{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "c6b75e30-ce16-46c4-ab91-9ec69b3a5a9a", "metadata": { "tags": [] }, "outputs": [], "source": [ "url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/processed.cleveland.data\"" ] }, { "cell_type": "code", "execution_count": 3, "id": "617f1d2a-49ab-4a1b-840c-73ca28e70ae1", "metadata": { "tags": [] }, "outputs": [], "source": [ "#Importation des données \n", "import pandas as pd\n", "import seaborn as sns\n", "data = pd.io.parsers.read_csv(url)" ] }, { "cell_type": "code", "execution_count": 4, "id": "ec96f94d-f855-42c0-b056-0520af40a8f5", "metadata": { "tags": [] }, "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", "
63.01.01.0.1145.0233.01.0.22.0150.00.02.33.00.0.16.00
067.01.04.0160.0286.00.02.0108.01.01.52.03.03.02
167.01.04.0120.0229.00.02.0129.01.02.62.02.07.01
237.01.03.0130.0250.00.00.0187.00.03.53.00.03.00
341.00.02.0130.0204.00.02.0172.00.01.41.00.03.00
456.01.02.0120.0236.00.00.0178.00.00.81.00.03.00
\n", "
" ], "text/plain": [ " 63.0 1.0 1.0.1 145.0 233.0 1.0.2 2.0 150.0 0.0 2.3 3.0 0.0.1 \\\n", "0 67.0 1.0 4.0 160.0 286.0 0.0 2.0 108.0 1.0 1.5 2.0 3.0 \n", "1 67.0 1.0 4.0 120.0 229.0 0.0 2.0 129.0 1.0 2.6 2.0 2.0 \n", "2 37.0 1.0 3.0 130.0 250.0 0.0 0.0 187.0 0.0 3.5 3.0 0.0 \n", "3 41.0 0.0 2.0 130.0 204.0 0.0 2.0 172.0 0.0 1.4 1.0 0.0 \n", "4 56.0 1.0 2.0 120.0 236.0 0.0 0.0 178.0 0.0 0.8 1.0 0.0 \n", "\n", " 6.0 0 \n", "0 3.0 2 \n", "1 7.0 1 \n", "2 3.0 0 \n", "3 3.0 0 \n", "4 3.0 0 " ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.head()" ] }, { "cell_type": "code", "execution_count": 21, "id": "0a9b659c-28c2-4463-8be8-204b6b45e5da", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "Int64Index([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13], dtype='int64')" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.columns" ] }, { "cell_type": "code", "execution_count": 22, "id": "a540a17a-deae-4d8d-affd-f987f9379cbe", "metadata": { "tags": [] }, "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", "
012345678910111213
29845.01.01.0110.0264.00.00.0132.00.01.22.00.07.01
29968.01.04.0144.0193.01.00.0141.00.03.42.02.07.02
30057.01.04.0130.0131.00.00.0115.01.01.22.01.07.03
30157.00.02.0130.0236.00.02.0174.00.00.02.01.03.01
30238.01.03.0138.0175.00.00.0173.00.00.01.0?3.00
\n", "
" ], "text/plain": [ " 0 1 2 3 4 5 6 7 8 9 10 11 12 \\\n", "298 45.0 1.0 1.0 110.0 264.0 0.0 0.0 132.0 0.0 1.2 2.0 0.0 7.0 \n", "299 68.0 1.0 4.0 144.0 193.0 1.0 0.0 141.0 0.0 3.4 2.0 2.0 7.0 \n", "300 57.0 1.0 4.0 130.0 131.0 0.0 0.0 115.0 1.0 1.2 2.0 1.0 7.0 \n", "301 57.0 0.0 2.0 130.0 236.0 0.0 2.0 174.0 0.0 0.0 2.0 1.0 3.0 \n", "302 38.0 1.0 3.0 138.0 175.0 0.0 0.0 173.0 0.0 0.0 1.0 ? 3.0 \n", "\n", " 13 \n", "298 1 \n", "299 2 \n", "300 3 \n", "301 1 \n", "302 0 " ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.tail()" ] }, { "cell_type": "code", "execution_count": 23, "id": "e8d814ee-52a0-40d4-bb25-9d3eff3629be", "metadata": { "tags": [] }, "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", "
01234567891013
count303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000303.000000
mean54.4389440.6798683.158416131.689769246.6930690.1485150.990099149.6072610.3267331.0396041.6006600.937294
std9.0386620.4672990.96012617.59974851.7769180.3561980.99497122.8750030.4697941.1610750.6162261.228536
min29.0000000.0000001.00000094.000000126.0000000.0000000.00000071.0000000.0000000.0000001.0000000.000000
25%48.0000000.0000003.000000120.000000211.0000000.0000000.000000133.5000000.0000000.0000001.0000000.000000
50%56.0000001.0000003.000000130.000000241.0000000.0000001.000000153.0000000.0000000.8000002.0000000.000000
75%61.0000001.0000004.000000140.000000275.0000000.0000002.000000166.0000001.0000001.6000002.0000002.000000
max77.0000001.0000004.000000200.000000564.0000001.0000002.000000202.0000001.0000006.2000003.0000004.000000
\n", "
" ], "text/plain": [ " 0 1 2 3 4 5 \\\n", "count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n", "mean 54.438944 0.679868 3.158416 131.689769 246.693069 0.148515 \n", "std 9.038662 0.467299 0.960126 17.599748 51.776918 0.356198 \n", "min 29.000000 0.000000 1.000000 94.000000 126.000000 0.000000 \n", "25% 48.000000 0.000000 3.000000 120.000000 211.000000 0.000000 \n", "50% 56.000000 1.000000 3.000000 130.000000 241.000000 0.000000 \n", "75% 61.000000 1.000000 4.000000 140.000000 275.000000 0.000000 \n", "max 77.000000 1.000000 4.000000 200.000000 564.000000 1.000000 \n", "\n", " 6 7 8 9 10 13 \n", "count 303.000000 303.000000 303.000000 303.000000 303.000000 303.000000 \n", "mean 0.990099 149.607261 0.326733 1.039604 1.600660 0.937294 \n", "std 0.994971 22.875003 0.469794 1.161075 0.616226 1.228536 \n", "min 0.000000 71.000000 0.000000 0.000000 1.000000 0.000000 \n", "25% 0.000000 133.500000 0.000000 0.000000 1.000000 0.000000 \n", "50% 1.000000 153.000000 0.000000 0.800000 2.000000 0.000000 \n", "75% 2.000000 166.000000 1.000000 1.600000 2.000000 2.000000 \n", "max 2.000000 202.000000 1.000000 6.200000 3.000000 4.000000 " ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data.describe()" ] }, { "cell_type": "code", "execution_count": 24, "id": "de75f7d4-dad0-451d-bdf8-5b2f32b7d34a", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "1.0 206\n", "0.0 97\n", "Name: 1, dtype: int64" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[1].value_counts()" ] }, { "cell_type": "code", "execution_count": 25, "id": "42f3cfc7-0daa-4c34-9c8d-9a9e37901735", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data[1].value_counts().plot.bar()" ] }, { "cell_type": "code", "execution_count": 7, "id": "973dcd7c-4c13-4992-bd5a-21419c245e11", "metadata": { "tags": [] }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": "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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.barplot(data)" ] }, { "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 }