{
 "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": []
  }
 ],
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