join.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import shutil\n",
    "from math import cos, pow, sqrt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('C:\\Python\\Python36-32\\examples\\_all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('./spec/house_all.csv')\n",
    "first= df.groupby('state_code').median().reset_index()[['state_code', 'unitPrice']]\n",
    "first.columns = ['state_code', 'house_unitPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .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>state_code</th>\n",
       "      <th>house_unitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>552226718</td>\n",
       "      <td>717000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>552326738</td>\n",
       "      <td>341000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>552326739</td>\n",
       "      <td>436000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>552326740</td>\n",
       "      <td>406000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>552326741</td>\n",
       "      <td>480000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state_code  house_unitPrice\n",
       "0   552226718         717000.0\n",
       "1   552326738         341000.0\n",
       "2   552326739         436000.0\n",
       "3   552326740         406000.0\n",
       "4   552326741         480000.0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "506\n",
      "503\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('./spec/lease_all.csv')\n",
    "lease= df.groupby('state_code').median().reset_index()[['state_code', 'unitPrice']]\n",
    "print(len(lease))\n",
    "lease = lease[lease['state_code']>550000000]\n",
    "print(len(lease))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "lease.columns = ['state_code', 'lease_unitPrice']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>state_code</th>\n",
       "      <th>lease_unitPrice</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>552226729</td>\n",
       "      <td>1250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>552326742</td>\n",
       "      <td>870.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>552426745</td>\n",
       "      <td>799.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>552426754</td>\n",
       "      <td>1142.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>552426757</td>\n",
       "      <td>937.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state_code  lease_unitPrice\n",
       "3   552226729           1250.0\n",
       "4   552326742            870.5\n",
       "5   552426745            799.0\n",
       "6   552426754           1142.0\n",
       "7   552426757            937.0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lease.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = pd.merge(first ,lease, how='outer', on='state_code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_code</th>\n",
       "      <th>house_unitPrice</th>\n",
       "      <th>lease_unitPrice</th>\n",
       "      <th>center</th>\n",
       "      <th>center_lat</th>\n",
       "      <th>center_lon</th>\n",
       "      <th>cit</th>\n",
       "      <th>dist</th>\n",
       "      <th>vil</th>\n",
       "      <th>restaurant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>552226718</td>\n",
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       "      <td>(24.97196447627833, 121.57556719359505)</td>\n",
       "      <td>24.971964</td>\n",
       "      <td>121.575567</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>老泉里</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>552326738</td>\n",
       "      <td>341000.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>24.976486</td>\n",
       "      <td>121.578657</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>552326739</td>\n",
       "      <td>436000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5832037836341)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.583204</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>552326740</td>\n",
       "      <td>406000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5877507349153)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.587751</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>552326741</td>\n",
       "      <td>480000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5922976861965)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.592298</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>552326742</td>\n",
       "      <td>368000.0</td>\n",
       "      <td>870.5</td>\n",
       "      <td>(24.976486334943115, 121.5968446374777)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.596845</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>政大里</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>552426752</td>\n",
       "      <td>519000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.55696370215156)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.556964</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>樟文里</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>552426754</td>\n",
       "      <td>456000.0</td>\n",
       "      <td>1142.0</td>\n",
       "      <td>(24.981008193607902, 121.56605122396837)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.566051</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>樟腳里</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>552426755</td>\n",
       "      <td>517000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.57059498487678)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.570595</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>老泉里</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>552426756</td>\n",
       "      <td>399000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.57513874578518)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.575139</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>552426757</td>\n",
       "      <td>496000.0</td>\n",
       "      <td>937.0</td>\n",
       "      <td>(24.981008193607902, 121.57968250669359)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.579683</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>552426758</td>\n",
       "      <td>439500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.58422626760199)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.584226</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>552426759</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.58877002851041)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.588770</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>552426760</td>\n",
       "      <td>383000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.59331378941881)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.593314</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>政大里</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>552426761</td>\n",
       "      <td>491500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.59785755032722)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.597858</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>政大里</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state_code  house_unitPrice  lease_unitPrice  \\\n",
       "0    552226718         717000.0              NaN   \n",
       "1    552326738         341000.0              NaN   \n",
       "2    552326739         436000.0              NaN   \n",
       "3    552326740         406000.0              NaN   \n",
       "4    552326741         480000.0              NaN   \n",
       "5    552326742         368000.0            870.5   \n",
       "6    552426752         519000.0              NaN   \n",
       "7    552426754         456000.0           1142.0   \n",
       "8    552426755         517000.0              NaN   \n",
       "9    552426756         399000.0              NaN   \n",
       "10   552426757         496000.0            937.0   \n",
       "11   552426758         439500.0              NaN   \n",
       "12   552426759         427500.0              NaN   \n",
       "13   552426760         383000.0              NaN   \n",
       "14   552426761         491500.0              NaN   \n",
       "\n",
       "                                      center  center_lat  center_lon  cit  \\\n",
       "0    (24.97196447627833, 121.57556719359505)   24.971964  121.575567  台北市   \n",
       "1    (24.976486334943115, 121.5786568323529)   24.976486  121.578657  台北市   \n",
       "2    (24.976486334943115, 121.5832037836341)   24.976486  121.583204  台北市   \n",
       "3    (24.976486334943115, 121.5877507349153)   24.976486  121.587751  台北市   \n",
       "4    (24.976486334943115, 121.5922976861965)   24.976486  121.592298  台北市   \n",
       "5    (24.976486334943115, 121.5968446374777)   24.976486  121.596845  台北市   \n",
       "6   (24.981008193607902, 121.55696370215156)   24.981008  121.556964  台北市   \n",
       "7   (24.981008193607902, 121.56605122396837)   24.981008  121.566051  台北市   \n",
       "8   (24.981008193607902, 121.57059498487678)   24.981008  121.570595  台北市   \n",
       "9   (24.981008193607902, 121.57513874578518)   24.981008  121.575139  台北市   \n",
       "10  (24.981008193607902, 121.57968250669359)   24.981008  121.579683  台北市   \n",
       "11  (24.981008193607902, 121.58422626760199)   24.981008  121.584226  台北市   \n",
       "12  (24.981008193607902, 121.58877002851041)   24.981008  121.588770  台北市   \n",
       "13  (24.981008193607902, 121.59331378941881)   24.981008  121.593314  台北市   \n",
       "14  (24.981008193607902, 121.59785755032722)   24.981008  121.597858  台北市   \n",
       "\n",
       "   dist  vil  restaurant  \n",
       "0   文山區  老泉里           1  \n",
       "1   文山區  指南里           4  \n",
       "2   文山區  指南里           3  \n",
       "3   文山區  指南里           7  \n",
       "4   文山區  指南里           5  \n",
       "5   文山區  政大里           4  \n",
       "6   文山區  樟文里          52  \n",
       "7   文山區  樟腳里         101  \n",
       "8   文山區  老泉里          61  \n",
       "9   文山區  指南里          78  \n",
       "10  文山區  指南里          24  \n",
       "11  文山區  指南里           3  \n",
       "12  文山區  指南里           0  \n",
       "13  文山區  政大里           1  \n",
       "14  文山區  政大里           1  "
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.head(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = []\n",
    "for root, dirs, files in os.walk('.', topdown=False):\n",
    "    for name in files:\n",
    "#             print(os.path.join(root, name))\n",
    "#         fullname = os.path.join(root, name)\n",
    "        if name.endswith('.csv'):\n",
    "            data.append(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ['106_traffic_done.csv',\n",
    " 'cloth_done.csv',\n",
    " 'convStore_done.csv',\n",
    " 'education_done.csv',\n",
    " 'edu_all_statecode.csv',\n",
    " 'hardware_done.csv',\n",
    " 'hsp_and_clc.csv',\n",
    " 'illegal_don.csv',\n",
    " 'job_stat_code.csv',\n",
    " 'mrt.csv',\n",
    " 'night_club.csv',\n",
    " 'park.csv',\n",
    " 'taxtion_done.csv',\n",
    " 'ubike.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>year</th>\n",
       "      <th>area</th>\n",
       "      <th>village</th>\n",
       "      <th>master</th>\n",
       "      <th>college</th>\n",
       "      <th>highSchool</th>\n",
       "      <th>secondary</th>\n",
       "      <th>total</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>state_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>106</td>\n",
       "      <td>松山區</td>\n",
       "      <td>莊敬里</td>\n",
       "      <td>0.081176</td>\n",
       "      <td>0.289560</td>\n",
       "      <td>0.456843</td>\n",
       "      <td>0.172421</td>\n",
       "      <td>4866</td>\n",
       "      <td>25.072157</td>\n",
       "      <td>121.566969</td>\n",
       "      <td>554427016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>106</td>\n",
       "      <td>松山區</td>\n",
       "      <td>東榮里</td>\n",
       "      <td>0.158182</td>\n",
       "      <td>0.387064</td>\n",
       "      <td>0.384340</td>\n",
       "      <td>0.070414</td>\n",
       "      <td>6973</td>\n",
       "      <td>25.060422</td>\n",
       "      <td>121.557694</td>\n",
       "      <td>554226992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106</td>\n",
       "      <td>松山區</td>\n",
       "      <td>三民里</td>\n",
       "      <td>0.134777</td>\n",
       "      <td>0.383161</td>\n",
       "      <td>0.392696</td>\n",
       "      <td>0.089367</td>\n",
       "      <td>6188</td>\n",
       "      <td>25.059660</td>\n",
       "      <td>121.562332</td>\n",
       "      <td>554126992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>106</td>\n",
       "      <td>松山區</td>\n",
       "      <td>新益里</td>\n",
       "      <td>0.103236</td>\n",
       "      <td>0.324120</td>\n",
       "      <td>0.437751</td>\n",
       "      <td>0.134893</td>\n",
       "      <td>4233</td>\n",
       "      <td>25.062045</td>\n",
       "      <td>121.567683</td>\n",
       "      <td>554226998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>106</td>\n",
       "      <td>松山區</td>\n",
       "      <td>富錦里</td>\n",
       "      <td>0.128194</td>\n",
       "      <td>0.364991</td>\n",
       "      <td>0.403961</td>\n",
       "      <td>0.102853</td>\n",
       "      <td>4696</td>\n",
       "      <td>25.060066</td>\n",
       "      <td>121.564829</td>\n",
       "      <td>554126993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year area village    master   college  highSchool  secondary  total  \\\n",
       "0   106  松山區     莊敬里  0.081176  0.289560    0.456843   0.172421   4866   \n",
       "1   106  松山區     東榮里  0.158182  0.387064    0.384340   0.070414   6973   \n",
       "2   106  松山區     三民里  0.134777  0.383161    0.392696   0.089367   6188   \n",
       "3   106  松山區     新益里  0.103236  0.324120    0.437751   0.134893   4233   \n",
       "4   106  松山區     富錦里  0.128194  0.364991    0.403961   0.102853   4696   \n",
       "\n",
       "         lat         lon  state_code  \n",
       "0  25.072157  121.566969   554427016  \n",
       "1  25.060422  121.557694   554226992  \n",
       "2  25.059660  121.562332   554126992  \n",
       "3  25.062045  121.567683   554226998  \n",
       "4  25.060066  121.564829   554126993  "
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.read_csv('education_done.csv').head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "for name in data:\n",
    "    df = pd.read_csv(name)\n",
    "    df_g = df.groupby('state_code').count().reset_index()[['state_code', 'lat']]\n",
    "    df_g.columns = ['state_code', name[:-4]]\n",
    "    f = pd.merge(f, df_g, how='outer', on='state_code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13f13e30>"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(f.isnull(),yticklabels=False,cbar=False,cmap='viridis')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "from main import centerProducer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# PRODUCE COLUMNS WHICH ARE LAT AND LON OF CENTER\n",
    "f['center'] = f['state_code'].apply(str).apply(centerProducer)\n",
    "f['center_lat'] = f['center'].apply(lambda x:x[0])\n",
    "f['center_lon'] = f['center'].apply(lambda x:x[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>state_code</th>\n",
       "      <th>house_unitPrice</th>\n",
       "      <th>lease_unitPrice</th>\n",
       "      <th>center</th>\n",
       "      <th>center_lat</th>\n",
       "      <th>center_lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>552226718</td>\n",
       "      <td>717000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.97196447627833, 121.57556719359505)</td>\n",
       "      <td>24.971964</td>\n",
       "      <td>121.575567</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>552326738</td>\n",
       "      <td>341000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5786568323529)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.578657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>552326739</td>\n",
       "      <td>436000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5832037836341)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.583204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>552326740</td>\n",
       "      <td>406000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5877507349153)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.587751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>552326741</td>\n",
       "      <td>480000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5922976861965)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.592298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state_code  house_unitPrice  lease_unitPrice  \\\n",
       "0   552226718         717000.0              NaN   \n",
       "1   552326738         341000.0              NaN   \n",
       "2   552326739         436000.0              NaN   \n",
       "3   552326740         406000.0              NaN   \n",
       "4   552326741         480000.0              NaN   \n",
       "\n",
       "                                    center  center_lat  center_lon  \n",
       "0  (24.97196447627833, 121.57556719359505)   24.971964  121.575567  \n",
       "1  (24.976486334943115, 121.5786568323529)   24.976486  121.578657  \n",
       "2  (24.976486334943115, 121.5832037836341)   24.976486  121.583204  \n",
       "3  (24.976486334943115, 121.5877507349153)   24.976486  121.587751  \n",
       "4  (24.976486334943115, 121.5922976861965)   24.976486  121.592298  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "import googlemaps\n",
    "class googleUClient:\n",
    "    def __init__(self,key):\n",
    "        self.gmaps = googlemaps.Client(key=key)\n",
    "    def getInformation(self,center):\n",
    "        res = self.gmaps.reverse_geocode(center,language='zh-TW')\n",
    "        res_str = str(res)\n",
    "        try:\n",
    "            political_1 = re.findall(\"'(..里)\", res_str)[0]\n",
    "            political_2 = re.findall(\"'(..區)\", res_str)[0]\n",
    "            political_3 = re.findall(\"'(..市)\", res_str)[0]\n",
    "            print(political_3)\n",
    "            return {'市': political_3,'區': political_2,'里': political_1}\n",
    "        except:\n",
    "            political_1 = None\n",
    "            political_2 = None\n",
    "            political_3 = None\n",
    "            print(res)\n",
    "            return {'市': political_3,'區': political_2,'里': political_1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "key = 'AIzaSyDebea1g0MYe_AM44BrW5IkEwVIQrjzof4'\n",
    "geoFunction = googleUClient(key).getInformation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "台北市\n",
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      "台北市\n",
      "基隆市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "基隆市\n",
      "新北市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "基隆市\n",
      "基隆市\n",
      "台北市\n",
      "台北市\n",
      "基隆市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n",
      "台北市\n"
     ]
    }
   ],
   "source": [
    "f['cit'] = f['center'].apply(lambda x: geoFunction(x)['市'])\n",
    "f['dist'] = f['center'].apply(lambda x: geoFunction(x)['區'])\n",
    "f['vil'] = f['center'].apply(lambda x: geoFunction(x)['里'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "873\n",
      "705\n"
     ]
    }
   ],
   "source": [
    "print(len(f))\n",
    "f = f[f['cit'] == '台北市']\n",
    "print(len(f))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.to_csv('numeric.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = pd.read_csv('numeric.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "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>state_code</th>\n",
       "      <th>house_unitPrice</th>\n",
       "      <th>lease_unitPrice</th>\n",
       "      <th>center</th>\n",
       "      <th>center_lat</th>\n",
       "      <th>center_lon</th>\n",
       "      <th>cit</th>\n",
       "      <th>dist</th>\n",
       "      <th>vil</th>\n",
       "      <th>km_x</th>\n",
       "      <th>km_y</th>\n",
       "      <th>restaurant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>552226718</td>\n",
       "      <td>717000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.97196447627833, 121.57556719359505)</td>\n",
       "      <td>24.971964</td>\n",
       "      <td>121.575567</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>老泉里</td>\n",
       "      <td>2761.25</td>\n",
       "      <td>13359.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state_code  house_unitPrice  lease_unitPrice  \\\n",
       "0   552226718         717000.0              NaN   \n",
       "\n",
       "                                    center  center_lat  center_lon  cit dist  \\\n",
       "0  (24.97196447627833, 121.57556719359505)   24.971964  121.575567  台北市  文山區   \n",
       "\n",
       "   vil     km_x      km_y  restaurant  \n",
       "0  老泉里  2761.25  13359.25           1  "
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "obj_df = pd.read_csv('restaurant_statcode.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "36693\n",
      "24831\n"
     ]
    }
   ],
   "source": [
    "print(len(obj_df))\n",
    "obj_df = obj_df[obj_df['status'] == '營業中']\n",
    "print(len(obj_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {},
   "outputs": [],
   "source": [
    "f_list = list(zip(f['center_lat'].tolist(),f['center_lon'].tolist()))\n",
    "obj_df_list = list(zip(obj_df['lat'].tolist(),obj_df['lon'].tolist()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(24.97196447627833, 121.57556719359505), (24.976486334943115, 121.5786568323529), (24.976486334943115, 121.58320378363409), (24.976486334943115, 121.58775073491529), (24.976486334943115, 121.5922976861965), (24.976486334943115, 121.5968446374777), (24.981008193607906, 121.55696370215156), (24.981008193607906, 121.56605122396836), (24.981008193607906, 121.57059498487679), (24.981008193607906, 121.57513874578518), (24.981008193607906, 121.57968250669359), (24.981008193607906, 121.584226267602), (24.981008193607906, 121.5887700285104), (24.981008193607906, 121.59331378941879), (24.981008193607906, 121.59785755032722)] 705 \n",
      " [(25.04153200644, 121.57863701133002), (25.051292417670002, 121.53433980168998), (25.039275700799998, 121.55518730441), (25.059803312550002, 121.61588460147), (25.04499500638, 121.58313600436001), (25.03745090875, 121.55171631351999), (25.050285011529997, 121.5715330108), (25.01715601696, 121.53252901423001), (25.051415513330003, 121.54191390173), (25.04530350072, 121.50486820353998), (25.04997800038, 121.57214571873999), (25.04146281940001, 121.56449931658999), (25.02992050967, 121.55402161437999), (24.99034901763, 121.54123000167), (25.029382806470004, 121.55708691051001)] 24831\n"
     ]
    }
   ],
   "source": [
    "print(f_list[:15],len(f_list),'\\n',obj_df_list[:15],len(obj_df_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "def lat_lon_to_km(lat,lon):\n",
    "    lat_to_km = lat * 110.574\n",
    "    lon_to_km = abs(lon * (111.320 * cos(lat)))\n",
    "    return (lat_to_km,lon_to_km)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def dis(x):\n",
    "    return round(sqrt(pow(x[0],2)+pow(x[1],2)),3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "sta = []\n",
    "for i in h_list:\n",
    "    count = 0\n",
    "    for j in res_list:\n",
    "        x_dis = i[0] - j[0]\n",
    "        y_dis = i[1] - j[1]\n",
    "        distance = dis(lat_lon_to_km(x_dis,y_dis))\n",
    "        if distance <= 1.5:\n",
    "            count+=1\n",
    "    sta.append(count)\n",
    "    print(f_list.index(i),count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[27, 134, 77, 48, 39, 31, 217, 303, 302, 240, 167, 109, 41, 19, 7, 333, 249, 280, 329, 316, 274, 200, 121, 63, 8, 2, 365, 385, 514, 570, 393, 391, 354, 322, 229, 169, 114, 44, 2, 1, 0, 382, 449, 526, 515, 363, 379, 382, 283, 200, 158, 121, 33, 5, 0, 409, 414, 508, 521, 309, 228, 223, 244, 182, 134, 115, 21, 9, 1, 658, 592, 516, 374, 224, 168, 151, 116, 30, 27, 20, 12, 9, 741, 752, 635, 423, 313, 264, 173, 102, 14, 1049, 1093, 942, 1000, 481, 333, 83, 200, 392, 1311, 1378, 1433, 1443, 1385, 1289, 901, 900, 767, 497, 275, 166, 58, 22, 2, 1, 0, 287, 499, 551, 596, 779, 1202, 1741, 1750, 1922, 1907, 1872, 1597, 1691, 1647, 1466, 979, 540, 272, 111, 37, 22, 1, 0, 74, 198, 453, 762, 913, 1001, 1066, 988, 1464, 1629, 1884, 2069, 2209, 2225, 1993, 2324, 2474, 2195, 1951, 1460, 918, 456, 233, 110, 10, 1, 378, 650, 986, 1297, 1710, 1685, 1385, 1646, 1683, 1947, 2112, 2388, 2782, 3429, 3836, 3680, 3352, 2645, 2125, 1685, 883, 407, 251, 7, 3, 337, 574, 1237, 1720, 2070, 2533, 2747, 2367, 2187, 1882, 2015, 2705, 3473, 4075, 4219, 4882, 4095, 3359, 2517, 2113, 1653, 632, 493, 219, 32, 6, 6, 25, 28, 24, 11, 2, 2, 354, 822, 1487, 1833, 2519, 2937, 3021, 3175, 3047, 2091, 2767, 3250, 3935, 4234, 4523, 5176, 4353, 3623, 2928, 2249, 1663, 816, 567, 202, 82, 84, 93, 78, 48, 1276, 1758, 2550, 2957, 3534, 3551, 3368, 3091, 3176, 3560, 4228, 4282, 4598, 4987, 4036, 3697, 2891, 2371, 1457, 1013, 719, 249, 209, 194, 217, 240, 221, 2304, 2824, 3487, 3770, 3748, 3637, 3323, 3753, 3956, 4309, 4406, 4472, 4004, 3411, 2867, 2455, 1767, 1148, 871, 434, 261, 221, 285, 289, 291, 1919, 2917, 3619, 3669, 4068, 3943, 3584, 3566, 3639, 3719, 3806, 3692, 2707, 2359, 2203, 1813, 1320, 870, 723, 349, 255, 228, 300, 295, 295, 280, 1373, 2539, 3230, 3643, 3524, 3177, 3314, 2885, 2668, 2442, 2327, 2058, 1530, 1539, 1402, 1060, 758, 682, 423, 225, 246, 285, 321, 400, 280, 950, 1727, 2435, 2903, 2827, 2682, 2418, 2074, 1979, 1571, 1555, 1303, 1027, 923, 921, 805, 571, 308, 266, 180, 261, 342, 452, 472, 380, 287, 1089, 1527, 1851, 2031, 1728, 1542, 1421, 1205, 1025, 850, 663, 502, 618, 595, 503, 453, 307, 206, 188, 219, 308, 362, 341, 502, 728, 1087, 1217, 1192, 1103, 932, 759, 842, 755, 662, 614, 516, 328, 317, 254, 227, 240, 235, 202, 352, 558, 705, 969, 498, 456, 559, 637, 716, 800, 756, 742, 688, 614, 453, 329, 324, 260, 221, 211, 188, 138, 308, 520, 925, 949, 864, 687, 557, 240, 282, 427, 600, 776, 784, 956, 799, 718, 781, 699, 565, 305, 289, 270, 192, 209, 192, 497, 972, 1028, 927, 883, 756, 381, 267, 420, 591, 753, 771, 758, 943, 701, 724, 614, 440, 254, 218, 236, 139, 71, 23, 80, 107, 233, 892, 1006, 1029, 984, 885, 737, 560, 587, 651, 687, 681, 599, 430, 369, 256, 212, 195, 101, 29, 70, 111, 230, 778, 1154, 1163, 1123, 961, 654, 397, 430, 402, 912, 1134, 1178, 1185, 926, 235, 53, 25, 145, 157, 15, 429, 736, 928, 1027, 898, 759, 313, 7, 8, 21, 422, 641, 974, 1174, 1072, 830, 586, 353, 61, 22, 21, 15, 15, 311, 397, 671, 975, 1076, 994, 754, 545, 341, 18, 15, 72, 73, 140, 327, 377, 460, 876, 1010, 941, 656, 553, 410, 117, 24, 74, 77, 77, 77, 278, 318, 463, 377, 744, 670, 532, 470, 217, 38, 21, 27, 26, 76, 77, 359, 404, 455, 531, 369, 410, 494, 398, 358, 349, 13, 25, 23, 77, 72, 69, 401, 416, 438, 424, 345, 167, 219, 302, 298, 46, 37, 412, 425, 423, 402, 269, 111, 96, 88, 46, 167, 368, 403, 422, 407, 315, 122, 108, 84, 80, 105, 205, 352, 369, 334, 83, 127, 87, 86, 51, 125, 167, 141, 50, 93, 80, 38, 32, 21, 0, 6, 3, 31, 13, 5, 36, 612, 1059, 483, 130, 237, 21, 162, 6, 13, 97, 26, 23, 18, 149, 88, 25, 48, 99, 81, 59, 30, 41, 37, 29, 23]\n"
     ]
    }
   ],
   "source": [
    "print(sta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [],
   "source": [
    "from math import radians, cos, sin, asin, sqrt\n",
    " \n",
    "def haversine(lon1, lat1, lon2, lat2): # 经度1，纬度1，经度2，纬度2 （十进制度数）\n",
    "    \"\"\"\n",
    "    Calculate the great circle distance between two points \n",
    "    on the earth (specified in decimal degrees)\n",
    "    \"\"\"\n",
    "    # 将十进制度数转化为弧度\n",
    "    lon1, lat1, lon2, lat2 = map(radians, [lon1, lat1, lon2, lat2])\n",
    " \n",
    "    # haversine公式\n",
    "    dlon = lon2 - lon1 \n",
    "    dlat = lat2 - lat1 \n",
    "    a = sin(dlat/2)**2 + cos(lat1) * cos(lat2) * sin(dlon/2)**2\n",
    "    c = 2 * asin(sqrt(a)) \n",
    "    r = 6371 # 地球平均半径，单位为公里\n",
    "    return c * r\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "672 3\n",
      "673 0\n",
      "674 0\n",
      "675 2\n",
      "676 0\n",
      "677 1\n",
      "678 0\n",
      "679 11\n",
      "680 11\n",
      "681 120\n",
      "682 178\n",
      "683 5\n",
      "684 0\n",
      "685 6\n",
      "686 124\n",
      "687 0\n",
      "688 6\n",
      "689 7\n",
      "690 8\n",
      "691 1\n",
      "692 1\n",
      "693 1\n",
      "694 21\n",
      "695 16\n",
      "696 9\n",
      "697 16\n",
      "698 1\n",
      "699 0\n",
      "700 4\n",
      "701 1\n",
      "702 5\n",
      "703 8\n",
      "704 5\n"
     ]
    }
   ],
   "source": [
    "sta_ = []\n",
    "for i in f_list:\n",
    "    count = 0\n",
    "    for j in obj_df_list:\n",
    "        distance = haversine(i[0], i[1], j[0], j[1])\n",
    "        if distance <= 0.5:\n",
    "            count+=1\n",
    "    sta_.append(count)\n",
    "    print(f_list.index(i),count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2528704699598607"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "haversine(25.048836073579693, 121.5942222397603, 25.04499500638, 121.58313600436001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {},
   "outputs": [],
   "source": [
    "f['restaurant'] = sta_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "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>state_code</th>\n",
       "      <th>house_unitPrice</th>\n",
       "      <th>lease_unitPrice</th>\n",
       "      <th>center</th>\n",
       "      <th>center_lat</th>\n",
       "      <th>center_lon</th>\n",
       "      <th>cit</th>\n",
       "      <th>dist</th>\n",
       "      <th>vil</th>\n",
       "      <th>km_x</th>\n",
       "      <th>km_y</th>\n",
       "      <th>restaurant</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>552226718</td>\n",
       "      <td>717000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.97196447627833, 121.57556719359505)</td>\n",
       "      <td>24.971964</td>\n",
       "      <td>121.575567</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>老泉里</td>\n",
       "      <td>2761.25</td>\n",
       "      <td>13359.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>552326738</td>\n",
       "      <td>341000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5786568323529)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.578657</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>2761.75</td>\n",
       "      <td>13369.25</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>552326739</td>\n",
       "      <td>436000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5832037836341)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.583204</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>2761.75</td>\n",
       "      <td>13369.75</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>552326740</td>\n",
       "      <td>406000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5877507349153)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.587751</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>2761.75</td>\n",
       "      <td>13370.25</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>552326741</td>\n",
       "      <td>480000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5922976861965)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.592298</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>2761.75</td>\n",
       "      <td>13370.75</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>552326742</td>\n",
       "      <td>368000.0</td>\n",
       "      <td>870.5</td>\n",
       "      <td>(24.976486334943115, 121.5968446374777)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.596845</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>政大里</td>\n",
       "      <td>2761.75</td>\n",
       "      <td>13371.25</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>552426752</td>\n",
       "      <td>519000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.55696370215156)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.556964</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>樟文里</td>\n",
       "      <td>2762.25</td>\n",
       "      <td>13376.25</td>\n",
       "      <td>52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>552426754</td>\n",
       "      <td>456000.0</td>\n",
       "      <td>1142.0</td>\n",
       "      <td>(24.981008193607902, 121.56605122396837)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.566051</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>樟腳里</td>\n",
       "      <td>2762.25</td>\n",
       "      <td>13377.25</td>\n",
       "      <td>101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>552426755</td>\n",
       "      <td>517000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.57059498487678)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.570595</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>老泉里</td>\n",
       "      <td>2762.25</td>\n",
       "      <td>13377.75</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>552426756</td>\n",
       "      <td>399000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.57513874578518)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.575139</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>2762.25</td>\n",
       "      <td>13378.25</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>552426757</td>\n",
       "      <td>496000.0</td>\n",
       "      <td>937.0</td>\n",
       "      <td>(24.981008193607902, 121.57968250669359)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.579683</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>2762.25</td>\n",
       "      <td>13378.75</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>552426758</td>\n",
       "      <td>439500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.58422626760199)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.584226</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>2762.25</td>\n",
       "      <td>13379.25</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>552426759</td>\n",
       "      <td>427500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.58877002851041)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.588770</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>2762.25</td>\n",
       "      <td>13379.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>552426760</td>\n",
       "      <td>383000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.59331378941881)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.593314</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>政大里</td>\n",
       "      <td>2762.25</td>\n",
       "      <td>13380.25</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>552426761</td>\n",
       "      <td>491500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.59785755032722)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.597858</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>政大里</td>\n",
       "      <td>2762.25</td>\n",
       "      <td>13380.75</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>552526768</td>\n",
       "      <td>621000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.985530052272686, 121.54686568959377)</td>\n",
       "      <td>24.985530</td>\n",
       "      <td>121.546866</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>試院里</td>\n",
       "      <td>2762.75</td>\n",
       "      <td>13384.25</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>552526770</td>\n",
       "      <td>346000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.985530052272686, 121.55594702517025)</td>\n",
       "      <td>24.985530</td>\n",
       "      <td>121.555947</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>樟林里</td>\n",
       "      <td>2762.75</td>\n",
       "      <td>13385.25</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>552526771</td>\n",
       "      <td>397500.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.985530052272686, 121.5604876929585)</td>\n",
       "      <td>24.985530</td>\n",
       "      <td>121.560488</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>樟林里</td>\n",
       "      <td>2762.75</td>\n",
       "      <td>13385.75</td>\n",
       "      <td>110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>552526772</td>\n",
       "      <td>405500.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>(24.985530052272686, 121.56502836074674)</td>\n",
       "      <td>24.985530</td>\n",
       "      <td>121.565028</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>忠順里</td>\n",
       "      <td>2762.75</td>\n",
       "      <td>13386.25</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>552526773</td>\n",
       "      <td>469000.0</td>\n",
       "      <td>1023.5</td>\n",
       "      <td>(24.985530052272686, 121.56956902853499)</td>\n",
       "      <td>24.985530</td>\n",
       "      <td>121.569569</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>木新里</td>\n",
       "      <td>2762.75</td>\n",
       "      <td>13386.75</td>\n",
       "      <td>103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>552526774</td>\n",
       "      <td>472000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.985530052272686, 121.57410969632323)</td>\n",
       "      <td>24.985530</td>\n",
       "      <td>121.574110</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>2762.75</td>\n",
       "      <td>13387.25</td>\n",
       "      <td>105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>552526775</td>\n",
       "      <td>460000.0</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>(24.985530052272686, 121.57865036411147)</td>\n",
       "      <td>24.985530</td>\n",
       "      <td>121.578650</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>萬興里</td>\n",
       "      <td>2762.75</td>\n",
       "      <td>13387.75</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>552526776</td>\n",
       "      <td>437500.0</td>\n",
       "      <td>1200.0</td>\n",
       "      <td>(24.985530052272686, 121.58319103189972)</td>\n",
       "      <td>24.985530</td>\n",
       "      <td>121.583191</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>政大里</td>\n",
       "      <td>2762.75</td>\n",
       "      <td>13388.25</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>552526777</td>\n",
       "      <td>418000.0</td>\n",
       "      <td>1068.0</td>\n",
       "      <td>(24.985530052272686, 121.58773169968796)</td>\n",
       "      <td>24.985530</td>\n",
       "      <td>121.587732</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>2762.75</td>\n",
       "      <td>13388.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>552526778</td>\n",
       "      <td>414000.0</td>\n",
       "      <td>1108.5</td>\n",
       "      <td>(24.985530052272686, 121.5922723674762)</td>\n",
       "      <td>24.985530</td>\n",
       "      <td>121.592272</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>政大里</td>\n",
       "      <td>2762.75</td>\n",
       "      <td>13389.25</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>552526779</td>\n",
       "      <td>454500.0</td>\n",
       "      <td>916.0</td>\n",
       "      <td>(24.985530052272686, 121.59681303526445)</td>\n",
       "      <td>24.985530</td>\n",
       "      <td>121.596813</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>政大里</td>\n",
       "      <td>2762.75</td>\n",
       "      <td>13389.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>552626784</td>\n",
       "      <td>442500.0</td>\n",
       "      <td>921.0</td>\n",
       "      <td>(24.99005191093747, 121.53926485947247)</td>\n",
       "      <td>24.990052</td>\n",
       "      <td>121.539265</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>景美里</td>\n",
       "      <td>2763.25</td>\n",
       "      <td>13392.25</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>552626785</td>\n",
       "      <td>428000.0</td>\n",
       "      <td>877.0</td>\n",
       "      <td>(24.99005191093747, 121.54380253106834)</td>\n",
       "      <td>24.990052</td>\n",
       "      <td>121.543803</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>景東里</td>\n",
       "      <td>2763.25</td>\n",
       "      <td>13392.75</td>\n",
       "      <td>247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>552626786</td>\n",
       "      <td>459500.0</td>\n",
       "      <td>1111.0</td>\n",
       "      <td>(24.99005191093747, 121.5483402026642)</td>\n",
       "      <td>24.990052</td>\n",
       "      <td>121.548340</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>華興里</td>\n",
       "      <td>2763.25</td>\n",
       "      <td>13393.25</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>552626787</td>\n",
       "      <td>412500.0</td>\n",
       "      <td>941.0</td>\n",
       "      <td>(24.99005191093747, 121.55287787426008)</td>\n",
       "      <td>24.990052</td>\n",
       "      <td>121.552878</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>華興里</td>\n",
       "      <td>2763.25</td>\n",
       "      <td>13393.75</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state_code  house_unitPrice  lease_unitPrice  \\\n",
       "0    552226718         717000.0              NaN   \n",
       "1    552326738         341000.0              NaN   \n",
       "2    552326739         436000.0              NaN   \n",
       "3    552326740         406000.0              NaN   \n",
       "4    552326741         480000.0              NaN   \n",
       "5    552326742         368000.0            870.5   \n",
       "6    552426752         519000.0              NaN   \n",
       "7    552426754         456000.0           1142.0   \n",
       "8    552426755         517000.0              NaN   \n",
       "9    552426756         399000.0              NaN   \n",
       "10   552426757         496000.0            937.0   \n",
       "11   552426758         439500.0              NaN   \n",
       "12   552426759         427500.0              NaN   \n",
       "13   552426760         383000.0              NaN   \n",
       "14   552426761         491500.0              NaN   \n",
       "15   552526768         621000.0              NaN   \n",
       "16   552526770         346000.0              NaN   \n",
       "17   552526771         397500.0              NaN   \n",
       "18   552526772         405500.0           1000.0   \n",
       "19   552526773         469000.0           1023.5   \n",
       "20   552526774         472000.0              NaN   \n",
       "21   552526775         460000.0           1000.0   \n",
       "22   552526776         437500.0           1200.0   \n",
       "23   552526777         418000.0           1068.0   \n",
       "24   552526778         414000.0           1108.5   \n",
       "25   552526779         454500.0            916.0   \n",
       "26   552626784         442500.0            921.0   \n",
       "27   552626785         428000.0            877.0   \n",
       "28   552626786         459500.0           1111.0   \n",
       "29   552626787         412500.0            941.0   \n",
       "\n",
       "                                      center  center_lat  center_lon  cit  \\\n",
       "0    (24.97196447627833, 121.57556719359505)   24.971964  121.575567  台北市   \n",
       "1    (24.976486334943115, 121.5786568323529)   24.976486  121.578657  台北市   \n",
       "2    (24.976486334943115, 121.5832037836341)   24.976486  121.583204  台北市   \n",
       "3    (24.976486334943115, 121.5877507349153)   24.976486  121.587751  台北市   \n",
       "4    (24.976486334943115, 121.5922976861965)   24.976486  121.592298  台北市   \n",
       "5    (24.976486334943115, 121.5968446374777)   24.976486  121.596845  台北市   \n",
       "6   (24.981008193607902, 121.55696370215156)   24.981008  121.556964  台北市   \n",
       "7   (24.981008193607902, 121.56605122396837)   24.981008  121.566051  台北市   \n",
       "8   (24.981008193607902, 121.57059498487678)   24.981008  121.570595  台北市   \n",
       "9   (24.981008193607902, 121.57513874578518)   24.981008  121.575139  台北市   \n",
       "10  (24.981008193607902, 121.57968250669359)   24.981008  121.579683  台北市   \n",
       "11  (24.981008193607902, 121.58422626760199)   24.981008  121.584226  台北市   \n",
       "12  (24.981008193607902, 121.58877002851041)   24.981008  121.588770  台北市   \n",
       "13  (24.981008193607902, 121.59331378941881)   24.981008  121.593314  台北市   \n",
       "14  (24.981008193607902, 121.59785755032722)   24.981008  121.597858  台北市   \n",
       "15  (24.985530052272686, 121.54686568959377)   24.985530  121.546866  台北市   \n",
       "16  (24.985530052272686, 121.55594702517025)   24.985530  121.555947  台北市   \n",
       "17   (24.985530052272686, 121.5604876929585)   24.985530  121.560488  台北市   \n",
       "18  (24.985530052272686, 121.56502836074674)   24.985530  121.565028  台北市   \n",
       "19  (24.985530052272686, 121.56956902853499)   24.985530  121.569569  台北市   \n",
       "20  (24.985530052272686, 121.57410969632323)   24.985530  121.574110  台北市   \n",
       "21  (24.985530052272686, 121.57865036411147)   24.985530  121.578650  台北市   \n",
       "22  (24.985530052272686, 121.58319103189972)   24.985530  121.583191  台北市   \n",
       "23  (24.985530052272686, 121.58773169968796)   24.985530  121.587732  台北市   \n",
       "24   (24.985530052272686, 121.5922723674762)   24.985530  121.592272  台北市   \n",
       "25  (24.985530052272686, 121.59681303526445)   24.985530  121.596813  台北市   \n",
       "26   (24.99005191093747, 121.53926485947247)   24.990052  121.539265  台北市   \n",
       "27   (24.99005191093747, 121.54380253106834)   24.990052  121.543803  台北市   \n",
       "28    (24.99005191093747, 121.5483402026642)   24.990052  121.548340  台北市   \n",
       "29   (24.99005191093747, 121.55287787426008)   24.990052  121.552878  台北市   \n",
       "\n",
       "   dist  vil     km_x      km_y  restaurant  \n",
       "0   文山區  老泉里  2761.25  13359.25           1  \n",
       "1   文山區  指南里  2761.75  13369.25           4  \n",
       "2   文山區  指南里  2761.75  13369.75           3  \n",
       "3   文山區  指南里  2761.75  13370.25           7  \n",
       "4   文山區  指南里  2761.75  13370.75           5  \n",
       "5   文山區  政大里  2761.75  13371.25           4  \n",
       "6   文山區  樟文里  2762.25  13376.25          52  \n",
       "7   文山區  樟腳里  2762.25  13377.25         101  \n",
       "8   文山區  老泉里  2762.25  13377.75          61  \n",
       "9   文山區  指南里  2762.25  13378.25          78  \n",
       "10  文山區  指南里  2762.25  13378.75          24  \n",
       "11  文山區  指南里  2762.25  13379.25           3  \n",
       "12  文山區  指南里  2762.25  13379.75           0  \n",
       "13  文山區  政大里  2762.25  13380.25           1  \n",
       "14  文山區  政大里  2762.25  13380.75           1  \n",
       "15  文山區  試院里  2762.75  13384.25          28  \n",
       "16  文山區  樟林里  2762.75  13385.25          48  \n",
       "17  文山區  樟林里  2762.75  13385.75         110  \n",
       "18  文山區  忠順里  2762.75  13386.25         143  \n",
       "19  文山區  木新里  2762.75  13386.75         103  \n",
       "20  文山區  指南里  2762.75  13387.25         105  \n",
       "21  文山區  萬興里  2762.75  13387.75          66  \n",
       "22  文山區  政大里  2762.75  13388.25           7  \n",
       "23  文山區  指南里  2762.75  13388.75           0  \n",
       "24  文山區  政大里  2762.75  13389.25           0  \n",
       "25  文山區  政大里  2762.75  13389.75           0  \n",
       "26  文山區  景美里  2763.25  13392.25         199  \n",
       "27  文山區  景東里  2763.25  13392.75         247  \n",
       "28  文山區  華興里  2763.25  13393.25          60  \n",
       "29  文山區  華興里  2763.25  13393.75          24  "
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['state_code', 'house_unitPrice', 'lease_unitPrice', 'center',\n",
       "       'center_lat', 'center_lon', 'cit', 'dist', 'vil', 'km_x', 'km_y',\n",
       "       'restaurant'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.drop(['km_x', 'km_y'],axis=1,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.to_csv('part.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('education_done.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "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>year</th>\n",
       "      <th>dist</th>\n",
       "      <th>vil</th>\n",
       "      <th>master</th>\n",
       "      <th>college</th>\n",
       "      <th>highSchool</th>\n",
       "      <th>secondary</th>\n",
       "      <th>total</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>state_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>106</td>\n",
       "      <td>松山區</td>\n",
       "      <td>莊敬里</td>\n",
       "      <td>0.081176</td>\n",
       "      <td>0.289560</td>\n",
       "      <td>0.456843</td>\n",
       "      <td>0.172421</td>\n",
       "      <td>4866</td>\n",
       "      <td>25.072157</td>\n",
       "      <td>121.566969</td>\n",
       "      <td>554427016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>106</td>\n",
       "      <td>松山區</td>\n",
       "      <td>東榮里</td>\n",
       "      <td>0.158182</td>\n",
       "      <td>0.387064</td>\n",
       "      <td>0.384340</td>\n",
       "      <td>0.070414</td>\n",
       "      <td>6973</td>\n",
       "      <td>25.060422</td>\n",
       "      <td>121.557694</td>\n",
       "      <td>554226992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>106</td>\n",
       "      <td>松山區</td>\n",
       "      <td>三民里</td>\n",
       "      <td>0.134777</td>\n",
       "      <td>0.383161</td>\n",
       "      <td>0.392696</td>\n",
       "      <td>0.089367</td>\n",
       "      <td>6188</td>\n",
       "      <td>25.059660</td>\n",
       "      <td>121.562332</td>\n",
       "      <td>554126992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>106</td>\n",
       "      <td>松山區</td>\n",
       "      <td>新益里</td>\n",
       "      <td>0.103236</td>\n",
       "      <td>0.324120</td>\n",
       "      <td>0.437751</td>\n",
       "      <td>0.134893</td>\n",
       "      <td>4233</td>\n",
       "      <td>25.062045</td>\n",
       "      <td>121.567683</td>\n",
       "      <td>554226998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>106</td>\n",
       "      <td>松山區</td>\n",
       "      <td>富錦里</td>\n",
       "      <td>0.128194</td>\n",
       "      <td>0.364991</td>\n",
       "      <td>0.403961</td>\n",
       "      <td>0.102853</td>\n",
       "      <td>4696</td>\n",
       "      <td>25.060066</td>\n",
       "      <td>121.564829</td>\n",
       "      <td>554126993</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year dist  vil    master   college  highSchool  secondary  total  \\\n",
       "0   106  松山區  莊敬里  0.081176  0.289560    0.456843   0.172421   4866   \n",
       "1   106  松山區  東榮里  0.158182  0.387064    0.384340   0.070414   6973   \n",
       "2   106  松山區  三民里  0.134777  0.383161    0.392696   0.089367   6188   \n",
       "3   106  松山區  新益里  0.103236  0.324120    0.437751   0.134893   4233   \n",
       "4   106  松山區  富錦里  0.128194  0.364991    0.403961   0.102853   4696   \n",
       "\n",
       "         lat         lon  state_code  \n",
       "0  25.072157  121.566969   554427016  \n",
       "1  25.060422  121.557694   554226992  \n",
       "2  25.059660  121.562332   554126992  \n",
       "3  25.062045  121.567683   554226998  \n",
       "4  25.060066  121.564829   554126993  "
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['year', 'dist', 'vil', 'master', 'college', 'highSchool',\n",
    "       'secondary', 'total', 'lat', 'lon', 'state_code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_c = df[['dist', 'vil', 'master', 'college', 'highSchool',\n",
    "       'secondary']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dist</th>\n",
       "      <th>vil</th>\n",
       "      <th>master</th>\n",
       "      <th>college</th>\n",
       "      <th>highSchool</th>\n",
       "      <th>secondary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>松山區</td>\n",
       "      <td>莊敬里</td>\n",
       "      <td>0.081176</td>\n",
       "      <td>0.289560</td>\n",
       "      <td>0.456843</td>\n",
       "      <td>0.172421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>松山區</td>\n",
       "      <td>東榮里</td>\n",
       "      <td>0.158182</td>\n",
       "      <td>0.387064</td>\n",
       "      <td>0.384340</td>\n",
       "      <td>0.070414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>松山區</td>\n",
       "      <td>三民里</td>\n",
       "      <td>0.134777</td>\n",
       "      <td>0.383161</td>\n",
       "      <td>0.392696</td>\n",
       "      <td>0.089367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>松山區</td>\n",
       "      <td>新益里</td>\n",
       "      <td>0.103236</td>\n",
       "      <td>0.324120</td>\n",
       "      <td>0.437751</td>\n",
       "      <td>0.134893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>松山區</td>\n",
       "      <td>富錦里</td>\n",
       "      <td>0.128194</td>\n",
       "      <td>0.364991</td>\n",
       "      <td>0.403961</td>\n",
       "      <td>0.102853</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dist  vil    master   college  highSchool  secondary\n",
       "0  松山區  莊敬里  0.081176  0.289560    0.456843   0.172421\n",
       "1  松山區  東榮里  0.158182  0.387064    0.384340   0.070414\n",
       "2  松山區  三民里  0.134777  0.383161    0.392696   0.089367\n",
       "3  松山區  新益里  0.103236  0.324120    0.437751   0.134893\n",
       "4  松山區  富錦里  0.128194  0.364991    0.403961   0.102853"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = pd.merge(f, df_c, how='inner', on=['dist', 'vil'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('taxtion_done.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>area</th>\n",
       "      <th>village</th>\n",
       "      <th>median</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>state_code</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>松山區</td>\n",
       "      <td>中崙里</td>\n",
       "      <td>803</td>\n",
       "      <td>25.046209</td>\n",
       "      <td>121.544850</td>\n",
       "      <td>553826959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>松山區</td>\n",
       "      <td>自強里</td>\n",
       "      <td>800</td>\n",
       "      <td>25.055099</td>\n",
       "      <td>121.563402</td>\n",
       "      <td>554026983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>松山區</td>\n",
       "      <td>鵬程里</td>\n",
       "      <td>746</td>\n",
       "      <td>25.055416</td>\n",
       "      <td>121.567683</td>\n",
       "      <td>554026984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>松山區</td>\n",
       "      <td>東榮里</td>\n",
       "      <td>906</td>\n",
       "      <td>25.060422</td>\n",
       "      <td>121.557694</td>\n",
       "      <td>554226992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>松山區</td>\n",
       "      <td>介壽里</td>\n",
       "      <td>856</td>\n",
       "      <td>25.057434</td>\n",
       "      <td>121.559121</td>\n",
       "      <td>554126987</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  area village  median        lat         lon  state_code\n",
       "0  松山區     中崙里     803  25.046209  121.544850   553826959\n",
       "1  松山區     自強里     800  25.055099  121.563402   554026983\n",
       "2  松山區     鵬程里     746  25.055416  121.567683   554026984\n",
       "3  松山區     東榮里     906  25.060422  121.557694   554226992\n",
       "4  松山區     介壽里     856  25.057434  121.559121   554126987"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['dist', 'vil', 'median', 'lat', 'lon', 'state_code']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_c = df[['dist', 'vil', 'median']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dist</th>\n",
       "      <th>vil</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>松山區</td>\n",
       "      <td>中崙里</td>\n",
       "      <td>803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>松山區</td>\n",
       "      <td>自強里</td>\n",
       "      <td>800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>松山區</td>\n",
       "      <td>鵬程里</td>\n",
       "      <td>746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>松山區</td>\n",
       "      <td>東榮里</td>\n",
       "      <td>906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>松山區</td>\n",
       "      <td>介壽里</td>\n",
       "      <td>856</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dist  vil  median\n",
       "0  松山區  中崙里     803\n",
       "1  松山區  自強里     800\n",
       "2  松山區  鵬程里     746\n",
       "3  松山區  東榮里     906\n",
       "4  松山區  介壽里     856"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_c.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = pd.merge(f, df_c, how='inner', on=['dist', 'vil'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "metadata": {},
   "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>state_code</th>\n",
       "      <th>house_unitPrice</th>\n",
       "      <th>lease_unitPrice</th>\n",
       "      <th>center</th>\n",
       "      <th>center_lat</th>\n",
       "      <th>center_lon</th>\n",
       "      <th>cit</th>\n",
       "      <th>dist</th>\n",
       "      <th>vil</th>\n",
       "      <th>restaurant</th>\n",
       "      <th>master</th>\n",
       "      <th>college</th>\n",
       "      <th>highSchool</th>\n",
       "      <th>secondary</th>\n",
       "      <th>median</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>552226718</td>\n",
       "      <td>717000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.97196447627833, 121.57556719359505)</td>\n",
       "      <td>24.971964</td>\n",
       "      <td>121.575567</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>老泉里</td>\n",
       "      <td>1</td>\n",
       "      <td>0.021583</td>\n",
       "      <td>0.160671</td>\n",
       "      <td>0.485612</td>\n",
       "      <td>0.332134</td>\n",
       "      <td>538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>552426755</td>\n",
       "      <td>517000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.57059498487678)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.570595</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>老泉里</td>\n",
       "      <td>61</td>\n",
       "      <td>0.021583</td>\n",
       "      <td>0.160671</td>\n",
       "      <td>0.485612</td>\n",
       "      <td>0.332134</td>\n",
       "      <td>538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>552326738</td>\n",
       "      <td>341000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5786568323529)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.578657</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>4</td>\n",
       "      <td>0.139898</td>\n",
       "      <td>0.250568</td>\n",
       "      <td>0.413167</td>\n",
       "      <td>0.196368</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>552326739</td>\n",
       "      <td>436000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5832037836341)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.583204</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>3</td>\n",
       "      <td>0.139898</td>\n",
       "      <td>0.250568</td>\n",
       "      <td>0.413167</td>\n",
       "      <td>0.196368</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>552326740</td>\n",
       "      <td>406000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5877507349153)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.587751</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>7</td>\n",
       "      <td>0.139898</td>\n",
       "      <td>0.250568</td>\n",
       "      <td>0.413167</td>\n",
       "      <td>0.196368</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   state_code  house_unitPrice  lease_unitPrice  \\\n",
       "0   552226718         717000.0              NaN   \n",
       "1   552426755         517000.0              NaN   \n",
       "2   552326738         341000.0              NaN   \n",
       "3   552326739         436000.0              NaN   \n",
       "4   552326740         406000.0              NaN   \n",
       "\n",
       "                                     center  center_lat  center_lon  cit dist  \\\n",
       "0   (24.97196447627833, 121.57556719359505)   24.971964  121.575567  台北市  文山區   \n",
       "1  (24.981008193607902, 121.57059498487678)   24.981008  121.570595  台北市  文山區   \n",
       "2   (24.976486334943115, 121.5786568323529)   24.976486  121.578657  台北市  文山區   \n",
       "3   (24.976486334943115, 121.5832037836341)   24.976486  121.583204  台北市  文山區   \n",
       "4   (24.976486334943115, 121.5877507349153)   24.976486  121.587751  台北市  文山區   \n",
       "\n",
       "   vil  restaurant    master   college  highSchool  secondary  median  \n",
       "0  老泉里           1  0.021583  0.160671    0.485612   0.332134     538  \n",
       "1  老泉里          61  0.021583  0.160671    0.485612   0.332134     538  \n",
       "2  指南里           4  0.139898  0.250568    0.413167   0.196368     750  \n",
       "3  指南里           3  0.139898  0.250568    0.413167   0.196368     750  \n",
       "4  指南里           7  0.139898  0.250568    0.413167   0.196368     750  "
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = ['cloth_done.csv',\n",
    " 'convStore_done.csv',\n",
    " 'hardware_done.csv',\n",
    " 'illegal_don.csv',\n",
    " 'mrt.csv',\n",
    " 'night_club.csv',]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cloth_done.csv\n",
      "cloth_done.csv 0 6\n",
      "cloth_done.csv 1 18\n",
      "cloth_done.csv 2 6\n",
      "cloth_done.csv 3 0\n",
      "cloth_done.csv 4 0\n",
      "cloth_done.csv 5 0\n",
      "cloth_done.csv 6 8\n",
      "cloth_done.csv 7 6\n",
      "cloth_done.csv 8 0\n",
      "cloth_done.csv 9 0\n",
      "cloth_done.csv 10 11\n",
      "cloth_done.csv 11 0\n",
      "cloth_done.csv 12 0\n",
      "cloth_done.csv 13 0\n",
      "cloth_done.csv 14 0\n",
      "cloth_done.csv 15 0\n",
      "cloth_done.csv 16 0\n",
      "cloth_done.csv 17 0\n",
      "cloth_done.csv 18 6\n",
      "cloth_done.csv 19 0\n",
      "cloth_done.csv 20 0\n",
      "cloth_done.csv 21 0\n",
      "cloth_done.csv 22 0\n",
      "cloth_done.csv 23 0\n",
      "cloth_done.csv 24 0\n",
      "cloth_done.csv 25 21\n",
      "cloth_done.csv 26 20\n",
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     ]
    }
   ],
   "source": [
    "f_list = list(zip(f['center_lat'].tolist(),f['center_lon'].tolist()))\n",
    "for name in data:\n",
    "    print(name)\n",
    "    obj_df = pd.read_csv(name)\n",
    "    obj_df_list = list(zip(obj_df['lat'].tolist(),obj_df['lon'].tolist()))\n",
    "    sta = []\n",
    "    for i in f_list:\n",
    "        count = 0\n",
    "        for j in obj_df_list:\n",
    "            distance = haversine(i[0], i[1], j[0], j[1])\n",
    "            if distance <= 1.5:\n",
    "                count+=1\n",
    "        sta.append(count)\n",
    "        print(name,f_list.index(i),count)\n",
    "    f[name[:-4]] = sta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "subdata = ['106_traffic_done.csv','job_stat_code.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "f_list = list(zip(f['center_lat'].tolist(),f['center_lon'].tolist()))\n",
    "for name in subdata:\n",
    "    print(name)\n",
    "    obj_df = pd.read_csv(name)\n",
    "    obj_df_list = list(zip(obj_df['lat'].tolist(),obj_df['lon'].tolist()))\n",
    "    sta = []\n",
    "    for i in f_list:\n",
    "        count = 0\n",
    "        for j in obj_df_list:\n",
    "            distance = haversine(i[0], i[1], j[0], j[1])\n",
    "            if distance <= 0.5:\n",
    "                count+=1\n",
    "        sta.append(count)\n",
    "        print(name,f_list.index(i),count)\n",
    "    f[name[:-4]] = sta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "metadata": {},
   "outputs": [],
   "source": [
    "less = [\n",
    " 'edu_all_statecode.csv',\n",
    " 'hsp_and_clc.csv',\n",
    " 'park.csv',\n",
    " 'ubike.csv']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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    },
    {
     "name": "stdout",
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     ]
    }
   ],
   "source": [
    "f_list = list(zip(f['center_lat'].tolist(),f['center_lon'].tolist()))\n",
    "for name in less:\n",
    "    print(name)\n",
    "    obj_df = pd.read_csv(name)\n",
    "    obj_df_list = list(zip(obj_df['lat'].tolist(),obj_df['lon'].tolist()))\n",
    "    sta = []\n",
    "    for i in f_list:\n",
    "        count = 0\n",
    "        for j in obj_df_list:\n",
    "            distance = haversine(i[0], i[1], j[0], j[1])\n",
    "            if distance <= 2.5:\n",
    "                count+=1\n",
    "        sta.append(count)\n",
    "        print(name,f_list.index(i),count)\n",
    "    f[name[:-4]] = sta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = pd.read_csv('bigTable.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state_code</th>\n",
       "      <th>house_unitPrice</th>\n",
       "      <th>lease_unitPrice</th>\n",
       "      <th>center</th>\n",
       "      <th>center_lat</th>\n",
       "      <th>center_lon</th>\n",
       "      <th>cit</th>\n",
       "      <th>dist</th>\n",
       "      <th>vil</th>\n",
       "      <th>restaurant</th>\n",
       "      <th>...</th>\n",
       "      <th>convStore_done</th>\n",
       "      <th>edu_all_statecode</th>\n",
       "      <th>hardware_done</th>\n",
       "      <th>hsp_and_clc</th>\n",
       "      <th>illegal_don</th>\n",
       "      <th>job_stat_code</th>\n",
       "      <th>mrt</th>\n",
       "      <th>night_club</th>\n",
       "      <th>park</th>\n",
       "      <th>ubike</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>552226718</td>\n",
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       "      <td>24.971964</td>\n",
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       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>老泉里</td>\n",
       "      <td>1</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>(24.981008193607902, 121.57059498487678)</td>\n",
       "      <td>24.981008</td>\n",
       "      <td>121.570595</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>老泉里</td>\n",
       "      <td>61</td>\n",
       "      <td>...</td>\n",
       "      <td>28</td>\n",
       "      <td>46</td>\n",
       "      <td>9</td>\n",
       "      <td>3</td>\n",
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       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>552326738</td>\n",
       "      <td>341000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5786568323529)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.578657</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>4</td>\n",
       "      <td>...</td>\n",
       "      <td>14</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>552326739</td>\n",
       "      <td>436000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>(24.976486334943115, 121.5832037836341)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.583204</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>指南里</td>\n",
       "      <td>3</td>\n",
       "      <td>...</td>\n",
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       "      <td>552326740</td>\n",
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       "      <td>(24.976486334943115, 121.5877507349153)</td>\n",
       "      <td>24.976486</td>\n",
       "      <td>121.587751</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   state_code  house_unitPrice  lease_unitPrice  \\\n",
       "0   552226718         717000.0              NaN   \n",
       "1   552426755         517000.0              NaN   \n",
       "2   552326738         341000.0              NaN   \n",
       "3   552326739         436000.0              NaN   \n",
       "4   552326740         406000.0              NaN   \n",
       "\n",
       "                                     center  center_lat  center_lon  cit dist  \\\n",
       "0   (24.97196447627833, 121.57556719359505)   24.971964  121.575567  台北市  文山區   \n",
       "1  (24.981008193607902, 121.57059498487678)   24.981008  121.570595  台北市  文山區   \n",
       "2   (24.976486334943115, 121.5786568323529)   24.976486  121.578657  台北市  文山區   \n",
       "3   (24.976486334943115, 121.5832037836341)   24.976486  121.583204  台北市  文山區   \n",
       "4   (24.976486334943115, 121.5877507349153)   24.976486  121.587751  台北市  文山區   \n",
       "\n",
       "   vil  restaurant  ...    convStore_done  edu_all_statecode  hardware_done  \\\n",
       "0  老泉里           1  ...                13                 40              4   \n",
       "1  老泉里          61  ...                28                 46              9   \n",
       "2  指南里           4  ...                14                 32              4   \n",
       "3  指南里           3  ...                 5                 25              2   \n",
       "4  指南里           7  ...                 3                 20              1   \n",
       "\n",
       "   hsp_and_clc  illegal_don  job_stat_code  mrt  night_club  park  ubike  \n",
       "0            2           12              0    0           1     1      1  \n",
       "1            3           29              1    3           1     2      2  \n",
       "2            1           11              0    3           1     2      0  \n",
       "3            0            6              0    1           1     1      0  \n",
       "4            0            4              0    0           0     1      0  \n",
       "\n",
       "[5 rows x 27 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_price(cols):\n",
    "    hou = cols[0]\n",
    "    dist = cols[1]\n",
    "    \n",
    "    if pd.isnull(hou):\n",
    "\n",
    "        if dist == '中正區':\n",
    "            return f[f.dist=='中正區'].house_unitPrice.median()\n",
    "\n",
    "        elif dist == '北投區':\n",
    "            return f[f.dist=='北投區'].house_unitPrice.median()\n",
    "\n",
    "        elif dist == '南港區':\n",
    "            return f[f.dist=='南港區'].house_unitPrice.median()\n",
    "\n",
    "        elif dist == '松山區':\n",
    "            return f[f.dist=='松山區'].house_unitPrice.median()\n",
    "\n",
    "        elif dist == '萬華區':\n",
    "            return f[f.dist=='萬華區'].house_unitPrice.median()\n",
    "\n",
    "        elif dist == '內湖區':\n",
    "            return f[f.dist=='內湖區'].house_unitPrice.median()\n",
    "\n",
    "        elif dist == '信義區':\n",
    "            return f[f.dist=='信義區'].house_unitPrice.median()\n",
    "\n",
    "        elif dist == '士林區':\n",
    "            return f[f.dist=='士林區'].house_unitPrice.median()\n",
    "\n",
    "        elif dist == '大安區':\n",
    "            return f[f.dist=='大安區'].house_unitPrice.median()\n",
    "\n",
    "        elif dist == '中山區':\n",
    "            return f[f.dist=='中山區'].house_unitPrice.median()\n",
    "        \n",
    "        else:\n",
    "            return f[f.dist=='文山區'].house_unitPrice.median()\n",
    "\n",
    "    else:\n",
    "        return hou"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "f['house_unitPrice'] = f[['house_unitPrice','dist']].apply(impute_price,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def impute_lease(cols):\n",
    "    lea = cols[0]\n",
    "    dist = cols[1]\n",
    "    \n",
    "    if pd.isnull(lea):\n",
    "\n",
    "        if dist == '中正區':\n",
    "            return f[f.dist=='中正區'].lease_unitPrice.median()\n",
    "\n",
    "        elif dist == '北投區':\n",
    "            return f[f.dist=='北投區'].lease_unitPrice.median()\n",
    "\n",
    "        elif dist == '南港區':\n",
    "            return f[f.dist=='南港區'].lease_unitPrice.median()\n",
    "\n",
    "        elif dist == '松山區':\n",
    "            return f[f.dist=='松山區'].lease_unitPrice.median()\n",
    "\n",
    "        elif dist == '萬華區':\n",
    "            return f[f.dist=='萬華區'].lease_unitPrice.median()\n",
    "\n",
    "        elif dist == '內湖區':\n",
    "            return f[f.dist=='內湖區'].lease_unitPrice.median()\n",
    "\n",
    "        elif dist == '信義區':\n",
    "            return f[f.dist=='信義區'].lease_unitPrice.median()\n",
    "\n",
    "        elif dist == '士林區':\n",
    "            return f[f.dist=='士林區'].lease_unitPrice.median()\n",
    "\n",
    "        elif dist == '大安區':\n",
    "            return f[f.dist=='大安區'].lease_unitPrice.median()\n",
    "\n",
    "        elif dist == '中山區':\n",
    "            return f[f.dist=='中山區'].lease_unitPrice.median()\n",
    "        \n",
    "        else:\n",
    "            return f[f.dist=='文山區'].lease_unitPrice.median()\n",
    "\n",
    "    else:\n",
    "        return lea"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['state_code', 'house_unitPrice', 'lease_unitPrice', 'center',\n",
       "       'center_lat', 'center_lon', 'cit', 'dist', 'vil', 'restaurant',\n",
       "       'master', 'college', 'highSchool', 'secondary', 'median',\n",
       "       '106_traffic_done', 'cloth_done', 'convStore_done', 'edu_all_statecode',\n",
       "       'hardware_done', 'hsp_and_clc', 'illegal_don', 'job_stat_code', 'mrt',\n",
       "       'night_club', 'park', 'ubike'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = f[['state_code', 'house_unitPrice', 'lease_unitPrice', 'center',\n",
    "       'center_lat', 'center_lon', 'cit', 'dist', 'vil',\n",
    "       'master', 'college', 'highSchool', 'secondary', 'median', 'restaurant',\n",
    "       '106_traffic_done', 'cloth_done', 'convStore_done', 'edu_all_statecode',\n",
    "       'hardware_done', 'hsp_and_clc', 'illegal_don', 'job_stat_code', 'mrt',\n",
    "       'night_club', 'park', 'ubike']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "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>state_code</th>\n",
       "      <th>house_unitPrice</th>\n",
       "      <th>lease_unitPrice</th>\n",
       "      <th>center</th>\n",
       "      <th>center_lat</th>\n",
       "      <th>center_lon</th>\n",
       "      <th>cit</th>\n",
       "      <th>dist</th>\n",
       "      <th>vil</th>\n",
       "      <th>master</th>\n",
       "      <th>...</th>\n",
       "      <th>convStore_done</th>\n",
       "      <th>edu_all_statecode</th>\n",
       "      <th>hardware_done</th>\n",
       "      <th>hsp_and_clc</th>\n",
       "      <th>illegal_don</th>\n",
       "      <th>job_stat_code</th>\n",
       "      <th>mrt</th>\n",
       "      <th>night_club</th>\n",
       "      <th>park</th>\n",
       "      <th>ubike</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>552226718</td>\n",
       "      <td>717000.0</td>\n",
       "      <td>1017.75</td>\n",
       "      <td>(24.97196447627833, 121.57556719359505)</td>\n",
       "      <td>24.971964</td>\n",
       "      <td>121.575567</td>\n",
       "      <td>台北市</td>\n",
       "      <td>文山區</td>\n",
       "      <td>老泉里</td>\n",
       "      <td>0.021583</td>\n",
       "      <td>...</td>\n",
       "      <td>13</td>\n",
       "      <td>40</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   state_code  house_unitPrice  lease_unitPrice  \\\n",
       "0   552226718         717000.0          1017.75   \n",
       "\n",
       "                                    center  center_lat  center_lon  cit dist  \\\n",
       "0  (24.97196447627833, 121.57556719359505)   24.971964  121.575567  台北市  文山區   \n",
       "\n",
       "   vil    master  ...    convStore_done  edu_all_statecode  hardware_done  \\\n",
       "0  老泉里  0.021583  ...                13                 40              4   \n",
       "\n",
       "   hsp_and_clc  illegal_don  job_stat_code  mrt  night_club  park  ubike  \n",
       "0            2           12              0    0           1     1      1  \n",
       "\n",
       "[1 rows x 27 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "f['lease_unitPrice'] = f[['lease_unitPrice','dist']].apply(impute_lease,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "ele = f[['state_code', 'house_unitPrice', 'lease_unitPrice', 'center',\n",
    "       'center_lat', 'center_lon', 'cit', 'dist', 'vil',\n",
    "       'master', 'college', 'highSchool', 'secondary', 'median']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "ele.to_csv('origin.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "f.to_csv('bigTable_full.csv',index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['中正區', '北投區', '南港區', '大同區', '松山區', '萬華區', '內湖區', '信義區', '士林區', '大安區', '中山區', '文山區']\n"
     ]
    }
   ],
   "source": [
    "print(list(set(f.dist.tolist())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "654000.0"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f[f.dist=='中正區'].house_unitPrice.median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "state_code         552226718.0\n",
       "house_unitPrice       717000.0\n",
       "Name: 0, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f[['state_code', 'house_unitPrice']].loc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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