12.8.1.카이제곱 검정(교차분석).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 필요한 패키지 설치\n",
    "\n",
    "import scipy.stats\n",
    "from scipy.stats import chi2_contingency\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>sex</th>\n",
       "      <th>smoke</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>male</td>\n",
       "      <td>Non-Smoker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>male</td>\n",
       "      <td>Smoker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>male</td>\n",
       "      <td>Non-Smoker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>male</td>\n",
       "      <td>Smoker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>male</td>\n",
       "      <td>Non-Smoker</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    sex       smoke\n",
       "0  male  Non-Smoker\n",
       "1  male      Smoker\n",
       "2  male  Non-Smoker\n",
       "3  male      Smoker\n",
       "4  male  Non-Smoker"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 데이터 불러오기\n",
    "# https://www.kaggle.com/datasets/sewonghwang/smoker\n",
    "df = pd.read_csv(\"datasets/smoker.csv\")\n",
    "\n",
    "# 데이터 샘플 확인\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "sex     smoke     \n",
       "female  Non-Smoker    50\n",
       "        Smoker        12\n",
       "male    Non-Smoker    40\n",
       "        Smoker        29\n",
       "Name: smoke, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 항목 별 집계\n",
    "\n",
    "df.groupby(['sex','smoke'])['smoke'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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>smoke</th>\n",
       "      <th>Non-Smoker</th>\n",
       "      <th>Smoker</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>female</th>\n",
       "      <td>50</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>male</th>\n",
       "      <td>40</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "smoke   Non-Smoker  Smoker\n",
       "sex                       \n",
       "female          50      12\n",
       "male            40      29"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 카이제곱 검정용 데이터셋 가공\n",
    "\n",
    "crosstab = pd.crosstab(df.sex, df.smoke)\n",
    "crosstab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 성별 별 흡연자 수 시각화\n",
    "\n",
    "%matplotlib inline\n",
    "crosstab.plot(kind='bar', figsize=(10,5))\n",
    "plt.grid()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi square: 7.8081404703715105\n",
      "P-value: 0.005201139711454792\n"
     ]
    }
   ],
   "source": [
    "# 카이제곱 검정\n",
    "\n",
    "chiresult = chi2_contingency(crosstab, correction=False)\n",
    "print('Chi square: {}'.format(chiresult[0]))\n",
    "print('P-value: {}'.format(chiresult[1]))"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}