rentallisting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import sys\n",
    "from scipy import stats\n",
    "from scipy.stats import ttest_ind\n",
    "import datetime\n",
    "import json\n",
    "import seaborn as sns\n",
    "from sklearn.feature_extraction import image\n",
    "import imageio\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import os\n",
    "from pylab import *\n",
    "from zipfile import *\n",
    "from PIL import Image\n",
    "#nltk.download()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#reading\n",
    "original=pd.read_json(\"train.json.zip\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Exploratory "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=original\n",
    "df['created'] = df['created'].astype('datetime64[ns]')\n",
    "df['created_hour']=df['created'].dt.hour\n",
    "df[\"location\"] = list(zip(df[\"latitude\"].values,df.longitude.values))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001B265984DD8>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B265953358>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#hist = df.hist(bins = 100)\n",
    "df[\"log_price\"]=np.log(df.price)\n",
    "df.hist(column = [\"latitude\",\"longitude\"],bins=20)\n",
    "df.hist(column = [\"price\",\"log_price\"],bins=30)\n",
    "#the histogram graphs aren't normal because some values are defaulted to 0 if left empty \n",
    "#and most values are relatively similar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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uSVJHDHZJkjpisEuS1BGDXZKkjhjskiR1xGCXJKkjBrskSR0x2CVJ6ojBLklSRwx2SZI6YrBLktQRg12SpI4Y7JIkdcRglySpIwa7JEkdMdglSeqIwS5JUkcMdkmSOmKwS5LUEYNdkqSOGOySJHXEYJckqSMGuyRJHTHYJUnqiMEuSVJHDHZJkjpisEuS1BGDXZKkjhjskiR1xGCXJKkjBrskSR0x2CVJ6ojBLklSRwx2SZI6MpZgT7JDkvOS/L8k1yd5VpKdklyc5MZ2uWPrmyTvSLI6yReT7DeynpWt/41JVo7juUiStJCMa8b+duAfqurJwNOB64GTgEuqajlwSbsNcCiwvP0dD7wLIMlOwMnAM4H9gZMnPgxIkrS1mvdgT/Jo4DnAGQBV9b2qugc4HDirdTsLOKJdPxw4uwaXAzsk2R04GLi4qtZV1d3AxcAh8/hUJElacKYV7EkumU7bND0eWAu8P8kXkrwvySOAXavqNoB2uUvrvydwy8jya1rbVO2T1X98klVJVq1du3aGZUuStPBtNNiTPLRt8t45yY5tP/hOSZYBe8zwMbcF9gPeVVX7At/mR5vdJy1jkrbaSPuDG6tOr6oVVbViyZIlm1uvJEmLxqZm7CcAVwFPbpcTf+cD75zhY64B1lTVFe32eQxBf0fbxE67vHOk/14jyy8Fbt1IuyRJW62NBntVvb2q9gb+R1U9vqr2bn9Pr6q/mMkDVtXtwC1JntSaDgSuAy4AJo5sX8nw4YHWfkw7Ov4A4N62qf4TwEFtS8KOwEGtTZKkrda20+lUVX+e5GeAZaPLVNXZM3zc3wI+lGR74MvAsQwfMs5NchxwM3BU63sR8HxgNfCd1peqWpfkTcCVrd8bq2rdDOuRJKkL0wr2JB8EngBcDdzfmguYUbBX1dXAiknuOnCSvgWcOMV6zgTOnEkNkiT1aFrBzhDC+7SQlSRJC9R0g/0aYDfgtjmsZau07KQLN3uZm049bA4qkST1YLrBvjNwXZLPAfdNNFbVC+ekKkmSNCPTDfbfn8siJEnS7JjuUfGfnetCJEnSlpvuUfHf5Ee/6rY9sB3w7ap69FwVJkmSNt90Z+yPGr2d5AiGM6pJkqQFZEZnd6uqjwPPm+VaJEnSFprupvgXjdx8CMP32v1OuyRJC8x0j4p/wcj19cBNDOdJlyRJC8h097EfO9eFSJKkLTetfexJlib5WJI7k9yR5G+SLJ3r4iRJ0uaZ7sFz72c4feoewJ7A37Y2SZK0gEw32JdU1furan37+wCwZA7rkiRJMzDdYP96kpcl2ab9vQy4ay4LkyRJm2+6wf7rwC8BtzOc4e0lgAfUSZK0wEz3625vAlZW1d0ASXYC3sIQ+JIkaYGY7oz9aROhDlBV64B956YkSZI0U9MN9ock2XHiRpuxT3e2L0mS5sl0w/mtwP9Nch7DT8n+EnDKnFUlSZJmZLq/PHd2klUMJ34J8KKqum5OK9OiseykCzd7mZtOPWwOKpEkTXtzegtyw1ySpAVsRqdtlSRJC5PBLklSRwx2SZI6YrBLktQRg12SpI4Y7JIkdcRglySpIwa7JEkdMdglSeqIwS5JUkcMdkmSOmKwS5LUEYNdkqSOTPvsbtJC5qljJWngjF2SpI4Y7JIkdcRglySpIwa7JEkdMdglSeqIwS5JUkcMdkmSOjK2YE+yTZIvJPm7dnvvJFckuTHJXyfZvrX/WLu9ut2/bGQdr2/tNyQ5eDzPRJKkhWOcM/ZXAdeP3P5j4LSqWg7cDRzX2o8D7q6qJwKntX4k2Qc4GngKcAjwl0m2mafaJUlakMYS7EmWAocB72u3AzwPOK91OQs4ol0/vN2m3X9g6384cE5V3VdVXwFWA/vPzzOQJGlhGteM/W3A7wA/aLcfA9xTVevb7TXAnu36nsAtAO3+e1v/H7ZPsswDJDk+yaokq9auXTubz0OSpAVl3oM9yX8B7qyqq0abJ+lam7hvY8s8sLHq9KpaUVUrlixZsln1SpK0mIzjJDDPBl6Y5PnAQ4FHM8zgd0iybZuVLwVubf3XAHsBa5JsC/w4sG6kfcLoMpIkbZXmfcZeVa+vqqVVtYzh4LdLq+qlwKeBl7RuK4Hz2/UL2m3a/ZdWVbX2o9tR83sDy4HPzdPTkCRpQVpIp219HXBOkjcDXwDOaO1nAB9Mspphpn40QFVdm+Rc4DpgPXBiVd0//2VLkrRwjDXYq+ozwGfa9S8zyVHtVfVd4Kgplj8FOGXuKpQkaXHxl+ckSeqIwS5JUkcMdkmSOmKwS5LUEYNdkqSOGOySJHXEYJckqSMGuyRJHTHYJUnqiMEuSVJHDHZJkjpisEuS1BGDXZKkjhjskiR1xGCXJKkjBrskSR0x2CVJ6ojBLklSRwx2SZI6YrBLktQRg12SpI4Y7JIkdcRglySpIwa7JEkdMdglSeqIwS5JUkcMdkmSOmKwS5LUEYNdkqSOGOySJHXEYJckqSMGuyRJHTHYJUnqiMEuSVJHDHZJkjpisEuS1BGDXZKkjhjskiR1xGCXJKkjBrskSR0x2CVJ6ojBLklSR+Y92JPsleTTSa5Pcm2SV7X2nZJcnOTGdrlja0+SdyRZneSLSfYbWdfK1v/GJCvn+7lIkrTQjGPGvh54bVX9JHAAcGKSfYCTgEuqajlwSbsNcCiwvP0dD7wLhg8CwMnAM4H9gZMnPgxIkrS1mvdgr6rbqurz7fo3geuBPYHDgbNat7OAI9r1w4Gza3A5sEOS3YGDgYural1V3Q1cDBwyj09FkqQFZ6z72JMsA/YFrgB2rarbYAh/YJfWbU/glpHF1rS2qdolSdpqjS3YkzwS+Bvg1VX1jY11naStNtI+2WMdn2RVklVr167d/GIlSVokxhLsSbZjCPUPVdVHW/MdbRM77fLO1r4G2Gtk8aXArRtpf5CqOr2qVlTViiVLlszeE5EkaYEZx1HxAc4Arq+qPxu56wJg4sj2lcD5I+3HtKPjDwDubZvqPwEclGTHdtDcQa1NkqSt1rZjeMxnAy8HvpTk6tb2u8CpwLlJjgNuBo5q910EPB9YDXwHOBagqtYleRNwZev3xqpaNz9PQZKkhWneg72q/onJ948DHDhJ/wJOnGJdZwJnzl51kiQtbv7ynCRJHTHYJUnqiMEuSVJHDHZJkjpisEuS1BGDXZKkjhjskiR1xGCXJKkjBrskSR0x2CVJ6ojBLklSRwx2SZI6YrBLktQRg12SpI4Y7JIkdcRglySpIwa7JEkdMdglSeqIwS5JUkcMdkmSOrLtuAuQFoplJ1242cvcdOphc1CJJM2cM3ZJkjpisEuS1BGDXZKkjhjskiR1xGCXJKkjBrskSR3x626SujMbX130649arJyxS5LUEWfskmaVM11pvJyxS5LUEWfskhYcZ/3SzDljlySpIwa7JEkdMdglSeqI+9glPYD7txeOLf238N9y6+SMXZKkjjhjl6Q54GxZ42KwS7PEN3JJC4HBLnXEDxeSDHZJ0pT8sLj4ePCcJEkdccYuSZoznkJ3/i36YE9yCPB2YBvgfVV16phLkmbMNzBp9m1t/68W9ab4JNsA7wQOBfYBfiXJPuOtSpKk8VnsM/b9gdVV9WWAJOcAhwPXzceDb22fAiVpa7WY3u8X9Ywd2BO4ZeT2mtYmSdJWKVU17hpmLMlRwMFV9Yp2++XA/lX1Wxv0Ox44vt18EnDDPJS3M/D1eXic3jmOs8exnB2O4+xxLDfP46pqyaY6LfZN8WuAvUZuLwVu3bBTVZ0OnD5fRQEkWVVVK+bzMXvkOM4ex3J2OI6zx7GcG4t9U/yVwPIkeyfZHjgauGDMNUmSNDaLesZeVeuTvBL4BMPX3c6sqmvHXJYkSWOzqIMdoKouAi4adx2TmNdN/x1zHGePYzk7HMfZ41jOgUV98JwkSXqgxb6PXZIkjTDYZ1mSQ5LckGR1kpPGXc9iluSmJF9KcnWSVeOuZzFJcmaSO5NcM9K2U5KLk9zYLnccZ42LwRTj+PtJvtZel1cnef44a1wMkuyV5NNJrk9ybZJXtXZfk3PAYJ9F/sTtnPj5qnqGX4nZbB8ADtmg7STgkqpaDlzSbmvjPsCDxxHgtPa6fEY7zkcbtx54bVX9JHAAcGJ7b/Q1OQcM9tn1w5+4rarvARM/cSvNq6q6DFi3QfPhwFnt+lnAEfNa1CI0xThqM1XVbVX1+Xb9m8D1DL8S6mtyDhjss8ufuJ1dBXwyyVXt1wO1ZXatqttgeKMFdhlzPYvZK5N8sW2qd/PxZkiyDNgXuAJfk3PCYJ9dmaTNrx3M3LOraj+GXRsnJnnOuAuSgHcBTwCeAdwGvHW85SweSR4J/A3w6qr6xrjr6ZXBPrum9RO3mp6qurVd3gl8jGFXh2bujiS7A7TLO8dcz6JUVXdU1f1V9QPgvfi6nJYk2zGE+oeq6qOt2dfkHDDYZ5c/cTtLkjwiyaMmrgMHAddsfCltwgXAynZ9JXD+GGtZtCaCqDkSX5eblCTAGcD1VfVnI3f5mpwD/kDNLGtffXkbP/qJ21PGXNKilOTxDLN0GH4h8a8cy+lL8mHguQxnz7oDOBn4OHAu8FjgZuCoqvLAsI2YYhyfy7AZvoCbgBMm9hNrckl+FvhH4EvAD1rz7zLsZ/c1OcsMdkmSOuKmeEmSOmKwS5LUEYNdkqSOGOySJHXEYJckqSMGu6QHSbJs9IxmkhYPg13SvEiy7bhrkLYGBrukqWyT5L3t/NmfTPKwJM9Icnk7AcrHJk6AkuQzSVa06zsnuald/7UkH0nytwwn9Nk9yWXtPObXJPm58T09qU8Gu6SpLAfeWVVPAe4BXgycDbyuqp7G8CtiJ09jPc8CVlbV84BfBT5RVc8Ang5cPSeVS1sxN41JmspXqmoieK9iOKPZDlX12dZ2FvCRaazn4pGfCb0SOLOdEOTjI+uXNEucsUuayn0j1+8HdthI3/X86P3koRvc9+2JK1V1GfAc4GvAB5McMwt1ShphsEuarnuBu0f2i78cmJi93wT8VLv+kqlWkORxwJ1V9V6Gs33tNzelSlsvN8VL2hwrgXcneTjwZeDY1v4W4NwkLwcu3cjyzwX+Z5LvA98CnLFLs8yzu0mS1BE3xUuS1BGDXZKkjhjskiR1xGCXJKkjBrskSR0x2CVJ6ojBLklSRwx2SZI68v8BXIqQtaMyxZ8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "hours=df.groupby(['created_hour']).count()\n",
    "fig = plt.figure()\n",
    "ax = fig.add_axes([0,0,1,1])\n",
    "ax.bar(hours.index,hours[\"bedrooms\"])\n",
    "plt.xlabel(\"hours\")\n",
    "plt.ylabel(\"count\")\n",
    "plt.title(\"Total Posts Created During the Hour\")\n",
    "plt.show()\n",
    "#in order of most posts created: 2, 3, 5, 1, 4\n",
    "\n",
    "#a possible explanation for the curve:the date time column may not be consistent\n",
    "#don't know if the system is always using a 24hr clock or AM/PM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### More pre-processing visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001B26596A208>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001B265CB9C18>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.hist(column = [\"bedrooms\",\"bathrooms\"],bins=8)\n",
    "#bedroom is relatively normal while bathroom is right skewed\n",
    "#but zscore outlier method will be used for both because the max/min values are relatively close to the mean\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Coordinate Map of the Data Points')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xmZlZjSt3OKvwl+tDyHomoysSkZmZDRjlDmd9reD5NmA58P6+D8fMzAaScpPI6RHxRGGBpGkViKd93x8HziJLWDdExLmp/DzgdGA78ImIuLlSMZiZWffKTSLXAsU3Y7wWOKhvwwFJbwOOB2ZHxGZJe6TymcCJwH7AXsCtkl4dEdv7OgYzMytPd3fxfS3Zl3ajpPcWrBpDdpVWJZwBXBoRmwEi4ulUfjxwdSpfLmkZcAjwhwrFYWZm3eiuJ/Ia4J3AWOC4gvKNwEcqFNOrgbdIuhjYBJwTEfcATcBdBfVWpjIzM6uSLpNIRFwPXC/psIjosyN+SbcCe5ZYdX6K6ZVkE2AdDFwjaR9Kz+m+0w8g0/7nAfMApkyZ0hchm5lZCd0NZ50bEV8BPijppOL1EfGJPC8aEXO6eM0zgF9ERAB3S9oBjCPreUwuqDoJWN3J/ucD8wGam5tLJhozM+u97oaz2u/U21LpQAr8iuyHjHdIejUwAngGWAD8l6Svk51Ynw7c3Y9xmZlZke6Gs36d/v1x/4QDwOXA5ZIWA1uAU1OvZImka4CHyS79PdNXZpmZVVe5v1j/NTuff2gl66H8R7rLb5+IiC1k85WUWncxcHFfvZaZmfVOuTMbPgE8D3w/PTYAa8mupPp+ZUIzM7NaV+6PDV8fEYcXLP9a0qKIOFzSkkoEZmZmta/cnsh4SS9dK5uej0uLW/o8KjMzGxDK7Yl8CrhT0uNkv9eYBnxM0i5Af550NzOzGlLu9Lg3SpoOvJYsiTxScDL9skoFZ2Zmta3cnghkN1ucmraZLYmIuKIiUZmZ2YBQ7iW+VwL7AveT3YYdskt+nUTMzOpYT+ZYn5l+9GdmZgaUf3XWYkrfMNHMzOpYuT2RccDDku4GNrcXRsS7KhKVmZkNCOUmkQsqGYSZWb07+6ctXP/A2g5lR0zfjR+dfliVIipPWcNZEfFb4BFgdHosTWVmZtZLpRIIwB2PPcdpP6ztyVvLvTrr/cBXgTvIfifybUmfjohrKxibmdmgsXRNKwsXr2XV+jaaxjYwd9YEZkxsBODGh57udLtFjz3XXyHmUu6J9fOBgyPi1Ig4hWxu889XLiwzs8Fj6ZpW5i9aTmvbViY2jqK1bSvzFy1n6ZpWALbt6PzC1x39FWRO5Z4TGRIRhanyWcpPQGZmdenob9zO0rUv7lQ+FHj1hF1YuHgtMyY2MmyI2NpFInn/f/yBUw+bwrGzmyoYbT7lJoKFkm6WdJqk04AbgBsrF5aZ2cB20EU3lUwgkP1ie+naF7jhwVUAHPO6PTrdz64jxIa2rVx606Mv1a8l5d4769OS3ge8ieycyPyI+GVFIzMzG0Daz3k8vKaVh1e18uyL3Q9ELVuXJZlvntQM7HxyfcyIIcxoGvvS8o//8Jea642Ufe+siLgOuK6CsZiZDUjt5zz+9sJmHlzZynMvbu3xPr55UjPfPCl7/tav3s4eu45gyJCXB4tGjxzK2g2dTyLb1Yn7SupyOEvSRkkbSjw2StpQ8ejMzAaAhYvXsmPHDh5atQFJvd7fhDGj2Lh5e4eyjZu3M2HMqJL1uztxX0ldJpGIGB0RY0o8RkfEmIpHZ2Y2AKxa38aa1k3siGDksPKvOeqs5qmHTeGFzdtobdvK85u28PjTz/OX515k91cML5kYFi5eS2PDcBobhjNEeun5wsU7//akr/kKKzOzXmoa28CzL2yhYfhQtu+Iss8THLf/hJLlx85u4jNHv4bhQ8STz7YxdIh4y6t2Z0JjQ8kexqr1bYwe1fFVR48axqr1bXneTo/0ZD4RMzMrYe6sCdy85K/sGB60vriVXUYNo3XTti63OX7/CemEemnHzm7iz2tf4KC2rTQ2DO+wrv3S4HZNYxtoLaq3cdM2msY25HxH5XMSMTPrpRkTG/n4kfvy7dseZ5eRwfYdwchh4oUt2xkiMWSImPzKV3DGEfv06OqqVevbmNjY8TxIqR7G3FkTmL9o+UvrN27KhsI+cPCk3r+5bjiJmJn1gWNnN7HP+F379AqpcnsYMyY2Mu/waR1e+wMHT+qXq7OcRMzM+siMiY19+sXdkx5GX792uWruxLqkAyTdJel+SS2SDknlkvQtScskPSjpwGrHamZWSe09jMaG4axp3URjw3DmHT6tKsmiM7XYE/kKcGFE3CTpmLR8BHA0MD093gB8L/1rZjZoVauHUa6a64kAAbT/BqURWJ2eHw9cEZm7gLGSJlYjQDMzy9RiT+SfgZsl/RtZkntjKm8CVhTUW5nK1hTvQNI8YB7AlClTKhqsmVk9q0oSkXQrsGeJVecDfwf8S0RclybD+iEwh+zGj8VK3js5IuYD8wGam5s7v7+ymZn1SlWSSETM6WydpCuAs9Piz4EfpOcrgckFVSfx8lCXmZlVQS2eE1kNvDU9PxJ4LD1fAJySrtI6FGiNiJ2GsszMrP/U4jmRjwDflDQM2EQ6t0E2CdYxwDLgReDD1QnPzMza1VwSiYg7gYNKlAdwZv9HZGZmnanF4SwzMxsgaq4nYmY2EFVrZsFqc0/EzKyXqjmzYLU5iZiZ9VI1ZxasNg9nmZn1UrnzfhQbDENg7omYmfVS09gGNhbNZNjdzIKDZQjMScTMrJfmzppAa9tWWtu2siPipedzZ5WeQx0GzxCYk4iZWS/lmfdj1fo2Ro/qeEahnCGwWuNzImZmfaCn836UO/VtrXNPxMysCvIMgdUiJxEzsyoYCFPflsPDWWZmVVLrU9+Wwz0RMzPLzT0RM7NBpL9/wOieiJnZIFGNHzA6iZiZDRLV+AGjk4iZ2SBRjR8wOomYmQ0See7h1VtOImZmg0Q1fsDoJGJmNkhU4weMvsTXzGwQ6e8fMLonYmZmuTmJmJlZbk4iZmaWW1WSiKQTJC2RtENSc9G68yQtk/SopHcUlM9NZcskfab/ozYzs2LV6oksBt4LLCoslDQTOBHYD5gLfFfSUElDge8ARwMzgZNSXTMzq6KqXJ0VEUsBJBWvOh64OiI2A8slLQMOSeuWRcQTaburU92H+ydiMzMrpdbOiTQBKwqWV6ayzspLkjRPUouklnXr1lUkUDMzq2BPRNKtwJ4lVp0fEdd3tlmJsqB0sovOXjsi5gPzAZqbmzutZ2ZmvVOxJBIRc3JsthKYXLA8CVidnndWbmZmVVJrw1kLgBMljZQ0DZgO3A3cA0yXNE3SCLKT7wuqGKeZmVGlE+uS3gN8GxgP3CDp/oh4R0QskXQN2QnzbcCZEbE9bXMWcDMwFLg8IpZUI3YzM3uZIgb3KYPm5uZoaWmpdhhmZgOGpHsjorn7mrU3nGVmZgOIk4iZmeXmJGJmZrk5iZiZWW5OImZmlpuTiJmZ5eYkYmZmuTmJmJlZbk4iZmaWm5OImZnl5iRiZma5OYmYmVluTiJmZpabk4iZmeXmJGJmZrk5iZiZWW5OImZmlpuTiJmZ5eYkYmZmuTmJmJlZbk4iZmaWm5OImZnlNqzaAZiZlWPpmlYWLl7LqvVtNI1tYO6sCcyY2FjtsOqeeyJmVvOWrmll/qLltLZtZWLjKFrbtjJ/0XKWrmmtdmh1z0nEzGrewsVraWwYTmPDcIZILz1fuHhttUOre1VJIpJOkLRE0g5JzQXlR0m6V9JD6d8jC9YdlMqXSfqWJFUjdjPrf6vWtzF6VMfR99GjhrFqfVuVIrJ21eqJLAbeCywqKn8GOC4iXgecClxZsO57wDxgenrM7Yc4zawGNI1tYOOmbR3KNm7aRtPYhipFZO2qkkQiYmlEPFqi/E8RsTotLgFGSRopaSIwJiL+EBEBXAG8ux9DNrMqmjtrAq1tW2lt28qOiJeez501odqh1b1aPifyPuBPEbEZaAJWFqxbmcpKkjRPUouklnXr1lU4TDOrtBkTG5l3+DQaG4azpnUTjQ3DmXf4NF+dVQMqdomvpFuBPUusOj8iru9m2/2A/wu8vb2oRLXobPuImA/MB2hubu60npkNHDMmNjpp1KCKJZGImJNnO0mTgF8Cp0TE46l4JTCpoNokYHXxtmZm1r9qajhL0ljgBuC8iPh9e3lErAE2Sjo0XZV1CtBlb8bMzCqvWpf4vkfSSuAw4AZJN6dVZwGvAj4v6f702COtOwP4AbAMeBy4qb/jNjOzjpRd7DR4NTc3R0tLS7XDMDMbMCTdGxHN3desseEsMzMbWAZ9T0TSOuCpnJuPI/sBpGXcHi9zW3Tk9uhooLfH3hExvpyKgz6J9IaklnK7dPXA7fEyt0VHbo+O6qk9PJxlZma5OYmYmVluTiJdm1/tAGqM2+NlbouO3B4d1U17+JyImZnl5p6ImZnl5iRiZma5OYmUIGmupEfTLIqfqXY8/U3S5ZKelrS4oGw3SbdIeiz9+8pqxtifJE2WdLukpWlGzrNTeV22iaRRku6W9EBqjwtT+TRJf0zt8TNJI6oda3+RNFTSnyT9d1qum7ZwEikiaSjwHeBoYCZwkqSZ1Y2q3/2InWeO/AxwW0RMB25Ly/ViG/CpiJgBHAqcmf4m6rVNNgNHRsT+wAHAXEmHkk3f8I3UHn8DTq9ijP3tbGBpwXLdtIWTyM4OAZZFxBMRsQW4Gji+yjH1q4hYBDxXVHw88OP0/MfU0cySEbEmIu5LzzeSfVk0UadtEpnn0+Lw9AjgSODaVF437ZGmrziW7AaxpDuN101bOInsrAlYUbDc5SyKdWRCuiV/+6359+im/qAkaSrweuCP1HGbpOGb+4GngVvI7qy9PiLaJ0Kvp/83lwHnAjvS8u7UUVs4ieysR7MoWv2QtCtwHfDPEbGh2vFUU0Rsj4gDyCaIOwSYUapa/0bV/yS9E3g6Iu4tLC5RddC2RcVmNhzAVgKTC5Y9i2JmraSJEbFG0kSyI9C6IWk4WQK5KiJ+kYrruk0AImK9pDvIzhWNlTQsHYHXy/+bNwHvknQMMAoYQ9YzqZu2cE9kZ/cA09PVFSOAE4EFVY6pFiwATk3PT6WOZpZMY9w/BJZGxNcLVtVlm0gan2YhRVIDMIfsPNHtwN+nanXRHhFxXkRMioipZN8Vv4mIk6mjtvAv1ktIRxWXAUOByyPi4iqH1K8k/RQ4gux21muBLwK/Aq4BpgB/AU6IiOKT74OSpDcDvwMe4uVx78+SnRepuzaRNJvsZPFQsgPRayLiIkn7kF2IshvwJ+BDEbG5epH2L0lHAOdExDvrqS2cRMzMLDcPZ5mZWW5OImZmlpuTiJmZ5eYkYmZmuTmJmJlZbk4iZj0k6flu1o+V9LGC5b0kXZueH5AuIe/pa14g6ZyeR2tWWU4iZn1vLPBSEomI1RHR/sOzA4AeJxGzWuUkYpaTpF0l3SbpPkkPSWq/2/OlwL6S7pf0VUlTJS1Od0C4CPhAWveB4h5Gqjc1PT8/zWtzK/Cagjr7Sloo6V5Jv5P02n5702ZFfO8ss/w2Ae+JiA2SxgF3SVpANq/IrHSDwvY7/xIRWyR9AWiOiLPSugtK7VjSQWS30Xg92f/T+4D2m/zNBz4aEY9JegPwXbJbj5v1OycRs/wEfFnS4WS3Q2kCJvTRvt8C/DIiXgRIyan9TsJvBH6e3dILgJF99JpmPeYkYpbfycB44KCI2CrpSbI7ufbENjoOKxduX+qeREPI5qo4oIevY1YRPidill8j2VwSWyW9Ddg7lW8ERneyTfG6J4EDASQdCExL5YuA90hqkDQaOA4gzWOyXNIJaRtJ2r/v3pJZzziJmOV3FdAsqYWsV/IIQEQ8C/w+nST/atE2twMz20+sk81RsluaJfAM4M9pH/cBPwPuT3V+V7CPk4HTJT0ALKHOpm+22uK7+JqZWW7uiZiZWW5OImZmlpuTiJmZ5eYkYmZmuTmJmJlZbk4iZmaWm5OImZnl9v8B8uWKpmMQgjUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.latitude,df.longitude,alpha=0.5)\n",
    "plt.xlabel(\"latitude\")\n",
    "plt.ylabel(\"longitude\")\n",
    "plt.title(\"Coordinate Map of the Data Points\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Quick visualizations with target variable (interest level)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#instead of using low/med/high, we'll represent the interest level with 1,2,3 respectively\n",
    "def interests(level):\n",
    "    if level == \"low\":\n",
    "        return 1\n",
    "    elif level == \"medium\":\n",
    "        return 2\n",
    "    elif level == \"high\":\n",
    "        return 3\n",
    "df[\"interest\"]=df[\"interest_level\"].apply(interests)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#just swapping 2 records so interest_level appears low,med,high for first 3 records\n",
    "temp = df.iloc[0]\n",
    "df.iloc[0]=df.iloc[1]\n",
    "df.iloc[1]=temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "distribution=df.interest_level.value_counts()\n",
    "labsl = \"low\", \"medium\", \"high\"\n",
    "colors = ['gold', 'orange', 'lightcoral']\n",
    "plt.pie(distribution,autopct='%1.1f%%',shadow = True, labels = labsl, colors= colors)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Logged Prices of the Interest Levels')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "avg_price = df.groupby([\"interest_level\"]).mean()\n",
    "plt.scatter(df.interest_level,np.log(df.price),alpha = 0.5)\n",
    "plt.scatter(avg_price.index,np.log(avg_price.price))\n",
    "plt.legend([\"logged prices\",\"Average logged prices\"])\n",
    "plt.xlabel(\"Interest Level\")\n",
    "plt.ylabel(\"logged prices\")\n",
    "plt.title(\"Logged Prices of the Interest Levels\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1b265c49128>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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zy+Xbn/+UXhAVEenQb6Cb2c+BucB77l7WMTYSeAQYB+wC5rv7B+krM+Hy8lIFuIhILwayQv8lcD/wq6PGFgNr3f0uM1vccf+fgi+vq3GLf3vM2K67Lk/3tJLFauujrNneQN3+ZkpHRKgqK2ZCSVGmyxJJSr/X0N29Gni/2/BVwIqO2yuAeQHXdYyewryvcZH+1NZHWV69k2hzjJKiQqLNMZZX76S2Pprp0kSSkuyLosXuXg/Q8VUbqkjorNneQFEkP/HeBrPO22u2N2S6NJGkpL3LxcwWmVmNmdU0NjamezqRAavb38ywwq5XHYcV5lG3vzlDFYmkJtlAbzCzEoCOr71+yoS7L3f3CnevGD16dJLTiQSvdESEgy1tXcYOtrRROiKSoYpEUpNsoP8GWNhxeyHwZDDliJw4VWXFRJtjifc2uHferiorznRpIknpN9DNbCWwEfi0me01s68BdwEXm9kO4OKO+2nVWzeLulwkWRNKilhUOZ6iSD710RaKIvksqhyvLhcJrX7bFt19QS/fujDgWvpVMjyP+gNtXe6LpGJCSZECXLJGaN76P+MHz3UJc4D6A23M+MFzGapIRGRwCU2gdw/z/sZFRE42oQl0ERHpmwJdRCRLhCbQe3sBVC+MiogkhCbQN95+yTHhXTI8j423X5KhikREBpdQLW976nIRScVvt9Z12WN/4Yyx2qJZQis0K3TttihB++3WOu569k0ONMc4/dQhHGiOcdezb/LbrXWZLk0kKaEJdJGgrdi4m1MK8hK7LebkUBTJ55SCPFZs3J3p0kSSokCXk1bDgRaGFeR2GRtWkEvDgZYMVSSSGgW6nLSKhxdysLW9y9jB1naKhxdmqCKR1CjQ5aS1cMZYPmxtS+y2GI8TbY7xYWsbC2eMzXRpIkkJTaBrt0UJ2uXlpSy+9NMMj+Tz3qHDDI/ks/jST6vLRULL3P2ETVZRUeE1NTUnbD4RkWxgZpvcvaK/x4WqD72nFkWt0EVEEkJzyUV96CIifQtNoIuISN8U6CIiWUKBLiKSJRToIiJZIqVAN7P/aWZ/MbPtZrbSzNL2Fjv1oYuI9C3ptkUzKwVuAia6e7OZrQa+BPwyoNpE0u7mlTU8s+092uJOXo5x2aTTWbag33ZfkT5lalvmVC+55AERM8sDhgL/mXpJPVPbogTt5pU1PLmlgba4k2vQFnee3NLAzSv15jdJXia3ZU460N29Drgb2A3UA1F3/11QhYmk2zPb3sOA/FwjJ8fIzzWsY1wkWZncljnpQDezjwFXAeOB/wKcYmZf7uFxi8ysxsxqGhsbk69UJGBHVuZHO7JSF0lWJrdlTuWSy0XATndvdPcY8Djw2e4Pcvfl7l7h7hWjR49OYTqRYOXlGO3dsrvdE+MiycrktsypBPpu4DwzG2pmBlwI1AZTlkj6XTbpdByItTvxuBNrd7xjXCRZmdyWOZVr6H8CHgX+DGzrONbygOo6htoWJWjLFlRw1eTizpV6Xo5x1eRidblISjK5LXOots/VbosicjIa6Pa5oXmnqNoWRUT6FppAFxGRvinQRUSyhAJdRCRLKNBFRLJEaAJdbYsiIn0L1YdEiwRNrbCSDlOXPkvTR/HO+6cNzWHTHZemfd7QrNDVtihB0zkl6dA9zAGaPoozdemzaZ87NIEuIhIG3cO8v/EgKdBFRLKEAl1EJEso0EVEAnTa0J5jtbfxIIUm0NW2KEHTOSXpsOmOS48J7xPV5RKq3RZFRE5GA91tMVR96OoZlqDV1kdZs72Buv3NlI6IUFVWzISSokyXJZKU0FxyUc+wBK22Psry6p1Em2OUFBUSbY6xvHontfXRTJcmkpTQBLpI0NZsb6Aokp/4dHazzttrtjdkujSRpCjQ5aRVt7+ZYYVdrzoOK8yjbn9zhioSSY0CXU5apSMiHGxp6zJ2sKWN0hGRDFUkkhoFupy0qsqKiTbHEp/O7t55u6qsONOliSQlpUA3sxFm9qiZvWFmtWY2I6jCulPPsARtQkkRiyrHUxTJpz7aQlEkn0WV49XlIqGVatviMmCNu/+jmQ0BhgZQk8gJc+myDV3uL1u7Q4sESVmmWqyTXqGb2XCgEngIwN0Pu/v+oArrTm2LEjSdU5IOmTyvUrnk8kmgEfiFmb1mZj8zs1MCqktERI5TKoGeB5wL/MTdzwE+BBZ3f5CZLTKzGjOraWxsTGE6ERHpSyqBvhfY6+5/6rj/KImA78Ldl7t7hbtXjB49OoXpRESkL0kHurv/DdhjZp/uGLoQeD2QqkRE5Lil2of+beBhM9sKTAF+kHpJPVPbogRN55SkQybPq5TaFt19M9Dvlo4ig1XJ8DzqD7R1uS+SqkwtCkLzTlG1mEnQZvzguS5hDlB/oI0ZP3guQxWJpCY0gS4StO5h3t+4yGCnQBcRyRIKdBGRLKFAl5NWby+A6oVRCavQBLpazCRoG2+/5JjwLhmex8bbL8lQRSKpMXc/YZNVVFR4TU3NCZtPRCQbmNkmd++3RTxUv1tmaktKyV46pyQdQrd97ommPnQJms4pSYewbp8rIiKDiAJdRCRLKNBFRLKEAl1EJEuEJtDVhy5B0zkl6RDa7XNFRORY2j63H2oxk6DpnJJsE5pAFxGRvinQRUSyhAJdRCRLKNBFRLJEaAJdLWYSNJ1Tkm1Sbls0s1ygBqhz97mplyQiEm5h3m3xZqA2gOP0SS1mEjSdU5IOod1t0czGAJcDPwumHBERSVaqK/R7gduAeG8PMLNFZlZjZjWNjY0pTiciIr1JOtDNbC7wnrtv6utx7r7c3SvcvWL06NHJTiciIv1IZYV+PnClme0CVgGfN7N/D6QqERE5bkkHurt/193HuPs44EvAC+7+5cAq60YtZhI0nVOSDtptcYD0gyZB0zkl6ZCp8yqQQHf3dcC6II7VF31CuwTtmgfWs2nPgc77Uz8+nMe+dUEGKxJJXmjeKaqeYQla9zAH2LTnANc8sD5DFYmkJjSBLhK07mHe37jIYKdAFxHJEgp0EZEsoUCXk9bUjw8/rnGRwS40ga6eYQnaY9+64JjwVpeLhFmo+tBFgtZTl4tIqsK8fe4JobZFCZrOKUmH0G6fKyIig0eoAv3KnA1sGHIT7xRcx4YhN3FlzoZMlyQhp3NKsklorqFfmbOBu/J/xlA7DMAY28dd+T+DGCQ+Y0Pk+OickmwTmhX6bXmrO3/wjhhqh7ktb3WGKpKw0zkl6ZKp3/xCE+hjcpqOa1ykPzqnJB12Xfchd+X/jDE5+8gxGJOT+M1v13Ufpn3u0AQ6RWOOb1ykPzqnJB3WLu3xNz/WLk371KEJ9Jsar+AjH9Jl7CMfwk2NV2SoIgm7uqm3cNgKuowdtgLqpt6SoYokK0T3Ht94gEIT6L+Jz2Rx7Ab2xkcRd2NvfBSLYzfwm/jMTJcmIbW69bM8NXYxBwrOwDEOFJzBU2MXs7r1s5kuTcIsg7/5habLBRKh/pvDCnAJRt3+ZuKll/PQmLmdY3F36vc3Z7AqCb0L74CnboLYUedRfiQxnmahWaGLBK10RISDLW1dxg62tFE6IpKhiiQrlM+HK+6Doo8Dlvh6xX2J8TQL1QpdJEhVZcUsr94JwLDCPA62tBFtjnHtNL0oKikqn39CAry70KzQtduiBG1CSRGLKsdTFMmnPtpCUSSfRZXjmVBSlOnSRJKS9ArdzD4O/Ao4A4gDy919WVCFiZwIly7r+oaPZWt3aJEgKQvjbottwP9y9wnAecC3zGxiMGUdSzvjSdB0Tkk6hHK3RXevd/c/d9w+CNQCpUEVJiIixyeQa+hmNg44B/hTD99bZGY1ZlbT2NgYxHQiItKDlAPdzE4FHgO+4+7HfNyLuy939wp3rxg9enSq04mISC9SCnQzyycR5g+7++PBlCQiIslIOtDNzICHgFp3/z/BldQztS1K0HROSTpk8rwyd0/uiWYzgfXANhJtiwC3u/szvT2noqLCa2pqkppPRORkZWab3L2iv8cl3Yfu7hsAS/b5yZj9o+fZ9X5r5/1xIwtYd9tFJ7IEEZFBKzTvFO0e5gC73m9l9o+ez1BFIiKDS2gCvXuY9zcuInKyCU2gi4hI3xToIiJZIjSBPm5kwXGNi4icbEIT6Otuu+iY8FaXi4jI34XqAy566nIRSUWmtjmV7BbG7XNPKG11KkHTOSXpEMrtc0VEZHBRoIuIZAkFuohIllCgi4hkidAEurY6laDpnJJ0COX2ucnQ9rkiIsdvoNvnhmaFLiIifVOgi4hkCQW6iEiWUKCLiGQJBbqISJZQoIuIZImUAt3MqszsTTP7q5ktDqooERE5fklvn2tmucADwMXAXuBVM/uNu78eVHHdaatTCdrSp7axuqaOllicwvwc5leUcscVkzJdloRcbX2UNdsbqNvfTOmICFVlxUwoKUr7vKms0KcDf3X3d9z9MLAKuCqYso6lrU4laEuf2saKl3dzuC1OQS4cbouz4uXdLH1qW6ZLkxCrrY+yvHon0eYYJUWFRJtjLK/eSW19NO1zpxLopcCeo+7v7RgTCYXVNXXk5hgFeTnk5ORQkJdDbo6xuqYu06VJiK3Z3kBRJJ+iSD45Zp2312xvSPvcqQS69TB2zD4CZrbIzGrMrKaxsTGF6USC1RKLk9/tJyA/JzEukqy6/c0MK+x6NXtYYR51+5vTPncqgb4X+PhR98cA/9n9Qe6+3N0r3L1i9OjRKUwnEqzC/By6Z3csnhgXSVbpiAgHW9q6jB1saaN0RCTtc6dy5r4K/IOZjTezIcCXgN8EU5ZI+s2vKKU97rS2xYnH47S2xWmPO/MrdOVQkldVVky0OUa0OUbcvfN2VVlx2udOOtDdvQ24EXgOqAVWu/tfgiqsO211KkG744pJLPzsWIbk5dDaDkPyclj42bHqcpGUTCgpYlHleIoi+dRHWyiK5LOocvwJ6XLR9rkiIoOcts8VETnJKNBFRLKEAl1EJEso0EVEsoQCXUQkS5zQLhczawTeDeBQo4B9ARxH5AidU5IOQZ1Xn3D3ft+ZeUIDPShmVjOQFh6RgdI5Jelwos8rXXIREckSCnQRkSwR1kBfnukCJOvonJJ0OKHnVSivoYuIyLHCukIXEZFuBm2gm9mhTNcgJw8zW2dmFR23nzGzEZmuSQYnMxtnZtt7GF9qZhf189wlZnZLumpL+kOiRbKVu1+W6RokfNz9jkzXMGhX6EdYwr+a2XYz22Zm13aM/18zu7Lj9hNm9vOO218zs+9nsmY5MTpWSm+Y2c86zo+HzewiM3vJzHaY2XQzO8XMfm5mr5rZa2Z2VcdzI2a2ysy2mtkjQOSo4+4ys1HdV2JmdouZLem4vc7M7jGzajOrNbNpZvZ4x7w6/7Jfrpn9PzP7i5n9ruN8+qWZ/SOAmV3WcW5uMLP7zOzpo547seP8ecfMbgqyqDCs0L8ATAEmk3jX1atmVg1UAxeQ+JSkUqCk4/EzgVUZqFMy478CXwQWkfgUretInANXArcDrwMvuPv1HZdRXjGz54FvAB+5e7mZlQN/TmLuw+5eaWY3A08CU4H3gbfN7B53b0r1DyeD1j8AC9z962a2GrjmyDfMrBB4EKh0951mtrLbc88CPgcMA940s5+4eyyIogb9Cp3ED+dKd2939wbgD8A0YD1wgZlNJPFD22BmJcAM4OWMVSsn2k533+buceAvwFpPtG5tA8YBc4DFZrYZWAcUAmOBSuDfAdx9K7A1ibmPfOTiNuAv7l7v7q3AO3T9vF3JPjvdfXPH7U0kzrUjzgLecfedHfe7B/pv3b3V3fcB7wGBfTZdGFbo1tOgu9eZ2ceAKhKr9ZHAfOCQux88gfVJZrUedTt+1P04ifO7HbjG3d88+klmBtBfz24bXRc9hb3MffS8R88t2evof+92jrpkRy+Z1cdzAztXwrBCrwauNbNcMxtNYmX1Ssf3NgLf6XjMeuCWjq8iRzwHfNs6EtzMzukYrwb+W8dYGVDew3MbgNPN7DQzKwDmnoB6JfzeAD5pZuM67l97oiYOwyriCRKXUbaQWFHd5u5/6/jeemCOu//VzN4lsUpXoMvRvgfcC2ztCPVdJIL5J8AvzGwrsJm/LxI6uXvMzJYCfwJ2kvhBFemTuzeb2f8A1phR1haNAAAAXklEQVTZPno4t9JF7xQVEQmYmZ3q7oc6FhEPADvc/Z50zxuGSy4iImHz9Y4X4v8CFJHoekk7rdBFRLKEVugiIllCgS4ikiUU6CIiWUKBLiKSJRToIiJZQoEuIpIl/j9fv3t5AQ5pdAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"rooms\"]=df.bathrooms+df.bedrooms\n",
    "avg_room = df.groupby([\"interest_level\"]).mean()\n",
    "plt.scatter(df.interest_level,df.rooms, alpha = 0.5)\n",
    "plt.scatter(avg_room.index,avg_room.rooms)\n",
    "plt.legend( [\"Rooms\",\"Average Rooms\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Average Interest Level of Most Popular Neighbourhoods')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "popular=df.display_address.value_counts()[:10].index.values\n",
    "pop=df[df.display_address.isin(popular)]\n",
    "pop=pop.groupby([\"display_address\"]).mean()\n",
    "avg = df.interest.mean()\n",
    "plt.figure(figsize=(20,6))\n",
    "plt.bar(pop.index,pop.interest)\n",
    "plt.bar(\"Total Average\",avg)\n",
    "plt.xlabel(\"10 Most Popular Neighbourhoods\",fontsize = 13)\n",
    "plt.ylabel(\"Interest Level (1=low, 3 = high)\", fontsize = 16)\n",
    "plt.title(\"Average Interest Level of Most Popular Neighbourhoods\", fontsize =18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outliers/Missing Values\n",
    "### Missing values are either empty strings or defaulted to the value 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#missing values for bedroom/bathrooms\n",
    "#difficult to know if an apartment actually has 0 bathrooms or 0 bedrooms or it's just default value\n",
    "#assume it's missing value if both bathroom and bedroom has value of 0\n",
    "#df[(df[\"bedrooms\"]==0) &  ( df[\"bathrooms\"]==0)].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2016-04-01 22:12:41\n",
      "2016-06-29 21:41:47\n"
     ]
    }
   ],
   "source": [
    "#no missing values for date as they're all within expected time frame\n",
    "print(df[\"created\"].min())\n",
    "print(df[\"created\"].max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bedroom  9475\n",
      "bathroom  313\n",
      "building id  8286\n",
      "description  1446\n",
      "display add  135\n",
      "features  3218\n",
      "lat  12\n",
      "listing id  0\n",
      "lon  12\n",
      "manager id missing values:  0\n",
      "photos with no link: 3615\n",
      "price missing values: 0\n",
      "street address missing values: 10\n",
      "missing value for interest level: 0\n"
     ]
    }
   ],
   "source": [
    "#although 0 is a valid longitude and latitude, we'll assume it is a missing value if it's exactly 0\n",
    "#\n",
    "print(\"bedroom \",df[df[\"bedrooms\"]==0].shape[0])\n",
    "print(\"bathroom \",df[df[\"bathrooms\"]==0].shape[0])\n",
    "print(\"building id \",df[df[\"building_id\"]==\"0\"].shape[0])\n",
    "print(\"description \",df[df[\"description\"]==\"\"].shape[0])\n",
    "print(\"display add \",df[df[\"display_address\"]==\"\"].shape[0])\n",
    "print(\"features \", df[df[\"features\"].str.len()==0].shape[0])\n",
    "print(\"lat \",df[df[\"latitude\"]==0].shape[0])\n",
    "print(\"listing id \", df[df[\"listing_id\"]==0].shape[0])\n",
    "print(\"lon \",df[df[\"longitude\"]==0].shape[0])\n",
    "#no missing value for manager_id\n",
    "print(\"manager id missing values: \",df[df[\"manager_id\"]==\"\"].shape[0])\n",
    "print(\"photos with no link:\", df[df[\"photos\"].str.len() == 0].shape[0])\n",
    "print(\"price missing values:\", df[df[\"price\"]==0].shape[0])\n",
    "print(\"street address missing values:\", df[df[\"street_address\"]==\"\"].shape[0])\n",
    "print(\"missing value for interest level:\", df[df[\"interest_level\"]==\"\"].shape[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n"
     ]
    }
   ],
   "source": [
    "#outlier for interst level if it isn't one of the 3 categories\n",
    "print(df[(df[\"interest_level\"] != \"medium\") & (df[\"interest_level\"] != \"high\") & (df[\"interest_level\"] != \"low\")].shape[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#too difficult for my computer to run the whole thing, so i split it \n",
    "from sklearn.cluster import DBSCAN\n",
    "outlier_detection = DBSCAN(\n",
    "  eps = 2,\n",
    "  min_samples = 5)\n",
    "clusters = outlier_detection.fit(df[[\"longitude\",\"latitude\"]].iloc[:25000])\n",
    "lab1=clusters.labels_\n",
    "clusters2 = outlier_detection.fit(df[[\"longitude\",\"latitude\"]].iloc[25000:])\n",
    "lab2=clusters2.labels_\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df[\"loc_out\"]=np.concatenate([lab1,lab2])\n",
    "#set all normal points to the same colour\n",
    "def nonoutlier(x):\n",
    "    if x >-1:\n",
    "        x = 0\n",
    "    return x\n",
    "df[\"loc_out\"] = df['loc_out'].apply(nonoutlier)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 648x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.figure(figsize=(9,6))\n",
    "plt.scatter(df.latitude[df.loc_out==-1],df.longitude[df.loc_out==-1], alpha=0.5,c=\"red\")\n",
    "plt.scatter(df.latitude[df.loc_out==0],df.longitude[df.loc_out==0], alpha=0.3, c=\"black\")\n",
    "plt.legend([\"outlier\",\"norm\"])\n",
    "plt.xlabel(\"latitude\")\n",
    "plt.ylabel(\"longitude\")\n",
    "plt.title(\"Coordinate Map of Data Points and Outliers\")\n",
    "plt.show()\n",
    "df[df.loc_out==-1].shape[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Outlier detection for price, bathrooms, bedrooms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "Q1 = df.price.quantile(0.25)\n",
    "Q2 = df.price.quantile(0.50)\n",
    "Q3 = df.price.quantile(0.75)\n",
    "IQR = Q3 - Q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "431"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#usually it's 1.5*IQR but that detects lots of valid points as outliers\n",
    "price_out=(df.price < (Q1 - 1.5 * IQR)) |(df.price > (Q3 + 6 * IQR)) \n",
    "df[price_out].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Outliers of Price Feature')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.index,np.log(df.price), alpha=0.3)\n",
    "plt.axhline(y=np.log(Q2), color=\"black\", linestyle='-') #median\n",
    "plt.scatter(df.index[price_out],np.log(df.price[price_out]),alpha=0.3,c=\"red\")\n",
    "plt.legend([\"median\",\"norm\",\"outlier\"])\n",
    "plt.xlabel(\"Record Index\")\n",
    "plt.ylabel(\"logged prices\")\n",
    "plt.title(\"Outliers of Price Feature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1b265966908>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x=df['bedrooms'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Under normal curve, 99.7% of data lies within 3 std dev of the mean\n",
    "#bathrooms distribution is a bit right skewed so we have 2% detected as outliers which is more than we want\n",
    "#so we modify it to 4*std dev of mean which is any apartment with more than 3 washrooms is an outlier\n",
    "bath_out = df.bathrooms>df.bathrooms.mean()+4*df.bathrooms.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(290, 21)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[bath_out].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatterplot of Bedrooms')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df.index,(df.bathrooms),alpha=0.3)\n",
    "\n",
    "plt.scatter(df.index[bath_out],(df.bathrooms[bath_out]),c=\"red\", alpha = 0.5)\n",
    "plt.ylabel(\"bathrooms\")\n",
    "plt.legend([\"norm\",\"outlier\"])\n",
    "plt.xlabel(\"record index\")\n",
    "plt.title(\"Scatterplot of Bedrooms\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "bed_out = df.bedrooms > df.bedrooms.mean()+3*df.bedrooms.std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scatterplot of Bathrooms')"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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h+uuv58gjj5x2nMZUw26PbA5oU74d8NatyZh9R0dyZH/BBdMevz8Q2e2RzVgHxO2RReRYYENF0VHAlap6XVptGsOJJx5UCd6Y2ZTmE68eB04CEBEfeB64Pa32DHRtvIee9RvwOjqIV66k9aJ1NfXEK1NbbHufurk6aXs28LSq2pmplHRtvIfeqz+HdPfAiiOQ7h56r/4cXRvvme/QZuxAGnacT9YP+xzM23ua5irhXwjcMkdt1aSe9RugtQ1vcRvie3iL26C1LSlfwOrq6tizZ0/NJztVZc+ePdTV1c13KAeEg3V7T1vqV+mISBY4H/jkft6/BLgEYOUMLp+rdV5HB6w4YlSZtLQgM7gG/UCwYsUKOjs76erqmu9Q5l1dXR0rxv6dQY06WLf3tM3FZZnnAvep6s7x3lTVG4AbILlKZw7iOSjFK1ci3T3I4rZymfb2ogv8QzSTybB69er5DsMcYA7W7T1tczGk8y5sOCd1rRetg55u4r3daBQT7+2Gnu6k3JiDjG3v05NqwheRBuCNwG1ptmOg/YzTabnicrStFTqfQ9taabnicrtqwRyUbHufHvvDK2OMWcCm8odXdi8dY4ypEZbwjTGmRljCN8aYGmEJ3xhjaoQlfGOMqRGW8I0xpkZYwjfGmBphCd8YY2qEJXxjjKkRlvCNMaZGWMI3xpgaYQnfGGNqhCV8Y4ypEZbwjTGmRljCN8aYGmEJ3xhjakSqz7QVkVbgq8AaQIH3qerdabY5ma6N99CzfgNeRwfxypXlR6JNVhaccALFu/4fjfdvRoCBk06l7eMfnvQJO9W2V6qnNL3/wANkXnyBzMgQqBJ7HrmBPuqHB4l9n+7lqwg//jes/suLq1ruZ794E/KF62l+voPYEwaXH0nxzDPLbVe26/X3ES9qIXrVq0bFVs1yTnf+ao3Xn+PV37XxHrqv/VeaHtiCNzJMsaGJ4qGHjRtTtXVOFtNky9618R56PnklSx+8Fz8s0nvoEaPWYWUcUSYLIviF/KQxVS6rAoMnr6Xtsr8G9r+dzaRvJ5q+2jan0+eT9c94bVcTz2yt/+nOP5dSfeKViNwM/FpVvyoiWaBBVXv2N33aT7zq2ngPvVd/DlrbkJYWtLcX79lnQIR41er9lz39FG2bNxEHPvnWJQAEQwMMHbGK5n++ZsIdsZr26Omm5YrLAZLpw5Cmh+6noa8HiSO8SMmGI3hA6GVQgdAPyDc00/cP10ya9J/94k00ffoKIs+jbqgfiWP8KGLXCacQL1mKf/75RD/8IYQhDU88horgaczQMcejgT/po+PKyznN+as1Xn+W+m5sYu2/7G9peG4bsefR2L0bL4oZamlj4ISTR8VUbZ0zXfaujfcweMmHaH/6UYrZHLF4ZAp5CnUN9P3DNTSdeHw5Dh0eovne3wHQf/prkbr6/cZUuaxhQxOQbJsjrUuIFy0adzvbXwKeSj9Mddseu36m2ueV84zXP1Xtx+O0M2vrf5rzz4YD4olXIrIIeD3wNQBVLUyU7OdCz/oN0NqGt7gN8T28xW1k9u4hu2f3hGXZF1/Ej4r4YYjW16P19YSNzWT27knqnGF7tLbRs35Defrsiy+SzY8Q5urwFIK4iLg6RSAOAjzAj4rIF66fdLnlC9dTaGgkCIvEfkBY10CYybLomSeTDfUL15fbjXN1yRFqXT2ZnTvKsVXTr9Odv1rj9ed49fes30Bm7x7Cxmayw0NEmSxhXT2Z/PBLYqq2zpkue8/6DTTv2E4xmyPK5tBMhrCuvrwOK+PIPf00YWMzYVMzuaefnDCmymUtb5tNzSzasX2/29lM+nai6SfatmfS1th5xuufavbj8dqZrfU/3fnnWppj+EcBXcCNInK/iHxVRBrHTiQil4jIZhHZ3NXVlWI44HV0IC0to8r8Qh6vkJ+wLBjoA1W8OCyXxblcMl1Hx4zbk5YWvI6O8vTBQB8Sx6jnIyieKuJSvmiMioeogioNXTsnXe6Grp1EjU0ExQLq+QBEmSy54UGkpYWGrp3lduNcLlm+bI6gv78c20Qq457O/NUarz/Hq9/r6Ei+6udy5WWOfR8vil4SU7V1ThbTZMvudXSQLeSJgmx53tj3y+uwMo5SXaV6JoqpclnL9WZzZCbYziZajplMP9G2PZO2xs4zXv9Usx+P185srf/pzj/X0kz4AXAK8EVVPRkYBC4fO5Gq3qCqa1V1bXt7e4rhQLxyZfKVq0KUTTacicrCpkXJV0Nv3ykPL59Pplu5csbtaW8v8cqV5enDpkWo5yFxhCLEIijJ0FuS7GNUBEQYal826XIPtS/DHxwgzGSROALALxbI1zeivb0MtS8rt+vlkx3EK+QJm5vLsU2kMu7pzF+t8fpzvPrjlSuJsjm8fL68zF4UEfv+S2Kqts7JYpps2eOVKylkc/hhoTyvF0XldVgZR6muUj0TxVS5rOV6C/lk2Gg/29lEyzGT6SfatmfS1th5xuufavbj8dqZrfU/3fnnWpoJvxPoVNVN7vWtJB8A86b1onXQ0028txuNYuK93RQXL6GwZOmEZYVDDyXyM0RBgAwPI8PDBIP9FBcvKZ8Ymkl79HTTetG68vSFQw+lkKsjyI8Qixu3d3WqgheGxEDkZ9APXTrpcuuHLiU7NEgYZPCikGBkiKBYoO+ol0NPd1KHa9fLj+D19eKPDFNctrwcWzX9Ot35qzVef45Xf+tF6yguXkIw2E+hvgG/WCAYGaaYq39JTNXWOdNlb71oHf3LjyRTyOMX8kixSDAyXF6HlXHkjz6aYLCfYKCf/NEvnzCmymUtb5sD/fQtP3K/29lM+nai6SfatmfS1th5xuufavbj8dqZrfU/3fnnWtonbX8NfEBVHxeRq4BGVf2b/U2f9klbsKt07Codu0pnpn070fTVtmlX6cyeqZy0TTvhn0RyWWYWeAZ4r6p272/6uUj4xhhzMJlKwk/1OnxVfQCoKhBjjDHpsr+0NcaYGmEJ3xhjaoQlfGOMqRGW8I0xpkZYwjfGmBphCd8YY2qEJXxjjKkRlvCNMaZGWMI3xpgaYQnfGGNqhCV8Y4ypEZbwjTGmRljCN8aYGmEJ3xhjaoQlfGOMqRGp3g9fRLYB/UAEhNXepH+6Sk/+WbRpI7nBfvINzfS//BXo0qVkn3yc5o5nqB8axA/zBIAAMcJgXQN9y48kamwAhbodnbT27MbXmIKfpWv1MVCXpXHHc4hCoa4egNzwEETJg809IAwyFOvq8VSJcnUMrVyFLllK8OILeP19+IOD1PXtpb63m1xYwAdioBBkGWhZQrF5EYMnryXzxnMIb/lO+clIA61LGHrZsWSf66BtRwe+RhT8LGEmS8NAD9k4xAMUKPoZBhoXkcuP4GvEYHMLe04+ncann6LlxU5UYKS+iaCYp2GwD1HI1zew5+XHEy1qofmJR8kNDRAGGbJ9PTSFeff4dIhEeP5layisOTF50lBfP41PP07z7p0EUUiYyTLYtpSRxmYWdb1AbmSYoaZmdr/xrQRtrWR+/SsaX3gOv1BEXaWe6wMRwS8UCIpFvKhIoHHSpucT+QGxlxybxEFAHEM2KiDAcF0D/csOJ1ssoMDQylUANHZswxsZJvY8MiMjZIcHwROGF7Ux0thM23PP0JAfQoDIC+hb3E7P8SeiS5biF/JEmSyydw/NTzxK494uMmERSPqq69TX4l94IcWf3lV+ytRISxuNndtp7d0NCAMNybNWmwd68OIoWU7PZ7Cukb3HvBLq6/f7dKzKp5MRhUSZHHEmQ7GhiaixgbrnO2np24unMcO5hnJ9ddueoXnPLrJh0hfFbI7BtqXJPrAk2QcaX0i24eGWxRRbWmjueJbG/h5i36dv8SEMvexYgr17yvsKnpAPsggQxCHFbB3dRx1T3lbqe7vx3HOSS+unkKtnpHXJqCeMAaOe5FbX200QFsr7TGZkhCAskG9spu81Z6JHHU3uJz+m+fkO/JERsvlhMnGyDiI/Q3/bUrr/4A3lJ3v1XfphDnvkfvw4YrCphV2nvY66F1+goWsnoeeDCEEUMtS+jOE1J9Lyy5+zZOdzeKqM5OrpesUJeAMDHLL9Sfw4YiTIMtLcivg++YYmii0t1PV0j3p6WnjIMhCh8XcbWbKzE19jRrL1dL59Hcfd8rXyely07WlyI8OIRkgcE2iE7/bXQpBj2zsv4rhbvjaj3FettJ94tQ1Yq6q7q5l+Jk+86tp4D/2X/S3NTz9Bbqif2PPxi0V8Dcn7OXLFEYIwxCPGg3ISKyniUczlCGNoLg4Tk6wQD0FQip7PSH0jfrFItpAHgaIfkAlDfGJGghyZsAAi5LP1DC1qpamvm5FcPWF9A16xQEvPbuI4JuueUCuuDSH5RNxzyOFIGJEb6icTRxRy9RBFNIwMEscRgXhEng9RSICOWobK3xWIEEI/g6qSi4sUgywj2TrqhwcJNHLTJQ9HV7eMkRcQ5nJECA3DA/jj9JMCfU2t7D36WA59+AEyYYFYPHxNeixEyKCE4jPUtIigkKeuMELXIYfT3NcNcUyukEc0xkPJewG5OHlGb+nr5tg2S+1C8uFQ+qCM8PBQPJTutkMoNDbS+uIO8IT+liU09HVTlx9O5nM7fRxFZIlH9X2p/8MgS3/rUnqPW0PLw1tpdPFm42L5gykSn9gTCtk6Co2LyLcuJujZy5LdO/BIPnBxbZTiLW1v6n5iYGDRYobaluJpzNAxx6OBT8sVlzOw9RGaPn0FkedR39dNXWEEiSKK2TpUISjmyRCjCKF4ZDQmRhnONlBfGEZcT5UOAPJ+BnyfQpAlE4WEQYBEEUEYkgnzIB6hH+CFISJJP+B5+FGyfhQtJ6fQCygGGXKF4WRb8QMyYRFfo30HBQiIR8F9+A+ccDLS35f0fVMTTQ/dT1P3HjJhkWKQIVsYSbZC8Rmpbyivq1x+iIHmNuoG+qgvDL9kKCJC6GtZQv/hR5LZu5v2XZ1Eno+KkCnm8YHuRUsoNDSyZNfzqEL3oUfg54dp3bsz2c5EUGVUnxX9DHEUkSNGgZ5Fi8mNDJMt5ulrWUKmmMeLYvL1DagfJAdFhSG3LyWxexrz7Cmvo63jWYhCFnXvxtdo1DKU+ivZHoQnL3zvtJP+VJ54ddAM6fSs30Bm7x78YoEoW0eUq8PTGBQa8kMEcYh6Mm6yBwiIkTimqTicrH4RlwaTjSETR4R1DWTDAnhJmsiFRdQTYvHIRfvKfY1p7O+hmM1RNzJEZmSY+qEBIs8flewr//eBxr4egjiivjAMqkTZHEEcEfkBGQCNCTNZ/Iq4hZcujwA+igY+mTj5BhKEBYI4Kk+c/KeoF5T7JIgjJIrxVcdN9qX5Gob6OeTx34OAesnyxp6HVi6fCNliAREh9nzad3YSBRlEBCT5sIrFS5K9eOUYxvbLeP2UvBZ896EXIzT37SU7PAwioEpjfw+eKupJeVmjICgn+8o6kw928KIIPyzQ+shW/DgEEbKu/9S1KZIsb31+CL9YQOvraRroLdflub4v1V+5vUlFWcNAb3J0X1dPZucOaG2jZ/0G5AvXU2hoJAiLBFFE6GfAE4KwgAhkSh8knk9ATOT6rrEwhLrtr/ThAkI2CkGV+pEhxG3Dfhzjx2ESj8bEQQbxkkgzGpOJwvJ27Y2JPRMWQTyCOCITFd3CyajlL/2fyQ+T2bmDzN49ZPfsJvvii2TzI8k68/2kLk/Kc/txTJSto2GoHxTqhwbIhsWXrK9S/XXDgyzasZ2lu54n8nziIIP6gdtvoWmwl+aePYR+QBxkaOruon5oIFnXbvsrTV+5nEF5r4eW/m4Xb0BTXzdRJktYV0/9YD9+sUBjwR1Q+AHq+eXtffWWjRQaGqkfGkBEy9GPv78qK27fwFxIO+Er8FMR2SIil4w3gYhcIiKbRWRzV1fXtBvyOjrwC3m8OCL2k7TgaYwo+O7/8RJYOQ5AVPcd+bl1NHZj8zR2K03dD6gIoqWUoIgqQVgkymTx4wgvjgjCYnKUOYFMWETiCE/V1UeyPJ5fThTlWCfpjyQuzx2luYQWR6Pmc/tquT5B8eJ41Ff08XhxTKaYL9dSOkJS2Vd7Uley7KEf4KFEQdbV7T5KpZScZNLl2d8yJvV4BFFEUCwktbn+F/atE0/TmPQfAAAWiElEQVST6fbXjpCsWy+KyA0PInFM6TvQvmNAt94VvFjL/ZS05XZojSfcqcofXG7eOJsj6O9HWlrwOjpo6NpJ1NhEUCwkrYo7iIjj8vZWqqgij1Skk33fJvbFq24f0HKZpzoq0VGxPZe3Lxn9LTSZL3brrVTH+OtEVPGiiKC/P9kvC3mCgT7ELUfpSLi8z7jtJfZ9vDgpD8Kim2a8flSCKCRTyONrNGrfKsUURMl+p+IR+x6ZsEgQFqn8gGJUX+1bf+UyTWKLPJ9AI9TziX2/vF/v2zJK6yQm9AN8NFmPrr2JCJArjkw4zWyZcsIXkTYRObHKyc9Q1VOAc4FLReT1YydQ1RtUda2qrm1vb59qOGXxypVE2VyyIUVuZxIPFYjc/xMNXilJwipt3Oq2dK14v1wn+3aP5MNBXbJLhkdUhDDIJN82PJ/Y8wmDzKSJtBhkkg1KpJw8Y8/Hi6PyUEA51kn6I4krJnYbd+zq0jHTlJaxtEyx5036wRR7HsVMrlxLabeXiuHBpK5k2YMoJEbww4Kr2w0jqZbT6XQGFksxJzuZT5jJJrW5/i99fCtCLMl0+2tHSdZt7Pvk6xtRd8RbqmPftwFBBWJPyv2UtFX60PMYP0XtaweScxMAXiFP2NyM9vYSr1zJUPsy/MEBwkw2aVWTpYw8r7y9lSrSisyuY/4dHa+4fUDKZbEkvb4v1n3bc3n70n11lbcP8dx6K9Ux/jpREWLfJ2xuTvbLbI6waRHqlsOLI2LxyuuntL14UeTO1yhhkHHTjNePQugHFLM5IvFH7VulmEI/2e9EY7wophhkCIOMa3Ofym96pfVXLpMkNj+OCMVPDsiiqLxfj/rIU9zBR0iEJOvRtTcRBfKZugmnmS1VJXwR+YWILBKRxcCDwI0i8vnJ5lPVHe7/XcDtwOkzCXYirReto7h4SXJUXRjBz48kG4vAUK6B0AuQWMtj82OFeKjnMZCpT1ah7hvdBih6PsHIEIUgC3GyWeeDDBInRz15f195JB6Dza1kCnlG6hoo1tUz3NCEH0cUKO10jPo/AgYXtRJ6PsPZehDBL+QJPR8/CilC8lW6WKDyY2O85F8aw5cwougl5+XDIJucvBqVIARx4+fJGK2P+h6RCNF++kmBoYZmdh27BhQkTpbXi2OkcvlUKWSyqCZHbl3LVuCHRVSTTKUkR9R5L8DTuBzD2H4Zr5+S10rp+MpD6V+0mEJ9fZIgRRhsbk2SfKzlZfXDkALeS+osfyD6PlGQpef4E4m8AFQpuP4rfXSoJss7nGtITuwODzPQ1LLvgMD1fan+yu2tcgx/qKkFr68Xf2SY4rLl0NNN60Xr0A9dSnZokDDIEPo+QVSEWAmDLKrJuSZIvq2FJOdOYmAw24C47W9fwlcKfgAiDNc1oG4bjjyPyAvKCc4Li6j7RlMUj6IflLfrsQcZxSCTDC16fnK+Qim3WVr+0v/FXD3FZcspLl5CYclSCoceSiFXl6yzKErqirU8d+R5+IURhhqaQWC4oYlCkHnJ+irVP1LfSN/yI9l9yOHJEXdYRKKwfAgy0NhCf+sSgijEC4sMtLUz3NCUrGu3/ZWmr1zOsOIbQG9zm4s3ZGBRG36xQDAyzHBjM1Emy2A2uYDDi0Ikjsrb+7OnnkF2aJDhhia04pN5/P1V6Hz7OuZCVSdtReR+VT1ZRD4AHKGqnxaRraq63yN9EWkEPFXtd7/fBXxWVf9rf/PM5KQt2FU6il2lY1fp2FU6tXaVzlRO2lab8B8C3gTcDPydqt5bRcI/iuSoHpLLP7+tqn8/UTszTfjGGFNrppLwq70O/7PAT4DfuGR/FPDkRDOo6jPAq6qs3xhjTMqqSviq+j3gexWvnwHekVZQxhhjZl9VCV9EVgN/BayqnEdVz08nLGOMMbOt2iGdO4CvAf8BE151Zowx5gBVbcIfUdV/SzUSY4wxqao24f+riHwa+ClQ+hNLVPW+VKIyxhgz66pN+CcAfw68gdF/8PmGNIIyxhgz+6pN+G8HjlLVQprBGGOMSU+199J5EGhNMxBjjDHpqvYIfxnwmIjcy+gxfLss0xhjFohqE/6nU43CGGNM6qr9S9tfisgy4DRXdI+7A6YxxpgFotrbI/8pcA/wJ8CfAptE5J1pBmaMMWZ2VTuk83fAaaWjehFpB/4fcGtagRljjJld1V6l440ZwtkzhXmNMcYcAKo9wv8vEfkJcIt7vQ74cTohGWOMSUO1J23/RkQuAM4keVDUDap6+ySzASAiPrAZeF5V3zbtSCfxs4df4FO3PcjOwYmfG2uMMQcCX+AVy5r42BuP4exXHjYnbVZ7hA+wESiS3FLhninM92HgUWDRFOaZkp89/AIf+c599BfTasEYY2ZXpPDIiwNc+cNHAOYk6U/1Kp13MoWrdERkBfBW4KszCXIyGzZ3WrI3xiw4CvQM5dmwuXNO2kv7Kp3rgE8AzfubQEQuAS4BWLlyZZXhjLazf2Ra8xljzHwrRjpnOSy1q3RE5G3ALlXdMtF0qnqDqq5V1bXt7e1VhjPasua6ac1njDHzLePLnOWwahP+f4nIT0TkYhG5GLiTya/SOQM4X0S2Ad8B3iAi35p2pBNYt3YFzZk0ajbGmPQI0NqQY93aFXPTnqpWN+Hoq3R+Ve1VOm7es4CPT3aVztq1a3Xz5s3VVjuKXaVjjFlIZusqHRHZoqprq5l20jF8d1nlT1T1HOC2aUeVsrNfeRib5ujSJmOMWYgmHdJR1QgYEpGW6Taiqr9I8xp8Y4wxk6v6IebAQyJyFzBYKlTVv04lKmOMMbOu2oR/p/sxxhizQFV7a4Wb0w7EGGNMuiZM+CLyEMkfg41LVU+c9YiMMcakYrIj/NKJ1kvd/990/18EDKUSkTHGmFRMmPBVdTuAiJyhqmdUvHW5iGwEPptmcMYYY2ZPtX9p2ygiZ5ZeiMjrgMZ0QjLGGJOGaq/SeT/wdXctvgK9wPtSi8oYY8ysq/YqnS3Aq0RkEcntGHrTDcsYY8xsq/Z++MtE5GvABlXtFZHjReT9KcdmjDFmFlU7hn8T8BNguXv9BPCRNAIyxhiTjmoT/lJV/S4QA6hqCNhtKY0xZgGpNuEPisgS3B9hichrSE7cGmOMWSCqvUrnY8APgaPc9fftJM+3NcYYs0BUm/AfAW4n+evafuAOknF8Y4wxC0S1QzrfAF4B/APw78DL2XebBWOMMQtAtUf4x6rqqype/7eIPDjRDCJSB/wKyLl2blXVT08vzMm99fM/4+Fdc/Pkd2OMmU3bPvfWOWmn2iP8+92JWgBE5NXAxknmyQNvcB8UJwFvrqxjNlmyN8YsZKsun5vHjVR7e+QM8G4R6XCvjyQZ198vTZ6OPuBeZtxPdU9MnyJL9sYYM7lqb488Le4B6FuAlwHXq+qmcaa5BLgEYOXKlTNpzhhjzASquj3ydLkHoJ8kIq3A7SKyRlV/P2aaG4AbANauXZvKNwBjjDHVj+HPiKr2AL8A3pxG/a88pC6Nao0x5qCSWsIXkXZ3ZI+I1APnAI+l0dadHzvbkr4xZsGaq6t0qr0sczoOA2524/ge8F1V/VFajd35sbPTqtoYYw4KqSV8Vd0KnJxW/cYYY6ZmTsbwjTHGzD9L+MYYUyMs4RtjTI2whG+MMTXCEr4xxtQIS/jGGFMjLOEbY0yNsIRvjDE1whK+McbUCEv4xhhTIyzhG2NMjbCEb4wxNcISvjHG1AhL+MYYUyMs4RtjTI1I7X74InIE8A3gUCAGblDVf02rvWMuv5NCWpUbY0yK5uqJV2ke4YfAZap6HPAa4FIROT6NhizZG2MWslWX3zkn7aSW8FX1BVW9z/3eDzwKHJ5GW5bsjTFmcnMyhi8iq0ged7hpnPcuEZHNIrK5q6trLsIxxpialHrCF5Em4PvAR1S1b+z7qnqDqq5V1bXt7e1ph2OMMTUr1YQvIhmSZL9eVW9Lq51sWhUbY8xBJLWELyICfA14VFU/n1Y7AE987q2W9I0xC9ZcXaWT2mWZwBnAnwMPicgDruxTqvrjNBp7Yo46zBhjFqrUEr6q/gaQtOo3xhgzNfaXtsYYUyMs4RtjTI2whG+MMTXCEr4xxtQIS/jGGFMjLOEbY0yNsIRvjDE1whK+McbUCEv4xhhTIyzhG2NMjbCEb4wxNcISvjHG1AhL+MYYUyMs4RtjTI2whG+MMTUitfvhi8jXgbcBu1R1TVrtlKy6/M60mzDGmFTM1ROv0jzCvwl4c4r1l1myN8YsZHOVw1JL+Kr6K2BvWvUbY4yZmnkfwxeRS0Rks4hs7urqmu9wjDHmoDXvCV9Vb1DVtaq6tr29fb7DMcaYg9a8J3xjjDFz46BI+HN1htsYY9IwVzkszcsybwHOApaKSCfwaVX9WlrtWdI3xpiJpZbwVfVdadVtjDFm6g6KIR1jjDGTs4RvjDE1whK+McbUCEv4xhhTIyzhG2NMjbCEb4wxNcISvjHG1AhL+MYYUyMs4RtjTI2whG+MMTXCEr4xxtQIS/jGGFMjLOEbY0yNsIRvjDE1whK+McbUiFQTvoi8WUQeF5GnROTyNNsyxhgzsTSfeOUD1wNvBDqBe0Xkh6r6SBrtHXP5nRTSqNgYY1I2V0/sS/MI/3TgKVV9RlULwHeAP06jIUv2xpiFbNXld85JO2km/MOB5yped7qyWWfJ3hhjJpdmwpdxyvQlE4lcIiKbRWRzV1dXiuEYY0xtSzPhdwJHVLxeAewYO5Gq3qCqa1V1bXt7e4rhGGNMbUsz4d8LvFxEVotIFrgQ+GEaDWXTqNQYYw4yqSV8VQ2B/wX8BHgU+K6qPpxGW0987q2W9I0xC9ZcXaWT2mWZAKr6Y+DHabZR8sQcdZgxxixU9pe2xhhTIyzhG2NMjbCEb4wxNcISvjHG1AhL+MYYUyNE9SV//DpvRKQL2D7DapYCu2chnPli8c+/hb4MFv/8m8tlOFJVq/qr1QMq4c8GEdmsqmvnO47psvjn30JfBot//h2oy2BDOsYYUyMs4RtjTI04GBP+DfMdwAxZ/PNvoS+DxT//DshlOOjG8I0xxozvYDzCN8YYMw5L+MYYUyMOmoQvIm8WkcdF5CkRuXyeYzlCRP5bRB4VkYdF5MOufLGI3CUiT7r/21y5iMi/udi3isgpFXW9x03/pIi8p6L8VBF5yM3zbyIy3hPGZrocvojcLyI/cq9Xi8gmF8sG95wDRCTnXj/l3l9VUccnXfnjIvJHFeWpry8RaRWRW0XkMbcuXruQ1oGIfNRtP78XkVtEpO5AXwci8nUR2SUiv68oS73P99fGLMX/f9w2tFVEbheR1or3ptS301l/s0pVF/wP4ANPA0eRPA/lQeD4eYznMOAU93sz8ARwPPBPwOWu/HLgGvf7W4D/JHks5GuATa58MfCM+7/N/d7m3rsHeK2b5z+Bc1NYjo8B3wZ+5F5/F7jQ/f4l4C/d7x8CvuR+vxDY4H4/3q2LHLDarSN/rtYXcDPwAfd7FmhdKOuA5PnPzwL1FX1/8YG+DoDXA6cAv68oS73P99fGLMX/JiBwv19TEf+U+3aq62/W94k0Kp3rH7cB/KTi9SeBT853XBXx/AB4I/A4cJgrOwx43P3+ZeBdFdM/7t5/F/DlivIvu7LDgMcqykdNN0sxrwB+BrwB+JHbwXZXbPjlPid5yM1r3e+Bm07GrofSdHOxvoBFJAlTxpQviHVAkvCfI0l6gVsHf7QQ1gGwitEJM/U+318bsxH/mPfeDqwfr88m69vp7EOzuU+o6kEzpFPaOUo6Xdm8c1/NTgY2ActU9QUA9/8hbrL9xT9Reec45bPpOuATQOxeLwF6NHmS2dg2y3G693vd9FNdrtl0FNAF3CjJsNRXRaSRBbIOVPV54FqgA3iBpE+3sLDWQclc9Pn+2pht7yP5ZsEkcY5XPp19aFYdLAl/vLHTeb/eVESagO8DH1HVvokmHadMp1E+K0TkbcAuVd1SWTxBmwdU/E5A8tX8i6p6MjBI8lV/fw6oZXBj0H9MMlSwHGgEzp2gzQMq/iotqJhF5O+AEFhfKtpPPNOJf06W7WBJ+J3AERWvVwA75ikWAEQkQ5Ls16vqba54p4gc5t4/DNjlyvcX/0TlK8Ypny1nAOeLyDbgOyTDOtcBrSJSeixmZZvlON37LcDeSeJPe311Ap2qusm9vpXkA2ChrINzgGdVtUtVi8BtwOtYWOugZC76fH9tzAp34vhtwEXqxl2mEf9upr7+ZtdsjxHNxw/J0dwzJEdDpZMkr5zHeAT4BnDdmPL/w+gTS//kfn8ro09e3ePKF5OMQ7e5n2eBxe69e920pZNXb0lpWc5i30nb7zH6hNOH3O+XMvqE03fd769k9EmtZ0hOaM3J+gJ+DRzrfr/K9f+CWAfAq4GHgQZX/83AXy2EdcBLx/BT7/P9tTFL8b8ZeARoHzPdlPt2qutv1veJNCqdjx+SM/5PkJwd/7t5juVMkq9jW4EH3M9bSMbkfgY86f4vbcQCXO9ifwhYW1HX+4Cn3M97K8rXAr938/xfUjjB49o5i30J/yiSqySechtuzpXXuddPufePqpj/71yMj1NxFctcrC/gJGCzWw93uOSxYNYB8BngMdfGN11iOaDXAXALyTmHIslR6/vnos/318Ysxf8Uyfh6aV/+0nT7djrrbzZ/7NYKxhhTIw6WMXxjjDGTsIRvjDE1whK+McbUCEv4xhhTIyzhG2NMjbCEb8wEROQmEXnnOOWfFZFzpljXNhFZOnvRGTM1weSTGHNgcbfEFVWNJ514avUGuu8+JxNS1Stns21j5oId4ZsFQURWSXJP+y8A9wFHiMibRORuEblPRL7n7l2EiJwmIr8VkQdF5B4RaXb3kr/R3Uv9fhH5H27ai928/wH81N2j/f+KyCMicif7uQlX5ZG/O3L/jIvjIRF5hStfIiI/de19mYr7pYjIn7nYHhCRL0vy7IEj3X3Sl4qIJyK/FpE3pdqxpqZYwjcLybHAN3TfzdCuAM5R1VNI/qL2Y+6BEhuAD6vqq0juSTNM8qfrqOoJJLfVvVlE6ly9rwXeo6pvILn97bHACcD/JLl/TTV2uzi+CHzclX0a+I2L94fASgAROQ5YB5yhqicBEck9WraT3G/9S8BlwCOq+tMp9pEx+2VDOmYh2a6qv3O/v4bkARQb3UOPssDdJMn6BVW9F0DdXUpF5Ezg313ZYyKyHTjG1XWXqpZuVPV64BZVjYAdIvLzKmMr3SBvC3BBRV0XuDbvFJFuV342cCpwr4u9HnezL1X9qoj8CfBBkltDGDNrLOGbhWSw4nchSdTvqpxARE5k/NvKTvT4wcExr6dzv5G8+z9i9H61v1huVtVPvuQNkQb23RGyCeifRizGjMuGdMxC9TvgDBF5GSSJUkSOIbnZ2HIROc2VN7vbzf4KuMiVHUMyvPL4OPX+CrjQjakfBvyPGcRY2ea5JDdvg+TmXu8UkUPce4tF5Ej33jUk91u/EvjKDNo25iXsCN8sSKraJSIXA7eISM4VX6GqT4jIOuDfRaSeZPz+HOALwJdE5CGSh1hcrKp5eelzx28nuf//QyR3O/zlDML8jIvvPldPh4v9ERG5guQksUdyZ8ZL3dPRTiMZ249E5B0i8l5VvXEGMRhTZnfLNMaYGmFDOsYYUyMs4RtjTI2whG+MMTXCEr4xxtQIS/jGGFMjLOEbY0yNsIRvjDE14v8H/ieZ86uSpWkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#5 or more bedrooms will be the outlier\n",
    "\n",
    "plt.scatter(df.index,(df.bedrooms),alpha = 0.3)\n",
    "plt.scatter(df.index[bed_out],(df.bedrooms[bed_out]),c=\"red\", alpha = 0.5)\n",
    "plt.ylabel(\"bedrooms\")\n",
    "plt.legend([\"norm\",\"outlier\"])\n",
    "plt.xlabel(\"record index\")\n",
    "plt.title(\"Scatterplot of Bathrooms\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "297"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[bed_out].shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  after removing the cwd from sys.path.\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: UserWarning: Boolean Series key will be reindexed to match DataFrame index.\n",
      "  \"\"\"\n"
     ]
    }
   ],
   "source": [
    "#removing outlier records\n",
    "original_record_num=df.shape[0]\n",
    "df.drop(df[bed_out].index,inplace=True)\n",
    "df.drop(df[bath_out].index,inplace=True)\n",
    "df.drop(df[price_out].index,inplace=True)\n",
    "df.drop(df[df.loc_out==-1].index,inplace=True)\n",
    "df.drop(df[df.latitude ==0].index,inplace= True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Imputation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "#set \"missing values\" as NaN \n",
    "(df.bedrooms[df.bedrooms==0])= np.nan\n",
    "(df[\"bathrooms\"][df.bathrooms==0])= np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#linear regression imputation\n",
    "index = df.index\n",
    "df.set_index('price', inplace=True)\n",
    "df['bedrooms'].interpolate(method='linear', inplace=True, limit_direction=\"both\")\n",
    "df['bathrooms'].interpolate(method='linear', inplace=True, limit_direction=\"both\")\n",
    "df.reset_index(inplace=True)\n",
    "df.index=index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "df.display_address[df[\"display_address\"]==\"\"]=df.street_address\n",
    "#there will be 10 more rows with missing addresses, so we'll just drop those 10\n",
    "df.drop(df[df[\"display_address\"]==\"\"].index,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "#drop building id because there are lots of missing values, the values aren't interpretable for a human\n",
    "#don't think bulding id or manager id is a good feature for predicting property popularity\n",
    "#because there is no order to it (not categorical nor continuous)\n",
    "#drop street_address because display_address is similar and sufficient in giving the neighbourhood\n",
    "df=df.drop(columns = [\"building_id\", \"street_address\",\"manager_id\"])\n",
    "\n",
    "#dropping columns created during transformation\n",
    "df=df.drop(columns=[\"loc_out\",\"rooms\",\"log_price\",\"location\",\"created_hour\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Extraction of Images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "imageZip = ZipFile('images_sample.zip')\n",
    "imageZip.extractall()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [],
   "source": [
    "for file in imageZip.namelist():\n",
    "    if file.endswith('.jpg'):\n",
    "        b = os.path.split(os.path.split(file)[0])[1]\n",
    "       # print(b)\n",
    "        img = Image.open(file)\n",
    "        greyscale = img.convert('L')\n",
    "        mgrey = mean(np.array(greyscale))\n",
    "      #  print(mgrey)\n",
    "        tempdf = pd.DataFrame([[b,mgrey]],columns=['listing_id','greyscale_means'])\n",
    "      #  df2 = pd.concat([df2,tempdf],sort=False)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Extraction of Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "#features and descriptions\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "textFeatures = df[['features', 'description', 'interest_level']]\n",
    "# print(textFeatures.iloc[0])\n",
    "#textFeatures"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  after removing the cwd from sys.path.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "10                                                       []\n",
       "10000     [A, Brand, New, 3, Bedroom, 1.5, bath, Apartme...\n",
       "100004    [Top, Top, West, Village, location,, beautiful...\n",
       "100007    [Building, Amenities, -, Garage, -, Garden, -,...\n",
       "100013    [Beautifully, renovated, 3, bedroom, flex, 4, ...\n",
       "100014                                                   []\n",
       "100016    [Stunning, unit, with, a, great, location, and...\n",
       "100020    [This, huge, sunny, ,plenty, of, lights, 1, be...\n",
       "100026                            [<p><a, website_redacted]\n",
       "100027    [This, is, a, spacious, four, bedroom, with, e...\n",
       "100030    [New, to, the, market!, Spacious, studio, loca...\n",
       "10004     [Check, out, this, one, bedroom, apartment, in...\n",
       "100044    [***LOW, FEE., Beautiful, CHERRY, OAK, WOODEN,...\n",
       "100048    [Lincoln, Square's, premier, full, service, bu...\n",
       "10005     [Spacious, 1-Bedroom, to, fit, King-sized, bed...\n",
       "100051    [Stunning, full, renovated, studio, unit., Hig...\n",
       "100052    [East, Village, is, a, great, community, with,...\n",
       "100053    [*Location:, Thayer, St., &amp;, Broadway,, Ma...\n",
       "100055                                                   []\n",
       "100058                                                   []\n",
       "100062    [BEAUTIFUL, 2, BEDROOM, POSSIBLE, CONVERSION, ...\n",
       "100063    [No, fee, huge, two, bedroom, apartment., Grea...\n",
       "100065                                                   []\n",
       "100066    [CooperCooper.com, ::, Web, ID, #179546;, Acce...\n",
       "10007     [SPRAWLING, 2, BEDROOM, FOUND!, ENJOY, THE, LU...\n",
       "100075    [Don't, miss, out, on, this, fantastic, apartm...\n",
       "100076    [The, TRUE, 1, bedroom, apartment, features, e...\n",
       "100079    [Currently, under, renovation,, this, one, bed...\n",
       "100081    [Gut, Renovated, Apartment, with, Stainless, s...\n",
       "100083    [Enjoy, the, Upper, West, Side, life-style!, T...\n",
       "                                ...                        \n",
       "99915     [Elegant,, spacious, one, bedroom, in, modern,...\n",
       "99917                                                    []\n",
       "99919     [(((((IMPORTANT, NOTICE))))), If, you, are, pl...\n",
       "99921     [WASHER, &, DRYER, IN, UNIT, ++, 5, MINUTE, WA...\n",
       "99923     [Tremendous, Classic, Six, in, a, Luxurious, P...\n",
       "99924     [Luxury, Living---, Full-Time, Doorman---, Con...\n",
       "99931     [Only, one, block, form, Washington, Square, P...\n",
       "99933     [THIS, RESIDENCE, FEATURES, STAINLESS, STEAL, ...\n",
       "99935     [This, newly, renovated, 1, bedroom, unit, is,...\n",
       "99937     [Bedroom, on, top,, Living, room, on, bottom<b...\n",
       "9994      [Don't, miss, out, on, this, spacious, and, be...\n",
       "99953     [This, exquisitely, appointed, 1, bedroom,, 1,...\n",
       "99956     [This, spacious, two, bedroom, apartment, feat...\n",
       "99960     [Great, Lower, East, Side, one, bedroom.Well, ...\n",
       "99961     [THIS, LARGE, AND, SPACIOUS, 3, BD/ONE, BA, AP...\n",
       "99964     [77TH, ST!, FULLY, GUT, RENOV, STUDIO!, HI, CE...\n",
       "99965     [Full-Time, Doorman, PET, FRIENDLYElevator, GY...\n",
       "99966     [This, is, a, Great, Opportunity, to, Rent, an...\n",
       "99979                                                    []\n",
       "99980     [Incredibly, sunny, and, spacious, studio, apa...\n",
       "99982     [LUXURY, BUILDING, IN, MIDTOWN, WEST,, 24, HR,...\n",
       "99984     [Huge, true, king, size, one, bedroom, in, an,...\n",
       "99986     [Charming, one, bedroom!, Hardwood, floors,, s...\n",
       "99987     [**89th, &, Park, Ave!, (block, from, the, cen...\n",
       "99988     [24hr, Doorman, Luxury, building, in, the, hea...\n",
       "9999      [30TH/3RD,, MASSIVE, CONV, 2BR, IN, LUXURY, FU...\n",
       "99991     [HIGH, END, condo, finishes,, swimming, pool,,...\n",
       "99992     [Large, Renovated, One, Bedroom, Apartment, wi...\n",
       "99993     [Stylishly, sleek, studio, apartment, with, un...\n",
       "99994     [Look, no, further!!!, This, giant, 2, bedroom...\n",
       "Name: description, Length: 48551, dtype: object"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# featuresBoW = textFeatures['features'].apply(lambda x: x.split(' '))\n",
    "descBoW = textFeatures.description.str.split()\n",
    "# print(descBoW)\n",
    "textFeatures['description'] = descBoW\n",
    "textFeatures['description']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:10: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # Remove the CWD from sys.path while we load stuff.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                                 features  \\\n",
      "10      ['Doorman', 'Elevator', 'Fitness Center', 'Cat...   \n",
      "10000                                                  []   \n",
      "100004  ['Laundry In Building', 'Dishwasher', 'Hardwoo...   \n",
      "100007                      ['Hardwood Floors', 'No Fee']   \n",
      "100013                                        ['Pre-War']   \n",
      "100014                                                 []   \n",
      "100016  ['prewar', 'elevator', 'Dogs Allowed', 'Cats A...   \n",
      "100020  ['Doorman', 'Elevator', 'Pre-War', 'Terrace', ...   \n",
      "100026  ['Cats Allowed', 'Dogs Allowed', 'Elevator', '...   \n",
      "100027                  ['Dishwasher', 'Hardwood Floors']   \n",
      "100030                                                 []   \n",
      "10004   ['prewar', 'dishwasher', 'HIGHRISE', 'ROOFDECK...   \n",
      "100044  ['Doorman', 'Elevator', 'Laundry in Building',...   \n",
      "100048  ['Swimming Pool', 'Doorman', 'Fitness Center',...   \n",
      "10005   ['Elevator', 'Multi-Level', 'Laundry in Buildi...   \n",
      "100051  ['Doorman', 'Elevator', 'Fitness Center', 'Lau...   \n",
      "100052                                ['Hardwood Floors']   \n",
      "100053  ['prewar', 'LOWRISE', 'EAT IN KITCHEN', 'SIMPL...   \n",
      "100055         ['No Fee', 'Cats Allowed', 'Dogs Allowed']   \n",
      "100058                   ['Cats Allowed', 'Dogs Allowed']   \n",
      "100062  ['Doorman', 'Elevator', 'Dishwasher', 'Hardwoo...   \n",
      "100063  ['Elevator', 'Central A/C', 'Walk in Closet(s)...   \n",
      "100065                   ['Cats Allowed', 'Dogs Allowed']   \n",
      "100066  ['Doorman', 'Fitness Center', 'Pool', 'Elevato...   \n",
      "10007   ['Balcony', 'Doorman', 'Elevator', 'Fitness Ce...   \n",
      "100075        ['Dishwasher', 'Hardwood Floors', 'No Fee']   \n",
      "100076  ['Roof Deck', 'Doorman', 'Elevator', 'Laundry ...   \n",
      "100079                                                 []   \n",
      "100081  ['Doorman', 'Elevator', 'Fitness Center', 'Pre...   \n",
      "100083  ['Doorman', 'Elevator', 'Pre-War', 'Exclusive'...   \n",
      "...                                                   ...   \n",
      "99915   ['Swimming Pool', 'Doorman', 'Fitness Center',...   \n",
      "99917   ['Cats Allowed', 'Dogs Allowed', 'Doorman', 'E...   \n",
      "99919   ['Elevator', 'Laundry in Building', 'Laundry i...   \n",
      "99921                    ['Furnished', 'Laundry In Unit']   \n",
      "99923   ['Doorman', 'Pre-War', 'Dogs Allowed', 'Cats A...   \n",
      "99924   ['Doorman', 'Laundry in Building', 'Laundry in...   \n",
      "99931                    ['Dogs Allowed', 'Cats Allowed']   \n",
      "99933   ['Roof Deck', 'Dining Room', 'Doorman', 'Eleva...   \n",
      "99935                                                  []   \n",
      "99937   ['Pre-War', 'Laundry in Building', 'Dishwasher...   \n",
      "9994    ['Doorman', 'Pre-War', 'Dogs Allowed', 'Cats A...   \n",
      "99953   ['Doorman', 'Elevator', 'Furnished', 'Pre-War'...   \n",
      "99956   ['Common Outdoor Space', 'Cats Allowed', 'Dogs...   \n",
      "99960                                 ['Hardwood Floors']   \n",
      "99961   ['Doorman', 'Elevator', 'Laundry in Building',...   \n",
      "99964   ['Loft', 'Multi-Level', 'Dishwasher', 'Hardwoo...   \n",
      "99965   ['Swimming Pool', 'Dining Room', 'Doorman', 'E...   \n",
      "99966   ['Roof Deck', 'Doorman', 'Elevator', 'Fitness ...   \n",
      "99979                                                  []   \n",
      "99980   ['Common Outdoor Space', 'Cats Allowed', 'Dogs...   \n",
      "99982   ['Doorman', 'Elevator', 'Fitness Center', 'Lau...   \n",
      "99984   ['Elevator', 'Multi-Level', 'Dishwasher', 'Har...   \n",
      "99986                     ['Elevator', 'Hardwood Floors']   \n",
      "99987                                      ['Dishwasher']   \n",
      "99988   ['Cats Allowed', 'No Fee', 'Doorman', 'Elevato...   \n",
      "9999    ['Elevator', 'Laundry in Building', 'Laundry i...   \n",
      "99991   ['Common Outdoor Space', 'Cats Allowed', 'Dogs...   \n",
      "99992   ['Doorman', 'Elevator', 'Pre-War', 'Dogs Allow...   \n",
      "99993   ['Doorman', 'Elevator', 'Pre-War', 'Dogs Allow...   \n",
      "99994                                 ['Hardwood Floors']   \n",
      "\n",
      "                                              description interest_level  \n",
      "10                                                     []            low  \n",
      "10000   ['A', 'Brand', 'New', '3', 'Bedroom', '15', 'b...         medium  \n",
      "100004  ['Top', 'Top', 'West', 'Village', 'location', ...           high  \n",
      "100007  ['Building', 'Amenities', '', 'Garage', '', 'G...            low  \n",
      "100013  ['Beautifully', 'renovated', '3', 'bedroom', '...            low  \n",
      "100014                                                 []         medium  \n",
      "100016  ['Stunning', 'unit', 'with', 'a', 'great', 'lo...            low  \n",
      "100020  ['This', 'huge', 'sunny', 'plenty', 'of', 'lig...            low  \n",
      "100026                          ['pa', 'websiteredacted']         medium  \n",
      "100027  ['This', 'is', 'a', 'spacious', 'four', 'bedro...            low  \n",
      "100030  ['New', 'to', 'the', 'market', 'Spacious', 'st...            low  \n",
      "10004   ['Check', 'out', 'this', 'one', 'bedroom', 'ap...            low  \n",
      "100044  ['LOW', 'FEE', 'Beautiful', 'CHERRY', 'OAK', '...           high  \n",
      "100048  ['Lincoln', 'Squares', 'premier', 'full', 'ser...            low  \n",
      "10005   ['Spacious', '1Bedroom', 'to', 'fit', 'Kingsiz...            low  \n",
      "100051  ['Stunning', 'full', 'renovated', 'studio', 'u...         medium  \n",
      "100052  ['East', 'Village', 'is', 'a', 'great', 'commu...            low  \n",
      "100053  ['Location', 'Thayer', 'St', 'amp', 'Broadway'...            low  \n",
      "100055                                                 []            low  \n",
      "100058                                                 []            low  \n",
      "100062  ['BEAUTIFUL', '2', 'BEDROOM', 'POSSIBLE', 'CON...            low  \n",
      "100063  ['No', 'fee', 'huge', 'two', 'bedroom', 'apart...         medium  \n",
      "100065                                                 []            low  \n",
      "100066  ['CooperCoopercom', '', 'Web', 'ID', '179546',...            low  \n",
      "10007   ['SPRAWLING', '2', 'BEDROOM', 'FOUND', 'ENJOY'...         medium  \n",
      "100075  ['Dont', 'miss', 'out', 'on', 'this', 'fantast...         medium  \n",
      "100076  ['The', 'TRUE', '1', 'bedroom', 'apartment', '...            low  \n",
      "100079  ['Currently', 'under', 'renovation', 'this', '...           high  \n",
      "100081  ['Gut', 'Renovated', 'Apartment', 'with', 'Sta...            low  \n",
      "100083  ['Enjoy', 'the', 'Upper', 'West', 'Side', 'lif...         medium  \n",
      "...                                                   ...            ...  \n",
      "99915   ['Elegant', 'spacious', 'one', 'bedroom', 'in'...            low  \n",
      "99917                                                  []            low  \n",
      "99919   ['IMPORTANT', 'NOTICE', 'If', 'you', 'are', 'p...         medium  \n",
      "99921   ['WASHER', '', 'DRYER', 'IN', 'UNIT', '', '5',...         medium  \n",
      "99923   ['Tremendous', 'Classic', 'Six', 'in', 'a', 'L...            low  \n",
      "99924   ['Luxury', 'Living', 'FullTime', 'Doorman', 'C...            low  \n",
      "99931   ['Only', 'one', 'block', 'form', 'Washington',...            low  \n",
      "99933   ['THIS', 'RESIDENCE', 'FEATURES', 'STAINLESS',...            low  \n",
      "99935   ['This', 'newly', 'renovated', '1', 'bedroom',...            low  \n",
      "99937   ['Bedroom', 'on', 'top', 'Living', 'room', 'on...            low  \n",
      "9994    ['Dont', 'miss', 'out', 'on', 'this', 'spaciou...            low  \n",
      "99953   ['This', 'exquisitely', 'appointed', '1', 'bed...            low  \n",
      "99956   ['This', 'spacious', 'two', 'bedroom', 'apartm...            low  \n",
      "99960   ['Great', 'Lower', 'East', 'Side', 'one', 'bed...         medium  \n",
      "99961   ['THIS', 'LARGE', 'AND', 'SPACIOUS', '3', 'BDO...            low  \n",
      "99964   ['77TH', 'ST', 'FULLY', 'GUT', 'RENOV', 'STUDI...         medium  \n",
      "99965   ['FullTime', 'Doorman', 'PET', 'FRIENDLYElevat...            low  \n",
      "99966   ['This', 'is', 'a', 'Great', 'Opportunity', 't...            low  \n",
      "99979                                                  []            low  \n",
      "99980   ['Incredibly', 'sunny', 'and', 'spacious', 'st...            low  \n",
      "99982   ['LUXURY', 'BUILDING', 'IN', 'MIDTOWN', 'WEST'...           high  \n",
      "99984   ['Huge', 'true', 'king', 'size', 'one', 'bedro...            low  \n",
      "99986   ['Charming', 'one', 'bedroom', 'Hardwood', 'fl...            low  \n",
      "99987   ['89th', '', 'Park', 'Ave', 'block', 'from', '...            low  \n",
      "99988   ['24hr', 'Doorman', 'Luxury', 'building', 'in'...         medium  \n",
      "9999    ['30TH3RD', 'MASSIVE', 'CONV', '2BR', 'IN', 'L...         medium  \n",
      "99991   ['HIGH', 'END', 'condo', 'finishes', 'swimming...            low  \n",
      "99992   ['Large', 'Renovated', 'One', 'Bedroom', 'Apar...            low  \n",
      "99993   ['Stylishly', 'sleek', 'studio', 'apartment', ...            low  \n",
      "99994   ['Look', 'no', 'further', 'This', 'giant', '2'...            low  \n",
      "\n",
      "[48551 rows x 3 columns]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  # This is added back by InteractiveShellApp.init_path()\n"
     ]
    }
   ],
   "source": [
    "# textFeatures['features'].to_string(textFeatures['test'])\n",
    "textFeatures['features'] = textFeatures['features'].astype(str)\n",
    "# textFeatures['features'] = textFeatures['features'].str.join('')\n",
    "#textFeatures\n",
    "# print(textFeatures['features'].iloc[0])\n",
    "# print(textFeatures)\n",
    "# textFeatures['features'].dtype\n",
    "stop = stopwords.words('english')\n",
    "# textFeatures['description'] = textFeatures['description'].apply(lambda x: [word for word in x if word not in stop])\n",
    "textFeatures['description'] = textFeatures['description'].apply(lambda x: [''.join(letter for letter in word if letter.isalnum()) for word in x])\n",
    "textFeatures['description'] = textFeatures['description'].astype(str)\n",
    "print(textFeatures)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorizer = TfidfVectorizer(max_features=50)\n",
    "# vectorizer.fit(textFeatures['features'])\n",
    "# textFeatures['features_idf'] = textFeatures['features'].apply(lambda x: vectorize(x, vectorizer))\n",
    "x = vectorizer.fit_transform(textFeatures['features'])\n",
    "x.toarray().shape\n",
    "first_vector = x[0]\n",
    "tfidf = pd.DataFrame(first_vector.T.todense(), index=vectorizer.get_feature_names(), columns=[\"tfidf\"])\n",
    "tfidf.sort_values(by=[\"tfidf\"],ascending=False)\n",
    "x = x.toarray().tolist()\n",
    "\n",
    "# print(vectorizer.vocabulary_)\n",
    "# print(vectorizer.idf_)\n",
    "# x = x.toarray()\n",
    "# x\n",
    "# textFeatures['idf'] = x\n",
    "# print(textFeatures['idf'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "textFeatures['features_idf'] = x\n",
    "#textFeatures[['features', 'features_idf']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Invalid otype specification",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-52-b95916c80c73>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mtextFeatures\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'description_idf'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtextFeatures\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'description'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mvectorize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvectorizer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36mapply\u001b[1;34m(self, func, convert_dtype, args, **kwds)\u001b[0m\n\u001b[0;32m   3589\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3590\u001b[0m                 \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mobject\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3591\u001b[1;33m                 \u001b[0mmapped\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmap_infer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mconvert_dtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3592\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3593\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmapped\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSeries\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.map_infer\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m<ipython-input-52-b95916c80c73>\u001b[0m in \u001b[0;36m<lambda>\u001b[1;34m(x)\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0my\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtoarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[0mtextFeatures\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'description_idf'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtextFeatures\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'description'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mvectorize\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvectorizer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\numpy\\lib\\function_base.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, pyfunc, otypes, doc, excluded, cache, signature)\u001b[0m\n\u001b[0;32m   2048\u001b[0m             \u001b[0motypes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0m_nx\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchar\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mx\u001b[0m \u001b[1;32min\u001b[0m \u001b[0motypes\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2049\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0motypes\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2050\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Invalid otype specification\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2051\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0motypes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0motypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2052\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Invalid otype specification"
     ]
    }
   ],
   "source": [
    "vectorizer = TfidfVectorizer(max_features=50)\n",
    "y = vectorizer.fit_transform(textFeatures['description'])\n",
    "y.toarray().shape\n",
    "y = y.toarray()\n",
    "textFeatures['description_idf'] = textFeatures['description'].apply(lambda x: vectorize(x, vectorizer))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  This is separate from the ipykernel package so we can avoid doing imports until\n"
     ]
    }
   ],
   "source": [
    "y = np.stack(y)\n",
    "y\n",
    "textFeatures['description_idf'] = y\n",
    "\n",
    "#textFeatures['description_idf']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"features\"]=textFeatures[\"features\"]\n",
    "df[\"description\"]=textFeatures[\"description\"]\n",
    "df[\"features_idf\"]=textFeatures[\"features_idf\"]\n",
    "df[\"description_idf\"]=textFeatures[\"description_idf\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## some new features for model training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"num_photos\"] = df[\"photos\"].apply(len)\n",
    "df[\"num_features\"] = df[\"features\"].apply(len)\n",
    "df[\"num_desc\"] = df[\"description\"].apply(len)\n",
    "df[\"day_of_week\"]=df.created.dt.dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_json('train_ETL.json.gz', compression='gzip')\n",
    "#read with pd.read_csv(\"train_ETL.csv.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>created</th>\n",
       "      <th>description</th>\n",
       "      <th>display_address</th>\n",
       "      <th>features</th>\n",
       "      <th>latitude</th>\n",
       "      <th>listing_id</th>\n",
       "      <th>longitude</th>\n",
       "      <th>photos</th>\n",
       "      <th>interest_level</th>\n",
       "      <th>interest</th>\n",
       "      <th>features_idf</th>\n",
       "      <th>description_idf</th>\n",
       "      <th>num_photos</th>\n",
       "      <th>num_features</th>\n",
       "      <th>num_desc</th>\n",
       "      <th>day_of_week</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>5465</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-06-12 12:19:27</td>\n",
       "      <td>[]</td>\n",
       "      <td>Columbus Avenue</td>\n",
       "      <td>['Doorman', 'Elevator', 'Fitness Center', 'Cat...</td>\n",
       "      <td>40.7947</td>\n",
       "      <td>7150865</td>\n",
       "      <td>-73.9667</td>\n",
       "      <td>[https://photos.renthop.com/2/7150865_be3306c5...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.5851074667834789, 0.0, 0.0, 0.29308358...</td>\n",
       "      <td></td>\n",
       "      <td>11</td>\n",
       "      <td>73</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10000</th>\n",
       "      <td>3000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016-06-24 07:54:24</td>\n",
       "      <td>['A', 'Brand', 'New', '3', 'Bedroom', '15', 'b...</td>\n",
       "      <td>Metropolitan Avenue</td>\n",
       "      <td>[]</td>\n",
       "      <td>40.7145</td>\n",
       "      <td>7211212</td>\n",
       "      <td>-73.9425</td>\n",
       "      <td>[https://photos.renthop.com/2/7211212_1ed4542e...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.1776163821148241\\n  (0, 8)\\t0.167...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>823</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100004</th>\n",
       "      <td>2850</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-17 03:26:41</td>\n",
       "      <td>['Top', 'Top', 'West', 'Village', 'location', ...</td>\n",
       "      <td>W 13 Street</td>\n",
       "      <td>['Laundry In Building', 'Dishwasher', 'Hardwoo...</td>\n",
       "      <td>40.7388</td>\n",
       "      <td>6887163</td>\n",
       "      <td>-74.0018</td>\n",
       "      <td>[https://photos.renthop.com/2/6887163_de85c427...</td>\n",
       "      <td>high</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.0, 0.3672803019787415, 0.0, 0.4164003778182...</td>\n",
       "      <td>(0, 30)\\t0.1420402417973664\\n  (0, 8)\\t0.134...</td>\n",
       "      <td>8</td>\n",
       "      <td>85</td>\n",
       "      <td>911</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100007</th>\n",
       "      <td>3275</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-18 02:22:02</td>\n",
       "      <td>['Building', 'Amenities', '', 'Garage', '', 'G...</td>\n",
       "      <td>East 49th Street</td>\n",
       "      <td>['Hardwood Floors', 'No Fee']</td>\n",
       "      <td>40.7539</td>\n",
       "      <td>6888711</td>\n",
       "      <td>-73.9677</td>\n",
       "      <td>[https://photos.renthop.com/2/6888711_6e660cee...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 3)\\t0.070735125312101\\n  (0, 36)\\t0.1840...</td>\n",
       "      <td>3</td>\n",
       "      <td>29</td>\n",
       "      <td>647</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100013</th>\n",
       "      <td>3350</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016-04-28 01:32:41</td>\n",
       "      <td>['Beautifully', 'renovated', '3', 'bedroom', '...</td>\n",
       "      <td>West 143rd Street</td>\n",
       "      <td>['Pre-War']</td>\n",
       "      <td>40.8241</td>\n",
       "      <td>6934781</td>\n",
       "      <td>-73.9493</td>\n",
       "      <td>[https://photos.renthop.com/2/6934781_1fa4b41a...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 8)\\t0.23775749541087743\\n  (0, 3)\\t0.322...</td>\n",
       "      <td>3</td>\n",
       "      <td>11</td>\n",
       "      <td>669</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100014</th>\n",
       "      <td>7995</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016-04-19 04:24:47</td>\n",
       "      <td>[]</td>\n",
       "      <td>West 18th Street</td>\n",
       "      <td>[]</td>\n",
       "      <td>40.7429</td>\n",
       "      <td>6894514</td>\n",
       "      <td>-74.0028</td>\n",
       "      <td>[https://photos.renthop.com/2/6894514_9abb8592...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td></td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100016</th>\n",
       "      <td>3600</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-04-27 03:19:56</td>\n",
       "      <td>['Stunning', 'unit', 'with', 'a', 'great', 'lo...</td>\n",
       "      <td>West 107th Street</td>\n",
       "      <td>['prewar', 'elevator', 'Dogs Allowed', 'Cats A...</td>\n",
       "      <td>40.8012</td>\n",
       "      <td>6930771</td>\n",
       "      <td>-73.9660</td>\n",
       "      <td>[https://photos.renthop.com/2/6930771_7e3622b6...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.3404048697805322, 0.0, 0.0, 0.17051069...</td>\n",
       "      <td>(0, 48)\\t0.3245981358259179\\n  (0, 43)\\t0.46...</td>\n",
       "      <td>10</td>\n",
       "      <td>98</td>\n",
       "      <td>806</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100020</th>\n",
       "      <td>5645</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-13 06:01:42</td>\n",
       "      <td>['This', 'huge', 'sunny', 'plenty', 'of', 'lig...</td>\n",
       "      <td>West 21st Street</td>\n",
       "      <td>['Doorman', 'Elevator', 'Pre-War', 'Terrace', ...</td>\n",
       "      <td>40.7427</td>\n",
       "      <td>6867392</td>\n",
       "      <td>-73.9957</td>\n",
       "      <td>[https://photos.renthop.com/2/6867392_b18283f6...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.18592672562653134\\n  (0, 3)\\t0.15...</td>\n",
       "      <td>5</td>\n",
       "      <td>114</td>\n",
       "      <td>1278</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100026</th>\n",
       "      <td>1725</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-20 02:36:35</td>\n",
       "      <td>['pa', 'websiteredacted']</td>\n",
       "      <td>Hamilton Terrace</td>\n",
       "      <td>['Cats Allowed', 'Dogs Allowed', 'Elevator', '...</td>\n",
       "      <td>40.8234</td>\n",
       "      <td>6898799</td>\n",
       "      <td>-73.9457</td>\n",
       "      <td>[https://photos.renthop.com/2/6898799_3759be4c...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.624237228556546, 0.0, 0.35386136476519...</td>\n",
       "      <td>(0, 45)\\t0.5807783354322145\\n  (0, 34)\\t0.81...</td>\n",
       "      <td>5</td>\n",
       "      <td>67</td>\n",
       "      <td>25</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100027</th>\n",
       "      <td>5800</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016-04-02 02:58:15</td>\n",
       "      <td>['This', 'is', 'a', 'spacious', 'four', 'bedro...</td>\n",
       "      <td>522 E 11th</td>\n",
       "      <td>['Dishwasher', 'Hardwood Floors']</td>\n",
       "      <td>40.7278</td>\n",
       "      <td>6814332</td>\n",
       "      <td>-73.9808</td>\n",
       "      <td>[https://photos.renthop.com/2/6814332_e19a8552...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 8)\\t0.15356530890167605\\n  (0, 3)\\t0.069...</td>\n",
       "      <td>9</td>\n",
       "      <td>33</td>\n",
       "      <td>1462</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100030</th>\n",
       "      <td>1950</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2016-04-14 01:10:30</td>\n",
       "      <td>['New', 'to', 'the', 'market', 'Spacious', 'st...</td>\n",
       "      <td>York Avenue</td>\n",
       "      <td>[]</td>\n",
       "      <td>40.7769</td>\n",
       "      <td>6869199</td>\n",
       "      <td>-73.9467</td>\n",
       "      <td>[https://photos.renthop.com/2/6869199_06b2601f...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.27484092145062755\\n  (0, 36)\\t0.3...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>245</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10004</th>\n",
       "      <td>1675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-06-03 03:21:22</td>\n",
       "      <td>['Check', 'out', 'this', 'one', 'bedroom', 'ap...</td>\n",
       "      <td>W. 173rd Street</td>\n",
       "      <td>['prewar', 'dishwasher', 'HIGHRISE', 'ROOFDECK...</td>\n",
       "      <td>40.8448</td>\n",
       "      <td>7102986</td>\n",
       "      <td>-73.9396</td>\n",
       "      <td>[https://photos.renthop.com/2/7102986_ca6af538...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 8)\\t0.21499865933864884\\n  (0, 3)\\t0.389...</td>\n",
       "      <td>5</td>\n",
       "      <td>89</td>\n",
       "      <td>960</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100044</th>\n",
       "      <td>3000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-04-19 05:37:25</td>\n",
       "      <td>['LOW', 'FEE', 'Beautiful', 'CHERRY', 'OAK', '...</td>\n",
       "      <td>E 38th St</td>\n",
       "      <td>['Doorman', 'Elevator', 'Laundry in Building',...</td>\n",
       "      <td>40.7488</td>\n",
       "      <td>6895442</td>\n",
       "      <td>-73.9770</td>\n",
       "      <td>[https://photos.renthop.com/2/6895442_34d617a5...</td>\n",
       "      <td>high</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.40179672989790755, 0.0, 0.0,...</td>\n",
       "      <td>(0, 8)\\t0.11989964628174397\\n  (0, 3)\\t0.108...</td>\n",
       "      <td>4</td>\n",
       "      <td>56</td>\n",
       "      <td>973</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100048</th>\n",
       "      <td>6895</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-04-09 01:22:11</td>\n",
       "      <td>['Lincoln', 'Squares', 'premier', 'full', 'ser...</td>\n",
       "      <td>West 63rd Street</td>\n",
       "      <td>['Swimming Pool', 'Doorman', 'Fitness Center',...</td>\n",
       "      <td>40.7707</td>\n",
       "      <td>6846213</td>\n",
       "      <td>-73.9817</td>\n",
       "      <td>[https://photos.renthop.com/2/6846213_83258754...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.4148146356728024, 0.0, 0.0, 0.20778296...</td>\n",
       "      <td>(0, 43)\\t0.14097350157983315\\n  (0, 41)\\t0.1...</td>\n",
       "      <td>6</td>\n",
       "      <td>88</td>\n",
       "      <td>780</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10005</th>\n",
       "      <td>3050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-06-01 03:11:01</td>\n",
       "      <td>['Spacious', '1Bedroom', 'to', 'fit', 'Kingsiz...</td>\n",
       "      <td>East 56th St..</td>\n",
       "      <td>['Elevator', 'Multi-Level', 'Laundry in Buildi...</td>\n",
       "      <td>40.7584</td>\n",
       "      <td>7089402</td>\n",
       "      <td>-73.9648</td>\n",
       "      <td>[https://photos.renthop.com/2/7089402_66038eaa...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.4196458341669265, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 43)\\t0.3368410620030332\\n  (0, 39)\\t0.12...</td>\n",
       "      <td>6</td>\n",
       "      <td>83</td>\n",
       "      <td>769</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100051</th>\n",
       "      <td>2350</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2016-04-18 02:36:00</td>\n",
       "      <td>['Stunning', 'full', 'renovated', 'studio', 'u...</td>\n",
       "      <td>East 34th Street</td>\n",
       "      <td>['Doorman', 'Elevator', 'Fitness Center', 'Lau...</td>\n",
       "      <td>40.7439</td>\n",
       "      <td>6889043</td>\n",
       "      <td>-73.9743</td>\n",
       "      <td>[https://photos.renthop.com/2/6889043_a3e1c004...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.3338940270308353, 0.0, 0.393...</td>\n",
       "      <td>(0, 14)\\t0.17133619942428127\\n  (0, 15)\\t0.1...</td>\n",
       "      <td>2</td>\n",
       "      <td>74</td>\n",
       "      <td>655</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100052</th>\n",
       "      <td>3650</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-04-22 05:57:16</td>\n",
       "      <td>['East', 'Village', 'is', 'a', 'great', 'commu...</td>\n",
       "      <td>1st Ave.</td>\n",
       "      <td>['Hardwood Floors']</td>\n",
       "      <td>40.7305</td>\n",
       "      <td>6913348</td>\n",
       "      <td>-73.9830</td>\n",
       "      <td>[https://photos.renthop.com/2/6913348_829f19ac...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 8)\\t0.2017915683423615\\n  (0, 43)\\t0.151...</td>\n",
       "      <td>5</td>\n",
       "      <td>19</td>\n",
       "      <td>458</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100053</th>\n",
       "      <td>1695</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-19 03:36:28</td>\n",
       "      <td>['Location', 'Thayer', 'St', 'amp', 'Broadway'...</td>\n",
       "      <td>Thayer St</td>\n",
       "      <td>['prewar', 'LOWRISE', 'EAT IN KITCHEN', 'SIMPL...</td>\n",
       "      <td>40.8643</td>\n",
       "      <td>6894111</td>\n",
       "      <td>-73.9280</td>\n",
       "      <td>[https://photos.renthop.com/2/6894111_571fa57d...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 8)\\t0.17155787166997313\\n  (0, 3)\\t0.155...</td>\n",
       "      <td>8</td>\n",
       "      <td>62</td>\n",
       "      <td>1681</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100055</th>\n",
       "      <td>5000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016-04-20 04:00:32</td>\n",
       "      <td>[]</td>\n",
       "      <td>West 106th Street</td>\n",
       "      <td>['No Fee', 'Cats Allowed', 'Dogs Allowed']</td>\n",
       "      <td>40.7999</td>\n",
       "      <td>6900220</td>\n",
       "      <td>-73.9638</td>\n",
       "      <td>[]</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.6771638443600325, 0.0, 0.0, 0.33919513...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>42</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100058</th>\n",
       "      <td>3973</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-09 03:49:42</td>\n",
       "      <td>[]</td>\n",
       "      <td>1st Avenue</td>\n",
       "      <td>['Cats Allowed', 'Dogs Allowed']</td>\n",
       "      <td>40.7328</td>\n",
       "      <td>6848536</td>\n",
       "      <td>-73.9799</td>\n",
       "      <td>[https://photos.renthop.com/2/6848536_9a3e1f87...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.8107329637511722, 0.0, 0.0, 0.40610065...</td>\n",
       "      <td></td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100062</th>\n",
       "      <td>4395</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016-04-12 02:39:45</td>\n",
       "      <td>['BEAUTIFUL', '2', 'BEDROOM', 'POSSIBLE', 'CON...</td>\n",
       "      <td>Madison Avenue</td>\n",
       "      <td>['Doorman', 'Elevator', 'Dishwasher', 'Hardwoo...</td>\n",
       "      <td>40.7454</td>\n",
       "      <td>6858062</td>\n",
       "      <td>-73.9845</td>\n",
       "      <td>[https://photos.renthop.com/2/6858062_5cfb9d90...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.31524046073526707\\n  (0, 8)\\t0.29...</td>\n",
       "      <td>6</td>\n",
       "      <td>56</td>\n",
       "      <td>1365</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100063</th>\n",
       "      <td>2999</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-04-07 02:11:45</td>\n",
       "      <td>['No', 'fee', 'huge', 'two', 'bedroom', 'apart...</td>\n",
       "      <td>E 30th St,</td>\n",
       "      <td>['Elevator', 'Central A/C', 'Walk in Closet(s)...</td>\n",
       "      <td>40.7427</td>\n",
       "      <td>6836760</td>\n",
       "      <td>-73.9794</td>\n",
       "      <td>[https://photos.renthop.com/2/6836760_6edeb636...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.18184836552957115\\n  (0, 8)\\t0.34...</td>\n",
       "      <td>5</td>\n",
       "      <td>137</td>\n",
       "      <td>405</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100065</th>\n",
       "      <td>2595</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-13 05:17:43</td>\n",
       "      <td>[]</td>\n",
       "      <td>East 35th Street</td>\n",
       "      <td>['Cats Allowed', 'Dogs Allowed']</td>\n",
       "      <td>40.7447</td>\n",
       "      <td>6866830</td>\n",
       "      <td>-73.9741</td>\n",
       "      <td>[]</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.8107329637511722, 0.0, 0.0, 0.40610065...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100066</th>\n",
       "      <td>3695</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-17 02:26:52</td>\n",
       "      <td>['CooperCoopercom', '', 'Web', 'ID', '179546',...</td>\n",
       "      <td>Liberty Street</td>\n",
       "      <td>['Doorman', 'Fitness Center', 'Pool', 'Elevato...</td>\n",
       "      <td>40.7074</td>\n",
       "      <td>6885927</td>\n",
       "      <td>-74.0081</td>\n",
       "      <td>[https://photos.renthop.com/2/6885927_3eb33bbc...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.3696849871373356, 0.0, 0.2095633328119...</td>\n",
       "      <td>(0, 3)\\t0.15650754291654204\\n  (0, 14)\\t0.21...</td>\n",
       "      <td>4</td>\n",
       "      <td>154</td>\n",
       "      <td>1168</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10007</th>\n",
       "      <td>7400</td>\n",
       "      <td>2.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2016-06-07 04:39:56</td>\n",
       "      <td>['SPRAWLING', '2', 'BEDROOM', 'FOUND', 'ENJOY'...</td>\n",
       "      <td>W 18 St.</td>\n",
       "      <td>['Balcony', 'Doorman', 'Elevator', 'Fitness Ce...</td>\n",
       "      <td>40.7391</td>\n",
       "      <td>7120132</td>\n",
       "      <td>-73.9936</td>\n",
       "      <td>[https://photos.renthop.com/2/7120132_8e47916c...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.29598397416109534, 0.3240414612033308,...</td>\n",
       "      <td>(0, 30)\\t0.11752156721780282\\n  (0, 8)\\t0.22...</td>\n",
       "      <td>6</td>\n",
       "      <td>191</td>\n",
       "      <td>1210</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100075</th>\n",
       "      <td>2295</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-25 02:29:50</td>\n",
       "      <td>['Dont', 'miss', 'out', 'on', 'this', 'fantast...</td>\n",
       "      <td>E 81st St.</td>\n",
       "      <td>['Dishwasher', 'Hardwood Floors', 'No Fee']</td>\n",
       "      <td>40.7728</td>\n",
       "      <td>6921632</td>\n",
       "      <td>-73.9502</td>\n",
       "      <td>[https://photos.renthop.com/2/6921632_563cdcee...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.1424544631151341\\n  (0, 3)\\t0.121...</td>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>557</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100076</th>\n",
       "      <td>3164</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-22 05:39:10</td>\n",
       "      <td>['The', 'TRUE', '1', 'bedroom', 'apartment', '...</td>\n",
       "      <td>West 57th Street</td>\n",
       "      <td>['Roof Deck', 'Doorman', 'Elevator', 'Laundry ...</td>\n",
       "      <td>40.7709</td>\n",
       "      <td>6913084</td>\n",
       "      <td>-73.9917</td>\n",
       "      <td>[https://photos.renthop.com/2/6913084_e1aef7e1...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.2131946462841425, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 8)\\t0.1014145294028751\\n  (0, 3)\\t0.0917...</td>\n",
       "      <td>6</td>\n",
       "      <td>125</td>\n",
       "      <td>983</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100079</th>\n",
       "      <td>1350</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-21 05:27:10</td>\n",
       "      <td>['Currently', 'under', 'renovation', 'this', '...</td>\n",
       "      <td>1215 Morris Avenue</td>\n",
       "      <td>[]</td>\n",
       "      <td>40.8335</td>\n",
       "      <td>6907079</td>\n",
       "      <td>-73.9141</td>\n",
       "      <td>[https://photos.renthop.com/2/6907079_2cc12101...</td>\n",
       "      <td>high</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 8)\\t0.22436092653277243\\n  (0, 43)\\t0.33...</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>218</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100081</th>\n",
       "      <td>5600</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-04-26 02:59:37</td>\n",
       "      <td>['Gut', 'Renovated', 'Apartment', 'with', 'Sta...</td>\n",
       "      <td>E 79th St.</td>\n",
       "      <td>['Doorman', 'Elevator', 'Fitness Center', 'Pre...</td>\n",
       "      <td>40.7716</td>\n",
       "      <td>6925264</td>\n",
       "      <td>-73.9506</td>\n",
       "      <td>[https://photos.renthop.com/2/6925264_59f5dc7a...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.25727016685225507, 0.0, 0.30...</td>\n",
       "      <td>(0, 3)\\t0.29568774835859046\\n  (0, 43)\\t0.24...</td>\n",
       "      <td>1</td>\n",
       "      <td>118</td>\n",
       "      <td>497</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100083</th>\n",
       "      <td>2750</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-04-21 02:17:28</td>\n",
       "      <td>['Enjoy', 'the', 'Upper', 'West', 'Side', 'lif...</td>\n",
       "      <td>250 West 88th Street</td>\n",
       "      <td>['Doorman', 'Elevator', 'Pre-War', 'Exclusive'...</td>\n",
       "      <td>40.7897</td>\n",
       "      <td>6904268</td>\n",
       "      <td>-73.9760</td>\n",
       "      <td>[https://photos.renthop.com/2/6904268_657c825a...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.4645633126290635, 0.0, 0.0, 0.23270234...</td>\n",
       "      <td>(0, 30)\\t0.0838593409637199\\n  (0, 8)\\t0.079...</td>\n",
       "      <td>4</td>\n",
       "      <td>79</td>\n",
       "      <td>1827</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99915</th>\n",
       "      <td>3126</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-25 01:13:37</td>\n",
       "      <td>['Elegant', 'spacious', 'one', 'bedroom', 'in'...</td>\n",
       "      <td>North 10th Street</td>\n",
       "      <td>['Swimming Pool', 'Doorman', 'Fitness Center',...</td>\n",
       "      <td>40.7176</td>\n",
       "      <td>6921019</td>\n",
       "      <td>-73.9531</td>\n",
       "      <td>[https://photos.renthop.com/2/6921019_05091585...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.4405599056825441, 0.0, 0.0, 0.22067890...</td>\n",
       "      <td>(0, 8)\\t0.13260875924697701\\n  (0, 3)\\t0.360...</td>\n",
       "      <td>6</td>\n",
       "      <td>78</td>\n",
       "      <td>762</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99917</th>\n",
       "      <td>3325</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-26 04:43:14</td>\n",
       "      <td>[]</td>\n",
       "      <td>W 58th St</td>\n",
       "      <td>['Cats Allowed', 'Dogs Allowed', 'Doorman', 'E...</td>\n",
       "      <td>40.7655</td>\n",
       "      <td>6926146</td>\n",
       "      <td>-73.9785</td>\n",
       "      <td>[https://photos.renthop.com/2/6926146_f82d7aff...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.493269763082621, 0.0, 0.0, 0.247081570...</td>\n",
       "      <td></td>\n",
       "      <td>9</td>\n",
       "      <td>79</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99919</th>\n",
       "      <td>2100</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2016-04-08 05:51:57</td>\n",
       "      <td>['IMPORTANT', 'NOTICE', 'If', 'you', 'are', 'p...</td>\n",
       "      <td>East 78th Street</td>\n",
       "      <td>['Elevator', 'Laundry in Building', 'Laundry i...</td>\n",
       "      <td>40.7721</td>\n",
       "      <td>6844805</td>\n",
       "      <td>-73.9539</td>\n",
       "      <td>[https://photos.renthop.com/2/6844805_b2a3a213...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.4047992390472882, 0.0, 0.2294685491866...</td>\n",
       "      <td>(0, 8)\\t0.10519315682314889\\n  (0, 3)\\t0.095...</td>\n",
       "      <td>4</td>\n",
       "      <td>129</td>\n",
       "      <td>950</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99921</th>\n",
       "      <td>3500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-04-29 15:03:29</td>\n",
       "      <td>['WASHER', '', 'DRYER', 'IN', 'UNIT', '', '5',...</td>\n",
       "      <td>Ridge Blvd.</td>\n",
       "      <td>['Furnished', 'Laundry In Unit']</td>\n",
       "      <td>40.6182</td>\n",
       "      <td>6943991</td>\n",
       "      <td>-74.0360</td>\n",
       "      <td>[https://photos.renthop.com/2/6943991_0a32ffd3...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 3)\\t0.1462737969273625\\n  (0, 36)\\t0.190...</td>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "      <td>607</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99923</th>\n",
       "      <td>11950</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-04-05 01:12:45</td>\n",
       "      <td>['Tremendous', 'Classic', 'Six', 'in', 'a', 'L...</td>\n",
       "      <td>West 81st Street</td>\n",
       "      <td>['Doorman', 'Pre-War', 'Dogs Allowed', 'Cats A...</td>\n",
       "      <td>40.7829</td>\n",
       "      <td>6822618</td>\n",
       "      <td>-73.9731</td>\n",
       "      <td>[https://photos.renthop.com/2/6822618_d04c2ece...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.5773152566220375, 0.0, 0.0, 0.28918042...</td>\n",
       "      <td>(0, 30)\\t0.16368032666036214\\n  (0, 3)\\t0.13...</td>\n",
       "      <td>2</td>\n",
       "      <td>54</td>\n",
       "      <td>723</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99924</th>\n",
       "      <td>2980</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-24 03:15:18</td>\n",
       "      <td>['Luxury', 'Living', 'FullTime', 'Doorman', 'C...</td>\n",
       "      <td>8th Avenue</td>\n",
       "      <td>['Doorman', 'Laundry in Building', 'Laundry in...</td>\n",
       "      <td>40.7637</td>\n",
       "      <td>6918969</td>\n",
       "      <td>-73.9847</td>\n",
       "      <td>[https://photos.renthop.com/2/6918969_e88539cc...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.2629911459641138, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 8)\\t0.15622373185253\\n  (0, 20)\\t0.17191...</td>\n",
       "      <td>3</td>\n",
       "      <td>96</td>\n",
       "      <td>881</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99931</th>\n",
       "      <td>2850</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-25 01:20:23</td>\n",
       "      <td>['Only', 'one', 'block', 'form', 'Washington',...</td>\n",
       "      <td>Thompson Street</td>\n",
       "      <td>['Dogs Allowed', 'Cats Allowed']</td>\n",
       "      <td>40.7293</td>\n",
       "      <td>6921162</td>\n",
       "      <td>-73.9987</td>\n",
       "      <td>[https://photos.renthop.com/2/6921162_5d6df102...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.8107329637511722, 0.0, 0.0, 0.40610065...</td>\n",
       "      <td>(0, 30)\\t0.3595029899102999\\n  (0, 43)\\t0.25...</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td>244</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99933</th>\n",
       "      <td>2885</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-04-03 03:51:21</td>\n",
       "      <td>['THIS', 'RESIDENCE', 'FEATURES', 'STAINLESS',...</td>\n",
       "      <td>Water</td>\n",
       "      <td>['Roof Deck', 'Dining Room', 'Doorman', 'Eleva...</td>\n",
       "      <td>40.7075</td>\n",
       "      <td>6819357</td>\n",
       "      <td>-74.0043</td>\n",
       "      <td>[https://photos.renthop.com/2/6819357_700208a6...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.2988137759459068, 0.0, 0.1693885685276...</td>\n",
       "      <td>(0, 30)\\t0.15188706712135894\\n  (0, 14)\\t0.3...</td>\n",
       "      <td>4</td>\n",
       "      <td>190</td>\n",
       "      <td>864</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99935</th>\n",
       "      <td>2950</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-19 02:58:55</td>\n",
       "      <td>['This', 'newly', 'renovated', '1', 'bedroom',...</td>\n",
       "      <td>East 12th Street</td>\n",
       "      <td>[]</td>\n",
       "      <td>40.7287</td>\n",
       "      <td>6893263</td>\n",
       "      <td>-73.9808</td>\n",
       "      <td>[https://photos.renthop.com/2/6893263_f1f39ce2...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.11274728402684481\\n  (0, 8)\\t0.10...</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>926</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99937</th>\n",
       "      <td>2650</td>\n",
       "      <td>1.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2016-04-14 05:39:33</td>\n",
       "      <td>['Bedroom', 'on', 'top', 'Living', 'room', 'on...</td>\n",
       "      <td>York Avenue</td>\n",
       "      <td>['Pre-War', 'Laundry in Building', 'Dishwasher...</td>\n",
       "      <td>40.7769</td>\n",
       "      <td>6873182</td>\n",
       "      <td>-73.9467</td>\n",
       "      <td>[https://photos.renthop.com/2/6873182_babdece5...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.30495355868409363, 0.0, 0.0,...</td>\n",
       "      <td>(0, 8)\\t0.1887565248085149\\n  (0, 36)\\t0.222...</td>\n",
       "      <td>7</td>\n",
       "      <td>77</td>\n",
       "      <td>414</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9994</th>\n",
       "      <td>4850</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-06-06 01:22:44</td>\n",
       "      <td>['Dont', 'miss', 'out', 'on', 'this', 'spaciou...</td>\n",
       "      <td>West 98th Street</td>\n",
       "      <td>['Doorman', 'Pre-War', 'Dogs Allowed', 'Cats A...</td>\n",
       "      <td>40.7957</td>\n",
       "      <td>7114685</td>\n",
       "      <td>-73.9705</td>\n",
       "      <td>[https://photos.renthop.com/2/7114685_7b7f598a...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.5773152566220375, 0.0, 0.0, 0.28918042...</td>\n",
       "      <td>(0, 8)\\t0.1534751557547402\\n  (0, 36)\\t0.180...</td>\n",
       "      <td>8</td>\n",
       "      <td>54</td>\n",
       "      <td>746</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99953</th>\n",
       "      <td>3475</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-26 02:18:16</td>\n",
       "      <td>['This', 'exquisitely', 'appointed', '1', 'bed...</td>\n",
       "      <td>West 58th Street</td>\n",
       "      <td>['Doorman', 'Elevator', 'Furnished', 'Pre-War'...</td>\n",
       "      <td>40.7655</td>\n",
       "      <td>6924210</td>\n",
       "      <td>-73.9785</td>\n",
       "      <td>[https://photos.renthop.com/2/6924210_27c32e80...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.4144424142167339, 0.0, 0.0, 0.20759651...</td>\n",
       "      <td>(0, 30)\\t0.08947465391823216\\n  (0, 8)\\t0.08...</td>\n",
       "      <td>1</td>\n",
       "      <td>93</td>\n",
       "      <td>1826</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99956</th>\n",
       "      <td>5815</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-04-16 16:39:50</td>\n",
       "      <td>['This', 'spacious', 'two', 'bedroom', 'apartm...</td>\n",
       "      <td>Nassau Street</td>\n",
       "      <td>['Common Outdoor Space', 'Cats Allowed', 'Dogs...</td>\n",
       "      <td>40.7095</td>\n",
       "      <td>6884807</td>\n",
       "      <td>-74.0084</td>\n",
       "      <td>[https://photos.renthop.com/2/6884807_158a7214...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.41581325034301647, 0.0, 0.0, 0.2082831...</td>\n",
       "      <td>(0, 8)\\t0.19794490578810875\\n  (0, 3)\\t0.179...</td>\n",
       "      <td>8</td>\n",
       "      <td>86</td>\n",
       "      <td>521</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99960</th>\n",
       "      <td>2050</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-05 04:35:32</td>\n",
       "      <td>['Great', 'Lower', 'East', 'Side', 'one', 'bed...</td>\n",
       "      <td>Baxter Street</td>\n",
       "      <td>['Hardwood Floors']</td>\n",
       "      <td>40.7179</td>\n",
       "      <td>6825168</td>\n",
       "      <td>-73.9989</td>\n",
       "      <td>[https://photos.renthop.com/2/6825168_f94cee7d...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.21674277230671993\\n  (0, 20)\\t0.2...</td>\n",
       "      <td>3</td>\n",
       "      <td>19</td>\n",
       "      <td>597</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99961</th>\n",
       "      <td>4600</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016-04-22 03:11:44</td>\n",
       "      <td>['THIS', 'LARGE', 'AND', 'SPACIOUS', '3', 'BDO...</td>\n",
       "      <td>East 3rd Street</td>\n",
       "      <td>['Doorman', 'Elevator', 'Laundry in Building',...</td>\n",
       "      <td>40.7222</td>\n",
       "      <td>6911061</td>\n",
       "      <td>-73.9824</td>\n",
       "      <td>[https://photos.renthop.com/2/6911061_25c3f3b1...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.3405778468498007, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 3)\\t0.12074897338505183\\n  (0, 14)\\t0.16...</td>\n",
       "      <td>8</td>\n",
       "      <td>89</td>\n",
       "      <td>936</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99964</th>\n",
       "      <td>2000</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016-04-29 05:20:49</td>\n",
       "      <td>['77TH', 'ST', 'FULLY', 'GUT', 'RENOV', 'STUDI...</td>\n",
       "      <td>E 77 St</td>\n",
       "      <td>['Loft', 'Multi-Level', 'Dishwasher', 'Hardwoo...</td>\n",
       "      <td>40.7708</td>\n",
       "      <td>6942494</td>\n",
       "      <td>-73.9525</td>\n",
       "      <td>[https://photos.renthop.com/2/6942494_edcfce0d...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 3)\\t0.10465041391457239\\n  (0, 43)\\t0.17...</td>\n",
       "      <td>3</td>\n",
       "      <td>56</td>\n",
       "      <td>879</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99965</th>\n",
       "      <td>9200</td>\n",
       "      <td>2.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016-04-03 05:23:23</td>\n",
       "      <td>['FullTime', 'Doorman', 'PET', 'FRIENDLYElevat...</td>\n",
       "      <td>East 87th Street</td>\n",
       "      <td>['Swimming Pool', 'Dining Room', 'Doorman', 'E...</td>\n",
       "      <td>40.7766</td>\n",
       "      <td>6819478</td>\n",
       "      <td>-73.9463</td>\n",
       "      <td>[https://photos.renthop.com/2/6819478_832d3d70...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.3580280900325466, 0.0, 0.2029553874192...</td>\n",
       "      <td>(0, 3)\\t0.3463732304372032\\n  (0, 48)\\t0.331...</td>\n",
       "      <td>4</td>\n",
       "      <td>143</td>\n",
       "      <td>901</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99966</th>\n",
       "      <td>4550</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2016-04-15 05:36:39</td>\n",
       "      <td>['This', 'is', 'a', 'Great', 'Opportunity', 't...</td>\n",
       "      <td>East 72nd Street</td>\n",
       "      <td>['Roof Deck', 'Doorman', 'Elevator', 'Fitness ...</td>\n",
       "      <td>40.7695</td>\n",
       "      <td>6878391</td>\n",
       "      <td>-73.9603</td>\n",
       "      <td>[https://photos.renthop.com/2/6878391_59754267...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.22154422578199173, 0.0, 0.26...</td>\n",
       "      <td>(0, 30)\\t0.11922670282684\\n  (0, 43)\\t0.2537...</td>\n",
       "      <td>4</td>\n",
       "      <td>128</td>\n",
       "      <td>1022</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99979</th>\n",
       "      <td>1900</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-14 03:32:24</td>\n",
       "      <td>[]</td>\n",
       "      <td>Eldert St</td>\n",
       "      <td>[]</td>\n",
       "      <td>40.6865</td>\n",
       "      <td>6871559</td>\n",
       "      <td>-73.9134</td>\n",
       "      <td>[]</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td></td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99980</th>\n",
       "      <td>2500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-27 12:52:12</td>\n",
       "      <td>['Incredibly', 'sunny', 'and', 'spacious', 'st...</td>\n",
       "      <td>John Street</td>\n",
       "      <td>['Common Outdoor Space', 'Cats Allowed', 'Dogs...</td>\n",
       "      <td>40.7090</td>\n",
       "      <td>6933865</td>\n",
       "      <td>-74.0105</td>\n",
       "      <td>[https://photos.renthop.com/2/6933865_9bdcae03...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.38087827762506266, 0.0, 0.215908473516...</td>\n",
       "      <td>(0, 3)\\t0.14746929321804764\\n  (0, 14)\\t0.19...</td>\n",
       "      <td>4</td>\n",
       "      <td>120</td>\n",
       "      <td>804</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99982</th>\n",
       "      <td>2500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-07 02:29:00</td>\n",
       "      <td>['LUXURY', 'BUILDING', 'IN', 'MIDTOWN', 'WEST'...</td>\n",
       "      <td>W 54 St.</td>\n",
       "      <td>['Doorman', 'Elevator', 'Fitness Center', 'Lau...</td>\n",
       "      <td>40.7645</td>\n",
       "      <td>6837242</td>\n",
       "      <td>-73.9840</td>\n",
       "      <td>[https://photos.renthop.com/2/6837242_3fbdc629...</td>\n",
       "      <td>high</td>\n",
       "      <td>3</td>\n",
       "      <td>[0.0, 0.35918061109311933, 0.0, 0.203608717045...</td>\n",
       "      <td>(0, 30)\\t0.12265908057307003\\n  (0, 3)\\t0.10...</td>\n",
       "      <td>3</td>\n",
       "      <td>159</td>\n",
       "      <td>1021</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99984</th>\n",
       "      <td>2675</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-02 03:17:03</td>\n",
       "      <td>['Huge', 'true', 'king', 'size', 'one', 'bedro...</td>\n",
       "      <td>E 76 Street</td>\n",
       "      <td>['Elevator', 'Multi-Level', 'Dishwasher', 'Har...</td>\n",
       "      <td>40.7728</td>\n",
       "      <td>6815109</td>\n",
       "      <td>-73.9591</td>\n",
       "      <td>[https://photos.renthop.com/2/6815109_1ec75160...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.448916456611514\\n  (0, 8)\\t0.2120...</td>\n",
       "      <td>4</td>\n",
       "      <td>70</td>\n",
       "      <td>494</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99986</th>\n",
       "      <td>3645</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-14 03:39:42</td>\n",
       "      <td>['Charming', 'one', 'bedroom', 'Hardwood', 'fl...</td>\n",
       "      <td>Charles Street</td>\n",
       "      <td>['Elevator', 'Hardwood Floors']</td>\n",
       "      <td>40.7350</td>\n",
       "      <td>6871681</td>\n",
       "      <td>-74.0007</td>\n",
       "      <td>[https://photos.renthop.com/2/6871681_f2e17c12...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.30769770799907714\\n  (0, 8)\\t0.29...</td>\n",
       "      <td>5</td>\n",
       "      <td>31</td>\n",
       "      <td>381</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99987</th>\n",
       "      <td>2179</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-11 03:29:05</td>\n",
       "      <td>['89th', '', 'Park', 'Ave', 'block', 'from', '...</td>\n",
       "      <td>East 89th Street</td>\n",
       "      <td>['Dishwasher']</td>\n",
       "      <td>40.7889</td>\n",
       "      <td>6856001</td>\n",
       "      <td>-73.9719</td>\n",
       "      <td>[https://photos.renthop.com/2/6856001_5d031c59...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.10307297174637345\\n  (0, 43)\\t0.0...</td>\n",
       "      <td>8</td>\n",
       "      <td>14</td>\n",
       "      <td>890</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99988</th>\n",
       "      <td>4500</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-22 15:44:11</td>\n",
       "      <td>['24hr', 'Doorman', 'Luxury', 'building', 'in'...</td>\n",
       "      <td>East 10th Street</td>\n",
       "      <td>['Cats Allowed', 'No Fee', 'Doorman', 'Elevato...</td>\n",
       "      <td>40.7296</td>\n",
       "      <td>6913833</td>\n",
       "      <td>-73.9869</td>\n",
       "      <td>[https://photos.renthop.com/2/6913833_a5587e14...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.27165997603414743, 0.0, 0.307991787333...</td>\n",
       "      <td>(0, 8)\\t0.3167884207146474\\n  (0, 3)\\t0.1433...</td>\n",
       "      <td>8</td>\n",
       "      <td>155</td>\n",
       "      <td>542</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9999</th>\n",
       "      <td>3200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-06-02 05:41:05</td>\n",
       "      <td>['30TH3RD', 'MASSIVE', 'CONV', '2BR', 'IN', 'L...</td>\n",
       "      <td>E 30 St</td>\n",
       "      <td>['Elevator', 'Laundry in Building', 'Laundry i...</td>\n",
       "      <td>40.7426</td>\n",
       "      <td>7098690</td>\n",
       "      <td>-73.9790</td>\n",
       "      <td>[https://photos.renthop.com/2/7098690_18396d32...</td>\n",
       "      <td>medium</td>\n",
       "      <td>2</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.2648440374482412, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.21656091778665967\\n  (0, 8)\\t0.20...</td>\n",
       "      <td>4</td>\n",
       "      <td>97</td>\n",
       "      <td>1071</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99991</th>\n",
       "      <td>3950</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-04 18:22:34</td>\n",
       "      <td>['HIGH', 'END', 'condo', 'finishes', 'swimming...</td>\n",
       "      <td>Rector Pl</td>\n",
       "      <td>['Common Outdoor Space', 'Cats Allowed', 'Dogs...</td>\n",
       "      <td>40.7102</td>\n",
       "      <td>6822449</td>\n",
       "      <td>-74.0163</td>\n",
       "      <td>[https://photos.renthop.com/2/6822449_b429587b...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.34700038455782867, 0.0, 0.196704111892...</td>\n",
       "      <td>(0, 30)\\t0.0703493609808237\\n  (0, 8)\\t0.066...</td>\n",
       "      <td>8</td>\n",
       "      <td>133</td>\n",
       "      <td>1665</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99992</th>\n",
       "      <td>2595</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2016-04-16 02:13:40</td>\n",
       "      <td>['Large', 'Renovated', 'One', 'Bedroom', 'Apar...</td>\n",
       "      <td>West 45th Street</td>\n",
       "      <td>['Doorman', 'Elevator', 'Pre-War', 'Dogs Allow...</td>\n",
       "      <td>40.7601</td>\n",
       "      <td>6881461</td>\n",
       "      <td>-73.9900</td>\n",
       "      <td>[https://photos.renthop.com/2/6881461_20a86530...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.5571217637212643, 0.0, 0.0, 0.27906539...</td>\n",
       "      <td>(0, 8)\\t0.24547916199145475\\n  (0, 3)\\t0.111...</td>\n",
       "      <td>3</td>\n",
       "      <td>66</td>\n",
       "      <td>856</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99993</th>\n",
       "      <td>3350</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2016-04-08 02:13:33</td>\n",
       "      <td>['Stylishly', 'sleek', 'studio', 'apartment', ...</td>\n",
       "      <td>Wall Street</td>\n",
       "      <td>['Doorman', 'Elevator', 'Pre-War', 'Dogs Allow...</td>\n",
       "      <td>40.7066</td>\n",
       "      <td>6841891</td>\n",
       "      <td>-74.0101</td>\n",
       "      <td>[https://photos.renthop.com/2/6841891_124c9c44...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.5571217637212643, 0.0, 0.0, 0.27906539...</td>\n",
       "      <td>(0, 3)\\t0.26247641369248653\\n  (0, 36)\\t0.34...</td>\n",
       "      <td>9</td>\n",
       "      <td>66</td>\n",
       "      <td>964</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99994</th>\n",
       "      <td>2200</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2016-04-12 02:48:07</td>\n",
       "      <td>['Look', 'no', 'further', 'This', 'giant', '2'...</td>\n",
       "      <td>Park Terrace East</td>\n",
       "      <td>['Hardwood Floors']</td>\n",
       "      <td>40.8699</td>\n",
       "      <td>6858245</td>\n",
       "      <td>-73.9172</td>\n",
       "      <td>[https://photos.renthop.com/2/6858245_c4380bde...</td>\n",
       "      <td>low</td>\n",
       "      <td>1</td>\n",
       "      <td>[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...</td>\n",
       "      <td>(0, 30)\\t0.20744733694239703\\n  (0, 8)\\t0.09...</td>\n",
       "      <td>7</td>\n",
       "      <td>19</td>\n",
       "      <td>1095</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>48551 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        price  bathrooms  bedrooms             created  \\\n",
       "10       5465        1.0       2.0 2016-06-12 12:19:27   \n",
       "10000    3000        1.5       3.0 2016-06-24 07:54:24   \n",
       "100004   2850        1.0       1.0 2016-04-17 03:26:41   \n",
       "100007   3275        1.0       1.0 2016-04-18 02:22:02   \n",
       "100013   3350        1.0       4.0 2016-04-28 01:32:41   \n",
       "100014   7995        2.0       4.0 2016-04-19 04:24:47   \n",
       "100016   3600        1.0       2.0 2016-04-27 03:19:56   \n",
       "100020   5645        2.0       1.0 2016-04-13 06:01:42   \n",
       "100026   1725        1.0       1.0 2016-04-20 02:36:35   \n",
       "100027   5800        2.0       4.0 2016-04-02 02:58:15   \n",
       "100030   1950        1.0       2.5 2016-04-14 01:10:30   \n",
       "10004    1675        1.0       1.0 2016-06-03 03:21:22   \n",
       "100044   3000        1.0       2.0 2016-04-19 05:37:25   \n",
       "100048   6895        2.0       2.0 2016-04-09 01:22:11   \n",
       "10005    3050        1.0       1.0 2016-06-01 03:11:01   \n",
       "100051   2350        1.0       1.5 2016-04-18 02:36:00   \n",
       "100052   3650        1.0       2.0 2016-04-22 05:57:16   \n",
       "100053   1695        1.0       1.0 2016-04-19 03:36:28   \n",
       "100055   5000        1.0       4.0 2016-04-20 04:00:32   \n",
       "100058   3973        1.0       1.0 2016-04-09 03:49:42   \n",
       "100062   4395        1.0       3.0 2016-04-12 02:39:45   \n",
       "100063   2999        1.0       2.0 2016-04-07 02:11:45   \n",
       "100065   2595        1.0       1.0 2016-04-13 05:17:43   \n",
       "100066   3695        1.0       1.0 2016-04-17 02:26:52   \n",
       "10007    7400        2.0       4.0 2016-06-07 04:39:56   \n",
       "100075   2295        1.0       1.0 2016-04-25 02:29:50   \n",
       "100076   3164        1.0       1.0 2016-04-22 05:39:10   \n",
       "100079   1350        1.0       1.0 2016-04-21 05:27:10   \n",
       "100081   5600        2.0       2.0 2016-04-26 02:59:37   \n",
       "100083   2750        1.0       2.0 2016-04-21 02:17:28   \n",
       "...       ...        ...       ...                 ...   \n",
       "99915    3126        1.0       1.0 2016-04-25 01:13:37   \n",
       "99917    3325        1.0       1.0 2016-04-26 04:43:14   \n",
       "99919    2100        1.0       1.5 2016-04-08 05:51:57   \n",
       "99921    3500        1.0       2.0 2016-04-29 15:03:29   \n",
       "99923   11950        2.0       2.0 2016-04-05 01:12:45   \n",
       "99924    2980        1.0       1.0 2016-04-24 03:15:18   \n",
       "99931    2850        1.0       1.0 2016-04-25 01:20:23   \n",
       "99933    2885        1.0       2.0 2016-04-03 03:51:21   \n",
       "99935    2950        1.0       1.0 2016-04-19 02:58:55   \n",
       "99937    2650        1.5       1.5 2016-04-14 05:39:33   \n",
       "9994     4850        1.0       2.0 2016-06-06 01:22:44   \n",
       "99953    3475        1.0       1.0 2016-04-26 02:18:16   \n",
       "99956    5815        2.0       2.0 2016-04-16 16:39:50   \n",
       "99960    2050        1.0       1.0 2016-04-05 04:35:32   \n",
       "99961    4600        2.0       3.0 2016-04-22 03:11:44   \n",
       "99964    2000        1.0       3.0 2016-04-29 05:20:49   \n",
       "99965    9200        2.0       3.0 2016-04-03 05:23:23   \n",
       "99966    4550        1.0       3.0 2016-04-15 05:36:39   \n",
       "99979    1900        1.0       1.0 2016-04-14 03:32:24   \n",
       "99980    2500        1.0       1.0 2016-04-27 12:52:12   \n",
       "99982    2500        1.0       1.0 2016-04-07 02:29:00   \n",
       "99984    2675        1.0       1.0 2016-04-02 03:17:03   \n",
       "99986    3645        1.0       1.0 2016-04-14 03:39:42   \n",
       "99987    2179        1.0       1.0 2016-04-11 03:29:05   \n",
       "99988    4500        1.0       1.0 2016-04-22 15:44:11   \n",
       "9999     3200        1.0       2.0 2016-06-02 05:41:05   \n",
       "99991    3950        1.0       1.0 2016-04-04 18:22:34   \n",
       "99992    2595        1.0       1.0 2016-04-16 02:13:40   \n",
       "99993    3350        1.0       1.5 2016-04-08 02:13:33   \n",
       "99994    2200        1.0       2.0 2016-04-12 02:48:07   \n",
       "\n",
       "                                              description  \\\n",
       "10                                                     []   \n",
       "10000   ['A', 'Brand', 'New', '3', 'Bedroom', '15', 'b...   \n",
       "100004  ['Top', 'Top', 'West', 'Village', 'location', ...   \n",
       "100007  ['Building', 'Amenities', '', 'Garage', '', 'G...   \n",
       "100013  ['Beautifully', 'renovated', '3', 'bedroom', '...   \n",
       "100014                                                 []   \n",
       "100016  ['Stunning', 'unit', 'with', 'a', 'great', 'lo...   \n",
       "100020  ['This', 'huge', 'sunny', 'plenty', 'of', 'lig...   \n",
       "100026                          ['pa', 'websiteredacted']   \n",
       "100027  ['This', 'is', 'a', 'spacious', 'four', 'bedro...   \n",
       "100030  ['New', 'to', 'the', 'market', 'Spacious', 'st...   \n",
       "10004   ['Check', 'out', 'this', 'one', 'bedroom', 'ap...   \n",
       "100044  ['LOW', 'FEE', 'Beautiful', 'CHERRY', 'OAK', '...   \n",
       "100048  ['Lincoln', 'Squares', 'premier', 'full', 'ser...   \n",
       "10005   ['Spacious', '1Bedroom', 'to', 'fit', 'Kingsiz...   \n",
       "100051  ['Stunning', 'full', 'renovated', 'studio', 'u...   \n",
       "100052  ['East', 'Village', 'is', 'a', 'great', 'commu...   \n",
       "100053  ['Location', 'Thayer', 'St', 'amp', 'Broadway'...   \n",
       "100055                                                 []   \n",
       "100058                                                 []   \n",
       "100062  ['BEAUTIFUL', '2', 'BEDROOM', 'POSSIBLE', 'CON...   \n",
       "100063  ['No', 'fee', 'huge', 'two', 'bedroom', 'apart...   \n",
       "100065                                                 []   \n",
       "100066  ['CooperCoopercom', '', 'Web', 'ID', '179546',...   \n",
       "10007   ['SPRAWLING', '2', 'BEDROOM', 'FOUND', 'ENJOY'...   \n",
       "100075  ['Dont', 'miss', 'out', 'on', 'this', 'fantast...   \n",
       "100076  ['The', 'TRUE', '1', 'bedroom', 'apartment', '...   \n",
       "100079  ['Currently', 'under', 'renovation', 'this', '...   \n",
       "100081  ['Gut', 'Renovated', 'Apartment', 'with', 'Sta...   \n",
       "100083  ['Enjoy', 'the', 'Upper', 'West', 'Side', 'lif...   \n",
       "...                                                   ...   \n",
       "99915   ['Elegant', 'spacious', 'one', 'bedroom', 'in'...   \n",
       "99917                                                  []   \n",
       "99919   ['IMPORTANT', 'NOTICE', 'If', 'you', 'are', 'p...   \n",
       "99921   ['WASHER', '', 'DRYER', 'IN', 'UNIT', '', '5',...   \n",
       "99923   ['Tremendous', 'Classic', 'Six', 'in', 'a', 'L...   \n",
       "99924   ['Luxury', 'Living', 'FullTime', 'Doorman', 'C...   \n",
       "99931   ['Only', 'one', 'block', 'form', 'Washington',...   \n",
       "99933   ['THIS', 'RESIDENCE', 'FEATURES', 'STAINLESS',...   \n",
       "99935   ['This', 'newly', 'renovated', '1', 'bedroom',...   \n",
       "99937   ['Bedroom', 'on', 'top', 'Living', 'room', 'on...   \n",
       "9994    ['Dont', 'miss', 'out', 'on', 'this', 'spaciou...   \n",
       "99953   ['This', 'exquisitely', 'appointed', '1', 'bed...   \n",
       "99956   ['This', 'spacious', 'two', 'bedroom', 'apartm...   \n",
       "99960   ['Great', 'Lower', 'East', 'Side', 'one', 'bed...   \n",
       "99961   ['THIS', 'LARGE', 'AND', 'SPACIOUS', '3', 'BDO...   \n",
       "99964   ['77TH', 'ST', 'FULLY', 'GUT', 'RENOV', 'STUDI...   \n",
       "99965   ['FullTime', 'Doorman', 'PET', 'FRIENDLYElevat...   \n",
       "99966   ['This', 'is', 'a', 'Great', 'Opportunity', 't...   \n",
       "99979                                                  []   \n",
       "99980   ['Incredibly', 'sunny', 'and', 'spacious', 'st...   \n",
       "99982   ['LUXURY', 'BUILDING', 'IN', 'MIDTOWN', 'WEST'...   \n",
       "99984   ['Huge', 'true', 'king', 'size', 'one', 'bedro...   \n",
       "99986   ['Charming', 'one', 'bedroom', 'Hardwood', 'fl...   \n",
       "99987   ['89th', '', 'Park', 'Ave', 'block', 'from', '...   \n",
       "99988   ['24hr', 'Doorman', 'Luxury', 'building', 'in'...   \n",
       "9999    ['30TH3RD', 'MASSIVE', 'CONV', '2BR', 'IN', 'L...   \n",
       "99991   ['HIGH', 'END', 'condo', 'finishes', 'swimming...   \n",
       "99992   ['Large', 'Renovated', 'One', 'Bedroom', 'Apar...   \n",
       "99993   ['Stylishly', 'sleek', 'studio', 'apartment', ...   \n",
       "99994   ['Look', 'no', 'further', 'This', 'giant', '2'...   \n",
       "\n",
       "             display_address  \\\n",
       "10           Columbus Avenue   \n",
       "10000    Metropolitan Avenue   \n",
       "100004           W 13 Street   \n",
       "100007      East 49th Street   \n",
       "100013     West 143rd Street   \n",
       "100014      West 18th Street   \n",
       "100016     West 107th Street   \n",
       "100020      West 21st Street   \n",
       "100026      Hamilton Terrace   \n",
       "100027            522 E 11th   \n",
       "100030           York Avenue   \n",
       "10004        W. 173rd Street   \n",
       "100044             E 38th St   \n",
       "100048      West 63rd Street   \n",
       "10005         East 56th St..   \n",
       "100051      East 34th Street   \n",
       "100052              1st Ave.   \n",
       "100053             Thayer St   \n",
       "100055     West 106th Street   \n",
       "100058            1st Avenue   \n",
       "100062        Madison Avenue   \n",
       "100063            E 30th St,   \n",
       "100065      East 35th Street   \n",
       "100066        Liberty Street   \n",
       "10007               W 18 St.   \n",
       "100075            E 81st St.   \n",
       "100076      West 57th Street   \n",
       "100079    1215 Morris Avenue   \n",
       "100081            E 79th St.   \n",
       "100083  250 West 88th Street   \n",
       "...                      ...   \n",
       "99915      North 10th Street   \n",
       "99917              W 58th St   \n",
       "99919       East 78th Street   \n",
       "99921           Ridge Blvd.    \n",
       "99923       West 81st Street   \n",
       "99924             8th Avenue   \n",
       "99931        Thompson Street   \n",
       "99933                  Water   \n",
       "99935       East 12th Street   \n",
       "99937            York Avenue   \n",
       "9994        West 98th Street   \n",
       "99953       West 58th Street   \n",
       "99956          Nassau Street   \n",
       "99960          Baxter Street   \n",
       "99961        East 3rd Street   \n",
       "99964                E 77 St   \n",
       "99965       East 87th Street   \n",
       "99966       East 72nd Street   \n",
       "99979              Eldert St   \n",
       "99980            John Street   \n",
       "99982               W 54 St.   \n",
       "99984            E 76 Street   \n",
       "99986         Charles Street   \n",
       "99987       East 89th Street   \n",
       "99988       East 10th Street   \n",
       "9999                 E 30 St   \n",
       "99991              Rector Pl   \n",
       "99992       West 45th Street   \n",
       "99993            Wall Street   \n",
       "99994      Park Terrace East   \n",
       "\n",
       "                                                 features  latitude  \\\n",
       "10      ['Doorman', 'Elevator', 'Fitness Center', 'Cat...   40.7947   \n",
       "10000                                                  []   40.7145   \n",
       "100004  ['Laundry In Building', 'Dishwasher', 'Hardwoo...   40.7388   \n",
       "100007                      ['Hardwood Floors', 'No Fee']   40.7539   \n",
       "100013                                        ['Pre-War']   40.8241   \n",
       "100014                                                 []   40.7429   \n",
       "100016  ['prewar', 'elevator', 'Dogs Allowed', 'Cats A...   40.8012   \n",
       "100020  ['Doorman', 'Elevator', 'Pre-War', 'Terrace', ...   40.7427   \n",
       "100026  ['Cats Allowed', 'Dogs Allowed', 'Elevator', '...   40.8234   \n",
       "100027                  ['Dishwasher', 'Hardwood Floors']   40.7278   \n",
       "100030                                                 []   40.7769   \n",
       "10004   ['prewar', 'dishwasher', 'HIGHRISE', 'ROOFDECK...   40.8448   \n",
       "100044  ['Doorman', 'Elevator', 'Laundry in Building',...   40.7488   \n",
       "100048  ['Swimming Pool', 'Doorman', 'Fitness Center',...   40.7707   \n",
       "10005   ['Elevator', 'Multi-Level', 'Laundry in Buildi...   40.7584   \n",
       "100051  ['Doorman', 'Elevator', 'Fitness Center', 'Lau...   40.7439   \n",
       "100052                                ['Hardwood Floors']   40.7305   \n",
       "100053  ['prewar', 'LOWRISE', 'EAT IN KITCHEN', 'SIMPL...   40.8643   \n",
       "100055         ['No Fee', 'Cats Allowed', 'Dogs Allowed']   40.7999   \n",
       "100058                   ['Cats Allowed', 'Dogs Allowed']   40.7328   \n",
       "100062  ['Doorman', 'Elevator', 'Dishwasher', 'Hardwoo...   40.7454   \n",
       "100063  ['Elevator', 'Central A/C', 'Walk in Closet(s)...   40.7427   \n",
       "100065                   ['Cats Allowed', 'Dogs Allowed']   40.7447   \n",
       "100066  ['Doorman', 'Fitness Center', 'Pool', 'Elevato...   40.7074   \n",
       "10007   ['Balcony', 'Doorman', 'Elevator', 'Fitness Ce...   40.7391   \n",
       "100075        ['Dishwasher', 'Hardwood Floors', 'No Fee']   40.7728   \n",
       "100076  ['Roof Deck', 'Doorman', 'Elevator', 'Laundry ...   40.7709   \n",
       "100079                                                 []   40.8335   \n",
       "100081  ['Doorman', 'Elevator', 'Fitness Center', 'Pre...   40.7716   \n",
       "100083  ['Doorman', 'Elevator', 'Pre-War', 'Exclusive'...   40.7897   \n",
       "...                                                   ...       ...   \n",
       "99915   ['Swimming Pool', 'Doorman', 'Fitness Center',...   40.7176   \n",
       "99917   ['Cats Allowed', 'Dogs Allowed', 'Doorman', 'E...   40.7655   \n",
       "99919   ['Elevator', 'Laundry in Building', 'Laundry i...   40.7721   \n",
       "99921                    ['Furnished', 'Laundry In Unit']   40.6182   \n",
       "99923   ['Doorman', 'Pre-War', 'Dogs Allowed', 'Cats A...   40.7829   \n",
       "99924   ['Doorman', 'Laundry in Building', 'Laundry in...   40.7637   \n",
       "99931                    ['Dogs Allowed', 'Cats Allowed']   40.7293   \n",
       "99933   ['Roof Deck', 'Dining Room', 'Doorman', 'Eleva...   40.7075   \n",
       "99935                                                  []   40.7287   \n",
       "99937   ['Pre-War', 'Laundry in Building', 'Dishwasher...   40.7769   \n",
       "9994    ['Doorman', 'Pre-War', 'Dogs Allowed', 'Cats A...   40.7957   \n",
       "99953   ['Doorman', 'Elevator', 'Furnished', 'Pre-War'...   40.7655   \n",
       "99956   ['Common Outdoor Space', 'Cats Allowed', 'Dogs...   40.7095   \n",
       "99960                                 ['Hardwood Floors']   40.7179   \n",
       "99961   ['Doorman', 'Elevator', 'Laundry in Building',...   40.7222   \n",
       "99964   ['Loft', 'Multi-Level', 'Dishwasher', 'Hardwoo...   40.7708   \n",
       "99965   ['Swimming Pool', 'Dining Room', 'Doorman', 'E...   40.7766   \n",
       "99966   ['Roof Deck', 'Doorman', 'Elevator', 'Fitness ...   40.7695   \n",
       "99979                                                  []   40.6865   \n",
       "99980   ['Common Outdoor Space', 'Cats Allowed', 'Dogs...   40.7090   \n",
       "99982   ['Doorman', 'Elevator', 'Fitness Center', 'Lau...   40.7645   \n",
       "99984   ['Elevator', 'Multi-Level', 'Dishwasher', 'Har...   40.7728   \n",
       "99986                     ['Elevator', 'Hardwood Floors']   40.7350   \n",
       "99987                                      ['Dishwasher']   40.7889   \n",
       "99988   ['Cats Allowed', 'No Fee', 'Doorman', 'Elevato...   40.7296   \n",
       "9999    ['Elevator', 'Laundry in Building', 'Laundry i...   40.7426   \n",
       "99991   ['Common Outdoor Space', 'Cats Allowed', 'Dogs...   40.7102   \n",
       "99992   ['Doorman', 'Elevator', 'Pre-War', 'Dogs Allow...   40.7601   \n",
       "99993   ['Doorman', 'Elevator', 'Pre-War', 'Dogs Allow...   40.7066   \n",
       "99994                                 ['Hardwood Floors']   40.8699   \n",
       "\n",
       "        listing_id  longitude  \\\n",
       "10         7150865   -73.9667   \n",
       "10000      7211212   -73.9425   \n",
       "100004     6887163   -74.0018   \n",
       "100007     6888711   -73.9677   \n",
       "100013     6934781   -73.9493   \n",
       "100014     6894514   -74.0028   \n",
       "100016     6930771   -73.9660   \n",
       "100020     6867392   -73.9957   \n",
       "100026     6898799   -73.9457   \n",
       "100027     6814332   -73.9808   \n",
       "100030     6869199   -73.9467   \n",
       "10004      7102986   -73.9396   \n",
       "100044     6895442   -73.9770   \n",
       "100048     6846213   -73.9817   \n",
       "10005      7089402   -73.9648   \n",
       "100051     6889043   -73.9743   \n",
       "100052     6913348   -73.9830   \n",
       "100053     6894111   -73.9280   \n",
       "100055     6900220   -73.9638   \n",
       "100058     6848536   -73.9799   \n",
       "100062     6858062   -73.9845   \n",
       "100063     6836760   -73.9794   \n",
       "100065     6866830   -73.9741   \n",
       "100066     6885927   -74.0081   \n",
       "10007      7120132   -73.9936   \n",
       "100075     6921632   -73.9502   \n",
       "100076     6913084   -73.9917   \n",
       "100079     6907079   -73.9141   \n",
       "100081     6925264   -73.9506   \n",
       "100083     6904268   -73.9760   \n",
       "...            ...        ...   \n",
       "99915      6921019   -73.9531   \n",
       "99917      6926146   -73.9785   \n",
       "99919      6844805   -73.9539   \n",
       "99921      6943991   -74.0360   \n",
       "99923      6822618   -73.9731   \n",
       "99924      6918969   -73.9847   \n",
       "99931      6921162   -73.9987   \n",
       "99933      6819357   -74.0043   \n",
       "99935      6893263   -73.9808   \n",
       "99937      6873182   -73.9467   \n",
       "9994       7114685   -73.9705   \n",
       "99953      6924210   -73.9785   \n",
       "99956      6884807   -74.0084   \n",
       "99960      6825168   -73.9989   \n",
       "99961      6911061   -73.9824   \n",
       "99964      6942494   -73.9525   \n",
       "99965      6819478   -73.9463   \n",
       "99966      6878391   -73.9603   \n",
       "99979      6871559   -73.9134   \n",
       "99980      6933865   -74.0105   \n",
       "99982      6837242   -73.9840   \n",
       "99984      6815109   -73.9591   \n",
       "99986      6871681   -74.0007   \n",
       "99987      6856001   -73.9719   \n",
       "99988      6913833   -73.9869   \n",
       "9999       7098690   -73.9790   \n",
       "99991      6822449   -74.0163   \n",
       "99992      6881461   -73.9900   \n",
       "99993      6841891   -74.0101   \n",
       "99994      6858245   -73.9172   \n",
       "\n",
       "                                                   photos interest_level  \\\n",
       "10      [https://photos.renthop.com/2/7150865_be3306c5...            low   \n",
       "10000   [https://photos.renthop.com/2/7211212_1ed4542e...         medium   \n",
       "100004  [https://photos.renthop.com/2/6887163_de85c427...           high   \n",
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       "100053  [https://photos.renthop.com/2/6894111_571fa57d...            low   \n",
       "100055                                                 []            low   \n",
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       "100065                                                 []            low   \n",
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       "...                                                   ...            ...   \n",
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       "99933   [https://photos.renthop.com/2/6819357_700208a6...            low   \n",
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       "99956   [https://photos.renthop.com/2/6884807_158a7214...            low   \n",
       "99960   [https://photos.renthop.com/2/6825168_f94cee7d...         medium   \n",
       "99961   [https://photos.renthop.com/2/6911061_25c3f3b1...            low   \n",
       "99964   [https://photos.renthop.com/2/6942494_edcfce0d...         medium   \n",
       "99965   [https://photos.renthop.com/2/6819478_832d3d70...            low   \n",
       "99966   [https://photos.renthop.com/2/6878391_59754267...            low   \n",
       "99979                                                  []            low   \n",
       "99980   [https://photos.renthop.com/2/6933865_9bdcae03...            low   \n",
       "99982   [https://photos.renthop.com/2/6837242_3fbdc629...           high   \n",
       "99984   [https://photos.renthop.com/2/6815109_1ec75160...            low   \n",
       "99986   [https://photos.renthop.com/2/6871681_f2e17c12...            low   \n",
       "99987   [https://photos.renthop.com/2/6856001_5d031c59...            low   \n",
       "99988   [https://photos.renthop.com/2/6913833_a5587e14...         medium   \n",
       "9999    [https://photos.renthop.com/2/7098690_18396d32...         medium   \n",
       "99991   [https://photos.renthop.com/2/6822449_b429587b...            low   \n",
       "99992   [https://photos.renthop.com/2/6881461_20a86530...            low   \n",
       "99993   [https://photos.renthop.com/2/6841891_124c9c44...            low   \n",
       "99994   [https://photos.renthop.com/2/6858245_c4380bde...            low   \n",
       "\n",
       "        interest                                       features_idf  \\\n",
       "10             1  [0.0, 0.5851074667834789, 0.0, 0.0, 0.29308358...   \n",
       "10000          2  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100004         3  [0.0, 0.3672803019787415, 0.0, 0.4164003778182...   \n",
       "100007         1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100013         1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100014         2  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100016         1  [0.0, 0.3404048697805322, 0.0, 0.0, 0.17051069...   \n",
       "100020         1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100026         2  [0.0, 0.624237228556546, 0.0, 0.35386136476519...   \n",
       "100027         1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100030         1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "10004          1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100044         3  [0.0, 0.0, 0.0, 0.40179672989790755, 0.0, 0.0,...   \n",
       "100048         1  [0.0, 0.4148146356728024, 0.0, 0.0, 0.20778296...   \n",
       "10005          1  [0.0, 0.0, 0.0, 0.4196458341669265, 0.0, 0.0, ...   \n",
       "100051         2  [0.0, 0.0, 0.0, 0.3338940270308353, 0.0, 0.393...   \n",
       "100052         1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100053         1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100055         1  [0.0, 0.6771638443600325, 0.0, 0.0, 0.33919513...   \n",
       "100058         1  [0.0, 0.8107329637511722, 0.0, 0.0, 0.40610065...   \n",
       "100062         1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100063         2  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100065         1  [0.0, 0.8107329637511722, 0.0, 0.0, 0.40610065...   \n",
       "100066         1  [0.0, 0.3696849871373356, 0.0, 0.2095633328119...   \n",
       "10007          2  [0.0, 0.29598397416109534, 0.3240414612033308,...   \n",
       "100075         2  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100076         1  [0.0, 0.0, 0.0, 0.2131946462841425, 0.0, 0.0, ...   \n",
       "100079         3  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "100081         1  [0.0, 0.0, 0.0, 0.25727016685225507, 0.0, 0.30...   \n",
       "100083         2  [0.0, 0.4645633126290635, 0.0, 0.0, 0.23270234...   \n",
       "...          ...                                                ...   \n",
       "99915          1  [0.0, 0.4405599056825441, 0.0, 0.0, 0.22067890...   \n",
       "99917          1  [0.0, 0.493269763082621, 0.0, 0.0, 0.247081570...   \n",
       "99919          2  [0.0, 0.4047992390472882, 0.0, 0.2294685491866...   \n",
       "99921          2  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "99923          1  [0.0, 0.5773152566220375, 0.0, 0.0, 0.28918042...   \n",
       "99924          1  [0.0, 0.0, 0.0, 0.2629911459641138, 0.0, 0.0, ...   \n",
       "99931          1  [0.0, 0.8107329637511722, 0.0, 0.0, 0.40610065...   \n",
       "99933          1  [0.0, 0.2988137759459068, 0.0, 0.1693885685276...   \n",
       "99935          1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "99937          1  [0.0, 0.0, 0.0, 0.30495355868409363, 0.0, 0.0,...   \n",
       "9994           1  [0.0, 0.5773152566220375, 0.0, 0.0, 0.28918042...   \n",
       "99953          1  [0.0, 0.4144424142167339, 0.0, 0.0, 0.20759651...   \n",
       "99956          1  [0.0, 0.41581325034301647, 0.0, 0.0, 0.2082831...   \n",
       "99960          2  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "99961          1  [0.0, 0.0, 0.0, 0.3405778468498007, 0.0, 0.0, ...   \n",
       "99964          2  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "99965          1  [0.0, 0.3580280900325466, 0.0, 0.2029553874192...   \n",
       "99966          1  [0.0, 0.0, 0.0, 0.22154422578199173, 0.0, 0.26...   \n",
       "99979          1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "99980          1  [0.0, 0.38087827762506266, 0.0, 0.215908473516...   \n",
       "99982          3  [0.0, 0.35918061109311933, 0.0, 0.203608717045...   \n",
       "99984          1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "99986          1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "99987          1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "99988          2  [0.0, 0.27165997603414743, 0.0, 0.307991787333...   \n",
       "9999           2  [0.0, 0.0, 0.0, 0.2648440374482412, 0.0, 0.0, ...   \n",
       "99991          1  [0.0, 0.34700038455782867, 0.0, 0.196704111892...   \n",
       "99992          1  [0.0, 0.5571217637212643, 0.0, 0.0, 0.27906539...   \n",
       "99993          1  [0.0, 0.5571217637212643, 0.0, 0.0, 0.27906539...   \n",
       "99994          1  [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, ...   \n",
       "\n",
       "                                          description_idf  num_photos  \\\n",
       "10                                                                 11   \n",
       "10000     (0, 30)\\t0.1776163821148241\\n  (0, 8)\\t0.167...           5   \n",
       "100004    (0, 30)\\t0.1420402417973664\\n  (0, 8)\\t0.134...           8   \n",
       "100007    (0, 3)\\t0.070735125312101\\n  (0, 36)\\t0.1840...           3   \n",
       "100013    (0, 8)\\t0.23775749541087743\\n  (0, 3)\\t0.322...           3   \n",
       "100014                                                              5   \n",
       "100016    (0, 48)\\t0.3245981358259179\\n  (0, 43)\\t0.46...          10   \n",
       "100020    (0, 30)\\t0.18592672562653134\\n  (0, 3)\\t0.15...           5   \n",
       "100026    (0, 45)\\t0.5807783354322145\\n  (0, 34)\\t0.81...           5   \n",
       "100027    (0, 8)\\t0.15356530890167605\\n  (0, 3)\\t0.069...           9   \n",
       "100030    (0, 30)\\t0.27484092145062755\\n  (0, 36)\\t0.3...           1   \n",
       "10004     (0, 8)\\t0.21499865933864884\\n  (0, 3)\\t0.389...           5   \n",
       "100044    (0, 8)\\t0.11989964628174397\\n  (0, 3)\\t0.108...           4   \n",
       "100048    (0, 43)\\t0.14097350157983315\\n  (0, 41)\\t0.1...           6   \n",
       "10005     (0, 43)\\t0.3368410620030332\\n  (0, 39)\\t0.12...           6   \n",
       "100051    (0, 14)\\t0.17133619942428127\\n  (0, 15)\\t0.1...           2   \n",
       "100052    (0, 8)\\t0.2017915683423615\\n  (0, 43)\\t0.151...           5   \n",
       "100053    (0, 8)\\t0.17155787166997313\\n  (0, 3)\\t0.155...           8   \n",
       "100055                                                              0   \n",
       "100058                                                             32   \n",
       "100062    (0, 30)\\t0.31524046073526707\\n  (0, 8)\\t0.29...           6   \n",
       "100063    (0, 30)\\t0.18184836552957115\\n  (0, 8)\\t0.34...           5   \n",
       "100065                                                              0   \n",
       "100066    (0, 3)\\t0.15650754291654204\\n  (0, 14)\\t0.21...           4   \n",
       "10007     (0, 30)\\t0.11752156721780282\\n  (0, 8)\\t0.22...           6   \n",
       "100075    (0, 30)\\t0.1424544631151341\\n  (0, 3)\\t0.121...           6   \n",
       "100076    (0, 8)\\t0.1014145294028751\\n  (0, 3)\\t0.0917...           6   \n",
       "100079    (0, 8)\\t0.22436092653277243\\n  (0, 43)\\t0.33...           1   \n",
       "100081    (0, 3)\\t0.29568774835859046\\n  (0, 43)\\t0.24...           1   \n",
       "100083    (0, 30)\\t0.0838593409637199\\n  (0, 8)\\t0.079...           4   \n",
       "...                                                   ...         ...   \n",
       "99915     (0, 8)\\t0.13260875924697701\\n  (0, 3)\\t0.360...           6   \n",
       "99917                                                               9   \n",
       "99919     (0, 8)\\t0.10519315682314889\\n  (0, 3)\\t0.095...           4   \n",
       "99921     (0, 3)\\t0.1462737969273625\\n  (0, 36)\\t0.190...           6   \n",
       "99923     (0, 30)\\t0.16368032666036214\\n  (0, 3)\\t0.13...           2   \n",
       "99924     (0, 8)\\t0.15622373185253\\n  (0, 20)\\t0.17191...           3   \n",
       "99931     (0, 30)\\t0.3595029899102999\\n  (0, 43)\\t0.25...           3   \n",
       "99933     (0, 30)\\t0.15188706712135894\\n  (0, 14)\\t0.3...           4   \n",
       "99935     (0, 30)\\t0.11274728402684481\\n  (0, 8)\\t0.10...           4   \n",
       "99937     (0, 8)\\t0.1887565248085149\\n  (0, 36)\\t0.222...           7   \n",
       "9994      (0, 8)\\t0.1534751557547402\\n  (0, 36)\\t0.180...           8   \n",
       "99953     (0, 30)\\t0.08947465391823216\\n  (0, 8)\\t0.08...           1   \n",
       "99956     (0, 8)\\t0.19794490578810875\\n  (0, 3)\\t0.179...           8   \n",
       "99960     (0, 30)\\t0.21674277230671993\\n  (0, 20)\\t0.2...           3   \n",
       "99961     (0, 3)\\t0.12074897338505183\\n  (0, 14)\\t0.16...           8   \n",
       "99964     (0, 3)\\t0.10465041391457239\\n  (0, 43)\\t0.17...           3   \n",
       "99965     (0, 3)\\t0.3463732304372032\\n  (0, 48)\\t0.331...           4   \n",
       "99966     (0, 30)\\t0.11922670282684\\n  (0, 43)\\t0.2537...           4   \n",
       "99979                                                               0   \n",
       "99980     (0, 3)\\t0.14746929321804764\\n  (0, 14)\\t0.19...           4   \n",
       "99982     (0, 30)\\t0.12265908057307003\\n  (0, 3)\\t0.10...           3   \n",
       "99984     (0, 30)\\t0.448916456611514\\n  (0, 8)\\t0.2120...           4   \n",
       "99986     (0, 30)\\t0.30769770799907714\\n  (0, 8)\\t0.29...           5   \n",
       "99987     (0, 30)\\t0.10307297174637345\\n  (0, 43)\\t0.0...           8   \n",
       "99988     (0, 8)\\t0.3167884207146474\\n  (0, 3)\\t0.1433...           8   \n",
       "9999      (0, 30)\\t0.21656091778665967\\n  (0, 8)\\t0.20...           4   \n",
       "99991     (0, 30)\\t0.0703493609808237\\n  (0, 8)\\t0.066...           8   \n",
       "99992     (0, 8)\\t0.24547916199145475\\n  (0, 3)\\t0.111...           3   \n",
       "99993     (0, 3)\\t0.26247641369248653\\n  (0, 36)\\t0.34...           9   \n",
       "99994     (0, 30)\\t0.20744733694239703\\n  (0, 8)\\t0.09...           7   \n",
       "\n",
       "        num_features  num_desc  day_of_week  \n",
       "10                73         2            6  \n",
       "10000              2       823            4  \n",
       "100004            85       911            6  \n",
       "100007            29       647            0  \n",
       "100013            11       669            3  \n",
       "100014             2         2            1  \n",
       "100016            98       806            2  \n",
       "100020           114      1278            2  \n",
       "100026            67        25            2  \n",
       "100027            33      1462            5  \n",
       "100030             2       245            3  \n",
       "10004             89       960            4  \n",
       "100044            56       973            1  \n",
       "100048            88       780            5  \n",
       "10005             83       769            2  \n",
       "100051            74       655            0  \n",
       "100052            19       458            4  \n",
       "100053            62      1681            1  \n",
       "100055            42         2            2  \n",
       "100058            32         2            5  \n",
       "100062            56      1365            1  \n",
       "100063           137       405            3  \n",
       "100065            32         2            2  \n",
       "100066           154      1168            6  \n",
       "10007            191      1210            1  \n",
       "100075            43       557            0  \n",
       "100076           125       983            4  \n",
       "100079             2       218            3  \n",
       "100081           118       497            1  \n",
       "100083            79      1827            3  \n",
       "...              ...       ...          ...  \n",
       "99915             78       762            0  \n",
       "99917             79         2            1  \n",
       "99919            129       950            4  \n",
       "99921             32       607            4  \n",
       "99923             54       723            1  \n",
       "99924             96       881            6  \n",
       "99931             32       244            0  \n",
       "99933            190       864            6  \n",
       "99935              2       926            1  \n",
       "99937             77       414            3  \n",
       "9994              54       746            0  \n",
       "99953             93      1826            1  \n",
       "99956             86       521            5  \n",
       "99960             19       597            1  \n",
       "99961             89       936            4  \n",
       "99964             56       879            4  \n",
       "99965            143       901            6  \n",
       "99966            128      1022            4  \n",
       "99979              2         2            3  \n",
       "99980            120       804            2  \n",
       "99982            159      1021            3  \n",
       "99984             70       494            5  \n",
       "99986             31       381            3  \n",
       "99987             14       890            0  \n",
       "99988            155       542            4  \n",
       "9999              97      1071            3  \n",
       "99991            133      1665            0  \n",
       "99992             66       856            5  \n",
       "99993             66       964            4  \n",
       "99994             19      1095            1  \n",
       "\n",
       "[48551 rows x 19 columns]"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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