208 lines (207 with data), 36.0 kB

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "import the needed libiraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy\n",
    "from scipy import stats"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "read the file and give it a name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"C:\\\\Users\\\\AOZ\\\\Desktop\\\\Statistics Project\\\\heart_data.csv\"\n",
    "df = pd.read_csv(url)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "show info about all col"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.series.Series'>\n",
      "RangeIndex: 319795 entries, 0 to 319794\n",
      "Series name: AlcoholDrinking\n",
      "Non-Null Count   Dtype \n",
      "--------------   ----- \n",
      "319795 non-null  object\n",
      "dtypes: object(1)\n",
      "memory usage: 2.4+ MB\n"
     ]
    }
   ],
   "source": [
    "df['AlcoholDrinking'].info()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "descriptive stat of BMI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count     319795\n",
       "unique         2\n",
       "top           No\n",
       "freq      298018\n",
       "Name: AlcoholDrinking, dtype: object"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['AlcoholDrinking'].describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percent of Drinkers that get heart diseases is  5.239472838315654\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Drinkers = df[df['AlcoholDrinking']=='Yes']\n",
    "Drinkers['HeartDisease'].hist()\n",
    "Drinkers_heart_disease = Drinkers[Drinkers['HeartDisease']=='Yes']\n",
    "Drinkers__heart_disease = Drinkers[Drinkers['HeartDisease']=='No']\n",
    "percent_of__Drinkers =  Drinkers_heart_disease.size/Drinkers.size*100\n",
    "print('The percent of Drinkers that get heart diseases is ',percent_of__Drinkers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The percent of non-Drinkers that get heart diseases is  8.802152890093886\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "nonDrinkers = df[df['AlcoholDrinking']=='No']\n",
    "nonDrinkers['HeartDisease'].hist()\n",
    "nonDrinkers_heart_disease = nonDrinkers[nonDrinkers['HeartDisease']=='Yes']\n",
    "nonDrinkers__heart_disease = nonDrinkers[nonDrinkers['HeartDisease']=='No']\n",
    "percent_of__nonDrinkers =  nonDrinkers_heart_disease.size/nonDrinkers.size*100\n",
    "print('The percent of non-Drinkers that get heart diseases is ',percent_of__nonDrinkers)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "thus, there is no relation between alcohol drinking and heart diseases"
   ]
  }
 ],
 "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": "3.10.8"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "05c317967a6bcc6983b33dfc32d6a00017438325114854c9651b783a874b92d3"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}