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+64.0,F,146.1,57.7,40.4,68.0,121.0,19.3,9.2,0.0,75.0,D +34.0,M,164.0,66.1,19.5,82.0,150.0,35.9,7.1,51.0,180.0,C diff --git a/body_performance.ipynb b/body_performance.ipynb new file mode 100644 index 0000000..2381d68 --- /dev/null +++ b/body_performance.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "74524ede", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import plotly.express as px\n", + "import seaborn as sns\n", + "\n", + "df = pd.read_csv(r'.\\body_performance.csv')\n", + "\n", + "#print(df.dtypes)\n", + "#df['BMI'] = float(df['weight_kg'])/(float(df['height_cm'])*0.01)^2\n", + "df['BMI'] = df['weight_kg']/(0.0001*df['height_cm']*df['height_cm'])\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0177f243", + "metadata": {}, + "outputs": [], + "source": [ + "df.duplicated().sum()\n", + "print(f'with duplicates:{df.shape}')\n", + "df.drop_duplicates(inplace=True)\n", + "print(f'without duplicates:{df.shape}')\n", + "df_copy = df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0f3ad57a", + "metadata": {}, + "outputs": [], + "source": [ + "df.describe(include='all')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5cd376cf", + "metadata": {}, + "outputs": [], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4857a167", + "metadata": {}, + "outputs": [], + "source": [ + "df[\"class\"].value_counts().plot(kind=\"bar\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "779157c0", + "metadata": {}, + "outputs": [], + "source": [ + "df[[\"class\",\"body fat_%\"]].groupby(\"class\").mean().plot(kind=\"bar\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "da14bf43", + "metadata": {}, + "outputs": [], + "source": [ + "sns.set_theme()\n", + "\n", + "sns.relplot(data = df.head(200), x = 'broad jump_cm', y = 'sit-ups counts', hue = 'class')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6597e57c", + "metadata": {}, + "outputs": [], + "source": [ + "sns.relplot(data = df[df['gender'] == 'M'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "957e1b2e", + "metadata": {}, + "outputs": [], + "source": [ + "sns.relplot(data = df[df['gender'] == 'F'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9f0394f0", + "metadata": {}, + "outputs": [], + "source": [ + "px.box(df, y=['height_cm',\n", + " 'weight_kg',\n", + " 'body fat_%',\n", + " 'diastolic',\n", + " 'systolic',\n", + " 'gripForce',\n", + " 'sit and bend forward_cm',\n", + " 'sit-ups counts',\n", + " 'broad jump_cm',\n", + " 'BMI'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "22542bba", + "metadata": {}, + "outputs": [], + "source": [ + "# this is taking too long time\n", + "#sns.pairplot(data=df.drop(columns=[\"gender\"]).head(500), hue=\"class\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "29730d20", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc21a9cb", + "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 +}