{ "cells": [ { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "data_base = pd.read_csv(\"csv/option4.csv\")" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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idgenderagehypertensionheart_diseaseever_marriedwork_typeResidence_typeavg_glucose_levelbmismoking_statusstroke
09046Male67.001YesPrivateUrban228.6936.6formerly smoked1
151676Female61.000YesSelf-employedRural202.21NaNnever smoked1
231112Male80.001YesPrivateRural105.9232.5never smoked1
360182Female49.000YesPrivateUrban171.2334.4smokes1
41665Female79.010YesSelf-employedRural174.1224.0never smoked1
\n", "
" ], "text/plain": [ " id gender age hypertension heart_disease ever_married \\\n", "0 9046 Male 67.0 0 1 Yes \n", "1 51676 Female 61.0 0 0 Yes \n", "2 31112 Male 80.0 0 1 Yes \n", "3 60182 Female 49.0 0 0 Yes \n", "4 1665 Female 79.0 1 0 Yes \n", "\n", " work_type Residence_type avg_glucose_level bmi smoking_status \\\n", "0 Private Urban 228.69 36.6 formerly smoked \n", "1 Self-employed Rural 202.21 NaN never smoked \n", "2 Private Rural 105.92 32.5 never smoked \n", "3 Private Urban 171.23 34.4 smokes \n", "4 Self-employed Rural 174.12 24.0 never smoked \n", "\n", " stroke \n", "0 1 \n", "1 1 \n", "2 1 \n", "3 1 \n", "4 1 " ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_base.head()" ] } ], "metadata": { "kernelspec": { "display_name": "venv", "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": "3.12.6" } }, "nbformat": 4, "nbformat_minor": 2 }