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226 lines
5.3 KiB
226 lines
5.3 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Exercise 2: Example for pandas using the heart.csv data set"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"import pandas as pd\n",
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"import matplotlib.pyplot as plt"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# read the csv Data \n",
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"df = pd.read_csv('heart.csv')"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# What is the number of columns and rows\n",
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"print(df.columns)\n",
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"print (df.info())\n",
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"print(df.dtypes)"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# get first 3 lines\n",
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"print(df.head(3))"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"#display statistics summary\n",
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"print(df.describe())"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"#display correlation\n",
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"print (df.corr())"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# Print mean values for each column with and without disease\n",
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"print(df.groupby('target').mean())"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# get table with selection on more than 1 column\n",
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"df1 = df[(df[\"sex\"] == 0) & (df[\"target\"] == 0) ]\n",
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"print (df1.head(5))"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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" Plots"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# age dirtibution group into male and female (1 = male; 0 = female)\n",
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"# male\n",
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"plt.title('age distribution according to Sex') \n",
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"df[df[\"sex\"] == 1]['age'].plot.hist()\n",
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"print(df[df[\"sex\"] > 0]['age'])\n",
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"# female\n",
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"df[df[\"sex\"] == 0]['age'].plot.hist()\n",
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"plt.xlabel('age [years]')\n",
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"plt.legend([\"male\", \"female\"])"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure()\n",
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"# Plot maximum heart rate\n",
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"# Heart disease (0 = no, 1 = yes)\n",
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"plt.title('maximum heart rate according to heart disease') \n",
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"df[df[\"target\"] == 1]['thalach'].plot.hist()\n",
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"# no disease\n",
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"df[df[\"target\"] == 0]['thalach'].plot.hist()\n",
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"plt.legend([\"disease\", \"no disease\"])\n",
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"plt.xlabel('max heart rate')"
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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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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# Plot sex and target in one histogramm via crosstab\n",
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"pd.crosstab(df.sex,df.target).plot(kind=\"bar\",color=['red','blue' ])\n",
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"plt.title('Heart Disease distribution according to Sex')\n",
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"plt.xlabel('Sex (0 = Female, 1 = Male)')\n",
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"plt.legend([\"no disease\", \"disease\"])"
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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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"metadata": {
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"scrolled": true
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},
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"outputs": [],
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"source": [
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"# Plot target and cp in one histogramm via crosstab\n",
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"pd.crosstab(df.cp,df.target).plot(kind=\"bar\",figsize=(15,6),color=['#11A5AA','#AA1190' ])\n",
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"plt.title('Heart Disease Distribution According To Chest Pain Type')\n",
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"plt.xlabel('Chest Pain Type')\n",
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"plt.xticks(rotation = 0)\n",
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"plt.ylabel('Frequency of Disease or Not')\n"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"# plot correlations for target\n",
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"plt.figure()\n",
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"plt.scatter(x=df.age[df.target==1], y=df.thalach[(df.target==1)], c=\"red\")\n",
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"plt.scatter(x=df.age[df.target==0], y=df.thalach[(df.target==0)])\n",
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"plt.title('Age-max Heart Rate Plot')\n",
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"plt.xlabel('age[years]')\n",
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"plt.ylabel('max. heart rate')\n",
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"plt.legend([\"Disease\", \"No Disease\"])"
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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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"metadata": {},
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"outputs": [],
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"source": [
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"plt.figure()\n",
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"plt.scatter(x=df.age[df.target==1], y=df.chol[(df.target==1)], c=\"red\")\n",
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"plt.scatter(x=df.age[df.target==0], y=df.chol[(df.target==0)])\n",
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"plt.title('Age-Cholesterol Plot')\n",
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"plt.xlabel('age[years]')\n",
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"plt.ylabel('Cholesterol')\n",
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"plt.legend([\"Disease\", \"No Disease\"])"
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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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"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": "3.8.16"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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