpj7ye8j+QWleREjWS3geMqbPM6LzN5yW9m1SLgtRM+W+Sls+18F0AWtj27cBGjQ/Vq0XE/RGxeX71J/GfATeQbsb7aERsGxEzIuKfAxS3ABindI/AcqTm+VbMAj6cr9EiafV8bgAWSNpM0jKkJ6mRl9kwIq6JiK8Bj5Bq9r8DPlG4PruxpJXqneea7d8J9EjaOq83StIb87ynSZc6BvJN0iWF0iLigHz8+5P4vqSn+X0SOBPYLCIOj4h5LRR7DfCv+brzCGAf4PetxFVjHqk2DPChFtdtdlyL5b78ZVzSBsDdEXE8qZXjX2jw+aj3t1rY9sbkp+7Z0OIaub1KRISkDwD/K2kqqaY9j1SDLvoBcE5OhlcAf8/TtyPdYPQ8sIh03W0t4LScOAD6awH7Aj+UdDipqf4s4Oaa7RwFnCnpBtI/z/vqhH0GcKGk2aRrp/2PYb1O0gW5zHtJ13efbGHbVwHHSlKhZtSqmaRnH7/Qykr5S8nXSUnknv59amH9v+R9uyQf9+dJyexe0uMZf036FcJtwMp5te/mG6FE+kd/M6nlYD3ghlyz7SNdq92O157n4vafy83xx+cWiZGkm9jmkK5TnyTpGWDr/ksfdfZhTj7vtV8SWnEvsE1EDHRZoaGImC/py6TPuUjPID9/MWI6CjglN/Vf02IszY7rMcBMSR/h1XeX7wXsl8/Vw6THsz7W4PPxDHX+VvMXuY1If0M2xPjpZzasSVo5IhZJWpGUmKdExA0trP894MKIuKxtQZoNcfnL/ZYR8dVux2Kv5aZ1G+6m52utNwDntJLEs6NJd+ibLc1GAsd2OwirzzVyMzOzCnON3MzMrMKcyM3MzCrMidzMzKzCnMjNzMwqzInczMyswpzIzczMKuz/AyRkfG+WO+1gAAAAAElFTkSuQmCC\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", "cell_type": "code",
"execution_count": null, "execution_count": null,
......
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