Homework/期末專題實作(二)/不動產時價登入分析.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ecrVHDiuAto7"
   },
   "source": [
    "哈摟，各位資料科學家大家好，我是來自於台北市\"頭期款不足\"的阿宏，我想買房子很久了，但是不知道要買哪個地方，想先從內政部不動產交易時價登入資料中先了解行情，我現在手邊蒐集了台北市Taipei(A_lvr_land_A.csv)、新北市New_Taipei(F_lvr_land_A.csv)、台中市Taichung(B_lvr_land_A.csv)、高雄市Kaohsiung(E_lvr_land_A.csv)共四份資料，請各位資料科學家幫幫忙分析了。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9_PILIgwAtpW"
   },
   "source": [
    "# 1.不動產資料檔案讀取並串接"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "54hz4CgWAtpX"
   },
   "source": [
    "資料分散在4個資料，先合併成一個資料方便後續分析，但是我發現有英文欄位跟中文欄位，留下中文刪除英文，並加入新的欄位用以分辨台北市Taipei、新北市New_Taipei、台中市Taichung、高雄市Kaohsiung"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "UAgGy_owAtpY"
   },
   "source": [
    "1. 利用 Pandas 中的 pd.read_csv() 分別讀取 A_lvr_land_A.csv、B_lvr_land_A.csv、E_lvr_land_A.csv、F_lvr_land_A.csv。\n",
    "2. 刪除csv中第二列英文翻譯，The villages and towns urban district,transaction sign,land sector position building sector house number plate,land shifting total area square meter,....。\n",
    "3. 生成 city 欄位，其中以地區分類台北市Taipei(A_lvr_land_A.csv)、新北市New_Taipei(F_lvr_land_A.csv)、台中市Taichung(B_lvr_land_A.csv)、高雄市Kaohsiung(E_lvr_land_A.csv)。\n",
    "4. 將以上四份資料運用 pd.concat() 串接。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "AtdUcGYAAtpY"
   },
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn  as sns\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "id": "qaBwVWbkAtpZ"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "台北市\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 8842 entries, 0 to 8841\n",
      "Data columns (total 32 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   鄉鎮市區           8842 non-null   object\n",
      " 1   交易標的           8842 non-null   object\n",
      " 2   土地區段位置建物區段門牌   8842 non-null   object\n",
      " 3   土地移轉總面積平方公尺    8842 non-null   object\n",
      " 4   都市土地使用分區       8751 non-null   object\n",
      " 5   非都市土地使用分區      1 non-null      object\n",
      " 6   非都市土地使用編定      1 non-null      object\n",
      " 7   交易年月日          8842 non-null   object\n",
      " 8   交易筆棟數          8842 non-null   object\n",
      " 9   移轉層次           7765 non-null   object\n",
      " 10  總樓層數           7756 non-null   object\n",
      " 11  建物型態           8842 non-null   object\n",
      " 12  主要用途           7589 non-null   object\n",
      " 13  主要建材           7764 non-null   object\n",
      " 14  建築完成年月         7322 non-null   object\n",
      " 15  建物移轉總面積平方公尺    8842 non-null   object\n",
      " 16  建物現況格局-房       8842 non-null   object\n",
      " 17  建物現況格局-廳       8842 non-null   object\n",
      " 18  建物現況格局-衛       8842 non-null   object\n",
      " 19  建物現況格局-隔間      8842 non-null   object\n",
      " 20  有無管理組織         8842 non-null   object\n",
      " 21  總價元            8842 non-null   object\n",
      " 22  單價元平方公尺        8412 non-null   object\n",
      " 23  車位類別           2945 non-null   object\n",
      " 24  車位移轉總面積(平方公尺)  8842 non-null   object\n",
      " 25  車位總價元          8842 non-null   object\n",
      " 26  備註             5156 non-null   object\n",
      " 27  編號             8842 non-null   object\n",
      " 28  主建物面積          8841 non-null   object\n",
      " 29  附屬建物面積         8841 non-null   object\n",
      " 30  陽台面積           8841 non-null   object\n",
      " 31  電梯             5184 non-null   object\n",
      "dtypes: object(32)\n",
      "memory usage: 2.2+ MB\n",
      "None \n",
      "\n",
      "新北市\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 19819 entries, 0 to 19818\n",
      "Data columns (total 32 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   鄉鎮市區           19819 non-null  object\n",
      " 1   交易標的           19819 non-null  object\n",
      " 2   土地區段位置建物區段門牌   19819 non-null  object\n",
      " 3   土地移轉總面積平方公尺    19819 non-null  object\n",
      " 4   都市土地使用分區       18385 non-null  object\n",
      " 5   非都市土地使用分區      1327 non-null   object\n",
      " 6   非都市土地使用編定      1325 non-null   object\n",
      " 7   交易年月日          19819 non-null  object\n",
      " 8   交易筆棟數          19819 non-null  object\n",
      " 9   移轉層次           17343 non-null  object\n",
      " 10  總樓層數           17335 non-null  object\n",
      " 11  建物型態           19819 non-null  object\n",
      " 12  主要用途           17336 non-null  object\n",
      " 13  主要建材           17341 non-null  object\n",
      " 14  建築完成年月         16844 non-null  object\n",
      " 15  建物移轉總面積平方公尺    19819 non-null  object\n",
      " 16  建物現況格局-房       19819 non-null  object\n",
      " 17  建物現況格局-廳       19819 non-null  object\n",
      " 18  建物現況格局-衛       19819 non-null  object\n",
      " 19  建物現況格局-隔間      19819 non-null  object\n",
      " 20  有無管理組織         19819 non-null  object\n",
      " 21  總價元            19819 non-null  object\n",
      " 22  單價元平方公尺        19183 non-null  object\n",
      " 23  車位類別           9209 non-null   object\n",
      " 24  車位移轉總面積(平方公尺)  19819 non-null  object\n",
      " 25  車位總價元          19819 non-null  object\n",
      " 26  備註             9300 non-null   object\n",
      " 27  編號             19819 non-null  object\n",
      " 28  主建物面積          19818 non-null  object\n",
      " 29  附屬建物面積         19818 non-null  object\n",
      " 30  陽台面積           19818 non-null  object\n",
      " 31  電梯             11669 non-null  object\n",
      "dtypes: object(32)\n",
      "memory usage: 4.8+ MB\n",
      "None \n",
      "\n",
      "台中市\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 16685 entries, 0 to 16684\n",
      "Data columns (total 32 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   鄉鎮市區           16685 non-null  object\n",
      " 1   交易標的           16685 non-null  object\n",
      " 2   土地區段位置建物區段門牌   16685 non-null  object\n",
      " 3   土地移轉總面積平方公尺    16685 non-null  object\n",
      " 4   都市土地使用分區       14303 non-null  object\n",
      " 5   非都市土地使用分區      1974 non-null   object\n",
      " 6   非都市土地使用編定      1973 non-null   object\n",
      " 7   交易年月日          16685 non-null  object\n",
      " 8   交易筆棟數          16685 non-null  object\n",
      " 9   移轉層次           13046 non-null  object\n",
      " 10  總樓層數           13014 non-null  object\n",
      " 11  建物型態           16685 non-null  object\n",
      " 12  主要用途           13016 non-null  object\n",
      " 13  主要建材           13017 non-null  object\n",
      " 14  建築完成年月         12976 non-null  object\n",
      " 15  建物移轉總面積平方公尺    16685 non-null  object\n",
      " 16  建物現況格局-房       16685 non-null  object\n",
      " 17  建物現況格局-廳       16685 non-null  object\n",
      " 18  建物現況格局-衛       16685 non-null  object\n",
      " 19  建物現況格局-隔間      16685 non-null  object\n",
      " 20  有無管理組織         16685 non-null  object\n",
      " 21  總價元            16685 non-null  object\n",
      " 22  單價元平方公尺        16570 non-null  object\n",
      " 23  車位類別           6678 non-null   object\n",
      " 24  車位移轉總面積(平方公尺)  16685 non-null  object\n",
      " 25  車位總價元          16685 non-null  object\n",
      " 26  備註             5633 non-null   object\n",
      " 27  編號             16685 non-null  object\n",
      " 28  主建物面積          16684 non-null  object\n",
      " 29  附屬建物面積         16684 non-null  object\n",
      " 30  陽台面積           16684 non-null  object\n",
      " 31  電梯             8994 non-null   object\n",
      "dtypes: object(32)\n",
      "memory usage: 4.1+ MB\n",
      "None \n",
      "\n",
      "高雄市\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 14134 entries, 0 to 14133\n",
      "Data columns (total 32 columns):\n",
      " #   Column         Non-Null Count  Dtype \n",
      "---  ------         --------------  ----- \n",
      " 0   鄉鎮市區           14134 non-null  object\n",
      " 1   交易標的           14134 non-null  object\n",
      " 2   土地區段位置建物區段門牌   14134 non-null  object\n",
      " 3   土地移轉總面積平方公尺    14134 non-null  object\n",
      " 4   都市土地使用分區       12662 non-null  object\n",
      " 5   非都市土地使用分區      1008 non-null   object\n",
      " 6   非都市土地使用編定      1007 non-null   object\n",
      " 7   交易年月日          14134 non-null  object\n",
      " 8   交易筆棟數          14134 non-null  object\n",
      " 9   移轉層次           11574 non-null  object\n",
      " 10  總樓層數           11563 non-null  object\n",
      " 11  建物型態           14134 non-null  object\n",
      " 12  主要用途           9952 non-null   object\n",
      " 13  主要建材           11562 non-null  object\n",
      " 14  建築完成年月         11552 non-null  object\n",
      " 15  建物移轉總面積平方公尺    14134 non-null  object\n",
      " 16  建物現況格局-房       14134 non-null  object\n",
      " 17  建物現況格局-廳       14134 non-null  object\n",
      " 18  建物現況格局-衛       14134 non-null  object\n",
      " 19  建物現況格局-隔間      14134 non-null  object\n",
      " 20  有無管理組織         14134 non-null  object\n",
      " 21  總價元            14134 non-null  object\n",
      " 22  單價元平方公尺        13995 non-null  object\n",
      " 23  車位類別           4857 non-null   object\n",
      " 24  車位移轉總面積(平方公尺)  14134 non-null  object\n",
      " 25  車位總價元          14134 non-null  object\n",
      " 26  備註             4674 non-null   object\n",
      " 27  編號             14134 non-null  object\n",
      " 28  主建物面積          14133 non-null  object\n",
      " 29  附屬建物面積         14133 non-null  object\n",
      " 30  陽台面積           14133 non-null  object\n",
      " 31  電梯             7097 non-null   object\n",
      "dtypes: object(32)\n",
      "memory usage: 3.5+ MB\n",
      "None \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 1.利用 Pandas中的 pd.read_csv()分別讀取 A_lvr_land_A.csv、B_lvr_land_A.csv、E_lvr_land_A.csv、F_lvr_land_A.csv。\n",
    "\n",
    "df_taipei = pd.read_csv( 'A_lvr_land_A.csv', dtype='unicode' )\n",
    "df_new_taipei = pd.read_csv( 'F_lvr_land_A.csv', dtype='unicode' )\n",
    "df_taichung = pd.read_csv( 'B_lvr_land_A.csv', dtype='unicode' )\n",
    "df_kaohsiung = pd.read_csv( 'E_lvr_land_A.csv', dtype='unicode' )\n",
    "\n",
    "for city, data in zip(['台北市','新北市','台中市','高雄市'],[df_taipei,df_new_taipei,df_taichung,df_kaohsiung]):\n",
    "    print( city )\n",
    "    print( data.info(), '\\n' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>鄉鎮市區</th>\n",
       "      <th>交易標的</th>\n",
       "      <th>土地區段位置建物區段門牌</th>\n",
       "      <th>土地移轉總面積平方公尺</th>\n",
       "      <th>都市土地使用分區</th>\n",
       "      <th>非都市土地使用分區</th>\n",
       "      <th>非都市土地使用編定</th>\n",
       "      <th>交易年月日</th>\n",
       "      <th>交易筆棟數</th>\n",
       "      <th>移轉層次</th>\n",
       "      <th>...</th>\n",
       "      <th>單價元平方公尺</th>\n",
       "      <th>車位類別</th>\n",
       "      <th>車位移轉總面積(平方公尺)</th>\n",
       "      <th>車位總價元</th>\n",
       "      <th>備註</th>\n",
       "      <th>編號</th>\n",
       "      <th>主建物面積</th>\n",
       "      <th>附屬建物面積</th>\n",
       "      <th>陽台面積</th>\n",
       "      <th>電梯</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>The villages and towns urban district</td>\n",
       "      <td>transaction sign</td>\n",
       "      <td>land sector position building sector house num...</td>\n",
       "      <td>land shifting total area square meter</td>\n",
       "      <td>the use zoning or compiles and checks</td>\n",
       "      <td>the non-metropolis land use district</td>\n",
       "      <td>non-metropolis land use</td>\n",
       "      <td>transaction year month and day</td>\n",
       "      <td>transaction pen number</td>\n",
       "      <td>shifting level</td>\n",
       "      <td>...</td>\n",
       "      <td>the unit price (NTD / square meter)</td>\n",
       "      <td>the berth category</td>\n",
       "      <td>berth shifting total area square meter</td>\n",
       "      <td>the berth total price NTD</td>\n",
       "      <td>the note</td>\n",
       "      <td>serial number</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>文山區</td>\n",
       "      <td>房地(土地+建物)+車位</td>\n",
       "      <td>臺北市文山區忠順街二段85巷1~30號</td>\n",
       "      <td>26.97</td>\n",
       "      <td>住</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090406</td>\n",
       "      <td>土地1建物1車位2</td>\n",
       "      <td>四層</td>\n",
       "      <td>...</td>\n",
       "      <td>156583</td>\n",
       "      <td>坡道平面</td>\n",
       "      <td>40.7</td>\n",
       "      <td>0</td>\n",
       "      <td>含增建或未登記建物。;內含獨立產權車位，未包含在棟數內。</td>\n",
       "      <td>RPVOMLMKNHPFFAA18CA</td>\n",
       "      <td>110.91</td>\n",
       "      <td>9.63</td>\n",
       "      <td>11.09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文山區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>臺北市文山區福興路91~120號</td>\n",
       "      <td>10.32</td>\n",
       "      <td>住</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090317</td>\n",
       "      <td>土地1建物1車位0</td>\n",
       "      <td>三層</td>\n",
       "      <td>...</td>\n",
       "      <td>149558</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>含增建或未登記建物。;</td>\n",
       "      <td>RPPPMLMKNHPFFAA67CA</td>\n",
       "      <td>24.58</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 32 columns</p>\n",
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      "text/plain": [
       "                                    鄉鎮市區              交易標的  \\\n",
       "0  The villages and towns urban district  transaction sign   \n",
       "1                                    文山區      房地(土地+建物)+車位   \n",
       "2                                    文山區         房地(土地+建物)   \n",
       "\n",
       "                                        土地區段位置建物區段門牌  \\\n",
       "0  land sector position building sector house num...   \n",
       "1                                臺北市文山區忠順街二段85巷1~30號   \n",
       "2                                   臺北市文山區福興路91~120號   \n",
       "\n",
       "                             土地移轉總面積平方公尺  \\\n",
       "0  land shifting total area square meter   \n",
       "1                                  26.97   \n",
       "2                                  10.32   \n",
       "\n",
       "                                都市土地使用分區  \\\n",
       "0  the use zoning or compiles and checks   \n",
       "1                                      住   \n",
       "2                                      住   \n",
       "\n",
       "                              非都市土地使用分區                非都市土地使用編定  \\\n",
       "0  the non-metropolis land use district  non-metropolis land use   \n",
       "1                                   NaN                      NaN   \n",
       "2                                   NaN                      NaN   \n",
       "\n",
       "                            交易年月日                   交易筆棟數            移轉層次  \\\n",
       "0  transaction year month and day  transaction pen number  shifting level   \n",
       "1                         1090406               土地1建物1車位2              四層   \n",
       "2                         1090317               土地1建物1車位0              三層   \n",
       "\n",
       "   ...                              單價元平方公尺                車位類別  \\\n",
       "0  ...  the unit price (NTD / square meter)  the berth category   \n",
       "1  ...                               156583                坡道平面   \n",
       "2  ...                               149558                 NaN   \n",
       "\n",
       "                            車位移轉總面積(平方公尺)                      車位總價元  \\\n",
       "0  berth shifting total area square meter  the berth total price NTD   \n",
       "1                                    40.7                          0   \n",
       "2                                     0.0                          0   \n",
       "\n",
       "                             備註                   編號   主建物面積 附屬建物面積   陽台面積  \\\n",
       "0                      the note        serial number     NaN    NaN    NaN   \n",
       "1  含增建或未登記建物。;內含獨立產權車位，未包含在棟數內。  RPVOMLMKNHPFFAA18CA  110.91   9.63  11.09   \n",
       "2                   含增建或未登記建物。;  RPPPMLMKNHPFFAA67CA   24.58   1.28    2.3   \n",
       "\n",
       "    電梯  \n",
       "0  NaN  \n",
       "1  NaN  \n",
       "2  NaN  \n",
       "\n",
       "[3 rows x 32 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_taipei.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "id": "ulv52OOeAtpZ"
   },
   "outputs": [],
   "source": [
    "# 2.刪除 csv中第二列英文翻譯\n",
    "\n",
    "for data in [df_taipei,df_new_taipei,df_taichung,df_kaohsiung]:\n",
    "    data.drop( index=0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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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       "        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>鄉鎮市區</th>\n",
       "      <th>交易標的</th>\n",
       "      <th>土地區段位置建物區段門牌</th>\n",
       "      <th>土地移轉總面積平方公尺</th>\n",
       "      <th>都市土地使用分區</th>\n",
       "      <th>非都市土地使用分區</th>\n",
       "      <th>非都市土地使用編定</th>\n",
       "      <th>交易年月日</th>\n",
       "      <th>交易筆棟數</th>\n",
       "      <th>移轉層次</th>\n",
       "      <th>...</th>\n",
       "      <th>單價元平方公尺</th>\n",
       "      <th>車位類別</th>\n",
       "      <th>車位移轉總面積(平方公尺)</th>\n",
       "      <th>車位總價元</th>\n",
       "      <th>備註</th>\n",
       "      <th>編號</th>\n",
       "      <th>主建物面積</th>\n",
       "      <th>附屬建物面積</th>\n",
       "      <th>陽台面積</th>\n",
       "      <th>電梯</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文山區</td>\n",
       "      <td>房地(土地+建物)+車位</td>\n",
       "      <td>臺北市文山區忠順街二段85巷1~30號</td>\n",
       "      <td>26.97</td>\n",
       "      <td>住</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090406</td>\n",
       "      <td>土地1建物1車位2</td>\n",
       "      <td>四層</td>\n",
       "      <td>...</td>\n",
       "      <td>156583</td>\n",
       "      <td>坡道平面</td>\n",
       "      <td>40.7</td>\n",
       "      <td>0</td>\n",
       "      <td>含增建或未登記建物。;內含獨立產權車位，未包含在棟數內。</td>\n",
       "      <td>RPVOMLMKNHPFFAA18CA</td>\n",
       "      <td>110.91</td>\n",
       "      <td>9.63</td>\n",
       "      <td>11.09</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文山區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>臺北市文山區福興路91~120號</td>\n",
       "      <td>10.32</td>\n",
       "      <td>住</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090317</td>\n",
       "      <td>土地1建物1車位0</td>\n",
       "      <td>三層</td>\n",
       "      <td>...</td>\n",
       "      <td>149558</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>含增建或未登記建物。;</td>\n",
       "      <td>RPPPMLMKNHPFFAA67CA</td>\n",
       "      <td>24.58</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文山區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>臺北市文山區木柵路四段121~150號</td>\n",
       "      <td>39.64</td>\n",
       "      <td>其他</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090418</td>\n",
       "      <td>土地2建物1車位0</td>\n",
       "      <td>六層</td>\n",
       "      <td>...</td>\n",
       "      <td>181472</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPSPMLMKNHPFFAA97CA</td>\n",
       "      <td>78.8</td>\n",
       "      <td>4.15</td>\n",
       "      <td>8.76</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  鄉鎮市區          交易標的         土地區段位置建物區段門牌 土地移轉總面積平方公尺 都市土地使用分區 非都市土地使用分區  \\\n",
       "1  文山區  房地(土地+建物)+車位  臺北市文山區忠順街二段85巷1~30號       26.97        住       NaN   \n",
       "2  文山區     房地(土地+建物)     臺北市文山區福興路91~120號       10.32        住       NaN   \n",
       "3  文山區     房地(土地+建物)  臺北市文山區木柵路四段121~150號       39.64       其他       NaN   \n",
       "\n",
       "  非都市土地使用編定    交易年月日      交易筆棟數 移轉層次  ... 單價元平方公尺  車位類別 車位移轉總面積(平方公尺) 車位總價元  \\\n",
       "1       NaN  1090406  土地1建物1車位2   四層  ...  156583  坡道平面          40.7     0   \n",
       "2       NaN  1090317  土地1建物1車位0   三層  ...  149558   NaN           0.0     0   \n",
       "3       NaN  1090418  土地2建物1車位0   六層  ...  181472   NaN           0.0     0   \n",
       "\n",
       "                             備註                   編號   主建物面積 附屬建物面積   陽台面積  \\\n",
       "1  含增建或未登記建物。;內含獨立產權車位，未包含在棟數內。  RPVOMLMKNHPFFAA18CA  110.91   9.63  11.09   \n",
       "2                   含增建或未登記建物。;  RPPPMLMKNHPFFAA67CA   24.58   1.28    2.3   \n",
       "3                           NaN  RPSPMLMKNHPFFAA97CA    78.8   4.15   8.76   \n",
       "\n",
       "    電梯  \n",
       "1  NaN  \n",
       "2  NaN  \n",
       "3  NaN  \n",
       "\n",
       "[3 rows x 32 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_taipei.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "id": "p7cqARS8Atpa"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  鄉鎮市區          交易標的         土地區段位置建物區段門牌 土地移轉總面積平方公尺 都市土地使用分區 非都市土地使用分區  \\\n",
      "1  文山區  房地(土地+建物)+車位  臺北市文山區忠順街二段85巷1~30號       26.97        住       NaN   \n",
      "2  文山區     房地(土地+建物)     臺北市文山區福興路91~120號       10.32        住       NaN   \n",
      "3  文山區     房地(土地+建物)  臺北市文山區木柵路四段121~150號       39.64       其他       NaN   \n",
      "\n",
      "  非都市土地使用編定    交易年月日      交易筆棟數 移轉層次  ...  車位類別 車位移轉總面積(平方公尺) 車位總價元  \\\n",
      "1       NaN  1090406  土地1建物1車位2   四層  ...  坡道平面          40.7     0   \n",
      "2       NaN  1090317  土地1建物1車位0   三層  ...   NaN           0.0     0   \n",
      "3       NaN  1090418  土地2建物1車位0   六層  ...   NaN           0.0     0   \n",
      "\n",
      "                             備註                   編號   主建物面積 附屬建物面積   陽台面積  \\\n",
      "1  含增建或未登記建物。;內含獨立產權車位，未包含在棟數內。  RPVOMLMKNHPFFAA18CA  110.91   9.63  11.09   \n",
      "2                   含增建或未登記建物。;  RPPPMLMKNHPFFAA67CA   24.58   1.28    2.3   \n",
      "3                           NaN  RPSPMLMKNHPFFAA97CA    78.8   4.15   8.76   \n",
      "\n",
      "    電梯      城市  \n",
      "1  NaN  Taipei  \n",
      "2  NaN  Taipei  \n",
      "3  NaN  Taipei  \n",
      "\n",
      "[3 rows x 33 columns] \n",
      "\n",
      "  鄉鎮市區          交易標的           土地區段位置建物區段門牌 土地移轉總面積平方公尺 都市土地使用分區 非都市土地使用分區  \\\n",
      "1  板橋區     房地(土地+建物)        新北市板橋區南雅東路1~30號        9.76        商       NaN   \n",
      "2  板橋區  房地(土地+建物)+車位  新北市板橋區縣民大道三段270巷1~30號       24.18        工       NaN   \n",
      "3  板橋區     房地(土地+建物)      新北市板橋區南雅南路一段1~30號         3.2        商       NaN   \n",
      "\n",
      "  非都市土地使用編定    交易年月日      交易筆棟數 移轉層次  ...  車位類別 車位移轉總面積(平方公尺) 車位總價元  \\\n",
      "1       NaN  1090405  土地2建物1車位0   六層  ...   NaN           0.0     0   \n",
      "2       NaN  1090401  土地1建物1車位1   八層  ...  坡道平面         38.09     0   \n",
      "3       NaN  1090316  土地1建物1車位0  十一層  ...   NaN           0.0     0   \n",
      "\n",
      "            備註                   編號   主建物面積 附屬建物面積  陽台面積   電梯          城市  \n",
      "1  含增建或未登記建物。;  RPQNMLMKNHPFFAF08CA   47.45    0.0   8.1  NaN  New Taipei  \n",
      "2  含增建或未登記建物。;  RPRNMLMKNHPFFAF18CA  116.29    0.0  11.8  NaN  New Taipei  \n",
      "3  含增建或未登記建物。;  RPTNMLMKNHPFFAF38CA   23.02    0.0  4.94  NaN  New Taipei  \n",
      "\n",
      "[3 rows x 33 columns] \n",
      "\n",
      "  鄉鎮市區          交易標的         土地區段位置建物區段門牌 土地移轉總面積平方公尺 都市土地使用分區 非都市土地使用分區  \\\n",
      "1   西區     房地(土地+建物)   臺中市西區美村路一段181~210號       28.34        住       NaN   \n",
      "2   西區     房地(土地+建物)   臺中市西區梅川西路一段91~120號        52.0        住       NaN   \n",
      "3   南區  房地(土地+建物)+車位  臺中市南區建國南路一段211~240號        23.4        住       NaN   \n",
      "\n",
      "  非都市土地使用編定    交易年月日      交易筆棟數 移轉層次  ...  車位類別 車位移轉總面積(平方公尺) 車位總價元   備註  \\\n",
      "1       NaN  1090331  土地1建物1車位0   五層  ...   NaN           0.0     0  NaN   \n",
      "2       NaN  1090331  土地1建物1車位0    全  ...   NaN           0.0     0  NaN   \n",
      "3       NaN  1090418  土地1建物1車位1  十一層  ...  坡道平面         24.05     0  NaN   \n",
      "\n",
      "                    編號   主建物面積 附屬建物面積 陽台面積   電梯        城市  \n",
      "1  RPVOMLLKNHPFFAB18CA   104.8  18.34  0.0  NaN  Taichung  \n",
      "2  RPPNMLMKNHPFFAB57CA    91.8    0.0  0.0  NaN  Taichung  \n",
      "3  RPUNMLMKNHPFFAB08CA  110.26  19.34  0.0  NaN  Taichung  \n",
      "\n",
      "[3 rows x 33 columns] \n",
      "\n",
      "  鄉鎮市區          交易標的       土地區段位置建物區段門牌 土地移轉總面積平方公尺 都市土地使用分區 非都市土地使用分區  \\\n",
      "1  鼓山區  房地(土地+建物)+車位  高雄市鼓山區明倫路121~150號        17.2        商       NaN   \n",
      "2  鼓山區  房地(土地+建物)+車位     高雄市鼓山區昌盛路1~30號        7.55        商       NaN   \n",
      "3  鹽埕區            建物     高雄市鹽埕區北斗街1~30號         0.0      NaN       NaN   \n",
      "\n",
      "  非都市土地使用編定    交易年月日      交易筆棟數      移轉層次  ...  車位類別 車位移轉總面積(平方公尺)   車位總價元  \\\n",
      "1       NaN  1090221  土地1建物2車位1  一層，二層，騎樓  ...  升降機械           0.0       0   \n",
      "2       NaN  1090410  土地1建物1車位1        七層  ...  坡道機械           0.0  800000   \n",
      "3       NaN  1090511  土地0建物1車位0        九層  ...   NaN           0.0       0   \n",
      "\n",
      "    備註                   編號   主建物面積 附屬建物面積 陽台面積   電梯         城市  \n",
      "1  NaN  RPTNMLLKNHPFFAE18CA  139.45   4.14  0.0  NaN  Kaohsiung  \n",
      "2  NaN  RPVNMLLKNHPFFAE38CA   46.55   6.41  0.0  NaN  Kaohsiung  \n",
      "3  NaN  RPUNMLMKNHPFFAE38CA   33.22   1.85  0.0  NaN  Kaohsiung  \n",
      "\n",
      "[3 rows x 33 columns] \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 3.生成 city 欄位，其中以地區分類台北市 Taipei(A_lvr_land_A.csv)、新北市 New_Taipei(F_lvr_land_A.csv)、台中市 Taichung(B_lvr_land_A.csv)、高雄市 Kaohsiung(E_lvr_land_A.csv)。\n",
    "\n",
    "for city_name, data in zip(['Taipei','New Taipei','Taichung','Kaohsiung'],[df_taipei,df_new_taipei,df_taichung,df_kaohsiung]):\n",
    "    data['城市'] = city_name\n",
    "    print( data.head(3), '\\n' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of Data\n",
      "台北市: (8841, 33)\n",
      "新北市: (19818, 33)\n",
      "台中市: (16684, 33)\n",
      "高雄市: (14133, 33)\n"
     ]
    }
   ],
   "source": [
    "print( 'Shape of Data' )\n",
    "print( '台北市:', df_taipei.shape )\n",
    "print( '新北市:', df_new_taipei.shape )\n",
    "print( '台中市:', df_taichung.shape )\n",
    "print( '高雄市:', df_kaohsiung.shape )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "5Sajh33BAtpb"
   },
   "outputs": [
    {
     "data": {
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       "    .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>鄉鎮市區</th>\n",
       "      <th>交易標的</th>\n",
       "      <th>土地區段位置建物區段門牌</th>\n",
       "      <th>土地移轉總面積平方公尺</th>\n",
       "      <th>都市土地使用分區</th>\n",
       "      <th>非都市土地使用分區</th>\n",
       "      <th>非都市土地使用編定</th>\n",
       "      <th>交易年月日</th>\n",
       "      <th>交易筆棟數</th>\n",
       "      <th>移轉層次</th>\n",
       "      <th>...</th>\n",
       "      <th>車位類別</th>\n",
       "      <th>車位移轉總面積(平方公尺)</th>\n",
       "      <th>車位總價元</th>\n",
       "      <th>備註</th>\n",
       "      <th>編號</th>\n",
       "      <th>主建物面積</th>\n",
       "      <th>附屬建物面積</th>\n",
       "      <th>陽台面積</th>\n",
       "      <th>電梯</th>\n",
       "      <th>城市</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文山區</td>\n",
       "      <td>房地(土地+建物)+車位</td>\n",
       "      <td>臺北市文山區忠順街二段85巷1~30號</td>\n",
       "      <td>26.97</td>\n",
       "      <td>住</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090406</td>\n",
       "      <td>土地1建物1車位2</td>\n",
       "      <td>四層</td>\n",
       "      <td>...</td>\n",
       "      <td>坡道平面</td>\n",
       "      <td>40.7</td>\n",
       "      <td>0</td>\n",
       "      <td>含增建或未登記建物。;內含獨立產權車位，未包含在棟數內。</td>\n",
       "      <td>RPVOMLMKNHPFFAA18CA</td>\n",
       "      <td>110.91</td>\n",
       "      <td>9.63</td>\n",
       "      <td>11.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文山區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>臺北市文山區福興路91~120號</td>\n",
       "      <td>10.32</td>\n",
       "      <td>住</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090317</td>\n",
       "      <td>土地1建物1車位0</td>\n",
       "      <td>三層</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>含增建或未登記建物。;</td>\n",
       "      <td>RPPPMLMKNHPFFAA67CA</td>\n",
       "      <td>24.58</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文山區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>臺北市文山區木柵路四段121~150號</td>\n",
       "      <td>39.64</td>\n",
       "      <td>其他</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090418</td>\n",
       "      <td>土地2建物1車位0</td>\n",
       "      <td>六層</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPSPMLMKNHPFFAA97CA</td>\n",
       "      <td>78.8</td>\n",
       "      <td>4.15</td>\n",
       "      <td>8.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中正區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>臺北市中正區和平西路一段31~60號</td>\n",
       "      <td>4.26</td>\n",
       "      <td>商</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090413</td>\n",
       "      <td>土地2建物1車位0</td>\n",
       "      <td>九層</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>含增建或未登記建物。;</td>\n",
       "      <td>RPPOMLMKNHPFFAA57CA</td>\n",
       "      <td>18.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>文山區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>臺北市文山區樟新街56巷1~30號</td>\n",
       "      <td>44.57</td>\n",
       "      <td>住</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090328</td>\n",
       "      <td>土地3建物1車位0</td>\n",
       "      <td>四層</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>含增建或未登記建物。;</td>\n",
       "      <td>RPWNMLNKNHPFFAA28CA</td>\n",
       "      <td>98.74</td>\n",
       "      <td>2.26</td>\n",
       "      <td>11.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14129</th>\n",
       "      <td>三民區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>建興路380巷1弄1~30號</td>\n",
       "      <td>32.0</td>\n",
       "      <td>其他</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090727</td>\n",
       "      <td>土地1建物1車位0</td>\n",
       "      <td>四層</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPSPMLLLQHPFFDE09DA</td>\n",
       "      <td>101.29</td>\n",
       "      <td>0.0</td>\n",
       "      <td>7.12</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kaohsiung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14130</th>\n",
       "      <td>三民區</td>\n",
       "      <td>房地(土地+建物)+車位</td>\n",
       "      <td>民族巷1~30號</td>\n",
       "      <td>7.01</td>\n",
       "      <td>其他</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090816</td>\n",
       "      <td>土地1建物1車位1</td>\n",
       "      <td>十三層</td>\n",
       "      <td>...</td>\n",
       "      <td>坡道平面</td>\n",
       "      <td>6.25</td>\n",
       "      <td>800000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPPQMLKLQHPFFDE78DA</td>\n",
       "      <td>51.16</td>\n",
       "      <td>9.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kaohsiung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14131</th>\n",
       "      <td>三民區</td>\n",
       "      <td>房地(土地+建物)+車位</td>\n",
       "      <td>大豐一路331~360號</td>\n",
       "      <td>27.23</td>\n",
       "      <td>其他</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090722</td>\n",
       "      <td>土地1建物1車位1</td>\n",
       "      <td>三層</td>\n",
       "      <td>...</td>\n",
       "      <td>坡道機械</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPSOMLSKQHPFFDE96DA</td>\n",
       "      <td>101.03</td>\n",
       "      <td>6.24</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kaohsiung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14132</th>\n",
       "      <td>三民區</td>\n",
       "      <td>房地(土地+建物)+車位</td>\n",
       "      <td>民族巷31~60號</td>\n",
       "      <td>10.32</td>\n",
       "      <td>其他</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090731</td>\n",
       "      <td>土地1建物1車位1</td>\n",
       "      <td>十層</td>\n",
       "      <td>...</td>\n",
       "      <td>坡道機械</td>\n",
       "      <td>26.06</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPWNMLLKQHPFFDE56DA</td>\n",
       "      <td>80.0</td>\n",
       "      <td>7.89</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kaohsiung</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14133</th>\n",
       "      <td>楠梓區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>青埔街61~90號</td>\n",
       "      <td>12.61</td>\n",
       "      <td>其他</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090704</td>\n",
       "      <td>土地1建物1車位0</td>\n",
       "      <td>十二層</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPXPMLPKPHPFFEE27DA</td>\n",
       "      <td>71.19</td>\n",
       "      <td>8.9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Kaohsiung</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>59476 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      鄉鎮市區          交易標的         土地區段位置建物區段門牌 土地移轉總面積平方公尺 都市土地使用分區 非都市土地使用分區  \\\n",
       "1      文山區  房地(土地+建物)+車位  臺北市文山區忠順街二段85巷1~30號       26.97        住       NaN   \n",
       "2      文山區     房地(土地+建物)     臺北市文山區福興路91~120號       10.32        住       NaN   \n",
       "3      文山區     房地(土地+建物)  臺北市文山區木柵路四段121~150號       39.64       其他       NaN   \n",
       "4      中正區     房地(土地+建物)   臺北市中正區和平西路一段31~60號        4.26        商       NaN   \n",
       "5      文山區     房地(土地+建物)    臺北市文山區樟新街56巷1~30號       44.57        住       NaN   \n",
       "...    ...           ...                  ...         ...      ...       ...   \n",
       "14129  三民區     房地(土地+建物)       建興路380巷1弄1~30號        32.0       其他       NaN   \n",
       "14130  三民區  房地(土地+建物)+車位             民族巷1~30號        7.01       其他       NaN   \n",
       "14131  三民區  房地(土地+建物)+車位         大豐一路331~360號       27.23       其他       NaN   \n",
       "14132  三民區  房地(土地+建物)+車位            民族巷31~60號       10.32       其他       NaN   \n",
       "14133  楠梓區     房地(土地+建物)            青埔街61~90號       12.61       其他       NaN   \n",
       "\n",
       "      非都市土地使用編定    交易年月日      交易筆棟數 移轉層次  ...  車位類別 車位移轉總面積(平方公尺)   車位總價元  \\\n",
       "1           NaN  1090406  土地1建物1車位2   四層  ...  坡道平面          40.7       0   \n",
       "2           NaN  1090317  土地1建物1車位0   三層  ...   NaN           0.0       0   \n",
       "3           NaN  1090418  土地2建物1車位0   六層  ...   NaN           0.0       0   \n",
       "4           NaN  1090413  土地2建物1車位0   九層  ...   NaN           0.0       0   \n",
       "5           NaN  1090328  土地3建物1車位0   四層  ...   NaN           0.0       0   \n",
       "...         ...      ...        ...  ...  ...   ...           ...     ...   \n",
       "14129       NaN  1090727  土地1建物1車位0   四層  ...   NaN           0.0       0   \n",
       "14130       NaN  1090816  土地1建物1車位1  十三層  ...  坡道平面          6.25  800000   \n",
       "14131       NaN  1090722  土地1建物1車位1   三層  ...  坡道機械           0.0       0   \n",
       "14132       NaN  1090731  土地1建物1車位1   十層  ...  坡道機械         26.06       0   \n",
       "14133       NaN  1090704  土地1建物1車位0  十二層  ...   NaN           0.0       0   \n",
       "\n",
       "                                 備註                   編號   主建物面積 附屬建物面積  \\\n",
       "1      含增建或未登記建物。;內含獨立產權車位，未包含在棟數內。  RPVOMLMKNHPFFAA18CA  110.91   9.63   \n",
       "2                       含增建或未登記建物。;  RPPPMLMKNHPFFAA67CA   24.58   1.28   \n",
       "3                               NaN  RPSPMLMKNHPFFAA97CA    78.8   4.15   \n",
       "4                       含增建或未登記建物。;  RPPOMLMKNHPFFAA57CA   18.34    0.0   \n",
       "5                       含增建或未登記建物。;  RPWNMLNKNHPFFAA28CA   98.74   2.26   \n",
       "...                             ...                  ...     ...    ...   \n",
       "14129                           NaN  RPSPMLLLQHPFFDE09DA  101.29    0.0   \n",
       "14130                           NaN  RPPQMLKLQHPFFDE78DA   51.16   9.73   \n",
       "14131                           NaN  RPSOMLSKQHPFFDE96DA  101.03   6.24   \n",
       "14132                           NaN  RPWNMLLKQHPFFDE56DA    80.0   7.89   \n",
       "14133                           NaN  RPXPMLPKPHPFFEE27DA   71.19    8.9   \n",
       "\n",
       "        陽台面積   電梯         城市  \n",
       "1      11.09  NaN     Taipei  \n",
       "2        2.3  NaN     Taipei  \n",
       "3       8.76  NaN     Taipei  \n",
       "4       6.45  NaN     Taipei  \n",
       "5      11.84  NaN     Taipei  \n",
       "...      ...  ...        ...  \n",
       "14129   7.12  NaN  Kaohsiung  \n",
       "14130    0.0  NaN  Kaohsiung  \n",
       "14131    0.0  NaN  Kaohsiung  \n",
       "14132    0.0  NaN  Kaohsiung  \n",
       "14133    0.0  NaN  Kaohsiung  \n",
       "\n",
       "[59476 rows x 33 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4.將以上四份資料運用 pd.concat()串接\n",
    "\n",
    "df = pd.concat( [df_taipei,df_new_taipei,df_taichung,df_kaohsiung], axis=0 )\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vf6GM8Z_Atpb"
   },
   "source": [
    "# 2. 資料清理與切片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QMPrmISNAtpb"
   },
   "source": [
    "因為我是想買來住的房子，所以幫忙刪除住宿用途以外的資料，並且限制\n",
    "1. 交易年月日，限制在109年\n",
    "2. 建物現況格局-房，1~5間 \n",
    "3. 建物現況格局-廳，1~2廳"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1C-ImCfTAtpc"
   },
   "source": [
    "以下 columns_mapping、analysis_columns、columns_type 提供給資料科學家配合使用\n",
    "1. 利用 .rename() 並配合以下 columns_mapping，將原中文欄位名稱改為英文方便之後分析。\n",
    "2. 取出主要用途(main_use)為'住家用'以及都市土地使用分區(use_zoning)為'住'的資料並針對以下欄位analysis_columns做分析並去除 NaN 值。\n",
    "   (提示:先取完之後再.dropna())\n",
    "3. 觀察欄位資料型態，並利用 .astype() 搭配以下提供的 columns_type 做欄位型態轉換。\n",
    "4. 做資料切片將<br>\n",
    "    新增欄位交易年月日(tx_dt_year)，從交易年月日(tx_dt)萃取出年份<br>\n",
    "    1.交易年月日(tx_dt_year)，限制在109年<br>\n",
    "    2.建物現況格局-房(room_number)，限制在1到5間<br>\n",
    "    3.建物現況格局-廳(hall_number)，限制在1到2廳<br>\n",
    "    4.最後運用.reset_index()重新定義索引"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "rBSElFmKAtpc"
   },
   "outputs": [],
   "source": [
    "columns_mapping = { '城市':'city', '鄉鎮市區':'towns', '交易標的':'transaction_sign',\n",
    "                    '土地區段位置建物區段門牌':'house_number', '土地移轉總面積平方公尺':'land_area_square_meter', \n",
    "                    '都市土地使用分區':'use_zoning', '非都市土地使用分區':'land_use_district', \n",
    "                    '非都市土地使用編定':'land_use', '交易年月日':'tx_dt', \n",
    "                    '交易筆棟數':'transaction_pen_number', '移轉層次':'shifting_level', \n",
    "                    '總樓層數':'total_floor_number', '建物型態':'building_state', \n",
    "                    '主要用途':'main_use', '主要建材':'main_materials',\n",
    "                    '建築完成年月':'complete_date', '建物移轉總面積平方公尺':'building_area_square_meter', \n",
    "                    '建物現況格局-房':'room_number', '建物現況格局-廳':'hall_number', \n",
    "                    '建物現況格局-衛':'health_number', '建物現況格局-隔間':'compartmented_number', \n",
    "                    '有無管理組織':'manages', '總價元':'total_price', \n",
    "                    '單價元平方公尺':'unit_price', '車位類別':'berth_category', \n",
    "                    '車位移轉總面積(平方公尺)':'berth_area_square_meter', '車位總價元':'berth_price', \n",
    "                    '備註':'note', '編號':'serial_number', \n",
    "                    '主建物面積':'main_building_area', '附屬建物面積':'auxiliary_building_area', \n",
    "                    '陽台面積':'balcony_area', '電梯':'elevator' }\n",
    "\n",
    "analysis_columns = [ 'city', 'towns', 'main_use', 'use_zoning', 'total_price', 'building_area_square_meter',\n",
    "                     'main_building_area', 'tx_dt', 'unit_price', 'room_number', 'hall_number', 'health_number' ]\n",
    "\n",
    "columns_type = { 'total_price':'int', 'unit_price':'float', 'building_area_square_meter':'float', \n",
    "                 'main_building_area':'float', 'room_number':'int', 'hall_number':'int', 'health_number':'int' }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "JFrw25keAtpe",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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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>towns</th>\n",
       "      <th>transaction_sign</th>\n",
       "      <th>house_number</th>\n",
       "      <th>land_area_square_meter</th>\n",
       "      <th>use_zoning</th>\n",
       "      <th>land_use_district</th>\n",
       "      <th>land_use</th>\n",
       "      <th>tx_dt</th>\n",
       "      <th>transaction_pen_number</th>\n",
       "      <th>shifting_level</th>\n",
       "      <th>...</th>\n",
       "      <th>berth_category</th>\n",
       "      <th>berth_area_square_meter</th>\n",
       "      <th>berth_price</th>\n",
       "      <th>note</th>\n",
       "      <th>serial_number</th>\n",
       "      <th>main_building_area</th>\n",
       "      <th>auxiliary_building_area</th>\n",
       "      <th>balcony_area</th>\n",
       "      <th>elevator</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文山區</td>\n",
       "      <td>房地(土地+建物)+車位</td>\n",
       "      <td>臺北市文山區忠順街二段85巷1~30號</td>\n",
       "      <td>26.97</td>\n",
       "      <td>住</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090406</td>\n",
       "      <td>土地1建物1車位2</td>\n",
       "      <td>四層</td>\n",
       "      <td>...</td>\n",
       "      <td>坡道平面</td>\n",
       "      <td>40.7</td>\n",
       "      <td>0</td>\n",
       "      <td>含增建或未登記建物。;內含獨立產權車位，未包含在棟數內。</td>\n",
       "      <td>RPVOMLMKNHPFFAA18CA</td>\n",
       "      <td>110.91</td>\n",
       "      <td>9.63</td>\n",
       "      <td>11.09</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文山區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>臺北市文山區福興路91~120號</td>\n",
       "      <td>10.32</td>\n",
       "      <td>住</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090317</td>\n",
       "      <td>土地1建物1車位0</td>\n",
       "      <td>三層</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>含增建或未登記建物。;</td>\n",
       "      <td>RPPPMLMKNHPFFAA67CA</td>\n",
       "      <td>24.58</td>\n",
       "      <td>1.28</td>\n",
       "      <td>2.3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文山區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>臺北市文山區木柵路四段121~150號</td>\n",
       "      <td>39.64</td>\n",
       "      <td>其他</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090418</td>\n",
       "      <td>土地2建物1車位0</td>\n",
       "      <td>六層</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RPSPMLMKNHPFFAA97CA</td>\n",
       "      <td>78.8</td>\n",
       "      <td>4.15</td>\n",
       "      <td>8.76</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>中正區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>臺北市中正區和平西路一段31~60號</td>\n",
       "      <td>4.26</td>\n",
       "      <td>商</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090413</td>\n",
       "      <td>土地2建物1車位0</td>\n",
       "      <td>九層</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>含增建或未登記建物。;</td>\n",
       "      <td>RPPOMLMKNHPFFAA57CA</td>\n",
       "      <td>18.34</td>\n",
       "      <td>0.0</td>\n",
       "      <td>6.45</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>文山區</td>\n",
       "      <td>房地(土地+建物)</td>\n",
       "      <td>臺北市文山區樟新街56巷1~30號</td>\n",
       "      <td>44.57</td>\n",
       "      <td>住</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1090328</td>\n",
       "      <td>土地3建物1車位0</td>\n",
       "      <td>四層</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>含增建或未登記建物。;</td>\n",
       "      <td>RPWNMLNKNHPFFAA28CA</td>\n",
       "      <td>98.74</td>\n",
       "      <td>2.26</td>\n",
       "      <td>11.84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Taipei</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  towns transaction_sign         house_number land_area_square_meter  \\\n",
       "1   文山區     房地(土地+建物)+車位  臺北市文山區忠順街二段85巷1~30號                  26.97   \n",
       "2   文山區        房地(土地+建物)     臺北市文山區福興路91~120號                  10.32   \n",
       "3   文山區        房地(土地+建物)  臺北市文山區木柵路四段121~150號                  39.64   \n",
       "4   中正區        房地(土地+建物)   臺北市中正區和平西路一段31~60號                   4.26   \n",
       "5   文山區        房地(土地+建物)    臺北市文山區樟新街56巷1~30號                  44.57   \n",
       "\n",
       "  use_zoning land_use_district land_use    tx_dt transaction_pen_number  \\\n",
       "1          住               NaN      NaN  1090406              土地1建物1車位2   \n",
       "2          住               NaN      NaN  1090317              土地1建物1車位0   \n",
       "3         其他               NaN      NaN  1090418              土地2建物1車位0   \n",
       "4          商               NaN      NaN  1090413              土地2建物1車位0   \n",
       "5          住               NaN      NaN  1090328              土地3建物1車位0   \n",
       "\n",
       "  shifting_level  ... berth_category berth_area_square_meter berth_price  \\\n",
       "1             四層  ...           坡道平面                    40.7           0   \n",
       "2             三層  ...            NaN                     0.0           0   \n",
       "3             六層  ...            NaN                     0.0           0   \n",
       "4             九層  ...            NaN                     0.0           0   \n",
       "5             四層  ...            NaN                     0.0           0   \n",
       "\n",
       "                           note        serial_number main_building_area  \\\n",
       "1  含增建或未登記建物。;內含獨立產權車位，未包含在棟數內。  RPVOMLMKNHPFFAA18CA             110.91   \n",
       "2                   含增建或未登記建物。;  RPPPMLMKNHPFFAA67CA              24.58   \n",
       "3                           NaN  RPSPMLMKNHPFFAA97CA               78.8   \n",
       "4                   含增建或未登記建物。;  RPPOMLMKNHPFFAA57CA              18.34   \n",
       "5                   含增建或未登記建物。;  RPWNMLNKNHPFFAA28CA              98.74   \n",
       "\n",
       "  auxiliary_building_area balcony_area elevator    city  \n",
       "1                    9.63        11.09      NaN  Taipei  \n",
       "2                    1.28          2.3      NaN  Taipei  \n",
       "3                    4.15         8.76      NaN  Taipei  \n",
       "4                     0.0         6.45      NaN  Taipei  \n",
       "5                    2.26        11.84      NaN  Taipei  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#1. 利用 .rename()並配合以下 columns_mapping，將原中文欄位名稱改為英文方便之後分析。\n",
    "\n",
    "df.rename( columns=columns_mapping, inplace=True )\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "GMPSUJFcAtpf"
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>towns</th>\n",
       "      <th>main_use</th>\n",
       "      <th>use_zoning</th>\n",
       "      <th>total_price</th>\n",
       "      <th>building_area_square_meter</th>\n",
       "      <th>main_building_area</th>\n",
       "      <th>tx_dt</th>\n",
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       "      <th>room_number</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>36000000</td>\n",
       "      <td>229.91</td>\n",
       "      <td>110.91</td>\n",
       "      <td>1090406</td>\n",
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       "      <th>2</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>5580000</td>\n",
       "      <td>37.31</td>\n",
       "      <td>24.58</td>\n",
       "      <td>1090317</td>\n",
       "      <td>149558</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>18280000</td>\n",
       "      <td>123.66</td>\n",
       "      <td>98.74</td>\n",
       "      <td>1090328</td>\n",
       "      <td>147825</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>8000000</td>\n",
       "      <td>124.25</td>\n",
       "      <td>90.78</td>\n",
       "      <td>1090417</td>\n",
       "      <td>64386</td>\n",
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       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>2200000</td>\n",
       "      <td>30.16</td>\n",
       "      <td>26.15</td>\n",
       "      <td>1090417</td>\n",
       "      <td>72944</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11925</th>\n",
       "      <td>Kaohsiung</td>\n",
       "      <td>路竹區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>5300000</td>\n",
       "      <td>140.10999999999999</td>\n",
       "      <td>86.24</td>\n",
       "      <td>1090718</td>\n",
       "      <td>37827</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12019</th>\n",
       "      <td>Kaohsiung</td>\n",
       "      <td>路竹區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>5050000</td>\n",
       "      <td>144.76</td>\n",
       "      <td>101.76</td>\n",
       "      <td>1090802</td>\n",
       "      <td>34885</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12236</th>\n",
       "      <td>Kaohsiung</td>\n",
       "      <td>茄萣區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>2720000</td>\n",
       "      <td>210.78</td>\n",
       "      <td>144.4</td>\n",
       "      <td>1090725</td>\n",
       "      <td>12904</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12284</th>\n",
       "      <td>Kaohsiung</td>\n",
       "      <td>路竹區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>4950000</td>\n",
       "      <td>94.48</td>\n",
       "      <td>94.48</td>\n",
       "      <td>1090726</td>\n",
       "      <td>52392</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
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       "    <tr>\n",
       "      <th>12492</th>\n",
       "      <td>Kaohsiung</td>\n",
       "      <td>路竹區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>9800000</td>\n",
       "      <td>80.4</td>\n",
       "      <td>80.4</td>\n",
       "      <td>1090701</td>\n",
       "      <td>121891</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>9</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>7818 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            city towns main_use use_zoning total_price  \\\n",
       "1         Taipei   文山區      住家用          住    36000000   \n",
       "2         Taipei   文山區      住家用          住     5580000   \n",
       "5         Taipei   文山區      住家用          住    18280000   \n",
       "7         Taipei   文山區      住家用          住     8000000   \n",
       "8         Taipei   文山區      住家用          住     2200000   \n",
       "...          ...   ...      ...        ...         ...   \n",
       "11925  Kaohsiung   路竹區      住家用          住     5300000   \n",
       "12019  Kaohsiung   路竹區      住家用          住     5050000   \n",
       "12236  Kaohsiung   茄萣區      住家用          住     2720000   \n",
       "12284  Kaohsiung   路竹區      住家用          住     4950000   \n",
       "12492  Kaohsiung   路竹區      住家用          住     9800000   \n",
       "\n",
       "      building_area_square_meter main_building_area    tx_dt unit_price  \\\n",
       "1                         229.91             110.91  1090406     156583   \n",
       "2                          37.31              24.58  1090317     149558   \n",
       "5                         123.66              98.74  1090328     147825   \n",
       "7                         124.25              90.78  1090417      64386   \n",
       "8                          30.16              26.15  1090417      72944   \n",
       "...                          ...                ...      ...        ...   \n",
       "11925         140.10999999999999              86.24  1090718      37827   \n",
       "12019                     144.76             101.76  1090802      34885   \n",
       "12236                     210.78              144.4  1090725      12904   \n",
       "12284                      94.48              94.48  1090726      52392   \n",
       "12492                       80.4               80.4  1090701     121891   \n",
       "\n",
       "      room_number hall_number health_number  \n",
       "1               3           2             2  \n",
       "2               2           1             1  \n",
       "5               3           2             2  \n",
       "7               3           1             2  \n",
       "8               5           2             2  \n",
       "...           ...         ...           ...  \n",
       "11925           3           2             2  \n",
       "12019           3           2             2  \n",
       "12236           5           2             3  \n",
       "12284           3           2             2  \n",
       "12492           9           0             9  \n",
       "\n",
       "[7818 rows x 12 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.取出主要用途(main_use)為'住家用'，及都市土地使用分區(use_zoning)為'住'的資料，並針對以下欄位 analysis_columns做分析且去除 NaN 值。 \n",
    "# 提示:先取完之後再.dropna()\n",
    "\n",
    "df_analysis = df.loc[ (df.main_use=='住家用')&(df.use_zoning=='住'), analysis_columns ]\n",
    "df_analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column Name</th>\n",
       "      <th>Missing Count</th>\n",
       "      <th>Percentage(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>unit_price</td>\n",
       "      <td>16</td>\n",
       "      <td>0.20%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>health_number</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>hall_number</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>room_number</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tx_dt</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>main_building_area</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>building_area_square_meter</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>total_price</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>use_zoning</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>main_use</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>towns</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>city</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Column Name  Missing Count Percentage(%)\n",
       "0                   unit_price             16         0.20%\n",
       "1                health_number              0         0.00%\n",
       "2                  hall_number              0         0.00%\n",
       "3                  room_number              0         0.00%\n",
       "4                        tx_dt              0         0.00%\n",
       "5           main_building_area              0         0.00%\n",
       "6   building_area_square_meter              0         0.00%\n",
       "7                  total_price              0         0.00%\n",
       "8                   use_zoning              0         0.00%\n",
       "9                     main_use              0         0.00%\n",
       "10                       towns              0         0.00%\n",
       "11                        city              0         0.00%"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 檢查訓練資料集中是否有遺失值 ?\n",
    "def Missing_Counts( Data, NoMissing=True ) : \n",
    "    missing = Data.isnull().sum()  \n",
    "    \n",
    "    if NoMissing==False :\n",
    "        missing = missing[ missing>0 ]\n",
    "        \n",
    "    missing.sort_values( ascending=False, inplace=True )  \n",
    "    Missing_Count = pd.DataFrame( { 'Column Name':missing.index, 'Missing Count':missing.values } ) \n",
    "    Missing_Count[ 'Percentage(%)' ] = Missing_Count['Missing Count'].apply( lambda x: '{:.2%}'.format(x/Data.shape[0] ))\n",
    "    return  Missing_Count\n",
    "\n",
    "Missing_Counts(df_analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Column Name</th>\n",
       "      <th>Missing Count</th>\n",
       "      <th>Percentage(%)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>health_number</td>\n",
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       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hall_number</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>room_number</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>unit_price</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tx_dt</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>main_building_area</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>building_area_square_meter</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>total_price</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>use_zoning</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>main_use</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>towns</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>city</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Column Name  Missing Count Percentage(%)\n",
       "0                health_number              0         0.00%\n",
       "1                  hall_number              0         0.00%\n",
       "2                  room_number              0         0.00%\n",
       "3                   unit_price              0         0.00%\n",
       "4                        tx_dt              0         0.00%\n",
       "5           main_building_area              0         0.00%\n",
       "6   building_area_square_meter              0         0.00%\n",
       "7                  total_price              0         0.00%\n",
       "8                   use_zoning              0         0.00%\n",
       "9                     main_use              0         0.00%\n",
       "10                       towns              0         0.00%\n",
       "11                        city              0         0.00%"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 去除 NaN 值\n",
    "df_analysis.dropna( inplace=True )\n",
    "\n",
    "Missing_Counts(df_analysis)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "Y5_BWtUpAtph"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7802 entries, 1 to 12492\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count  Dtype \n",
      "---  ------                      --------------  ----- \n",
      " 0   city                        7802 non-null   object\n",
      " 1   towns                       7802 non-null   object\n",
      " 2   main_use                    7802 non-null   object\n",
      " 3   use_zoning                  7802 non-null   object\n",
      " 4   total_price                 7802 non-null   object\n",
      " 5   building_area_square_meter  7802 non-null   object\n",
      " 6   main_building_area          7802 non-null   object\n",
      " 7   tx_dt                       7802 non-null   object\n",
      " 8   unit_price                  7802 non-null   object\n",
      " 9   room_number                 7802 non-null   object\n",
      " 10  hall_number                 7802 non-null   object\n",
      " 11  health_number               7802 non-null   object\n",
      "dtypes: object(12)\n",
      "memory usage: 792.4+ KB\n"
     ]
    }
   ],
   "source": [
    "# 3.觀察欄位資料型態，並利用 .astype() 搭配以下提供的 columns_type 做欄位型態轉換。\n",
    "\n",
    "df_analysis.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 7802 entries, 1 to 12492\n",
      "Data columns (total 12 columns):\n",
      " #   Column                      Non-Null Count  Dtype  \n",
      "---  ------                      --------------  -----  \n",
      " 0   city                        7802 non-null   object \n",
      " 1   towns                       7802 non-null   object \n",
      " 2   main_use                    7802 non-null   object \n",
      " 3   use_zoning                  7802 non-null   object \n",
      " 4   total_price                 7802 non-null   int32  \n",
      " 5   building_area_square_meter  7802 non-null   float64\n",
      " 6   main_building_area          7802 non-null   float64\n",
      " 7   tx_dt                       7802 non-null   object \n",
      " 8   unit_price                  7802 non-null   float64\n",
      " 9   room_number                 7802 non-null   int32  \n",
      " 10  hall_number                 7802 non-null   int32  \n",
      " 11  health_number               7802 non-null   int32  \n",
      "dtypes: float64(3), int32(4), object(5)\n",
      "memory usage: 670.5+ KB\n"
     ]
    }
   ],
   "source": [
    "# 欄位型態轉換\n",
    "df_analysis = df_analysis.astype(columns_type)\n",
    "\n",
    "df_analysis.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "AxF8yHDeAtph",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data = (7802, 13)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\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>city</th>\n",
       "      <th>towns</th>\n",
       "      <th>main_use</th>\n",
       "      <th>use_zoning</th>\n",
       "      <th>total_price</th>\n",
       "      <th>building_area_square_meter</th>\n",
       "      <th>main_building_area</th>\n",
       "      <th>tx_dt</th>\n",
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       "      <th>room_number</th>\n",
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       "      <th>tx_dt_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>36000000</td>\n",
       "      <td>229.91</td>\n",
       "      <td>110.91</td>\n",
       "      <td>1090406</td>\n",
       "      <td>156583.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>5580000</td>\n",
       "      <td>37.31</td>\n",
       "      <td>24.58</td>\n",
       "      <td>1090317</td>\n",
       "      <td>149558.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>18280000</td>\n",
       "      <td>123.66</td>\n",
       "      <td>98.74</td>\n",
       "      <td>1090328</td>\n",
       "      <td>147825.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>8000000</td>\n",
       "      <td>124.25</td>\n",
       "      <td>90.78</td>\n",
       "      <td>1090417</td>\n",
       "      <td>64386.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>2200000</td>\n",
       "      <td>30.16</td>\n",
       "      <td>26.15</td>\n",
       "      <td>1090417</td>\n",
       "      <td>72944.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     city towns main_use use_zoning  total_price  building_area_square_meter  \\\n",
       "1  Taipei   文山區      住家用          住     36000000                      229.91   \n",
       "2  Taipei   文山區      住家用          住      5580000                       37.31   \n",
       "5  Taipei   文山區      住家用          住     18280000                      123.66   \n",
       "7  Taipei   文山區      住家用          住      8000000                      124.25   \n",
       "8  Taipei   文山區      住家用          住      2200000                       30.16   \n",
       "\n",
       "   main_building_area    tx_dt  unit_price  room_number  hall_number  \\\n",
       "1              110.91  1090406    156583.0            3            2   \n",
       "2               24.58  1090317    149558.0            2            1   \n",
       "5               98.74  1090328    147825.0            3            2   \n",
       "7               90.78  1090417     64386.0            3            1   \n",
       "8               26.15  1090417     72944.0            5            2   \n",
       "\n",
       "   health_number  tx_dt_year  \n",
       "1              2         109  \n",
       "2              1         109  \n",
       "5              2         109  \n",
       "7              2         109  \n",
       "8              2         109  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4.資料切片將\n",
    "#   新增欄位交易年月日(tx_dt_year)，從交易年月日(tx_dt)萃取出年份\n",
    "#   1.交易年月日(tx_dt_year)，限制在109年\n",
    "#   2.建物現況格局-房(room_number)，限制在1到5間\n",
    "#   3.建物現況格局-廳(hall_number)，限制在1到2廳\n",
    "#   4.最後運用.reset_index()重新定義索引\n",
    "\n",
    "df_analysis['tx_dt_year'] = df_analysis['tx_dt'].apply( lambda x: int(x[:-4]) )\n",
    "print( 'Shape of data =', df_analysis.shape )\n",
    "df_analysis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of data = (6894, 13)\n"
     ]
    }
   ],
   "source": [
    "# 篩選資料:\n",
    "# 交易年月日(tx_dt_year)，限制在109年\n",
    "# 建物現況格局-房(room_number)，限制在1到5間\n",
    "# 建物現況格局-廳(hall_number)，限制在1到2廳\n",
    "\n",
    "df_analysis = df_analysis.loc[ (df_analysis['tx_dt_year']>=109)&(df_analysis['room_number']>=1)&(df_analysis['room_number']<=5)&(df_analysis['hall_number']>=1)&(df_analysis['hall_number']<=2)]\n",
    "print( 'Shape of data =', df_analysis.shape )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>city</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>36000000</td>\n",
       "      <td>229.91</td>\n",
       "      <td>110.91</td>\n",
       "      <td>1090406</td>\n",
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       "      <td>3</td>\n",
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       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>5580000</td>\n",
       "      <td>37.31</td>\n",
       "      <td>24.58</td>\n",
       "      <td>1090317</td>\n",
       "      <td>149558.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>18280000</td>\n",
       "      <td>123.66</td>\n",
       "      <td>98.74</td>\n",
       "      <td>1090328</td>\n",
       "      <td>147825.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>8000000</td>\n",
       "      <td>124.25</td>\n",
       "      <td>90.78</td>\n",
       "      <td>1090417</td>\n",
       "      <td>64386.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>2200000</td>\n",
       "      <td>30.16</td>\n",
       "      <td>26.15</td>\n",
       "      <td>1090417</td>\n",
       "      <td>72944.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     city towns main_use use_zoning  total_price  building_area_square_meter  \\\n",
       "1  Taipei   文山區      住家用          住     36000000                      229.91   \n",
       "2  Taipei   文山區      住家用          住      5580000                       37.31   \n",
       "5  Taipei   文山區      住家用          住     18280000                      123.66   \n",
       "7  Taipei   文山區      住家用          住      8000000                      124.25   \n",
       "8  Taipei   文山區      住家用          住      2200000                       30.16   \n",
       "\n",
       "   main_building_area    tx_dt  unit_price  room_number  hall_number  \\\n",
       "1              110.91  1090406    156583.0            3            2   \n",
       "2               24.58  1090317    149558.0            2            1   \n",
       "5               98.74  1090328    147825.0            3            2   \n",
       "7               90.78  1090417     64386.0            3            1   \n",
       "8               26.15  1090417     72944.0            5            2   \n",
       "\n",
       "   health_number  tx_dt_year  \n",
       "1              2         109  \n",
       "2              1         109  \n",
       "5              2         109  \n",
       "7              2         109  \n",
       "8              2         109  "
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_analysis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>towns</th>\n",
       "      <th>main_use</th>\n",
       "      <th>use_zoning</th>\n",
       "      <th>total_price</th>\n",
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       "      <th>main_building_area</th>\n",
       "      <th>tx_dt</th>\n",
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       "      <th>room_number</th>\n",
       "      <th>hall_number</th>\n",
       "      <th>health_number</th>\n",
       "      <th>tx_dt_year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
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       "      <td>109</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>5580000</td>\n",
       "      <td>37.31</td>\n",
       "      <td>24.58</td>\n",
       "      <td>1090317</td>\n",
       "      <td>149558.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>18280000</td>\n",
       "      <td>123.66</td>\n",
       "      <td>98.74</td>\n",
       "      <td>1090328</td>\n",
       "      <td>147825.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>8000000</td>\n",
       "      <td>124.25</td>\n",
       "      <td>90.78</td>\n",
       "      <td>1090417</td>\n",
       "      <td>64386.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>2200000</td>\n",
       "      <td>30.16</td>\n",
       "      <td>26.15</td>\n",
       "      <td>1090417</td>\n",
       "      <td>72944.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     city towns main_use use_zoning  total_price  building_area_square_meter  \\\n",
       "0  Taipei   文山區      住家用          住     36000000                      229.91   \n",
       "1  Taipei   文山區      住家用          住      5580000                       37.31   \n",
       "2  Taipei   文山區      住家用          住     18280000                      123.66   \n",
       "3  Taipei   文山區      住家用          住      8000000                      124.25   \n",
       "4  Taipei   文山區      住家用          住      2200000                       30.16   \n",
       "\n",
       "   main_building_area    tx_dt  unit_price  room_number  hall_number  \\\n",
       "0              110.91  1090406    156583.0            3            2   \n",
       "1               24.58  1090317    149558.0            2            1   \n",
       "2               98.74  1090328    147825.0            3            2   \n",
       "3               90.78  1090417     64386.0            3            1   \n",
       "4               26.15  1090417     72944.0            5            2   \n",
       "\n",
       "   health_number  tx_dt_year  \n",
       "0              2         109  \n",
       "1              1         109  \n",
       "2              2         109  \n",
       "3              2         109  \n",
       "4              2         109  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 重新定義索引\n",
    "\n",
    "df_analysis.reset_index( drop=True,  inplace=True )\n",
    "df_analysis.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zTOTmNDxAtpi"
   },
   "source": [
    "# 3. 建立自定義特徵加入分析\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cyL2au1aAtpi"
   },
   "source": [
    "以台灣來說大家都是以坪為單位計算使用面積，應該是不會問說你家有幾平方公尺吧?，但是偏偏資料中沒有以坪為單位計算，所以接下來請各位幫忙產生新的欄位以坪為單位計算面積，轉換公式我也幫你找好了，在下面的定義。<br>\n",
    "定義 : 1平方公尺相當於0.3025坪 <br>\n",
    "建立新特徵\n",
    "1. 建物移轉總面積坪(building_area_square_feet) : 建物移轉總面積平方公尺\\*0.3025\n",
    "2. 主建物面積坪(main_building_area_square_feet) : 主建物面積\\*0.3025\n",
    "3. 單價元坪(unit_price_square_feet) : 單價元平方公尺/0.3025"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O-BL5boZAtpi"
   },
   "source": [
    "可以利用.describe()做一下資料觀察，是否有奇怪的資料?<br>\n",
    "如果有請將資料移除，並說明為什麼移除此資料?<br>\n",
    "移除後再用.describe()觀察是否還有奇怪的資料?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "id": "vWILawwvAtpj"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
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       "      <th>use_zoning</th>\n",
       "      <th>total_price</th>\n",
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       "      <th>unit_price_square_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
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       "      <td>33.550275</td>\n",
       "      <td>517629.752066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>5580000</td>\n",
       "      <td>37.31</td>\n",
       "      <td>24.58</td>\n",
       "      <td>1090317</td>\n",
       "      <td>149558.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>11.286275</td>\n",
       "      <td>7.435450</td>\n",
       "      <td>494406.611570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>18280000</td>\n",
       "      <td>123.66</td>\n",
       "      <td>98.74</td>\n",
       "      <td>1090328</td>\n",
       "      <td>147825.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>37.407150</td>\n",
       "      <td>29.868850</td>\n",
       "      <td>488677.685950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>8000000</td>\n",
       "      <td>124.25</td>\n",
       "      <td>90.78</td>\n",
       "      <td>1090417</td>\n",
       "      <td>64386.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>37.585625</td>\n",
       "      <td>27.460950</td>\n",
       "      <td>212846.280992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>2200000</td>\n",
       "      <td>30.16</td>\n",
       "      <td>26.15</td>\n",
       "      <td>1090417</td>\n",
       "      <td>72944.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>9.123400</td>\n",
       "      <td>7.910375</td>\n",
       "      <td>241137.190083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     city towns main_use use_zoning  total_price  building_area_square_meter  \\\n",
       "0  Taipei   文山區      住家用          住     36000000                      229.91   \n",
       "1  Taipei   文山區      住家用          住      5580000                       37.31   \n",
       "2  Taipei   文山區      住家用          住     18280000                      123.66   \n",
       "3  Taipei   文山區      住家用          住      8000000                      124.25   \n",
       "4  Taipei   文山區      住家用          住      2200000                       30.16   \n",
       "\n",
       "   main_building_area    tx_dt  unit_price  room_number  hall_number  \\\n",
       "0              110.91  1090406    156583.0            3            2   \n",
       "1               24.58  1090317    149558.0            2            1   \n",
       "2               98.74  1090328    147825.0            3            2   \n",
       "3               90.78  1090417     64386.0            3            1   \n",
       "4               26.15  1090417     72944.0            5            2   \n",
       "\n",
       "   health_number  tx_dt_year  building_area_square_feet  \\\n",
       "0              2         109                  69.547775   \n",
       "1              1         109                  11.286275   \n",
       "2              2         109                  37.407150   \n",
       "3              2         109                  37.585625   \n",
       "4              2         109                   9.123400   \n",
       "\n",
       "   main_building_area_square_feet  unit_price_square_feet  \n",
       "0                       33.550275           517629.752066  \n",
       "1                        7.435450           494406.611570  \n",
       "2                       29.868850           488677.685950  \n",
       "3                       27.460950           212846.280992  \n",
       "4                        7.910375           241137.190083  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 建立新特徵\n",
    "# 1.建物移轉總面積坪(building_area_square_feet) : 建物移轉總面積平方公尺(building_area_square_meter)*0.3025\n",
    "# 2.主建物面積坪(main_building_area_square_feet) : 主建物面積(main_building_area)*0.3025\n",
    "# 3.單價元坪(unit_price_square_feet) : 單價元平方公尺(unit_price)/0.3025\n",
    "\n",
    "df_analysis['building_area_square_feet'] = df_analysis['building_area_square_meter']*0.3025\n",
    "df_analysis['main_building_area_square_feet'] = df_analysis['main_building_area']*0.3025\n",
    "df_analysis['unit_price_square_feet'] = df_analysis['unit_price']/0.3025\n",
    "\n",
    "df_analysis.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "11 Numeric Features:\n",
      " ['total_price', 'building_area_square_meter', 'main_building_area', 'unit_price', 'room_number', 'hall_number', 'health_number', 'tx_dt_year', 'building_area_square_feet', 'main_building_area_square_feet', 'unit_price_square_feet']\n",
      "\n",
      "5 Category Features:\n",
      " ['city', 'towns', 'main_use', 'use_zoning', 'tx_dt']\n"
     ]
    }
   ],
   "source": [
    "num_features = []\n",
    "cat_features = []\n",
    "\n",
    "for dtype, feature in zip(df_analysis.dtypes,df_analysis.columns):\n",
    "    if dtype == 'float64' or dtype=='int32' or dtype=='int64':\n",
    "        num_features.append(feature)\n",
    "    elif dtype == 'object':\n",
    "        cat_features.append(feature)\n",
    "    else:\n",
    "        print( feature, 'is', df_analysis[feature].dtypes, 'type.' )\n",
    "        \n",
    "print( f'{len(num_features)} Numeric Features:\\n {num_features}\\n' )\n",
    "print( f'{len(cat_features)} Category Features:\\n {cat_features}' )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "ZuAZPbnNAtpj",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "<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>total_price</th>\n",
       "      <th>building_area_square_meter</th>\n",
       "      <th>main_building_area</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>room_number</th>\n",
       "      <th>hall_number</th>\n",
       "      <th>health_number</th>\n",
       "      <th>tx_dt_year</th>\n",
       "      <th>building_area_square_feet</th>\n",
       "      <th>main_building_area_square_feet</th>\n",
       "      <th>unit_price_square_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.894000e+03</td>\n",
       "      <td>6894.000000</td>\n",
       "      <td>6894.000000</td>\n",
       "      <td>6894.000000</td>\n",
       "      <td>6894.000000</td>\n",
       "      <td>6894.000000</td>\n",
       "      <td>6894.000000</td>\n",
       "      <td>6894.0</td>\n",
       "      <td>6894.000000</td>\n",
       "      <td>6894.000000</td>\n",
       "      <td>6.894000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.138712e+07</td>\n",
       "      <td>121.712089</td>\n",
       "      <td>82.086667</td>\n",
       "      <td>94364.671163</td>\n",
       "      <td>2.850885</td>\n",
       "      <td>1.793299</td>\n",
       "      <td>1.733536</td>\n",
       "      <td>109.0</td>\n",
       "      <td>36.817907</td>\n",
       "      <td>24.831217</td>\n",
       "      <td>3.119493e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.159445e+07</td>\n",
       "      <td>62.871712</td>\n",
       "      <td>37.363166</td>\n",
       "      <td>58317.311227</td>\n",
       "      <td>0.898498</td>\n",
       "      <td>0.404969</td>\n",
       "      <td>0.681085</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.018693</td>\n",
       "      <td>11.302358</td>\n",
       "      <td>1.927845e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.970000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>0.293425</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.580000e+06</td>\n",
       "      <td>82.965000</td>\n",
       "      <td>60.682500</td>\n",
       "      <td>53532.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>25.096912</td>\n",
       "      <td>18.356456</td>\n",
       "      <td>1.769661e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.550000e+06</td>\n",
       "      <td>109.390000</td>\n",
       "      <td>78.990000</td>\n",
       "      <td>79610.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>33.090475</td>\n",
       "      <td>23.894475</td>\n",
       "      <td>2.631736e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.329500e+07</td>\n",
       "      <td>147.517500</td>\n",
       "      <td>95.797500</td>\n",
       "      <td>118728.250000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>44.624044</td>\n",
       "      <td>28.978744</td>\n",
       "      <td>3.924901e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.250000e+08</td>\n",
       "      <td>1444.950000</td>\n",
       "      <td>335.760000</td>\n",
       "      <td>610104.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>437.097375</td>\n",
       "      <td>101.567400</td>\n",
       "      <td>2.016873e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_price  building_area_square_meter  main_building_area  \\\n",
       "count  6.894000e+03                 6894.000000         6894.000000   \n",
       "mean   1.138712e+07                  121.712089           82.086667   \n",
       "std    1.159445e+07                   62.871712           37.363166   \n",
       "min    0.000000e+00                    0.970000            0.000000   \n",
       "25%    5.580000e+06                   82.965000           60.682500   \n",
       "50%    8.550000e+06                  109.390000           78.990000   \n",
       "75%    1.329500e+07                  147.517500           95.797500   \n",
       "max    2.250000e+08                 1444.950000          335.760000   \n",
       "\n",
       "          unit_price  room_number  hall_number  health_number  tx_dt_year  \\\n",
       "count    6894.000000  6894.000000  6894.000000    6894.000000      6894.0   \n",
       "mean    94364.671163     2.850885     1.793299       1.733536       109.0   \n",
       "std     58317.311227     0.898498     0.404969       0.681085         0.0   \n",
       "min         0.000000     1.000000     1.000000       0.000000       109.0   \n",
       "25%     53532.250000     2.000000     2.000000       1.000000       109.0   \n",
       "50%     79610.000000     3.000000     2.000000       2.000000       109.0   \n",
       "75%    118728.250000     3.000000     2.000000       2.000000       109.0   \n",
       "max    610104.000000     5.000000     2.000000       6.000000       109.0   \n",
       "\n",
       "       building_area_square_feet  main_building_area_square_feet  \\\n",
       "count                6894.000000                     6894.000000   \n",
       "mean                   36.817907                       24.831217   \n",
       "std                    19.018693                       11.302358   \n",
       "min                     0.293425                        0.000000   \n",
       "25%                    25.096912                       18.356456   \n",
       "50%                    33.090475                       23.894475   \n",
       "75%                    44.624044                       28.978744   \n",
       "max                   437.097375                      101.567400   \n",
       "\n",
       "       unit_price_square_feet  \n",
       "count            6.894000e+03  \n",
       "mean             3.119493e+05  \n",
       "std              1.927845e+05  \n",
       "min              0.000000e+00  \n",
       "25%              1.769661e+05  \n",
       "50%              2.631736e+05  \n",
       "75%              3.924901e+05  \n",
       "max              2.016873e+06  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 可以利用.describe()做一下資料觀察，是否有奇怪的資料?\n",
    "# 如果有請將資料移除，並說明為什麼移除此資料?\n",
    "\n",
    "df_analysis.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "異常資料:   \n",
    "- main_building_area(主建物面積) 最小值為 0  \n",
    "- main_building_area_square_feet(主建物面積坪) 最小值為 0   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>total_price</th>\n",
       "      <th>building_area_square_meter</th>\n",
       "      <th>main_building_area</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>room_number</th>\n",
       "      <th>hall_number</th>\n",
       "      <th>health_number</th>\n",
       "      <th>tx_dt_year</th>\n",
       "      <th>building_area_square_feet</th>\n",
       "      <th>main_building_area_square_feet</th>\n",
       "      <th>unit_price_square_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.854000e+03</td>\n",
       "      <td>6854.000000</td>\n",
       "      <td>6854.000000</td>\n",
       "      <td>6854.000000</td>\n",
       "      <td>6854.000000</td>\n",
       "      <td>6854.000000</td>\n",
       "      <td>6854.000000</td>\n",
       "      <td>6854.0</td>\n",
       "      <td>6854.000000</td>\n",
       "      <td>6854.000000</td>\n",
       "      <td>6.854000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.141669e+07</td>\n",
       "      <td>121.768404</td>\n",
       "      <td>82.565725</td>\n",
       "      <td>94548.453020</td>\n",
       "      <td>2.849869</td>\n",
       "      <td>1.793114</td>\n",
       "      <td>1.733149</td>\n",
       "      <td>109.0</td>\n",
       "      <td>36.834942</td>\n",
       "      <td>24.976132</td>\n",
       "      <td>3.125569e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.161669e+07</td>\n",
       "      <td>62.849105</td>\n",
       "      <td>36.940424</td>\n",
       "      <td>58374.690321</td>\n",
       "      <td>0.898343</td>\n",
       "      <td>0.405103</td>\n",
       "      <td>0.680869</td>\n",
       "      <td>0.0</td>\n",
       "      <td>19.011854</td>\n",
       "      <td>11.174478</td>\n",
       "      <td>1.929742e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.100000e+05</td>\n",
       "      <td>7.440000</td>\n",
       "      <td>7.440000</td>\n",
       "      <td>3239.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2.250600</td>\n",
       "      <td>2.250600</td>\n",
       "      <td>1.070744e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.600000e+06</td>\n",
       "      <td>83.012500</td>\n",
       "      <td>61.062500</td>\n",
       "      <td>53597.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>25.111281</td>\n",
       "      <td>18.471406</td>\n",
       "      <td>1.771810e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.600000e+06</td>\n",
       "      <td>109.405000</td>\n",
       "      <td>79.110000</td>\n",
       "      <td>79742.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>33.095012</td>\n",
       "      <td>23.930775</td>\n",
       "      <td>2.636116e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.330000e+07</td>\n",
       "      <td>147.445000</td>\n",
       "      <td>95.970000</td>\n",
       "      <td>118880.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>44.602112</td>\n",
       "      <td>29.030925</td>\n",
       "      <td>3.929917e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.250000e+08</td>\n",
       "      <td>1444.950000</td>\n",
       "      <td>335.760000</td>\n",
       "      <td>610104.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>437.097375</td>\n",
       "      <td>101.567400</td>\n",
       "      <td>2.016873e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_price  building_area_square_meter  main_building_area  \\\n",
       "count  6.854000e+03                 6854.000000         6854.000000   \n",
       "mean   1.141669e+07                  121.768404           82.565725   \n",
       "std    1.161669e+07                   62.849105           36.940424   \n",
       "min    4.100000e+05                    7.440000            7.440000   \n",
       "25%    5.600000e+06                   83.012500           61.062500   \n",
       "50%    8.600000e+06                  109.405000           79.110000   \n",
       "75%    1.330000e+07                  147.445000           95.970000   \n",
       "max    2.250000e+08                 1444.950000          335.760000   \n",
       "\n",
       "          unit_price  room_number  hall_number  health_number  tx_dt_year  \\\n",
       "count    6854.000000  6854.000000  6854.000000    6854.000000      6854.0   \n",
       "mean    94548.453020     2.849869     1.793114       1.733149       109.0   \n",
       "std     58374.690321     0.898343     0.405103       0.680869         0.0   \n",
       "min      3239.000000     1.000000     1.000000       0.000000       109.0   \n",
       "25%     53597.250000     2.000000     2.000000       1.000000       109.0   \n",
       "50%     79742.500000     3.000000     2.000000       2.000000       109.0   \n",
       "75%    118880.000000     3.000000     2.000000       2.000000       109.0   \n",
       "max    610104.000000     5.000000     2.000000       6.000000       109.0   \n",
       "\n",
       "       building_area_square_feet  main_building_area_square_feet  \\\n",
       "count                6854.000000                     6854.000000   \n",
       "mean                   36.834942                       24.976132   \n",
       "std                    19.011854                       11.174478   \n",
       "min                     2.250600                        2.250600   \n",
       "25%                    25.111281                       18.471406   \n",
       "50%                    33.095012                       23.930775   \n",
       "75%                    44.602112                       29.030925   \n",
       "max                   437.097375                      101.567400   \n",
       "\n",
       "       unit_price_square_feet  \n",
       "count            6.854000e+03  \n",
       "mean             3.125569e+05  \n",
       "std              1.929742e+05  \n",
       "min              1.070744e+04  \n",
       "25%              1.771810e+05  \n",
       "50%              2.636116e+05  \n",
       "75%              3.929917e+05  \n",
       "max              2.016873e+06  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 移除主建物面積為 0 的資料\n",
    "df_analysis = df_analysis[ df_analysis['main_building_area']!=0 ]\n",
    "\n",
    "df_analysis.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 離群值判定 by IQR Method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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TuWvA/6DcRfnvRdtrlLsoP6xLi/L/o/SbRZlF+U1R+3eVuyj/TLTdqvR61Kro421JrWXUYJKekvSDvPaGqkXSakkro+3LJR2UdEej1ZFX0226tAbccHVI+pyk5bHtV5T+odiItRyU9DvR9s6ohrqto9rBt0npd+uHJD1UzXMVmMO/S3pP0kWlf0r1Kb1m8xNJb0aPrbHjH4rme1rRO59Re5ekE9G+x3TptwiXSdor6S2l3zm9NtbnT6L2tyT9cZl13KL0f2mOS3o1+tjUaLVI+oqkn0d1nJD0N1F7Q9WRV9NtuhTADVeH0munr0UfJxX9W23QWr4qaTD6/vovpcOwbuvgV5EBwAm/CQcATghgAHBCAAOAEwIYAJwQwADghAAGACcEMOqSmXWZ2Q+j7dvM7OYKjftFM9tXibGAcnEdMOqeme2UNBVC+H6Z4zSH9A1UgLrAK2DUhJl1Wu5N8f/KzHaa2c/M7BFL36T9DTO7Ndp/m5k9H9357T5JfxHdLPzWOcb/sZk9bmYHo3HuiNq3mtleM3tO6bt9ZecR3ZXt+9GNt4+b2fao/WtmdiC6M9hA5j4CQKU1e08AkNQcQlhrZpsk/a2k2zM7QgjvmNnjKu4VcKek35X0ZUk/NbPfjto3SPpKCOFs/C8YSNqm9D0AbgwhTEd/OeEySbsk/UEIYdTM/lDS3yv9a6ZARRHAqAeZO7sdVTpEF+qZEEJK0ptmNizpuqj9pRDC2VmOv13pG6tMS1IU0DdIukHSS9FfoWlS+l4iQMURwKiVaeUueS2LbZ+PHpMq73sy/w2NzPOP5zjeZuljkk6GEDaUMQ+gKKwBo1Z+LekLZnalmS1V+haUxZpU+u/gzeduM1tiZl9W+g5fp+c5/kVJ90X3dZWZtUZ9VpvZhqjtMjNbU8JcgaIRwKiJEMJFSX+n9J9Rel7SqRK6Pydpc6E34SKnJR1Q+v6t94UQzs0z7o8k/VLScTN7TdIfhRAuSNoi6ZGo7VVJFbkEDsjHZWhYFMzsx0rfk5drfNEweAUMAE54Ew4NxcweknR3XvPeEMJWh+kAZWEJAgCcsAQBAE4IYABwQgADgBMCGACc/D/sj/04yIYFSwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 盒鬚圖\n",
    "for f in [ i for i in num_features if i!='tx_dt_year']:\n",
    "    sns.boxplot( x=df_analysis[f], whis=1.5, orient='h', color='skyblue' )\n",
    "    plt.xlabel( f )\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "總價元(total_price)有 1 筆資料超過 2 億元\n"
     ]
    },
    {
     "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>city</th>\n",
       "      <th>towns</th>\n",
       "      <th>main_use</th>\n",
       "      <th>use_zoning</th>\n",
       "      <th>total_price</th>\n",
       "      <th>building_area_square_meter</th>\n",
       "      <th>main_building_area</th>\n",
       "      <th>tx_dt</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>room_number</th>\n",
       "      <th>hall_number</th>\n",
       "      <th>health_number</th>\n",
       "      <th>tx_dt_year</th>\n",
       "      <th>building_area_square_feet</th>\n",
       "      <th>main_building_area_square_feet</th>\n",
       "      <th>unit_price_square_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>749</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>士林區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>225000000</td>\n",
       "      <td>618.19</td>\n",
       "      <td>288.89</td>\n",
       "      <td>1090427</td>\n",
       "      <td>363966.0</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>109</td>\n",
       "      <td>187.002475</td>\n",
       "      <td>87.389225</td>\n",
       "      <td>1.203193e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city towns main_use use_zoning  total_price  \\\n",
       "749  Taipei   士林區      住家用          住    225000000   \n",
       "\n",
       "     building_area_square_meter  main_building_area    tx_dt  unit_price  \\\n",
       "749                      618.19              288.89  1090427    363966.0   \n",
       "\n",
       "     room_number  hall_number  health_number  tx_dt_year  \\\n",
       "749            4            2              5         109   \n",
       "\n",
       "     building_area_square_feet  main_building_area_square_feet  \\\n",
       "749                 187.002475                       87.389225   \n",
       "\n",
       "     unit_price_square_feet  \n",
       "749            1.203193e+06  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_2e8_index = df_analysis.index[ df_analysis['total_price']>2*(10**8) ]\n",
    "print( '總價元(total_price)有', len(over_2e8_index), '筆資料超過 2 億元' )\n",
    "\n",
    "df_analysis.loc[ over_2e8_index ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 移除上列資料，因為此筆資料的總價異常高，且其單價元平方公尺(unit_price) 363,966 亦遠高於平均約 94,548。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>total_price</th>\n",
       "      <th>building_area_square_meter</th>\n",
       "      <th>main_building_area</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>room_number</th>\n",
       "      <th>hall_number</th>\n",
       "      <th>health_number</th>\n",
       "      <th>tx_dt_year</th>\n",
       "      <th>building_area_square_feet</th>\n",
       "      <th>main_building_area_square_feet</th>\n",
       "      <th>unit_price_square_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.853000e+03</td>\n",
       "      <td>6853.000000</td>\n",
       "      <td>6853.000000</td>\n",
       "      <td>6853.000000</td>\n",
       "      <td>6853.000000</td>\n",
       "      <td>6853.000000</td>\n",
       "      <td>6853.000000</td>\n",
       "      <td>6853.0</td>\n",
       "      <td>6853.000000</td>\n",
       "      <td>6853.000000</td>\n",
       "      <td>6.853000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.138552e+07</td>\n",
       "      <td>121.695965</td>\n",
       "      <td>82.535618</td>\n",
       "      <td>94509.139209</td>\n",
       "      <td>2.849701</td>\n",
       "      <td>1.793083</td>\n",
       "      <td>1.732672</td>\n",
       "      <td>109.0</td>\n",
       "      <td>36.813029</td>\n",
       "      <td>24.967024</td>\n",
       "      <td>3.124269e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.132733e+07</td>\n",
       "      <td>62.566891</td>\n",
       "      <td>36.858926</td>\n",
       "      <td>58288.136556</td>\n",
       "      <td>0.898302</td>\n",
       "      <td>0.405125</td>\n",
       "      <td>0.679774</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.926485</td>\n",
       "      <td>11.149825</td>\n",
       "      <td>1.926881e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.100000e+05</td>\n",
       "      <td>7.440000</td>\n",
       "      <td>7.440000</td>\n",
       "      <td>3239.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2.250600</td>\n",
       "      <td>2.250600</td>\n",
       "      <td>1.070744e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.600000e+06</td>\n",
       "      <td>83.010000</td>\n",
       "      <td>61.060000</td>\n",
       "      <td>53595.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>25.110525</td>\n",
       "      <td>18.470650</td>\n",
       "      <td>1.771736e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.600000e+06</td>\n",
       "      <td>109.400000</td>\n",
       "      <td>79.090000</td>\n",
       "      <td>79737.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>33.093500</td>\n",
       "      <td>23.924725</td>\n",
       "      <td>2.635934e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.330000e+07</td>\n",
       "      <td>147.430000</td>\n",
       "      <td>95.940000</td>\n",
       "      <td>118871.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>44.597575</td>\n",
       "      <td>29.021850</td>\n",
       "      <td>3.929620e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.888800e+08</td>\n",
       "      <td>1444.950000</td>\n",
       "      <td>335.760000</td>\n",
       "      <td>610104.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>437.097375</td>\n",
       "      <td>101.567400</td>\n",
       "      <td>2.016873e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_price  building_area_square_meter  main_building_area  \\\n",
       "count  6.853000e+03                 6853.000000         6853.000000   \n",
       "mean   1.138552e+07                  121.695965           82.535618   \n",
       "std    1.132733e+07                   62.566891           36.858926   \n",
       "min    4.100000e+05                    7.440000            7.440000   \n",
       "25%    5.600000e+06                   83.010000           61.060000   \n",
       "50%    8.600000e+06                  109.400000           79.090000   \n",
       "75%    1.330000e+07                  147.430000           95.940000   \n",
       "max    1.888800e+08                 1444.950000          335.760000   \n",
       "\n",
       "          unit_price  room_number  hall_number  health_number  tx_dt_year  \\\n",
       "count    6853.000000  6853.000000  6853.000000    6853.000000      6853.0   \n",
       "mean    94509.139209     2.849701     1.793083       1.732672       109.0   \n",
       "std     58288.136556     0.898302     0.405125       0.679774         0.0   \n",
       "min      3239.000000     1.000000     1.000000       0.000000       109.0   \n",
       "25%     53595.000000     2.000000     2.000000       1.000000       109.0   \n",
       "50%     79737.000000     3.000000     2.000000       2.000000       109.0   \n",
       "75%    118871.000000     3.000000     2.000000       2.000000       109.0   \n",
       "max    610104.000000     5.000000     2.000000       6.000000       109.0   \n",
       "\n",
       "       building_area_square_feet  main_building_area_square_feet  \\\n",
       "count                6853.000000                     6853.000000   \n",
       "mean                   36.813029                       24.967024   \n",
       "std                    18.926485                       11.149825   \n",
       "min                     2.250600                        2.250600   \n",
       "25%                    25.110525                       18.470650   \n",
       "50%                    33.093500                       23.924725   \n",
       "75%                    44.597575                       29.021850   \n",
       "max                   437.097375                      101.567400   \n",
       "\n",
       "       unit_price_square_feet  \n",
       "count            6.853000e+03  \n",
       "mean             3.124269e+05  \n",
       "std              1.926881e+05  \n",
       "min              1.070744e+04  \n",
       "25%              1.771736e+05  \n",
       "50%              2.635934e+05  \n",
       "75%              3.929620e+05  \n",
       "max              2.016873e+06  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 移除總價元(total_price)超過 2 億元的資料\n",
    "df_analysis.drop( over_2e8_index[0], inplace=True )\n",
    "\n",
    "df_analysis.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "建物移轉總面積平方公尺(building_area_square_meter)有 1 筆資料超過 1000 平方公尺\n",
      "建物移轉總面積坪(building_area_square_feet)有 1 筆資料超過 300 坪\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>city</th>\n",
       "      <th>towns</th>\n",
       "      <th>main_use</th>\n",
       "      <th>use_zoning</th>\n",
       "      <th>total_price</th>\n",
       "      <th>building_area_square_meter</th>\n",
       "      <th>main_building_area</th>\n",
       "      <th>tx_dt</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>room_number</th>\n",
       "      <th>hall_number</th>\n",
       "      <th>health_number</th>\n",
       "      <th>tx_dt_year</th>\n",
       "      <th>building_area_square_feet</th>\n",
       "      <th>main_building_area_square_feet</th>\n",
       "      <th>unit_price_square_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5553</th>\n",
       "      <td>Taichung</td>\n",
       "      <td>龍井區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>4680000</td>\n",
       "      <td>1444.95</td>\n",
       "      <td>73.54</td>\n",
       "      <td>1090529</td>\n",
       "      <td>3239.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>437.097375</td>\n",
       "      <td>22.24585</td>\n",
       "      <td>10707.438017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          city towns main_use use_zoning  total_price  \\\n",
       "5553  Taichung   龍井區      住家用          住      4680000   \n",
       "\n",
       "      building_area_square_meter  main_building_area    tx_dt  unit_price  \\\n",
       "5553                     1444.95               73.54  1090529      3239.0   \n",
       "\n",
       "      room_number  hall_number  health_number  tx_dt_year  \\\n",
       "5553            3            2              2         109   \n",
       "\n",
       "      building_area_square_feet  main_building_area_square_feet  \\\n",
       "5553                 437.097375                        22.24585   \n",
       "\n",
       "      unit_price_square_feet  \n",
       "5553            10707.438017  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "over_1000_index = df_analysis.index[ df_analysis['building_area_square_meter']>1000 ]\n",
    "print( '建物移轉總面積平方公尺(building_area_square_meter)有', len(over_1000_index), '筆資料超過 1000 平方公尺' )\n",
    "\n",
    "over_300_index = df_analysis.index[ df_analysis['building_area_square_feet']>300 ]\n",
    "print( '建物移轉總面積坪(building_area_square_feet)有', len(over_300_index), '筆資料超過 300 坪' )\n",
    "\n",
    "df_analysis.loc[ over_1000_index ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 移除上列資料，因為此筆資料的坪數過大，但其單價元平方公尺(unit_price) 3,239 卻遠低於平均約 94,509。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_price</th>\n",
       "      <th>building_area_square_meter</th>\n",
       "      <th>main_building_area</th>\n",
       "      <th>unit_price</th>\n",
       "      <th>room_number</th>\n",
       "      <th>hall_number</th>\n",
       "      <th>health_number</th>\n",
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       "      <th>main_building_area_square_feet</th>\n",
       "      <th>unit_price_square_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>6.852000e+03</td>\n",
       "      <td>6852.000000</td>\n",
       "      <td>6852.000000</td>\n",
       "      <td>6852.000000</td>\n",
       "      <td>6852.000000</td>\n",
       "      <td>6852.000000</td>\n",
       "      <td>6852.000000</td>\n",
       "      <td>6852.0</td>\n",
       "      <td>6852.000000</td>\n",
       "      <td>6852.000000</td>\n",
       "      <td>6.852000e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.138650e+07</td>\n",
       "      <td>121.502846</td>\n",
       "      <td>82.536931</td>\n",
       "      <td>94522.459428</td>\n",
       "      <td>2.849679</td>\n",
       "      <td>1.793053</td>\n",
       "      <td>1.732633</td>\n",
       "      <td>109.0</td>\n",
       "      <td>36.754611</td>\n",
       "      <td>24.967422</td>\n",
       "      <td>3.124709e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.132787e+07</td>\n",
       "      <td>60.494352</td>\n",
       "      <td>36.861456</td>\n",
       "      <td>58281.958479</td>\n",
       "      <td>0.898365</td>\n",
       "      <td>0.405147</td>\n",
       "      <td>0.679815</td>\n",
       "      <td>0.0</td>\n",
       "      <td>18.299542</td>\n",
       "      <td>11.150590</td>\n",
       "      <td>1.926676e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.100000e+05</td>\n",
       "      <td>7.440000</td>\n",
       "      <td>7.440000</td>\n",
       "      <td>6768.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>2.250600</td>\n",
       "      <td>2.250600</td>\n",
       "      <td>2.237355e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.600000e+06</td>\n",
       "      <td>83.010000</td>\n",
       "      <td>61.045000</td>\n",
       "      <td>53601.750000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>25.110525</td>\n",
       "      <td>18.466113</td>\n",
       "      <td>1.771959e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.600000e+06</td>\n",
       "      <td>109.395000</td>\n",
       "      <td>79.110000</td>\n",
       "      <td>79742.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>33.091988</td>\n",
       "      <td>23.930775</td>\n",
       "      <td>2.636116e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.330000e+07</td>\n",
       "      <td>147.430000</td>\n",
       "      <td>95.950000</td>\n",
       "      <td>118874.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>44.597575</td>\n",
       "      <td>29.024875</td>\n",
       "      <td>3.929719e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.888800e+08</td>\n",
       "      <td>658.340000</td>\n",
       "      <td>335.760000</td>\n",
       "      <td>610104.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>109.0</td>\n",
       "      <td>199.147850</td>\n",
       "      <td>101.567400</td>\n",
       "      <td>2.016873e+06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        total_price  building_area_square_meter  main_building_area  \\\n",
       "count  6.852000e+03                 6852.000000         6852.000000   \n",
       "mean   1.138650e+07                  121.502846           82.536931   \n",
       "std    1.132787e+07                   60.494352           36.861456   \n",
       "min    4.100000e+05                    7.440000            7.440000   \n",
       "25%    5.600000e+06                   83.010000           61.045000   \n",
       "50%    8.600000e+06                  109.395000           79.110000   \n",
       "75%    1.330000e+07                  147.430000           95.950000   \n",
       "max    1.888800e+08                  658.340000          335.760000   \n",
       "\n",
       "          unit_price  room_number  hall_number  health_number  tx_dt_year  \\\n",
       "count    6852.000000  6852.000000  6852.000000    6852.000000      6852.0   \n",
       "mean    94522.459428     2.849679     1.793053       1.732633       109.0   \n",
       "std     58281.958479     0.898365     0.405147       0.679815         0.0   \n",
       "min      6768.000000     1.000000     1.000000       0.000000       109.0   \n",
       "25%     53601.750000     2.000000     2.000000       1.000000       109.0   \n",
       "50%     79742.500000     3.000000     2.000000       2.000000       109.0   \n",
       "75%    118874.000000     3.000000     2.000000       2.000000       109.0   \n",
       "max    610104.000000     5.000000     2.000000       6.000000       109.0   \n",
       "\n",
       "       building_area_square_feet  main_building_area_square_feet  \\\n",
       "count                6852.000000                     6852.000000   \n",
       "mean                   36.754611                       24.967422   \n",
       "std                    18.299542                       11.150590   \n",
       "min                     2.250600                        2.250600   \n",
       "25%                    25.110525                       18.466113   \n",
       "50%                    33.091988                       23.930775   \n",
       "75%                    44.597575                       29.024875   \n",
       "max                   199.147850                      101.567400   \n",
       "\n",
       "       unit_price_square_feet  \n",
       "count            6.852000e+03  \n",
       "mean             3.124709e+05  \n",
       "std              1.926676e+05  \n",
       "min              2.237355e+04  \n",
       "25%              1.771959e+05  \n",
       "50%              2.636116e+05  \n",
       "75%              3.929719e+05  \n",
       "max              2.016873e+06  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 移除建物移轉總面積坪(building_area_square_feet)超過 300 坪的資料\n",
    "df_analysis.drop( over_300_index[0], inplace=True )\n",
    "\n",
    "df_analysis.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uRcOUtYPAtpk"
   },
   "source": [
    "# 4. 找出台北市時價登入總價高度相關的變數"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "sMgZ6PbwAtpk"
   },
   "source": [
    "阿宏我是台北人他想找出影響台北市總價、單價元坪的因子<br>\n",
    "相關係數 0.3 以下為低相關，0.3\\~0.7 為中等相關，0.7 以上為高度相關<br>\n",
    "1. 資料切片切出 city 欄位為台北市的資料，並找出時價登入總價(total_price)高度相關的變數\n",
    "2. 資料切片切出 city 欄位為台北市的資料，找出單價元坪(unit_price_square_feet)高度相關的變數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>36000000</td>\n",
       "      <td>229.91</td>\n",
       "      <td>110.91</td>\n",
       "      <td>1090406</td>\n",
       "      <td>156583.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>69.547775</td>\n",
       "      <td>33.550275</td>\n",
       "      <td>517629.752066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>5580000</td>\n",
       "      <td>37.31</td>\n",
       "      <td>24.58</td>\n",
       "      <td>1090317</td>\n",
       "      <td>149558.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>109</td>\n",
       "      <td>11.286275</td>\n",
       "      <td>7.435450</td>\n",
       "      <td>494406.611570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>18280000</td>\n",
       "      <td>123.66</td>\n",
       "      <td>98.74</td>\n",
       "      <td>1090328</td>\n",
       "      <td>147825.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>37.407150</td>\n",
       "      <td>29.868850</td>\n",
       "      <td>488677.685950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>8000000</td>\n",
       "      <td>124.25</td>\n",
       "      <td>90.78</td>\n",
       "      <td>1090417</td>\n",
       "      <td>64386.0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>37.585625</td>\n",
       "      <td>27.460950</td>\n",
       "      <td>212846.280992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Taipei</td>\n",
       "      <td>文山區</td>\n",
       "      <td>住家用</td>\n",
       "      <td>住</td>\n",
       "      <td>2200000</td>\n",
       "      <td>30.16</td>\n",
       "      <td>26.15</td>\n",
       "      <td>1090417</td>\n",
       "      <td>72944.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>109</td>\n",
       "      <td>9.123400</td>\n",
       "      <td>7.910375</td>\n",
       "      <td>241137.190083</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     city towns main_use use_zoning  total_price  building_area_square_meter  \\\n",
       "0  Taipei   文山區      住家用          住     36000000                      229.91   \n",
       "1  Taipei   文山區      住家用          住      5580000                       37.31   \n",
       "2  Taipei   文山區      住家用          住     18280000                      123.66   \n",
       "3  Taipei   文山區      住家用          住      8000000                      124.25   \n",
       "4  Taipei   文山區      住家用          住      2200000                       30.16   \n",
       "\n",
       "   main_building_area    tx_dt  unit_price  room_number  hall_number  \\\n",
       "0              110.91  1090406    156583.0            3            2   \n",
       "1               24.58  1090317    149558.0            2            1   \n",
       "2               98.74  1090328    147825.0            3            2   \n",
       "3               90.78  1090417     64386.0            3            1   \n",
       "4               26.15  1090417     72944.0            5            2   \n",
       "\n",
       "   health_number  tx_dt_year  building_area_square_feet  \\\n",
       "0              2         109                  69.547775   \n",
       "1              1         109                  11.286275   \n",
       "2              2         109                  37.407150   \n",
       "3              2         109                  37.585625   \n",
       "4              2         109                   9.123400   \n",
       "\n",
       "   main_building_area_square_feet  unit_price_square_feet  \n",
       "0                       33.550275           517629.752066  \n",
       "1                        7.435450           494406.611570  \n",
       "2                       29.868850           488677.685950  \n",
       "3                       27.460950           212846.280992  \n",
       "4                        7.910375           241137.190083  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 資料切片切出 city 欄位為台北市的資料\n",
    "\n",
    "df_taipei = df_analysis[ df_analysis['city']=='Taipei' ]\n",
    "df_taipei.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
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       "      <th>total_price</th>\n",
       "      <th>building_area_square_meter</th>\n",
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       "      <th>hall_number</th>\n",
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      "text/plain": [
       "                                total_price  building_area_square_meter  \\\n",
       "total_price                        1.000000                    0.862308   \n",
       "building_area_square_meter         0.862308                    1.000000   \n",
       "main_building_area                 0.705892                    0.833087   \n",
       "unit_price                         0.582932                    0.207651   \n",
       "room_number                        0.329024                    0.488802   \n",
       "hall_number                        0.277600                    0.384049   \n",
       "health_number                      0.588484                    0.692117   \n",
       "tx_dt_year                              NaN                         NaN   \n",
       "building_area_square_feet          0.862308                    1.000000   \n",
       "main_building_area_square_feet     0.705892                    0.833087   \n",
       "unit_price_square_feet             0.582932                    0.207651   \n",
       "\n",
       "                                main_building_area  unit_price  room_number  \\\n",
       "total_price                               0.705892    0.582932     0.329024   \n",
       "building_area_square_meter                0.833087    0.207651     0.488802   \n",
       "main_building_area                        1.000000    0.102358     0.636596   \n",
       "unit_price                                0.102358    1.000000    -0.103544   \n",
       "room_number                               0.636596   -0.103544     1.000000   \n",
       "hall_number                               0.469032    0.003756     0.509411   \n",
       "health_number                             0.652984    0.118247     0.545231   \n",
       "tx_dt_year                                     NaN         NaN          NaN   \n",
       "building_area_square_feet                 0.833087    0.207651     0.488802   \n",
       "main_building_area_square_feet            1.000000    0.102358     0.636596   \n",
       "unit_price_square_feet                    0.102358    1.000000    -0.103544   \n",
       "\n",
       "                                hall_number  health_number  tx_dt_year  \\\n",
       "total_price                        0.277600       0.588484         NaN   \n",
       "building_area_square_meter         0.384049       0.692117         NaN   \n",
       "main_building_area                 0.469032       0.652984         NaN   \n",
       "unit_price                         0.003756       0.118247         NaN   \n",
       "room_number                        0.509411       0.545231         NaN   \n",
       "hall_number                        1.000000       0.407677         NaN   \n",
       "health_number                      0.407677       1.000000         NaN   \n",
       "tx_dt_year                              NaN            NaN         NaN   \n",
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       "main_building_area_square_feet     0.469032       0.652984         NaN   \n",
       "unit_price_square_feet             0.003756       0.118247         NaN   \n",
       "\n",
       "                                building_area_square_feet  \\\n",
       "total_price                                      0.862308   \n",
       "building_area_square_meter                       1.000000   \n",
       "main_building_area                               0.833087   \n",
       "unit_price                                       0.207651   \n",
       "room_number                                      0.488802   \n",
       "hall_number                                      0.384049   \n",
       "health_number                                    0.692117   \n",
       "tx_dt_year                                            NaN   \n",
       "building_area_square_feet                        1.000000   \n",
       "main_building_area_square_feet                   0.833087   \n",
       "unit_price_square_feet                           0.207651   \n",
       "\n",
       "                                main_building_area_square_feet  \\\n",
       "total_price                                           0.705892   \n",
       "building_area_square_meter                            0.833087   \n",
       "main_building_area                                    1.000000   \n",
       "unit_price                                            0.102358   \n",
       "room_number                                           0.636596   \n",
       "hall_number                                           0.469032   \n",
       "health_number                                         0.652984   \n",
       "tx_dt_year                                                 NaN   \n",
       "building_area_square_feet                             0.833087   \n",
       "main_building_area_square_feet                        1.000000   \n",
       "unit_price_square_feet                                0.102358   \n",
       "\n",
       "                                unit_price_square_feet  \n",
       "total_price                                   0.582932  \n",
       "building_area_square_meter                    0.207651  \n",
       "main_building_area                            0.102358  \n",
       "unit_price                                    1.000000  \n",
       "room_number                                  -0.103544  \n",
       "hall_number                                   0.003756  \n",
       "health_number                                 0.118247  \n",
       "tx_dt_year                                         NaN  \n",
       "building_area_square_feet                     0.207651  \n",
       "main_building_area_square_feet                0.102358  \n",
       "unit_price_square_feet                        1.000000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 時價登入中，各變數間的相關係數\n",
    "correlation = df_taipei.corr()#[['total_price','unit_price_square_feet']]\n",
    "correlation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                                total_price\n",
       "total_price                        1.000000\n",
       "building_area_square_meter         0.862308\n",
       "building_area_square_feet          0.862308\n",
       "main_building_area                 0.705892\n",
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     "execution_count": 31,
     "metadata": {},
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   ],
   "source": [
    "# 時價登入中，與總價(total_price)高度相關的變數\n",
    "high_corr_1 = correlation[ ['total_price'] ].sort_values( by='total_price',  ascending=False )\n",
    "high_corr_1[ high_corr_1['total_price']>0.7 ]"
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  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>unit_price_square_feet</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>unit_price</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unit_price_square_feet</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        unit_price_square_feet\n",
       "unit_price                                 1.0\n",
       "unit_price_square_feet                     1.0"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 時價登入中，與單價元坪(unit_price_square_feet)高度相關的變數\n",
    "high_corr_2 = correlation[['unit_price_square_feet']].sort_values( by='unit_price_square_feet',  ascending=False )\n",
    "high_corr_2[ high_corr_2['unit_price_square_feet']>0.7 ]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-y15EpRMAtpm"
   },
   "source": [
    "# 5. 資料視覺化並解釋\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "TuAbtSFfAtpn"
   },
   "source": [
    "我想要以視覺化的方式來看房價資料，並且請各位資料科學家解釋圖表給阿宏我知道\n",
    "1. 以城市(city)為 x 軸，以單價元坪(unit_price_square_feet)為 y 軸畫出 boxplot，並找出單價元坪(unit_price_square_feet)中位數最高的地區\n",
    "2. 進一步對台北市的資料做圖，以建物現況格局-房(room_number)為 x 軸，以總價元(total_price)為 y 軸畫出 boxplot，並找出總價元(total_price)中位數最高的房間數。hint:資料切片找出city欄位為台北市的資料，再進一步畫圖\n",
    "3. 對台北市的資料做圖，先將地區(twons)做編碼在進行，再以地區(twon)為 x 軸，以單價元坪(unit_price_square_feet)為 y 軸畫出boxplot，並找出單價元坪(unit_price_square_feet)中位數最高的地區。hint:運用LabelEncoder()對地區(twons)做編碼，運用.inverse_transform()反查編碼的地區"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "AeGKqgwIAtpn",
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 1.以城市(city)為 x 軸，以單價元坪(unit_price_square_feet)為 y 軸畫出boxplot，並找出單價元坪(unit_price_square_feet)中位數最高的地區。\n",
    "\n",
    "fig = plt.figure( figsize=(8,5) )\n",
    "sns.boxplot( x='city', y='unit_price_square_feet', data=df_analysis, whis=1.5, color='skyblue' )\n",
    "plt.xlabel( 'City' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "2_sM72QLAtpn"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2.進一步對台北市的資料做圖，以建物現況格局-房(room_number)為 x 軸，以總價元(total_price)為 y 軸畫出 boxplot，並找出總價元(total_price)中位數最高的房間數。\n",
    "\n",
    "fig = plt.figure( figsize=(8,5) )\n",
    "sns.boxplot( x='room_number', y='total_price', data=df_taipei, whis=1.5, color='skyblue' )\n",
    "plt.xlabel( 'Room Number' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "PatbpuqLAtpo"
   },
   "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>towns</th>\n",
       "      <th>unit_price_square_feet</th>\n",
       "      <th>Label_towns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>文山區</td>\n",
       "      <td>517629.752066</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>文山區</td>\n",
       "      <td>494406.611570</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>文山區</td>\n",
       "      <td>488677.685950</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>文山區</td>\n",
       "      <td>212846.280992</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>文山區</td>\n",
       "      <td>241137.190083</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1373</th>\n",
       "      <td>萬華區</td>\n",
       "      <td>561590.082645</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1374</th>\n",
       "      <td>內湖區</td>\n",
       "      <td>494479.338843</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1375</th>\n",
       "      <td>大安區</td>\n",
       "      <td>817047.933884</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1376</th>\n",
       "      <td>信義區</td>\n",
       "      <td>465831.404959</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1377</th>\n",
       "      <td>中山區</td>\n",
       "      <td>456707.438017</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1375 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     towns  unit_price_square_feet  Label_towns\n",
       "0      文山區           517629.752066            9\n",
       "1      文山區           494406.611570            9\n",
       "2      文山區           488677.685950            9\n",
       "3      文山區           212846.280992            9\n",
       "4      文山區           241137.190083            9\n",
       "...    ...                     ...          ...\n",
       "1373   萬華區           561590.082645           11\n",
       "1374   內湖區           494479.338843            3\n",
       "1375   大安區           817047.933884            8\n",
       "1376   信義區           465831.404959            2\n",
       "1377   中山區           456707.438017            0\n",
       "\n",
       "[1375 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3.對台北市的資料做圖，先將地區(twons)做編碼在進行，再以地區(twon)為 x 軸，以單價元坪(unit_price_square_feet)為 y 軸畫出boxplot，並找出單價元坪(unit_price_square_feet)中位數最高的地區。\n",
    "\n",
    "from sklearn.preprocessing import LabelEncoder\n",
    "\n",
    "df_taipei_town = df_taipei[['towns','unit_price_square_feet']].copy()\n",
    "df_taipei_town['Label_towns'] = LabelEncoder().fit_transform( df_taipei_town['towns'] )\n",
    "df_taipei_town"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure( figsize=(10,5) )\n",
    "sns.boxplot( x='Label_towns', y='unit_price_square_feet', data=df_taipei_town, whis=1.5, color='skyblue' )\n",
    "plt.xlabel( 'Towns(Label)' )\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "單價元坪(unit_price_square_feet)中位數最高的地區為 大安區\n"
     ]
    }
   ],
   "source": [
    "le = LabelEncoder().fit( df_taipei_town['towns'] )\n",
    "print( '單價元坪(unit_price_square_feet)中位數最高的地區為', le.inverse_transform([8])[0] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Label 0 : 中山區\n",
      "Label 1 : 中正區\n",
      "Label 2 : 信義區\n",
      "Label 3 : 內湖區\n",
      "Label 4 : 北投區\n",
      "Label 5 : 南港區\n",
      "Label 6 : 士林區\n",
      "Label 7 : 大同區\n",
      "Label 8 : 大安區\n",
      "Label 9 : 文山區\n",
      "Label 10 : 松山區\n",
      "Label 11 : 萬華區\n"
     ]
    }
   ],
   "source": [
    "for i in np.arange(12):\n",
    "    print( 'Label', i, ':', le.inverse_transform([i])[0] )"
   ]
  }
 ],
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