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--- a +++ b/HIMA/kidney.ipynb @@ -0,0 +1,300 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 44, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "heart_data = pd.read_csv('../heart_data.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "heart_data['HeartDisease'] = heart_data['HeartDisease']\n", + "Kidney = heart_data['KidneyDisease']\n", + "KidneyDisease = heart_data[heart_data['KidneyDisease']=='Yes']\n", + "dont_have_KidneyDisease = heart_data[heart_data['KidneyDisease']=='No']\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "count 319795\n", + "unique 2\n", + "top No\n", + "freq 308016\n", + "Name: KidneyDisease, dtype: object" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "Kidney.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "KidneyDisease.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of kidney patients that don't have heart disease 70.66813821207234\n", + "Percentage of kidney patients that have heart disease 29.331861787927668\n" + ] + } + ], + "source": [ + "print(\"Percentage of kidney patients that don't have heart disease\", 100 * KidneyDisease[KidneyDisease[\"HeartDisease\"] == \"No\"].size / KidneyDisease.size )\n", + "print(\"Percentage of kidney patients that have heart disease\", 100 * KidneyDisease[KidneyDisease[\"HeartDisease\"] == \"Yes\"].size / KidneyDisease.size )" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "<div>\n", + "<style scoped>\n", + " .dataframe tbody tr th:only-of-type {\n", + " vertical-align: middle;\n", + " }\n", + "\n", + " .dataframe tbody tr th {\n", + " vertical-align: top;\n", + " }\n", + "\n", + " .dataframe thead th {\n", + " text-align: right;\n", + " }\n", + "</style>\n", + "<table border=\"1\" class=\"dataframe\">\n", + " <thead>\n", + " <tr style=\"text-align: right;\">\n", + " <th></th>\n", + " <th>BMI</th>\n", + " <th>PhysicalHealth</th>\n", + " <th>MentalHealth</th>\n", + " <th>SleepTime</th>\n", + " </tr>\n", + " </thead>\n", + " <tbody>\n", + " <tr>\n", + " <th>count</th>\n", + " <td>308016.000000</td>\n", + " <td>308016.000000</td>\n", + " <td>308016.000000</td>\n", + " <td>308016.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>mean</th>\n", + " <td>28.262296</td>\n", + " <td>3.150619</td>\n", + " <td>3.840369</td>\n", + " <td>7.095323</td>\n", + " </tr>\n", + " <tr>\n", + " <th>std</th>\n", + " <td>6.311631</td>\n", + " <td>7.667615</td>\n", + " <td>7.879449</td>\n", + " <td>1.416586</td>\n", + " </tr>\n", + " <tr>\n", + " <th>min</th>\n", + " <td>12.020000</td>\n", + " <td>0.000000</td>\n", + " <td>0.000000</td>\n", + " <td>1.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>25%</th>\n", + " <td>23.960000</td>\n", + " <td>0.000000</td>\n", + " <td>0.000000</td>\n", + " <td>6.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>50%</th>\n", + " <td>27.270000</td>\n", + " <td>0.000000</td>\n", + " <td>0.000000</td>\n", + " <td>7.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>75%</th>\n", + " <td>31.320000</td>\n", + " <td>1.000000</td>\n", + " <td>3.000000</td>\n", + " <td>8.000000</td>\n", + " </tr>\n", + " <tr>\n", + " <th>max</th>\n", + " <td>94.850000</td>\n", + " <td>30.000000</td>\n", + " <td>30.000000</td>\n", + " <td>24.000000</td>\n", + " </tr>\n", + " </tbody>\n", + "</table>\n", + "</div>" + ], + "text/plain": [ + " BMI PhysicalHealth MentalHealth SleepTime\n", + "count 308016.000000 308016.000000 308016.000000 308016.000000\n", + "mean 28.262296 3.150619 3.840369 7.095323\n", + "std 6.311631 7.667615 7.879449 1.416586\n", + "min 12.020000 0.000000 0.000000 1.000000\n", + "25% 23.960000 0.000000 0.000000 6.000000\n", + "50% 27.270000 0.000000 0.000000 7.000000\n", + "75% 31.320000 1.000000 3.000000 8.000000\n", + "max 94.850000 30.000000 30.000000 24.000000" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dont_have_KidneyDisease.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Percentage of non kidney that don't have heart disease 92.2348189704431\n", + "Percentage of non kidney that have heart disease 7.765181029556906\n" + ] + } + ], + "source": [ + "print(\"Percentage of non kidney that don't have heart disease\", 100 * dont_have_KidneyDisease[dont_have_KidneyDisease[\"HeartDisease\"] == \"No\"].size / dont_have_KidneyDisease.size )\n", + "print(\"Percentage of non kidney that have heart disease\", 100 * dont_have_KidneyDisease[dont_have_KidneyDisease[\"HeartDisease\"] == \"Yes\"].size / dont_have_KidneyDisease.size )" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<AxesSubplot: >" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "KidneyDisease['HeartDisease'].hist()" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "<AxesSubplot: >" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "<Figure size 640x480 with 1 Axes>" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dont_have_KidneyDisease['HeartDisease'].hist()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.11.0" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "9328ff5b7eb661541ab3edfa5748581be07fc9da53f0de3fac60dfd343d1146b" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}