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Commit f89a0dff authored by 625d4bd70e112d69188d5fafc86902fa's avatar 625d4bd70e112d69188d5fafc86902fa
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amélioration avec ajout d'un plot (en barre) via pandas

parent 2f7f0be7
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Showing with 125 additions and 58 deletions
module3/exo3/exercice_fr.ipynb
View file @ f89a0dff
...@@ -25,7 +25,7 @@ ...@@ -25,7 +25,7 @@
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...@@ -496,7 +496,7 @@ ...@@ -496,7 +496,7 @@
"[1314 rows x 3 columns]" "[1314 rows x 3 columns]"
] ]
}, },
"execution_count": 14, "execution_count": 2,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -508,7 +508,7 @@ ...@@ -508,7 +508,7 @@
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"hidePrompt": true "hidePrompt": true
...@@ -585,7 +585,7 @@ ...@@ -585,7 +585,7 @@
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"metadata": { "metadata": {
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"hidePrompt": true "hidePrompt": true
...@@ -599,7 +599,7 @@ ...@@ -599,7 +599,7 @@
"Name: Smoker, dtype: int64" "Name: Smoker, dtype: int64"
] ]
}, },
"execution_count": 39, "execution_count": 4,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -610,7 +610,7 @@ ...@@ -610,7 +610,7 @@
}, },
{ {
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"execution_count": 40, "execution_count": 5,
"metadata": { "metadata": {
"hideCode": false, "hideCode": false,
"hidePrompt": true, "hidePrompt": true,
...@@ -625,7 +625,7 @@ ...@@ -625,7 +625,7 @@
"Name: Status, dtype: int64" "Name: Status, dtype: int64"
] ]
}, },
"execution_count": 40, "execution_count": 5,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -650,7 +650,7 @@ ...@@ -650,7 +650,7 @@
}, },
{ {
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"execution_count": 41, "execution_count": 6,
"metadata": { "metadata": {
"hideCode": false, "hideCode": false,
"hidePrompt": true "hidePrompt": true
...@@ -718,7 +718,7 @@ ...@@ -718,7 +718,7 @@
" Dead 139" " Dead 139"
] ]
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"execution_count": 41, "execution_count": 6,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -731,7 +731,7 @@ ...@@ -731,7 +731,7 @@
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"metadata": { "metadata": {
"hideCode": false, "hideCode": false,
"hidePrompt": true "hidePrompt": true
...@@ -763,7 +763,7 @@ ...@@ -763,7 +763,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 55, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -834,7 +834,7 @@ ...@@ -834,7 +834,7 @@
"All 1314" "All 1314"
] ]
}, },
"execution_count": 55, "execution_count": 8,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -852,7 +852,7 @@ ...@@ -852,7 +852,7 @@
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{ {
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"execution_count": 70, "execution_count": 9,
"metadata": { "metadata": {
"scrolled": true "scrolled": true
}, },
...@@ -920,7 +920,7 @@ ...@@ -920,7 +920,7 @@
"All 945 369 1314" "All 945 369 1314"
] ]
}, },
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"metadata": {}, "metadata": {},
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} }
...@@ -932,7 +932,7 @@ ...@@ -932,7 +932,7 @@
}, },
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{ {
...@@ -963,7 +963,7 @@ ...@@ -963,7 +963,7 @@
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{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 68, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1038,7 +1038,7 @@ ...@@ -1038,7 +1038,7 @@
"All 945 369 1314" "All 945 369 1314"
] ]
}, },
"execution_count": 68, "execution_count": 11,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -1050,7 +1050,7 @@ ...@@ -1050,7 +1050,7 @@
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"execution_count": 69, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
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...@@ -1084,7 +1084,7 @@ ...@@ -1084,7 +1084,7 @@
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"cell_type": "code", "cell_type": "code",
"execution_count": 73, "execution_count": 13,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1155,7 +1155,7 @@ ...@@ -1155,7 +1155,7 @@
"All 945 369 1314 0.280822" "All 945 369 1314 0.280822"
] ]
}, },
"execution_count": 73, "execution_count": 13,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -1176,7 +1176,7 @@ ...@@ -1176,7 +1176,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 72, "execution_count": 14,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1242,7 +1242,7 @@ ...@@ -1242,7 +1242,7 @@
"All 40.020000 66.155285 47.359361" "All 40.020000 66.155285 47.359361"
] ]
}, },
"execution_count": 72, "execution_count": 14,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -1260,7 +1260,7 @@ ...@@ -1260,7 +1260,7 @@
}, },
{ {
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"execution_count": 74, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -1279,7 +1279,7 @@ ...@@ -1279,7 +1279,7 @@
}, },
{ {
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"execution_count": 75, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1809,7 +1809,7 @@ ...@@ -1809,7 +1809,7 @@
"[1314 rows x 4 columns]" "[1314 rows x 4 columns]"
] ]
}, },
"execution_count": 75, "execution_count": 16,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -1820,7 +1820,7 @@ ...@@ -1820,7 +1820,7 @@
}, },
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"execution_count": 84, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1891,7 +1891,7 @@ ...@@ -1891,7 +1891,7 @@
"All 403 13 416 0.031250" "All 403 13 416 0.031250"
] ]
}, },
"execution_count": 84, "execution_count": 17,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -1904,7 +1904,7 @@ ...@@ -1904,7 +1904,7 @@
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"execution_count": 86, "execution_count": 18,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1975,7 +1975,7 @@ ...@@ -1975,7 +1975,7 @@
"All 362 58 420 0.138095" "All 362 58 420 0.138095"
] ]
}, },
"execution_count": 86, "execution_count": 18,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -1988,7 +1988,7 @@ ...@@ -1988,7 +1988,7 @@
}, },
{ {
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"execution_count": 87, "execution_count": 19,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -2059,7 +2059,7 @@ ...@@ -2059,7 +2059,7 @@
"All 147 102 249 0.409639" "All 147 102 249 0.409639"
] ]
}, },
"execution_count": 87, "execution_count": 19,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -2072,7 +2072,7 @@ ...@@ -2072,7 +2072,7 @@
}, },
{ {
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"execution_count": 88, "execution_count": 20,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -2143,7 +2143,7 @@ ...@@ -2143,7 +2143,7 @@
"All 34 207 241 0.858921" "All 34 207 241 0.858921"
] ]
}, },
"execution_count": 88, "execution_count": 20,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -2163,24 +2163,24 @@ ...@@ -2163,24 +2163,24 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 98, "execution_count": 43,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f7ef506e5c0>" "<matplotlib.axes._subplots.AxesSubplot at 0x7f788f02f4a8>"
] ]
}, },
"execution_count": 98, "execution_count": 43,
"metadata": {}, "metadata": {},
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}, },
{ {
"data": { "data": {
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\n", 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\n",
"text/plain": [ "text/plain": [
"<Figure size 720x1080 with 1 Axes>" "<Figure size 1080x504 with 1 Axes>"
] ]
}, },
"metadata": { "metadata": {
...@@ -2190,49 +2190,116 @@ ...@@ -2190,49 +2190,116 @@
} }
], ],
"source": [ "source": [
"copy = donnees.copy()\n", "tableAge = donnees.pivot_table(index = 'tranche',values='Age', columns=['Smoker','Status'], aggfunc='count')\n",
"copy['Smoker']=donnees['Smoker'].replace({'Yes':1, 'No':0})\n", "tableAge.plot.bar(stacked=False,figsize=(15,7))"
"copy.plot.scatter(x='Smoker', y='Age', figsize=(10,15),s=1)"
] ]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Le graphe si dessus n'est pas si parlant, on peut sinon simplement calculer la proportion de fumeuses par tranche d'age et voir que cette proportion s'effondre pour les personnes de plus de 65 ans." "On peut sinon simplement calculer la proportion de fumeuses par tranche d'age et voir que cette proportion s'effondre pour les personnes de plus de 65 ans."
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 99, "execution_count": 48,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
"data": { "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>Smoker</th>\n",
" <th>No</th>\n",
" <th>Yes</th>\n",
" <th>All</th>\n",
" <th>ratio</th>\n",
" </tr>\n",
" <tr>\n",
" <th>tranche</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>18-34 ans</th>\n",
" <td>219</td>\n",
" <td>181</td>\n",
" <td>400</td>\n",
" <td>0.452500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35-54 ans</th>\n",
" <td>199</td>\n",
" <td>237</td>\n",
" <td>436</td>\n",
" <td>0.543578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55-65 ans</th>\n",
" <td>122</td>\n",
" <td>115</td>\n",
" <td>237</td>\n",
" <td>0.485232</td>\n",
" </tr>\n",
" <tr>\n",
" <th>&gt;65 ans</th>\n",
" <td>192</td>\n",
" <td>49</td>\n",
" <td>241</td>\n",
" <td>0.203320</td>\n",
" </tr>\n",
" <tr>\n",
" <th>All</th>\n",
" <td>732</td>\n",
" <td>582</td>\n",
" <td>1314</td>\n",
" <td>0.442922</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [ "text/plain": [
"tranche\n", "Smoker No Yes All ratio\n",
"18-34 ans 0.452500\n", "tranche \n",
"35-54 ans 0.543578\n", "18-34 ans 219 181 400 0.452500\n",
"55-65 ans 0.485232\n", "35-54 ans 199 237 436 0.543578\n",
">65 ans 0.203320\n", "55-65 ans 122 115 237 0.485232\n",
"Name: Smoker, dtype: float64" ">65 ans 192 49 241 0.203320\n",
"All 732 582 1314 0.442922"
] ]
}, },
"execution_count": 99, "execution_count": 48,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"copy.groupby('tranche')['Smoker'].mean()" "fumeuseAge = donnees.pivot_table(index = 'tranche',values='Age', columns='Smoker', aggfunc='count',margins=True)\n",
"fumeuseAge['ratio']=fumeuseAge['Yes']/fumeuseAge['All']\n",
"fumeuseAge"
] ]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
} }
], ],
"metadata": { "metadata": {
......
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