05-Pandas/01-10mins-to-pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandas 모듈 import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## csv파일을 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./data/01_house_price_2019_03.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 살펴보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가격(㎡)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
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       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879</td>\n",
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      "text/plain": [
       "  지역명               규모구분    연도   월 분양가격(㎡)\n",
       "0  서울                 전체  2015  10    5841\n",
       "1  서울         전용면적 60㎡이하  2015  10    5652\n",
       "2  서울   전용면적 60㎡초과 85㎡이하  2015  10    5882\n",
       "3  서울  전용면적 85㎡초과 102㎡이하  2015  10    5721\n",
       "4  서울        전용면적 102㎡초과  2015  10    5879"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['test'] = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "      <th>연도</th>\n",
       "      <th>월</th>\n",
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       "      <td>2019</td>\n",
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       "      <td>True</td>\n",
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       "      <th>3566</th>\n",
       "      <td>제주</td>\n",
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       "      <td>제주</td>\n",
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       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3504</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3568</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3226</td>\n",
       "      <td>True</td>\n",
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       "      <th>3569</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>2952</td>\n",
       "      <td>True</td>\n",
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      "text/plain": [
       "     지역명               규모구분    연도  월 분양가격(㎡)  test\n",
       "3565  제주                 전체  2019  3    3424  True\n",
       "3566  제주         전용면적 60㎡이하  2019  3    3804  True\n",
       "3567  제주   전용면적 60㎡초과 85㎡이하  2019  3    3504  True\n",
       "3568  제주  전용면적 85㎡초과 102㎡이하  2019  3    3226  True\n",
       "3569  제주        전용면적 102㎡초과  2019  3    2952  True"
      ]
     },
     "execution_count": 6,
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   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 열의 이름 바꾸기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=df.rename(columns={'분양가격(㎡)':'분양가'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "      <th>연도</th>\n",
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       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
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       "      <td>2015</td>\n",
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       "      <th>1</th>\n",
       "      <td>서울</td>\n",
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       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721</td>\n",
       "      <td>True</td>\n",
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       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879</td>\n",
       "      <td>True</td>\n",
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      "text/plain": [
       "  지역명               규모구분    연도   월   분양가  test\n",
       "0  서울                 전체  2015  10  5841  True\n",
       "1  서울         전용면적 60㎡이하  2015  10  5652  True\n",
       "2  서울   전용면적 60㎡초과 85㎡이하  2015  10  5882  True\n",
       "3  서울  전용면적 85㎡초과 102㎡이하  2015  10  5721  True\n",
       "4  서울        전용면적 102㎡초과  2015  10  5879  True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data type 보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "지역명     object\n",
       "규모구분    object\n",
       "연도       int64\n",
       "월        int64\n",
       "분양가     object\n",
       "test      bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
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       "      <td>서울</td>\n",
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       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879</td>\n",
       "      <td>True</td>\n",
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      "text/plain": [
       "  지역명               규모구분    연도   월   분양가  test\n",
       "0  서울                 전체  2015  10  5841  True\n",
       "1  서울         전용면적 60㎡이하  2015  10  5652  True\n",
       "2  서울   전용면적 60㎡초과 85㎡이하  2015  10  5882  True\n",
       "3  서울  전용면적 85㎡초과 102㎡이하  2015  10  5721  True\n",
       "4  서울        전용면적 102㎡초과  2015  10  5879  True"
      ]
     },
     "execution_count": 10,
     "metadata": {},
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    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['분양가'] = df['분양가'].convert_objects(convert_numeric=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "지역명      object\n",
       "규모구분     object\n",
       "연도        int64\n",
       "월         int64\n",
       "분양가     float64\n",
       "test       bool\n",
       "dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <td>서울</td>\n",
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      "text/plain": [
       "  지역명               규모구분    연도   월     분양가  test\n",
       "0  서울                 전체  2015  10  5841.0  True\n",
       "1  서울         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  서울   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  서울  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  서울        전용면적 102㎡초과  2015  10  5879.0  True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## numpy array로 변환하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = df.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['서울', '전체', 2015, 10, 5841.0, True],\n",
       "       ['서울', '전용면적 60㎡이하', 2015, 10, 5652.0, True],\n",
       "       ['서울', '전용면적 60㎡초과 85㎡이하', 2015, 10, 5882.0, True],\n",
       "       ...,\n",
       "       ['제주', '전용면적 60㎡초과 85㎡이하', 2019, 3, 3504.0, True],\n",
       "       ['제주', '전용면적 85㎡초과 102㎡이하', 2019, 3, 3226.0, True],\n",
       "       ['제주', '전용면적 102㎡초과', 2019, 3, 2952.0, True]], dtype=object)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3570"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array(['서울', '전체', 2015, 10, 5841.0, True], dtype=object),\n",
       " array(['서울', '전용면적 60㎡이하', 2015, 10, 5652.0, True], dtype=object))"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0], arr[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 간단한 통계 보기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>3570.000000</td>\n",
       "      <td>3570.000000</td>\n",
       "      <td>3273.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>3130.001833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.069195</td>\n",
       "      <td>3.634017</td>\n",
       "      <td>1141.740958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2015.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1868.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2016.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2387.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>6.500000</td>\n",
       "      <td>2787.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2018.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>3383.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2019.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>8191.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                연도            월          분양가\n",
       "count  3570.000000  3570.000000  3273.000000\n",
       "mean   2017.000000     6.500000  3130.001833\n",
       "std       1.069195     3.634017  1141.740958\n",
       "min    2015.000000     1.000000  1868.000000\n",
       "25%    2016.000000     3.000000  2387.000000\n",
       "50%    2017.000000     6.500000  2787.000000\n",
       "75%    2018.000000    10.000000  3383.000000\n",
       "max    2019.000000    12.000000  8191.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transposing 하기 (축 변환하기)"
   ]
  },
  {
   "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",
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       "\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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  지역명              규모구분    연도   월     분양가  test\n",
       "0  서울                전체  2015  10  5841.0  True\n",
       "1  서울        전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  서울  전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 갯수를 지정하여 출력\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>3</th>\n",
       "      <th>4</th>\n",
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       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>3560</th>\n",
       "      <th>3561</th>\n",
       "      <th>3562</th>\n",
       "      <th>3563</th>\n",
       "      <th>3564</th>\n",
       "      <th>3565</th>\n",
       "      <th>3566</th>\n",
       "      <th>3567</th>\n",
       "      <th>3568</th>\n",
       "      <th>3569</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>지역명</th>\n",
       "      <td>서울</td>\n",
       "      <td>서울</td>\n",
       "      <td>서울</td>\n",
       "      <td>서울</td>\n",
       "      <td>서울</td>\n",
       "      <td>인천</td>\n",
       "      <td>인천</td>\n",
       "      <td>인천</td>\n",
       "      <td>인천</td>\n",
       "      <td>인천</td>\n",
       "      <td>...</td>\n",
       "      <td>경남</td>\n",
       "      <td>경남</td>\n",
       "      <td>경남</td>\n",
       "      <td>경남</td>\n",
       "      <td>경남</td>\n",
       "      <td>제주</td>\n",
       "      <td>제주</td>\n",
       "      <td>제주</td>\n",
       "      <td>제주</td>\n",
       "      <td>제주</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>규모구분</th>\n",
       "      <td>전체</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>전체</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>...</td>\n",
       "      <td>전체</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>전체</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>연도</th>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>2015</td>\n",
       "      <td>...</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019</td>\n",
       "      <td>2019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>월</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>분양가</th>\n",
       "      <td>5841</td>\n",
       "      <td>5652</td>\n",
       "      <td>5882</td>\n",
       "      <td>5721</td>\n",
       "      <td>5879</td>\n",
       "      <td>3163</td>\n",
       "      <td>3488</td>\n",
       "      <td>3119</td>\n",
       "      <td>3545</td>\n",
       "      <td>3408</td>\n",
       "      <td>...</td>\n",
       "      <td>2877</td>\n",
       "      <td>2776</td>\n",
       "      <td>2855</td>\n",
       "      <td>3173</td>\n",
       "      <td>4303</td>\n",
       "      <td>3424</td>\n",
       "      <td>3804</td>\n",
       "      <td>3504</td>\n",
       "      <td>3226</td>\n",
       "      <td>2952</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 3570 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      0           1                 2                  3            4     \\\n",
       "지역명     서울          서울                서울                 서울           서울   \n",
       "규모구분    전체  전용면적 60㎡이하  전용면적 60㎡초과 85㎡이하  전용면적 85㎡초과 102㎡이하  전용면적 102㎡초과   \n",
       "연도    2015        2015              2015               2015         2015   \n",
       "월       10          10                10                 10           10   \n",
       "분양가   5841        5652              5882               5721         5879   \n",
       "\n",
       "      5           6                 7                  8            9     ...  \\\n",
       "지역명     인천          인천                인천                 인천           인천  ...   \n",
       "규모구분    전체  전용면적 60㎡이하  전용면적 60㎡초과 85㎡이하  전용면적 85㎡초과 102㎡이하  전용면적 102㎡초과  ...   \n",
       "연도    2015        2015              2015               2015         2015  ...   \n",
       "월       10          10                10                 10           10  ...   \n",
       "분양가   3163        3488              3119               3545         3408  ...   \n",
       "\n",
       "      3560        3561              3562               3563         3564  \\\n",
       "지역명     경남          경남                경남                 경남           경남   \n",
       "규모구분    전체  전용면적 60㎡이하  전용면적 60㎡초과 85㎡이하  전용면적 85㎡초과 102㎡이하  전용면적 102㎡초과   \n",
       "연도    2019        2019              2019               2019         2019   \n",
       "월        3           3                 3                  3            3   \n",
       "분양가   2877        2776              2855               3173         4303   \n",
       "\n",
       "      3565        3566              3567               3568         3569  \n",
       "지역명     제주          제주                제주                 제주           제주  \n",
       "규모구분    전체  전용면적 60㎡이하  전용면적 60㎡초과 85㎡이하  전용면적 85㎡초과 102㎡이하  전용면적 102㎡초과  \n",
       "연도    2019        2019              2019               2019         2019  \n",
       "월        3           3                 3                  3            3  \n",
       "분양가   3424        3804              3504               3226         2952  \n",
       "\n",
       "[5 rows x 3570 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.T.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  지역명               규모구분    연도   월     분양가  test\n",
       "0  서울                 전체  2015  10  5841.0  True\n",
       "1  서울         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  서울   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  서울  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  서울        전용면적 102㎡초과  2015  10  5879.0  True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Index 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3569</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>2952.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3568</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3226.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3567</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3566</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3804.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3565</th>\n",
       "      <td>제주</td>\n",
       "      <td>전체</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3424.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도  월     분양가  test\n",
       "3569  제주        전용면적 102㎡초과  2019  3  2952.0  True\n",
       "3568  제주  전용면적 85㎡초과 102㎡이하  2019  3  3226.0  True\n",
       "3567  제주   전용면적 60㎡초과 85㎡이하  2019  3  3504.0  True\n",
       "3566  제주         전용면적 60㎡이하  2019  3  3804.0  True\n",
       "3565  제주                 전체  2019  3  3424.0  True"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 내림차순 정렬\n",
    "df.sort_index(axis=0, ascending=False)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  지역명               규모구분    연도   월     분양가  test\n",
       "0  서울                 전체  2015  10  5841.0  True\n",
       "1  서울         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  서울   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  서울  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  서울        전용면적 102㎡초과  2015  10  5879.0  True"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 오름차순 정렬\n",
    "df.sort_index(axis=0, ascending=True)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Value 정렬"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "\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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1831</th>\n",
       "      <td>강원</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2017</td>\n",
       "      <td>7</td>\n",
       "      <td>2167.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>896</th>\n",
       "      <td>강원</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>2073.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>895</th>\n",
       "      <td>강원</td>\n",
       "      <td>전체</td>\n",
       "      <td>2016</td>\n",
       "      <td>8</td>\n",
       "      <td>2098.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>215</th>\n",
       "      <td>강원</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>2171.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>216</th>\n",
       "      <td>강원</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>2292.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명        규모구분    연도   월     분양가  test\n",
       "1831  강원  전용면적 60㎡이하  2017   7  2167.0  True\n",
       "896   강원  전용면적 60㎡이하  2016   8  2073.0  True\n",
       "895   강원          전체  2016   8  2098.0  True\n",
       "215   강원          전체  2015  12  2171.0  True\n",
       "216   강원  전용면적 60㎡이하  2015  12  2292.0  True"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 지역명 별로 정렬\n",
    "df.sort_values(by='지역명')[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>경남</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>2322.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163</th>\n",
       "      <td>경남</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>2980.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>164</th>\n",
       "      <td>경남</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>3043.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>제주</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>2232.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    지역명               규모구분    연도   월     분양가  test\n",
       "0    서울                 전체  2015  10  5841.0  True\n",
       "162  경남   전용면적 60㎡초과 85㎡이하  2015  11  2322.0  True\n",
       "163  경남  전용면적 85㎡초과 102㎡이하  2015  11  2980.0  True\n",
       "164  경남        전용면적 102㎡초과  2015  11  3043.0  True\n",
       "165  제주                 전체  2015  11  2232.0  True"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 연도 별로 정렬\n",
    "df.sort_values(by='연도')[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 선택 (Selection)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Column 이름으로 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    서울\n",
       "1    서울\n",
       "2    서울\n",
       "3    서울\n",
       "4    서울\n",
       "Name: 지역명, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['지역명'][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    2015\n",
       "1    2015\n",
       "2    2015\n",
       "3    2015\n",
       "4    2015\n",
       "Name: 연도, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['연도'][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Index 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  지역명               규모구분    연도   월     분양가  test\n",
       "0  서울                 전체  2015  10  5841.0  True\n",
       "1  서울         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  서울   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  서울  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  서울        전용면적 102㎡초과  2015  10  5879.0  True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index 0 부터 5 미만까지 선택\n",
    "# index 지정시 : 기준으로 왼쪽은 포함, : 기준으로 오른쪽은 미만\n",
    "df[0:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3565</th>\n",
       "      <td>제주</td>\n",
       "      <td>전체</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3424.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3566</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3804.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3567</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3568</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3226.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3569</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>2952.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도  월     분양가  test\n",
       "3565  제주                 전체  2019  3  3424.0  True\n",
       "3566  제주         전용면적 60㎡이하  2019  3  3804.0  True\n",
       "3567  제주   전용면적 60㎡초과 85㎡이하  2019  3  3504.0  True\n",
       "3568  제주  전용면적 85㎡초과 102㎡이하  2019  3  3226.0  True\n",
       "3569  제주        전용면적 102㎡초과  2019  3  2952.0  True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 끝에 5개 출력\n",
    "df[-5:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. label로 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>지역명</th>\n",
       "      <th>연도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>서울</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  지역명    연도\n",
       "0  서울  2015\n",
       "1  서울  2015\n",
       "2  서울  2015\n",
       "3  서울  2015\n",
       "4  서울  2015"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['지역명', '연도']][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "      <th>연도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  지역명    연도\n",
       "0  서울  2015\n",
       "1  서울  2015\n",
       "2  서울  2015\n",
       "3  서울  2015\n",
       "4  서울  2015\n",
       "5  인천  2015\n",
       "6  인천  2015"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# (주의) 여기서는 :를 기준으로 우측에 있는 범위인 6을 포함하여 출력\n",
    "df.loc[:6,['지역명', '연도']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "      <th>연도</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>6</th>\n",
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       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>경기</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>경기</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>경기</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>경기</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>경기</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>부산</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   지역명    연도\n",
       "6   인천  2015\n",
       "7   인천  2015\n",
       "8   인천  2015\n",
       "9   인천  2015\n",
       "10  경기  2015\n",
       "11  경기  2015\n",
       "12  경기  2015\n",
       "13  경기  2015\n",
       "14  경기  2015\n",
       "15  부산  2015"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 조건을 삽입 가능\n",
    "df.loc[df.index > 5, ['지역명', '연도']][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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",
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       "      <th>지역명</th>\n",
       "      <th>연도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
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       "    <tr>\n",
       "      <th>91</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94</th>\n",
       "      <td>인천</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   지역명    연도\n",
       "5   인천  2015\n",
       "6   인천  2015\n",
       "7   인천  2015\n",
       "8   인천  2015\n",
       "9   인천  2015\n",
       "90  인천  2015\n",
       "91  인천  2015\n",
       "92  인천  2015\n",
       "93  인천  2015\n",
       "94  인천  2015"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 지역명 column의 값이 '인천'인 행의 '지역명', '연도' 출력\n",
    "df.loc[df['지역명']=='인천',['지역명', '연도']][:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. iloc을 활용한 인덱스 지정 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <td>True</td>\n",
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       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  지역명               규모구분    연도   월     분양가  test\n",
       "0  서울                 전체  2015  10  5841.0  True\n",
       "1  서울         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  서울   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  서울  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  서울        전용면적 102㎡초과  2015  10  5879.0  True"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'전용면적 60㎡초과 85㎡이하'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'전용면적 60㎡초과 85㎡이하'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# df.iloc과 동작방식은 동일하지만, 범위 지정은 불가\n",
    "df.iat[2, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "지역명                   서울\n",
       "규모구분    전용면적 60㎡초과 85㎡이하\n",
       "연도                  2015\n",
       "월                     10\n",
       "분양가                 5882\n",
       "test                True\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[2, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                   전체\n",
       "1           전용면적 60㎡이하\n",
       "2     전용면적 60㎡초과 85㎡이하\n",
       "3    전용면적 85㎡초과 102㎡이하\n",
       "4          전용면적 102㎡초과\n",
       "Name: 규모구분, dtype: object"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[:5, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>규모구분</th>\n",
       "      <th>연도</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                규모구분    연도\n",
       "0                 전체  2015\n",
       "1         전용면적 60㎡이하  2015\n",
       "2   전용면적 60㎡초과 85㎡이하  2015\n",
       "3  전용면적 85㎡초과 102㎡이하  2015\n",
       "4        전용면적 102㎡초과  2015"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# iloc으로 인덱스 지정시 : 기준으로 왼쪽은 포함, : 기준으로 오른쪽은 미만\n",
    "df.iloc[:5, 1:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5. 범위 조건 지정 선택"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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",
       "    .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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3566</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3804.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3567</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3504.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3568</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>3226.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3569</th>\n",
       "      <td>제주</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>2952.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도  월     분양가  test\n",
       "3566  제주         전용면적 60㎡이하  2019  3  3804.0  True\n",
       "3567  제주   전용면적 60㎡초과 85㎡이하  2019  3  3504.0  True\n",
       "3568  제주  전용면적 85㎡초과 102㎡이하  2019  3  3226.0  True\n",
       "3569  제주        전용면적 102㎡초과  2019  3  2952.0  True"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.index > 3565]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5-a. df.연도  와  df['연도'] 의 column 지정방식은 동일하다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7600.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3316</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7400.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3317</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>8105.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3318</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>6842.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3319</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7787.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도  월     분양가  test\n",
       "3315  서울                 전체  2019  1  7600.0  True\n",
       "3316  서울         전용면적 60㎡이하  2019  1  7400.0  True\n",
       "3317  서울   전용면적 60㎡초과 85㎡이하  2019  1  8105.0  True\n",
       "3318  서울  전용면적 85㎡초과 102㎡이하  2019  1  6842.0  True\n",
       "3319  서울        전용면적 102㎡초과  2019  1  7787.0  True"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df.연도 == 2019][:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    <tr style=\"text-align: right;\">\n",
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       "      <th>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3315</th>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7600.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3316</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7400.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3317</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>8105.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3318</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>6842.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3319</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7787.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도  월     분양가  test\n",
       "3315  서울                 전체  2019  1  7600.0  True\n",
       "3316  서울         전용면적 60㎡이하  2019  1  7400.0  True\n",
       "3317  서울   전용면적 60㎡초과 85㎡이하  2019  1  8105.0  True\n",
       "3318  서울  전용면적 85㎡초과 102㎡이하  2019  1  6842.0  True\n",
       "3319  서울        전용면적 102㎡초과  2019  1  7787.0  True"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['연도'] == 2019][:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Copy로 복사"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>연도</th>\n",
       "      <th>월</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  지역명               규모구분    연도   월     분양가  test\n",
       "0  서울                 전체  2015  10  5841.0  True\n",
       "1  서울         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  서울   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  서울  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  서울        전용면적 102㎡초과  2015  10  5879.0  True"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "copy = df.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>서울</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>서울</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  지역명               규모구분    연도   월     분양가  test\n",
       "0  서울                 전체  2015  10  5841.0  True\n",
       "1  서울         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  서울   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  서울  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  서울        전용면적 102㎡초과  2015  10  5879.0  True"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 값 지정"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     서울\n",
       "1     서울\n",
       "2     서울\n",
       "3     서울\n",
       "4     서울\n",
       "85    서울\n",
       "86    서울\n",
       "87    서울\n",
       "88    서울\n",
       "89    서울\n",
       "Name: 지역명, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 지역명 == 서울 인 지역만 선택\n",
    "df.loc[df['지역명']=='서울','지역명'][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 지역명 == 서울 인 지역을 Seoul로 변경(값을 set)\n",
    "df.loc[df['지역명']=='서울','지역명'] = 'Seoul'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test\n",
       "0  Seoul                 전체  2015  10  5841.0  True\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## reindex를 통한 지정 행과 새로운 열을 추가하여 새로운 dataframe으로 생성"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df.reindex(index=df.index[:7], columns=list(df.columns) + ['extra'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
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       "      <td>2015</td>\n",
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       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test  extra\n",
       "0  Seoul                 전체  2015  10  5841.0  True    NaN\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True    NaN\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True    NaN\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True    NaN\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True    NaN\n",
       "5     인천                 전체  2015  10  3163.0  True    NaN\n",
       "6     인천         전용면적 60㎡이하  2015  10  3488.0  True    NaN"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1.loc[:4, 'extra'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
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       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
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       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test  extra\n",
       "0  Seoul                 전체  2015  10  5841.0  True  False\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True  False\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True  False\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True  False\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True  False\n",
       "5     인천                 전체  2015  10  3163.0  True    NaN\n",
       "6     인천         전용면적 60㎡이하  2015  10  3488.0  True    NaN"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 빈 데이터 처리"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "      <th>extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test  extra\n",
       "0  Seoul                 전체  2015  10  5841.0  True  False\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True  False\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True  False\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True  False\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True  False\n",
       "5     인천                 전체  2015  10  3163.0  True    NaN\n",
       "6     인천         전용면적 60㎡이하  2015  10  3488.0  True    NaN"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. NaN 값이 있는 행을 제거"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>연도</th>\n",
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       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
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       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
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       "      <td>5652.0</td>\n",
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       "      <td>False</td>\n",
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       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test  extra\n",
       "0  Seoul                 전체  2015  10  5841.0  True  False\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True  False\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True  False\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True  False\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True  False"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.dropna(how='any')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1-a 하지만 원본 데이터에는 반영이 안되어 있음"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
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       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test  extra\n",
       "0  Seoul                 전체  2015  10  5841.0  True  False\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True  False\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True  False\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True  False\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True  False\n",
       "5     인천                 전체  2015  10  3163.0  True    NaN\n",
       "6     인천         전용면적 60㎡이하  2015  10  3488.0  True    NaN"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1-b 원본 데이터에 반영을 시키기 위해서는 inplace=True를 적용"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2.dropna(how='any', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "      <td>Seoul</td>\n",
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       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test  extra\n",
       "0  Seoul                 전체  2015  10  5841.0  True  False\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True  False\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True  False\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True  False\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True  False"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# NaN 값이 있는 행이 제거됨\n",
    "df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. NaN 값이 있는 행에 값을 채움"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = df1.copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test  extra\n",
       "0  Seoul                 전체  2015  10  5841.0  True  False\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True  False\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True  False\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True  False\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True  False\n",
       "5     인천                 전체  2015  10  3163.0  True    NaN\n",
       "6     인천         전용면적 60㎡이하  2015  10  3488.0  True    NaN"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "      <th>extra</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test  extra\n",
       "0  Seoul                 전체  2015  10  5841.0  True  False\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True  False\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True  False\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True  False\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True  False\n",
       "5     인천                 전체  2015  10  3163.0  True   True\n",
       "6     인천         전용면적 60㎡이하  2015  10  3488.0  True   True"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.fillna(value=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. NaN 값이 있는 데이터를 Boolean 값으로 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "      <th>extra</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명   규모구분     연도      월    분양가   test  extra\n",
       "0  False  False  False  False  False  False  False\n",
       "1  False  False  False  False  False  False  False\n",
       "2  False  False  False  False  False  False  False\n",
       "3  False  False  False  False  False  False  False\n",
       "4  False  False  False  False  False  False  False\n",
       "5  False  False  False  False  False  False   True\n",
       "6  False  False  False  False  False  False   True"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.isna(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 연산 (Operation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
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       "      <td>5841.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
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       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
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       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
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       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test\n",
       "0  Seoul                 전체  2015  10  5841.0  True\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 열(column) 기준 평균"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "연도      2017.000000\n",
       "월          6.500000\n",
       "분양가     3130.001833\n",
       "test       1.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 행(row) 기준 평균"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1966.75\n",
       "1    1919.50\n",
       "2    1977.00\n",
       "3    1936.75\n",
       "4    1976.25\n",
       "dtype: float64"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1)[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test\n",
       "0  Seoul                 전체  2015  10  5841.0  True\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 행을 2칸 뒤로 밀기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명              규모구분      연도     월     분양가  test\n",
       "0    NaN               NaN     NaN   NaN     NaN   NaN\n",
       "1    NaN               NaN     NaN   NaN     NaN   NaN\n",
       "2  Seoul                전체  2015.0  10.0  5841.0  True\n",
       "3  Seoul        전용면적 60㎡이하  2015.0  10.0  5652.0  True\n",
       "4  Seoul  전용면적 60㎡초과 85㎡이하  2015.0  10.0  5882.0  True"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shift(2)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 행을 2칸 당기기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "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",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분      연도     월     분양가  test\n",
       "0  Seoul   전용면적 60㎡초과 85㎡이하  2015.0  10.0  5882.0  True\n",
       "1  Seoul  전용면적 85㎡초과 102㎡이하  2015.0  10.0  5721.0  True\n",
       "2  Seoul        전용면적 102㎡초과  2015.0  10.0  5879.0  True\n",
       "3     인천                 전체  2015.0  10.0  3163.0  True\n",
       "4     인천         전용면적 60㎡이하  2015.0  10.0  3488.0  True"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shift(-2)[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broadcasting을 이용한 subtract (빼기)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = df[['연도', '월']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "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",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
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       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도   월\n",
       "0  2015  10\n",
       "1  2015  10\n",
       "2  2015  10\n",
       "3  2015  10\n",
       "4  2015  10"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3570, 2)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = np.ones(df.shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3570,)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <td>2015</td>\n",
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       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
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       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도   월\n",
       "0  2015  10\n",
       "1  2015  10\n",
       "2  2015  10\n",
       "3  2015  10\n",
       "4  2015  10"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., ..., 1., 1., 1.])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "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",
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       "    .dataframe thead th {\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>연도</th>\n",
       "      <th>월</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       연도    월\n",
       "0  2014.0  9.0\n",
       "1  2014.0  9.0\n",
       "2  2014.0  9.0\n",
       "3  2014.0  9.0\n",
       "4  2014.0  9.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sub(s, axis=0).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "s = np.ones(df1.shape[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1.])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2014.0</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       연도    월\n",
       "0  2014.0  9.0\n",
       "1  2014.0  9.0\n",
       "2  2014.0  9.0\n",
       "3  2014.0  9.0\n",
       "4  2014.0  9.0"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.sub(s, axis=1).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Apply"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "np.cumsum : cumulative sum의 함수형으로 누적 합을 구하는 함수"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4030</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6045</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8060</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10075</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>12090</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>14105</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>16120</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>18135</td>\n",
       "      <td>90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>20150</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      연도    월\n",
       "0   2015   10\n",
       "1   4030   20\n",
       "2   6045   30\n",
       "3   8060   40\n",
       "4  10075   50\n",
       "5  12090   60\n",
       "6  14105   70\n",
       "7  16120   80\n",
       "8  18135   90\n",
       "9  20150  100"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.apply(np.cumsum)[:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 연도 column\n",
    "x.max() 는 최대값인 2019년도의 2019\n",
    "x.min() 은 최소값인 2015년도의 2015\n",
    "2019 - 2015 = 4가 출력\n",
    "\n",
    "#### 월 column\n",
    "x.max() 는 최대값인 12\n",
    "x.min() 은 최소값인 1\n",
    "12 - 1 = 11이 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "연도     4\n",
       "월     11\n",
       "dtype: int64"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.apply(lambda x: x.max() - x.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "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>연도</th>\n",
       "      <th>월</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도   월\n",
       "0  2015  10\n",
       "1  2015  10\n",
       "2  2015  10\n",
       "3  2015  10\n",
       "4  2015  10"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 값들의 종류별 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2017    1020\n",
       "2018    1020\n",
       "2016    1020\n",
       "2019     255\n",
       "2015     255\n",
       "Name: 연도, dtype: int64"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['연도'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 데이터 합치기 (Data Merge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test\n",
       "0  Seoul                 전체  2015  10  5841.0  True\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = df[['연도', '월']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\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>연도</th>\n",
       "      <th>월</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도   월\n",
       "0  2015  10\n",
       "1  2015  10\n",
       "2  2015  10\n",
       "3  2015  10\n",
       "4  2015  10"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "right = df[['지역명']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "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",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명\n",
       "0  Seoul\n",
       "1  Seoul\n",
       "2  Seoul\n",
       "3  Seoul\n",
       "4  Seoul"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Concat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "part_1 = pd.DataFrame(np.random.randn(3, 4))\n",
    "part_2 = pd.DataFrame(np.random.randn(4, 4))\n",
    "part_3 = pd.DataFrame(np.random.randn(5, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.725924</td>\n",
       "      <td>1.841674</td>\n",
       "      <td>0.660353</td>\n",
       "      <td>-2.383965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.950517</td>\n",
       "      <td>0.604445</td>\n",
       "      <td>1.899723</td>\n",
       "      <td>1.510485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.253384</td>\n",
       "      <td>-1.023495</td>\n",
       "      <td>1.177039</td>\n",
       "      <td>1.075810</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  1.725924  1.841674  0.660353 -2.383965\n",
       "1 -1.950517  0.604445  1.899723  1.510485\n",
       "2  0.253384 -1.023495  1.177039  1.075810"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.000298</td>\n",
       "      <td>-0.876631</td>\n",
       "      <td>-0.536867</td>\n",
       "      <td>0.793464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.087293</td>\n",
       "      <td>-2.380273</td>\n",
       "      <td>0.328481</td>\n",
       "      <td>0.196080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.927580</td>\n",
       "      <td>0.052453</td>\n",
       "      <td>-1.163826</td>\n",
       "      <td>0.440285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.028867</td>\n",
       "      <td>0.104983</td>\n",
       "      <td>0.371043</td>\n",
       "      <td>-0.985374</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0 -0.000298 -0.876631 -0.536867  0.793464\n",
       "1 -2.087293 -2.380273  0.328481  0.196080\n",
       "2 -0.927580  0.052453 -1.163826  0.440285\n",
       "3 -0.028867  0.104983  0.371043 -0.985374"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.132537</td>\n",
       "      <td>1.236222</td>\n",
       "      <td>3.393376</td>\n",
       "      <td>1.125493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.257455</td>\n",
       "      <td>-0.106632</td>\n",
       "      <td>0.278487</td>\n",
       "      <td>-0.535576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.361710</td>\n",
       "      <td>-0.542086</td>\n",
       "      <td>0.380133</td>\n",
       "      <td>-1.838153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.566501</td>\n",
       "      <td>1.465961</td>\n",
       "      <td>1.459668</td>\n",
       "      <td>0.623596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.907765</td>\n",
       "      <td>0.529168</td>\n",
       "      <td>0.536374</td>\n",
       "      <td>0.347229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  0.132537  1.236222  3.393376  1.125493\n",
       "1  1.257455 -0.106632  0.278487 -0.535576\n",
       "2  0.361710 -0.542086  0.380133 -1.838153\n",
       "3  1.566501  1.465961  1.459668  0.623596\n",
       "4  0.907765  0.529168  0.536374  0.347229"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "part = [part_1, part_2, part_3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[          0         1         2         3\n",
       " 0  1.725924  1.841674  0.660353 -2.383965\n",
       " 1 -1.950517  0.604445  1.899723  1.510485\n",
       " 2  0.253384 -1.023495  1.177039  1.075810,\n",
       "           0         1         2         3\n",
       " 0 -0.000298 -0.876631 -0.536867  0.793464\n",
       " 1 -2.087293 -2.380273  0.328481  0.196080\n",
       " 2 -0.927580  0.052453 -1.163826  0.440285\n",
       " 3 -0.028867  0.104983  0.371043 -0.985374,\n",
       "           0         1         2         3\n",
       " 0  0.132537  1.236222  3.393376  1.125493\n",
       " 1  1.257455 -0.106632  0.278487 -0.535576\n",
       " 2  0.361710 -0.542086  0.380133 -1.838153\n",
       " 3  1.566501  1.465961  1.459668  0.623596\n",
       " 4  0.907765  0.529168  0.536374  0.347229]"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.725924</td>\n",
       "      <td>1.841674</td>\n",
       "      <td>0.660353</td>\n",
       "      <td>-2.383965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-1.950517</td>\n",
       "      <td>0.604445</td>\n",
       "      <td>1.899723</td>\n",
       "      <td>1.510485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.253384</td>\n",
       "      <td>-1.023495</td>\n",
       "      <td>1.177039</td>\n",
       "      <td>1.075810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.000298</td>\n",
       "      <td>-0.876631</td>\n",
       "      <td>-0.536867</td>\n",
       "      <td>0.793464</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.087293</td>\n",
       "      <td>-2.380273</td>\n",
       "      <td>0.328481</td>\n",
       "      <td>0.196080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.927580</td>\n",
       "      <td>0.052453</td>\n",
       "      <td>-1.163826</td>\n",
       "      <td>0.440285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.028867</td>\n",
       "      <td>0.104983</td>\n",
       "      <td>0.371043</td>\n",
       "      <td>-0.985374</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.132537</td>\n",
       "      <td>1.236222</td>\n",
       "      <td>3.393376</td>\n",
       "      <td>1.125493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.257455</td>\n",
       "      <td>-0.106632</td>\n",
       "      <td>0.278487</td>\n",
       "      <td>-0.535576</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.361710</td>\n",
       "      <td>-0.542086</td>\n",
       "      <td>0.380133</td>\n",
       "      <td>-1.838153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.566501</td>\n",
       "      <td>1.465961</td>\n",
       "      <td>1.459668</td>\n",
       "      <td>0.623596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.907765</td>\n",
       "      <td>0.529168</td>\n",
       "      <td>0.536374</td>\n",
       "      <td>0.347229</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3\n",
       "0  1.725924  1.841674  0.660353 -2.383965\n",
       "1 -1.950517  0.604445  1.899723  1.510485\n",
       "2  0.253384 -1.023495  1.177039  1.075810\n",
       "0 -0.000298 -0.876631 -0.536867  0.793464\n",
       "1 -2.087293 -2.380273  0.328481  0.196080\n",
       "2 -0.927580  0.052453 -1.163826  0.440285\n",
       "3 -0.028867  0.104983  0.371043 -0.985374\n",
       "0  0.132537  1.236222  3.393376  1.125493\n",
       "1  1.257455 -0.106632  0.278487 -0.535576\n",
       "2  0.361710 -0.542086  0.380133 -1.838153\n",
       "3  1.566501  1.465961  1.459668  0.623596\n",
       "4  0.907765  0.529168  0.536374  0.347229"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(part)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Join"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Join을 Column(열)을 기준으로 합칠 경우에 on='합치고자하는 열의 이름'를 자주 사용하게 되는데, 값이 고유하지 않다면, 매우 혼란스러울 수 있다"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 예시1: key가 고유한 경우"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "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>연도</th>\n",
       "      <th>월</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도  월\n",
       "0  2015  1\n",
       "1  2016  2\n",
       "2  2017  3\n",
       "3  2018  4\n",
       "4  2019  5"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'연도': ['2015', '2016', '2017', '2018', '2019'], '월': ['1', '2', '3', '4', '5']})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "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>이름</th>\n",
       "      <th>월</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>홍길동</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>김영희</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>이철수</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>방탄소년단</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>QUEEN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      이름  월\n",
       "0    홍길동  1\n",
       "1    김영희  2\n",
       "2    이철수  3\n",
       "3  방탄소년단  4\n",
       "4  QUEEN  5"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = pd.DataFrame({'이름': ['홍길동', '김영희', '이철수', '방탄소년단', 'QUEEN'], '월': ['1', '2', '3', '4', '5']})\n",
    "right"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((5, 2), (5, 2))"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left.shape, right.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 매우 깔끔하게 합쳐진 것을 볼 수 있다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "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>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>이름</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>홍길동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>2</td>\n",
       "      <td>김영희</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>이철수</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>방탄소년단</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>QUEEN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도  월     이름\n",
       "0  2015  1    홍길동\n",
       "1  2016  2    김영희\n",
       "2  2017  3    이철수\n",
       "3  2018  4  방탄소년단\n",
       "4  2019  5  QUEEN"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='월')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 예시2: 중복되는 key가 있을 때 합치려는 경우 (1월을 중복으로 넣어놨음)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "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>연도</th>\n",
       "      <th>월</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도  월\n",
       "0  2015  1\n",
       "1  2016  1\n",
       "2  2017  3\n",
       "3  2018  4\n",
       "4  2019  5"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left = pd.DataFrame({'연도': ['2015', '2016', '2017', '2018', '2019'], '월': ['1', '1', '3', '4', '5']})\n",
    "left"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "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>이름</th>\n",
       "      <th>월</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>홍길동</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>김영희</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>이철수</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>방탄소년단</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>QUEEN</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      이름  월\n",
       "0    홍길동  1\n",
       "1    김영희  1\n",
       "2    이철수  3\n",
       "3  방탄소년단  4\n",
       "4  QUEEN  5"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "right = pd.DataFrame({'이름': ['홍길동', '김영희', '이철수', '방탄소년단', 'QUEEN'], '월': ['1', '1', '3', '4', '5']})\n",
    "right"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 행이 2개가 더 추가로 합쳐진 모습이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>이름</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>홍길동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
       "      <td>1</td>\n",
       "      <td>김영희</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>홍길동</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016</td>\n",
       "      <td>1</td>\n",
       "      <td>김영희</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>이철수</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>방탄소년단</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2019</td>\n",
       "      <td>5</td>\n",
       "      <td>QUEEN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     연도  월     이름\n",
       "0  2015  1    홍길동\n",
       "1  2015  1    김영희\n",
       "2  2016  1    홍길동\n",
       "3  2016  1    김영희\n",
       "4  2017  3    이철수\n",
       "5  2018  4  방탄소년단\n",
       "6  2019  5  QUEEN"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left, right, on='월')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Append"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test = np.arange(0, 50)\n",
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1d -> 2d로 변환\n",
    "test = test.reshape(10, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24],\n",
       "       [25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34],\n",
       "       [35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44],\n",
       "       [45, 46, 47, 48, 49]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_1 = test[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_2 = test[3:7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24],\n",
       "       [25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [],
   "source": [
    "test_3 = test[7:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44],\n",
       "       [45, 46, 47, 48, 49]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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      ]
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     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df1 = pd.DataFrame(test_1)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    0   1   2   3   4\n",
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       "3  30  31  32  33  34"
      ]
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     "execution_count": 113,
     "metadata": {},
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   "source": [
    "df2 = pd.DataFrame(test_2)\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    0   1   2   3   4\n",
       "0  35  36  37  38  39\n",
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       "2  45  46  47  48  49"
      ]
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     "execution_count": 114,
     "metadata": {},
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   ],
   "source": [
    "df3 = pd.DataFrame(test_3)\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>3</th>\n",
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       "      <td>31</td>\n",
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       "      <td>33</td>\n",
       "      <td>34</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4\n",
       "0   0   1   2   3   4\n",
       "1   5   6   7   8   9\n",
       "2  10  11  12  13  14\n",
       "0  15  16  17  18  19\n",
       "1  20  21  22  23  24\n",
       "2  25  26  27  28  29\n",
       "3  30  31  32  33  34"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.append(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### index가 이상하게 출력되는 부분은 ignore_index=True로 해결할 수 있다"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>29</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>33</td>\n",
       "      <td>34</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    0   1   2   3   4\n",
       "0   0   1   2   3   4\n",
       "1   5   6   7   8   9\n",
       "2  10  11  12  13  14\n",
       "3  15  16  17  18  19\n",
       "4  20  21  22  23  24\n",
       "5  25  26  27  28  29\n",
       "6  30  31  32  33  34"
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     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df1.append(df2, ignore_index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 그룹화 (Grouping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test\n",
       "0  Seoul                 전체  2015  10  5841.0  True\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3570, 6)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "지역명    연도    월 \n",
       "Seoul  2015  10    28975.0\n",
       "             11    31977.0\n",
       "             12    31392.0\n",
       "       2016  1     31635.0\n",
       "             2     31559.0\n",
       "             3     32400.0\n",
       "             4     33504.0\n",
       "             5     33185.0\n",
       "             6     33085.0\n",
       "             7     33369.0\n",
       "             8     33174.0\n",
       "             9     33246.0\n",
       "             10    34239.0\n",
       "             11    32672.0\n",
       "             12    33449.0\n",
       "       2017  1     33413.0\n",
       "             2     33202.0\n",
       "             3     32526.0\n",
       "             4     32519.0\n",
       "             5     32536.0\n",
       "             6     33637.0\n",
       "             7     33285.0\n",
       "             8     31812.0\n",
       "             9     33788.0\n",
       "             10    33160.0\n",
       "             11        0.0\n",
       "             12    33973.0\n",
       "       2018  1     33772.0\n",
       "             2     34315.0\n",
       "             3     35647.0\n",
       "                    ...   \n",
       "충북     2016  10    11276.0\n",
       "             11    11283.0\n",
       "             12    11497.0\n",
       "       2017  1     11588.0\n",
       "             2     11367.0\n",
       "             3     11327.0\n",
       "             4     11136.0\n",
       "             5     11387.0\n",
       "             6     11309.0\n",
       "             7     11309.0\n",
       "             8     11323.0\n",
       "             9     11332.0\n",
       "             10    11343.0\n",
       "             11        0.0\n",
       "             12    11131.0\n",
       "       2018  1     11131.0\n",
       "             2     11435.0\n",
       "             3     12212.0\n",
       "             4     12961.0\n",
       "             5     13028.0\n",
       "             6     12552.0\n",
       "             7     12506.0\n",
       "             8     12506.0\n",
       "             9     12494.0\n",
       "             10    12494.0\n",
       "             11    12425.0\n",
       "             12    12425.0\n",
       "       2019  1     12425.0\n",
       "             2     12158.0\n",
       "             3     12235.0\n",
       "Name: 분양가, Length: 714, dtype: float64"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(['지역명', '연도', '월'])['분양가'].agg('sum')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 분양가 column이 현재 int 타입이 아닌 object 타입으로 되어 있기 때문에 연산이 잘 이뤄지지 않음. 따라서 int 타입으로 변경"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3570 entries, 0 to 3569\n",
      "Data columns (total 6 columns):\n",
      "지역명     3570 non-null object\n",
      "규모구분    3570 non-null object\n",
      "연도      3570 non-null int64\n",
      "월       3570 non-null int64\n",
      "분양가     3273 non-null float64\n",
      "test    3570 non-null bool\n",
      "dtypes: bool(1), float64(1), int64(2), object(2)\n",
      "memory usage: 143.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3570 entries, 0 to 3569\n",
      "Data columns (total 6 columns):\n",
      "지역명     3570 non-null object\n",
      "규모구분    3570 non-null object\n",
      "연도      3570 non-null int64\n",
      "월       3570 non-null int64\n",
      "분양가     3273 non-null float64\n",
      "test    3570 non-null bool\n",
      "dtypes: bool(1), float64(1), int64(2), object(2)\n",
      "memory usage: 143.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "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",
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       "\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></th>\n",
       "      <th></th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지역명</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">Seoul</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015</th>\n",
       "      <th>10</th>\n",
       "      <td>28975.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>31977.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31392.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2016</th>\n",
       "      <th>1</th>\n",
       "      <td>31635.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31559.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32400.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33504.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33185.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33085.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33369.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>33174.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33246.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>34239.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>32672.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>33449.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2017</th>\n",
       "      <th>1</th>\n",
       "      <td>33413.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33202.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32526.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32519.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32536.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33637.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33285.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31812.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33788.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>33160.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>33973.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2018</th>\n",
       "      <th>1</th>\n",
       "      <td>33772.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34315.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35647.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"30\" valign=\"top\">충북</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2016</th>\n",
       "      <th>10</th>\n",
       "      <td>11276.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11283.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11497.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2017</th>\n",
       "      <th>1</th>\n",
       "      <td>11588.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11367.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>11327.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11136.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11387.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11309.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11309.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11323.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11332.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11343.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11131.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2018</th>\n",
       "      <th>1</th>\n",
       "      <td>11131.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>11435.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12212.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>12961.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>13028.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12552.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>12506.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>12506.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>12494.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>12494.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12425.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>12425.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2019</th>\n",
       "      <th>1</th>\n",
       "      <td>12425.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12158.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12235.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>714 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   분양가  test\n",
       "지역명   연도   월                \n",
       "Seoul 2015 10  28975.0   5.0\n",
       "           11  31977.0   5.0\n",
       "           12  31392.0   5.0\n",
       "      2016 1   31635.0   5.0\n",
       "           2   31559.0   5.0\n",
       "           3   32400.0   5.0\n",
       "           4   33504.0   5.0\n",
       "           5   33185.0   5.0\n",
       "           6   33085.0   5.0\n",
       "           7   33369.0   5.0\n",
       "           8   33174.0   5.0\n",
       "           9   33246.0   5.0\n",
       "           10  34239.0   5.0\n",
       "           11  32672.0   5.0\n",
       "           12  33449.0   5.0\n",
       "      2017 1   33413.0   5.0\n",
       "           2   33202.0   5.0\n",
       "           3   32526.0   5.0\n",
       "           4   32519.0   5.0\n",
       "           5   32536.0   5.0\n",
       "           6   33637.0   5.0\n",
       "           7   33285.0   5.0\n",
       "           8   31812.0   5.0\n",
       "           9   33788.0   5.0\n",
       "           10  33160.0   5.0\n",
       "           11      0.0   5.0\n",
       "           12  33973.0   5.0\n",
       "      2018 1   33772.0   5.0\n",
       "           2   34315.0   5.0\n",
       "           3   35647.0   5.0\n",
       "...                ...   ...\n",
       "충북    2016 10  11276.0   5.0\n",
       "           11  11283.0   5.0\n",
       "           12  11497.0   5.0\n",
       "      2017 1   11588.0   5.0\n",
       "           2   11367.0   5.0\n",
       "           3   11327.0   5.0\n",
       "           4   11136.0   5.0\n",
       "           5   11387.0   5.0\n",
       "           6   11309.0   5.0\n",
       "           7   11309.0   5.0\n",
       "           8   11323.0   5.0\n",
       "           9   11332.0   5.0\n",
       "           10  11343.0   5.0\n",
       "           11      0.0   5.0\n",
       "           12  11131.0   5.0\n",
       "      2018 1   11131.0   5.0\n",
       "           2   11435.0   5.0\n",
       "           3   12212.0   5.0\n",
       "           4   12961.0   5.0\n",
       "           5   13028.0   5.0\n",
       "           6   12552.0   5.0\n",
       "           7   12506.0   5.0\n",
       "           8   12506.0   5.0\n",
       "           9   12494.0   5.0\n",
       "           10  12494.0   5.0\n",
       "           11  12425.0   5.0\n",
       "           12  12425.0   5.0\n",
       "      2019 1   12425.0   5.0\n",
       "           2   12158.0   5.0\n",
       "           3   12235.0   5.0\n",
       "\n",
       "[714 rows x 2 columns]"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped = df.groupby(['지역명', '연도', '월']).sum()\n",
    "grouped"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지역명</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Seoul</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">2015</th>\n",
       "      <th>10</th>\n",
       "      <td>28975.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>31977.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31392.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2016</th>\n",
       "      <th>1</th>\n",
       "      <td>31635.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31559.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   분양가  test\n",
       "지역명   연도   월                \n",
       "Seoul 2015 10  28975.0   5.0\n",
       "           11  31977.0   5.0\n",
       "           12  31392.0   5.0\n",
       "      2016 1   31635.0   5.0\n",
       "           2   31559.0   5.0"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "지역명    연도    월       \n",
       "Seoul  2015  10  분양가     28975.0\n",
       "                 test        5.0\n",
       "             11  분양가     31977.0\n",
       "                 test        5.0\n",
       "             12  분양가     31392.0\n",
       "                 test        5.0\n",
       "       2016  1   분양가     31635.0\n",
       "                 test        5.0\n",
       "             2   분양가     31559.0\n",
       "                 test        5.0\n",
       "             3   분양가     32400.0\n",
       "                 test        5.0\n",
       "             4   분양가     33504.0\n",
       "                 test        5.0\n",
       "             5   분양가     33185.0\n",
       "                 test        5.0\n",
       "             6   분양가     33085.0\n",
       "                 test        5.0\n",
       "             7   분양가     33369.0\n",
       "                 test        5.0\n",
       "             8   분양가     33174.0\n",
       "                 test        5.0\n",
       "             9   분양가     33246.0\n",
       "                 test        5.0\n",
       "             10  분양가     34239.0\n",
       "                 test        5.0\n",
       "             11  분양가     32672.0\n",
       "                 test        5.0\n",
       "             12  분양가     33449.0\n",
       "                 test        5.0\n",
       "                          ...   \n",
       "충북     2018  1   분양가     11131.0\n",
       "                 test        5.0\n",
       "             2   분양가     11435.0\n",
       "                 test        5.0\n",
       "             3   분양가     12212.0\n",
       "                 test        5.0\n",
       "             4   분양가     12961.0\n",
       "                 test        5.0\n",
       "             5   분양가     13028.0\n",
       "                 test        5.0\n",
       "             6   분양가     12552.0\n",
       "                 test        5.0\n",
       "             7   분양가     12506.0\n",
       "                 test        5.0\n",
       "             8   분양가     12506.0\n",
       "                 test        5.0\n",
       "             9   분양가     12494.0\n",
       "                 test        5.0\n",
       "             10  분양가     12494.0\n",
       "                 test        5.0\n",
       "             11  분양가     12425.0\n",
       "                 test        5.0\n",
       "             12  분양가     12425.0\n",
       "                 test        5.0\n",
       "       2019  1   분양가     12425.0\n",
       "                 test        5.0\n",
       "             2   분양가     12158.0\n",
       "                 test        5.0\n",
       "             3   분양가     12235.0\n",
       "                 test        5.0\n",
       "Length: 1428, dtype: float64"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped.stack()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**group이 되어 있는 DataFrame을 unstack의 '레벨'을 조정하여 보여줄 수 있다.**\n",
    "위의 grouped에서 '도시' = level 1, '연도' = level 2, '월'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3119.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3545.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3408.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test\n",
       "0  Seoul                 전체  2015  10  5841.0  True\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True\n",
       "5     인천                 전체  2015  10  3163.0  True\n",
       "6     인천         전용면적 60㎡이하  2015  10  3488.0  True\n",
       "7     인천   전용면적 60㎡초과 85㎡이하  2015  10  3119.0  True\n",
       "8     인천  전용면적 85㎡초과 102㎡이하  2015  10  3545.0  True\n",
       "9     인천        전용면적 102㎡초과  2015  10  3408.0  True"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### stack & unstack을 활용한 column별 데이터 그룹핑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"3\" valign=\"top\">2015</th>\n",
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       "      <td>16679.0</td>\n",
       "      <td>12739.0</td>\n",
       "      <td>11027.0</td>\n",
       "      <td>7112.0</td>\n",
       "      <td>13147.0</td>\n",
       "      <td>9928.0</td>\n",
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       "      <td>5.0</td>\n",
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       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>31977.0</td>\n",
       "      <td>10888.0</td>\n",
       "      <td>16494.0</td>\n",
       "      <td>12843.0</td>\n",
       "      <td>11472.0</td>\n",
       "      <td>7119.0</td>\n",
       "      <td>13358.0</td>\n",
       "      <td>9928.0</td>\n",
       "      <td>15846.0</td>\n",
       "      <td>13279.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31392.0</td>\n",
       "      <td>10888.0</td>\n",
       "      <td>17104.0</td>\n",
       "      <td>12869.0</td>\n",
       "      <td>11429.0</td>\n",
       "      <td>7360.0</td>\n",
       "      <td>14490.0</td>\n",
       "      <td>9928.0</td>\n",
       "      <td>15806.0</td>\n",
       "      <td>13355.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2016</th>\n",
       "      <th>1</th>\n",
       "      <td>31635.0</td>\n",
       "      <td>10894.0</td>\n",
       "      <td>17104.0</td>\n",
       "      <td>12698.0</td>\n",
       "      <td>11406.0</td>\n",
       "      <td>7546.0</td>\n",
       "      <td>14805.0</td>\n",
       "      <td>9928.0</td>\n",
       "      <td>15929.0</td>\n",
       "      <td>13355.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31559.0</td>\n",
       "      <td>9013.0</td>\n",
       "      <td>16831.0</td>\n",
       "      <td>12692.0</td>\n",
       "      <td>11420.0</td>\n",
       "      <td>7546.0</td>\n",
       "      <td>14759.0</td>\n",
       "      <td>9928.0</td>\n",
       "      <td>15982.0</td>\n",
       "      <td>13355.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32400.0</td>\n",
       "      <td>9014.0</td>\n",
       "      <td>16958.0</td>\n",
       "      <td>12839.0</td>\n",
       "      <td>11532.0</td>\n",
       "      <td>7661.0</td>\n",
       "      <td>14896.0</td>\n",
       "      <td>9928.0</td>\n",
       "      <td>15915.0</td>\n",
       "      <td>13374.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33504.0</td>\n",
       "      <td>8816.0</td>\n",
       "      <td>16848.0</td>\n",
       "      <td>13029.0</td>\n",
       "      <td>11526.0</td>\n",
       "      <td>14268.0</td>\n",
       "      <td>14923.0</td>\n",
       "      <td>7403.0</td>\n",
       "      <td>15886.0</td>\n",
       "      <td>13403.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>33185.0</td>\n",
       "      <td>8669.0</td>\n",
       "      <td>17932.0</td>\n",
       "      <td>13203.0</td>\n",
       "      <td>11534.0</td>\n",
       "      <td>14284.0</td>\n",
       "      <td>14965.0</td>\n",
       "      <td>7474.0</td>\n",
       "      <td>15828.0</td>\n",
       "      <td>13403.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33085.0</td>\n",
       "      <td>8678.0</td>\n",
       "      <td>18269.0</td>\n",
       "      <td>13026.0</td>\n",
       "      <td>11828.0</td>\n",
       "      <td>14319.0</td>\n",
       "      <td>15266.0</td>\n",
       "      <td>7757.0</td>\n",
       "      <td>16167.0</td>\n",
       "      <td>13372.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33369.0</td>\n",
       "      <td>8648.0</td>\n",
       "      <td>18315.0</td>\n",
       "      <td>12969.0</td>\n",
       "      <td>11828.0</td>\n",
       "      <td>14329.0</td>\n",
       "      <td>15295.0</td>\n",
       "      <td>7757.0</td>\n",
       "      <td>16409.0</td>\n",
       "      <td>13368.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>33174.0</td>\n",
       "      <td>8705.0</td>\n",
       "      <td>18250.0</td>\n",
       "      <td>12984.0</td>\n",
       "      <td>11798.0</td>\n",
       "      <td>14293.0</td>\n",
       "      <td>15573.0</td>\n",
       "      <td>7757.0</td>\n",
       "      <td>16594.0</td>\n",
       "      <td>13393.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33246.0</td>\n",
       "      <td>8690.0</td>\n",
       "      <td>17479.0</td>\n",
       "      <td>12787.0</td>\n",
       "      <td>11812.0</td>\n",
       "      <td>13853.0</td>\n",
       "      <td>15573.0</td>\n",
       "      <td>7611.0</td>\n",
       "      <td>16611.0</td>\n",
       "      <td>13428.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>34239.0</td>\n",
       "      <td>10608.0</td>\n",
       "      <td>17852.0</td>\n",
       "      <td>12821.0</td>\n",
       "      <td>12070.0</td>\n",
       "      <td>13862.0</td>\n",
       "      <td>15573.0</td>\n",
       "      <td>16079.0</td>\n",
       "      <td>16489.0</td>\n",
       "      <td>13525.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>32672.0</td>\n",
       "      <td>10383.0</td>\n",
       "      <td>18362.0</td>\n",
       "      <td>12699.0</td>\n",
       "      <td>12076.0</td>\n",
       "      <td>14208.0</td>\n",
       "      <td>17464.0</td>\n",
       "      <td>14944.0</td>\n",
       "      <td>16427.0</td>\n",
       "      <td>13546.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>33449.0</td>\n",
       "      <td>10752.0</td>\n",
       "      <td>18254.0</td>\n",
       "      <td>12739.0</td>\n",
       "      <td>12141.0</td>\n",
       "      <td>14224.0</td>\n",
       "      <td>17854.0</td>\n",
       "      <td>14944.0</td>\n",
       "      <td>17100.0</td>\n",
       "      <td>13529.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2017</th>\n",
       "      <th>1</th>\n",
       "      <td>33413.0</td>\n",
       "      <td>10711.0</td>\n",
       "      <td>18260.0</td>\n",
       "      <td>12745.0</td>\n",
       "      <td>12185.0</td>\n",
       "      <td>14215.0</td>\n",
       "      <td>17917.0</td>\n",
       "      <td>14944.0</td>\n",
       "      <td>17082.0</td>\n",
       "      <td>13529.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>33202.0</td>\n",
       "      <td>10734.0</td>\n",
       "      <td>18571.0</td>\n",
       "      <td>12922.0</td>\n",
       "      <td>12183.0</td>\n",
       "      <td>14215.0</td>\n",
       "      <td>17992.0</td>\n",
       "      <td>15106.0</td>\n",
       "      <td>17080.0</td>\n",
       "      <td>13529.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>32526.0</td>\n",
       "      <td>10682.0</td>\n",
       "      <td>18838.0</td>\n",
       "      <td>12853.0</td>\n",
       "      <td>12215.0</td>\n",
       "      <td>14119.0</td>\n",
       "      <td>18486.0</td>\n",
       "      <td>15106.0</td>\n",
       "      <td>16971.0</td>\n",
       "      <td>13529.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>32519.0</td>\n",
       "      <td>10682.0</td>\n",
       "      <td>18849.0</td>\n",
       "      <td>12877.0</td>\n",
       "      <td>12311.0</td>\n",
       "      <td>14392.0</td>\n",
       "      <td>18137.0</td>\n",
       "      <td>15201.0</td>\n",
       "      <td>16970.0</td>\n",
       "      <td>13696.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>32536.0</td>\n",
       "      <td>10767.0</td>\n",
       "      <td>18200.0</td>\n",
       "      <td>12893.0</td>\n",
       "      <td>12468.0</td>\n",
       "      <td>12005.0</td>\n",
       "      <td>18137.0</td>\n",
       "      <td>15201.0</td>\n",
       "      <td>17252.0</td>\n",
       "      <td>13696.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>33637.0</td>\n",
       "      <td>11026.0</td>\n",
       "      <td>18379.0</td>\n",
       "      <td>13223.0</td>\n",
       "      <td>12513.0</td>\n",
       "      <td>12049.0</td>\n",
       "      <td>17782.0</td>\n",
       "      <td>15201.0</td>\n",
       "      <td>17374.0</td>\n",
       "      <td>13715.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>33285.0</td>\n",
       "      <td>11076.0</td>\n",
       "      <td>18630.0</td>\n",
       "      <td>13345.0</td>\n",
       "      <td>12512.0</td>\n",
       "      <td>12013.0</td>\n",
       "      <td>16024.0</td>\n",
       "      <td>15448.0</td>\n",
       "      <td>17479.0</td>\n",
       "      <td>13715.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31812.0</td>\n",
       "      <td>11401.0</td>\n",
       "      <td>18853.0</td>\n",
       "      <td>13547.0</td>\n",
       "      <td>12864.0</td>\n",
       "      <td>12031.0</td>\n",
       "      <td>15961.0</td>\n",
       "      <td>15448.0</td>\n",
       "      <td>17782.0</td>\n",
       "      <td>13722.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33788.0</td>\n",
       "      <td>11237.0</td>\n",
       "      <td>18872.0</td>\n",
       "      <td>14758.0</td>\n",
       "      <td>13166.0</td>\n",
       "      <td>11797.0</td>\n",
       "      <td>15190.0</td>\n",
       "      <td>15932.0</td>\n",
       "      <td>17697.0</td>\n",
       "      <td>13840.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>33160.0</td>\n",
       "      <td>11174.0</td>\n",
       "      <td>18806.0</td>\n",
       "      <td>13387.0</td>\n",
       "      <td>12922.0</td>\n",
       "      <td>14490.0</td>\n",
       "      <td>15190.0</td>\n",
       "      <td>11240.0</td>\n",
       "      <td>18048.0</td>\n",
       "      <td>13819.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>33973.0</td>\n",
       "      <td>11736.0</td>\n",
       "      <td>18825.0</td>\n",
       "      <td>13896.0</td>\n",
       "      <td>10165.0</td>\n",
       "      <td>11427.0</td>\n",
       "      <td>14134.0</td>\n",
       "      <td>11091.0</td>\n",
       "      <td>18943.0</td>\n",
       "      <td>12651.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">2018</th>\n",
       "      <th>1</th>\n",
       "      <td>33772.0</td>\n",
       "      <td>12157.0</td>\n",
       "      <td>20740.0</td>\n",
       "      <td>13896.0</td>\n",
       "      <td>10268.0</td>\n",
       "      <td>11422.0</td>\n",
       "      <td>14236.0</td>\n",
       "      <td>11189.0</td>\n",
       "      <td>18943.0</td>\n",
       "      <td>12651.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>34315.0</td>\n",
       "      <td>12346.0</td>\n",
       "      <td>20954.0</td>\n",
       "      <td>13905.0</td>\n",
       "      <td>10268.0</td>\n",
       "      <td>11422.0</td>\n",
       "      <td>13793.0</td>\n",
       "      <td>11334.0</td>\n",
       "      <td>18967.0</td>\n",
       "      <td>12651.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35647.0</td>\n",
       "      <td>12800.0</td>\n",
       "      <td>20861.0</td>\n",
       "      <td>13910.0</td>\n",
       "      <td>10174.0</td>\n",
       "      <td>11495.0</td>\n",
       "      <td>13860.0</td>\n",
       "      <td>11334.0</td>\n",
       "      <td>18782.0</td>\n",
       "      <td>12651.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35662.0</td>\n",
       "      <td>12260.0</td>\n",
       "      <td>20891.0</td>\n",
       "      <td>13816.0</td>\n",
       "      <td>10121.0</td>\n",
       "      <td>11004.0</td>\n",
       "      <td>16635.0</td>\n",
       "      <td>11514.0</td>\n",
       "      <td>18874.0</td>\n",
       "      <td>15615.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>36217.0</td>\n",
       "      <td>12333.0</td>\n",
       "      <td>21368.0</td>\n",
       "      <td>13825.0</td>\n",
       "      <td>10574.0</td>\n",
       "      <td>11046.0</td>\n",
       "      <td>18482.0</td>\n",
       "      <td>11514.0</td>\n",
       "      <td>18924.0</td>\n",
       "      <td>15615.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>35161.0</td>\n",
       "      <td>12317.0</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>13831.0</td>\n",
       "      <td>10619.0</td>\n",
       "      <td>11233.0</td>\n",
       "      <td>18445.0</td>\n",
       "      <td>11514.0</td>\n",
       "      <td>19043.0</td>\n",
       "      <td>15615.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>35595.0</td>\n",
       "      <td>12434.0</td>\n",
       "      <td>21708.0</td>\n",
       "      <td>13813.0</td>\n",
       "      <td>10702.0</td>\n",
       "      <td>11103.0</td>\n",
       "      <td>18767.0</td>\n",
       "      <td>8383.0</td>\n",
       "      <td>19496.0</td>\n",
       "      <td>15615.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>33783.0</td>\n",
       "      <td>12577.0</td>\n",
       "      <td>22203.0</td>\n",
       "      <td>13679.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>11103.0</td>\n",
       "      <td>18790.0</td>\n",
       "      <td>8383.0</td>\n",
       "      <td>19514.0</td>\n",
       "      <td>15717.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>32407.0</td>\n",
       "      <td>12590.0</td>\n",
       "      <td>22170.0</td>\n",
       "      <td>14211.0</td>\n",
       "      <td>13177.0</td>\n",
       "      <td>11293.0</td>\n",
       "      <td>18901.0</td>\n",
       "      <td>14108.0</td>\n",
       "      <td>20655.0</td>\n",
       "      <td>15717.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>36028.0</td>\n",
       "      <td>12667.0</td>\n",
       "      <td>22191.0</td>\n",
       "      <td>14471.0</td>\n",
       "      <td>13401.0</td>\n",
       "      <td>9353.0</td>\n",
       "      <td>19194.0</td>\n",
       "      <td>14067.0</td>\n",
       "      <td>20683.0</td>\n",
       "      <td>15717.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>36199.0</td>\n",
       "      <td>12611.0</td>\n",
       "      <td>22352.0</td>\n",
       "      <td>14628.0</td>\n",
       "      <td>13492.0</td>\n",
       "      <td>9666.0</td>\n",
       "      <td>19106.0</td>\n",
       "      <td>14067.0</td>\n",
       "      <td>20213.0</td>\n",
       "      <td>15717.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>37073.0</td>\n",
       "      <td>12349.0</td>\n",
       "      <td>22557.0</td>\n",
       "      <td>15609.0</td>\n",
       "      <td>13492.0</td>\n",
       "      <td>9773.0</td>\n",
       "      <td>19469.0</td>\n",
       "      <td>12149.0</td>\n",
       "      <td>20269.0</td>\n",
       "      <td>15327.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">2019</th>\n",
       "      <th>1</th>\n",
       "      <td>37734.0</td>\n",
       "      <td>12618.0</td>\n",
       "      <td>22750.0</td>\n",
       "      <td>15609.0</td>\n",
       "      <td>13322.0</td>\n",
       "      <td>9831.0</td>\n",
       "      <td>20013.0</td>\n",
       "      <td>12243.0</td>\n",
       "      <td>20269.0</td>\n",
       "      <td>15327.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37592.0</td>\n",
       "      <td>12618.0</td>\n",
       "      <td>22879.0</td>\n",
       "      <td>15604.0</td>\n",
       "      <td>13430.0</td>\n",
       "      <td>9831.0</td>\n",
       "      <td>20022.0</td>\n",
       "      <td>12243.0</td>\n",
       "      <td>20269.0</td>\n",
       "      <td>15327.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>37761.0</td>\n",
       "      <td>12706.0</td>\n",
       "      <td>22696.0</td>\n",
       "      <td>15984.0</td>\n",
       "      <td>13536.0</td>\n",
       "      <td>9831.0</td>\n",
       "      <td>19994.0</td>\n",
       "      <td>11718.0</td>\n",
       "      <td>20394.0</td>\n",
       "      <td>15327.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>42 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             분양가                                                        \\\n",
       "지역명        Seoul       강원       경기       경남       경북       광주       대구   \n",
       "연도   월                                                                   \n",
       "2015 10  28975.0  10897.0  16679.0  12739.0  11027.0   7112.0  13147.0   \n",
       "     11  31977.0  10888.0  16494.0  12843.0  11472.0   7119.0  13358.0   \n",
       "     12  31392.0  10888.0  17104.0  12869.0  11429.0   7360.0  14490.0   \n",
       "2016 1   31635.0  10894.0  17104.0  12698.0  11406.0   7546.0  14805.0   \n",
       "     2   31559.0   9013.0  16831.0  12692.0  11420.0   7546.0  14759.0   \n",
       "     3   32400.0   9014.0  16958.0  12839.0  11532.0   7661.0  14896.0   \n",
       "     4   33504.0   8816.0  16848.0  13029.0  11526.0  14268.0  14923.0   \n",
       "     5   33185.0   8669.0  17932.0  13203.0  11534.0  14284.0  14965.0   \n",
       "     6   33085.0   8678.0  18269.0  13026.0  11828.0  14319.0  15266.0   \n",
       "     7   33369.0   8648.0  18315.0  12969.0  11828.0  14329.0  15295.0   \n",
       "     8   33174.0   8705.0  18250.0  12984.0  11798.0  14293.0  15573.0   \n",
       "     9   33246.0   8690.0  17479.0  12787.0  11812.0  13853.0  15573.0   \n",
       "     10  34239.0  10608.0  17852.0  12821.0  12070.0  13862.0  15573.0   \n",
       "     11  32672.0  10383.0  18362.0  12699.0  12076.0  14208.0  17464.0   \n",
       "     12  33449.0  10752.0  18254.0  12739.0  12141.0  14224.0  17854.0   \n",
       "2017 1   33413.0  10711.0  18260.0  12745.0  12185.0  14215.0  17917.0   \n",
       "     2   33202.0  10734.0  18571.0  12922.0  12183.0  14215.0  17992.0   \n",
       "     3   32526.0  10682.0  18838.0  12853.0  12215.0  14119.0  18486.0   \n",
       "     4   32519.0  10682.0  18849.0  12877.0  12311.0  14392.0  18137.0   \n",
       "     5   32536.0  10767.0  18200.0  12893.0  12468.0  12005.0  18137.0   \n",
       "     6   33637.0  11026.0  18379.0  13223.0  12513.0  12049.0  17782.0   \n",
       "     7   33285.0  11076.0  18630.0  13345.0  12512.0  12013.0  16024.0   \n",
       "     8   31812.0  11401.0  18853.0  13547.0  12864.0  12031.0  15961.0   \n",
       "     9   33788.0  11237.0  18872.0  14758.0  13166.0  11797.0  15190.0   \n",
       "     10  33160.0  11174.0  18806.0  13387.0  12922.0  14490.0  15190.0   \n",
       "     11      0.0      0.0      0.0      0.0      0.0      0.0      0.0   \n",
       "     12  33973.0  11736.0  18825.0  13896.0  10165.0  11427.0  14134.0   \n",
       "2018 1   33772.0  12157.0  20740.0  13896.0  10268.0  11422.0  14236.0   \n",
       "     2   34315.0  12346.0  20954.0  13905.0  10268.0  11422.0  13793.0   \n",
       "     3   35647.0  12800.0  20861.0  13910.0  10174.0  11495.0  13860.0   \n",
       "     4   35662.0  12260.0  20891.0  13816.0  10121.0  11004.0  16635.0   \n",
       "     5   36217.0  12333.0  21368.0  13825.0  10574.0  11046.0  18482.0   \n",
       "     6   35161.0  12317.0  21249.0  13831.0  10619.0  11233.0  18445.0   \n",
       "     7   35595.0  12434.0  21708.0  13813.0  10702.0  11103.0  18767.0   \n",
       "     8   33783.0  12577.0  22203.0  13679.0  10500.0  11103.0  18790.0   \n",
       "     9   32407.0  12590.0  22170.0  14211.0  13177.0  11293.0  18901.0   \n",
       "     10  36028.0  12667.0  22191.0  14471.0  13401.0   9353.0  19194.0   \n",
       "     11  36199.0  12611.0  22352.0  14628.0  13492.0   9666.0  19106.0   \n",
       "     12  37073.0  12349.0  22557.0  15609.0  13492.0   9773.0  19469.0   \n",
       "2019 1   37734.0  12618.0  22750.0  15609.0  13322.0   9831.0  20013.0   \n",
       "     2   37592.0  12618.0  22879.0  15604.0  13430.0   9831.0  20022.0   \n",
       "     3   37761.0  12706.0  22696.0  15984.0  13536.0   9831.0  19994.0   \n",
       "\n",
       "                                    ... test                                \\\n",
       "지역명           대전       부산       세종  ...   대전   부산   세종   울산   인천   전남   전북   \n",
       "연도   월                              ...                                      \n",
       "2015 10   9928.0  15518.0  13207.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     11   9928.0  15846.0  13279.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     12   9928.0  15806.0  13355.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "2016 1    9928.0  15929.0  13355.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     2    9928.0  15982.0  13355.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     3    9928.0  15915.0  13374.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     4    7403.0  15886.0  13403.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     5    7474.0  15828.0  13403.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     6    7757.0  16167.0  13372.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     7    7757.0  16409.0  13368.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     8    7757.0  16594.0  13393.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     9    7611.0  16611.0  13428.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     10  16079.0  16489.0  13525.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     11  14944.0  16427.0  13546.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     12  14944.0  17100.0  13529.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "2017 1   14944.0  17082.0  13529.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     2   15106.0  17080.0  13529.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     3   15106.0  16971.0  13529.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     4   15201.0  16970.0  13696.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     5   15201.0  17252.0  13696.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     6   15201.0  17374.0  13715.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     7   15448.0  17479.0  13715.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     8   15448.0  17782.0  13722.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     9   15932.0  17697.0  13840.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     10  11240.0  18048.0  13819.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     11      0.0      0.0      0.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     12  11091.0  18943.0  12651.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "2018 1   11189.0  18943.0  12651.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     2   11334.0  18967.0  12651.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     3   11334.0  18782.0  12651.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     4   11514.0  18874.0  15615.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     5   11514.0  18924.0  15615.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     6   11514.0  19043.0  15615.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     7    8383.0  19496.0  15615.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     8    8383.0  19514.0  15717.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     9   14108.0  20655.0  15717.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     10  14067.0  20683.0  15717.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     11  14067.0  20213.0  15717.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     12  12149.0  20269.0  15327.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "2019 1   12243.0  20269.0  15327.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     2   12243.0  20269.0  15327.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "     3   11718.0  20394.0  15327.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "\n",
       "                        \n",
       "지역명       제주   충남   충북  \n",
       "연도   월                  \n",
       "2015 10  5.0  5.0  5.0  \n",
       "     11  5.0  5.0  5.0  \n",
       "     12  5.0  5.0  5.0  \n",
       "2016 1   5.0  5.0  5.0  \n",
       "     2   5.0  5.0  5.0  \n",
       "     3   5.0  5.0  5.0  \n",
       "     4   5.0  5.0  5.0  \n",
       "     5   5.0  5.0  5.0  \n",
       "     6   5.0  5.0  5.0  \n",
       "     7   5.0  5.0  5.0  \n",
       "     8   5.0  5.0  5.0  \n",
       "     9   5.0  5.0  5.0  \n",
       "     10  5.0  5.0  5.0  \n",
       "     11  5.0  5.0  5.0  \n",
       "     12  5.0  5.0  5.0  \n",
       "2017 1   5.0  5.0  5.0  \n",
       "     2   5.0  5.0  5.0  \n",
       "     3   5.0  5.0  5.0  \n",
       "     4   5.0  5.0  5.0  \n",
       "     5   5.0  5.0  5.0  \n",
       "     6   5.0  5.0  5.0  \n",
       "     7   5.0  5.0  5.0  \n",
       "     8   5.0  5.0  5.0  \n",
       "     9   5.0  5.0  5.0  \n",
       "     10  5.0  5.0  5.0  \n",
       "     11  5.0  5.0  5.0  \n",
       "     12  5.0  5.0  5.0  \n",
       "2018 1   5.0  5.0  5.0  \n",
       "     2   5.0  5.0  5.0  \n",
       "     3   5.0  5.0  5.0  \n",
       "     4   5.0  5.0  5.0  \n",
       "     5   5.0  5.0  5.0  \n",
       "     6   5.0  5.0  5.0  \n",
       "     7   5.0  5.0  5.0  \n",
       "     8   5.0  5.0  5.0  \n",
       "     9   5.0  5.0  5.0  \n",
       "     10  5.0  5.0  5.0  \n",
       "     11  5.0  5.0  5.0  \n",
       "     12  5.0  5.0  5.0  \n",
       "2019 1   5.0  5.0  5.0  \n",
       "     2   5.0  5.0  5.0  \n",
       "     3   5.0  5.0  5.0  \n",
       "\n",
       "[42 rows x 34 columns]"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# level0: 열을 도시별로 출력\n",
    "grouped.unstack(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"5\" halign=\"left\">분양가</th>\n",
       "      <th colspan=\"5\" halign=\"left\">test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>연도</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "      <th>2018</th>\n",
       "      <th>2019</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지역명</th>\n",
       "      <th>월</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">Seoul</th>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31635.0</td>\n",
       "      <td>33413.0</td>\n",
       "      <td>33772.0</td>\n",
       "      <td>37734.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>31559.0</td>\n",
       "      <td>33202.0</td>\n",
       "      <td>34315.0</td>\n",
       "      <td>37592.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>32400.0</td>\n",
       "      <td>32526.0</td>\n",
       "      <td>35647.0</td>\n",
       "      <td>37761.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33504.0</td>\n",
       "      <td>32519.0</td>\n",
       "      <td>35662.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33185.0</td>\n",
       "      <td>32536.0</td>\n",
       "      <td>36217.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33085.0</td>\n",
       "      <td>33637.0</td>\n",
       "      <td>35161.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33369.0</td>\n",
       "      <td>33285.0</td>\n",
       "      <td>35595.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33174.0</td>\n",
       "      <td>31812.0</td>\n",
       "      <td>33783.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>33246.0</td>\n",
       "      <td>33788.0</td>\n",
       "      <td>32407.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28975.0</td>\n",
       "      <td>34239.0</td>\n",
       "      <td>33160.0</td>\n",
       "      <td>36028.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>31977.0</td>\n",
       "      <td>32672.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>36199.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31392.0</td>\n",
       "      <td>33449.0</td>\n",
       "      <td>33973.0</td>\n",
       "      <td>37073.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">강원</th>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10894.0</td>\n",
       "      <td>10711.0</td>\n",
       "      <td>12157.0</td>\n",
       "      <td>12618.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9013.0</td>\n",
       "      <td>10734.0</td>\n",
       "      <td>12346.0</td>\n",
       "      <td>12618.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>9014.0</td>\n",
       "      <td>10682.0</td>\n",
       "      <td>12800.0</td>\n",
       "      <td>12706.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8816.0</td>\n",
       "      <td>10682.0</td>\n",
       "      <td>12260.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8669.0</td>\n",
       "      <td>10767.0</td>\n",
       "      <td>12333.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8678.0</td>\n",
       "      <td>11026.0</td>\n",
       "      <td>12317.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8648.0</td>\n",
       "      <td>11076.0</td>\n",
       "      <td>12434.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8705.0</td>\n",
       "      <td>11401.0</td>\n",
       "      <td>12577.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>8690.0</td>\n",
       "      <td>11237.0</td>\n",
       "      <td>12590.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10897.0</td>\n",
       "      <td>10608.0</td>\n",
       "      <td>11174.0</td>\n",
       "      <td>12667.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10888.0</td>\n",
       "      <td>10383.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12611.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10888.0</td>\n",
       "      <td>10752.0</td>\n",
       "      <td>11736.0</td>\n",
       "      <td>12349.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">경기</th>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17104.0</td>\n",
       "      <td>18260.0</td>\n",
       "      <td>20740.0</td>\n",
       "      <td>22750.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16831.0</td>\n",
       "      <td>18571.0</td>\n",
       "      <td>20954.0</td>\n",
       "      <td>22879.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16958.0</td>\n",
       "      <td>18838.0</td>\n",
       "      <td>20861.0</td>\n",
       "      <td>22696.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16848.0</td>\n",
       "      <td>18849.0</td>\n",
       "      <td>20891.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>17932.0</td>\n",
       "      <td>18200.0</td>\n",
       "      <td>21368.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>18269.0</td>\n",
       "      <td>18379.0</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">제주</th>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14122.0</td>\n",
       "      <td>21366.0</td>\n",
       "      <td>12906.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>14211.0</td>\n",
       "      <td>21366.0</td>\n",
       "      <td>12906.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>16407.0</td>\n",
       "      <td>11752.0</td>\n",
       "      <td>12906.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9614.0</td>\n",
       "      <td>16407.0</td>\n",
       "      <td>11752.0</td>\n",
       "      <td>12906.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>9614.0</td>\n",
       "      <td>16461.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>16675.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9685.0</td>\n",
       "      <td>16510.0</td>\n",
       "      <td>15454.0</td>\n",
       "      <td>16910.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">충남</th>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12017.0</td>\n",
       "      <td>12448.0</td>\n",
       "      <td>9507.0</td>\n",
       "      <td>12773.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12035.0</td>\n",
       "      <td>12451.0</td>\n",
       "      <td>9507.0</td>\n",
       "      <td>12994.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11440.0</td>\n",
       "      <td>12453.0</td>\n",
       "      <td>9507.0</td>\n",
       "      <td>13113.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11803.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>9507.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>11809.0</td>\n",
       "      <td>12499.0</td>\n",
       "      <td>9871.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12048.0</td>\n",
       "      <td>12417.0</td>\n",
       "      <td>12623.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12048.0</td>\n",
       "      <td>12420.0</td>\n",
       "      <td>12623.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12167.0</td>\n",
       "      <td>12440.0</td>\n",
       "      <td>12774.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>12168.0</td>\n",
       "      <td>12440.0</td>\n",
       "      <td>12774.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11591.0</td>\n",
       "      <td>12159.0</td>\n",
       "      <td>12569.0</td>\n",
       "      <td>12774.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11410.0</td>\n",
       "      <td>12488.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12627.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11953.0</td>\n",
       "      <td>12513.0</td>\n",
       "      <td>9519.0</td>\n",
       "      <td>12603.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">충북</th>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10518.0</td>\n",
       "      <td>11588.0</td>\n",
       "      <td>11131.0</td>\n",
       "      <td>12425.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10518.0</td>\n",
       "      <td>11367.0</td>\n",
       "      <td>11435.0</td>\n",
       "      <td>12158.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10463.0</td>\n",
       "      <td>11327.0</td>\n",
       "      <td>12212.0</td>\n",
       "      <td>12235.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10430.0</td>\n",
       "      <td>11136.0</td>\n",
       "      <td>12961.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10476.0</td>\n",
       "      <td>11387.0</td>\n",
       "      <td>13028.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10681.0</td>\n",
       "      <td>11309.0</td>\n",
       "      <td>12552.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10788.0</td>\n",
       "      <td>11309.0</td>\n",
       "      <td>12506.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10881.0</td>\n",
       "      <td>11323.0</td>\n",
       "      <td>12506.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NaN</td>\n",
       "      <td>10886.0</td>\n",
       "      <td>11332.0</td>\n",
       "      <td>12494.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10455.0</td>\n",
       "      <td>11276.0</td>\n",
       "      <td>11343.0</td>\n",
       "      <td>12494.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10085.0</td>\n",
       "      <td>11283.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>12425.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10500.0</td>\n",
       "      <td>11497.0</td>\n",
       "      <td>11131.0</td>\n",
       "      <td>12425.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>204 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              분양가                                     test                    \n",
       "연도           2015     2016     2017     2018     2019 2015 2016 2017 2018 2019\n",
       "지역명   월                                                                       \n",
       "Seoul 1       NaN  31635.0  33413.0  33772.0  37734.0  NaN  5.0  5.0  5.0  5.0\n",
       "      2       NaN  31559.0  33202.0  34315.0  37592.0  NaN  5.0  5.0  5.0  5.0\n",
       "      3       NaN  32400.0  32526.0  35647.0  37761.0  NaN  5.0  5.0  5.0  5.0\n",
       "      4       NaN  33504.0  32519.0  35662.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      5       NaN  33185.0  32536.0  36217.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      6       NaN  33085.0  33637.0  35161.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      7       NaN  33369.0  33285.0  35595.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      8       NaN  33174.0  31812.0  33783.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      9       NaN  33246.0  33788.0  32407.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      10  28975.0  34239.0  33160.0  36028.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "      11  31977.0  32672.0      0.0  36199.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "      12  31392.0  33449.0  33973.0  37073.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "강원    1       NaN  10894.0  10711.0  12157.0  12618.0  NaN  5.0  5.0  5.0  5.0\n",
       "      2       NaN   9013.0  10734.0  12346.0  12618.0  NaN  5.0  5.0  5.0  5.0\n",
       "      3       NaN   9014.0  10682.0  12800.0  12706.0  NaN  5.0  5.0  5.0  5.0\n",
       "      4       NaN   8816.0  10682.0  12260.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      5       NaN   8669.0  10767.0  12333.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      6       NaN   8678.0  11026.0  12317.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      7       NaN   8648.0  11076.0  12434.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      8       NaN   8705.0  11401.0  12577.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      9       NaN   8690.0  11237.0  12590.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      10  10897.0  10608.0  11174.0  12667.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "      11  10888.0  10383.0      0.0  12611.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "      12  10888.0  10752.0  11736.0  12349.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "경기    1       NaN  17104.0  18260.0  20740.0  22750.0  NaN  5.0  5.0  5.0  5.0\n",
       "      2       NaN  16831.0  18571.0  20954.0  22879.0  NaN  5.0  5.0  5.0  5.0\n",
       "      3       NaN  16958.0  18838.0  20861.0  22696.0  NaN  5.0  5.0  5.0  5.0\n",
       "      4       NaN  16848.0  18849.0  20891.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      5       NaN  17932.0  18200.0  21368.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      6       NaN  18269.0  18379.0  21249.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "...           ...      ...      ...      ...      ...  ...  ...  ...  ...  ...\n",
       "제주    7       NaN  14122.0  21366.0  12906.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      8       NaN  14211.0  21366.0  12906.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      9       NaN  16407.0  11752.0  12906.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      10   9614.0  16407.0  11752.0  12906.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "      11   9614.0  16461.0      0.0  16675.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "      12   9685.0  16510.0  15454.0  16910.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "충남    1       NaN  12017.0  12448.0   9507.0  12773.0  NaN  5.0  5.0  5.0  5.0\n",
       "      2       NaN  12035.0  12451.0   9507.0  12994.0  NaN  5.0  5.0  5.0  5.0\n",
       "      3       NaN  11440.0  12453.0   9507.0  13113.0  NaN  5.0  5.0  5.0  5.0\n",
       "      4       NaN  11803.0  12500.0   9507.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      5       NaN  11809.0  12499.0   9871.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      6       NaN  12048.0  12417.0  12623.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      7       NaN  12048.0  12420.0  12623.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      8       NaN  12167.0  12440.0  12774.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      9       NaN  12168.0  12440.0  12774.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      10  11591.0  12159.0  12569.0  12774.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "      11  11410.0  12488.0      0.0  12627.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "      12  11953.0  12513.0   9519.0  12603.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "충북    1       NaN  10518.0  11588.0  11131.0  12425.0  NaN  5.0  5.0  5.0  5.0\n",
       "      2       NaN  10518.0  11367.0  11435.0  12158.0  NaN  5.0  5.0  5.0  5.0\n",
       "      3       NaN  10463.0  11327.0  12212.0  12235.0  NaN  5.0  5.0  5.0  5.0\n",
       "      4       NaN  10430.0  11136.0  12961.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      5       NaN  10476.0  11387.0  13028.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      6       NaN  10681.0  11309.0  12552.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      7       NaN  10788.0  11309.0  12506.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      8       NaN  10881.0  11323.0  12506.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      9       NaN  10886.0  11332.0  12494.0      NaN  NaN  5.0  5.0  5.0  NaN\n",
       "      10  10455.0  11276.0  11343.0  12494.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "      11  10085.0  11283.0      0.0  12425.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "      12  10500.0  11497.0  11131.0  12425.0      NaN  5.0  5.0  5.0  5.0  NaN\n",
       "\n",
       "[204 rows x 10 columns]"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# level1: 열을 연도별로 출력\n",
    "grouped.unstack(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"10\" halign=\"left\">분양가</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"10\" halign=\"left\">test</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>월</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
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       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>지역명</th>\n",
       "      <th>연도</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Seoul</th>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>28975.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>31635.0</td>\n",
       "      <td>31559.0</td>\n",
       "      <td>32400.0</td>\n",
       "      <td>33504.0</td>\n",
       "      <td>33185.0</td>\n",
       "      <td>33085.0</td>\n",
       "      <td>33369.0</td>\n",
       "      <td>33174.0</td>\n",
       "      <td>33246.0</td>\n",
       "      <td>34239.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>33413.0</td>\n",
       "      <td>33202.0</td>\n",
       "      <td>32526.0</td>\n",
       "      <td>32519.0</td>\n",
       "      <td>32536.0</td>\n",
       "      <td>33637.0</td>\n",
       "      <td>33285.0</td>\n",
       "      <td>31812.0</td>\n",
       "      <td>33788.0</td>\n",
       "      <td>33160.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>33772.0</td>\n",
       "      <td>34315.0</td>\n",
       "      <td>35647.0</td>\n",
       "      <td>35662.0</td>\n",
       "      <td>36217.0</td>\n",
       "      <td>35161.0</td>\n",
       "      <td>35595.0</td>\n",
       "      <td>33783.0</td>\n",
       "      <td>32407.0</td>\n",
       "      <td>36028.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>37734.0</td>\n",
       "      <td>37592.0</td>\n",
       "      <td>37761.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">강원</th>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10897.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>10894.0</td>\n",
       "      <td>9013.0</td>\n",
       "      <td>9014.0</td>\n",
       "      <td>8816.0</td>\n",
       "      <td>8669.0</td>\n",
       "      <td>8678.0</td>\n",
       "      <td>8648.0</td>\n",
       "      <td>8705.0</td>\n",
       "      <td>8690.0</td>\n",
       "      <td>10608.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>10711.0</td>\n",
       "      <td>10734.0</td>\n",
       "      <td>10682.0</td>\n",
       "      <td>10682.0</td>\n",
       "      <td>10767.0</td>\n",
       "      <td>11026.0</td>\n",
       "      <td>11076.0</td>\n",
       "      <td>11401.0</td>\n",
       "      <td>11237.0</td>\n",
       "      <td>11174.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>12157.0</td>\n",
       "      <td>12346.0</td>\n",
       "      <td>12800.0</td>\n",
       "      <td>12260.0</td>\n",
       "      <td>12333.0</td>\n",
       "      <td>12317.0</td>\n",
       "      <td>12434.0</td>\n",
       "      <td>12577.0</td>\n",
       "      <td>12590.0</td>\n",
       "      <td>12667.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>12618.0</td>\n",
       "      <td>12618.0</td>\n",
       "      <td>12706.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">경기</th>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16679.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>17104.0</td>\n",
       "      <td>16831.0</td>\n",
       "      <td>16958.0</td>\n",
       "      <td>16848.0</td>\n",
       "      <td>17932.0</td>\n",
       "      <td>18269.0</td>\n",
       "      <td>18315.0</td>\n",
       "      <td>18250.0</td>\n",
       "      <td>17479.0</td>\n",
       "      <td>17852.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>18260.0</td>\n",
       "      <td>18571.0</td>\n",
       "      <td>18838.0</td>\n",
       "      <td>18849.0</td>\n",
       "      <td>18200.0</td>\n",
       "      <td>18379.0</td>\n",
       "      <td>18630.0</td>\n",
       "      <td>18853.0</td>\n",
       "      <td>18872.0</td>\n",
       "      <td>18806.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>20740.0</td>\n",
       "      <td>20954.0</td>\n",
       "      <td>20861.0</td>\n",
       "      <td>20891.0</td>\n",
       "      <td>21368.0</td>\n",
       "      <td>21249.0</td>\n",
       "      <td>21708.0</td>\n",
       "      <td>22203.0</td>\n",
       "      <td>22170.0</td>\n",
       "      <td>22191.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>22750.0</td>\n",
       "      <td>22879.0</td>\n",
       "      <td>22696.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">경남</th>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12739.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>12698.0</td>\n",
       "      <td>12692.0</td>\n",
       "      <td>12839.0</td>\n",
       "      <td>13029.0</td>\n",
       "      <td>13203.0</td>\n",
       "      <td>13026.0</td>\n",
       "      <td>12969.0</td>\n",
       "      <td>12984.0</td>\n",
       "      <td>12787.0</td>\n",
       "      <td>12821.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>12745.0</td>\n",
       "      <td>12922.0</td>\n",
       "      <td>12853.0</td>\n",
       "      <td>12877.0</td>\n",
       "      <td>12893.0</td>\n",
       "      <td>13223.0</td>\n",
       "      <td>13345.0</td>\n",
       "      <td>13547.0</td>\n",
       "      <td>14758.0</td>\n",
       "      <td>13387.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>13896.0</td>\n",
       "      <td>13905.0</td>\n",
       "      <td>13910.0</td>\n",
       "      <td>13816.0</td>\n",
       "      <td>13825.0</td>\n",
       "      <td>13831.0</td>\n",
       "      <td>13813.0</td>\n",
       "      <td>13679.0</td>\n",
       "      <td>14211.0</td>\n",
       "      <td>14471.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>15609.0</td>\n",
       "      <td>15604.0</td>\n",
       "      <td>15984.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">경북</th>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11027.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>11406.0</td>\n",
       "      <td>11420.0</td>\n",
       "      <td>11532.0</td>\n",
       "      <td>11526.0</td>\n",
       "      <td>11534.0</td>\n",
       "      <td>11828.0</td>\n",
       "      <td>11828.0</td>\n",
       "      <td>11798.0</td>\n",
       "      <td>11812.0</td>\n",
       "      <td>12070.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>12185.0</td>\n",
       "      <td>12183.0</td>\n",
       "      <td>12215.0</td>\n",
       "      <td>12311.0</td>\n",
       "      <td>12468.0</td>\n",
       "      <td>12513.0</td>\n",
       "      <td>12512.0</td>\n",
       "      <td>12864.0</td>\n",
       "      <td>13166.0</td>\n",
       "      <td>12922.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>10268.0</td>\n",
       "      <td>10268.0</td>\n",
       "      <td>10174.0</td>\n",
       "      <td>10121.0</td>\n",
       "      <td>10574.0</td>\n",
       "      <td>10619.0</td>\n",
       "      <td>10702.0</td>\n",
       "      <td>10500.0</td>\n",
       "      <td>13177.0</td>\n",
       "      <td>13401.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>13322.0</td>\n",
       "      <td>13430.0</td>\n",
       "      <td>13536.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">광주</th>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7112.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>7546.0</td>\n",
       "      <td>7546.0</td>\n",
       "      <td>7661.0</td>\n",
       "      <td>14268.0</td>\n",
       "      <td>14284.0</td>\n",
       "      <td>14319.0</td>\n",
       "      <td>14329.0</td>\n",
       "      <td>14293.0</td>\n",
       "      <td>13853.0</td>\n",
       "      <td>13862.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>14215.0</td>\n",
       "      <td>14215.0</td>\n",
       "      <td>14119.0</td>\n",
       "      <td>14392.0</td>\n",
       "      <td>12005.0</td>\n",
       "      <td>12049.0</td>\n",
       "      <td>12013.0</td>\n",
       "      <td>12031.0</td>\n",
       "      <td>11797.0</td>\n",
       "      <td>14490.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>11422.0</td>\n",
       "      <td>11422.0</td>\n",
       "      <td>11495.0</td>\n",
       "      <td>11004.0</td>\n",
       "      <td>11046.0</td>\n",
       "      <td>11233.0</td>\n",
       "      <td>11103.0</td>\n",
       "      <td>11103.0</td>\n",
       "      <td>11293.0</td>\n",
       "      <td>9353.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>9831.0</td>\n",
       "      <td>9831.0</td>\n",
       "      <td>9831.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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 rowspan=\"5\" valign=\"top\">인천</th>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>16723.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>16582.0</td>\n",
       "      <td>16585.0</td>\n",
       "      <td>16577.0</td>\n",
       "      <td>18092.0</td>\n",
       "      <td>17208.0</td>\n",
       "      <td>16567.0</td>\n",
       "      <td>16517.0</td>\n",
       "      <td>16617.0</td>\n",
       "      <td>16289.0</td>\n",
       "      <td>16904.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>16926.0</td>\n",
       "      <td>17011.0</td>\n",
       "      <td>17100.0</td>\n",
       "      <td>17100.0</td>\n",
       "      <td>17497.0</td>\n",
       "      <td>17415.0</td>\n",
       "      <td>17862.0</td>\n",
       "      <td>18171.0</td>\n",
       "      <td>18403.0</td>\n",
       "      <td>18216.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>18346.0</td>\n",
       "      <td>18426.0</td>\n",
       "      <td>18889.0</td>\n",
       "      <td>18889.0</td>\n",
       "      <td>18762.0</td>\n",
       "      <td>13592.0</td>\n",
       "      <td>13592.0</td>\n",
       "      <td>13533.0</td>\n",
       "      <td>13533.0</td>\n",
       "      <td>16773.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>18757.0</td>\n",
       "      <td>19151.0</td>\n",
       "      <td>19585.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">전남</th>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>10252.0</td>\n",
       "      <td>10281.0</td>\n",
       "      <td>10280.0</td>\n",
       "      <td>10275.0</td>\n",
       "      <td>10279.0</td>\n",
       "      <td>10549.0</td>\n",
       "      <td>10550.0</td>\n",
       "      <td>10546.0</td>\n",
       "      <td>10563.0</td>\n",
       "      <td>10648.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>10996.0</td>\n",
       "      <td>11033.0</td>\n",
       "      <td>11021.0</td>\n",
       "      <td>11053.0</td>\n",
       "      <td>11171.0</td>\n",
       "      <td>10938.0</td>\n",
       "      <td>10939.0</td>\n",
       "      <td>11074.0</td>\n",
       "      <td>11074.0</td>\n",
       "      <td>11660.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>12124.0</td>\n",
       "      <td>11798.0</td>\n",
       "      <td>11982.0</td>\n",
       "      <td>12024.0</td>\n",
       "      <td>12026.0</td>\n",
       "      <td>12026.0</td>\n",
       "      <td>12029.0</td>\n",
       "      <td>12029.0</td>\n",
       "      <td>12074.0</td>\n",
       "      <td>12100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>11972.0</td>\n",
       "      <td>12314.0</td>\n",
       "      <td>12236.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">전북</th>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10864.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>10534.0</td>\n",
       "      <td>10535.0</td>\n",
       "      <td>10382.0</td>\n",
       "      <td>10374.0</td>\n",
       "      <td>10400.0</td>\n",
       "      <td>10371.0</td>\n",
       "      <td>10402.0</td>\n",
       "      <td>10407.0</td>\n",
       "      <td>10410.0</td>\n",
       "      <td>10431.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>10951.0</td>\n",
       "      <td>10947.0</td>\n",
       "      <td>11070.0</td>\n",
       "      <td>11192.0</td>\n",
       "      <td>11273.0</td>\n",
       "      <td>11414.0</td>\n",
       "      <td>11500.0</td>\n",
       "      <td>11509.0</td>\n",
       "      <td>8870.0</td>\n",
       "      <td>8752.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>11354.0</td>\n",
       "      <td>11366.0</td>\n",
       "      <td>11345.0</td>\n",
       "      <td>12641.0</td>\n",
       "      <td>12720.0</td>\n",
       "      <td>12720.0</td>\n",
       "      <td>12780.0</td>\n",
       "      <td>12737.0</td>\n",
       "      <td>12737.0</td>\n",
       "      <td>12551.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>13114.0</td>\n",
       "      <td>13229.0</td>\n",
       "      <td>13358.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">제주</th>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9614.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>9685.0</td>\n",
       "      <td>9685.0</td>\n",
       "      <td>4628.0</td>\n",
       "      <td>13651.0</td>\n",
       "      <td>13569.0</td>\n",
       "      <td>14122.0</td>\n",
       "      <td>14122.0</td>\n",
       "      <td>14211.0</td>\n",
       "      <td>16407.0</td>\n",
       "      <td>16407.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>16490.0</td>\n",
       "      <td>16453.0</td>\n",
       "      <td>16412.0</td>\n",
       "      <td>18250.0</td>\n",
       "      <td>20348.0</td>\n",
       "      <td>20762.0</td>\n",
       "      <td>21366.0</td>\n",
       "      <td>21366.0</td>\n",
       "      <td>11752.0</td>\n",
       "      <td>11752.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>15557.0</td>\n",
       "      <td>15862.0</td>\n",
       "      <td>16055.0</td>\n",
       "      <td>16055.0</td>\n",
       "      <td>16055.0</td>\n",
       "      <td>16055.0</td>\n",
       "      <td>12906.0</td>\n",
       "      <td>12906.0</td>\n",
       "      <td>12906.0</td>\n",
       "      <td>12906.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>16910.0</td>\n",
       "      <td>16910.0</td>\n",
       "      <td>16910.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">충남</th>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11591.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>12017.0</td>\n",
       "      <td>12035.0</td>\n",
       "      <td>11440.0</td>\n",
       "      <td>11803.0</td>\n",
       "      <td>11809.0</td>\n",
       "      <td>12048.0</td>\n",
       "      <td>12048.0</td>\n",
       "      <td>12167.0</td>\n",
       "      <td>12168.0</td>\n",
       "      <td>12159.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>12448.0</td>\n",
       "      <td>12451.0</td>\n",
       "      <td>12453.0</td>\n",
       "      <td>12500.0</td>\n",
       "      <td>12499.0</td>\n",
       "      <td>12417.0</td>\n",
       "      <td>12420.0</td>\n",
       "      <td>12440.0</td>\n",
       "      <td>12440.0</td>\n",
       "      <td>12569.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>9507.0</td>\n",
       "      <td>9507.0</td>\n",
       "      <td>9507.0</td>\n",
       "      <td>9507.0</td>\n",
       "      <td>9871.0</td>\n",
       "      <td>12623.0</td>\n",
       "      <td>12623.0</td>\n",
       "      <td>12774.0</td>\n",
       "      <td>12774.0</td>\n",
       "      <td>12774.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>12773.0</td>\n",
       "      <td>12994.0</td>\n",
       "      <td>13113.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">충북</th>\n",
       "      <th>2015</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10455.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>10518.0</td>\n",
       "      <td>10518.0</td>\n",
       "      <td>10463.0</td>\n",
       "      <td>10430.0</td>\n",
       "      <td>10476.0</td>\n",
       "      <td>10681.0</td>\n",
       "      <td>10788.0</td>\n",
       "      <td>10881.0</td>\n",
       "      <td>10886.0</td>\n",
       "      <td>11276.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>11588.0</td>\n",
       "      <td>11367.0</td>\n",
       "      <td>11327.0</td>\n",
       "      <td>11136.0</td>\n",
       "      <td>11387.0</td>\n",
       "      <td>11309.0</td>\n",
       "      <td>11309.0</td>\n",
       "      <td>11323.0</td>\n",
       "      <td>11332.0</td>\n",
       "      <td>11343.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2018</th>\n",
       "      <td>11131.0</td>\n",
       "      <td>11435.0</td>\n",
       "      <td>12212.0</td>\n",
       "      <td>12961.0</td>\n",
       "      <td>13028.0</td>\n",
       "      <td>12552.0</td>\n",
       "      <td>12506.0</td>\n",
       "      <td>12506.0</td>\n",
       "      <td>12494.0</td>\n",
       "      <td>12494.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019</th>\n",
       "      <td>12425.0</td>\n",
       "      <td>12158.0</td>\n",
       "      <td>12235.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                분양가                                                        \\\n",
       "월                1        2        3        4        5        6        7    \n",
       "지역명   연도                                                                    \n",
       "Seoul 2015      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "      2016  31635.0  31559.0  32400.0  33504.0  33185.0  33085.0  33369.0   \n",
       "      2017  33413.0  33202.0  32526.0  32519.0  32536.0  33637.0  33285.0   \n",
       "      2018  33772.0  34315.0  35647.0  35662.0  36217.0  35161.0  35595.0   \n",
       "      2019  37734.0  37592.0  37761.0      NaN      NaN      NaN      NaN   \n",
       "강원    2015      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "      2016  10894.0   9013.0   9014.0   8816.0   8669.0   8678.0   8648.0   \n",
       "      2017  10711.0  10734.0  10682.0  10682.0  10767.0  11026.0  11076.0   \n",
       "      2018  12157.0  12346.0  12800.0  12260.0  12333.0  12317.0  12434.0   \n",
       "      2019  12618.0  12618.0  12706.0      NaN      NaN      NaN      NaN   \n",
       "경기    2015      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "      2016  17104.0  16831.0  16958.0  16848.0  17932.0  18269.0  18315.0   \n",
       "      2017  18260.0  18571.0  18838.0  18849.0  18200.0  18379.0  18630.0   \n",
       "      2018  20740.0  20954.0  20861.0  20891.0  21368.0  21249.0  21708.0   \n",
       "      2019  22750.0  22879.0  22696.0      NaN      NaN      NaN      NaN   \n",
       "경남    2015      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "      2016  12698.0  12692.0  12839.0  13029.0  13203.0  13026.0  12969.0   \n",
       "      2017  12745.0  12922.0  12853.0  12877.0  12893.0  13223.0  13345.0   \n",
       "      2018  13896.0  13905.0  13910.0  13816.0  13825.0  13831.0  13813.0   \n",
       "      2019  15609.0  15604.0  15984.0      NaN      NaN      NaN      NaN   \n",
       "경북    2015      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "      2016  11406.0  11420.0  11532.0  11526.0  11534.0  11828.0  11828.0   \n",
       "      2017  12185.0  12183.0  12215.0  12311.0  12468.0  12513.0  12512.0   \n",
       "      2018  10268.0  10268.0  10174.0  10121.0  10574.0  10619.0  10702.0   \n",
       "      2019  13322.0  13430.0  13536.0      NaN      NaN      NaN      NaN   \n",
       "광주    2015      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "      2016   7546.0   7546.0   7661.0  14268.0  14284.0  14319.0  14329.0   \n",
       "      2017  14215.0  14215.0  14119.0  14392.0  12005.0  12049.0  12013.0   \n",
       "      2018  11422.0  11422.0  11495.0  11004.0  11046.0  11233.0  11103.0   \n",
       "      2019   9831.0   9831.0   9831.0      NaN      NaN      NaN      NaN   \n",
       "...             ...      ...      ...      ...      ...      ...      ...   \n",
       "인천    2015      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "      2016  16582.0  16585.0  16577.0  18092.0  17208.0  16567.0  16517.0   \n",
       "      2017  16926.0  17011.0  17100.0  17100.0  17497.0  17415.0  17862.0   \n",
       "      2018  18346.0  18426.0  18889.0  18889.0  18762.0  13592.0  13592.0   \n",
       "      2019  18757.0  19151.0  19585.0      NaN      NaN      NaN      NaN   \n",
       "전남    2015      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "      2016  10252.0  10281.0  10280.0  10275.0  10279.0  10549.0  10550.0   \n",
       "      2017  10996.0  11033.0  11021.0  11053.0  11171.0  10938.0  10939.0   \n",
       "      2018  12124.0  11798.0  11982.0  12024.0  12026.0  12026.0  12029.0   \n",
       "      2019  11972.0  12314.0  12236.0      NaN      NaN      NaN      NaN   \n",
       "전북    2015      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "      2016  10534.0  10535.0  10382.0  10374.0  10400.0  10371.0  10402.0   \n",
       "      2017  10951.0  10947.0  11070.0  11192.0  11273.0  11414.0  11500.0   \n",
       "      2018  11354.0  11366.0  11345.0  12641.0  12720.0  12720.0  12780.0   \n",
       "      2019  13114.0  13229.0  13358.0      NaN      NaN      NaN      NaN   \n",
       "제주    2015      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "      2016   9685.0   9685.0   4628.0  13651.0  13569.0  14122.0  14122.0   \n",
       "      2017  16490.0  16453.0  16412.0  18250.0  20348.0  20762.0  21366.0   \n",
       "      2018  15557.0  15862.0  16055.0  16055.0  16055.0  16055.0  12906.0   \n",
       "      2019  16910.0  16910.0  16910.0      NaN      NaN      NaN      NaN   \n",
       "충남    2015      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "      2016  12017.0  12035.0  11440.0  11803.0  11809.0  12048.0  12048.0   \n",
       "      2017  12448.0  12451.0  12453.0  12500.0  12499.0  12417.0  12420.0   \n",
       "      2018   9507.0   9507.0   9507.0   9507.0   9871.0  12623.0  12623.0   \n",
       "      2019  12773.0  12994.0  13113.0      NaN      NaN      NaN      NaN   \n",
       "충북    2015      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
       "      2016  10518.0  10518.0  10463.0  10430.0  10476.0  10681.0  10788.0   \n",
       "      2017  11588.0  11367.0  11327.0  11136.0  11387.0  11309.0  11309.0   \n",
       "      2018  11131.0  11435.0  12212.0  12961.0  13028.0  12552.0  12506.0   \n",
       "      2019  12425.0  12158.0  12235.0      NaN      NaN      NaN      NaN   \n",
       "\n",
       "                                       ... test                                \\\n",
       "월                8        9        10  ...   3    4    5    6    7    8    9    \n",
       "지역명   연도                               ...                                      \n",
       "Seoul 2015      NaN      NaN  28975.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "      2016  33174.0  33246.0  34239.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2017  31812.0  33788.0  33160.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2018  33783.0  32407.0  36028.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2019      NaN      NaN      NaN  ...  5.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "강원    2015      NaN      NaN  10897.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "      2016   8705.0   8690.0  10608.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2017  11401.0  11237.0  11174.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2018  12577.0  12590.0  12667.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2019      NaN      NaN      NaN  ...  5.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "경기    2015      NaN      NaN  16679.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "      2016  18250.0  17479.0  17852.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2017  18853.0  18872.0  18806.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2018  22203.0  22170.0  22191.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2019      NaN      NaN      NaN  ...  5.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "경남    2015      NaN      NaN  12739.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "      2016  12984.0  12787.0  12821.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2017  13547.0  14758.0  13387.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2018  13679.0  14211.0  14471.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2019      NaN      NaN      NaN  ...  5.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "경북    2015      NaN      NaN  11027.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "      2016  11798.0  11812.0  12070.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2017  12864.0  13166.0  12922.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2018  10500.0  13177.0  13401.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2019      NaN      NaN      NaN  ...  5.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "광주    2015      NaN      NaN   7112.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "      2016  14293.0  13853.0  13862.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2017  12031.0  11797.0  14490.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2018  11103.0  11293.0   9353.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2019      NaN      NaN      NaN  ...  5.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "...             ...      ...      ...  ...  ...  ...  ...  ...  ...  ...  ...   \n",
       "인천    2015      NaN      NaN  16723.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "      2016  16617.0  16289.0  16904.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2017  18171.0  18403.0  18216.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2018  13533.0  13533.0  16773.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2019      NaN      NaN      NaN  ...  5.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "전남    2015      NaN      NaN  10400.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "      2016  10546.0  10563.0  10648.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2017  11074.0  11074.0  11660.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2018  12029.0  12074.0  12100.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2019      NaN      NaN      NaN  ...  5.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "전북    2015      NaN      NaN  10864.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "      2016  10407.0  10410.0  10431.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2017  11509.0   8870.0   8752.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2018  12737.0  12737.0  12551.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2019      NaN      NaN      NaN  ...  5.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "제주    2015      NaN      NaN   9614.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "      2016  14211.0  16407.0  16407.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2017  21366.0  11752.0  11752.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2018  12906.0  12906.0  12906.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2019      NaN      NaN      NaN  ...  5.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "충남    2015      NaN      NaN  11591.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "      2016  12167.0  12168.0  12159.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2017  12440.0  12440.0  12569.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2018  12774.0  12774.0  12774.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2019      NaN      NaN      NaN  ...  5.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "충북    2015      NaN      NaN  10455.0  ...  NaN  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "      2016  10881.0  10886.0  11276.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2017  11323.0  11332.0  11343.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2018  12506.0  12494.0  12494.0  ...  5.0  5.0  5.0  5.0  5.0  5.0  5.0   \n",
       "      2019      NaN      NaN      NaN  ...  5.0  NaN  NaN  NaN  NaN  NaN  NaN   \n",
       "\n",
       "                           \n",
       "월            10   11   12  \n",
       "지역명   연도                   \n",
       "Seoul 2015  5.0  5.0  5.0  \n",
       "      2016  5.0  5.0  5.0  \n",
       "      2017  5.0  5.0  5.0  \n",
       "      2018  5.0  5.0  5.0  \n",
       "      2019  NaN  NaN  NaN  \n",
       "강원    2015  5.0  5.0  5.0  \n",
       "      2016  5.0  5.0  5.0  \n",
       "      2017  5.0  5.0  5.0  \n",
       "      2018  5.0  5.0  5.0  \n",
       "      2019  NaN  NaN  NaN  \n",
       "경기    2015  5.0  5.0  5.0  \n",
       "      2016  5.0  5.0  5.0  \n",
       "      2017  5.0  5.0  5.0  \n",
       "      2018  5.0  5.0  5.0  \n",
       "      2019  NaN  NaN  NaN  \n",
       "경남    2015  5.0  5.0  5.0  \n",
       "      2016  5.0  5.0  5.0  \n",
       "      2017  5.0  5.0  5.0  \n",
       "      2018  5.0  5.0  5.0  \n",
       "      2019  NaN  NaN  NaN  \n",
       "경북    2015  5.0  5.0  5.0  \n",
       "      2016  5.0  5.0  5.0  \n",
       "      2017  5.0  5.0  5.0  \n",
       "      2018  5.0  5.0  5.0  \n",
       "      2019  NaN  NaN  NaN  \n",
       "광주    2015  5.0  5.0  5.0  \n",
       "      2016  5.0  5.0  5.0  \n",
       "      2017  5.0  5.0  5.0  \n",
       "      2018  5.0  5.0  5.0  \n",
       "      2019  NaN  NaN  NaN  \n",
       "...         ...  ...  ...  \n",
       "인천    2015  5.0  5.0  5.0  \n",
       "      2016  5.0  5.0  5.0  \n",
       "      2017  5.0  5.0  5.0  \n",
       "      2018  5.0  5.0  5.0  \n",
       "      2019  NaN  NaN  NaN  \n",
       "전남    2015  5.0  5.0  5.0  \n",
       "      2016  5.0  5.0  5.0  \n",
       "      2017  5.0  5.0  5.0  \n",
       "      2018  5.0  5.0  5.0  \n",
       "      2019  NaN  NaN  NaN  \n",
       "전북    2015  5.0  5.0  5.0  \n",
       "      2016  5.0  5.0  5.0  \n",
       "      2017  5.0  5.0  5.0  \n",
       "      2018  5.0  5.0  5.0  \n",
       "      2019  NaN  NaN  NaN  \n",
       "제주    2015  5.0  5.0  5.0  \n",
       "      2016  5.0  5.0  5.0  \n",
       "      2017  5.0  5.0  5.0  \n",
       "      2018  5.0  5.0  5.0  \n",
       "      2019  NaN  NaN  NaN  \n",
       "충남    2015  5.0  5.0  5.0  \n",
       "      2016  5.0  5.0  5.0  \n",
       "      2017  5.0  5.0  5.0  \n",
       "      2018  5.0  5.0  5.0  \n",
       "      2019  NaN  NaN  NaN  \n",
       "충북    2015  5.0  5.0  5.0  \n",
       "      2016  5.0  5.0  5.0  \n",
       "      2017  5.0  5.0  5.0  \n",
       "      2018  5.0  5.0  5.0  \n",
       "      2019  NaN  NaN  NaN  \n",
       "\n",
       "[85 rows x 24 columns]"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# level2: 열을 연도별로 출력\n",
    "grouped.unstack(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pivot Table"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Pivot Table이란?\n",
    "다시 말해 피벗 테이블이란 여러 데이터 중에서 자신이 원하는 데이터만을 가지고 원하는 행과 열에 데이터를 배치하여 새로운 보고서를 만드는 기능이다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3119.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3545.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3408.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test\n",
       "0  Seoul                 전체  2015  10  5841.0  True\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True\n",
       "5     인천                 전체  2015  10  3163.0  True\n",
       "6     인천         전용면적 60㎡이하  2015  10  3488.0  True\n",
       "7     인천   전용면적 60㎡초과 85㎡이하  2015  10  3119.0  True\n",
       "8     인천  전용면적 85㎡초과 102㎡이하  2015  10  3545.0  True\n",
       "9     인천        전용면적 102㎡초과  2015  10  3408.0  True"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>11</th>\n",
       "      <th>12</th>\n",
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       "    <tr>\n",
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       "      <th>지역명</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2568.60</td>\n",
       "      <td>2573.800000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2205.400000</td>\n",
       "      <td>2294.40</td>\n",
       "      <td>2285.800000</td>\n",
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       "    <tr>\n",
       "      <th>광주</th>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>대구</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>2482.000000</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3103.600000</td>\n",
       "      <td>3169.20</td>\n",
       "      <td>3161.200000</td>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2641.400000</td>\n",
       "      <td>2655.80</td>\n",
       "      <td>2671.000000</td>\n",
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       "    <tr>\n",
       "      <th>울산</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2831.200000</td>\n",
       "      <td>2842.40</td>\n",
       "      <td>2842.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3344.600000</td>\n",
       "      <td>3316.80</td>\n",
       "      <td>3316.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전남</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2080.000000</td>\n",
       "      <td>2050.40</td>\n",
       "      <td>2050.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전북</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2172.800000</td>\n",
       "      <td>2180.40</td>\n",
       "      <td>2110.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2403.500000</td>\n",
       "      <td>2403.50</td>\n",
       "      <td>2421.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충남</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2318.200000</td>\n",
       "      <td>2282.00</td>\n",
       "      <td>2390.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2091.000000</td>\n",
       "      <td>2017.00</td>\n",
       "      <td>2100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"13\" valign=\"top\">2016</th>\n",
       "      <th>Seoul</th>\n",
       "      <td>6327.000000</td>\n",
       "      <td>6311.800000</td>\n",
       "      <td>6480.000000</td>\n",
       "      <td>6700.800000</td>\n",
       "      <td>6637.000000</td>\n",
       "      <td>6617.000000</td>\n",
       "      <td>6673.800000</td>\n",
       "      <td>6634.800000</td>\n",
       "      <td>6649.20</td>\n",
       "      <td>6847.800000</td>\n",
       "      <td>6534.40</td>\n",
       "      <td>6689.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원</th>\n",
       "      <td>2178.800000</td>\n",
       "      <td>2253.250000</td>\n",
       "      <td>2253.500000</td>\n",
       "      <td>2204.000000</td>\n",
       "      <td>2167.250000</td>\n",
       "      <td>2169.500000</td>\n",
       "      <td>2162.000000</td>\n",
       "      <td>2176.250000</td>\n",
       "      <td>2172.50</td>\n",
       "      <td>2121.600000</td>\n",
       "      <td>2076.60</td>\n",
       "      <td>2150.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경기</th>\n",
       "      <td>3420.800000</td>\n",
       "      <td>3366.200000</td>\n",
       "      <td>3391.600000</td>\n",
       "      <td>3369.600000</td>\n",
       "      <td>3586.400000</td>\n",
       "      <td>3653.800000</td>\n",
       "      <td>3663.000000</td>\n",
       "      <td>3650.000000</td>\n",
       "      <td>3495.80</td>\n",
       "      <td>3570.400000</td>\n",
       "      <td>3672.40</td>\n",
       "      <td>3650.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경남</th>\n",
       "      <td>2539.600000</td>\n",
       "      <td>2538.400000</td>\n",
       "      <td>2567.800000</td>\n",
       "      <td>2605.800000</td>\n",
       "      <td>2640.600000</td>\n",
       "      <td>2605.200000</td>\n",
       "      <td>2593.800000</td>\n",
       "      <td>2596.800000</td>\n",
       "      <td>2557.40</td>\n",
       "      <td>2564.200000</td>\n",
       "      <td>2539.80</td>\n",
       "      <td>2547.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경북</th>\n",
       "      <td>2281.200000</td>\n",
       "      <td>2284.000000</td>\n",
       "      <td>2306.400000</td>\n",
       "      <td>2305.200000</td>\n",
       "      <td>2306.800000</td>\n",
       "      <td>2365.600000</td>\n",
       "      <td>2365.600000</td>\n",
       "      <td>2359.600000</td>\n",
       "      <td>2362.40</td>\n",
       "      <td>2414.000000</td>\n",
       "      <td>2415.20</td>\n",
       "      <td>2428.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주</th>\n",
       "      <td>2515.333333</td>\n",
       "      <td>2515.333333</td>\n",
       "      <td>2553.666667</td>\n",
       "      <td>2853.600000</td>\n",
       "      <td>2856.800000</td>\n",
       "      <td>2863.800000</td>\n",
       "      <td>2865.800000</td>\n",
       "      <td>2858.600000</td>\n",
       "      <td>2770.60</td>\n",
       "      <td>2772.400000</td>\n",
       "      <td>2841.60</td>\n",
       "      <td>2844.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>2961.000000</td>\n",
       "      <td>2951.800000</td>\n",
       "      <td>2979.200000</td>\n",
       "      <td>2984.600000</td>\n",
       "      <td>2993.000000</td>\n",
       "      <td>3053.200000</td>\n",
       "      <td>3059.000000</td>\n",
       "      <td>3114.600000</td>\n",
       "      <td>3114.60</td>\n",
       "      <td>3114.600000</td>\n",
       "      <td>3492.80</td>\n",
       "      <td>3570.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대전</th>\n",
       "      <td>2482.000000</td>\n",
       "      <td>2482.000000</td>\n",
       "      <td>2482.000000</td>\n",
       "      <td>2467.666667</td>\n",
       "      <td>2491.333333</td>\n",
       "      <td>2585.666667</td>\n",
       "      <td>2585.666667</td>\n",
       "      <td>2585.666667</td>\n",
       "      <td>2537.00</td>\n",
       "      <td>3215.800000</td>\n",
       "      <td>2988.80</td>\n",
       "      <td>2988.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>3185.800000</td>\n",
       "      <td>3196.400000</td>\n",
       "      <td>3183.000000</td>\n",
       "      <td>3177.200000</td>\n",
       "      <td>3165.600000</td>\n",
       "      <td>3233.400000</td>\n",
       "      <td>3281.800000</td>\n",
       "      <td>3318.800000</td>\n",
       "      <td>3322.20</td>\n",
       "      <td>3297.800000</td>\n",
       "      <td>3285.40</td>\n",
       "      <td>3420.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>세종</th>\n",
       "      <td>2671.000000</td>\n",
       "      <td>2671.000000</td>\n",
       "      <td>2674.800000</td>\n",
       "      <td>2680.600000</td>\n",
       "      <td>2680.600000</td>\n",
       "      <td>2674.400000</td>\n",
       "      <td>2673.600000</td>\n",
       "      <td>2678.600000</td>\n",
       "      <td>2685.60</td>\n",
       "      <td>2705.000000</td>\n",
       "      <td>2709.20</td>\n",
       "      <td>2705.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>울산</th>\n",
       "      <td>2844.000000</td>\n",
       "      <td>2844.000000</td>\n",
       "      <td>2889.000000</td>\n",
       "      <td>2872.800000</td>\n",
       "      <td>2877.400000</td>\n",
       "      <td>2866.000000</td>\n",
       "      <td>2866.000000</td>\n",
       "      <td>2830.600000</td>\n",
       "      <td>2830.60</td>\n",
       "      <td>2936.200000</td>\n",
       "      <td>3142.75</td>\n",
       "      <td>3141.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>3316.400000</td>\n",
       "      <td>3317.000000</td>\n",
       "      <td>3315.400000</td>\n",
       "      <td>3618.400000</td>\n",
       "      <td>3441.600000</td>\n",
       "      <td>3313.400000</td>\n",
       "      <td>3303.400000</td>\n",
       "      <td>3323.400000</td>\n",
       "      <td>3257.80</td>\n",
       "      <td>3380.800000</td>\n",
       "      <td>3387.40</td>\n",
       "      <td>3385.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전남</th>\n",
       "      <td>2050.400000</td>\n",
       "      <td>2056.200000</td>\n",
       "      <td>2056.000000</td>\n",
       "      <td>2055.000000</td>\n",
       "      <td>2055.800000</td>\n",
       "      <td>2109.800000</td>\n",
       "      <td>2110.000000</td>\n",
       "      <td>2109.200000</td>\n",
       "      <td>2112.60</td>\n",
       "      <td>2129.600000</td>\n",
       "      <td>2180.20</td>\n",
       "      <td>2199.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">2018</th>\n",
       "      <th>경남</th>\n",
       "      <td>2779.200000</td>\n",
       "      <td>2781.000000</td>\n",
       "      <td>2782.000000</td>\n",
       "      <td>2763.200000</td>\n",
       "      <td>2765.000000</td>\n",
       "      <td>2766.200000</td>\n",
       "      <td>2762.600000</td>\n",
       "      <td>2735.800000</td>\n",
       "      <td>2842.20</td>\n",
       "      <td>2894.200000</td>\n",
       "      <td>2925.60</td>\n",
       "      <td>3121.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경북</th>\n",
       "      <td>2567.000000</td>\n",
       "      <td>2567.000000</td>\n",
       "      <td>2543.500000</td>\n",
       "      <td>2530.250000</td>\n",
       "      <td>2643.500000</td>\n",
       "      <td>2654.750000</td>\n",
       "      <td>2675.500000</td>\n",
       "      <td>2625.000000</td>\n",
       "      <td>2635.40</td>\n",
       "      <td>2680.200000</td>\n",
       "      <td>2698.40</td>\n",
       "      <td>2698.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주</th>\n",
       "      <td>2855.500000</td>\n",
       "      <td>2855.500000</td>\n",
       "      <td>2873.750000</td>\n",
       "      <td>2751.000000</td>\n",
       "      <td>2761.500000</td>\n",
       "      <td>2808.250000</td>\n",
       "      <td>2775.750000</td>\n",
       "      <td>2775.750000</td>\n",
       "      <td>2823.25</td>\n",
       "      <td>3117.666667</td>\n",
       "      <td>3222.00</td>\n",
       "      <td>3257.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>3559.000000</td>\n",
       "      <td>3448.250000</td>\n",
       "      <td>3465.000000</td>\n",
       "      <td>3327.000000</td>\n",
       "      <td>3696.400000</td>\n",
       "      <td>3689.000000</td>\n",
       "      <td>3753.400000</td>\n",
       "      <td>3758.000000</td>\n",
       "      <td>3780.20</td>\n",
       "      <td>3838.800000</td>\n",
       "      <td>3821.20</td>\n",
       "      <td>3893.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대전</th>\n",
       "      <td>2797.250000</td>\n",
       "      <td>2833.500000</td>\n",
       "      <td>2833.500000</td>\n",
       "      <td>2878.500000</td>\n",
       "      <td>2878.500000</td>\n",
       "      <td>2878.500000</td>\n",
       "      <td>2794.333333</td>\n",
       "      <td>2794.333333</td>\n",
       "      <td>3527.00</td>\n",
       "      <td>3516.750000</td>\n",
       "      <td>3516.75</td>\n",
       "      <td>4049.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>3788.600000</td>\n",
       "      <td>3793.400000</td>\n",
       "      <td>3756.400000</td>\n",
       "      <td>3774.800000</td>\n",
       "      <td>3784.800000</td>\n",
       "      <td>3808.600000</td>\n",
       "      <td>3899.200000</td>\n",
       "      <td>3902.800000</td>\n",
       "      <td>4131.00</td>\n",
       "      <td>4136.600000</td>\n",
       "      <td>4042.60</td>\n",
       "      <td>4053.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>세종</th>\n",
       "      <td>3162.750000</td>\n",
       "      <td>3162.750000</td>\n",
       "      <td>3162.750000</td>\n",
       "      <td>3123.000000</td>\n",
       "      <td>3123.000000</td>\n",
       "      <td>3123.000000</td>\n",
       "      <td>3123.000000</td>\n",
       "      <td>3143.400000</td>\n",
       "      <td>3143.40</td>\n",
       "      <td>3143.400000</td>\n",
       "      <td>3143.40</td>\n",
       "      <td>3065.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>울산</th>\n",
       "      <td>3162.000000</td>\n",
       "      <td>3174.000000</td>\n",
       "      <td>3174.000000</td>\n",
       "      <td>3174.000000</td>\n",
       "      <td>3174.000000</td>\n",
       "      <td>3125.000000</td>\n",
       "      <td>3125.000000</td>\n",
       "      <td>3125.000000</td>\n",
       "      <td>3125.00</td>\n",
       "      <td>2890.000000</td>\n",
       "      <td>2890.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>3669.200000</td>\n",
       "      <td>3685.200000</td>\n",
       "      <td>3777.800000</td>\n",
       "      <td>3777.800000</td>\n",
       "      <td>3752.400000</td>\n",
       "      <td>3398.000000</td>\n",
       "      <td>3398.000000</td>\n",
       "      <td>3383.250000</td>\n",
       "      <td>3383.25</td>\n",
       "      <td>3354.600000</td>\n",
       "      <td>3732.40</td>\n",
       "      <td>3725.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전남</th>\n",
       "      <td>2424.800000</td>\n",
       "      <td>2359.600000</td>\n",
       "      <td>2396.400000</td>\n",
       "      <td>2404.800000</td>\n",
       "      <td>2405.200000</td>\n",
       "      <td>2405.200000</td>\n",
       "      <td>2405.800000</td>\n",
       "      <td>2405.800000</td>\n",
       "      <td>2414.80</td>\n",
       "      <td>2420.000000</td>\n",
       "      <td>2386.00</td>\n",
       "      <td>2407.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전북</th>\n",
       "      <td>2270.800000</td>\n",
       "      <td>2273.200000</td>\n",
       "      <td>2269.000000</td>\n",
       "      <td>2528.200000</td>\n",
       "      <td>2544.000000</td>\n",
       "      <td>2544.000000</td>\n",
       "      <td>2556.000000</td>\n",
       "      <td>2547.400000</td>\n",
       "      <td>2547.40</td>\n",
       "      <td>2510.200000</td>\n",
       "      <td>2512.80</td>\n",
       "      <td>2622.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주</th>\n",
       "      <td>3889.250000</td>\n",
       "      <td>3965.500000</td>\n",
       "      <td>4013.750000</td>\n",
       "      <td>4013.750000</td>\n",
       "      <td>4013.750000</td>\n",
       "      <td>4013.750000</td>\n",
       "      <td>3226.500000</td>\n",
       "      <td>3226.500000</td>\n",
       "      <td>3226.50</td>\n",
       "      <td>3226.500000</td>\n",
       "      <td>3335.00</td>\n",
       "      <td>3382.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충남</th>\n",
       "      <td>2376.750000</td>\n",
       "      <td>2376.750000</td>\n",
       "      <td>2376.750000</td>\n",
       "      <td>2376.750000</td>\n",
       "      <td>2467.750000</td>\n",
       "      <td>2524.600000</td>\n",
       "      <td>2524.600000</td>\n",
       "      <td>2554.800000</td>\n",
       "      <td>2554.80</td>\n",
       "      <td>2554.800000</td>\n",
       "      <td>2525.40</td>\n",
       "      <td>2520.600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북</th>\n",
       "      <td>2226.200000</td>\n",
       "      <td>2287.000000</td>\n",
       "      <td>2442.400000</td>\n",
       "      <td>2592.200000</td>\n",
       "      <td>2605.600000</td>\n",
       "      <td>2510.400000</td>\n",
       "      <td>2501.200000</td>\n",
       "      <td>2501.200000</td>\n",
       "      <td>2498.80</td>\n",
       "      <td>2498.800000</td>\n",
       "      <td>2485.00</td>\n",
       "      <td>2485.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"16\" valign=\"top\">2019</th>\n",
       "      <th>Seoul</th>\n",
       "      <td>7546.800000</td>\n",
       "      <td>7518.400000</td>\n",
       "      <td>7552.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>강원</th>\n",
       "      <td>2523.600000</td>\n",
       "      <td>2523.600000</td>\n",
       "      <td>2541.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경기</th>\n",
       "      <td>4550.000000</td>\n",
       "      <td>4575.800000</td>\n",
       "      <td>4539.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경남</th>\n",
       "      <td>3121.800000</td>\n",
       "      <td>3120.800000</td>\n",
       "      <td>3196.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>경북</th>\n",
       "      <td>2664.400000</td>\n",
       "      <td>2686.000000</td>\n",
       "      <td>2707.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>광주</th>\n",
       "      <td>3277.000000</td>\n",
       "      <td>3277.000000</td>\n",
       "      <td>3277.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대구</th>\n",
       "      <td>4002.600000</td>\n",
       "      <td>4004.400000</td>\n",
       "      <td>3998.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>대전</th>\n",
       "      <td>4081.000000</td>\n",
       "      <td>4081.000000</td>\n",
       "      <td>3906.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>부산</th>\n",
       "      <td>4053.800000</td>\n",
       "      <td>4053.800000</td>\n",
       "      <td>4078.800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>세종</th>\n",
       "      <td>3065.400000</td>\n",
       "      <td>3065.400000</td>\n",
       "      <td>3065.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>인천</th>\n",
       "      <td>3751.400000</td>\n",
       "      <td>3830.200000</td>\n",
       "      <td>3917.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전남</th>\n",
       "      <td>2394.400000</td>\n",
       "      <td>2462.800000</td>\n",
       "      <td>2447.200000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>전북</th>\n",
       "      <td>2622.800000</td>\n",
       "      <td>2645.800000</td>\n",
       "      <td>2671.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>제주</th>\n",
       "      <td>3382.000000</td>\n",
       "      <td>3382.000000</td>\n",
       "      <td>3382.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충남</th>\n",
       "      <td>2554.600000</td>\n",
       "      <td>2598.800000</td>\n",
       "      <td>2622.600000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>충북</th>\n",
       "      <td>2485.000000</td>\n",
       "      <td>2431.600000</td>\n",
       "      <td>2447.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>84 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "월                    1            2            3            4            5   \\\n",
       "연도   지역명                                                                      \n",
       "2015 Seoul          NaN          NaN          NaN          NaN          NaN   \n",
       "     강원             NaN          NaN          NaN          NaN          NaN   \n",
       "     경기             NaN          NaN          NaN          NaN          NaN   \n",
       "     경남             NaN          NaN          NaN          NaN          NaN   \n",
       "     경북             NaN          NaN          NaN          NaN          NaN   \n",
       "     광주             NaN          NaN          NaN          NaN          NaN   \n",
       "     대구             NaN          NaN          NaN          NaN          NaN   \n",
       "     대전             NaN          NaN          NaN          NaN          NaN   \n",
       "     부산             NaN          NaN          NaN          NaN          NaN   \n",
       "     세종             NaN          NaN          NaN          NaN          NaN   \n",
       "     울산             NaN          NaN          NaN          NaN          NaN   \n",
       "     인천             NaN          NaN          NaN          NaN          NaN   \n",
       "     전남             NaN          NaN          NaN          NaN          NaN   \n",
       "     전북             NaN          NaN          NaN          NaN          NaN   \n",
       "     제주             NaN          NaN          NaN          NaN          NaN   \n",
       "     충남             NaN          NaN          NaN          NaN          NaN   \n",
       "     충북             NaN          NaN          NaN          NaN          NaN   \n",
       "2016 Seoul  6327.000000  6311.800000  6480.000000  6700.800000  6637.000000   \n",
       "     강원     2178.800000  2253.250000  2253.500000  2204.000000  2167.250000   \n",
       "     경기     3420.800000  3366.200000  3391.600000  3369.600000  3586.400000   \n",
       "     경남     2539.600000  2538.400000  2567.800000  2605.800000  2640.600000   \n",
       "     경북     2281.200000  2284.000000  2306.400000  2305.200000  2306.800000   \n",
       "     광주     2515.333333  2515.333333  2553.666667  2853.600000  2856.800000   \n",
       "     대구     2961.000000  2951.800000  2979.200000  2984.600000  2993.000000   \n",
       "     대전     2482.000000  2482.000000  2482.000000  2467.666667  2491.333333   \n",
       "     부산     3185.800000  3196.400000  3183.000000  3177.200000  3165.600000   \n",
       "     세종     2671.000000  2671.000000  2674.800000  2680.600000  2680.600000   \n",
       "     울산     2844.000000  2844.000000  2889.000000  2872.800000  2877.400000   \n",
       "     인천     3316.400000  3317.000000  3315.400000  3618.400000  3441.600000   \n",
       "     전남     2050.400000  2056.200000  2056.000000  2055.000000  2055.800000   \n",
       "...                 ...          ...          ...          ...          ...   \n",
       "2018 경남     2779.200000  2781.000000  2782.000000  2763.200000  2765.000000   \n",
       "     경북     2567.000000  2567.000000  2543.500000  2530.250000  2643.500000   \n",
       "     광주     2855.500000  2855.500000  2873.750000  2751.000000  2761.500000   \n",
       "     대구     3559.000000  3448.250000  3465.000000  3327.000000  3696.400000   \n",
       "     대전     2797.250000  2833.500000  2833.500000  2878.500000  2878.500000   \n",
       "     부산     3788.600000  3793.400000  3756.400000  3774.800000  3784.800000   \n",
       "     세종     3162.750000  3162.750000  3162.750000  3123.000000  3123.000000   \n",
       "     울산     3162.000000  3174.000000  3174.000000  3174.000000  3174.000000   \n",
       "     인천     3669.200000  3685.200000  3777.800000  3777.800000  3752.400000   \n",
       "     전남     2424.800000  2359.600000  2396.400000  2404.800000  2405.200000   \n",
       "     전북     2270.800000  2273.200000  2269.000000  2528.200000  2544.000000   \n",
       "     제주     3889.250000  3965.500000  4013.750000  4013.750000  4013.750000   \n",
       "     충남     2376.750000  2376.750000  2376.750000  2376.750000  2467.750000   \n",
       "     충북     2226.200000  2287.000000  2442.400000  2592.200000  2605.600000   \n",
       "2019 Seoul  7546.800000  7518.400000  7552.200000          NaN          NaN   \n",
       "     강원     2523.600000  2523.600000  2541.200000          NaN          NaN   \n",
       "     경기     4550.000000  4575.800000  4539.200000          NaN          NaN   \n",
       "     경남     3121.800000  3120.800000  3196.800000          NaN          NaN   \n",
       "     경북     2664.400000  2686.000000  2707.200000          NaN          NaN   \n",
       "     광주     3277.000000  3277.000000  3277.000000          NaN          NaN   \n",
       "     대구     4002.600000  4004.400000  3998.800000          NaN          NaN   \n",
       "     대전     4081.000000  4081.000000  3906.000000          NaN          NaN   \n",
       "     부산     4053.800000  4053.800000  4078.800000          NaN          NaN   \n",
       "     세종     3065.400000  3065.400000  3065.400000          NaN          NaN   \n",
       "     인천     3751.400000  3830.200000  3917.000000          NaN          NaN   \n",
       "     전남     2394.400000  2462.800000  2447.200000          NaN          NaN   \n",
       "     전북     2622.800000  2645.800000  2671.600000          NaN          NaN   \n",
       "     제주     3382.000000  3382.000000  3382.000000          NaN          NaN   \n",
       "     충남     2554.600000  2598.800000  2622.600000          NaN          NaN   \n",
       "     충북     2485.000000  2431.600000  2447.000000          NaN          NaN   \n",
       "\n",
       "월                    6            7            8        9            10  \\\n",
       "연도   지역명                                                                  \n",
       "2015 Seoul          NaN          NaN          NaN      NaN  5795.000000   \n",
       "     강원             NaN          NaN          NaN      NaN  2179.400000   \n",
       "     경기             NaN          NaN          NaN      NaN  3335.800000   \n",
       "     경남             NaN          NaN          NaN      NaN  2547.800000   \n",
       "     경북             NaN          NaN          NaN      NaN  2205.400000   \n",
       "     광주             NaN          NaN          NaN      NaN  2370.666667   \n",
       "     대구             NaN          NaN          NaN      NaN  2629.400000   \n",
       "     대전             NaN          NaN          NaN      NaN  2482.000000   \n",
       "     부산             NaN          NaN          NaN      NaN  3103.600000   \n",
       "     세종             NaN          NaN          NaN      NaN  2641.400000   \n",
       "     울산             NaN          NaN          NaN      NaN  2831.200000   \n",
       "     인천             NaN          NaN          NaN      NaN  3344.600000   \n",
       "     전남             NaN          NaN          NaN      NaN  2080.000000   \n",
       "     전북             NaN          NaN          NaN      NaN  2172.800000   \n",
       "     제주             NaN          NaN          NaN      NaN  2403.500000   \n",
       "     충남             NaN          NaN          NaN      NaN  2318.200000   \n",
       "     충북             NaN          NaN          NaN      NaN  2091.000000   \n",
       "2016 Seoul  6617.000000  6673.800000  6634.800000  6649.20  6847.800000   \n",
       "     강원     2169.500000  2162.000000  2176.250000  2172.50  2121.600000   \n",
       "     경기     3653.800000  3663.000000  3650.000000  3495.80  3570.400000   \n",
       "     경남     2605.200000  2593.800000  2596.800000  2557.40  2564.200000   \n",
       "     경북     2365.600000  2365.600000  2359.600000  2362.40  2414.000000   \n",
       "     광주     2863.800000  2865.800000  2858.600000  2770.60  2772.400000   \n",
       "     대구     3053.200000  3059.000000  3114.600000  3114.60  3114.600000   \n",
       "     대전     2585.666667  2585.666667  2585.666667  2537.00  3215.800000   \n",
       "     부산     3233.400000  3281.800000  3318.800000  3322.20  3297.800000   \n",
       "     세종     2674.400000  2673.600000  2678.600000  2685.60  2705.000000   \n",
       "     울산     2866.000000  2866.000000  2830.600000  2830.60  2936.200000   \n",
       "     인천     3313.400000  3303.400000  3323.400000  3257.80  3380.800000   \n",
       "     전남     2109.800000  2110.000000  2109.200000  2112.60  2129.600000   \n",
       "...                 ...          ...          ...      ...          ...   \n",
       "2018 경남     2766.200000  2762.600000  2735.800000  2842.20  2894.200000   \n",
       "     경북     2654.750000  2675.500000  2625.000000  2635.40  2680.200000   \n",
       "     광주     2808.250000  2775.750000  2775.750000  2823.25  3117.666667   \n",
       "     대구     3689.000000  3753.400000  3758.000000  3780.20  3838.800000   \n",
       "     대전     2878.500000  2794.333333  2794.333333  3527.00  3516.750000   \n",
       "     부산     3808.600000  3899.200000  3902.800000  4131.00  4136.600000   \n",
       "     세종     3123.000000  3123.000000  3143.400000  3143.40  3143.400000   \n",
       "     울산     3125.000000  3125.000000  3125.000000  3125.00  2890.000000   \n",
       "     인천     3398.000000  3398.000000  3383.250000  3383.25  3354.600000   \n",
       "     전남     2405.200000  2405.800000  2405.800000  2414.80  2420.000000   \n",
       "     전북     2544.000000  2556.000000  2547.400000  2547.40  2510.200000   \n",
       "     제주     4013.750000  3226.500000  3226.500000  3226.50  3226.500000   \n",
       "     충남     2524.600000  2524.600000  2554.800000  2554.80  2554.800000   \n",
       "     충북     2510.400000  2501.200000  2501.200000  2498.80  2498.800000   \n",
       "2019 Seoul          NaN          NaN          NaN      NaN          NaN   \n",
       "     강원             NaN          NaN          NaN      NaN          NaN   \n",
       "     경기             NaN          NaN          NaN      NaN          NaN   \n",
       "     경남             NaN          NaN          NaN      NaN          NaN   \n",
       "     경북             NaN          NaN          NaN      NaN          NaN   \n",
       "     광주             NaN          NaN          NaN      NaN          NaN   \n",
       "     대구             NaN          NaN          NaN      NaN          NaN   \n",
       "     대전             NaN          NaN          NaN      NaN          NaN   \n",
       "     부산             NaN          NaN          NaN      NaN          NaN   \n",
       "     세종             NaN          NaN          NaN      NaN          NaN   \n",
       "     인천             NaN          NaN          NaN      NaN          NaN   \n",
       "     전남             NaN          NaN          NaN      NaN          NaN   \n",
       "     전북             NaN          NaN          NaN      NaN          NaN   \n",
       "     제주             NaN          NaN          NaN      NaN          NaN   \n",
       "     충남             NaN          NaN          NaN      NaN          NaN   \n",
       "     충북             NaN          NaN          NaN      NaN          NaN   \n",
       "\n",
       "월                11           12  \n",
       "연도   지역명                          \n",
       "2015 Seoul  6395.40  6278.400000  \n",
       "     강원     2177.60  2177.600000  \n",
       "     경기     3298.80  3420.800000  \n",
       "     경남     2568.60  2573.800000  \n",
       "     경북     2294.40  2285.800000  \n",
       "     광주     2373.00  2453.333333  \n",
       "     대구     2671.60  2898.000000  \n",
       "     대전     2482.00  2482.000000  \n",
       "     부산     3169.20  3161.200000  \n",
       "     세종     2655.80  2671.000000  \n",
       "     울산     2842.40  2842.400000  \n",
       "     인천     3316.80  3316.800000  \n",
       "     전남     2050.40  2050.400000  \n",
       "     전북     2180.40  2110.800000  \n",
       "     제주     2403.50  2421.250000  \n",
       "     충남     2282.00  2390.600000  \n",
       "     충북     2017.00  2100.000000  \n",
       "2016 Seoul  6534.40  6689.800000  \n",
       "     강원     2076.60  2150.400000  \n",
       "     경기     3672.40  3650.800000  \n",
       "     경남     2539.80  2547.800000  \n",
       "     경북     2415.20  2428.200000  \n",
       "     광주     2841.60  2844.800000  \n",
       "     대구     3492.80  3570.800000  \n",
       "     대전     2988.80  2988.800000  \n",
       "     부산     3285.40  3420.000000  \n",
       "     세종     2709.20  2705.800000  \n",
       "     울산     3142.75  3141.750000  \n",
       "     인천     3387.40  3385.200000  \n",
       "     전남     2180.20  2199.200000  \n",
       "...             ...          ...  \n",
       "2018 경남     2925.60  3121.800000  \n",
       "     경북     2698.40  2698.400000  \n",
       "     광주     3222.00  3257.666667  \n",
       "     대구     3821.20  3893.800000  \n",
       "     대전     3516.75  4049.666667  \n",
       "     부산     4042.60  4053.800000  \n",
       "     세종     3143.40  3065.400000  \n",
       "     울산     2890.00          NaN  \n",
       "     인천     3732.40  3725.800000  \n",
       "     전남     2386.00  2407.400000  \n",
       "     전북     2512.80  2622.800000  \n",
       "     제주     3335.00  3382.000000  \n",
       "     충남     2525.40  2520.600000  \n",
       "     충북     2485.00  2485.000000  \n",
       "2019 Seoul      NaN          NaN  \n",
       "     강원         NaN          NaN  \n",
       "     경기         NaN          NaN  \n",
       "     경남         NaN          NaN  \n",
       "     경북         NaN          NaN  \n",
       "     광주         NaN          NaN  \n",
       "     대구         NaN          NaN  \n",
       "     대전         NaN          NaN  \n",
       "     부산         NaN          NaN  \n",
       "     세종         NaN          NaN  \n",
       "     인천         NaN          NaN  \n",
       "     전남         NaN          NaN  \n",
       "     전북         NaN          NaN  \n",
       "     제주         NaN          NaN  \n",
       "     충남         NaN          NaN  \n",
       "     충북         NaN          NaN  \n",
       "\n",
       "[84 rows x 12 columns]"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.pivot_table(df, values='분양가', index=['연도','지역명'], columns=['월'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Categoricals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3119.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3545.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3408.0</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test\n",
       "0  Seoul                 전체  2015  10  5841.0  True\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True\n",
       "5     인천                 전체  2015  10  3163.0  True\n",
       "6     인천         전용면적 60㎡이하  2015  10  3488.0  True\n",
       "7     인천   전용면적 60㎡초과 85㎡이하  2015  10  3119.0  True\n",
       "8     인천  전용면적 85㎡초과 102㎡이하  2015  10  3545.0  True\n",
       "9     인천        전용면적 102㎡초과  2015  10  3408.0  True"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3273.000000\n",
       "mean     3130.001833\n",
       "std      1141.740958\n",
       "min      1868.000000\n",
       "25%      2387.000000\n",
       "50%      2787.000000\n",
       "75%      3383.000000\n",
       "max      8191.000000\n",
       "Name: 분양가, dtype: float64"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['분양가'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['평가'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
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       "      <td>5841.0</td>\n",
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       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
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       "      <td>2015</td>\n",
       "      <td>10</td>\n",
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       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
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       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3119.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3545.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3408.0</td>\n",
       "      <td>True</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test  평가\n",
       "0  Seoul                 전체  2015  10  5841.0  True   0\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True   0\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True   0\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True   0\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True   0\n",
       "5     인천                 전체  2015  10  3163.0  True   0\n",
       "6     인천         전용면적 60㎡이하  2015  10  3488.0  True   0\n",
       "7     인천   전용면적 60㎡초과 85㎡이하  2015  10  3119.0  True   0\n",
       "8     인천  전용면적 85㎡초과 102㎡이하  2015  10  3545.0  True   0\n",
       "9     인천        전용면적 102㎡초과  2015  10  3408.0  True   0"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**가격대 별 평가**\n",
    "\n",
    "low: 25% = 2387 보다 싼 분양가 \n",
    "\n",
    "mid: 50% = 2387 ~ 3130 \n",
    "\n",
    "high: 75% = 3130 ~ 3383\n",
    "\n",
    "very high: 75% ~ 100% = 3383 보다 비싼 분양가 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.select를 활용하여 조건에 맞는 값을 대입하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "conditions = [\n",
    "    (df['분양가'] < 2387),\n",
    "    (df['분양가'] >= 2387) & (df['분양가'] < 3130),\n",
    "    (df['분양가'] >= 3130) & (df['분양가'] < 3383),\n",
    "    (df['분양가'] >= 3383),\n",
    "    (df['분양가'] == np.nan)\n",
    "]\n",
    "choices = ['저렴', '보통', '비쌈', '매우 비쌈', '-']\n",
    "df['평가'] = np.select(conditions, choices, default=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
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       "      <td>2015</td>\n",
       "      <td>10</td>\n",
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       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
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       "      <td>2015</td>\n",
       "      <td>10</td>\n",
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       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "      <td>비쌈</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3119.0</td>\n",
       "      <td>True</td>\n",
       "      <td>보통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3545.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3408.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>경기</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3138.0</td>\n",
       "      <td>True</td>\n",
       "      <td>비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>경기</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3126.0</td>\n",
       "      <td>True</td>\n",
       "      <td>보통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>경기</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3239.0</td>\n",
       "      <td>True</td>\n",
       "      <td>비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>경기</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3496.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>경기</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3680.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>부산</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3112.0</td>\n",
       "      <td>True</td>\n",
       "      <td>보통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>부산</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2950.0</td>\n",
       "      <td>True</td>\n",
       "      <td>보통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>부산</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2999.0</td>\n",
       "      <td>True</td>\n",
       "      <td>보통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>부산</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2957.0</td>\n",
       "      <td>True</td>\n",
       "      <td>보통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>부산</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3500.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      지역명               규모구분    연도   월     분양가  test     평가\n",
       "0   Seoul                 전체  2015  10  5841.0  True  매우 비쌈\n",
       "1   Seoul         전용면적 60㎡이하  2015  10  5652.0  True  매우 비쌈\n",
       "2   Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True  매우 비쌈\n",
       "3   Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True  매우 비쌈\n",
       "4   Seoul        전용면적 102㎡초과  2015  10  5879.0  True  매우 비쌈\n",
       "5      인천                 전체  2015  10  3163.0  True     비쌈\n",
       "6      인천         전용면적 60㎡이하  2015  10  3488.0  True  매우 비쌈\n",
       "7      인천   전용면적 60㎡초과 85㎡이하  2015  10  3119.0  True     보통\n",
       "8      인천  전용면적 85㎡초과 102㎡이하  2015  10  3545.0  True  매우 비쌈\n",
       "9      인천        전용면적 102㎡초과  2015  10  3408.0  True  매우 비쌈\n",
       "10     경기                 전체  2015  10  3138.0  True     비쌈\n",
       "11     경기         전용면적 60㎡이하  2015  10  3126.0  True     보통\n",
       "12     경기   전용면적 60㎡초과 85㎡이하  2015  10  3239.0  True     비쌈\n",
       "13     경기  전용면적 85㎡초과 102㎡이하  2015  10  3496.0  True  매우 비쌈\n",
       "14     경기        전용면적 102㎡초과  2015  10  3680.0  True  매우 비쌈\n",
       "15     부산                 전체  2015  10  3112.0  True     보통\n",
       "16     부산         전용면적 60㎡이하  2015  10  2950.0  True     보통\n",
       "17     부산   전용면적 60㎡초과 85㎡이하  2015  10  2999.0  True     보통\n",
       "18     부산  전용면적 85㎡초과 102㎡이하  2015  10  2957.0  True     보통\n",
       "19     부산        전용면적 102㎡초과  2015  10  3500.0  True  매우 비쌈"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
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       "      <th>평가</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th>0</th>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>297</td>\n",
       "      <td>0</td>\n",
       "      <td>297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>매우 비쌈</th>\n",
       "      <td>819</td>\n",
       "      <td>819</td>\n",
       "      <td>819</td>\n",
       "      <td>819</td>\n",
       "      <td>819</td>\n",
       "      <td>819</td>\n",
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       "      <td>1285</td>\n",
       "      <td>1285</td>\n",
       "      <td>1285</td>\n",
       "      <td>1285</td>\n",
       "      <td>1285</td>\n",
       "      <td>1285</td>\n",
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       "    <tr>\n",
       "      <th>비쌈</th>\n",
       "      <td>352</td>\n",
       "      <td>352</td>\n",
       "      <td>352</td>\n",
       "      <td>352</td>\n",
       "      <td>352</td>\n",
       "      <td>352</td>\n",
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       "    <tr>\n",
       "      <th>저렴</th>\n",
       "      <td>817</td>\n",
       "      <td>817</td>\n",
       "      <td>817</td>\n",
       "      <td>817</td>\n",
       "      <td>817</td>\n",
       "      <td>817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        지역명  규모구분    연도     월   분양가  test\n",
       "평가                                       \n",
       "0       297   297   297   297     0   297\n",
       "매우 비쌈   819   819   819   819   819   819\n",
       "보통     1285  1285  1285  1285  1285  1285\n",
       "비쌈      352   352   352   352   352   352\n",
       "저렴      817   817   817   817   817   817"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(by='평가').count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3487</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>3</td>\n",
       "      <td>8191.0</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3402</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>2</td>\n",
       "      <td>8141.0</td>\n",
       "      <td>True</td>\n",
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       "    <tr>\n",
       "      <th>3317</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>8105.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
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       "    <tr>\n",
       "      <th>2638</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2018</td>\n",
       "      <td>5</td>\n",
       "      <td>8098.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2016</td>\n",
       "      <td>4</td>\n",
       "      <td>8096.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3232</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2018</td>\n",
       "      <td>12</td>\n",
       "      <td>7890.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2017</td>\n",
       "      <td>9</td>\n",
       "      <td>7887.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2553</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2018</td>\n",
       "      <td>4</td>\n",
       "      <td>7823.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2468</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2018</td>\n",
       "      <td>3</td>\n",
       "      <td>7823.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3319</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2019</td>\n",
       "      <td>1</td>\n",
       "      <td>7787.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        지역명               규모구분    연도   월     분양가  test     평가\n",
       "3487  Seoul   전용면적 60㎡초과 85㎡이하  2019   3  8191.0  True  매우 비쌈\n",
       "3402  Seoul   전용면적 60㎡초과 85㎡이하  2019   2  8141.0  True  매우 비쌈\n",
       "3317  Seoul   전용면적 60㎡초과 85㎡이하  2019   1  8105.0  True  매우 비쌈\n",
       "2638  Seoul  전용면적 85㎡초과 102㎡이하  2018   5  8098.0  True  매우 비쌈\n",
       "513   Seoul  전용면적 85㎡초과 102㎡이하  2016   4  8096.0  True  매우 비쌈\n",
       "3232  Seoul   전용면적 60㎡초과 85㎡이하  2018  12  7890.0  True  매우 비쌈\n",
       "1958  Seoul  전용면적 85㎡초과 102㎡이하  2017   9  7887.0  True  매우 비쌈\n",
       "2553  Seoul  전용면적 85㎡초과 102㎡이하  2018   4  7823.0  True  매우 비쌈\n",
       "2468  Seoul  전용면적 85㎡초과 102㎡이하  2018   3  7823.0  True  매우 비쌈\n",
       "3319  Seoul        전용면적 102㎡초과  2019   1  7787.0  True  매우 비쌈"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='분양가', ascending=False)[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "지역명      object\n",
       "규모구분     object\n",
       "연도        int64\n",
       "월         int64\n",
       "분양가     float64\n",
       "test       bool\n",
       "평가       object\n",
       "dtype: object"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['평가'] = df['평가'].astype('category')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "지역명       object\n",
       "규모구분      object\n",
       "연도         int64\n",
       "월          int64\n",
       "분양가      float64\n",
       "test        bool\n",
       "평가      category\n",
       "dtype: object"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>지역명</th>\n",
       "      <th>규모구분</th>\n",
       "      <th>연도</th>\n",
       "      <th>월</th>\n",
       "      <th>분양가</th>\n",
       "      <th>test</th>\n",
       "      <th>평가</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5841.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5652.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5882.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5721.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Seoul</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>5879.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>인천</td>\n",
       "      <td>전체</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3163.0</td>\n",
       "      <td>True</td>\n",
       "      <td>비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3488.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 60㎡초과 85㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3119.0</td>\n",
       "      <td>True</td>\n",
       "      <td>보통</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 85㎡초과 102㎡이하</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3545.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>인천</td>\n",
       "      <td>전용면적 102㎡초과</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>3408.0</td>\n",
       "      <td>True</td>\n",
       "      <td>매우 비쌈</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     지역명               규모구분    연도   월     분양가  test     평가\n",
       "0  Seoul                 전체  2015  10  5841.0  True  매우 비쌈\n",
       "1  Seoul         전용면적 60㎡이하  2015  10  5652.0  True  매우 비쌈\n",
       "2  Seoul   전용면적 60㎡초과 85㎡이하  2015  10  5882.0  True  매우 비쌈\n",
       "3  Seoul  전용면적 85㎡초과 102㎡이하  2015  10  5721.0  True  매우 비쌈\n",
       "4  Seoul        전용면적 102㎡초과  2015  10  5879.0  True  매우 비쌈\n",
       "5     인천                 전체  2015  10  3163.0  True     비쌈\n",
       "6     인천         전용면적 60㎡이하  2015  10  3488.0  True  매우 비쌈\n",
       "7     인천   전용면적 60㎡초과 85㎡이하  2015  10  3119.0  True     보통\n",
       "8     인천  전용면적 85㎡초과 102㎡이하  2015  10  3545.0  True  매우 비쌈\n",
       "9     인천        전용면적 102㎡초과  2015  10  3408.0  True  매우 비쌈"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    매우 비쌈\n",
       "1    매우 비쌈\n",
       "2    매우 비쌈\n",
       "3    매우 비쌈\n",
       "4    매우 비쌈\n",
       "Name: 평가, dtype: category\n",
       "Categories (5, object): [0, 매우 비쌈, 보통, 비쌈, 저렴]"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['평가'].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['평가'].cat.categories = ['해당없음', '개비쌈', '평균', '쫌비쌈', '쌈']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    개비쌈\n",
       "1    개비쌈\n",
       "2    개비쌈\n",
       "3    개비쌈\n",
       "4    개비쌈\n",
       "Name: 평가, dtype: category\n",
       "Categories (5, object): [해당없음, 개비쌈, 평균, 쫌비쌈, 쌈]"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['평가'].head()"
   ]
  }
 ],
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