Data Clean.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "data = pd.read_csv('listings.csv', header=0, index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Select columns\n",
    "\n",
    "selected_cols = ['zipcode', 'property_type', 'room_type', 'accommodates', 'bathrooms', 'bedrooms', 'beds', \n",
    "                'amenities', 'price', 'minimum_nights', 'cancellation_policy']\n",
    "data = data[selected_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Create dummy variables\n",
    "\n",
    "categorical_variable = ['property_type', 'room_type', 'cancellation_policy']\n",
    "temp = pd.get_dummies(data[categorical_variable])\n",
    "data = pd.concat([data, temp], axis=1).drop(categorical_variable, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Convert price format\n",
    "\n",
    "data['price'] = (data['price'].str.replace(r'[\\$,]', '').astype(float))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Drop any entries that are inconsistent; i.e. 'accommodates', 'bedrooms', 'beds', or 'price' with a value of 0\n",
    "\n",
    "data = data[data['accommodates'] != 0]\n",
    "data = data[data['bedrooms'] != 0]\n",
    "data = data[data['beds'] != 0]\n",
    "data = data[data['price'] != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Handle invalid zipcodes\n",
    "\n",
    "data['zipcode'] = pd.to_numeric(data['zipcode'], errors='coerce')\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Create feature vectors\n",
    "\n",
    "from collections import Counter\n",
    "\n",
    "amenities = data['amenities']\n",
    "x = []\n",
    "for i in range(len(amenities)):\n",
    "    row = amenities.iloc[i][1: -1].split(',')\n",
    "    for item in row:\n",
    "        item = item.replace('\"', '')\n",
    "        x.append(item)\n",
    "\n",
    "c = Counter(x)\n",
    "del c['']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "all_amenities = list(c.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "for i in range(len(amenities)):\n",
    "    row = amenities.iloc[i][1: -1].split(',')\n",
    "    for item in row:\n",
    "        item = item.replace('\"', '')\n",
    "        if item not in data.columns:\n",
    "            data[item] = 0\n",
    "        data.set_value(data.index[i], item, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
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       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
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       "      <th>beds</th>\n",
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       "      <th>property_type_Bed &amp; Breakfast</th>\n",
       "      <th>property_type_Boat</th>\n",
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       "      <th>Suitable for events</th>\n",
       "      <th>Self Check-In</th>\n",
       "      <th>Keypad</th>\n",
       "      <th>Lockbox</th>\n",
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       "      <th></th>\n",
       "      <th>Free parking on street</th>\n",
       "      <th>Doorman Entry</th>\n",
       "      <th>Washer / Dryer</th>\n",
       "      <th>Paid parking off premises</th>\n",
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      "text/plain": [
       "          zipcode  accommodates  bathrooms  bedrooms  beds  price  \\\n",
       "id                                                                  \n",
       "685006    11225.0             2        1.0       1.0   1.0   75.0   \n",
       "9461238   11211.0             5        1.0       1.0   2.0  117.0   \n",
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       "15359479  10003.0             4        1.0       1.0   1.0  200.0   \n",
       "\n",
       "          minimum_nights  property_type_Apartment  \\\n",
       "id                                                  \n",
       "685006                 2                      1.0   \n",
       "9461238                2                      0.0   \n",
       "4873690                1                      1.0   \n",
       "12738047               5                      0.0   \n",
       "15359479               5                      1.0   \n",
       "\n",
       "          property_type_Bed & Breakfast  property_type_Boat  \\\n",
       "id                                                            \n",
       "685006                              0.0                 0.0   \n",
       "9461238                             0.0                 0.0   \n",
       "4873690                             0.0                 0.0   \n",
       "12738047                            0.0                 0.0   \n",
       "15359479                            0.0                 0.0   \n",
       "\n",
       "                    ...              Suitable for events  Self Check-In  \\\n",
       "id                  ...                                                   \n",
       "685006              ...                                0              0   \n",
       "9461238             ...                                0              0   \n",
       "4873690             ...                                0              0   \n",
       "12738047            ...                                0              0   \n",
       "15359479            ...                                0              0   \n",
       "\n",
       "          Keypad  Lockbox  Other pet(s)     Free parking on street  \\\n",
       "id                                                                   \n",
       "685006         0        0             0  0                       0   \n",
       "9461238        0        0             0  0                       0   \n",
       "4873690        0        0             0  0                       0   \n",
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       "\n",
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       "id                                                                  \n",
       "685006                0               0                          0  \n",
       "9461238               0               0                          0  \n",
       "4873690               0               0                          0  \n",
       "12738047              0               0                          0  \n",
       "15359479              0               0                          0  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.drop('amenities', axis=1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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      ],
      "text/plain": [
       "          zipcode  accommodates  bathrooms  bedrooms  beds  minimum_nights  \\\n",
       "id                                                                           \n",
       "685006    11225.0             2        1.0       1.0   1.0               2   \n",
       "9461238   11211.0             5        1.0       1.0   2.0               2   \n",
       "4873690   11101.0             2        1.0       1.0   1.0               1   \n",
       "12738047  10025.0             2        1.0       1.0   1.0               5   \n",
       "15359479  10003.0             4        1.0       1.0   1.0               5   \n",
       "\n",
       "          property_type_Apartment  property_type_Bed & Breakfast  \\\n",
       "id                                                                 \n",
       "685006                        1.0                            0.0   \n",
       "9461238                       0.0                            0.0   \n",
       "4873690                       1.0                            0.0   \n",
       "12738047                      0.0                            0.0   \n",
       "15359479                      1.0                            0.0   \n",
       "\n",
       "          property_type_Boat  property_type_Boutique hotel  ...    \\\n",
       "id                                                          ...     \n",
       "685006                   0.0                           0.0  ...     \n",
       "9461238                  0.0                           0.0  ...     \n",
       "4873690                  0.0                           0.0  ...     \n",
       "12738047                 0.0                           0.0  ...     \n",
       "15359479                 0.0                           0.0  ...     \n",
       "\n",
       "          Self Check-In  Keypad  Lockbox  Other pet(s)     \\\n",
       "id                                                          \n",
       "685006                0       0        0             0  0   \n",
       "9461238               0       0        0             0  0   \n",
       "4873690               0       0        0             0  0   \n",
       "12738047              0       0        0             0  0   \n",
       "15359479              0       0        0             0  0   \n",
       "\n",
       "          Free parking on street  Doorman Entry  Washer / Dryer  \\\n",
       "id                                                                \n",
       "685006                         0              0               0   \n",
       "9461238                        0              0               0   \n",
       "4873690                        0              0               0   \n",
       "12738047                       0              0               0   \n",
       "15359479                       0              0               0   \n",
       "\n",
       "          Paid parking off premises      Y  \n",
       "id                                          \n",
       "685006                            0   75.0  \n",
       "9461238                           0  117.0  \n",
       "4873690                           0   89.0  \n",
       "12738047                          0   80.0  \n",
       "15359479                          0  200.0  \n",
       "\n",
       "[5 rows x 90 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Y'] = data['price']\n",
    "data.pop('price')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "del data['']\n",
    "del data.index.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(36096, 89)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.to_csv('airbnb_v1.csv')\n",
    "data.shape"
   ]
  }
 ],
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