column_t.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import shutil\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('C:\\projectData\\leaseCleaned\\cle_csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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",
    "#         data.append(fullname)\n",
    "        data.append(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['lease_100_2008.csv',\n",
       " 'lease_100_2009.csv',\n",
       " 'lease_100_2010.csv',\n",
       " 'lease_100_2011.csv',\n",
       " 'lease_100_2012.csv',\n",
       " 'lease_100_2013.csv',\n",
       " 'lease_100_2014.csv',\n",
       " 'lease_100_2015.csv',\n",
       " 'lease_100_2016.csv',\n",
       " 'lease_100_2017.csv',\n",
       " 'lease_100_2018.csv',\n",
       " 'lease_103_2008.csv',\n",
       " 'lease_103_2009.csv',\n",
       " 'lease_103_2010.csv',\n",
       " 'lease_103_2011.csv',\n",
       " 'lease_103_2012.csv',\n",
       " 'lease_103_2013.csv',\n",
       " 'lease_103_2014.csv',\n",
       " 'lease_103_2015.csv',\n",
       " 'lease_103_2016.csv',\n",
       " 'lease_103_2017.csv',\n",
       " 'lease_103_2018.csv',\n",
       " 'lease_104_2008.csv',\n",
       " 'lease_104_2009.csv',\n",
       " 'lease_104_2010.csv',\n",
       " 'lease_104_2011.csv',\n",
       " 'lease_104_2012.csv',\n",
       " 'lease_104_2013.csv',\n",
       " 'lease_104_2014.csv',\n",
       " 'lease_104_2015.csv',\n",
       " 'lease_104_2016.csv',\n",
       " 'lease_104_2017.csv',\n",
       " 'lease_104_2018.csv',\n",
       " 'lease_105_2008.csv',\n",
       " 'lease_105_2009.csv',\n",
       " 'lease_105_2010.csv',\n",
       " 'lease_105_2011.csv',\n",
       " 'lease_105_2012.csv',\n",
       " 'lease_105_2013.csv',\n",
       " 'lease_105_2014.csv',\n",
       " 'lease_105_2015.csv',\n",
       " 'lease_105_2016.csv',\n",
       " 'lease_105_2017.csv',\n",
       " 'lease_105_2018.csv',\n",
       " 'lease_106_2008.csv',\n",
       " 'lease_106_2009.csv',\n",
       " 'lease_106_2010.csv',\n",
       " 'lease_106_2011.csv',\n",
       " 'lease_106_2012.csv',\n",
       " 'lease_106_2013.csv',\n",
       " 'lease_106_2014.csv',\n",
       " 'lease_106_2015.csv',\n",
       " 'lease_106_2016.csv',\n",
       " 'lease_106_2017.csv',\n",
       " 'lease_106_2018.csv',\n",
       " 'lease_108_2008.csv',\n",
       " 'lease_108_2009.csv',\n",
       " 'lease_108_2010.csv',\n",
       " 'lease_108_2011.csv',\n",
       " 'lease_108_2012.csv',\n",
       " 'lease_108_2013.csv',\n",
       " 'lease_108_2014.csv',\n",
       " 'lease_108_2015.csv',\n",
       " 'lease_108_2016.csv',\n",
       " 'lease_108_2017.csv',\n",
       " 'lease_110_2008.csv',\n",
       " 'lease_110_2009.csv',\n",
       " 'lease_110_2010.csv',\n",
       " 'lease_110_2011.csv',\n",
       " 'lease_110_2012.csv',\n",
       " 'lease_110_2013.csv',\n",
       " 'lease_110_2014.csv',\n",
       " 'lease_110_2015.csv',\n",
       " 'lease_110_2016.csv',\n",
       " 'lease_110_2017.csv',\n",
       " 'lease_110_2018.csv',\n",
       " 'lease_111_2008.csv',\n",
       " 'lease_111_2009.csv',\n",
       " 'lease_111_2010.csv',\n",
       " 'lease_111_2011.csv',\n",
       " 'lease_111_2012.csv',\n",
       " 'lease_111_2013.csv',\n",
       " 'lease_111_2014.csv',\n",
       " 'lease_111_2015.csv',\n",
       " 'lease_111_2016.csv',\n",
       " 'lease_111_2017.csv',\n",
       " 'lease_111_2018.csv',\n",
       " 'lease_112_2008.csv',\n",
       " 'lease_112_2009.csv',\n",
       " 'lease_112_2010.csv',\n",
       " 'lease_112_2011.csv',\n",
       " 'lease_112_2012.csv',\n",
       " 'lease_112_2013.csv',\n",
       " 'lease_112_2014.csv',\n",
       " 'lease_112_2015.csv',\n",
       " 'lease_112_2016.csv',\n",
       " 'lease_112_2017.csv',\n",
       " 'lease_112_2018.csv',\n",
       " 'lease_114_2008.csv',\n",
       " 'lease_114_2009.csv',\n",
       " 'lease_114_2010.csv',\n",
       " 'lease_114_2011.csv',\n",
       " 'lease_114_2012.csv',\n",
       " 'lease_114_2013.csv',\n",
       " 'lease_114_2014.csv',\n",
       " 'lease_114_2015.csv',\n",
       " 'lease_114_2016.csv',\n",
       " 'lease_114_2017.csv',\n",
       " 'lease_114_2018.csv',\n",
       " 'lease_115_2008.csv',\n",
       " 'lease_115_2009.csv',\n",
       " 'lease_115_2010.csv',\n",
       " 'lease_115_2011.csv',\n",
       " 'lease_115_2012.csv',\n",
       " 'lease_115_2013.csv',\n",
       " 'lease_115_2014.csv',\n",
       " 'lease_115_2015.csv',\n",
       " 'lease_115_2016.csv',\n",
       " 'lease_115_2017.csv',\n",
       " 'lease_115_2018.csv',\n",
       " 'lease_116_2008.csv',\n",
       " 'lease_116_2009.csv',\n",
       " 'lease_116_2010.csv',\n",
       " 'lease_116_2011.csv',\n",
       " 'lease_116_2012.csv',\n",
       " 'lease_116_2013.csv',\n",
       " 'lease_116_2014.csv',\n",
       " 'lease_116_2015.csv',\n",
       " 'lease_116_2016.csv',\n",
       " 'lease_116_2017.csv',\n",
       " 'lease_116_2018.csv']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'lease_100_2008.csv'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(data[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['district'] = df['Address'].apply(lambda x:x[:3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>dealTime</th>\n",
       "      <th>Usage</th>\n",
       "      <th>Type</th>\n",
       "      <th>Address</th>\n",
       "      <th>Pattern</th>\n",
       "      <th>Floor</th>\n",
       "      <th>floorSum</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>unitPrice</th>\n",
       "      <th>district</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>電梯大樓</td>\n",
       "      <td>中正區中華路二段</td>\n",
       "      <td>2房1廳1衛</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>25.028453</td>\n",
       "      <td>121.505532</td>\n",
       "      <td>941</td>\n",
       "      <td>中正區</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>公寓</td>\n",
       "      <td>中正區林森南路</td>\n",
       "      <td>3房2廳1衛</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>25.038463</td>\n",
       "      <td>121.521444</td>\n",
       "      <td>750</td>\n",
       "      <td>中正區</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>公寓</td>\n",
       "      <td>中正區汀州路一段</td>\n",
       "      <td>1房2廳1衛</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>25.029342</td>\n",
       "      <td>121.508230</td>\n",
       "      <td>488</td>\n",
       "      <td>中正區</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>透天厝</td>\n",
       "      <td>中正區汀州路三段</td>\n",
       "      <td>2房1廳1衛</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25.015911</td>\n",
       "      <td>121.531042</td>\n",
       "      <td>681</td>\n",
       "      <td>中正區</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>電梯大樓</td>\n",
       "      <td>中正區南昌路一段</td>\n",
       "      <td>2房1廳1衛</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>25.031562</td>\n",
       "      <td>121.516954</td>\n",
       "      <td>1176</td>\n",
       "      <td>中正區</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dealTime Usage  Type   Address Pattern  Floor  floorSum        lat  \\\n",
       "0  2008-12  整層住家  電梯大樓  中正區中華路二段  2房1廳1衛      2         7  25.028453   \n",
       "1  2008-12  整層住家    公寓   中正區林森南路  3房2廳1衛      3         4  25.038463   \n",
       "2  2008-12  整層住家    公寓  中正區汀州路一段  1房2廳1衛      2         3  25.029342   \n",
       "3  2008-12  整層住家   透天厝  中正區汀州路三段  2房1廳1衛      1         3  25.015911   \n",
       "4  2008-12  整層住家  電梯大樓  中正區南昌路一段  2房1廳1衛      4         7  25.031562   \n",
       "\n",
       "         long  unitPrice district  \n",
       "0  121.505532        941      中正區  \n",
       "1  121.521444        750      中正區  \n",
       "2  121.508230        488      中正區  \n",
       "3  121.531042        681      中正區  \n",
       "4  121.516954       1176      中正區  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dealTime', 'Usage', 'Type', 'Address', 'Pattern', 'Floor', 'floorSum', 'lat', 'long', 'unitPrice', 'district']\n"
     ]
    }
   ],
   "source": [
    "print(list(df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = ['dealTime', 'usage', 'houseType', 'address', 'pattern', 'floor', 'floorSum', 'lat', 'lon', 'unitPrice', 'district']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['dealTime', 'usage', 'houseType', 'address', 'pattern', 'floor', 'floorSum', 'lat', 'lon', 'unitPrice', 'district']\n"
     ]
    }
   ],
   "source": [
    "print(list(df.columns))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['address'] = df['address'].apply(lambda x:x[3:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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>dealTime</th>\n",
       "      <th>usage</th>\n",
       "      <th>houseType</th>\n",
       "      <th>address</th>\n",
       "      <th>pattern</th>\n",
       "      <th>floor</th>\n",
       "      <th>floorSum</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>unitPrice</th>\n",
       "      <th>district</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>電梯大樓</td>\n",
       "      <td>中華路二段</td>\n",
       "      <td>2房1廳1衛</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>25.028453</td>\n",
       "      <td>121.505532</td>\n",
       "      <td>941</td>\n",
       "      <td>中正區</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>公寓</td>\n",
       "      <td>林森南路</td>\n",
       "      <td>3房2廳1衛</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>25.038463</td>\n",
       "      <td>121.521444</td>\n",
       "      <td>750</td>\n",
       "      <td>中正區</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>公寓</td>\n",
       "      <td>汀州路一段</td>\n",
       "      <td>1房2廳1衛</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>25.029342</td>\n",
       "      <td>121.508230</td>\n",
       "      <td>488</td>\n",
       "      <td>中正區</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>透天厝</td>\n",
       "      <td>汀州路三段</td>\n",
       "      <td>2房1廳1衛</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>25.015911</td>\n",
       "      <td>121.531042</td>\n",
       "      <td>681</td>\n",
       "      <td>中正區</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>電梯大樓</td>\n",
       "      <td>南昌路一段</td>\n",
       "      <td>2房1廳1衛</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>25.031562</td>\n",
       "      <td>121.516954</td>\n",
       "      <td>1176</td>\n",
       "      <td>中正區</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dealTime usage houseType address pattern  floor  floorSum        lat  \\\n",
       "0  2008-12  整層住家      電梯大樓   中華路二段  2房1廳1衛      2         7  25.028453   \n",
       "1  2008-12  整層住家        公寓    林森南路  3房2廳1衛      3         4  25.038463   \n",
       "2  2008-12  整層住家        公寓   汀州路一段  1房2廳1衛      2         3  25.029342   \n",
       "3  2008-12  整層住家       透天厝   汀州路三段  2房1廳1衛      1         3  25.015911   \n",
       "4  2008-12  整層住家      電梯大樓   南昌路一段  2房1廳1衛      4         7  25.031562   \n",
       "\n",
       "          lon  unitPrice district  \n",
       "0  121.505532        941      中正區  \n",
       "1  121.521444        750      中正區  \n",
       "2  121.508230        488      中正區  \n",
       "3  121.531042        681      中正區  \n",
       "4  121.516954       1176      中正區  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df[['dealTime', 'district', 'address', 'lat', 'lon', 'usage', 'houseType', 'pattern', 'unitPrice', 'floor', 'floorSum']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dealTime</th>\n",
       "      <th>district</th>\n",
       "      <th>address</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>usage</th>\n",
       "      <th>houseType</th>\n",
       "      <th>pattern</th>\n",
       "      <th>unitPrice</th>\n",
       "      <th>floor</th>\n",
       "      <th>floorSum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>中正區</td>\n",
       "      <td>中華路二段</td>\n",
       "      <td>25.028453</td>\n",
       "      <td>121.505532</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>電梯大樓</td>\n",
       "      <td>2房1廳1衛</td>\n",
       "      <td>941</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>中正區</td>\n",
       "      <td>林森南路</td>\n",
       "      <td>25.038463</td>\n",
       "      <td>121.521444</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>公寓</td>\n",
       "      <td>3房2廳1衛</td>\n",
       "      <td>750</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>中正區</td>\n",
       "      <td>汀州路一段</td>\n",
       "      <td>25.029342</td>\n",
       "      <td>121.508230</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>公寓</td>\n",
       "      <td>1房2廳1衛</td>\n",
       "      <td>488</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>中正區</td>\n",
       "      <td>汀州路三段</td>\n",
       "      <td>25.015911</td>\n",
       "      <td>121.531042</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>透天厝</td>\n",
       "      <td>2房1廳1衛</td>\n",
       "      <td>681</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2008-12</td>\n",
       "      <td>中正區</td>\n",
       "      <td>南昌路一段</td>\n",
       "      <td>25.031562</td>\n",
       "      <td>121.516954</td>\n",
       "      <td>整層住家</td>\n",
       "      <td>電梯大樓</td>\n",
       "      <td>2房1廳1衛</td>\n",
       "      <td>1176</td>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  dealTime district address        lat         lon usage houseType pattern  \\\n",
       "0  2008-12      中正區   中華路二段  25.028453  121.505532  整層住家      電梯大樓  2房1廳1衛   \n",
       "1  2008-12      中正區    林森南路  25.038463  121.521444  整層住家        公寓  3房2廳1衛   \n",
       "2  2008-12      中正區   汀州路一段  25.029342  121.508230  整層住家        公寓  1房2廳1衛   \n",
       "3  2008-12      中正區   汀州路三段  25.015911  121.531042  整層住家       透天厝  2房1廳1衛   \n",
       "4  2008-12      中正區   南昌路一段  25.031562  121.516954  整層住家      電梯大樓  2房1廳1衛   \n",
       "\n",
       "   unitPrice  floor  floorSum  \n",
       "0        941      2         7  \n",
       "1        750      3         4  \n",
       "2        488      2         3  \n",
       "3        681      1         3  \n",
       "4       1176      4         7  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "lease_100_2008.csv\n",
      "lease_100_2009.csv\n",
      "lease_100_2010.csv\n",
      "lease_100_2011.csv\n",
      "lease_100_2012.csv\n",
      "lease_100_2013.csv\n",
      "lease_100_2014.csv\n",
      "lease_100_2015.csv\n",
      "lease_100_2016.csv\n",
      "lease_100_2017.csv\n",
      "lease_100_2018.csv\n",
      "lease_103_2008.csv\n",
      "lease_103_2009.csv\n",
      "lease_103_2010.csv\n",
      "lease_103_2011.csv\n",
      "lease_103_2012.csv\n",
      "lease_103_2013.csv\n",
      "lease_103_2014.csv\n",
      "lease_103_2015.csv\n",
      "lease_103_2016.csv\n",
      "lease_103_2017.csv\n",
      "lease_103_2018.csv\n",
      "lease_104_2008.csv\n",
      "lease_104_2009.csv\n",
      "lease_104_2010.csv\n",
      "lease_104_2011.csv\n",
      "lease_104_2012.csv\n",
      "lease_104_2013.csv\n",
      "lease_104_2014.csv\n",
      "lease_104_2015.csv\n",
      "lease_104_2016.csv\n",
      "lease_104_2017.csv\n",
      "lease_104_2018.csv\n",
      "lease_105_2008.csv\n",
      "lease_105_2009.csv\n",
      "lease_105_2010.csv\n",
      "lease_105_2011.csv\n",
      "lease_105_2012.csv\n",
      "lease_105_2013.csv\n",
      "lease_105_2014.csv\n",
      "lease_105_2015.csv\n",
      "lease_105_2016.csv\n",
      "lease_105_2017.csv\n",
      "lease_105_2018.csv\n",
      "lease_106_2008.csv\n",
      "lease_106_2009.csv\n",
      "lease_106_2010.csv\n",
      "lease_106_2011.csv\n",
      "lease_106_2012.csv\n",
      "lease_106_2013.csv\n",
      "lease_106_2014.csv\n",
      "lease_106_2015.csv\n",
      "lease_106_2016.csv\n",
      "lease_106_2017.csv\n",
      "lease_106_2018.csv\n",
      "lease_108_2008.csv\n",
      "lease_108_2009.csv\n",
      "lease_108_2010.csv\n",
      "lease_108_2011.csv\n",
      "lease_108_2012.csv\n",
      "lease_108_2013.csv\n",
      "lease_108_2014.csv\n",
      "lease_108_2015.csv\n",
      "lease_108_2016.csv\n",
      "lease_108_2017.csv\n",
      "lease_110_2008.csv\n",
      "lease_110_2009.csv\n",
      "lease_110_2010.csv\n",
      "lease_110_2011.csv\n",
      "lease_110_2012.csv\n",
      "lease_110_2013.csv\n",
      "lease_110_2014.csv\n",
      "lease_110_2015.csv\n",
      "lease_110_2016.csv\n",
      "lease_110_2017.csv\n",
      "lease_110_2018.csv\n",
      "lease_111_2008.csv\n",
      "lease_111_2009.csv\n",
      "lease_111_2010.csv\n",
      "lease_111_2011.csv\n",
      "lease_111_2012.csv\n",
      "lease_111_2013.csv\n",
      "lease_111_2014.csv\n",
      "lease_111_2015.csv\n",
      "lease_111_2016.csv\n",
      "lease_111_2017.csv\n",
      "lease_111_2018.csv\n",
      "lease_112_2008.csv\n",
      "lease_112_2009.csv\n",
      "lease_112_2010.csv\n",
      "lease_112_2011.csv\n",
      "lease_112_2012.csv\n",
      "lease_112_2013.csv\n",
      "lease_112_2014.csv\n",
      "lease_112_2015.csv\n",
      "lease_112_2016.csv\n",
      "lease_112_2017.csv\n",
      "lease_112_2018.csv\n",
      "lease_114_2008.csv\n",
      "lease_114_2009.csv\n",
      "lease_114_2010.csv\n",
      "lease_114_2011.csv\n",
      "lease_114_2012.csv\n",
      "lease_114_2013.csv\n",
      "lease_114_2014.csv\n",
      "lease_114_2015.csv\n",
      "lease_114_2016.csv\n",
      "lease_114_2017.csv\n",
      "lease_114_2018.csv\n",
      "lease_115_2008.csv\n",
      "lease_115_2009.csv\n",
      "lease_115_2010.csv\n",
      "lease_115_2011.csv\n",
      "lease_115_2012.csv\n",
      "lease_115_2013.csv\n",
      "lease_115_2014.csv\n",
      "lease_115_2015.csv\n",
      "lease_115_2016.csv\n",
      "lease_115_2017.csv\n",
      "lease_115_2018.csv\n",
      "lease_116_2008.csv\n",
      "lease_116_2009.csv\n",
      "lease_116_2010.csv\n",
      "lease_116_2011.csv\n",
      "lease_116_2012.csv\n",
      "lease_116_2013.csv\n",
      "lease_116_2014.csv\n",
      "lease_116_2015.csv\n",
      "lease_116_2016.csv\n",
      "lease_116_2017.csv\n",
      "lease_116_2018.csv\n"
     ]
    }
   ],
   "source": [
    "for name in data:\n",
    "    print(name)\n",
    "    df = pd.read_csv(name)\n",
    "    df['district'] = df['Address'].apply(lambda x:x[:3])\n",
    "    df['Address'] = df['Address'].apply(lambda x:x[3:])\n",
    "    df = df[['dealTime', 'district', 'Address', 'lat', 'long', 'Usage', 'Type', 'unitPrice', 'Pattern', 'Floor', 'floorSum']]\n",
    "    df.columns = ['dealTime', 'district', 'address', 'lat', 'lon', 'usage', 'houseType', 'unitPrice', \n",
    "                  'pattern', 'floor', 'floorSum']\n",
    "    df.to_csv(name, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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