177 lines
3.7 KiB
Plaintext
177 lines
3.7 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "74524ede",
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"metadata": {},
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"outputs": [],
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"source": [
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"import pandas as pd\n",
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"import plotly.express as px\n",
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"import seaborn as sns\n",
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"\n",
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"df = pd.read_csv(r'.\\body_performance.csv')\n",
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"\n",
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"#print(df.dtypes)\n",
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"#df['BMI'] = float(df['weight_kg'])/(float(df['height_cm'])*0.01)^2\n",
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"df['BMI'] = df['weight_kg']/(0.0001*df['height_cm']*df['height_cm'])\n",
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"df.head()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0177f243",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.duplicated().sum()\n",
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"print(f'with duplicates:{df.shape}')\n",
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"df.drop_duplicates(inplace=True)\n",
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"print(f'without duplicates:{df.shape}')\n",
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"df_copy = df.copy()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0f3ad57a",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.describe(include='all')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5cd376cf",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.info()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "4857a167",
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"metadata": {},
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"outputs": [],
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"source": [
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"df[\"class\"].value_counts().plot(kind=\"bar\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "779157c0",
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"metadata": {},
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"outputs": [],
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"source": [
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"df[[\"class\",\"body fat_%\"]].groupby(\"class\").mean().plot(kind=\"bar\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "da14bf43",
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"metadata": {},
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"outputs": [],
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"source": [
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"sns.set_theme()\n",
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"\n",
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"sns.relplot(data = df.head(200), x = 'broad jump_cm', y = 'sit-ups counts', hue = 'class')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6597e57c",
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"metadata": {},
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"outputs": [],
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"source": [
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"sns.relplot(data = df[df['gender'] == 'M'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "957e1b2e",
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"metadata": {},
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"outputs": [],
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"source": [
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"sns.relplot(data = df[df['gender'] == 'F'].head(200), x = 'body fat_%', y = 'BMI', hue = 'class')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9f0394f0",
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"metadata": {},
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"outputs": [],
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"source": [
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"px.box(df, y=['height_cm',\n",
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" 'weight_kg',\n",
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" 'body fat_%',\n",
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" 'diastolic',\n",
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" 'systolic',\n",
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" 'gripForce',\n",
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" 'sit and bend forward_cm',\n",
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" 'sit-ups counts',\n",
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" 'broad jump_cm',\n",
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" 'BMI'])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "22542bba",
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"metadata": {},
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"outputs": [],
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"source": [
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"# this is taking too long time\n",
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"#sns.pairplot(data=df.drop(columns=[\"gender\"]).head(500), hue=\"class\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "29730d20",
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "dc21a9cb",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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