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Tarata/.ipynb_checkpoints/test-checkpoint.ipynb
thpc-ensae e5e5d9f93a deuxieme essai
2023-05-04 11:42:53 +00:00

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{
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"id": "c6b75e30-ce16-46c4-ab91-9ec69b3a5a9a",
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"source": [
"url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\""
]
},
{
"cell_type": "code",
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"id": "617f1d2a-49ab-4a1b-840c-73ca28e70ae1",
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"source": [
"#Importation des données \n",
"import pandas as pd\n",
"data = pd.io.parsers.read_csv(url)"
]
},
{
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"execution_count": 27,
"id": "ec96f94d-f855-42c0-b056-0520af40a8f5",
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" <td>202.000000</td>\n",
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" 0 1 2 3 4 5 \\\n",
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"std 9.038662 0.467299 0.960126 17.599748 51.776918 0.356198 \n",
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"\n",
" 6 7 8 9 10 13 \n",
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"min 0.000000 71.000000 0.000000 0.000000 1.000000 0.000000 \n",
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"metadata": {},
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"id": "de75f7d4-dad0-451d-bdf8-5b2f32b7d34a",
"metadata": {
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{
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"id": "42f3cfc7-0daa-4c34-9c8d-9a9e37901735",
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{
"data": {
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"<Axes: >"
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"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data[1].value_counts().plot.bar()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "973dcd7c-4c13-4992-bd5a-21419c245e11",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "b13946d0-7731-459d-b1bd-e2a89752df0e",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.10.9"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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