소비자심리지수 코로나 이전 이후 비교.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "from lxml import html\n",
    "from urllib.request import Request, urlopen\n",
    "from urllib.parse import urlencode, quote_plus, unquote\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import font_manager, rc\n",
    "\n",
    "from scipy.stats import mannwhitneyu\n",
    "from scipy.stats import wilcoxon\n",
    "\n",
    "# - 표시 오류 해결\n",
    "mpl.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "# 한글폰트 굴림으로 설정\n",
    "font_location = 'C:/Windows/Fonts/gulim.ttc'\n",
    "font_name = font_manager.FontProperties(fname = font_location).get_name()\n",
    "rc('font', family=font_name)\n",
    "mpl.rcParams.update({'font.size': 15})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지수코드별</th>\n",
       "      <th>분류코드별</th>\n",
       "      <th>항목</th>\n",
       "      <th>단위</th>\n",
       "      <th>2018. 01 월</th>\n",
       "      <th>2018. 02 월</th>\n",
       "      <th>2018. 03 월</th>\n",
       "      <th>2018. 04 월</th>\n",
       "      <th>2018. 05 월</th>\n",
       "      <th>2018. 06 월</th>\n",
       "      <th>...</th>\n",
       "      <th>2020. 01 월</th>\n",
       "      <th>2020. 02 월</th>\n",
       "      <th>2020. 03 월</th>\n",
       "      <th>2020. 04 월</th>\n",
       "      <th>2020. 05 월</th>\n",
       "      <th>2020. 06 월</th>\n",
       "      <th>2020. 07 월</th>\n",
       "      <th>2020. 08 월</th>\n",
       "      <th>2020. 09 월</th>\n",
       "      <th>2020. 10 월</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>현재생활형편CSI</td>\n",
       "      <td>전체</td>\n",
       "      <td>소비자동향</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>현재생활형편CSI</td>\n",
       "      <td>남자</td>\n",
       "      <td>소비자동향</td>\n",
       "      <td>NaN</td>\n",
       "      <td>94.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>...</td>\n",
       "      <td>93.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>현재생활형편CSI</td>\n",
       "      <td>여자</td>\n",
       "      <td>소비자동향</td>\n",
       "      <td>NaN</td>\n",
       "      <td>95.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>...</td>\n",
       "      <td>95.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>현재생활형편CSI</td>\n",
       "      <td>40세미만</td>\n",
       "      <td>소비자동향</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>98.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>...</td>\n",
       "      <td>97.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>91.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>92.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>현재생활형편CSI</td>\n",
       "      <td>40-50세</td>\n",
       "      <td>소비자동향</td>\n",
       "      <td>NaN</td>\n",
       "      <td>96.0</td>\n",
       "      <td>93.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>95.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>94.0</td>\n",
       "      <td>...</td>\n",
       "      <td>96.0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>77.0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>86.0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>89.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>519</th>\n",
       "      <td>임금수준전망CSI</td>\n",
       "      <td>200-300만원</td>\n",
       "      <td>소비자동향</td>\n",
       "      <td>NaN</td>\n",
       "      <td>122.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>...</td>\n",
       "      <td>118.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>임금수준전망CSI</td>\n",
       "      <td>300-400만원</td>\n",
       "      <td>소비자동향</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>...</td>\n",
       "      <td>118.0</td>\n",
       "      <td>115.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>99.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>109.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>521</th>\n",
       "      <td>임금수준전망CSI</td>\n",
       "      <td>400-500만원</td>\n",
       "      <td>소비자동향</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>124.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>...</td>\n",
       "      <td>117.0</td>\n",
       "      <td>114.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>102.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>108.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>109.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>522</th>\n",
       "      <td>임금수준전망CSI</td>\n",
       "      <td>500만원이상</td>\n",
       "      <td>소비자동향</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126.0</td>\n",
       "      <td>125.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>...</td>\n",
       "      <td>121.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>101.0</td>\n",
       "      <td>104.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>110.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523</th>\n",
       "      <td>임금수준전망CSI</td>\n",
       "      <td>자가</td>\n",
       "      <td>소비자동향</td>\n",
       "      <td>NaN</td>\n",
       "      <td>126.0</td>\n",
       "      <td>123.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>120.0</td>\n",
       "      <td>121.0</td>\n",
       "      <td>119.0</td>\n",
       "      <td>...</td>\n",
       "      <td>121.0</td>\n",
       "      <td>117.0</td>\n",
       "      <td>111.0</td>\n",
       "      <td>103.0</td>\n",
       "      <td>105.0</td>\n",
       "      <td>106.0</td>\n",
       "      <td>112.0</td>\n",
       "      <td>113.0</td>\n",
       "      <td>107.0</td>\n",
       "      <td>111.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>524 rows × 38 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         지수코드별      분류코드별     항목  단위  2018. 01 월  2018. 02 월  2018. 03 월  \\\n",
       "0    현재생활형편CSI         전체  소비자동향 NaN        94.0        94.0        95.0   \n",
       "1    현재생활형편CSI         남자  소비자동향 NaN        94.0        94.0        95.0   \n",
       "2    현재생활형편CSI         여자  소비자동향 NaN        95.0        94.0        95.0   \n",
       "3    현재생활형편CSI      40세미만  소비자동향 NaN        96.0        98.0        99.0   \n",
       "4    현재생활형편CSI     40-50세  소비자동향 NaN        96.0        93.0        96.0   \n",
       "..         ...        ...    ...  ..         ...         ...         ...   \n",
       "519  임금수준전망CSI  200-300만원  소비자동향 NaN       122.0       119.0       117.0   \n",
       "520  임금수준전망CSI  300-400만원  소비자동향 NaN       126.0       123.0       121.0   \n",
       "521  임금수준전망CSI  400-500만원  소비자동향 NaN       129.0       122.0       124.0   \n",
       "522  임금수준전망CSI    500만원이상  소비자동향 NaN       126.0       125.0       121.0   \n",
       "523  임금수준전망CSI         자가  소비자동향 NaN       126.0       123.0       122.0   \n",
       "\n",
       "     2018. 04 월  2018. 05 월  2018. 06 월  ...  2020. 01 월  2020. 02 월  \\\n",
       "0          95.0        95.0        94.0  ...        93.0        91.0   \n",
       "1          94.0        95.0        94.0  ...        93.0        91.0   \n",
       "2          95.0        97.0        95.0  ...        95.0        94.0   \n",
       "3         100.0        98.0        99.0  ...        97.0        96.0   \n",
       "4          95.0        96.0        94.0  ...        96.0        96.0   \n",
       "..          ...         ...         ...  ...         ...         ...   \n",
       "519       116.0       117.0       115.0  ...       118.0       114.0   \n",
       "520       119.0       121.0       117.0  ...       118.0       115.0   \n",
       "521       121.0       121.0       120.0  ...       117.0       114.0   \n",
       "522       120.0       120.0       120.0  ...       121.0       117.0   \n",
       "523       120.0       121.0       119.0  ...       121.0       117.0   \n",
       "\n",
       "     2020. 03 월  2020. 04 월  2020. 05 월  2020. 06 월  2020. 07 월  2020. 08 월  \\\n",
       "0          83.0        77.0        79.0        84.0        85.0        85.0   \n",
       "1          82.0        76.0        79.0        84.0        85.0        85.0   \n",
       "2          84.0        79.0        80.0        85.0        86.0        86.0   \n",
       "3          90.0        87.0        87.0        90.0        90.0        91.0   \n",
       "4          85.0        77.0        81.0        86.0        87.0        86.0   \n",
       "..          ...         ...         ...         ...         ...         ...   \n",
       "519       111.0       103.0       105.0       107.0       111.0       112.0   \n",
       "520       108.0        99.0       100.0       102.0       107.0       109.0   \n",
       "521       106.0       103.0       102.0       104.0       108.0       111.0   \n",
       "522       110.0       101.0       104.0       105.0       111.0       111.0   \n",
       "523       111.0       103.0       105.0       106.0       112.0       113.0   \n",
       "\n",
       "     2020. 09 월  2020. 10 월  \n",
       "0          81.0        86.0  \n",
       "1          81.0        86.0  \n",
       "2          81.0        86.0  \n",
       "3          87.0        92.0  \n",
       "4          83.0        89.0  \n",
       "..          ...         ...  \n",
       "519       107.0       109.0  \n",
       "520       102.0       108.0  \n",
       "521       104.0       109.0  \n",
       "522       105.0       110.0  \n",
       "523       107.0       111.0  \n",
       "\n",
       "[524 rows x 38 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "customer = pd.read_excel(\"월별소비자동향조사.xlsx\")\n",
    "customer.head(-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#코로나 이전|이후 비교\n",
    "\n",
    "df = customer.loc[customer['지수코드별'] == '소비자심리지수']\n",
    "df1 = df.iloc[:, 4:28]\n",
    "df2 = df.iloc[:, 28:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df1.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df2.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>528</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2018. 01 월</th>\n",
       "      <td>110.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018. 02 월</th>\n",
       "      <td>108.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018. 03 월</th>\n",
       "      <td>108.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018. 04 월</th>\n",
       "      <td>107.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018. 05 월</th>\n",
       "      <td>108.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018. 06 월</th>\n",
       "      <td>105.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018. 07 월</th>\n",
       "      <td>100.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018. 08 월</th>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018. 09 월</th>\n",
       "      <td>100.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018. 10 월</th>\n",
       "      <td>99.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018. 11 월</th>\n",
       "      <td>95.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018. 12 월</th>\n",
       "      <td>96.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019. 01 월</th>\n",
       "      <td>97.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019. 02 월</th>\n",
       "      <td>99.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019. 03 월</th>\n",
       "      <td>99.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019. 04 월</th>\n",
       "      <td>101.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019. 05 월</th>\n",
       "      <td>97.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019. 06 월</th>\n",
       "      <td>97.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019. 07 월</th>\n",
       "      <td>95.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019. 08 월</th>\n",
       "      <td>92.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019. 09 월</th>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019. 10 월</th>\n",
       "      <td>98.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019. 11 월</th>\n",
       "      <td>101.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019. 12 월</th>\n",
       "      <td>100.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              528\n",
       "2018. 01 월  110.3\n",
       "2018. 02 월  108.4\n",
       "2018. 03 월  108.3\n",
       "2018. 04 월  107.3\n",
       "2018. 05 월  108.2\n",
       "2018. 06 월  105.6\n",
       "2018. 07 월  100.9\n",
       "2018. 08 월   99.0\n",
       "2018. 09 월  100.1\n",
       "2018. 10 월   99.3\n",
       "2018. 11 월   95.7\n",
       "2018. 12 월   96.9\n",
       "2019. 01 월   97.5\n",
       "2019. 02 월   99.6\n",
       "2019. 03 월   99.8\n",
       "2019. 04 월  101.7\n",
       "2019. 05 월   97.9\n",
       "2019. 06 월   97.6\n",
       "2019. 07 월   95.9\n",
       "2019. 08 월   92.4\n",
       "2019. 09 월   97.0\n",
       "2019. 10 월   98.6\n",
       "2019. 11 월  101.0\n",
       "2019. 12 월  100.5"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = ['18.01','18.02','18.03','18.04','18.05','18.06','18.07','18.08','18.09','18.10','18.11','18.12',\n",
    "    '19.01','19.02','19.03','19.04','19.05','19.06','19.07','19.08','19.09','19.10','19.11','19.12',\n",
    "    '20.01','20.02','20.03','20.04','20.05','20.06','20.07','20.08','20.09','20.10']\n",
    "y = [110.3, 108.4, 108.3, 107.3, 108.2, 105.6, 100.9, 99, 100.1, 99.3, 95.7, 96.9, 97.5, 99.6, 99.8, 101.7, 97.9, 97.6, 95.9, 92.4, 97.0, 98.6, 101, 100.5, 104.2, 96.9, 78.4, 70.8, 77.6, 81.8, 84.2, 88.2, 79.4, 91.6]\n",
    "plt.figure(figsize=(30,10))\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('기간')\n",
    "plt.ylabel('소비자심리지수')\n",
    "plt.title('기간별 소비자심리지수')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>528</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020. 01 월</th>\n",
       "      <td>104.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 02 월</th>\n",
       "      <td>96.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 03 월</th>\n",
       "      <td>78.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 04 월</th>\n",
       "      <td>70.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 05 월</th>\n",
       "      <td>77.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 06 월</th>\n",
       "      <td>81.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 07 월</th>\n",
       "      <td>84.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 08 월</th>\n",
       "      <td>88.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 09 월</th>\n",
       "      <td>79.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 10 월</th>\n",
       "      <td>91.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              528\n",
       "2020. 01 월  104.2\n",
       "2020. 02 월   96.9\n",
       "2020. 03 월   78.4\n",
       "2020. 04 월   70.8\n",
       "2020. 05 월   77.6\n",
       "2020. 06 월   81.8\n",
       "2020. 07 월   84.2\n",
       "2020. 08 월   88.2\n",
       "2020. 09 월   79.4\n",
       "2020. 10 월   91.6"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=21.5, pvalue=0.00010597307437608049)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(df1, df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>528</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020. 02 월</th>\n",
       "      <td>96.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 03 월</th>\n",
       "      <td>78.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 04 월</th>\n",
       "      <td>70.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 05 월</th>\n",
       "      <td>77.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             528\n",
       "2020. 02 월  96.9\n",
       "2020. 03 월  78.4\n",
       "2020. 04 월  70.8\n",
       "2020. 05 월  77.6"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#코로나 이후 1차, 2차 비교\n",
    "df3 = df2.iloc[1:5]\n",
    "df4 = df2.iloc[7:]\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>528</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020. 08 월</th>\n",
       "      <td>88.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 09 월</th>\n",
       "      <td>79.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020. 10 월</th>\n",
       "      <td>91.6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             528\n",
       "2020. 08 월  88.2\n",
       "2020. 09 월  79.4\n",
       "2020. 10 월  91.6"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MannwhitneyuResult(statistic=3.0, pvalue=0.188379558905791)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mannwhitneyu(df3, df4)"
   ]
  }
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