6_seasonality_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Goal: \n",
    "Find:   \n",
    "- Reservation seasonality in general\n",
    "- Reservation seasonality with location (zipcode)\n",
    "- Reservation seasonality with pricing\n",
    "- Which places are undervalued?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Big assumption for seasonality analysis: \n",
    "- Nubmer of reviews/month has strong positive correlation with reservation frequency/month. \n",
    "- So below, I treat review frequency as reservation frequency. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.cross_validation import train_test_split\n",
    "from gensim.models.word2vec import Word2Vec\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from collections import Counter\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reservation seasonality in general"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data and preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 132034 entries, 0 to 132331\n",
      "Data columns (total 4 columns):\n",
      "id             132034 non-null int64\n",
      "listing_url    132034 non-null object\n",
      "date           132034 non-null object\n",
      "zipcode        132034 non-null float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 5.0+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>date</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                           listing_url        date  zipcode\n",
       "0  1994427  https://www.airbnb.com/rooms/1994427  2014-02-27    94110"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/df_new_en.csv').drop('Unnamed: 0', 1)\n",
    "df = df[['id', 'listing_url','date', 'zipcode']]\n",
    "df =  df.dropna()\n",
    "print df.info()\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>date</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2014-02</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2015-10</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2015-10</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2015-10</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2015-10</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                           listing_url     date  zipcode\n",
       "0  1994427  https://www.airbnb.com/rooms/1994427  2014-02    94110\n",
       "1  1994427  https://www.airbnb.com/rooms/1994427  2015-10    94110\n",
       "2  1994427  https://www.airbnb.com/rooms/1994427  2015-10    94110\n",
       "3  1994427  https://www.airbnb.com/rooms/1994427  2015-10    94110\n",
       "4  1994427  https://www.airbnb.com/rooms/1994427  2015-10    94110"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dates to year-month\n",
    "df.date = [month[:7] for month in df.date]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Convert to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>date</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2014002</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2015010</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2015010</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2015010</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2015010</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                           listing_url     date  zipcode\n",
       "0  1994427  https://www.airbnb.com/rooms/1994427  2014002    94110\n",
       "1  1994427  https://www.airbnb.com/rooms/1994427  2015010    94110\n",
       "2  1994427  https://www.airbnb.com/rooms/1994427  2015010    94110\n",
       "3  1994427  https://www.airbnb.com/rooms/1994427  2015010    94110\n",
       "4  1994427  https://www.airbnb.com/rooms/1994427  2015010    94110"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_month_df = df[:][:]\n",
    "import datetime\n",
    "def convert_datetime(x):\n",
    "    dt = datetime.datetime.strptime(x, \"%Y-%m\")\n",
    "#     return dt.date()\n",
    "    return 1000*dt.year + dt.month\n",
    "year_month_df['date'] = year_month_df['date'].apply(convert_datetime)\n",
    "year_month_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Let's see seasonality based on review frequency "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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r8xj3tmPuub765/o8xr3tmHuur/65Po9xbzvmnuurf653adw3Wnp9yyijD4ka\nfWJ/WFXjzyc9PMk/SfJPgSOq+QtA03a8/e8meUOSbwbeluQnkjw+yUuAz661E1X11aq6tKq+G3gi\ncBVw0ViTJPl7SU4HjkzyHc3KXdz/F8H/NsnJzeunAXc227jzUH8bX07y/CT3jHGSw5KcA3x5IvMv\ngd1VddLkAtw21u6eS9RVdVdV/TCjE+/9wOQl9XlkthrPqrqA0X9E3pjk5ax/a3TbMYcpx73FmEP7\ncW875tB+3NuOD7Qfo3lktj6H5zDunuv05lyHOY2753ovznWY/bh7rrfL7My53mTMatw91/tzrkN3\nxn191bJy7OICvB44ao31JwN/MrHuDYyq/kPLob9GPAZ4/0TblzQHxF8BdwCfAn4ZePhEuz9uuZ/f\nA9zE6NL0acC1wI2Mfltxz0TbZzA6YG9s3nNms/7RwK+MtTuJ0eXqLwI3NMsXm3UnTWT+GPDkdfbt\ngrHHvwN89xptfgi4awGZrcezWX848HJGl+P/zxqvtx7ztuPedsynGfe2Yz7NuLcdn2nGaE6ZU435\nrMe9zZhPM+5tx3yacW875nM8L1f+XJ/HuLcd82nGve2YTzPubcdnyvOyd+f6rMe97ZhPM+5tx3yO\n5+Ugz/V5jHvbMZ9m3NuOz5Tnped6j8Z9o6XX3yHcSJLDqvmrxKpJEuCbgC+utY/NXwseWVVfbJn1\nyObpl6qnA77ReCY5jtHJ8u4F79ZUNhr3acZ8LKvX477ZOdyFcfdcn57n+ppZvR53z/U1s3o95uC5\nvk5Wr8e9D+c6dHPcO/3D9G0k2QnsAQ59qfIW4D1VdfsabR8OfPdmbccyj2tWHdwgs9X2J7ZdhzKB\n+2UCRwHPbE6MbNSn5sD5q4ltPbuqrlgj937atl2r3RR9n2aMtpyZZOcmn3vbbW847ts45jYa99Zj\nDtsb9+0cH4say2kytzPunuvt2nXhXG/aznTcPdf7c65PZM5k3D3X+3OuT7mfnusDO9fX2H7nxr3X\n3yFM8oPAR4DdjGYzOoLR5dqPJjl3jbYf3aztROaRzbJR5qbbX2Pbm2UeanvkRn3awG+2bDdN2/u0\nm7Lv04zRzDLbjvkameuO+zaPuY0ytzvmsMWxbNt2mWO5hUzP9a237dy5PtZ2ZuPuub70sVzp/8Z7\nri/9+Fjp/8Z7rvfnXF9n+50b917fMprRb5WcvkbF/wjgQFXtmrZthzLfucFH88yqGv8tnlZtp8xc\n+c+zQ2OriDhSAAALd0lEQVQ5TeY8xrJt5rL7PtTjw3N9mMeH5/oKZ3qu92cs55HpuW5mi8yZj/tG\nen/L6DqmqYLbtl21zKcCLwa+NtEmwBlbbDtNZtv93G67eWSu2lhO024eY7ndce/y57lq216rref6\namV6rrezap/nojI9182ctp3n+jAzF3kO974g/EXgI0ney+geXYDHAmcBP7/Ftl3JvAr4m6raP/F+\nknx6YlXbttNkduHz7MpYTpM5j7Fs23bZfR/q8eG5Pszjw3N9tTM91/szlvPI9Fw3cxnjvq5e3zIK\nkORo4B9z3y+OvreqJn+zp3XbrmQuWxc+z66MZd/GfJq2Hh/9GfeufJ5d2PayLbvvXcj0XO/PWM4j\ns29jPk1bM1dv3HtfEB6S5JEAVfWlWbXtUObRTbtND7a2bafMXPnPs0NjOU3mPMaybeay+z7U48Nz\nfZjHh+f6Cmd6rvdnLOeR6bluZovMmY/7/VTLHyzs4gI8Hngzox9yvLFZvtisO3Erbc3sT2bf+mOm\nx4eZHh9mrkZm3/pjpseHmd3P3Ghp1airC/Ah4Bxgx9i6HcALgA9tpa2Z/cnsW3/M9Pgw0+PDzNXI\n7Ft/zPT4MLP7mRstrRp1dQFuaPta27Zm9iezb/0x0+PDTI8PM1cjs2/9MdPjw8zuZ2607KDfPprk\nNcAlwOeadY8DzgWu3mJbM/uT2bf+mOnxYabHh5mrkdm3/pjp8WFm9zPX1etJZZI8CDgPeA5wfLP6\nIPAO4PVVdee0bc3sT2bf+mOmx4eZi8vsW3/M9Pgwc3GZfeuPmd3I3EivC0JJkiRJ0voOW/YOzFuS\nZyT5r0l+P8nvJbk4ycnbaWtm68w9Sf5bknc2y2uT7Nlqu3lkLnPbfcxcT5J/P+u2y8zsW3+2m9kc\nH+clOXFi/Uu30q4rmX3rT9u2SR6Q5EWH/huQ5Nwk/6V5Xybe16pt3zL71p/1JPmjzdpM027ImavY\nnySPnnj+4iT/OcmPrHHMtWpr5vIyN9LrK4RJLgaOBd4PnA18Brge+JfAL1fVZdO2NbN15quAXcCl\njC5dA5wAvBi4saoumKbdPDKXue0+Zm4kyeeq6rGbtZum7TIz+9af7WQm+WXgO4GPAt8HvKqqXt28\ndnVVffs07bqS2bf+TJn5euDhwAOB/wc8CHgr8L3AZ6vqp8YyW7XtW2bf+tO0vQYoYPwfmU9g9O+A\nqqonTdNuyJkd6s/4ef9zwHcBb2T034fPVdVPjGW2amvm8jI3VC1nn+niAnxi7PEO4E+bx48APrmV\ntma2zlxzZiNG//G5cdp288hc5rZ7mnnHBsvdE+9t1XaZmX3rzxwzPwE8oHm8E/hD4Neb4+Pqadt1\nJbNv/Zky85PN/z4A+DLwoOb5DuDjE5mt2vYts2/9ada9A/gd4BRGv312IqNJLB7PfX8XrVW7IWd2\nqD/j5/3VwEPHjpdPTGS2amvm8jI3Wvp+y+jfJXlk8/h4mltkq+or22hrZrvMryc5fY31pzP6K+S0\n7eaRucxt9zHzK8CuqjpqcgH+z8R727ZdZmbf+jOvzMOr6i6Aqrqd0V8lHwa8hdFVh2nbdSWzb/2Z\npu2hNncBH65m0oKqupvRVQe20LZvmX3rD1X1HEZXD18HPLmqbmL0B6Kbm8dTtRtyZlf6AxyR5ClJ\nTmP0x6KvNe+/C/g77qttWzOXl7m+alk5dnFh9EONNwPvY/SXj+9t1n8T8MattDWzdeZpwAHgU8AV\nzfIp4CrgtGnbzSNzmdvuaeYvAqevcy7+ysTzVm2Xmdm3/swx813A09Zo9wvAN6Zt15XMvvVnyszL\naf4KPdHuMcCBiXWt2vYts2/9mXjtocCvAb8PHFyrzTTthpy56v0B9gNXji3HNesfBfz5VtqaubzM\njZZef4cQoLmi9c2Mbnu7fRZtzWyX2bR/DGPT4FbV5NWFqdrNI3OZ2+5jpoYlyREAVTV5ZZkkJ1TV\nLdO060pm3/ozbdu1JHkI8JCq+sJG7aZp27fMPvUnyZOBM6vqv22S1ardkDO70p+x9oczur34b2bV\n1szlZQKDKAgDnAEc16w6yOivXvfreNu2ZrbLXE+SJ1bVdbNqN4/MZW7bzNXP7Ft/zPT4MHNxmX3r\nj5keH2b2ILPPBWGSs4DXADcCh/6yeQKjGRPPr6r3TNvWzHaZG8kKzZC4its2c/Uz+9YfMz0+zFxc\nZt/607T9bFU9blbthpzZof4s+5gzc4aZO9o06rBXA8+qiS/TJjmJ0expT9xCWzNbZCb5z6xv57Tt\n5pG5zG2bufqZfeuPmR4fZi4us2/9adH2EdO2G3JmT/qz7GPOzCkzN9L3K4Q3AKdWM2va2PoHAtdW\n1cnTtjWzdeYdwE8Cd3LfmcoC/GpVPXKadvPIXOa2zVz9zL71x0yPDzMXl9m3/pjp8WFm9zM3VC1n\nn+niAlwEfAz4GeAHmuXCZt3PbqWtma0zrwS+c51xuWnadvPIXOa2zVz9zL71x0yPDzM9Psxcjcy+\n9cfMbmRutPT6CiFAklOB53LfSVDeUVXXbrWtma3aHQ18vTafBalVu3lkLnPbZq5+Zt/6Y6bHh5mL\ny+xbf8z0+DCz+5kb5vS9IJQkSZIkraPtpcQuLoy+THkxcB3wFeDLzeOLgZ1baWtmfzL71h8zPT7M\n9PgwczUy+9YfMz0+zOx+5kbLYfTbZYw+nN3A0VV1NPB04Pbmta20NbM/mX3rj5keH2Z6fJi5Gpl9\n64+ZHh9mdj9zfW0rxy4uwPVtX2vb1sz+ZPatP2Z6fJjp8WHmamT2rT9menyY2f3MjZa+XyG8OclP\nJznm0Iokxyb5GeCzW2xrZn8y+9YfMz0+zPT4MHM1MvvWHzM9Pszsfua6+l4QngM8CvhAkq8k+Qqw\nH3gk8PwttjWzP5l964+ZHh9menyYuRqZfeuPmR4fZnY/c31tLyV2dQFOAZ4FHDWxfs9W25rZn8y+\n9cdMjw8zPT7MXI3MvvXHTI8PM7ufud7SqlFXF+AC4NPA24GbgbPHXrt6K23N7E9m3/pjpseHmR4f\nZq5GZt/6Y6bHh5ndz9xoadWoqwvwCeChzeMTgY8AP77OB9+qrZn9yexbf8z0+DDT48PM1cjsW3/M\n9Pgws/uZGy076LdU1dcAquqmJE8D3prk8UC22NbM/mT2rT9menyY6fFh5mpk9q0/Znp8mNn9zHX1\nfVKZLyR58qEnzQf2vYy+aPmkLbY1sz+ZfeuPmR4fZnp8mLkamX3rj5keH2Z2P3N91fJSYhcX4LHA\nsWusD/DUrbQ1sz+ZfeuPmR4fZnp8mLkamX3rj5keH2Z2P3OjJc2bJEmSJEkD0/dbRiVJkiRJ67Ag\nlCRJkqSBsiCUJEmSpIGyIJQkSZKkgbIglCRpRSTx/5clSQvl//FIkrQFSfYlefnY819MckGSn0py\nIMlfJNk79vrbkvx5kk8k+eGx9V9L8h+TfAw4c7G9kCQNnQWhJElb85vAD8I9V/bOAW4FTq6q04Fv\nB05L8l1N+5dW1T8EvgO4IMkjmvVHAh+qqidX1Z8utAeSpMHbsewdkCSpi6rq5iRfSvJk4FjgakbF\n3llJrm6aPQQ4Gfgg8PIkZzfrHwvsAg4Afwe8daE7L0lSw4JQkqSt+w3gJcAxjK4YPhP45ap63Xij\nJLub186sqq8nuRJ4cPPy16uqFrfLkiTdy1tGJUnaurcBe4B/CFwOvAd4aZKHACQ5PsmjgYcBX2mK\nwSfidwUlSSvCK4SSJG1RVd2V5I8YFXsFXJHkFODPkgDcAbyIUbH4siTXAp8G/mw8ZsG7LUnSPeJd\nKpIkbU0zmcxHgOdV1f9e9v5IkjQtbxmVJGkLkpwK3AC8z2JQktRVXiGUJEmSpIHyCqEkSZIkDZQF\noSRJkiQNlAWhJEmSJA2UBaEkSZIkDZQFoSRJkiQNlAWhJEmSJA3U/wculqHmUaEB8QAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ad66c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "labels, values = zip(*sorted(Counter(year_month_df.date).items()))\n",
    "\n",
    "indexes = np.arange(len(labels))\n",
    "width = 1\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.bar(indexes, values, width)\n",
    "plt.xticks(indexes + width * 0.5, labels,  rotation='vertical')\n",
    "plt.ylabel('review count')\n",
    "plt.xlabel('year')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Two findings:  \n",
    "- REALLY quick growth \n",
    "- Possible seasonality (increase the counts during summer time)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Number of visiters at least overnight visitors in SF (in millions)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Let's see if continous increase of counts in airbnb data is due to number of SF visitors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Domestic\n",
    "<img src=\"data/sfvisiter_dom.png\" alt=\"\" style=\"width: 700px;\"/>\n",
    "#### International\n",
    "<img src=\"data/sfvisitor_int.png\" alt=\"\" style=\"width: 700px;\"/>  \n",
    "Source: statista.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The growth rate of the number of SF visitors is not comparable to airbnb's growth  -> hotels should be scared by the service :D\n",
    "- It'll be interesting to further research on which visitor groups (younger students, business people etc) contribute to the growth"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's see monthly (ignore year)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>date</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>10</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>10</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>10</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>10</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                           listing_url  date  zipcode\n",
       "0  1994427  https://www.airbnb.com/rooms/1994427     2    94110\n",
       "1  1994427  https://www.airbnb.com/rooms/1994427    10    94110\n",
       "2  1994427  https://www.airbnb.com/rooms/1994427    10    94110\n",
       "3  1994427  https://www.airbnb.com/rooms/1994427    10    94110\n",
       "4  1994427  https://www.airbnb.com/rooms/1994427    10    94110"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "def convert_datetime_to_month(x):\n",
    "    dt = datetime.datetime.strptime(x, \"%Y-%m\")\n",
    "    return dt.month\n",
    "df['date'] = df['date'].apply(convert_datetime_to_month)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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V5H1JfrO7P7jf5+tD3fFla6roySr6spq+rKYvB9KT1fRlNX1ZTV8OpCer6ctq+rJapbsP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\nTKgDAAAY2P8HE2JRgu/c/ZQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x113b95fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "labels, values = zip(*sorted(Counter(df.date).items()))\n",
    "\n",
    "indexes = np.arange(len(labels))\n",
    "width = 1\n",
    "plt.figure(figsize=(15,5))\n",
    "plt.bar(indexes, values, width)\n",
    "plt.xticks(indexes + width * 0.5, labels,  rotation='vertical')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It seems that seasonality is present. Let's find that out with chi-square test."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Null: number of reviews is equal across months\n",
    "\n",
    "Alternative: number of reviews is different across months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6270, 6106, 8377, 8891, 11419, 12218, 13857, 17914, 18167, 17534, 5868, 5413]\n",
      "[11002.833333333334, 11002.833333333334, 11002.833333333334, 11002.833333333334, 11002.833333333334, 11002.833333333334, 11002.833333333334, 11002.833333333334, 11002.833333333334, 11002.833333333334, 11002.833333333334, 11002.833333333334]\n"
     ]
    }
   ],
   "source": [
    "obs_val = Counter(df.date).values()\n",
    "null_val = [np.mean(obs_val)]*12\n",
    "print obs_val\n",
    "print null_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=24256.299225956947, pvalue=0.0)"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as ss\n",
    "ss.chisquare(obs_val,null_val)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Since p-value = 0.00, we can reject the null. So, we have enough evidence to conclude that seasonality is present."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reservation seasonality with location (zipcode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 142919 entries, 0 to 143236\n",
      "Data columns (total 4 columns):\n",
      "id             142919 non-null int64\n",
      "listing_url    142919 non-null object\n",
      "date           142919 non-null object\n",
      "zipcode        142919 non-null float64\n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 5.5+ MB\n",
      "None\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>date</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                           listing_url        date  zipcode\n",
       "0  1994427  https://www.airbnb.com/rooms/1994427  2014-02-27    94110"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/df_new.csv').drop('Unnamed: 0', 1)\n",
    "df = df[['id', 'listing_url','date', 'zipcode']]\n",
    "df =  df.dropna()\n",
    "print df.info()\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Convert to datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>date</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2015-10-07</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2015-10-12</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2015-10-17</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>2015-10-26</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        id                           listing_url        date  zipcode\n",
       "0  1994427  https://www.airbnb.com/rooms/1994427  2014-02-27    94110\n",
       "1  1994427  https://www.airbnb.com/rooms/1994427  2015-10-07    94110\n",
       "2  1994427  https://www.airbnb.com/rooms/1994427  2015-10-12    94110\n",
       "3  1994427  https://www.airbnb.com/rooms/1994427  2015-10-17    94110\n",
       "4  1994427  https://www.airbnb.com/rooms/1994427  2015-10-26    94110"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import datetime\n",
    "def convert_datetime(x):\n",
    "    dt = datetime.datetime.strptime(x, \"%Y-%m-%d\")\n",
    "    return dt.date()\n",
    "df['date'] = df['date'].apply(convert_datetime)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's see if there is seasonality with season"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1119be990>"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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K2gu8DzghSZL3A9uNMY9n5DOBe7cD/w0Y9+4vAF4CMMbUgIPqghoRERERERERERERERGR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J7Aoci+EanLZzXegPK+EDcGnEX63a/hXHJ0a542vjMU1l7E/ctupP/dgkOZ9etms6b/IQ49+zAO\npOxZnO2oIaA7Ml4X3f5LXtOgMq+18LWvbcnOPHND3UXSM8/8ePa1r205FqcZQJwyNGPvBwdvyNrb\nP1A6H8dTc7yVDakyw61Za+s6/b0lA78cdql9TfA8y+DjmVxa3pBIuyWDdcH/D+TEN5qXRmg0C+oQ\nXtI3wIzVER6vL+BnZSaAJuG7NVk9CMdNKo8wD5GxPA9pbwnoWdoYUEGWwbUR2jdl8JFE/Msiz0wP\n/PIWXeC3Mqzz0vnpU7LzAWmuVNpbMrlQH9Kwzw1ZvZ4Y/Zsi8dZ69brao+n/b1Y3U2V5IPE8BGK5\nO8sHE4rparMX6309KANYUZaXSwreG+BKEchFWX6K4ufxnYpfFljCz79IPmVl2wzARFj2lN5t8d6b\nTheBchTlXxbc5Nasvl29t0SZmgGLaEbvfVttn5uyxvZnevpy1V+WCahVWV2yTxmgqLy6KWNTY3Tf\nn9XbNpPTAyq/LVm8L8wyuMOr51/K4M4IH6HtDPmysUKZ/uEG5TfWt37QS/dAJM+rNL3/7H2Z9Al+\nnnlyzCub9VNhvjcqz3kyNH7L9pU3ZG6M4addHXw2JGiG7cF4D/tM04Own7wzUobVQZy8vjXk7YFM\n+pIPJngukvuGrFG+4XP/XagfWzKnB2HZybLsBKjMaybYDuXnPnePtxt5Sd3OZbNAOkU04++v5z/+\nx//Bk0+Wy8fx1BxvZUOqzAsWjPHRj/4Mjz9+jzqv30ulsp6+vsV897vjHDoEsiv488CHEAj6WRw8\n/VZ93qG8r0dWiE5BLjDfg1thqyAr1h/C+Y66HFn1m0KOL/hH71YgYCKpkAcs4YfvIytee5DdpPfi\nVrffgRwtm0BW5C5CVkdryOrUv1can0J2IMxH0bQ++zKyIzep9GeRlbarNJ0dq12EHDs7SWVj8rNg\nMn4I538oDGPIruhlyArdXuDnkF2qWIidtTc9sLzvwd0N89+HYTGySvcYIs8fIjK9Buenaj1uBfcS\nZAf5GkQfzO/hCgTKey/wW0r7A4hM9iI68Mf62+rpAKInvi5dr/EeQ+Q/jey+PolbTX1eafQg9Wb8\nPYHsGsV083eRlXDj6YeIDpiu34Psypm+hiu0pns/j+wE2W6RHV/xdynWaZ5jyGrvb3txzU2BTwNk\nxd6O+H0I0dWbgF+KxL1eZbNE6dmuhh3z83e8btQybVTZhLTs2OwQjbu6FpZ66YqOEVt8C80CEDQT\nP8VLCpSjDGACpMHFyqQNwbT89tiCHLUy/fq69z60D83KJI8nvzw3ICcKBpDjs09771PpZ3DH1crU\nRTP1tQL4T9TLZx6N7Q/qwZTy+G1BdmCL4nVqvI3EwUBAym79Wi9ynDQWni3IL2afW5Bjpf+eelAw\nPzyn329C2vs1SstOK/wvxK79AW7H/VMBz6s03/+B7GxvQspdQXzrnYnrc8KxgPFl/djvIvVj6cb0\nt/nl7EHs09c1vtnVU3Buafy+xtKbv8d3I32hb8PvQI7IX4Pzd7dDf/t95i3Utyk/7zd7ZRvQ/K/A\n+S69HNlNs7QpGV4G/AbS7zzhybBN6frjpOuV1kNIW9sF/Lo+m8Dt1j+vZa4h/VyvlnUKqdsx4D8r\nL8Na1suUd/NvaX3lU7jdv50az3xuTqqMP6X5+3lP4fralbiTAM8hIDZGu0Xp7td008jJhFmVxzSi\nN13ISSx/rLgXaetoPpfj/K3+k/Izg4zlViJjzz3I+KpHed6n8p6v8lqBXB1YTyycmBD+Kw6XX74i\n94hsHpDO8dKMvf/sZzdHJ4T5wDvN81YmpMr8zneeyqZNt0bfXXzx3WzebDytQLbrNwWxViCGO6P+\niN59EYr3RJ6vwiE3rkCOWfhhMY2IXxZSoA6hrNqQyYl/kds/gmjlN/5iRxQ3kz4qmToyafTs+zKk\nrDHZzCLlt2MlsXCOpl3lledWGu+vWZiMPPP1IJZf3oX4xdTLp6j8v6k8h7Q7kElFmO5unJ75dO0e\nWBgeU34uRQZBry/JV4xeKu6luM4/lFfMHswG7+5BBhUzSssfwHxYy/AMclTMf/dmRH7LcJPu1we0\nTU52rOyAF9cmwBbXJrNPAT+FHFez/OxI02ZkwvcAojs2mLNJ8Q+V3p9Eyo2+P0lpDSfi+EA+vd7z\nZgEImomf0ukUKEcZwARIg4uVSRsD0zL9AtcWYu9T7fV4gR0s2F22WWRCaPlfgRwltnxT6e0oaCxO\nrC6ara/5ONuZspEgA98Y3TA8hQzOi+KdjSwUPkTa1rYhx6gtpPibpL7dhSFmn0EmaGYbY2ECqace\npA5D+2Z90OWI/TsbGfT/dxpDn+a1GDle+PP6O+xzNnlpjC9/rFDmasFm6vXa+heI98UhvbAv3Uzc\njl/h8e/LMcyboGxhfrdSb/9T+Z2P2HU7nmlxVuEW1EL+V3hxHo78zuvP/tSL8ywyaY+NU9D3IeAM\n1I/P7LdNsiq4o6MHkClUT4RO0VgojLOKxjrcikwI2zXfMdw1mgwZP4D0qTWkTfUhk9Ua0hdeSz16\neX54TYHKnAj1oRlAnFcrHxe38TLty8Hb8ZT5ox8dUgCevAvacll6cHCXB9YzROOl6jtxdzYsGKCL\nf8n4AhovBo/QeIl4DStWnBQABBlNv0wGXGMXwFMANzfhOug8oIe8C/R+MLncyeCgDSJOwq00++H6\ngOftpIGONgAX0NKyriD/2CVrkJX16yLP1gVliuX9NHL3Jg9gwE9zDtIpmsxfoL6Oiy6UDxHXCZ+n\ndcDHkY66DPBECkRoO3EAqU4a5bWLOJDQnUj9Wl2uQFY770AmVM8hnd8m/f5TpE1cgNxZ89/tQDrj\nOWQCdov+vpZG/Vuhaf4Lgkx4CzIp8zvEFYh83q10PoZ0sps07qNK71H9vwiZZPcoH1/B1cM1kbKv\nw+2AP4DURyyOD8jl24lYXW2nvk58kIBUG32Bcu3T14PQXqUAJPxgQEIpgAwLMUCXDTiblEr3NGnZ\n+PwV5W92K0Xbwhqk/mYQXbLB6WqkHZu9CW2gn/4I+WARIQhHLM66yHMQmR2hHjQuZsfXajliefth\nHaKj15Cvg2ZnViDtYWUkzmpEbkV53om7q7gJaWcxfVkd4eEXydflIdxOUgrQZA0ymF6PW6Bal4hr\nOnozcopmNsi/rO1uRj/9NEV6lKJnfZQf1uHA1or4D+kW9fep+riIeD/n92Vl+qei+Gup170h3E59\njO/d+j7Vn/q/1yILk2cBtyN3mM8EPoHsgE9E6HyfRv1dS/34xtfxsG+3+75/hdzB/0vNsx3Z4bXn\njyKYDMuBv9bnm5Cd5Y2aZ6wtx8NrBlTmtVDO4wnNAOK8WvlY3B079rFnzxGWLh1k+fK+l4234ynz\nI49s5ROfeJinn97B1NQcLS1ztLd3MjAwwNjYKIOD8zn55CXHJpZGf2RkByMjMxw8CDDNggUZMMPe\nvXNMTLQA01Srh5ib66NazajVfECCEdyF5knkOMQMcnywm2r1KKtWvYmvfOVTx/j7/vf3MjU1gnS2\nC3DgLODAFRbp/wncBec24ChdXXNceeXbOHiw/Rj/lUoHfX2LGR3dT5ZNcfDgBC+8cAg55mGXyAVI\npqdnB52dXfT29uXK5Tvf+VuOHKkguyPttLYe5ayzuti2bYq5OQGQaWuDnp5pRkc7mJ1tVxkI8E9H\nR4Vzzz2NK644l8cf331MV6rVGocOTdHe3kuWjQGTzM62Mz196JjcRB4jtLbC7GwfWdZDpTJNlhkQ\nix17McCaLtyqG1rWLo1XU9lN44Bq2rXOxjTdEuTYiF3qBhlY9OIuvs/pu+UqT7u4/13NaxRZ/RtT\n/WhTXbBjveEu2EPI8RED9plFVjM7tN5HcMeWWnCgPSbjPtW9cZVDv6Z7E7KrZm4J7EhSn9I4qnSm\nlDejuyHg72Et39Sx+hB9NUAak6WBRwzrex/kaVa/23FHwqx8BvowhgN2sPIcQHZZMtyRbQNhOIRb\nkW1VHmpIOzEQqF4tZzcOVKJN6Yx65TB5G1DSPM3/iMZp1/w6vXJ2afkMEGBE6c7hwAOOquwGA557\nlb5f5z3Kw17c0Wnja0Z561f6VU1T1fxnlF43TgcPI7sk3VqOCRxASE2/WzRuF669jStts0kGMNWO\n6Oli5X+/pu/TOGNaT6ZL+7RMvcjg1gfi2oEcn+pUuZlMTCer+qxH/3fiAE6M5j8hk44QIONx4F3A\n3yKnG+y4/AxOdwB+ATn6tUn/93p5d+Ha02KVlwHHHPHKfVT5n4ezC1ZfI7hj/O36fQRnTzpUZgZE\n4rfvmtafyXI6qF8Du+lC9MV4ntV8VwDfQfoPK7eBoBjYyJ8gOtDtyfkAUu9dOJAZA0TpxYHidOCO\nutnOsbWDw0rL2pbpWobTvUmcvu/Wb/uYvqH5G9AK+vywx8ukR99AdSb0/bCm7VEZGJCZtalZLYu1\nNdN1o9WmHwMXa9O8MhygW5/KzK6CzHj5mQ5P40BEOnHAgfOUzgHlp1vfW1sym2L8VXF9VoemPYLo\nTov+7sDZ3arSOIKbeBvQ0hiuzU0qT/M1/k4cyJP1X7uRnTY7BtmpfE0qLQMH6tE89uH6JX93zuLb\njpm1jYOIzlj7btN0XcAZOCA5s6tHcDpm/X0LDhTKdGIGBwAzgzs+ugB3pPag1qkParcXOenSjrS7\n9wJ/gzsOu0/lZXY8BoZzCTLpC8Ol+m11YmCN83FgTr6uPUoWAZU5MSE8EUqFRx7Zymc/u5mpqVY6\nOmp89KNDDZOoWBygMN3LycPLGf9nf3YZX/7y3/DjH0+TZd10d8+wcuUbGB5ui9LctOlBPv/5LdRq\nXbS2TnDbbRckj6E2W+577nmIZ57ZzdRUKy0ts7S3t3PaaaeybFlvQ7msHDt37ueFF14EusmydmZm\n9jAz006W9VGpjNPaepiOjmW0tvZwxhk93HffNVH5bNr0IL/1W19jfHyaLOuko6OH1tZpxsfNlYhz\nqH3uuft58sn/0iCPubnDdHdPMzbWwdRUK5XKGLOzU3R2LqNaPciSJYs45ZTX0dFRY3JyJ3//9wfI\nsh6ybB9LlizirLPOYWRkB0eOjLJ371idHAYGuhgePkStNket1kFLyyzV6jQtLfNobe1hYEA6fkvX\n0dHDG9/YX1de43V09CjT08OYYW5vn6Cra5TR0Rbm5gQdtKVlgiyborW1i1ptgvb2+bS0dDE3d4Rq\nFbKslZaWFgYGuti163lmZmzyUaGlpUZ7e6WOt/nzF/L004c4enQM6TBmkQGdHXlchDta+RDSucwh\nHWg7rvNfiuuUZ5EjMTIo6O2tsGRJxvz5JzFv3pJoe/D1Zs+ewyxdupRly3r52Z9dxj/8wy6+8Y0d\nHDlyMrLC6uq9Wv02Z5+9jOXLF2ub2cL27cPMzRl/Y7hjOk5XOjq+w7ve9XZGRvYB0+zYsYP9+22i\n1Yl0tLP6jf62wWG/xpGJabXaT0fHLK973SDLly8+Znvuuechtm8fY2pqgpaWFk477VTa2qbZtWs7\ne/eO4FDYWnETy3aVryELTyAdbDduoHsUGVDYhMEGUj2e/G1Av0jjbMchFHZoOVpxk4Y51bsfI4ML\nK2cP7u7LYtzA0I4OGarjAs1nFDeIscHgYU+uEyq3hcrrTtzA2ybIlyM7R1sRJML5SsMmYDaJtvqy\nSbNNTgw1zxacppCJ0nPIgBkc2q7J1uozQwZiFerRim3wZgNQH20Z5fU3cAOuGZXJfq/epvV3C3Jk\nuIbos7WDD0Togpxe+CPkSPN5SvNFpflVL3/T76dUDnNeea3MtojQr7IcwNVDLw75uqplnqf5jOEm\nErZg2KlpJnCIjxWVYYZbMJjDTQTHldZS3KLEHLKT4NqnyMXuttk952nqJ01t+rsf2Tl5FofOOi9S\ndpsctSG60qV5T+EWgiYQ3ZyPHL9u1XgHcBP6cWQAPQ+3eGDoqzYhXoTTUbMhtsC6H3cPz8pli4E1\nZEDfR33bNtRO0yFb9LPJd4f+nsJhCdhxPr+NdKhcjJ6hSBo90w9rs7aI6C9ImTvL8uIAACAASURB\nVMzwyjCGm2BNKJ+HkMWwXuVtP2KzbOFgWMtoi1KjONtqdWw+K81OWd9kEzBDEHf9XP3imE1wTSem\nVJ42oe7GTYJmtN5sAmW21spwBOeDz9pAF27hx+rJgj/RtcX0A7gJtiGU24KxtYcO3OKMLTaazbQJ\nrE24zX75i5Et1N/LN90/hJvEG08mJ3ALn1WVp9+ntyM7v736fqn+HkKuVYzi0FgNXdZ00Xiuajn/\nNDohbECZ+df4kWKeCMcb4uiiG+oQS+MIo6uzwcE7c9O9nDy8vPG3KEJUiK61VlGa6mnee+8DHkqp\nfFpb12X33vtA02UN+RocjKFrxcvlypFCp0ojUA0O3tkgn3vvfSCrVj+YxZHEDIHNPevqWpeQRwwh\n05C4fLq/kjUipPkoXjGUsRgCWCpdY3kdrzFEtQeyRrRPK3sMdXV95pDJLsocAmiZOslUnu+LPP9A\nIr87M4fcGubj100jKl5ab+r1JNTrOCKb1GWl8oGErqyKpDGZZyqr90XimDysXnz98PPeUvd7cPDO\nrL+/CMX1g1mjjsUQBS3++7z/v5I1ovtt0HKarI2nBwI9SNX9Bq98sXKuy+rR8cqiy8UQEY2/BzKH\ngheW91ciecX4XufRWZUj8zUZXJAVt4GYflkbtvghAnFKnjFUXKuLmP5cEsS1z3sz165iSKex/K9X\nenl2Kw89MLRFv5I5W2Q6FbMFpsdZJJ9Y27P6DhGgN2QO/TIl31DXzTb+Yib6kOq7UnLwy5LqC+x/\nHm955StCewzT+2WMIXSH/ZpfD8dT9vUZ/JzWtS+/PORN62t8OjEE0vrxC1yXOZsVQxN9X9aI6Hqn\n0jY+LJ1frosi8g51Z0XWiJjrt8U1GbwlUq61SjvWBtYqP+k+qlGWfrljvK7V+kiNS1J9st8OLW7I\n7/rM9UMXZfU2LqX3YR1tyODdWo4bsrheh3TIsiwyV4o9/Nf2OTEhfGmhjHuKeJyXz3VEsy4yXnr8\njVnazcOqBpoLF8bjLly4qumyNvLlw8jHy/XWt67JhoY2Zt3d71G+r8kE8vuWTDqslRl8LHPQ7fny\n+drXtmRDQxuz1tarlF7YKWzR5/fqu1v098asWn131jiwSsGUX6rvjM7QMTr1ea7R/K4N3vny2aL0\n3qPxbslk0OCn2+KV/95s4cKrs87OC/X/tSonP85VnhzvzerlVwTjbrDvft2VgXeP6VJRfql8jFfT\nC18HbNHjKv2O6UUzcP2XZmk+88p6r5c2FcdkUsbFiv1+X4m8b8lkcHC18rAyk4FYvU6LbPw88vi4\nOogfc+uQ59Iij37M5UGoL2XdRdyq+VjbeCBzbXFjJoOMZvhembmJWp5Lj+N1UWHtytr5zSXSpGx4\nzCXPDRlcmDUO8D6eic336zGcCBa5oAjtd55rEPuEvK/04uWVK6Rb1r1G+D/LnNuIIhr2bf3Ce4Ky\npPQoj7+VkfixcjVbvlhd5KXP4zWvbVmfdW/mbELZsvu2NKzzjZFPzF6kaK/KGsuZGu8U6Zmfzi9X\nXt3F6jdVb6kFmruzNG9X5+R5dfAdljuvPmJximxVmfpIjRdSeh+j8d5EPaTSkWXZCbcTJ8JxhDLu\nKeJxitO9nDy8vPHtmEos1Lt5mJxsoVaLx63VUi4hyoU4X2HYyrZtbTzxxBCQEXdG/V+8+BuRYxON\nYXKyhUce2crHPvYoP/yhD7d/i9I0twOPIvfAGp1Qz83dRL0LhzTfckTSECIfpR7K3/IEOW7ycOSd\n0d2NOKZ+q8dLzBHzGuQohrg2GB62OJ9MxIk52Tb5+e4C/GA61qPfKRj5MlD8RXH9/MJ8fB0wvbBg\nZfTvKcT0ohm4/iU571LPz0buW12fEyd0TRALe7zfJg+Dxs/L+//FIZFaPefVuYUe4qFF6T7qxY+5\ndUjx1BN8h6Gber336adop/I6AvyF999cfNjxyTWRvFK0zkfq8dqCeHavM49eKm0L0s4fRdwHGGLs\nC6SRllM2POwHDNJ/kEZXLJcgMP52wsrnz2T1+zn5x+SXqqO8eu3x3pctVxm7kfoP0qbz6LR4336/\nsCkSt4wc/OC3gTydLuIt/B/mXZQ+L14qrR3F9PssA/cw/c8re3fkfco9DMTtRYp+OCbxj6SGoYye\nhXzZ3b4iPvJsqIXenDgp3sI2F77zv8NxXipdbyJOWd0rEzccL8TS5NnVME4ZPuvDK4oyWqlUPlap\nVL5XqVSerFQqHwve/XKlUpmrVCoD+n+gUqn8baVSGa1UKp8L4p6vdJ6tVCqf8Z53VCqVh/X545VK\n5bRXsjyv1VDGPUU8zsvnOqJZFxkvPX6Nekh0P9S7MejsnKW1NR63tTXm8qB8EL7sA3GZbmZy8neR\n+x/3H3smv/1nFu4n5cKis3OWz352czAZBPGn9FhAO/xt4b/SaBDjfLsJYIyO5blZf8femWzsfpNP\nYzP1k0E0zm83ESdPfnkw/DXcINWvuyJ4fIjrXZg2zC+WT54OhHKAuF4060IgxWcR7xM5cXyQiVR7\n8ieEYRvPy7sb8UtYpNP3Uw/jn+fqZTaIH3PrkOJpPPgOw5k4vbcQ6ktZdxFnBP+/gGvjIJOcMK+i\nejy1IN7rkUldHr28PA7j6sZQZR8ibatTz0MdORP4aWSwbnQ34ZBGfwo3IQx1fAUCsJXKP2a/U7bA\nt/ch7+NevLxyhXSL6iz138+niMYs9e2mFnxCGik5+MFvAzGdzusTfd7C/7G6yEufx2sqbUZjv3I/\n0p5M/4vKHsrPr4vwE7MXKdqhDTWdielUkZ756Xw55dWdhTLussZy4uS1+aK+1ec55vomDGOJOEVj\nAD80O16IpSnTR+a1t/zwik0IK5XKeQjm6TsQS7uyUqmcqe9OQfBXX/CSTCLIA/9HhNzvAmuyLHsD\n8IZKpXKJPl8DDOvz30E8SJ4IL3Mo46ohFqfeBUM83cvJw8sbf4i4m4e1+H7XjOZtt11Aa2t93NbW\nddx220tDRRWXF76LiEbI5c7OH1uOfu6RZ35YSggHPjh4J7ffflFyd9U11zIrT4M0QtaH0N4/9n7n\n5Zn3ziCkK5F4ZVZ0i+Lkraz50P4W1uNg+KdohPj2IeJj8Nl3IhfDbwmeh64E/Pi7AnqWz/acMuTp\nRQqG3MIzxCHdLyDtzuQHiE+3MI21xws0TsxNi6G63Yx0WTEY7Rbv90WI3C8g1WZcPHPeXabO/U71\nHOKw+M97ZRrU7wsodjVg6Q0W/hyke4vxbHofg2Q32nn/0f8xWxgu5IR55cnS3t9SEG+igF7MRcRq\nfZ63Kx/L74LI85uoL/sGBNDlIkTP1ib47seVLdTxoUi61UgfErPfKVsw5MUPYfHPwbnrMZ3yg+8C\nxHdhEGuPoVui9Ui7Dstdpg2t0W+/3QwhLmlSfVdKDn5ZrC1AY/5GP88FhV8+058w7zLpfVcSsXqP\nuVPpJx4MTCilRyBlNyThmCuqoeCdgTyFfDxN3PWBPya5HgECMjcNoZ2ItQdzKxS6b/DlOYWzXynd\nGSG/T7gJWQCKuUHaRbwNrFV+dpPuo0JZ+uVOubQYI66LqT48bIcWN+R3PVJPvszy6Mds+fU4kJ7V\nxPU6xmdjeMVQRiuVylXAJVmWrdX/dwNTWZb9ZqVS+Sqy9PY/gfOzLDvopbten92u/5cCX8+y7Gz9\nfw1wYZZlN1cqlb8C7s2y7BuVSqUV2J1l2eIIL9krVc7XSijjqiEWB3jZ3Fo06y7ipcZ/17uWBiij\nNVaufP0xlwwhTUGq3Eqt1klr6yS33bbiFUQZ7eC0005h+fI+9u0b5oknHqQ5Z/GX0dZ2hI6OpbS1\n9XDGGb382q+t4vLLV3DxxXezeXMszSU4WONHgnzCcA8yeNuKHFE5THv7MG1ti5iaaqNSGWNmZhY5\nYpRH52La29uYnv5aw5tK5TJ6e7sYGOjkxRcPMjd3PmnnvalnRXHSfMkA6CgOrXEE6mDQwcG8ZziE\nulbkOJEhgS3EIUXOR47bzgA/gzu2Zp3kJgRxrR05ljSocXYhuxzPIDs/NWRC+GhJOVi4DFfHZyqf\nS4EtyFFIg4u/FvGzZ8/MR9lWZCV8FociaZDfz+EgvseB/0D94OQDKsNWHFT8HG5wtU+ffxxZafdl\n8xsq4wxZg9yN7D6bs/p9noz6PH7N4XHZOq8i9VVDjhkOaJlqWt5bvTJd4b1/DkGP8911GMqooV+i\ncV/UuGPIwMUv5wocxLihjPruTPoQvbgU545hG26ybrRMPmHwHSeDDFgGgCeUN0NLNQTWcz2+fDkZ\nmukADonV4l2MQ8MzuHcftXEc577CkPuOIn7A9gCNtkDK6+vFfhyaobkYMATKTPNfqL97EH1egdi4\n+Yivr1Du96jcliL6tAMHab8Hd0rBkFpriD7b0WVwcP8dOB0wpE2DxM9wUPFTOHc5c/oxFwmGftin\nMjOkTLM/Gc6NySBib2o4aHxDLjyCDFwfQpAKY236N5WXARyS7XzkSOn/1ueTuH4BxJYswy1MmVsG\nc/B9EIe6ak61O1RmZyjtaWShyHhepO9mEfh9sydmVy3eiKZvQXTHECOryuuLOJcUhphrblBmlLde\nxHZPe+8zXFs35M09yOTV15eUo/aVWvaTVIaTEXqrgF9H9Pkh3ITP3KQYiuQ4DlEW4iijh1UO5vpg\nH05HxxDUy15NU9Nn/ThkzhqNKKPWhjo9eRjKqLnPmcG5r+jFIcQaIuyRoM4MObaGa5e/gNjsGcSv\nn6GsjuHshSHtGhqp/bdgdTyBQxkdBk7X+JM4JOB+nEsQc+Ex7tE0lFFz82H6sQuHHDqmednxbotr\nbmPMhVMXznWNodRmytuA8m7ufyaUVqvK33SgittZNOTbIpTRNqRNPkT2aqKMImcsntbSdQN/D3wW\ncb7xOxrneWAgSHcd8Dnv/9uBx7z/Pwf8hf7+HrDMe/dcSE+fvyRgjxPhRPhJDnGEyDSC2plnfjwX\n6TWO0OrS1COIxlC27shCxLpYnkV0LE0RP8azoLHmI6lWq1dlCxasr4sTomgODt7gxYmV7+MZ+Mik\nIZrZxzMBs4mhrBUhlJnsYu9uyupRE4vq20dSC2l9OGsECroxc0hnIaLpFi++n1dY9jsyQU0L5XFD\n1oiSV89TT8+qrKUlRGG7IRMkNvsfQ5a7KYOLM0ElDBHvwov3IZLikJarqF7WKG0f0TMPlTRsAzF0\nuBuzOBqdpY8hB96qfIT85iHK+nXnyzV8Zrz49WZ19UuROvWR93y67/PyjbedekTVlNx8BMA8WX88\nh1YMsfe6LI4+avFjyI8mi6sidXVj5kCaYmitRYiflq9Pw/Qs1obL2Xnh+cOZoDnm2WtD4Mzj78PK\nS8zWbcmc7Yi1Vcvz+syhpKbyWp3Bz2SNKLnvj8S9KJO2H0MNzUOdDp+HKL2Z1nOImJvieVXWqHvv\nD2Th18dQJu0pjn7tbGUo6/dH8rlD5eUjYYc6FNqxB0qU6VrlMaUv1k5SZbg+g7dG+F2TwZWZ05uh\nLB9ZM4ayGdI0Wu/P6ttaXhsO6+YOfZ5KawieMT6KkEC3ZHGkcut3YvWV6hN8e+YjRBfV5x2ZQyC9\nIdM5EeHnFfVDWKlUViNLpuPIFL8FOT46lGXZSKVSeR54eyYeoS3NdfrMdgjfDvx6lmUX6f+fA34l\ny7IrKpXK94CLsyzbpe+eA96ZeTuO+jy79957j/2/8MILufDCC1+pYp8IJ8KrHmx30xy0L106SFvb\nGJVKB1NTtWPPli/vK7VLW7S7aruhExMVJif30t7eS0tLD3NzY1Src4h/utZju5ipPP1d1dnZ/Zx0\n0iJOPvmMhjzL7lCL37lxoJ0zzujlrLN62bz5hbpd23e847yGneDHH9/dsLP9iU88zPPPjzE9fYBq\ntY2BgcWMjY0yODifk09ecizdjh37+PGP9zI3102lMg1MMDDQzcGD4vS6ra2HBQum6O9fSF/fYkZG\ndlCpdNT9np5uY/fuPQwOzqe9fbah3trbZzh8eA+HDrVz9OgItVqFjo4+/v/27j/azqq+8/j7k4Rc\nCCAWBA1JUBpjkdr6AxVsi6Z1iCylwmLZgTpjrcVZq9IhGeuMLdJZZqZmWbpcWGGNdmxxpHbpkpHV\nljYtP/wRcHVEWhQhxWDMCDUIQYOSBEjIDd/54zxP7sklmdybe87NTZ73a6277nn2s5999j77nOR+\nz97P3rNmbeGYY45m69bZPPXUpub5wqxZs5k1ax47dmxkdPQY2vufkiOo2snYZtPtxsFPMHfuE8yf\nv5DNm3/C6Ogutm/vfes4a9ZRHH30Ts4772e4//5t416To3n00S3s2NF+0/4EJ5wwj23bZrNjR7tp\n8xMsWjSfE0547u42PP30kzzzzHZmzz52jxHqf/qntbvfV08/vY0FC57D7Nm72LhxC08/3W7I/Bi9\nb2Tbb4V/DISRkZOZNWsrO3c+yehou/l1gCOZM2eUX/iF53H//T9g06Z2Y+neBsKzZs3hmWf6R3Hb\nPdLa0Zdt9EacjmNs76ujGduDqh0BOoLet7jtxtk7GfumeWvz044mP948XzsS1u7J1baxPW73oWs3\ne368afOJfdfPYWyj6PZb/CcY29etf5Po8fuAPY+xPckeARaw5+jAHHrfmLdTa9tRnieZPbs47bTn\ncv/9o+zaNY+qrZx44jPs3DnKT35yRPMa9TZInjXrWObOHWXOnF286EVLOOKIbWzZspMHH9zA6Gi7\n/+HTTZt2kBzBrFnHs2vXw4xt+tyOkFXfa93u33gEvX3LTmJs38anmj5sN7du94l7lN635G1/tyM7\n1VzTjhQdNe452lH52Yzt6be1ueanmjr073nYjkC0e9LNbc6neT1Hmjzt3nMnMLb/3FGMjcy0IwLP\n0BsVmMPYKGr7fmzv/jmCsX0wn+z7PZex9+8PGZu62G5Y/lRfHz/d93o92bRxDr3v9tsN0I9qXtf2\nNWw3Fm9HONtZAD+m9x5r87Wbix/bvK7t/oNHMvYZaUc/T2Bs5GgTYyMy7eve7r/avjfakakj+vqs\n3W+ubVPbh/0jse37pR1tfLLpg2MZG3FpR2yfYWyUtZ0V0t43fMy4+j6Hsc3WdzB2b9sWxmY9tP3W\nbqbePvfxjL3Xj26ufZyx/ena/SRHGXvftXsytpuctyOF7WbuTzb1OoGxjebbkc9R9vyMtCNg7b8n\n7SIuo/RGxI7l2dPL2xkhTzQ/7XvvZMZGKNsR9jbu+CFjI4cwtkdlO3viOHr91T8S/ihjI6Ht7QSz\nmtd1hLF/937UXNe2cRdje0C2+x+2ffNk3/l5fWW1n8N2BP8FjPXxj5p6zGvqO5uxzerb/y/aUcaH\nGBstndu8xtsY24O0HUV8irH34Y+a1+A5jO0z2o4Ktp+ZjX2vTbsXZ/uaHNPXH5ua52T3cR3MjemT\nrGpqdQW9VxhgIb1X6rVV9WiTb3xAOH7K6K8Dr6+q9zRTRldW1R1OGZUkSZKkvUuy14Bw2KuMntT8\nPgW4EPh0VT2/qk6tqlPphbevaoPB9rL+MqrqYWBLkjOTBHgHvXsPAW6kN8UU4G3Al4bXGkmSJEk6\nvExsc4oD94UkJ9Abv7y0qraMO7/HsF2SB+iNfc5NcgFwTlWtozft9NP0xm//vqpuai65FvhMkvX0\nxpUvHlZDJEmSJOlwM21TRg8mp4xKkiRJ6rKDMmVUkiRJkjRzGRBKkiRJUkcZEEqSJElSRxkQSpIk\nSVJHGRBKkiRJUkcZEEqSJElSRxkQSpIkSVJHGRBKkiRJUkcZEEqSJElSRxkQSpIkSVJHGRBKkiRJ\nUkcZEEqSJElSRxkQSpIkSVJH7TcgTHJ0kv+a5M+a4yVJzht+1SRJkiRJwzSREcL/BTwN/EJz/ANg\n1dBqJEmSJEmaFhMJCBdX1ZX0gkKq6onhVkmSJEmSNB0mEhDuSHJUe5BkMbBjeFWSJEmSJE2HORPI\nsxK4CViY5LPALwK/OcQ6SZIkSZKmQapq/5mS5wFnNYd3VNWPhlqrAUtSE2mnJEmSJB2OklBVeVb6\nvgKlJGcA40+mTauqbwy6ksNiQChJkiSpyw4kIFxDL/g7CjgDuKc59fPAP1fV64ZT1cEzIJQkSZLU\nZfsKCPe5qExVLa2qX6a3zcSrquqMqjoDeGWTJkmSJEk6hE1kldHTqure9qCq1gIvHV6VJEmSJEnT\nYSKrjN6T5M+Bv6R3D+HbgW8NtVaSJEmSpKHb7yqjzR6E7wHObpJuBz5RVduHXLeB8R5CSZIkSV02\n6UVlxl08ArykOVxXVTsHXL+hMiCUJEmS1GWTXlSm78KlwHeA/9H8rE/yhgk+6Yok9yZZm2TFuHPv\nS/JMkuP70i5Psj7JuiTL+tLf1ZTzrST/kOSEJn0kyeeba+5I8sKJ1EuSJEmSNLFFZa4CllXV66vq\n9cAy4KP7uyjJy4B3A68BXg6cl2Rxc24RcA7wYF/+04GLgNOBc4GPp2cu8BHgDVX1cnrbX/zH5rJL\ngM1VtaSp05UTaI8kSZIkiYkFhHOq6v72oKq+w8QWozkN+HpVba+qXcBtwIXNuauA94/Lfz7wuara\nWVUPAN8FXguMAj8GjkkS4DjgoeaatwLXNY9vAN44gXpJkiRJkphYQHhXkj9PsjTJLzcrjv7zBK5b\nC5yd5Pgk84A3A4uSnA9srKp7xuU/GdjYd7wRWFhVzwArmvIeorflxbVNngXA9wGqahR4vH8KqiRJ\nkiRp3yYy0vce4HeA5c3xV4GP7++iqlqX5ErgFuAJ4G5gBLic3rTT1rNubOwvJslzgKuBl1fV95Jc\nA3wAWDWBuu+2cuXK3Y+XLl3K0qVLJ3O5JEmSJB0y1qxZw5o1a/abbyLbThwNtNM+STIbGKmqJydT\noSSrgE3AFUB77UJ6o35nAu8CqKo/avLfBHywybeqqv5Nk/564Peq6i1NnpVVdUeSOcDDVXXiXp7b\nVUYlSZIkddYBrzIKfBk4qu94HvDFCT7pSc3vU+jdP/jpqnp+VZ1aVafSmxb6qqraBNwIXJxkbpJT\ngSXAncD/BU5L8rym2HOA+5rHNwLvbB6/DfjSROolSZIkSZrYlNGRqtrWHlTV1uaewIn4QrNFxE7g\n0qraMu787mG7qrovyfX0gr3RJn8BP0zyAeArSZ4BHgB+s7nsWuAzSdYDm4GLJ1gvSZIkSeq8iUwZ\n/UdgeVXd1Ry/Grimql43DfUbCKeMSpIkSeqyfU0ZncgI4X8Crk/ycHM8n95+gZIkSZKkQ9h+RwgB\nms3hX9Ic3l9VO4daqwFzhFCSJElSlx3wCGGSo4BLgV+id8/fV5N8oqq2D76akiRJkqTpMpF7CP83\nsAX4S3p7Br4dOK6qfm341RsMRwglSZIkddm+RggnEhDeV1Wn7y9tJjMglCRJktRlU9mH8BtJdq8o\nmuQs4K5BVk6SJEmSNP0mMkK4jt6CMt+ndw/hKcD99PYKrKr6+WFXcqocIZQkSZLUZVPZduLcIdRH\nkiRJknSQ7TMgTPKcqtpCb0GZZ6mqx4ZWK0mSJEnS0O1zymiS1VX1liTf28vpqqqfHm7VBscpo5Ik\nSZK6bNKLylTVW5qH3wMuq6pT2x/gS0OqpyRJkiRpmkxkldEXAe9P8sG+tFcPpzqSJEmSpOkykYDw\nceBXgOcn+dskzx1ynSRJkiRJ02AiASFVNVpVlwI3AF8FThxqrSRJkiRJQzeRbSf+tH1QVZ9Oci/w\nO8OrkiRJkiRpOux3Y/rDgauMSpIkSeqySa8yKkmSJEk6vE1kyqgkSZIkaQZZvfp2rr76FnbsmMPI\nyCjLly/jLW95/aTLMSCUJEmSpEPI6tW3s2LFzWzYsGp32oYNVwBMOih0yqgkSZIkHUKuvvqWPYJB\ngA0bVnHNNbdOuiwDQkmSJEk6hOzYsfeJntu3z550WQaEkiRJknQIGRkZ3Wv6kUfumnRZBoSSJEmS\ndAhZvnwZixdfsUfa4sUf4LLLzpl0We5DKEmSJEmHmNWrb+eaa25l+/bZHHnkLi677Jz/74Iy+9qH\n0IBQkiRJkg5zbkwvSZIkSdrDUAPCJCuS3JtkbZIV4869L8kzSY7vS7s8yfok65Is60ufm+STSe5P\n8u0kFzbpI0k+31xzR5IXDrM9kiRJknQ4GdrG9EleBrwbeA2wE7gpyd9V1YYki4BzgAf78p8OXASc\nDiwAvphkSTPX8wrgkar6mSbvCc1llwCbq2pJkouAK4GLh9UmSZIkSTqcDHOE8DTg61W1vap2AbcB\nFzbnrgLePy7/+cDnqmpnVT0AfBd4bXPuXcCH24xVtbl5+FbguubxDcAbB90ISZIkSTpcDTMgXAuc\nneT4JPOANwOLkpwPbKyqe8blPxnY2He8EViQ5LnN8YeS3JXk+iQnNWkLgO8DVNUo8Hj/FFRJkiRJ\n0r4NbcpoVa1LciVwC/AEcDcwAlwOLOvL+qyVbsaZAywE/rGq3pfkvcBHgN+YTH1Wrly5+/HSpUtZ\nunTpZC6XJEmSpEPGmjVrWLNmzX7zTdu2E0lWAZvo3Q/4ZJO8EHgIOJPetFCq6o+a/DcBHwTuBLZW\n1TFN+iLgH6rqZU2elVV1R5I5wMNVdeJentttJyRJkiR11kHZdqKd2pnkFHr3D366qp5fVadW1an0\npoW+qqo2ATcCFzcrip4KLAHubCK5v03yy02xbwT+pXl8I/DO5vHbgC8Nsz2SJEmSdDgZ2pTRxhea\nFUF3ApdW1ZZx53cP21XVfUmuB+4DRpv87fnfAz6T5E+AR2lGE4Frm/T1wGZcYVSSJEmSJmzapowe\nTE4ZlSRJktRlB2XKqCRJkiRp5jIglCRJkqSOMiCUJEmSpI4yIJQkSZKkjjIglCRJkqSOMiCUJEmS\npI4yIJQkSZKkjjIglCRJkqSOMiCUJEmSpI4yIJQkSZKkjjIglCRJkqSOMiCUJEmSpI4yIJQkSZKk\njjIglCRJkqSOMiCUJEmSpI4yIJQkSZKkjjIglCRJkqSOMiCUJEmSpI4yRU4dtAAAEMZJREFUIJQk\nSZKkjjIglCRJkqSOMiCUJEmSpI4yIJQkSZKkjjIglCRJkqSOMiCUJEmSpI4yIJQkSZKkjhpqQJhk\nRZJ7k6xNsmLcufcleSbJ8X1plydZn2RdkmV7Ke/GJPf2HY8k+XxzzR1JXjjM9kiSJEnS4WRoAWGS\nlwHvBl4DvBw4L8ni5twi4Bzgwb78pwMXAacD5wIfTzKr7/yFwFag+p7mEmBzVS0BPgpcOaz2SJIk\nSdLhZpgjhKcBX6+q7VW1C7gNuLA5dxXw/nH5zwc+V1U7q+oB4LvAawGSHAO8F/gQkL5r3gpc1zy+\nAXjjENohSZIkSYelYQaEa4GzkxyfZB7wZmBRkvOBjVV1z7j8JwMb+443NmkAfwh8BHhy3DULgO8D\nVNUo8Hj/FFRJkiRJ0r7NGVbBVbUuyZXALcATwN3ACHA50H9/YPZy+e5zSV4B/HRVvTfJi4ZUXUmS\nJEnqnKEFhABV9SngUwBJVgGbgAuAbyUBWAjcleRM4CFgUd/lC+mNEp4FvDrJ95r6npTky1X1K801\npwA/SDIHOK6qHttbXVauXLn78dKlS1m6dOngGipJkiRJM8iaNWtYs2bNfvOlqvab6UAlOamqHk1y\nCnAzcGZVbek7/z3gjKp6rFlU5rP07htcAHwReHH1VbBZRfTvqurnmuNLgZ+rqvckuRi4oKou3ks9\napjtlCRJkqSZLAlV9azZmUMdIQS+kOQEYCdwaX8w2NgdpVXVfUmuB+4DRpv846O4sOcqo9cCn0my\nHtgMPCsYlCRJkiTt3VBHCGcKRwglSZIkddm+RgiHujG9JEmSJGnmMiCUJEmSpI4yIJQkSZKkjjIg\nlCRJkqSOMiCUJEmSpI4yIJQkSZKkjjIglCRJkqSOMiCUJEmSpI4yIJQkSZKkjjIglCRJkqSOmnOw\nKyBJkiRNxOrVt3P11bewY8ccRkZGed3rTuZrX/sBDz30Qx555CfMnz+fk08+huXLl/GWt7x+SmUf\nSBnDLn8qZQy7fVPRX7ctWzYCc3n66V1T7tNhmQmv5SDrYEAoSZKkGW/16ttZseJmNmxY1aTczpe/\n/FlGR98O3Az8TzZvhrVrYcOGKwAmFSztWfbkyxh2+VMpY9jtm4o963Y7vb58E1Pt02GZCa/lwOtQ\nVYf9T6+ZkiRJOlQtW3ZFQfX9XDHu954/b3rTH0yh7MmXMezyp1LGsNs3FXvWbXB9Oj31PTh1O9A6\nNDHRs2Il7yGUJEnSjLdjx/iJbXPG/d7T9u2zp1D25MsYdvlTKWPY7ZuKPes2uD4dlpnwWg66DgaE\nkiRJmvFGRkbHpYyO+72nI4/cNYWyJ1/GsMufShnDbt9U7Fm3wfXpsMyE13LQdTAglCRJ0oy3fPky\nFi++oi9lGXPm/DawDLhij7yLF3+Ayy47ZwplT76MYZc/lTKG3b6p2LNubV9OvU+HZSa8loOuQ3rT\nSQ9vSaoL7ZQkSTqcrV59O9dccyvbt8/myCN3cdZZ87njjofZuPFRHnnkcebPfwELFhzLZZedc0Ar\nePaXfSBlDLv8qZQx7PZNRX/dtmzZSDLCjh2jU+7TYZkJr+WB1CEJVZVnpXchUDIglCRJktRl+woI\nnTIqSZIkSR1lQChJkiRJHWVAKEmSJEkdZUAoSZIkSR1lQChJkiRJHWVAKEmSJEkdZUAoSZIkSR1l\nQChJkiRJHTXUgDDJiiT3JlmbZMW4c+9L8kyS4/vSLk+yPsm6JMuatKOSrE7y7aacD/flH0ny+eaa\nO5K8cJjtkSRJkqTDydACwiQvA94NvAZ4OXBeksXNuUXAOcCDfflPBy4CTgfOBT6eJM3pP66qlwKv\nBH4xyblN+iXA5qpaAnwUuHJY7ZEkSZKkw80wRwhPA75eVdurahdwG3Bhc+4q4P3j8p8PfK6qdlbV\nA8B3gTOr6qmqug2gqnYC3wAWNNe8FbiueXwD8MZhNUaSJEmSDjfDDAjXAmcnOT7JPODNwKIk5wMb\nq+qecflPBjb2HW9kLPADIMlzgV8FvtQkLQC+D1BVo8Dj/VNQJUmSJEn7NmdYBVfVuiRXArcATwB3\nAyPA5cCyvqzZy+W7i9mdKZkDfA74WDOCKEmSJEmagqEFhABV9SngUwBJVgGbgAuAbzW3By4E7kpy\nJvAQsKjv8oVNWuuTwP1VdXVf2kPAKcAPmoDxuKp6bG91Wbly5e7HS5cuZenSpVNpmiRJkiTNWGvW\nrGHNmjX7zZeq2m+mA5XkpKp6NMkpwM307gnc0nf+e8AZVfVYs6jMZ4HX0psK+kXgxVVVST5E757E\nX6u+Cie5FPi5qnpPkouBC6rq4r3Uo4bZTkmSJEmayZJQVc+anTnUEULgC0lOAHYCl/YHg43dUVpV\n3ZfkeuA+YLTJX0kWAh8Avg18oxlZvKYZfbwW+EyS9cBm4FnBoCRJkiRp74Y6QjhTOEIoSZIkqcv2\nNUI41I3pJUmSJEkzlwGhJEmSJHWUAaEkSZIkddSwF5WRJEmSJE3Q6tW3c/XVt7BjxxxGRkbZvv0h\nvvrVB6maB8wFfgJsax7PBY5qfm8DdtAb85sHHAlsobe+5wn7fD4XlZEkSZKkGWD16ttZseJmNmxY\n1aT8HrAB+BmgTfs4cCtwPPCCvnSAt9Hbpv2qvrQrgDcBb3BRGUmSJEmaqa6++pa+YBB6O/Kdxp5B\n323AzwLzx6XT5L1qXNoqegHk3jllVJIkSZJmgB07xodnR/PskO2ovaS19pU+e5/PaUAoSZIkSTPA\nyMjouJQngPFpT+0lrbWv9F37fE6njEqSJEnSDLB8+TIWL76iL+V0YB29+wBbbwD+BXh4XDrAt4Hf\nHZf2AeCcfT6ni8pIkiRJ0gyxevXtXHPNrWzfPpsjj9zFU09tbFYZPRo4Angc2AqMNMfzmt/9q4we\n3ZzfCjxNb5XRv9rrojIGhJIkSZJ0mEviKqOSJEmSpDEGhJIkSZLUUQaEkiRJktRRBoSSJEmS1FEG\nhJIkSZLUUQaEkiRJktRRBoSSJEmS1FEGhJIkSZLUUQaEkiRJktRRBoSSJEmS1FEGhJIkSZLUUQaE\nkiRJktRRBoSSJEmS1FEGhJIkSZLUUQaEkiRJktRRQw0Ik6xIcm+StUlWjDv3viTPJDm+L+3yJOuT\nrEuyrC/9jKac9Uk+1pc+kuTzTfodSV44zPZIkiRJ0uFkaAFhkpcB7wZeA7wcOC/J4ubcIuAc4MG+\n/KcDFwGnA+cCH0+S5vQngEuqagmwJMm5TfolwOYm/aPAlcNqjw7cmjVrDnYVdJDY991l33eXfd9d\n9n132feHtmGOEJ4GfL2qtlfVLuA24MLm3FXA+8flPx/4XFXtrKoHgO8CZyaZDxxbVXc2+f4CuKB5\n/FbguubxDcAbh9ISTYn/SHSXfd9d9n132ffdZd93l31/aBtmQLgWODvJ8UnmAW8GFiU5H9hYVfeM\ny38ysLHveCOwYC/pDzXpNL+/D1BVo8Dj/VNQJUmSJEn7NmdYBVfVuiRXArcATwB3AyPA5cCyvqzZ\ny+WSJEmSpCFLVU3PEyWrgE3AFcCTTfJCeiN+ZwLvAqiqP2ry3wR8kN59hl+pqpc26b8OvL6q3tPk\nWVlVdySZAzxcVSfu5bmnp5GSJEmSNENV1bMG44Y2QgiQ5KSqejTJKfTuHzyzqq7uO/894IyqeizJ\njcBnk1xFbyroEuDOqqokW5KcCdwJvANoy7gReCdwB/A24Et7q8feGi5JkiRJXTfUgBD4QpITgJ3A\npVW1Zdz53SN3VXVfkuuB+4DRJn97/lLg08BRwN9X1U1N+rXAZ5KsBzYDFw+tJZIkSZJ0mJm2KaOS\nJEmSpJllqBvT6/CUZFGSryT5lyRrkyxv0o9PcmuS7yS5Jclz+9K/kmRrkmvGlXVGknuTrE/ysYPR\nHk3cgPt+VZJ/TbL1YLRFkzOovk9yVJLVSb7dlPPhg9UmTcyAP/c3Jbm7KevaJEccjDZpYgbZ931l\n3pjk3ulshyZvwJ/7NUnWJflm8/O8g9Em7ZsBoQ7ETuC9VfWzwFnA7yR5KfD7wK1V9RJ693P+fpN/\nO/AHwH/eS1mfAC6pqiXAkiTnDr32mopB9v3fAK8dfpU1IIPs+z9uFgp7JfCLfu5nvEH2/duq6hVN\nWccBFw299pqKQfY9SS4EttJ3y5BmrEH2fQFvr6pXNj8/Gn71NRkGhJq0qnqkqu5uHm8Dvk1vIaC3\nAtc12a4DLmjyPFlV/wjs6C8nyXzg2Kq6s0n6i/YazUyD6vvm3J1V9ci0VFxTNqi+r6qnquq25vFO\n4BuM7S2rGWjAn/ttAM3I4FzAPwxnsEH2fZJjgPcCH8Itx2a8QfZ9wz6fwQwINSVJXkTvW/6vA8+v\nqk3NqU3A88dlH/+N4AJgY9/xQ/iH4SFjin2vQ9ig+r6ZavSr7GOFaM08g+j7JDc3+Z/qWyROM9wA\n+v4PgY8wtvWYDhED+jf/uma66B8MpZKaEgNCHbDm274bgBVVtcd9YM0KsQYBhyn7vrsG1ffp7R37\nOeBjVfXAoOupwRtU31fVm4D5wEiSdw68ohq4qfZ9klcAP11Vf4MjRYeUAX3u/11VvQw4Gzg7yTsG\nX1NNhQGhDkgz3ecG4DNV9ddN8qYkL2jOzwce3U8xDwEL+44XNmmawQbU9zoEDbjvPwnc3783rWau\nQX/uq2pHU95rBl1XDdaA+v4s4NXp7T/9VeAlSb48rDprMAb1ua+qHzS/twGfxfUDZhwDQk1aktDb\nA/K+qvqTvlM3Au23ve8E/nr8pf0HVfUwsCXJmU2Z79jLNZpBBtX3OvQMsu+TfAh4Dr37iTTDDarv\nkxzd/AHZjhCfB3xzKJXWQAzw//s/raoFVXUq8EvAd6rqV4ZUbQ3AAD/3s9tVRZsA81cBV5mdYdyH\nUJOW5JeA24F7GJsqcDlwJ3A9cArwAPBvq+onzTUPAMfSW0TgJ8A5VbUuyRnAp4GjgL+vquXT1hBN\n2oD7/o+BX6c3dexh4M+q6r9PW2M0KYPqe2Ab8K/0Fih4uinnmqr61HS0Q5M3wL5/DPg7YITeH403\nA+8v/xCZsQbQ9z8GllXVur4yXwTcWFU/Px1t0IEZ4Of+X5tyjgBmA7cCv+vnfmYxIJQkSZKkjnLK\nqCRJkiR1lAGhJEmSJHWUAaEkSZIkdZQBoSRJkiR1lAGhJEmSJHWUAaEkSZIkdZQBoSRJkiR1lAGh\nJEkzRBL/X5YkTSv/45Ek6QAk+W9JVvQdr0qyPMl/SXJnkm8lWdl3/q+S/HOStUn+Q1/6tiQfSXI3\ncNb0tkKS1HUGhJIkHZhPAb8Bu0f2LgIeAV5cVa8FXgmckeTsJv9vVdWrgdcAy5P8VJM+D7ijql5R\nVf9nWlsgSeq8OQe7ApIkHYqq6sEkm5O8AngB8E16wd6yJN9ssh0NvBj4KrAiyQVN+iJgCXAnsAu4\nYVorL0lSw4BQkqQD9+fAu4Dn0xsxfCPw4ar6ZH+mJEubc2dV1fYkXwGObE5vr6qavipLkjTGKaOS\nJB24vwLOBV4N3ATcDPxWkqMBkixIciLwHODHTTB4Gt4rKEmaIRwhlCTpAFXVziRfphfsFXBrkpcC\nX0sCsBX49/SCxd9Och9wP/C1/mKmudqSJO0WZ6lIknRgmsVk7gLeVlUbDnZ9JEmaLKeMSpJ0AJKc\nDqwHvmgwKEk6VDlCKEmSJEkd5QihJEmSJHWUAaEkSZIkdZQBoSRJkiR1lAGhJEmSJHWUAaEkSZIk\ndZQBoSRJkiR11P8DR0fsA3eEEoAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x110b682d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pylab\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(15,5))\n",
    "pylab.plot_date(df.date, df.zipcode)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('zipcode')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Only cirtain areas are popular. Probably city or near city areas.\n",
    "- Can't really tell.. Let's dig deeper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2014, 2, 27)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.date[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Focus on zipcode from 94100 to 94140"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df.zipcode = df['zipcode'].apply(lambda x:int(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x116f23810>]"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VnPMkzZfk93pC/g5iT4RKpYPWOlc/F706+HRN81m2H/Ot3ez+6ePz+GgjazxtfSbJ8/nQ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6TM9RSyx/TjdviKdJ8/v4YLFy5iamosJWtiCzJrYg2l0gR27Cji0qULaDaHITMhbSCb3VBZEycB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\nRPFlff08KytnWF6usbnZsVQTS1o1sWqpJnY5cOAaLl6sDXBuWq0uZ86sc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ydOnNtmii0u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a2KY+XDWxTf6cJvkLyF41ccqKbRERZ9YwVRM55/iczlUT7bEaEdcX85tVaKxdNbFN83LFvTbJ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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196f8550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = df[df['zipcode'] > 94100]\n",
    "df2 = df2[df2['zipcode'] < 94140]\n",
    "\n",
    "plt.figure(figsize=(15,5))\n",
    "pylab.plot_date(df2.date, df2.zipcode)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Can't really tell anything..\n",
    "\n",
    "### What about per year?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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6uvlkMuOMjIyUxmBn53L+8R+7yeezmIUCly5Vi55jxyYxc3+UHE1GzI+V5FeJ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\nzZi/lEN+Ox/r898ZXYnhY29/FXpM32rou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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ba02f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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b53lWuN62bQ133XVLvaJ1pZKlVhulpydNNtvJL35xlGIxixBlstkyn/jEldx1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oAAAg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\nK7z44lbgK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AN5HFwmwDwaI5drZ3x8P0EFdMEHKhTmUSodQF4PZpH3z9ebtfoa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\n08gTlVcDs0me1+Fag3uARUqpJ5RSdyqllpm2eaL9o+bo+kcr+1LgF247c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      "text/plain": [
       "<matplotlib.figure.Figure at 0x114386a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Umcfa2xhR5n4bcWj3fbtubeR2r+kTj3SMR8PZSF+7Ns4gtnrOzNON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M173nMtb3jDq/jN33yYv/u7L3Py5KTKmMS9pp+kq6uXK664kqee+hpnzhxBXs31I6/sZrEDFbu6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\n450ME2EM1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ezut30UwW2YHmtpBUnvBG5FcAX+tZtwtosBW4dxjM17cYBCv882BXqPIHgI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\nAL90zm1xzk0Af4/SYOeSSy655JJLLrn8t5JGH5aOQsNevPhcJ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      "text/plain": [
       "<matplotlib.figure.Figure at 0x12278f450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2012 = df2[df2['date'] >datetime.date(2012, 1, 1)]\n",
    "df2012 = df2012[df2012['date'] < datetime.date(2012, 12, 30)]\n",
    "df2013 = df2[df2['date'] >datetime.date(2013, 1, 1)]\n",
    "df2013 = df2013[df2013['date'] < datetime.date(2013, 12, 30)]\n",
    "df2014 = df2[df2['date'] >datetime.date(2014, 1, 1)]\n",
    "df2014 = df2014[df2014['date'] < datetime.date(2014, 12, 30)]\n",
    "import pylab\n",
    "%matplotlib inline\n",
    "plt.figure(figsize=(10,3))\n",
    "pylab.plot_date(df2012.date, df2012.zipcode)\n",
    "plt.title('2012')\n",
    "plt.show()\n",
    "plt.figure(figsize=(10,3))\n",
    "pylab.plot_date(df2013.date, df2013.zipcode)\n",
    "plt.title('2013')\n",
    "plt.show()\n",
    "plt.figure(figsize=(10,3))\n",
    "pylab.plot_date(df2014.date, df2014.zipcode)\n",
    "plt.title('2014')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We can't visually find a pattern. I'll stop here for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Reservation seasonality with pricing\n",
    "- Are Airbnb places price responsive/sensitive on demand?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Read data and preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-02-27</td>\n",
       "      <td>$170.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-10-07</td>\n",
       "      <td>$170.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-10-12</td>\n",
       "      <td>$170.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-10-17</td>\n",
       "      <td>$170.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-10-26</td>\n",
       "      <td>$170.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date    price\n",
       "0  2014-02-27  $170.00\n",
       "1  2015-10-07  $170.00\n",
       "2  2015-10-12  $170.00\n",
       "3  2015-10-17  $170.00\n",
       "4  2015-10-26  $170.00"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "df = pd.read_csv('data/df_new_en.csv').drop('Unnamed: 0', 1)\n",
    "price_season = df[['date','price']]\n",
    "price_season.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "from re import sub\n",
    "from decimal import Decimal\n",
    "\n",
    "def doller_to_int(x):\n",
    "    x = str(x)\n",
    "    x = Decimal(sub(r'[^\\d.]', '', x))\n",
    "    x = float(x)\n",
    "    return x\n",
    "\n",
    "# doller_to_int(df.price[0])\n",
    "price_season.price = price_season['price'].apply(doller_to_int)\n",
    "\n",
    "price_count  = Counter(price_season.price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Review frequency based on price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11748c310>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H1L3scIqWvo3lcX35xh19zuLFi7cdz5o1i1mzZjXrK1RCf//SMvQVW7MNDhZlBj9JksZn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\nue7y+dMTpie8JOHEunNXl4+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      "text/plain": [
       "<matplotlib.figure.Figure at 0x115100990>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,7))\n",
    "plt.scatter(price_count.keys(), price_count.values())\n",
    "plt.xlabel('price')\n",
    "plt.ylabel('review frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Most places are between 0 and 2,000 dollar range"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get rid of price points above 2500 since they could affect the averag price too much"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "(preferablly I should've run tests on influencial points as well as leverage points if I had more time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132332, 132325)"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_season2 = price_season[price_season['price'] < 2500]\n",
    "len(price_season), len(price_season2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get rid of days (only year and month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kimitakanakazawa/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:9: 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"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2014-02-01</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         date  price\n",
       "0  2014-02-01    170\n",
       "1  2015-10-01    170\n",
       "2  2015-10-01    170\n",
       "3  2015-10-01    170\n",
       "4  2015-10-01    170"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# dates to year-month\n",
    "price_season2.date = [month[:7] for month in price_season2.date]\n",
    "\n",
    "# convert to datetime \n",
    "import datetime\n",
    "def convert_datetime(x):\n",
    "    dt = datetime.datetime.strptime(x, \"%Y-%m\")\n",
    "    return dt.date()\n",
    "price_season2['date'] = price_season2['date'].apply(convert_datetime)\n",
    "price_season2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Group by listing id and make average price for each month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2009-03-01</th>\n",
       "      <td>181.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-05-01</th>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-06-01</th>\n",
       "      <td>250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-07-01</th>\n",
       "      <td>210.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-08-01</th>\n",
       "      <td>197.9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            price\n",
       "date             \n",
       "2009-03-01  181.0\n",
       "2009-05-01  250.0\n",
       "2009-06-01  250.0\n",
       "2009-07-01  210.0\n",
       "2009-08-01  197.9"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_month = price_season2.groupby('date').aggregate(np.mean)\n",
    "groupby_month.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's see if there is pricing pattern "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1142fb790>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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6ocEDAADohAYPAACgE4MavKo6paoeO739Q1X1I+NuFgAAALPascGrql9N8rYkfzhddUKS\nd4y5UQAAAMxuyBm8f5Lk0Um+lSSttc8nue+YGwUAAMDshjR4322tfXf/D1V1RA78BmcAAAAWbEiD\n95+r6sVJfqiq/n4ml2tePu5mAQAAMKshDd55Sb6a5Kokz07yn5K8ZMyNAgAAYHZHDKi5a5KLW2t/\nlCRVdXiSuyX5zpgbBgAAwGyGnMH7s0wauv1+KMn7x9kcAAAAdmtIg3eX1tot+39orX07kyYPAACA\nJTKkwfv/quqR+3+oqr+T5L+Nt0kAAADsxpD34P3TJJdW1ZenPx+X5KnjbRIAAAC7sWOD11r7r1X1\n0CQ/msn3332utXbb6FsGAADATA7a4FXVz7fWPlBVT86ksavpXQ+uqrTW/mRPthAAAIBBtjuD93eT\nfCDJL2bS4G2mwQMAAFgiB23wWmsvrarDkvxpa+3f7+E2AQAAsAvbfopma+17SX5jj7YFAACAQzDk\naxLeV1W/XlUnVtUx+5fRtwwAAICZDPmahHMyeQ/eP9m0/tT5bw4AAAC7NaTBe2gmzd2jk3wvyYeS\nvH7MjQIAAGB2Qxq8tyT5VpLXZPJVCb88Xfc/jbhdAAAAzGhIg/djrbXTN/z8Z1V19VgbBAAAwO4M\n+ZCVj1fVz+z/oap+OsnHxtskAAAAdmPIGby/k+S/VNW+TD5s5aQkn6uqq5K01tqPj7mBAAAADDOk\nwTtr9K0AAADgkO3Y4LXWrt2D7QAAAOAQDXkPHgAAACtAgwcAANAJDR4AAEAnNHgAAACd0OABAAB0\nQoMHAADQCQ0eAABAJzR4AAAAndDgAQAAdEKDBwAA0IlRG7yqemNV3VRVV21Yd0xVva+qPl9V762q\ne2247/yq+kJVfbaqHjfmtgEAAPRm7DN4b0py1qZ15yV5X2vtwUk+MP05VXV6kqcmOX36mNdVlTOM\nAAAAA43aQLXWPpjk5k2rn5DkzdPbb05y9vT2E5Nc0lq7rbV2bZJrkpwx5vYBAAD0ZBFnyO7XWrtp\nevumJPeb3n5Akhs21N2Q5Pi93DAAAIBVdsQin7y11qqqbVey1coLLrhg/60kZ04XAACAHl2ZZGMf\ndHDV2nb91aGrqlOSXN5ae9j0588mObO19pWqOi7JFa21h1TVeUnSWrtoWvfuJC9trX1kU15rraWq\nsnX/V9k8pq1rh9atSuaBdeucudpzOUbm6s7lGJmrPZdjZK7uXI6RudpzOUbm6s7lGJmrPZdjZK7u\nXK5K5mrvH2Nkru5czjOzqtJaqwOeIIu5RPOyJOdOb5+b5J0b1p9TVUdV1alJTkvy0QVsHwAAwEoa\n9RLNqrokyc8mObaq9iX5rSQXJbm0qp6V5NokT0mS1trVVXVpkquT3J7kOW3s04sAAAAdGbXBa609\n7SB3PfYg9RcmuXC8LQIAAOiX75kDAADohAYPAACgExo8AACATmjwAAAAOqHBAwAA6IQGDwAAoBMa\nPAAAgE5o8AAAADqhwQMAAOiEBg8AAKATGjwAAIBOaPAAAAA6ocEDAADohAYPAACgExo8AACATmjw\nAAAAOqHBAwAA6IQGDwAAoBMaPAAAgE5o8AAAADqhwQMAAOiEBg8AAKATGjwAAIBOaPAAAAA6ocED\nAADohAYPAACgExo8AACATmjwAAAAOqHBAwAA6IQGDwAAoBMaPAAAgE5o8AAAADqhwQMAAOiEBg8A\nAKATGjwAAIBOaPAAAAA6ocEDAADohAYPAACgExo8AACATmjwAAAAOqHBAwAA6IQGDwAAoBMaPAAA\ngE5o8AAAADqhwQMAAOiEBg8AAKATRyzqiavq2iTfSnJHkttaa2dU1TFJ/n2Sk5Ncm+QprbVvLGob\nAQAAVskiz+C1JGe21h7RWjtjuu68JO9rrT04yQemPwMAADDAoi/RrE0/PyHJm6e335zk7L3dHAAA\ngNW16DN476+qv6yqX5muu19r7abp7ZuS3G8xmwYAALB6FvYevCSPaq19uaruk+R9VfXZjXe21lpV\nta0eeMEFF+y/leTM6QIAANCjK5Ns7IMOrlrbsofaU1X10iS3JPmVTN6X95WqOi7JFa21h2yqba21\nVFUmJwEPSMvmMW1dO7RuVTIPrFvnzNWeyzEyV3cux8hc7bkcI3N153KMzNWeyzEyV3cux8hc7bkc\nI3N153JVMld7/xgjc3Xncp6ZVZXW2ua3uyVZ0CWaVfVDVXWP6e0fTvK4JFcluSzJudOyc5O8cxHb\nBwAAsIoWdYnm/ZK8Y9KZ5ogkf9xae29V/WWSS6vqWZl+TcKCtg8AAGDlLKTBa619KcnDt1j/9SSP\n3fstAgAAWH2L/poEAAAA5kSDBwAA0AkNHgAAQCc0eAAAAJ3Q4AEAAHRCgwcAANAJDR4AAEAnNHgA\nAACd0OABAAB0QoMHAADQCQ0eAABAJzR4AAAAndDgAQAAdEKDBwAA0AkNHgAAQCc0eAAAAJ3Q4AEA\nAHRCgwcAANAJDR4AAEAnNHgAAACd0OABAAB0QoMHAADQCQ0eAABAJzR4AAAAndDgAQAAdEKDBwAA\n0AkNHgAAQCc0eAAAAJ3Q4AEAAHRCgwcAANAJDR4AAEAnNHgAAACd0OABAAB0QoMHAADQCQ0eAABA\nJzR4AAAAndDgAQAAdEKDBwAA0AkNHgAAQCc0eAAAAJ3Q4AEAAHRCgwcAANAJDR4AAEAnNHgAAACd\n0OABAAB0QoMHAADQCQ0eAABAJ5auwauqs6rqs1X1hap64bBHXTnDMwytnXedzPXMXORzy1z+zEU+\nt8zlz1zkc8tc/sxFPrfM9cxc5HPLnMVSNXhVdXiS1yY5K8npSZ5WVQ/d+ZFXzvAsQ2vnXSdzPTMX\n+dwylz9zkc8tc/kzF/ncMpc/c5HPLXM9Mxf53DJnsVQNXpIzklzTWru2tXZbkn+X5IkL3iYAAICV\nsGwN3vFJ9m34+YbpOgAAAHZQrbVFb8OdqurJSc5qrf3K9OenJ/mp1trzNtQszwYDAAAsQGuttlp/\nxF5vyA5uTHLihp9PzOQs3p0ONhAAAIB1t2yXaP5lktOq6pSqOirJU5NctuBtAgAAWAlLdQavtXZ7\nVT03yXuSHJ7k4tbaZxa8WQAAACthqd6DBwAAwO4t2yWaAAAA7FI3DV5V/f151867TuZ6ZvY2Hpn2\nD5l7l9nbeGTaP2SudmZv4+kxM0nSWutiSbJv3rXzrpO5npm9jUem/UOm/UPmcmT2Nh6Zy5/Z23h6\nzGytLdeHrOykqi7f5u5776Z23nUy1zOzt/HItH/I3LvM3sYj0/4hc7UzextPj5k7WakGL8mjkzwj\nyS0b1rUkleSndlk77zqZ65nZ23hk2j9k2j9kLkdmb+ORufyZvY2nx8xtrVqD95Ek32mtXbn5jqr6\n3C5r510ncz0zexuPTPuHzL3L7G08Mu0fMlc7s7fx9Ji5LV+TAAAA0ImV/RTNqjqmqo6ZZ+2862Su\nZ2Zv45Fp/5C5d5m9jUem/UPmamf2Np4eM7c09NNYlmFJcnKSf5fkq0mumS5fna47ZTe1866TuZ6Z\nvY1Hpv1Dpv1D5nJk9jYemcuf2dt4eszcaRlUtCxLkr9I8tQkR2xYd0SSc5L8xW5q510ncz0zexuP\nTPuHTPuHzOXI7G08Mpc/s7fx9Ji50zKoaFmWJF8Yet/Q2nnXyVzPzN7GI9P+IdP+IXM5Mnsbj8zl\nz+xtPD1m7rSs2qdofryqXpfkzUn2TdedlOTcJJ/YZe2862SuZ2Zv45Fp/5Bp/5C5HJm9jUfm8mf2\nNp4eM7e1Up+iWVV3SfKsJE9Icvx09Y1JLktycWvtu7PWzrtO5npm9jYemfYPmXuX2dt4ZNo/ZK52\nZm/j6TFzJyvV4AEAAHBwK/s1CftV1cfnXTvvOpnrmdnbeGTaP2TuXWZv45Fp/5C52pm9jafHzI1W\nvsFLUiPUzrtO5npm9jYemf08t8zlz+xtPDL7eW6Z65nZ23h6zLxTDw3efxqh9j/OuW6W517keGap\nHSNzlrEvMnOM5x5j/1jkXI5xbCw6c97z7lhf/sxF/o5f9Hgc6/N77llqHeuLyfT33Hyf27E+38xZ\nxpQk3oO3WVXdM8lpSb7YWrt5jrn3aa19dZv7j05yR2vtWwOyjkmS1trX57V9q6aqHtla+9gc8+Y+\n7zvN+bRm0Lyb89WY82muY32O1m3ezXk/cz6tMe8D9TLvfscP18ucT2uW61gf+n0Ky7Ak+e+TvD+T\nb3M/NckVSb6Z5INJHrSp9pkbbp+Q5ANJvpHkw0kevOG+P05y7PT2P0hy/fQ5rk/ylA11Nyf510l+\nPtPGeJvt/IUkX0ryoSSPSPLpJF/M5FNwHruh7vgkb5mO4XuZfBzqviQXJDlyU+bJOcRvtk9y1aaf\nT5o+/kNJXrTxOZO8c8zMGefyJ6bLIzf894b962ed8zHmfeiczzLv85jzzXM0jzk/lMyh8z50zsc4\n1seY96Fz7lhf/mN9jHmfx5w71pfjWN/reY9jfaWO9VWZ91nyhs770Dl3rA+f921rhxYuw5Lk/03y\ni0meluTL0/8eNl333k21n9hw+21JfjXJ4Ul+KckHNtz3qU35p0xvH5vkrzfc97kkz53uXH+T5DVJ\nfvog2/lXSR6a5GeSfH1/3XTdxu26Isnfy+Ta2icleXWSuyd5eZI/2pQ56Jvtkzx5i+VJ0/9+bVPm\n+5P82nQHfu10bMdu8fqNkTnLXH5vmnPFhuW/7b8965yPMe9D53yWeR8657PM0dD5GTFz0LwPnfMx\njvUx5n3onDvWl/9YH2Peh865Y335j/Ux5n3o/DjWl/9YX5V5H5rnWF/c7/idlkFFy7JsmvBrDnbf\nFrWbJ/mTG25/Osk9p7c/lOTwjfcdJO/kJC9M8vFMuvwLt3nufds8919tuu/jG3fETfcN+mb7JLdl\n8uWIb9q0/Jskt2zeiTf9/PQkVyd54KYxjJE5y1w+OcmfJ/kfN6z70g77x0HnfIx5Hzrns8z70Dmf\nZY6Gzs+ImYPmfeiczzLvQ+d8jHkfOuezzPvQ+ZlljkbK7OpYH2Peh875LHM0dH5GzFzLY32MeR86\nP7PM0UiZjvUd5nxV5n1o3izzPnTOZ5n3oXM+y7wPnfNZ5n3onM8679stR2S1HL7h9qs23Xfkpp9P\nqKo/yKSrPraqjmyt3Ta9b+O4fzvJFVX12iT/JcmlVXV5kjOTvHurjWitXZfknyf551X1kEy68o1u\nqapnJ7lnkm9V1T9LcmmSx2Zyinm/r1XVM5L8WSY7/peSpKoOy4GfmDP0m+2vSvIvWmtXbd7uqvr5\nTauOqKq7ttZunY7r31bVV5K8J8kPj5w5eC5ba2+vqvcm+d2q+sdJfn3zdkwNnfNk/vM+dM6T4fM+\ndM6T4XM0dH7Gyhw07zPMeTLisT7dlnnMu2N9oodjPZn/vDvW+znWk/nPu2O9n2M9WY15d6wv/99z\n2xvaCS7Dksnp4ntssf5BSV69ad0/mr5w+/97zHT9/XPgv9CcluSVSd6R5D8keX2Sf7Cp5lUzbOeD\nMum0X5HkRzK55vfqJH+S5IEb6k7O5NTzpzK5jvi46fp7J3nypsy7JHlOJjvsVdPl3dN1d9lQ93eT\nnHyQ7frJTT//b0nO3KLuEUneN3Lm4LncdP9PJLkyyVe3uG/wnM973ofO+SzzPnTOZ5mjofMzYubM\n877dnM8670PmfIx5Hzrns8z70PmZ8bh0rC9g3ofO+YjHpWN9TnM+xrwPnZ8Zj0vH+hznfeicr8q8\nD83b7bxvN+ezzvuQOZ9l3ofO+SzzPnTOZ5337RafosnKqarK5JfJjp9QRR/M+Xoy7+vHnK8n875+\nzPm4Vq7Bq6qzkpydySfXJJNPtXlna+2AU7FDa7eouyHJuwbUzfLch5x5MFX1W62135lX3Va1e/S6\n7+nrOe/MoXmzbudWDmUuZ6nbo7ncs9dzkZmO9X6O9bEyt+JY7zvzYBzrfWcezDLNu2N9uY71mWtX\nqcGrqtdkcir2LZm8gMnk41OfkckbO58/a+2868bK3OF12ddaO3FedZtrFz32Vcgc47m3s9u5nKVu\nFV73Vcl0rMvcKfNgHOv9Zm7Hsd5v5naWZd4X/Rr1lrmdMX7HJ1m59+Bt+Sk0SSoHfnLPoNp5142Y\n+e1tlttuQMu6AAALYUlEQVRnrZsxc9FjX/rMkZ57jLmcJXPpX/dVyZzxuR3ra5Y50lw61pc/07G+\nnplLP+9L8Br1ljn3udxpOSyr5daqOmOL9Wdk8l0au6mdd91YmTcnOa21do/NSybfOzJr3Sy1ix77\nKmSO8dxjzOUsmavwuq9KpmNd5nZ1jvX1zHSsr2fmKsz7ol+j3jLHmMttrdrXJPyjJK+vqntkcj1s\nMjkV+q3pfbupnXfdWJn/dyYfqfqVHOiSXdTNUjvLdg6t7S1zjOceYy5nyRy6nUPr1jlzlud2rK9f\npmN9PTMd6+uZuQrzPrRO5uJ+x29rpd6Dt19VHZcNb3xsrW31IsxUO++6sTIXadFjX4XMMZ570Vbh\ndV+VzN7mfJZamf3M+6Jfo94yF2lVXqMeMxdpVV6j3jL3VBt4LeeyLkkumHftvOtkrmdmb+ORaf+Q\naf+QuRyZvY1H5vJn9jaeHjM3Lqv2HrytPHGE2nnXyVzPzN7GI7Of55a5/Jm9jUdmP88tcz0zextP\nj5l36qHBqxFq510ncz0zexuPzMU99yL19rqvUuYircprtK6ZvY1H5vJn9jaeHjO//4Dpqb+VVVWH\ntda+N8/aedfJXM/M3sYzrT28tXbHvOrWObO38cjsbv9Yld9Ja5nZ23hkLn9mb+PpMXOjlTuDV1U/\nV1X/qqouq6p3JLmwqh50KLXzrus086yqekNVXV5Vlyf5V1V11kEyB9X2ltnbeLay/4/DqvqtedSt\nc+ayjme6fzyrqk7ZVPfMzRlDa2Uu/XMfWVVP3/97oKrOTfIH08fWrHUy55s5xnMfxPt3uH83tTKX\nKLOqjt308zOSvKaqfnXTPjeoTubiMneyUmfwquqiJPdP8oEkZyf5UpLPJ/lfk7yitXbprLXzrus0\n8zVJTkvyliQ3Th9+QpJnZPJFjs/fkDmotrfM3sazk6ra11o7cV5165y5TOOpqlckeVSSjyf5xSSv\naa39wfS+T7TWHrHhcYNqZS73c09/vjjJPZMclcl3N90lyduTPD7J9a21/2OWOpnzzRzpua9K0pIf\nuPTrwZn8DdBaaz++IXNQrcyVyNz4O+IlSR6T5K2Z/I7Y11r7Z7PUyVxc5o7ajJ/Kssglyac23D4i\nyYent49O8und1M67rtPMLxxkPiqTpiCz1vaW2dt4puu+vc1y+6x165y5QuP5VJIjp7fvleRPk7x6\nun98YvPvjyG1Mpf7uaf3f3r63yOTfD3JXaY/H5Hkr2etkznfzJGe+7Ikf5zkoUlOTnJKkn37b2/K\nHFQrcyUyN/6O+ESSu2/YXz41a53MxWXutKzaJZp3VNW9p7ePz/QS09bazYdQO++6HjNvraoztnj8\nGZn8C+FuanvL7G08SXJzktNaa/fYvCT58i7q1jlzVcZzeGvttiRprX0jk38x/JEkb8vkjEB2UStz\nuZ87SfbX3Zbkv7bWvjv9+fZMzgrMWidzvplzf+7W2hMyObP3R0ke3lq7NpN/7Lluejuz1spc/swk\nd6uqn6iqR2byD0C3TB9/W5I7dlEnc3GZ22sDO8FlWJI8Ncl1mVxXvC/J46fr75vkrbupnXddp5mP\nTPLRJJ9J8r7p8pkkH0nyyE2Zg2p7y+xtPNPalyc54yDH4itnrVvnzBUaz39M8rNb1Lwsyfc2rRtU\nK3O5n3u67t2Z/ivxpvXHJfnorHUy55s5xnNvWH/3JP9nkncluXHz/buplbm8mUmuTHLFhuUB0/XH\nJvnLWetkLi5zp2Wl3oOXJNMzTv9dJpeafWMetfOu6zFzWntcJmf6kskvjs1nCWau7S2zt/GwXqrq\nbknSWtt8NjdVdUJr7YZZa2Uu93Nvp6p+OMkPt9b+dh51MuebOc/nrqqHJ/np1tobBmQNqpW5/Jkb\n6g/P5HLe78yjTubiMu80tBNcliWT9w/8dJInTZefSiaN6m5r513XY+Y28/GQedf2ltnbeGTaP2Ta\nP2QuR2Zv45G5/Jm9jafHzNZW7AxeVT0uyeuSXJNk/78+npDJpwI+p7X2nllr513XY+Z2aok+AXBZ\nM3sbj0z7h8y9y+xtPDJXYv+4vrV20sDMQbUyVyJz6fd3mcNrjxhStET+IMlj26Y3l1bVqZl8QthD\ndlE777ruMqvqX+bg7rXpsYNqe8vsbTwy7R8y9y6zt/HIXPn94+gZMo+etU7m0mYuxf4uc3jtdlbt\nDN4Xkpzepp8MtmH9UUmubq09aNbaedd1mvntJL+e5Lv5wU/sqiS/31q794bHDqrtLbO38ci0f8jc\nu8zexiPT/iFztTN7G0+PmTtqA6/lXIYlyflJPpnkhUn+5+ly3nTdi3ZTO++6TjOvSPKog8zJtZt+\nHlTbW2Zv45Fp/5Bp/5C5HJm9jUfm8mf2Np4eM3daVuoMXpJU1elJnpjkAdNVNya5rLV29W5r513X\nW2ZVHZPk1jbsU34G1faW2dt4ZNo/ZO5dZm/jkWn/kLnamb2Np8fMHXNWrcEDAADgIIae6luGJZM3\nF16U5LNJbk7y9enti5Lcaze1866TuZ6ZvY1Hpv1Dpv1D5nJk9jYemcuf2dt4eszcaTksq+XSTAZ7\nZpJjWmvHJPl7Sb4xvW83tfOuk7memb2NR6b9Q6b9Q+ZyZPY2HpnLn9nbeHrM3N7QTnAZliSfH3rf\n0Np518lcz8zexiPT/iHT/iFzOTJ7G4/M5c/sbTw9Zu60rNoZvOuq6jeq6n77V1TV/avqhUmu32Xt\nvOtkrmdmb+ORaf+Qaf+QuRyZvY1H5vJn9jaeHjO3tWoN3lOTHJvkP1fVzVV1c5Irk9w7yVN2WTvv\nOpnrmdnbeGTaP2TaP2QuR2Zv45G5/Jm9jafHzO0NPdW3LEuShyZ5bJJ7bFp/1m5r510ncz0zexuP\nTPuHTPuHzOXI7G08Mpc/s7fx9Ji53TKoaFmWJM9P8rkk70xyXZKzN9z3id3UzrtO5npm9jYemfYP\nmfYPmcuR2dt4ZC5/Zm/j6TFzp2VQ0bIsST6V5O7T26ck+ViSf3qQF2hQ7bzrZK5nZm/jkWn/kGn/\nkLkcmb2NR+byZ/Y2nh4zd1qOyGqp1totSdJau7aqfjbJ26vq5CS1y9p518lcz8zexiPT/iHT/iFz\nOTJ7G4/M5c/sbTw9Zm5r1T5k5W+r6uH7f5i+AI/P5I2HP77L2nnXyVzPzN7GI9P+IdP+IXM5Mnsb\nj8zlz+xtPD1mbq8NPNW3DEuSE5Pcf4v1leTRu6mdd53M9czsbTwy7R8y7R8ylyOzt/HIXP7M3sbT\nY+ZOS00fBAAAwIpbtUs0AQAAOAgNHgAAQCc0eAAAAJ3Q4AHAFqrqgqr637e5/4lV9dC93CYA2IkG\nDwC2ttOnkP1SktP3YkMAYCifogkAU1X14iT/S5K/TbIvyceSfDPJryY5Ksk1SZ6R5BFJLp/e980k\nT8rkH01fm+Q+Sb6T5Fdaa5/b4yEAsOY0eACQpKoemeRNSc5IcmSSjyd5fZJ/01r7+rTmd5Pc1Fp7\nbVW9KcnlrbU/md73gSTPbq1dU1U/leTC1trPL2IsAKyvIxa9AQCwJB6T5E9aa7cmubWqLsvky2Uf\nVlUvS3LPJHdP8u4Nj6kkqaq7J/mZJG+rqv33HbVXGw4A+2nwAGCiZdqwbfKmJE9srV1VVecmOXPT\nY5LJ5ZnfaK09YtxNBIDt+ZAVAJj48yRnV9Vdq+oeSX5xuv4eSb5SVUcmeXq+39R9O8mPJElr7VtJ\nvlRV/zBJauLH93TrASDegwcAd6qqFyU5N5MPWbkuk/fhfSfJbyT5apKPJLl7a+2ZVfU/JPm/ktya\n5B9m0vi9PslxmbyH75LW2sv2fBAArDUNHgAAQCdcogkAANAJDR4AAEAnNHgAAACd0OABAAB0QoMH\nAADQCQ0eAABAJzR4AAAAnfj/AXQjg0y3kV7JAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1143703d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby_month.plot(kind='bar',figsize=(15,5))\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Possible decrease in price from June to March (possibly August) between 2009-2010. Possible reasons:  \n",
    "- SF visitor counts directly affected host's pricing strategy.\n",
    "- Early adopter hosts would have had higher prices (due to un-educated pricing or monopoly-like situation) and as growing number of hosts being added (so more competitive and hosts get smarter), the price would have been optimized to the right market level."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### But can't really say anything for seasonality.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What about monthly?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date  price\n",
       "0     2    170\n",
       "1    10    170\n",
       "2    10    170\n",
       "3    10    170\n",
       "4    10    170"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_season3 = price_season2[:][:]\n",
    "import datetime\n",
    "def convert_datetime_to_month(x):\n",
    "    return x.month\n",
    "price_season3['date'] = price_season3['date'].apply(convert_datetime_to_month)\n",
    "price_season3.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>183.627507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>176.760987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>173.045980</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>177.832417</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>171.902930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>173.119794</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>179.676557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>178.754106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>175.042630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>176.611468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>177.645918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>182.136355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           price\n",
       "date            \n",
       "1     183.627507\n",
       "2     176.760987\n",
       "3     173.045980\n",
       "4     177.832417\n",
       "5     171.902930\n",
       "6     173.119794\n",
       "7     179.676557\n",
       "8     178.754106\n",
       "9     175.042630\n",
       "10    176.611468\n",
       "11    177.645918\n",
       "12    182.136355"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupby_month2 = price_season3.groupby('date').aggregate(np.mean)\n",
    "groupby_month2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x10e1e1e90>"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZBQEAAHD3bGSRlb+qqu/dsVNVT0zy4eWVBAAAwN2xkRm8707yf+b3seskxyT5ZFV9PEl3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11ad5d450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "groupby_month2.plot(kind='bar',figsize=(15,5))\n",
    "plt.ylabel('price')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looks like no relationship or price is pretty much fixed at macro level (seasonality ignorant) but let's check if that's right."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hypothesis\n",
    "\n",
    "Null: pricing is equal across months\n",
    "\n",
    "Alternative: pricing is different across months"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[183.62750716332377, 176.76098676686817, 173.04597974985111, 177.83241665731282, 171.90292960209882, 173.11979421852033, 179.67655743608211, 178.75410566163782, 175.04263033565897, 176.61146822913705, 177.64591836734695, 182.13635526073338]\n",
      "[177.17972078738092, 177.17972078738092, 177.17972078738092, 177.17972078738092, 177.17972078738092, 177.17972078738092, 177.17972078738092, 177.17972078738092, 177.17972078738092, 177.17972078738092, 177.17972078738092, 177.17972078738092]\n"
     ]
    }
   ],
   "source": [
    "obs_monthly_price = list(groupby_month2.price)\n",
    "null_monthly_price = [np.mean(obs_monthly_price)]*12\n",
    "print obs_monthly_price\n",
    "print null_monthly_price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Power_divergenceResult(statistic=0.80132847911568961, pvalue=0.99998380219808847)"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scipy.stats as ss\n",
    "ss.chisquare(obs_monthly_price,null_monthly_price)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-No seasonality."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find undervalued places with sentiment, price, review count"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Lastly, just because I was moving to San Francsico and for my benefit, I wanted to search undere-valued places on airbnb."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read data and preprocess"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>price</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10612780</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>$170.00</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49925683</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>$170.00</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50537634</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>$170.00</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51077613</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>$170.00</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52109034</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>$170.00</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_id       id                           listing_url    price  zipcode\n",
       "0   10612780  1994427  https://www.airbnb.com/rooms/1994427  $170.00    94110\n",
       "1   49925683  1994427  https://www.airbnb.com/rooms/1994427  $170.00    94110\n",
       "2   50537634  1994427  https://www.airbnb.com/rooms/1994427  $170.00    94110\n",
       "3   51077613  1994427  https://www.airbnb.com/rooms/1994427  $170.00    94110\n",
       "4   52109034  1994427  https://www.airbnb.com/rooms/1994427  $170.00    94110"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/df_new_en.csv').drop('Unnamed: 0', 1)\n",
    "df = df[['review_id','id','listing_url','price','zipcode']]\n",
    "df.head()\n",
    "# print df.info()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>bagging_sgd_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10612780</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49925683</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50537634</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51077613</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52109034</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_id  bagging_sgd_sentiment\n",
       "0   10612780                      1\n",
       "1   49925683                      1\n",
       "2   50537634                      1\n",
       "3   51077613                      1\n",
       "4   52109034                      1"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sentiment_df = pd.read_csv('data/df_new_en_labeled_bagging_sgd_sentiment.csv').drop('Unnamed: 0', 1)\n",
    "sentiment_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({-1.0: 1649, 1.0: 130683})"
      ]
     },
     "execution_count": 310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(sentiment_df.bagging_sgd_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(132332, 132332)"
      ]
     },
     "execution_count": 311,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df),len(sentiment_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>price</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>bagging_sgd_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10612780</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>$170.00</td>\n",
       "      <td>94110</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>$170.00</td>\n",
       "      <td>94110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50537634</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>$170.00</td>\n",
       "      <td>94110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51077613</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>$170.00</td>\n",
       "      <td>94110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52109034</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>$170.00</td>\n",
       "      <td>94110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_id       id                           listing_url    price  zipcode  \\\n",
       "0   10612780  1994427  https://www.airbnb.com/rooms/1994427  $170.00    94110   \n",
       "1   49925683  1994427  https://www.airbnb.com/rooms/1994427  $170.00    94110   \n",
       "2   50537634  1994427  https://www.airbnb.com/rooms/1994427  $170.00    94110   \n",
       "3   51077613  1994427  https://www.airbnb.com/rooms/1994427  $170.00    94110   \n",
       "4   52109034  1994427  https://www.airbnb.com/rooms/1994427  $170.00    94110   \n",
       "\n",
       "   bagging_sgd_sentiment  \n",
       "0                      1  \n",
       "1                      1  \n",
       "2                      1  \n",
       "3                      1  \n",
       "4                      1  "
      ]
     },
     "execution_count": 312,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = pd.merge(df,sentiment_df,on='review_id')\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "132332"
      ]
     },
     "execution_count": 313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>price</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>bagging_sgd_sentiment</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10612780</td>\n",
       "      <td>1994427</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50537634</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>170</td>\n",
       "      <td>94110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51077613</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>170</td>\n",
       "      <td>94110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52109034</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>170</td>\n",
       "      <td>94110</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_id       id                           listing_url  price  zipcode  \\\n",
       "0   10612780  1994427  https://www.airbnb.com/rooms/1994427    170    94110   \n",
       "1   49925683  1994427  https://www.airbnb.com/rooms/1994427    170    94110   \n",
       "2   50537634  1994427  https://www.airbnb.com/rooms/1994427    170    94110   \n",
       "3   51077613  1994427  https://www.airbnb.com/rooms/1994427    170    94110   \n",
       "4   52109034  1994427  https://www.airbnb.com/rooms/1994427    170    94110   \n",
       "\n",
       "   bagging_sgd_sentiment  \n",
       "0                      1  \n",
       "1                      1  \n",
       "2                      1  \n",
       "3                      1  \n",
       "4                      1  "
      ]
     },
     "execution_count": 314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def doller_to_int(x):\n",
    "    x = str(x)\n",
    "    x = Decimal(sub(r'[^\\d.]', '', x))\n",
    "    x = float(x)\n",
    "    return x\n",
    "\n",
    "# doller_to_int(df.price[0])\n",
    "df2.price = df2['price'].apply(doller_to_int)\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Counter({-1.0: 1649, 1.0: 130683})"
      ]
     },
     "execution_count": 315,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Counter(df2.bagging_sgd_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kimitakanakazawa/anaconda/lib/python2.7/site-packages/ipykernel/__main__.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",
      "  if __name__ == '__main__':\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Counter({0.0: 1649, 1.0: 130683})"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['bagging_sgd_sentiment'][df2['bagging_sgd_sentiment'] == -1] = 0\n",
    "Counter(df2.bagging_sgd_sentiment)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>review_id</th>\n",
       "      <th>id</th>\n",
       "      <th>listing_url</th>\n",
       "      <th>price</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>bagging_sgd_sentiment</th>\n",
       "      <th>review_count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10612780</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>170</td>\n",
       "      <td>94110</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>49925683</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>170</td>\n",
       "      <td>94110</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>50537634</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>170</td>\n",
       "      <td>94110</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>51077613</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>170</td>\n",
       "      <td>94110</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>52109034</td>\n",
       "      <td>1994427</td>\n",
       "      <td>https://www.airbnb.com/rooms/1994427</td>\n",
       "      <td>170</td>\n",
       "      <td>94110</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   review_id       id                           listing_url  price  zipcode  \\\n",
       "0   10612780  1994427  https://www.airbnb.com/rooms/1994427    170    94110   \n",
       "1   49925683  1994427  https://www.airbnb.com/rooms/1994427    170    94110   \n",
       "2   50537634  1994427  https://www.airbnb.com/rooms/1994427    170    94110   \n",
       "3   51077613  1994427  https://www.airbnb.com/rooms/1994427    170    94110   \n",
       "4   52109034  1994427  https://www.airbnb.com/rooms/1994427    170    94110   \n",
       "\n",
       "   bagging_sgd_sentiment  review_count  \n",
       "0                      1             1  \n",
       "1                      1             1  \n",
       "2                      1             1  \n",
       "3                      1             1  \n",
       "4                      1             1  "
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['review_count'] = 1\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group by id to see count of total positive sentiment and total review count."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5667\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bagging_sgd_sentiment</th>\n",
       "      <th>review_count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5193</th>\n",
       "      <td>180</td>\n",
       "      <td>184</td>\n",
       "    </tr>\n",
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       "      <th>5841</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5858</th>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      bagging_sgd_sentiment  review_count\n",
       "id                                       \n",
       "958                      34            34\n",
       "2822                     41            41\n",
       "5193                    180           184\n",
       "5841                     35            35\n",
       "5858                    104           104"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_by_listing = df2[['id','bagging_sgd_sentiment','review_count']].groupby('id').aggregate(sum)\n",
    "print len(review_by_listing)\n",
    "review_by_listing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Give sentiment rate (positive sentiment review count /total review count)\n",
    "- How likely the place has positive sentiment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bagging_sgd_sentiment</th>\n",
       "      <th>review_count</th>\n",
       "      <th>sentiment_rate</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5193</th>\n",
       "      <td>180</td>\n",
       "      <td>184</td>\n",
       "      <td>0.978261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5841</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5858</th>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      bagging_sgd_sentiment  review_count  sentiment_rate\n",
       "id                                                       \n",
       "958                      34            34        1.000000\n",
       "2822                     41            41        1.000000\n",
       "5193                    180           184        0.978261\n",
       "5841                     35            35        1.000000\n",
       "5858                    104           104        1.000000"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_by_listing['sentiment_rate'] = review_by_listing['bagging_sgd_sentiment']/(review_by_listing['review_count'])\n",
    "review_by_listing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Sentiment rate distribution looks like this (< 0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x123c0e2d0>"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEACAYAAACpoOGTAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAE7dJREFUeJzt3X2wZHV95/H3BwZWJwrjBDMQHhbUIOIqKwghPlRaEw1a\nBliz5UMSl0TyUGtczZZ5GMzuzmzVrqumEmMqZSoVgztxS1IohoItsmEwTGR3CfgwEHTAETesjoYx\noiBREgf57h99Zqb38mPm3Hun+3Qz71dVF+ecPn37c3u4/elzfqfPSVUhSdJSRwwdQJI0nywISVKT\nBSFJarIgJElNFoQkqcmCkCQ1Ta0gklyeZHeSOyaW/WaSO5PcnuSjSY6duO+yJJ9PcleSl08rlySp\nn2luQXwAuGDJsuuBZ1fVWcBO4DKAJGcCrwXO7B7zviRu3UjSgKb2JlxVNwHfWLJsa1U90s3eApzU\nTV8EXFFVe6rqHuBu4LxpZZMkHdyQn9LfCFzXTX8/sGvivl3AiTNPJEnaZ5CCSPIbwHeq6kMHWM1z\ngEjSgNbM+gmT/AzwSuBHJhZ/GTh5Yv6kbtnSx1oakrQCVZXlPmamWxBJLgB+Fbioqv5h4q5rgNcl\nOTrJacAPALe2fkZVzd1t06ZNg2cwk5kOx1xm6ndbqaltQSS5Avhh4LgkXwI2MT5q6WhgaxKAm6vq\nTVW1I8mVwA7gYeBNtZrfSpK0alMriKp6fWPx5QdY/x3AO6aVR5K0PH7X4BAYjUZDR3gUM/Vjpv7m\nMZeZpiuLtCcniXueJGmZklDzPkgtSVocFoQkqcmCkCQ1WRCSpCYLQpLUZEFIkposCElSkwUhSWqy\nICRJTRaEJKlp5teDkKTDRXfW6oVlQUjSVM3D+eNWVlTuYpIkNVkQkqQmC0KS1GRBSJKaLAhJUpMF\nIUlqsiAkSU0WhCSpyYKQJDVZEJKkJgtCktRkQUiSmiwISVKTBSFJappaQSS5PMnuJHdMLFufZGuS\nnUmuT7Ju4r7Lknw+yV1JXj6tXJKkfqa5BfEB4IIlyzYCW6vqdOBj3TxJzgReC5zZPeZ9Sdy6kaQB\nTe1NuKpuAr6xZPGFwJZuegtwcTd9EXBFVe2pqnuAu4HzppVNknRws/6UvqGqdnfTu4EN3fT3A7sm\n1tsFnDjLYJKk/99gu3Gqqjjwtfjm4Tp9knTYmvU1qXcnOb6q7k1yAvDVbvmXgZMn1jupW/Yomzdv\n3jc9Go0YjUbTSSpJC2tbd1udjD/IT0eSU4Frq+o53fy7gfuq6l1JNgLrqmpjN0j9IcbjDicCNwDP\nqCXhkixdJElzKwnzsTMkVFWW+6ipbUEkuQL4YeC4JF8C/gPwTuDKJJcC9wCvAaiqHUmuBHYADwNv\nsgkkaVhT3YI41NyCkLRIFn0Lwu8aSJKaLAhJUpMFIUlqsiAkSU0WhCSpyYKQJDVZEJKkJgtCktRk\nQUiSmiwISVKTBSFJarIgJElNFoQkqcmCkCQ1WRCSpCYLQpLUZEFIkposCElSkwUhSWqyICRJTRaE\nJKnJgpAkNVkQkqQmC0KS1GRBSJKaLAhJUpMFIUlqsiAkSU0WhCSpaZCCSHJZks8muSPJh5L8kyTr\nk2xNsjPJ9UnWDZFNkjQ284JIcirw88DZVfUc4EjgdcBGYGtVnQ58rJuXJA1kiC2IbwJ7gLVJ1gBr\nga8AFwJbunW2ABcPkE2S1Jl5QVTV14HfAr7IuBjur6qtwIaq2t2tthvYMOtskqT91sz6CZM8Hfhl\n4FTgAeDDSX56cp2qqiTVevzmzZv3TY9GI0aj0bSiStKC2tbdVidVzffhqUnyWuBlVfVz3fwbgPOB\nlwIvqap7k5wA3FhVZyx5bM06ryStVBJgHt6zQlVluY8aYgziLuD8JE/M+NX7UWAHcC1wSbfOJcDV\nA2STJHVmvgUBkOTXGJfAI8CngZ8DngxcCZwC3AO8pqruX/I4tyAkLYxF34IYpCBWyoKQtEgWvSD8\nJrUkqcmCkCQ1WRCSpCYLQpLUZEFIkposCElSkwUhSWqyICRJTQctiCTPmUUQSdJ86bMF8ftJPpHk\nTUmOnXoiSdJcOGhBVNWLgJ9ifI6kTye5IsnLp55MkjSo3udi6q7+djHwu4yv43AE8Paqump68R6V\nwXMxSVoYj/tzMSU5K8l7gDsZX7PhVVX1LOAlwHuWnVOStBAOugWR5C+BPwI+UlXfXnLfv6qqP55i\nvqVZ3IKQtDAWfQuiT0E8CXioqr7bzR8JPKGqvrWinKtgQUhaJIteEH2OYroBeOLE/Fpg63KfSJK0\nWPoUxBOq6u/3zlTVg4xLQpL0ONanIL6V5Jy9M0meDzw0vUiSpHmwpsc6vwxcmeRvu/kTgNdOL5Ik\naR70+h5EkqOBZzIebflcVe2ZdrDHyOEgtaSFseiD1H0L4gXAaYy3OApgloe3TuSwICQtjEUviIPu\nYkry34CnAbcB3524a+YFIUmanT5jEOcAZ/rRXZIOL32OYvoM44FpSdJhpM8WxFOBHUluBf6xW1ZV\ndeH0YkmShtanIDZ3/y0gE9OSpMexvkcxnQo8o6puSLIWWFNV35xytlYOh0IkLYxFP4qpz+m+fwH4\nMPAH3aKTgD9d7hNJkhZLn0HqXwJeBHwToKp2At+3midNsi7JR5LcmWRHkh9Msj7J1iQ7k1yfZN1q\nnkOStDp9CuIfq2rv4PTeK8utdpvpvcB13YWHngvcBWwEtlbV6cDHunlJ0kD6FMRfJvkNYG2SlzHe\n3XTtSp8wybHAi6vqcoCqeriqHgAuBLZ0q21hfHlTSdJA+lww6EjgUuDl3aI/B96/0tHiJP+c8XjG\nDuAs4FOMTwi4q6qe0q0T4Ot75yce6yC1pIWx6IPUvY5iOpS604XfDLygqj6R5HeAB4E3TxZCkq9X\n1folj61Nmzbtmx+NRoxGo9kEl6RlGq4gtnW3vf7j1C45+jeNxVVVT1vuk3U/73jg5qo6rZt/EXAZ\n4/M9vaSq7k1yAnBjVZ2x5LFuQUhaGIu+BdHni3LnTkw/AfiXwPcu94n26grgS0lO746I+lHgs93t\nEuBd3X+vXulzSJJWb0W7mJJ8uqrOXvGTJmcB7weOBr4A/CxwJHAlcApwD/Caqrp/yePcgpC0MBZ9\nC6LPLqZz2P8bHgE8H/jXVXXWsjOukgUhaZEsekH02cX0W+z/DR+m+3S/3CeSJC2WmR/FtBpuQUha\nJI/7LYgkb+PRv+G+s7pW1W8v90klSfOv7xXlzgWuYVwMrwI+AeycYi5J0sD6DFLfBLyyqh7s5p/M\n+DxKL55BvqVZ3MUkaWEs+i6mPudi+j5gz8T8HlZ5NldJ0vzrs4vpj4Fbk3yU8S6mi9l/Uj1J0uNU\n3yvKncP4mhAAH6+q7VNN9dg53MUkaWEcDruYANYCD1bVe4FdSU5b7hNJkhZLn0HqzYyPZHpmVZ2e\n5ETgyqp64QzyLc3iFoSkhXE4bEH8C+Ai4FsAVfVl4MnLfSJJ0mLpe8nRR/bOJPmeKeaRJM2JPgXx\n4SR/AKxL8guMrxf9/unGkiQN7YBjEN2lP08GzmDikqNVtXUG2Vp5HIOQtDAWfQyiT0HcUVX/bDXR\nDhULQtIiWfSCOOAupu7d+FNJzltxLknSQupzmOvngGcA/5fuSCbG3fHcKWdrZXELQlIv40/v82Ae\n3rMO8em+k5xSVV8Efozxbzgvr7Yk9TT0m/Niv20+5hZEku1V9bxu+qqq+omZJmtncgtCUi/zsf9/\nHjLAtE+18bTl/mBJ0mLrWxCSpMPMgXYxfRf4djf7ROChiburqo6ZcrZWJncxSerFXUyTDvEgdVUd\nubpAkqRF5i4mSVKTBSFJarIgJElNFoQkqcmCkCQ1DVYQSY5Msj3Jtd38+iRbk+xMcn2SdUNlkyQN\nuwXxVmAH+w8S3ghsrarTGV+UaONQwSRJAxVEkpOAVzK+Mt3eL29cCGzpprcAFw8QTZLUGWoL4j3A\nrwKPTCzbUFW7u+ndwIaZp5Ik7fOY36SeliSvAr5aVduTjFrrVFUlaX4/ffPmzfumR6MRo1HzR0jS\nYWxbd1udg14w6FBL8g7gDcDDwBOAY4CPAucCo6q6N8kJwI1VdcaSx3ouJkm9eC6mSdM93fchU1Vv\nr6qTq+o04HXAX1TVG4BrgEu61S4Brp51NknSfvPwPYi99fpO4GVJdgIv7eYlSQOZ+S6m1XAXk6S+\n3MU0aUF2MUmSFoMFIUlqsiAkSU0WhCSpyYKQJDVZEJKkJgtCktRkQUiSmiwISVKTBSFJarIgJElN\nFoQkqcmCkCQ1WRCSpCYLQpLUZEFIkposCElSkwUhSWqyICRJTRaEJKnJgpAkNVkQkqQmC0KS1GRB\nSJKaLAhJUpMFIUlqsiAkSU0WhCSpaeYFkeTkJDcm+WySzyR5S7d8fZKtSXYmuT7JullnkyTtl6qa\n7RMmxwPHV9VtSZ4EfAq4GPhZ4GtV9e4kvw48pao2LnlszTqvpMWUBBj6/WIeMgCEqspyHzXzLYiq\nureqbuum/x64EzgRuBDY0q22hXFpSJIGMugYRJJTgecBtwAbqmp3d9duYMNAsSRJDFgQ3e6lq4C3\nVtWDk/d1+5HmYbtMkg5ba4Z40iRHMS6HD1bV1d3i3UmOr6p7k5wAfLX12M2bN++bHo1GjEajKaeV\npEWzrbutzhCD1GE8xnBfVf3bieXv7pa9K8lGYJ2D1JJWykHqSSsbpB6iIF4EfBz4a/a/cpcBtwJX\nAqcA9wCvqar7lzzWgpDUiwUxaUEKYjUsCM2z8RvS8PwbGbMgJq2sIAYZg5Aev4Z+M5iPktLjg6fa\nkCQ1WRCSpCYLQpLUZEFIkpocpJZ0SM3L0VxaPQtC0hQMfTQXeETX6rmLSZLUZEFIkposCElSkwUh\nSWqyICRJTRaEJKnJgpAkNVkQkqQmC0KS1GRBSJKaFu5UG5de+uZBn/+II+DSS3+a888/f9AckjRt\nC1cQl19+xqDPf/TRH+SFL3y+BSHpcW/hCgKG3YI46qhPDvr8kjQrjkFIkposCElSkwUhSWqyICRJ\nTRaEJKnJgpAkNVkQkqQmC0KS1DRXBZHkgiR3Jfl8kl8fOo8kHc7mpiCSHAn8HnABcCbw+iTPGjZV\nP9u2bRs6wqPMKlOSubit1Dz+28G2oQM0+Vr1tW3oAIfM3BQEcB5wd1XdU1V7gD8BLho4Uy/z+Icz\n20zV87ZpGesu57Zy8/hvN69vML5WfW0bOsAhM08FcSLwpYn5Xd0ySdIA5ulkfb0+Ch5zzI9PO8cB\nfec724HRoBkkaRZStbpN9EMlyfnA5qq6oJu/DHikqt41sc58hJWkBVNVyx6sm6eCWAN8DvgR4CvA\nrcDrq+rOQYNJ0mFqbnYxVdXDSd4M/DlwJPBHloMkDWdutiAkSfNlno5i2qfPF+aS/G53/+1Jnjd0\npiRnJLk5yT8kedu08ywj1091r9FfJ/lfSZ47B5ku6jJtT/KpJC8dOtPEeucmeTjJq4fOlGSU5IHu\nddqe5N8NnWki1/Ykn0mybdqZ+uRK8isTr9Md3b/huoEzHZfkfyS5rXutfmaaeXpmekqSP+3+/m5J\n8uwD/sCqmqsb491LdwOnAkcBtwHPWrLOK4HruukfBP5qDjI9FXg+8J+At83Ra/VDwLHd9AVz8lp9\nz8T0cxh//2XQTBPr/QXw34GfGDoT48PlrpnF/0vLyLQO+CxwUjd/3DzkWrL+q4Abhs4EbAb+y97X\nCbgPWDNwpt8E/n03/cyDvU7zuAXR5wtzFwJbAKrqFmBdkg1DZqqqv6uqTwJ7pphjJblurqoHutlb\ngJPmINO3JmafBHxt6EydfwN8BPi7KedZTqaVf018Opl+EriqqnYBVNW0/+365lqa8Yo5yPS3wDHd\n9DHAfVX18MCZngXcCFBVnwNOTfLUx/qB81gQfb4w11pnmm988/olvuXmuhS4bqqJemZKcnGSO4E/\nA94ydKYkJzL+Y/r9btG0B+f6vE4FvKDbHXBdkjPnINMPAOuT3Jjkk0neMOVMfXMBkGQt8GPAVXOQ\n6Q+BZyf5CnA78NY5yHQ78GqAJOcB/5QDvHfOzVFME/r+YS79ZDXNP+h5HcnvnSvJS4A3Ai+cXhyg\nZ6aquhq4OsmLgQ8y3twdMtPvABurqjI+udO0P7n3yfRp4OSq+naSVwBXA6cPnOko4GzGh6OvBW5O\n8ldV9fmBc+3148D/rKr7pxWm0yfT24HbqmqU5OnA1iRnVdWDA2Z6J/DeJNuBO4DtwHcfa+V5LIgv\nAydPzJ/MuAkPtM5J3bIhMw2hV65uYPoPgQuq6hvzkGmvqropyZok31tV9w2Y6RzgT7oT/x0HvCLJ\nnqq6ZqhMk28kVfVnSd6XZH1VfX2oTIw/oX6tqh4CHkryceAsYJoFsZz/p17H9HcvQb9MLwD+M0BV\nfSHJ3zD+IPTJoTJ1/0+9ce98l+n/POZPnOZAzgoHWtYAX2A80HI0Bx+kPp/pD7weNNPEupuZ3SB1\nn9fqFMYDV+fPUaans/8Q67OBLwydacn6HwBePXQmYMPE63QecM8cZDoDuIHxgOhaxp9Czxw6V7fe\nsYwHgp84zTzLeK1+G9g08W+5C1g/cKZjgaO76Z8H/usBf+a0X8gV/qKvYPyt6ruBy7plvwj84sQ6\nv9fdfztw9tCZgOMZf7p6APgG8EXgSXOQ6/3dH8327nbrHGT6NeAzXZ6bgHOHzrRk3akXRM/X6Ze6\n1+k24H8zg5Lv+bf3K4yPZLoDeMu0My0j1yXAh2aRp+e/33HAtd171B3AT85Bph/q7r+L8QEZxx7o\n5/lFOUlS0zwexSRJmgMWhCSpyYKQJDVZEJKkJgtCktRkQUiSmiwISVKTBSFJavp/H8jhjS3Bh2cA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x121915790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "review_by_listing[review_by_listing.sentiment_rate < 0.9]['sentiment_rate'].plot(kind='hist')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limit only sentiment_rate = 1 (places with only good reviews)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "review_by_listing_all_positive = review_by_listing[review_by_listing.sentiment_rate == 1]\n",
    "# Counter(review_by_listing_all_positive.sentiment_rate)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Limit only review_count > 10 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1844"
      ]
     },
     "execution_count": 331,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review_by_listing_all_positive = review_by_listing_all_positive[review_by_listing_all_positive.review_count >10]\n",
    "# Counter(review_by_listing_all_positive.sentiment_rate)\n",
    "len(review_by_listing_all_positive)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are 1844 potentially undervalued places."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Id (airbnb place) with average price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>price</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5193</th>\n",
       "      <td>160</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5841</th>\n",
       "      <td>181</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5858</th>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      price\n",
       "id         \n",
       "958     170\n",
       "2822    138\n",
       "5193    160\n",
       "5841    181\n",
       "5858    250"
      ]
     },
     "execution_count": 332,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_by_listing = df2[['id','price']].groupby('id').aggregate(np.mean)\n",
    "price_by_listing.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Id with zipcode"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 333,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5667\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>id</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>958</th>\n",
       "      <td>94117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2822</th>\n",
       "      <td>94124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5193</th>\n",
       "      <td>94109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5841</th>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5858</th>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      zipcode\n",
       "id           \n",
       "958     94117\n",
       "2822    94124\n",
       "5193    94109\n",
       "5841    94110\n",
       "5858    94110"
      ]
     },
     "execution_count": 333,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zipcode_df = df2[['id','zipcode']].groupby('id').first()\n",
    "print len(zipcode_df)\n",
    "zipcode_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge the grouped-by dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1844\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>bagging_sgd_sentiment</th>\n",
       "      <th>review_count</th>\n",
       "      <th>sentiment_rate</th>\n",
       "      <th>price</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>958</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>170</td>\n",
       "      <td>94117</td>\n",
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       "      <th>1</th>\n",
       "      <td>2822</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>1</td>\n",
       "      <td>138</td>\n",
       "      <td>94124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5841</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>1</td>\n",
       "      <td>181</td>\n",
       "      <td>94110</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5858</td>\n",
       "      <td>104</td>\n",
       "      <td>104</td>\n",
       "      <td>1</td>\n",
       "      <td>250</td>\n",
       "      <td>94110</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8339</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>395</td>\n",
       "      <td>94117</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     id  bagging_sgd_sentiment  review_count  sentiment_rate  price  zipcode\n",
       "0   958                     34            34               1    170    94117\n",
       "1  2822                     41            41               1    138    94124\n",
       "2  5841                     35            35               1    181    94110\n",
       "3  5858                    104           104               1    250    94110\n",
       "4  8339                     18            18               1    395    94117"
      ]
     },
     "execution_count": 334,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "undervalued_listing_df = pd.merge(review_by_listing_all_positive.reset_index(), price_by_listing.reset_index(), how='inner', on=['id'])\n",
    "undervalued_listing_df = pd.merge(undervalued_listing_df, zipcode_df.reset_index(), how='inner',on=['id'])\n",
    "\n",
    "print len(undervalued_listing_df)\n",
    "undervalued_listing_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Top undervalued rooms (Ranked by price and nubmer of reviews)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Popular undervalued places. Great for short stay"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kimitakanakazawa/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>bagging_sgd_sentiment</th>\n",
       "      <th>review_count</th>\n",
       "      <th>sentiment_rate</th>\n",
       "      <th>price</th>\n",
       "      <th>zipcode</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>25463</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>94117</td>\n",
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       "    <tr>\n",
       "      <th>419</th>\n",
       "      <td>918341</td>\n",
       "      <td>196</td>\n",
       "      <td>196</td>\n",
       "      <td>1</td>\n",
       "      <td>190</td>\n",
       "      <td>94107</td>\n",
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       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>23630</td>\n",
       "      <td>192</td>\n",
       "      <td>192</td>\n",
       "      <td>1</td>\n",
       "      <td>165</td>\n",
       "      <td>94114</td>\n",
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       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>87640</td>\n",
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       "      <td>125</td>\n",
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       "      <th>218</th>\n",
       "      <td>465723</td>\n",
       "      <td>188</td>\n",
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       "      <td>1</td>\n",
       "      <td>150</td>\n",
       "      <td>94116</td>\n",
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       "    <tr>\n",
       "      <th>266</th>\n",
       "      <td>567239</td>\n",
       "      <td>187</td>\n",
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       "      <td>1</td>\n",
       "      <td>89</td>\n",
       "      <td>94131</td>\n",
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       "      <th>847</th>\n",
       "      <td>1995487</td>\n",
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       "      <th>455</th>\n",
       "      <td>992676</td>\n",
       "      <td>183</td>\n",
       "      <td>183</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>94131</td>\n",
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       "    <tr>\n",
       "      <th>167</th>\n",
       "      <td>311259</td>\n",
       "      <td>181</td>\n",
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       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>94134</td>\n",
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       "    <tr>\n",
       "      <th>328</th>\n",
       "      <td>727866</td>\n",
       "      <td>178</td>\n",
       "      <td>178</td>\n",
       "      <td>1</td>\n",
       "      <td>119</td>\n",
       "      <td>94116</td>\n",
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       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>131015</td>\n",
       "      <td>176</td>\n",
       "      <td>176</td>\n",
       "      <td>1</td>\n",
       "      <td>110</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>1117756</td>\n",
       "      <td>172</td>\n",
       "      <td>172</td>\n",
       "      <td>1</td>\n",
       "      <td>114</td>\n",
       "      <td>94110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>322</th>\n",
       "      <td>713877</td>\n",
       "      <td>164</td>\n",
       "      <td>164</td>\n",
       "      <td>1</td>\n",
       "      <td>145</td>\n",
       "      <td>94117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>741178</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>1</td>\n",
       "      <td>120</td>\n",
       "      <td>94122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>18904</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>115</td>\n",
       "      <td>94117</td>\n",
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       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>173464</td>\n",
       "      <td>162</td>\n",
       "      <td>162</td>\n",
       "      <td>1</td>\n",
       "      <td>159</td>\n",
       "      <td>94118</td>\n",
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       "    <tr>\n",
       "      <th>192</th>\n",
       "      <td>406930</td>\n",
       "      <td>161</td>\n",
       "      <td>161</td>\n",
       "      <td>1</td>\n",
       "      <td>100</td>\n",
       "      <td>94110</td>\n",
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       "    <tr>\n",
       "      <th>180</th>\n",
       "      <td>351881</td>\n",
       "      <td>157</td>\n",
       "      <td>157</td>\n",
       "      <td>1</td>\n",
       "      <td>129</td>\n",
       "      <td>94114</td>\n",
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       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>2587269</td>\n",
       "      <td>156</td>\n",
       "      <td>156</td>\n",
       "      <td>1</td>\n",
       "      <td>350</td>\n",
       "      <td>94114</td>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26843</td>\n",
       "      <td>149</td>\n",
       "      <td>149</td>\n",
       "      <td>1</td>\n",
       "      <td>111</td>\n",
       "      <td>94131</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          id  bagging_sgd_sentiment  review_count  sentiment_rate  price  \\\n",
       "22     25463                    200           200               1    115   \n",
       "419   918341                    196           196               1    190   \n",
       "17     23630                    192           192               1    165   \n",
       "68     87640                    190           190               1    125   \n",
       "218   465723                    188           188               1    150   \n",
       "266   567239                    187           187               1     89   \n",
       "847  1995487                    186           186               1    199   \n",
       "455   992676                    183           183               1    120   \n",
       "167   311259                    181           181               1    120   \n",
       "328   727866                    178           178               1    119   \n",
       "83    131015                    176           176               1    110   \n",
       "516  1117756                    172           172               1    114   \n",
       "322   713877                    164           164               1    145   \n",
       "341   741178                    163           163               1    120   \n",
       "13     18904                    162           162               1    115   \n",
       "103   173464                    162           162               1    159   \n",
       "192   406930                    161           161               1    100   \n",
       "180   351881                    157           157               1    129   \n",
       "997  2587269                    156           156               1    350   \n",
       "25     26843                    149           149               1    111   \n",
       "\n",
       "     zipcode  \n",
       "22     94117  \n",
       "419    94107  \n",
       "17     94114  \n",
       "68     94131  \n",
       "218    94116  \n",
       "266    94131  \n",
       "847    94110  \n",
       "455    94131  \n",
       "167    94134  \n",
       "328    94116  \n",
       "83     94110  \n",
       "516    94110  \n",
       "322    94117  \n",
       "341    94122  \n",
       "13     94117  \n",
       "103    94118  \n",
       "192    94110  \n",
       "180    94114  \n",
       "997    94114  \n",
       "25     94131  "
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "undervalued_listing_df.sort(columns=['review_count', 'price'], ascending=[False, True])[:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hidden undervalued places (funkier). Great for students and longer stayer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 337,
   "metadata": {
    "collapsed": false,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kimitakanakazawa/anaconda/lib/python2.7/site-packages/ipykernel/__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th>id</th>\n",
       "      <th>bagging_sgd_sentiment</th>\n",
       "      <th>review_count</th>\n",
       "      <th>sentiment_rate</th>\n",
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       "      <th>zipcode</th>\n",
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       "  </thead>\n",
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       "      <th>96</th>\n",
       "      <td>152517</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>94112</td>\n",
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       "      <th>651</th>\n",
       "      <td>1446043</td>\n",
       "      <td>18</td>\n",
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       "      <td>27</td>\n",
       "      <td>94122</td>\n",
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       "      <th>564</th>\n",
       "      <td>1218290</td>\n",
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       "      <td>7262134</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
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       "      <td>32</td>\n",
       "      <td>94122</td>\n",
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       "    <tr>\n",
       "      <th>428</th>\n",
       "      <td>939833</td>\n",
       "      <td>26</td>\n",
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       "      <th>1794</th>\n",
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       "      <td>21</td>\n",
       "      <td>21</td>\n",
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       "      <th>1795</th>\n",
       "      <td>7462926</td>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>94121</td>\n",
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       "    <tr>\n",
       "      <th>1796</th>\n",
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       "      <td>18</td>\n",
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       "      <td>40</td>\n",
       "      <td>94121</td>\n",
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       "    <tr>\n",
       "      <th>1797</th>\n",
       "      <td>7462935</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>94121</td>\n",
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       "      <th>1765</th>\n",
       "      <td>7204241</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>94121</td>\n",
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       "    <tr>\n",
       "      <th>682</th>\n",
       "      <td>1530807</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>94107</td>\n",
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       "    <tr>\n",
       "      <th>1798</th>\n",
       "      <td>7462937</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>94121</td>\n",
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       "    <tr>\n",
       "      <th>1166</th>\n",
       "      <td>3491545</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>1</td>\n",
       "      <td>40</td>\n",
       "      <td>94117</td>\n",
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       "    <tr>\n",
       "      <th>1793</th>\n",
       "      <td>7462873</td>\n",
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       "      <td>11</td>\n",
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       "      <td>40</td>\n",
       "      <td>94121</td>\n",
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       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>74758</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>1</td>\n",
       "      <td>41</td>\n",
       "      <td>94112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>69549</td>\n",
       "      <td>29</td>\n",
       "      <td>29</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>94112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1559</th>\n",
       "      <td>5561787</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>43</td>\n",
       "      <td>94127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>162</th>\n",
       "      <td>292723</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>45</td>\n",
       "      <td>94112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>438</th>\n",
       "      <td>958573</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>46</td>\n",
       "      <td>94122</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           id  bagging_sgd_sentiment  review_count  sentiment_rate  price  \\\n",
       "96     152517                     16            16               1     22   \n",
       "651   1446043                     18            18               1     27   \n",
       "564   1218290                     16            16               1     27   \n",
       "563   1218239                     18            18               1     30   \n",
       "1769  7262134                     16            16               1     32   \n",
       "428    939833                     26            26               1     40   \n",
       "1794  7462925                     21            21               1     40   \n",
       "1795  7462926                     19            19               1     40   \n",
       "1796  7462930                     18            18               1     40   \n",
       "1797  7462935                     18            18               1     40   \n",
       "1765  7204241                     13            13               1     40   \n",
       "682   1530807                     12            12               1     40   \n",
       "1798  7462937                     12            12               1     40   \n",
       "1166  3491545                     11            11               1     40   \n",
       "1793  7462873                     11            11               1     40   \n",
       "64      74758                     26            26               1     41   \n",
       "60      69549                     29            29               1     43   \n",
       "1559  5561787                     13            13               1     43   \n",
       "162    292723                     14            14               1     45   \n",
       "438    958573                     13            13               1     46   \n",
       "\n",
       "      zipcode  \n",
       "96      94112  \n",
       "651     94122  \n",
       "564     94122  \n",
       "563     94122  \n",
       "1769    94122  \n",
       "428     94117  \n",
       "1794    94121  \n",
       "1795    94121  \n",
       "1796    94121  \n",
       "1797    94121  \n",
       "1765    94121  \n",
       "682     94107  \n",
       "1798    94121  \n",
       "1166    94117  \n",
       "1793    94121  \n",
       "64      94112  \n",
       "60      94112  \n",
       "1559    94127  \n",
       "162     94112  \n",
       "438     94122  "
      ]
     },
     "execution_count": 337,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "undervalued_listing_df.sort(columns=['price','review_count'], ascending=[True,False])[:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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