Airbnb_PricexTime.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import re\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from math import * \n",
    "import scipy as sp\n",
    "from scipy.stats import mode\n",
    "from sklearn import linear_model\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn import discriminant_analysis\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn import preprocessing\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "IndentationError",
     "evalue": "expected an indented block (<ipython-input-103-0e28329195cb>, line 5)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  File \u001b[1;32m\"<ipython-input-103-0e28329195cb>\"\u001b[1;36m, line \u001b[1;32m5\u001b[0m\n\u001b[1;33m    \u001b[0m\n\u001b[1;37m    ^\u001b[0m\n\u001b[1;31mIndentationError\u001b[0m\u001b[1;31m:\u001b[0m expected an indented block\n"
     ]
    }
   ],
   "source": [
    "#for i in range(len(prices)):\n",
    "    #prices[i] = prices[i].replace(',', '')\n",
    "    #prices[i] = prices[i].strip('$')\n",
    "    #prices[i] = float(prices[i])\n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calendar Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "time_data = pd.read_csv('calendar.csv', delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Regex the price and generate log(price)\n",
    "time_data['price'] = time_data['price'].replace('[\\$,)]','', regex=True).replace('[(]','-', regex=True).astype(float)\n",
    "time_data['log_price'] = np.log(time_data['price'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": 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>listing_id</th>\n",
       "      <th>date</th>\n",
       "      <th>available</th>\n",
       "      <th>price</th>\n",
       "      <th>questionable</th>\n",
       "      <th>log_price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3604481</td>\n",
       "      <td>1/1/2015</td>\n",
       "      <td>t</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.39693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3604481</td>\n",
       "      <td>1/2/2015</td>\n",
       "      <td>t</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.39693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3604481</td>\n",
       "      <td>1/3/2015</td>\n",
       "      <td>t</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.39693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3604481</td>\n",
       "      <td>1/4/2015</td>\n",
       "      <td>t</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.39693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3604481</td>\n",
       "      <td>1/5/2015</td>\n",
       "      <td>t</td>\n",
       "      <td>600.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.39693</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   listing_id      date available  price  questionable  log_price\n",
       "0     3604481  1/1/2015         t  600.0           NaN    6.39693\n",
       "1     3604481  1/2/2015         t  600.0           NaN    6.39693\n",
       "2     3604481  1/3/2015         t  600.0           NaN    6.39693\n",
       "3     3604481  1/4/2015         t  600.0           NaN    6.39693\n",
       "4     3604481  1/5/2015         t  600.0           NaN    6.39693"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5242875"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time_data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Only consider data for which there is data\n",
    "nan_adjusted = time_data[np.isfinite(time_data['log_price'])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4641912"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Still left with a reasonable amount of observations\n",
    "nan_adjusted.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Group by listing_id, then find each listing id's average price\n",
    "grouped = nan_adjusted.groupby(['listing_id'], as_index = False)\n",
    "\n",
    "means = grouped['price'].mean().values\n",
    "log_means = grouped['log_price'].mean().values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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phnxc3vaC+BBNu+hn08znMjZj308Az2q/s71+M8nRaSZuvYB92xXzuZ7mD+03JPnRNJNi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\na7z/AF6VWkTHlFbOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb9cba20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine Distribution of average price by listing_id\n",
    "\n",
    "fig, ((ax1, ax2)) = plt.subplots(1, 2, figsize=(15, 5))\n",
    "\n",
    "ax1.hist(means, color='red', bins=40, alpha=0.5)\n",
    "ax1.set_title('distribution of mean price by listing_id')\n",
    "\n",
    "ax2.hist(log_means, color='blue', bins=40, alpha=0.5)\n",
    "ax2.set_title('distribution of the mean of log(price) by listing_id')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Goals: \n",
    "# 1)Get the average price for each specific listing [done]\n",
    "# 2)Compute the distance between each day's price and the average price [done]\n",
    "# 3)Visualize spikes [done]\n",
    "# 4)For each day, check the change in price [done]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Find high demand dates by checking for positive distance between a listings average price and it's current price.\n",
    "# For these purposes, high demand is categorized as a date which experienced, on average, properties being \n",
    "# priced above their average list price"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of unique properties: 2815\n"
     ]
    }
   ],
   "source": [
    "print \"number of unique properties:\", len(nan_adjusted['listing_id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Excaliburxiv\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:1: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  if __name__ == '__main__':\n",
      "C:\\Users\\Excaliburxiv\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\pandas\\core\\frame.py:3218: 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",
      "  na_position=na_position)\n",
      "C:\\Users\\Excaliburxiv\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:2: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    }
   ],
   "source": [
    "# Let's sort to make things easier on pattern matching algorithm\n",
    "\n",
    "nan_adjusted.sort(['listing_id'], inplace=True)\n",
    "time_data.sort(['listing_id'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0          2684\n",
       "1          3330\n",
       "2          4611\n",
       "3          5099\n",
       "4          5110\n",
       "5          5172\n",
       "6          5203\n",
       "7          5270\n",
       "8          5441\n",
       "9          5489\n",
       "10         5818\n",
       "11         5862\n",
       "12         5945\n",
       "13         6171\n",
       "14         6628\n",
       "15         7036\n",
       "16         7097\n",
       "17         7249\n",
       "18         7626\n",
       "19         8270\n",
       "20         9357\n",
       "21         9529\n",
       "22         9680\n",
       "23         9704\n",
       "24         9782\n",
       "25         9783\n",
       "26         9896\n",
       "27        10423\n",
       "28        10950\n",
       "29        10962\n",
       "         ...   \n",
       "2785    4857477\n",
       "2786    4857675\n",
       "2787    4860430\n",
       "2788    4860442\n",
       "2789    4860598\n",
       "2790    4860988\n",
       "2791    4861791\n",
       "2792    4861859\n",
       "2793    4861911\n",
       "2794    4862061\n",
       "2795    4862166\n",
       "2796    4862175\n",
       "2797    4862501\n",
       "2798    4862702\n",
       "2799    4862870\n",
       "2800    4862933\n",
       "2801    4863048\n",
       "2802    4863090\n",
       "2803    4863255\n",
       "2804    4868986\n",
       "2805    4872405\n",
       "2806    4884213\n",
       "2807    4884427\n",
       "2808    4905337\n",
       "2809    4908335\n",
       "2810    4909427\n",
       "2811    4915452\n",
       "2812    4916824\n",
       "2813    4922047\n",
       "2814    4922220\n",
       "Name: listing_id, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_df = grouped.mean()\n",
    "mean_df['listing_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Maybe I need to reset the index, who the hell knows if I'll use this later\n",
    "nan_adjusted.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Excaliburxiv\\AppData\\Local\\Continuum\\Anaconda2\\lib\\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",
      "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",
      "  if __name__ == '__main__':\n"
     ]
    }
   ],
   "source": [
    "# Initialize an empty column for the price differences \n",
    "nan_adjusted['price_diff'] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Excaliburxiv\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\ipykernel\\__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n"
     ]
    },
    {
     "ename": "IndexError",
     "evalue": "single positional indexer is out-of-bounds",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-25-7b5c6aae852b>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mi\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnan_adjusted\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'listing_id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcount\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m     \u001b[0mnan_adjusted\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price_diff'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnan_adjusted\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m-\u001b[0m \u001b[0mmean_df\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mnan_adjusted\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'listing_id'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'price'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Excaliburxiv\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1284\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1285\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1286\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1287\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1288\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Excaliburxiv\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1571\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1572\u001b[0m                 \u001b[1;31m# validate the location\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1573\u001b[1;33m                 \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_valid_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1574\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1575\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\Excaliburxiv\\AppData\\Local\\Continuum\\Anaconda2\\lib\\site-packages\\pandas\\core\\indexing.pyc\u001b[0m in \u001b[0;36m_is_valid_integer\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1485\u001b[0m         \u001b[0ml\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1486\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0ml\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m<\u001b[0m \u001b[1;33m-\u001b[0m\u001b[0ml\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1487\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"single positional indexer is out-of-bounds\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1488\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1489\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: single positional indexer is out-of-bounds"
     ]
    }
   ],
   "source": [
    "for i in range(nan_adjusted['listing_id'].count()):\n",
    "    nan_adjusted.iloc[i]['price_diff'] = nan_adjusted.iloc[i]['price'] - mean_df.iloc[nan_adjusted.iloc[i]['listing_id']]['price']\n",
    "    \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Subset dataframe of averages for left-merge\n",
    "mean_df_new = mean_df[['listing_id','price']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Left-Merge\n",
    "new = nan_adjusted.merge(mean_df_new,how='left', left_on='listing_id', right_on='listing_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Compute price differences \n",
    "new['price_diff'] = new['price_x'] - new['price_y']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['2015-01-09', '2015-10-02', '2015-10-03', '2015-10-04',\n",
       "               '2015-10-05', '2015-10-06', '2015-10-07', '2015-10-08',\n",
       "               '2015-10-09', '2015-10-10',\n",
       "               ...\n",
       "               '2015-09-01', '2015-09-02', '2015-09-03', '2015-09-04',\n",
       "               '2015-09-05', '2015-09-06', '2015-09-07', '2015-09-08',\n",
       "               '2015-09-10', '2015-08-05'],\n",
       "              dtype='datetime64[ns]', length=773652, freq=None)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert dates to datetime\n",
    "dates = pd.to_datetime(new['date'].values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Add new datetime object\n",
    "new['datetime'] = dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Groupby datetime, get average price difference\n",
    "final = new.groupby(['datetime'], as_index = False)\n",
    "final_sums = final['price_diff'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of days average price difference was positive: 154\n"
     ]
    }
   ],
   "source": [
    "print \"Number of days average price difference was positive:\", sum(final_sums['price_diff']>0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0xe0fc4a8>"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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XtagjYlfgLcDy1ttn5n79JChJkiRJkiRJUquuM6wj4sfAf1CsW33v1OWZOfJ1\nrJ1hLUmSJEmSJEn1N2czrIF7MvMjc5CTJEmSJEmSJEkzqnLSxbMj4oiI2CYitpr6GnhmkiRJkiRJ\nkqSxUmVJkCunuTgz8/cHk1J1LgkiSZIkSZIkSfVXdUmQrg3rOrNhLUmSJEmSJEn1V7Vh3XVJkIh4\nSES8IyI+Vm4/IiIOmIskJUmSJEmSJEmaUmUN608AdwFPKbfXAscNLCNJkiRJkiRJ0liq0rDeOTPf\nA9wNkJl3AF2nbkuSJEmSJEmS1IsqDeu7IuLBFAtDExE7A3cONCtJkiRJkiRJ0thZWOE2xwBfBnaI\niE8DTwVePcikpkTEKuBW4D7g7szcexjHlSRJkiRJkiQNX2TmzFdGBLA9cAfwZIqlQL6TmTcMJbmI\nXwF/kJk3z3B9mXwCQWZSpLx+e+nS5UxOrmbJkmWsW7dqGGlLkiRJkiRJklpEBJnZdanpjg3rMtBP\nM/PRc5ZZDyLiSuAJmXnjDNd3bVi3b0uSJElNtHTpcgAnYUiSJKmRqjasq6xh/cOIeOIc5DQbCZwX\nEd+LiNeOKAdJkiRp5CYnVzM5uXrUaUiSJEkDVaVh/STg2xHxy4j4SUT8NCJ+MujESk/NzL2A5wKv\ni4inDem4kiRJ0kgtXbqciLh/ZrUkSZI0DqqcdPFPBp7FDDLz2vL79RHxv8DewAUPvOUEACtWrBha\nbpIkSdIgz5dSzKZOJie7fmpSkiRJqp0VK1bMql/bdQ1rgHJm8yMy8xMR8XDgYZl5Zc9H6yWxiIcA\nCzLz1xHxUOBc4NjMPLflNq5hLUmSVFPtzdxBrcE83XEG1URujz3I95rTv6/F97SSJElqpDlbwzoi\njgGOBI4uL1oE/Hd/6VWyBLggIn4EfAc4u7VZLUmSpHpbP0N49f3bg1iDebrjtG4P8ljzQfvSI4Nc\nimSYxxqV+ViTJEnSMFVZw/pFwAuA3wBk5jXApoNMqjzOlZn5uMx8fGY+OjOPH/QxJUmab1obJzZR\nJE1nlA3/QR5rWM3x9rjzoaZhm/odNfWz/zCRJGm8dV0SJCIuysy9I+KHmblXuTzHtzPzMcNJsWNu\nLgkiSVIHrb8HC/5O7MeglrSY7jjDWtJikMca1pIWw3z/N/pjOX51O9Z8rAlG8VrBvBo/SZL0QHO2\nJAhwekR8FNgiIl4LfBU4sd8EJUmqwhnCzTBflxSYbubkMGZNzpcZrpKay9cKSZI0KjPOsI6IB2Xm\nneXPfwwEWONkAAAgAElEQVTsTzE96yuZed7wUpyZM6wlqR6GNUOz0PyZZMM0rPGbrzMMneE6iGM5\nfv0dy/Gr27HmY02jOdb8nmE9X95XSJLUj7mYYf3tMtCnMvO8zHxrZr6lLs1qSdLoDHM26LDMx3VV\nJUnSaMzH90qSJA3Lwg7XbRwRhwBPiYgXt1+Zmf8zuLQkSXW2/o+urv8Y1TTax8/xlCRpfvF3e3+c\nkS5J80fra3pVnWZY/xXwdGAL4PltXwf0kackSZIkSZoDU+f5mPp5PpxDon1Gup9Gk6S5N6zX9dl8\nyqjTGtZ/mplnRMRfZubH5ijHOeUa1pI0GqNaQ7PQ/HUtHb+mH8s1hPs7luPX37Ecv7odaz7WNJpj\nza81rB2/5hyrlTO7JY2TUbxXAvpew/ro8vtf9Z2ZJEmSJElSzbTOKPQcJs3h+EnzW6eG9Y0RcS6w\nU0Sc1f41rAQlSZIkSZIGYVgnxPQE3/3xRKbNMR8ebxq9TkuCbAzsBXwK+Iv26zPz/MGm1p1LgkjS\naLikRTOPVZhfNbkkQxOP5fj1dyzHr27Hmo81jeZYLmnR37Ecv7k4VmF+1TToY7WfTG1QS6rY62kO\n75vmaOSSIJl5V2Z+B3hKZp7f/jVXSUuSBsP/bEuSJEkapNaZzs561nSGfTJY//6dH2ZsWEfEB8of\nT3JJEElqHt8wSpIkSVJv5uOSKqM0yL9Lp1tqZ779/Tuuj7+FHa77VPn9X4aRiCRJkiRJkjRK65ug\nMe32XGpdUmXdulX3NyXnekmV6Y4zqOVbNLcG+firs05Lgvyg/H4+sBJY6ZIgktSfYX4cSpIkSZJU\nX8OaITzMk36qP/5dX5ixYQ0QERMRcQNwGfCLiLg+Iv5hOKlJ0vzQuo7WMD8OJUmSJEmS+jPMZWL8\nu77QaQ3rvwOeCjwxM7fKzC2BJwFPjYi/HVaCktR083EdLUmSJEmSxoEz1Iev0wzrVwIHZ+aVUxdk\n5q+AVwCHDjoxSZIkSZIkSdJ46dSwXpSZN7RfmJnXA4sGl5IkSZIkSZIkaRx1aljfNcvrJKn2hrkG\nlSRJkiRJkqpZ2OG6x0bEbdNcHsAmA8pHku63dOlyJidXs2TJMtatWzWnsdevORXTbkuSJEmSJGn4\nZpxhnZkbZeZm03xtmpkuCSJpzrXPcvZEBpIkSZIkSeOl0wxrSRoqZzlLkiRJkiSNt05rWEuSJEmS\nJEmSNDQ2rCVJkiRJkiRJtVCpYR0RyyLiWeXPD46ITQebliRJkiRJkiRp3HRtWEfEa4HPAh8tL9oe\nOHOQSUmSJEmSJEmSxk+VGdavA54K3AaQmZcDWw8yKUmSJEmSJEnS+KnSsL4zM++a2oiIhUAOLiVJ\nkiRJkiRJ0jiq0rA+PyL+HnhwRPwxcAZw9mDTkiRJkiRJkiSNmyoN66OA64GfAocD5wDvGGRSkiRJ\nkiRJkqTxs7DCbR4MnJSZJwJExEblZXcMMjFJkiRJkiRJ0nipMsP6/yga1FMeDHx1MOlIkiRJkiRJ\nksZVlYb1Jpn566mN8ueHDC4lSZIkSZIkSdI4qtKw/k1E7DW1ERF/APx2cClJkiRJkiRJksZRlTWs\n3wScERHXAAEsBV4+0KwkSZIkSZIkSWOna8M6M78XEY8Edisvuiwz7x5sWpIkSZIkSZKkcTPjkiAR\nsV/5/cXA84Fdy6/nl5cNXEQ8OyJ+HhG/iIgjh3FMSZIkSZIkSdJodFrD+o/K78+f5uuAAedFRCwA\nPgj8CbAncHA501tqjKVLlxMRLF26fNSpSJKkecT3GJIkSZqvZlwSJDOPKZvGX8rM04eY05S9gcsz\nczVARJwGvBD4+QhykWZlcnI1kExOxqhTkSRJNbd06XImJ1ezZMky1q1b1fG2vseQJElSXbW+r52N\nTjOsycz7gLfNKnL/tgPWtGxfXV4mSZIk1VL7zOelS5dv8HP7da3b65vQq/s+7lzqlncvNc/mWKOo\nqanHmo9m8/ib2p6Lx5/3lSRJvevnfS10aViXvhoRb4mIHSJiq6mvWR1tAB760M2B4KEP3ZwVK1aU\nnfu4v4Pfvt3PHxH9vDmfyzdadcuz7jUPc/wmJiaICCYmJoBqj7+m1zyXsWbz/J1tzVWONVc1L1my\nbFY19VrzfB6/qdh1Hb9ea+7lWHUdv/aax2H8pranfm5/zW/fHvb4Va2527Fa6+hWY7ftfsavl+1e\nauq2PZuaWrf70S12p98n7eZy/OZSP8eai8ffII7V7/hNF3u2NfXz2O7lWHP5/J2KXffHX93fK9X9\nvWavNftes5nvlXyvObfvNes2fnP1XrNJ4ze1Xdfxa7+f52L8prbn6r7ZcsslG/Ruq4rM7HyDiCun\nuTgz8/crH2UWIuLJwERmPrvcPqo87rtbbpPd8p8mLpBAkJnlNi0/t1/X6/Zcxiq2C3MTaxxrHub4\nze7x1+yamzp+1Y41NzXPtqZe72fHb3Tj1+vzpJdjtedZl/Frr3kcx6+bpUs3XN6hPfbUm7luSz9M\nF6vTNtDxtpKk3nV7Tfe90ih+1zt+dX+v5HvN+TZ+1WqeX+M398eay/Hr9bHdy98FgxqviCAzY8Yb\nlGZcw3pKZu7U7TYD8j1gl4hYBlwLHAQcPKJcJEmSetL+JnDJkmVMTq6fwdBL87j9tt22q14nSaqm\n22t6+7YkSXUzyL8L5vr34IxLgkTEIyLi8xHxs4g4NSKGun50Zt4LvB44F7gEOC0zLx1mDpIkaW70\ns9RBrx8P73T7Knn0cqxerFu3isy0gSxJ80D7a3qvr/HDWgZlvurlfYPjK0mDN9d/63SaYX0S8Eng\nG8ALgH8HXjwnR60oM78M7DbMY0qS+ucfBP1p/e/0bE9S0etxZrtdVS8zhHuZPdztWMXH3mae1dzv\nsSRJmo1Ov/d6/X3cSb+/63uZLdfL+4Z+Z6j38r7B3+2S1L9hf5KoU8N608w8sfz5vRHxw2EkJElq\nvtY/DPr9Q6mT+fpx3Nbxm1qTbNDHmYvtOmpCjpIktZrL37/D/N0+zH9O92O+vF+UpEHqZznDudCp\nYb1JRDye9SuCP7h1OzNtYEuSuhrkH0rd9p3LWUOjMsiGvyRJ0rjp5b2m77Mkjatur5WDfj3s1LC+\nFnhfy/a6lu0E9htIRtKA+SZD6m6+PE/mcvbOMD/m2inn+TDrWZIkqQmGOTlimO81h7X83DDNl79f\npLk0yOfFoP/unLFhnZn7DvTI0pCM+mMMg9LpTcp8edOh0en2ZnwcNeVjrpIkSRqOpi6p0ul8H3Va\nQ72f86VoePyHyWh1+mdWk58XnWZYS/NCk5+gnXR6kzLXa952egHUeJivzyNJkiRpnI1y+b5BfnKw\nn+a3Ouu3KdpP78J+ROfxn09/t9uwltTVfH0BlCRJkiTNP52a301ZUmWYzdleZr/PZU+g11n39iPG\nZwxsWEuSJEmSJGksNGVJlV7XTJ/LJVWGtcThMGfZq1m6NqwjYq9pLr4VWJ2Z98x9SpIkSZIkSVK9\nDXOZE0/G3ps61d/phKw21qdXZYb1h4G9gJ8AATwKuATYPCL+OjPPHWB+kiRJkiRJkjRr/Z4EtJ/Y\nnZrndWqs10mVhvU1wGsy8xKAiNgDeCfwNuB/ABvWktRw/ldXkiRJkjRfjfIEo+pdlYb1rlPNaoDM\nXBkRj8zMX/V6Ns/5zkaPpCYZ1IkzJEmSJEmSZqtKw/qSiPgIcFq5/XJgZUQ8CLh7YJk1kA0fSXXm\nGZYlSZIkSVLdVWlYvxo4AnhTuf0t4C0Uzep9B5OWJGmu2aCWJEmSJEl1V6Vh/Rzgg5n5r9Nc9+s5\nzqdRhrXmaz8Lv0uqN5/PkiRJkiRJ61VpWD8feH9EfAP4DPDlzLxnsGk1w7BmK7p4uzR/dXs+d/uH\nlc1uSZIkSZI0n3RtWGfmn0fEIoqZ1gcDH4qI8zLzLwaencZKayNucnL1qNORZq2fWdPd1pn2H1aS\nJEmSJGk+qzLDmsy8OyK+BCTwYOBAoJENaz9+Xx+dGnMRMaKspEI/rxH9NJVtSEuSJEmSpHHWtWEd\nEc8BXg7sA6wA/hN42UCzGiCbQfXhfaE66/T4dF15SZIkSZKkwagyw/pQirWrD8/MOwecjyTVjst0\nSJIkSZIkDUeVNawPbt2OiKcBB2fm6waWlWrDmaOSDWlJkiRJkqRhWVDlRhHx+Ih4b0SsAv4R+PlA\ns1JtrFu3isy0YadaKP5xsuFSHFPbna6bbluSJEmSJEn1M+MM64jYFTi4/LqBYlmQyMx9h5SbpAbo\ntp7zdNsz7dtNL0txuGyHJEmSJElS80RmTn9FxH3AN4HXZOYV5WW/yszfH2J+HUVEzpR/DzEAyMzy\n5wSi0nb3uNVjDf9Y09e8dOlyJidXs2TJsmkbfL3EGsfx66ZT7ELzxk+SJEmSJEnqJiLIzOh2u05r\nWL8YOAj4ekR8GTiN9V01zVPOQpUkSZIkSZI0KjOuYZ2ZZ2bmQcAjga8DbwK2joiPRMT+w0pQkiRJ\nkiRJkjQeup50MTN/k5mnZObzge2BHwFHDjwzSZIkSZIkSdJY6dqwbpWZN2fmxzLzmYNKSJIkSZIk\nSZI0nnpqWEuSJEmSJEmSNCg2rCVJkiRJkiRJtWDDWpIkSZIkSZJUCzasJUmSJEmSJEm1YMNakiRJ\nkiRJklQLNqwlSZIkSZIkSbVgw1qSJEmSJEmSVAs2rCVJkiRJkiRJtWDDWpIkSZIkSZJUC7VsWEfE\nMRFxdUT8sPx69qhzkiRJkiRJkiQN1sJRJ9DB+zLzfaNOQpIkSZIkSZI0HLWcYV2KUScgzbUlS5YB\nUX6XJEmSJEmS1KrODevXR8TFEfGfEbH5qJOR5sK6davITNatWzXqVCRJkiRJkqTaGdmSIBFxHrCk\n9SIggbcDHwbemZkZEccB7wNeM12ciYmJ+3/eZ5992GeffQaUsSRJkiRJkiSpihUrVrBixYqe94vM\nnPts5lBELAPOzszHTHNd9pt/RLHySGaWPycQlba7x60ea/jHmr7mauNVLZbj113r/oXmjZ8kSZIk\nSZLUTUSQmV2Xga7lkiARsbRl88XAz0aViyRJkiRJkiRpOEa2JEgX74mIxwH3AauAwwd1IE9+p1Fa\nsmQZk5NRfl896nQkSZIkSZKkkaplwzozDx3WsTz5nUap9fE3tdSIJEmSJEmSNK5quSSINI6K2f7h\nrH9JkiRJkiSNrVrOsJbGkbP9JUmSJEmSNO6cYS01hDOwJUmSJEmSNN85w1qqqdYTMoIzsCVJkiRJ\nkjT/2bCWaqqfBnV7s1uSJEmSJElqAhvWUh/q2hh2NrYkSZIkSZKayDWsVUtNWa953bpVZGbtG8RN\nGU9JkiRJkiSNNxvWqqV+G8G9NGjHoZnbPp7dah6HMZEkSZIkSVL9uCRIi7ou76De9dLo7rUpPh8e\nH+01e4JHSZIkSZIk1YEN6xY26eqrvaHabXuQ5uPjZD7WJEmSJEmSpOaJzBx1DrMWETnI/CMCSCDI\nzAds97Nvr9tzfyx6Ps7Mx5o+Vj81jVJT85YkSZIkSZLqKiLIzOh2O9ewltq0r9/ses6SJEmSJEnS\ncLgkyIC4HnZztS+P4XIZkiRJkiRJ0nDYsB6QYTY5bY5LkiRJkiRJmg9cEqSDpiwNsW7dKjLTmcCS\nJEmSJEmSGs2TLg7JIE+6OLtjedJFSZIkSZIkScPhSRdrpq6zsyVJkiRJkiSpLlzDekhcrsO1tiVJ\nkiRJkiR15gzrEWnqjOt+8natbUmSJEmSJEmduIZ1TTRlDevpj1VtDWtJkiRJkiRJ48k1rCVJkiRJ\nkiRJjWLDuiZGtURIv8ddsmTZjPs2ddkTSZIkSZIkSaPhkiA1tXTpciYnV7NkybKe13xu37fTkiBz\nySVAJEmSJEmSJE2n6pIgNqzHwLAa1v002SVJkiRJkiTNX1Ub1guHkYxGa8mSZUxODn5pDpvUkiRJ\nkiRJkvphw3oMtDeSXVNakiRJkiRJUh25JIgkSZIkSZIkaaCqLgmyYBjJSJIkSZIkSZLUjQ1rSZIk\nSZIkSVIt2LCWJEmSJEmSJNWCDWtJkiRJkiRJUi3YsJYkSZIkSZIk1YINa0mSJEmSJElSLdiwliRJ\nkiRJkiTVgg1rSZIkSZIkSVItjKxhHREvjYifRcS9EbFX23VHR8TlEXFpROw/qhwlSZIkSZIkScMz\nyhnWPwVeBJzfemFE7A68DNgdeA7w4YiIToFWrFgx6+1+9h1m7LrGakqe1lyfWE3J05rrG3scYjUl\nT2uub+y6xmpKnnWN1ZQ8rbm+scchVlPytOb6xq5rrKbkWddYTcnTmusbexxiDTPPbkbWsM7MyzLz\ncqC9Gf1C4LTMvCczVwGXA3t3ilWXwR9k7LrGakqe1lyfWE3J05rrG3scYjUlT2uub+y6xmpKnnWN\n1ZQ8rbm+scchVlPytOb6xq5rrKbkWddYTcnTmusbexxiDTPPbuq4hvV2wJqW7bXlZZIkSZIkSZKk\neWzhIINHxHnAktaLgATenplnD/LYkiRJkiRJkqRmicwcbQIRXwfenJk/LLePAjIz311ufxk4JjO/\nO82+o01ekiRJkiRJklRJZnY8VyEMeIZ1D1oTPQv4dES8n2IpkF2Ai6bbqUqBkiRJkiRJkqRmGNka\n1hFxYESsAZ4MfCEivgSQmSuB04GVwDnAETnqaeCSJEmSJEmSpIEb+ZIgkiRJkiRJkiTBCGdYS5Ik\nSZIkSZLUyoa1JEmSJEmSJKkW6nLSxcoiIoC9KU7ICLC2/F51ewvgllnuO8zYdY3VlDytuT6xmpKn\nNfcXqyl51jVWU/K05v5iNSVPa65PrKbkac39xWpKnnWN1ZQ8rbm/WE3J05rrE6speVpzf7Gakmdd\nYw0zz4t6OUdho9awjoj9gQ8Dl1MUux3wjPLqb5TfZ9q+BNgNuAvYGLgM2LPivsOMXddYTcnTmusT\nqyl5WrM1O37WPOhYTcnTmusTqyl5WrM1O37WPOhYTcnTmusTqyl5WrM1j8v4XQgEsAtwRGaeSwVN\nm2F9AvCszFwFEBGXUgxgAOeUt5lp++fAbpm5KiJ2mtquuO8wY9c1VlPytOb6xGpKntZszY6fNVuz\nNdctVlPytGZrdvys2ZqtuW6xmpKnNVvzuIzfOZm5+9TPwO5U0LSG9ULg6mm2FwCLgOywHS37rm3Z\nrrLvMGPXNVZT8rTm+sRqSp7WbM2OnzVbszXXLVZT8rRma3b8rNmarblusZqSpzVb87iM36Jpfu5q\nYdUb1sRJwPci4jRgDfADYHV53TfL7zNtXwpcERErgT3K7ar7DjN2XWM1JU9rrk+spuRpzdbs+Fmz\nNVtz3WI1JU9rtmbHz5qt2ZrrFqspeVqzNY/L+H0nIo4EDgI+TkWNWsMaICL2AF7A+kW87y6/L6qw\nvSWwGLgRuLnHfYcZu66xmpKnNdcnVlPytGZrHmWspuRpzdbca6ym5FnXWE3J05qteZSxmpKnNVtz\nr7GakmddYzUlT2u25lHGGmaea4GzMnMlFTWuYS1JkiRJkiRJmp8atSRIRGwOHA0cCGxdXnwfxfoo\nCyjWTOm0vUn5/T7gd8C9Pew7zNh1jdWUPK25PrGakqc1W7PjZ83WbM11i9WUPK3Zmh0/a7Zma65b\nrKbkac3WPA7j91tgEvg8cHxm3kIFjZphHRFfAb4GnJyZ68rti8qrn0QxQDNt/zXwXoozUj4XeCvw\nkYr7DjN2XWM1JU9rrk+spuRpzdbs+FmzNVtz3WI1JU9rtmbHz5qt2ZrrFqspeVqzNY/L+D0TOBR4\nFfDMzNyfKjKzMV/AZTNtA5d12b6rbd+7eth3mLHrGqspeVpzfWI1JU9rtmbHz5qt2ZrrFqspeVqz\nNTt+1mzN1ly3WE3J05qteVzG77Lpfu721aglQYDVEfE2ihnWk+X2OymmnK8B7uuwfXu57xeBA8rt\nqvsOM3ZdYzUlT2uuT6ym5GnN1uz4WbM1W3PdYjUlT2u2ZsfPmq3ZmusWqyl5WrM1j8v4rYmIJcCr\ny9tW0rQlQbYEjgJeCCwpL55aU2UjiinoM20vBDZm/doqvwPuqbjvMGPXNVZT8rTm+sRqSp7WbM2O\nnzVbszXXLVZT8rRma3b8rNmarblusZqSpzVb87iM32+BdcBZwLsz8yYqaFTDWpIkSZIkSZI0fy0Y\ndQKzFRF7tW0fUnU7IvZq3b+XfYcZu66xmpKnNdcnVlPytGZrHmWspuRpzdZcp9jjEKspeVqzNY8y\nVlPytGZrrlPscYjVlDyt2ZpHGWvIeW7Qy+2o6mLXdfsCTmzb/nnVbeDE1v172XeYsesaqyl5WnN9\nYjUlT2u2ZsfPmq3ZmusWqyl5WrM1O37WbM3WXLdYTcnTmq15jMZvg15upy+XBJEkSZIkSZIk1cLC\nUSfQq4gIYG9gu/KiteX3qttbALfMct9hxq5rrKbkac31idWUPK25v1hNybOusZqSpzX3F6speVpz\nfWI1JU9r7i9WU/Ksa6ym5GnN/cVqSp7WXJ9YTcnTmvuL1ZQ86xprmHlelD3Mmm7UDOuI2B/4MHA5\nRbHbAc8or/5G+X2m7UuA3YC7KM5WeRmwZ8V9hxm7rrGakqc11ydWU/K0Zmt2/Kx50LGakqc11ydW\nU/K0Zmt2/Kx50LGakqc11ydWU/K0Zmsel/G7EAhgF+CIzDyXCpo2w/oE4FmZuQogIi6lGMAAzilv\nM9P2z4HdMnNVROw0tV1x32HGrmuspuRpzfWJ1ZQ8rdmaHT9rtmZrrluspuRpzdbs+FmzNVtz3WI1\nJU9rtuZxGb9zMnP3qZ+B3amgaQ3rhcDV02wvABYB2WE7WvZd27JdZd9hxq5rrKbkac31idWUPK3Z\nmh0/a7Zma65brKbkac3W7PhZszVbc91iNSVPa7bmcRm/RdP83NXCqjesiZOA70XEacAa4AfA6vK6\nb5bfZ9q+FLgiIlYCe5TbVfcdZuy6xmpKntZcn1hNydOardnxs2Zrtua6xWpKntZszY6fNVuzNdct\nVlPytGZrHpfx+05EHAkcBHycihq1hjVAROwBvID1i3jfXX5fVGF7S2AxcCNwc4/7DjN2XWM1JU9r\nrk+spuRpzdY8ylhNydOarbnXWE3Js66xmpKnNVvzKGM1JU9rtuZeYzUlz7rGakqe1mzNo4w1zDzX\nAmdl5koqalzDWpIkSZIkSZI0PzVqSZCI2Bw4GjgQ2Lq8+D6K9VEWUKyZ0ml7k/L7fcDvgHt72HeY\nsesaqyl5WnN9YjUlT2u2ZsfPmq3ZmusWqyl5WrM1O37WbM3WXLdYTcnTmq15HMbvt8Ak8Hng+My8\nhQoaNcM6Ir4CfA04OTPXldsXlVc/iWKAZtr+a+C9FGekfC7wVuAjFfcdZuy6xmpKntZcn1hNydOa\nrdnxs2Zrtua6xWpKntZszY6fNVuzNdctVlPytGZrHpfxeyZwKPAq4JmZuT9VZGZjvoDLZtoGLuuy\nfVfbvnf1sO8wY9c1VlPytOb6xGpKntZszY6fNVuzNdctVlPytGZrdvys2ZqtuW6xmpKnNVvzuIzf\nZdP93O2rUUuCAKsj4m0UM6wny+13Ukw5XwPc12H79nLfLwIHlNtV9x1m7LrGakqe1lyfWE3J05qt\n2fGzZmu25rrFakqe1mzNjp81W7M11y1WU/K0Zmsel/FbExFLgFeXt62kaUuCbAkcBbwQWFJePLWm\nykYUU9Bn2l4IbMz6tVV+B9xTcd9hxq5rrKbkac31idWUPK3Zmh0/a7Zma65brKbkac3W7PhZszVb\nc91iNSVPa7bmcRm/3wLrgLOAd2fmTVTQqIa1JEmSJEmSJGn+WjDqBGYrIvZq2z6k6nZE7NW6fy/7\nDjN2XWM1JU9rrk+spuRpzdY8ylhNydOarblOscchVlPytGZrHmWspuRpzdZcp9jjEKspeVqzNY8y\n1pDz3KCX21HVxa7r9gWc2Lb986rbwImt+/ey7zBj1zVWU/K05vrEakqe1mzNjp81W7M11y1WU/K0\nZmt2/KzZmq25brGakqc1W/MYjd8GvdxOXy4JIkmSJEmSJEmqhYWjTqBXERHA3sB25UVry+9Vt7cA\nbpnlvsOMXddYTcnTmusTqyl5WnN/sZqSZ11jNSVPa+4vVlPytOb6xGpKntbcX6ym5FnXWE3J05r7\ni9WUPK25PrGakqc19xerKXnWNdYw87woe5g13agZ1hGxP/Bh4HKKYrcDnlFe/Y3y+0zblwC7AXdR\nnK3yMmDPivsOM3ZdYzUlT2uuT6ym5GnN1uz4WfOgYzUlT2uuT6ym5GnN1uz4WfOgYzUlT2uuT6ym\n5GnN1jwu43chEMAuwBGZeS4VNG2G9QnAszJzFUBEXEoxgAGcU95mpu2fA7tl5qqI2Glqu+K+w4xd\n11hNydOa6xOrKXlaszU7ftZszdZct1hNydOardnxs2Zrtua6xWpKntZszeMyfudk5u5TPwO7U0HT\nGtYLgaun2V4ALAKyw3a07Lu2ZbvKvsOMXddYTcnTmusTqyl5WrM1O37WbM3WXLdYTcnTmq3Z8bNm\na7bmusVqSp7WbM3jMn6Lpvm5q4VVb1gTJwHfi4jTgDXAD4DV5XXfLL/PtH0pcEVErAT2KLer7jvM\n2HWN1ZQ8rbk+sZqSpzVbs+NnzdZszXWL1ZQ8rdmaHT9rtmZrrluspuRpzdY8LuP3nYg4EjgI+DgV\nNWoNa4CI2AN4AesX8b67/L6owvaWwGLgRuDmHvcdZuy6xmpKntZcn1hNydOarXmUsZqSpzVbc6+x\nmpJnXWM1JU9rtuZRxmpKntZszb3GakqedY3VlDyt2ZpHGWuYea4F/n97Zx4uV1Wm+98XCINMMigo\nXAbnEUWvA20LKoOCXAUHbPoqoN1qS9tCO+F0r+1wtUUv3SpXbIdG7VZpxb6CVzHQyhUnFA0gYYgB\nTIhIQiAkZDo5Sc7qP9aqZGd7TtXep2rv+lbV+3ue/aS+U7XefO/6au1VtfauvS8LIdxMRbJbsBZC\nCK8Q61sAACAASURBVCGEEEIIIYQQQggxmmR1SRAz2wt4N3Ay8ND05yni9VHmEK+Z0i3eJf07BUwA\nW2q0bVPbq1YuecqzH61c8pRneVb/ybM8y7M3rVzylGd5Vv/JszzLszetXPKUZ3keh/7bACwHLgX+\nPoSwigpkdYa1mc0Dfgh8OYSwLMW/TE8/i9hBM8VvAj5OvCPlicA7gAsrtm1T26tWLnnKsx+tXPKU\nZ3lW/8mzPMuzN61c8pRneVb/ybM8y7M3rVzylGd5Hpf+OwY4HTgDOCaEcDxVCCFkswELZ4qBhT3i\nyVLbyRpt29T2qpVLnvLsRyuXPOVZntV/8izP8uxNK5c85Vme1X/yLM/y7E0rlzzlWZ7Hpf8WTve4\n15bVJUGAJWb2TuIZ1stT/EHiKedLgaku8ZrU9rvASSmu2rZNba9aueQpz360cslTnuVZ/SfP8izP\n3rRyyVOe5Vn9J8/yLM/etHLJU57leVz6b6mZ7Q+cmV5bidwuCbI38C7gpcRrWBvbX1OFLvEOwM5s\nu4bKRmBzxbZtanvVyiVPefajlUue8izP6j95lmd59qaVS57yLM/qP3mWZ3n2ppVLnvIsz+PSfxPA\nMuAy4GMhhJVUIKsFayGEEEIIIYQQQgghhBCjy5zeLxFCCCGEEEIIIYQQQgghmkcL1kIIIYQQQggh\nhBBCCCFcoAVrIYQQQgghhBBCCCGEEC7YcdgJ9IuZ7Qk8BHhECOFKMzsgPfVQ4G7gucDCEMJN6fUf\nCSG8x8yeAxwE3AAsBP4RmAvcCHw+hLDZzI4ClocQFqbXHw3sDqwgXkz898BOwMPT/3kXMC+EsKqP\nvA4GdgWeCNwMvMlDXulxsc8eA+wPHJ7y/DzwJ7PNq1TT44BfeO6/EMLtKc8bPfRfy3kdCdwSQvhu\naj+sMTgXeDBwHxAovZ+0b5jdGDSz41JeLvMctbzS41HZN9R6vxX7rJNXCGGZmR0N7Fsxrz8ao17z\nmuY9dizwGuINTRb0yqvDdGPUa//1Ggtt9t8o5FWspeZQX3NAijWHjtgcSgF9PxlqXtnM9YPMK9e5\nystnI095tphXLnN9U3m5nKu85pUetzaHhhCuLD/uRdY3XTSzU4mD4x7gCcAngNPYdpfKnYiD4OHA\nJuDfiDuWHwMnAl8FngXcBrwIWA8sB+4AbgGeQVzUnwecCuyWtHcDbiIWchL4Wmp3EHAccDnwsop5\n/Ro4jDgxfgU4E1gJXAW8BNgbWOcgr8OAFwIXpz57FPGOodemfjsQWD3LvD4QQvhKoa73ptd57r+5\n6e8rHfRfm3kdk/6/o4Hr0uuGMQYfTLzb7N3AnsA3k/fjgA8Q70KrfcMsxqCZ3Qm8vUb/5bIP85rX\nqOwbar3f2H6MPik9vwn4GPAp4LcV8yqP0bnED0re8iq/x+5Mr19IfM+sBPaaKa/SHFkeo177rzwW\n9iWOuc57bnP6/xrvv0zz0hyazxygOXQ051B9P/HzGSSXuV5zaIt59fhs5CbPFvsvl7l+kHnlMld5\nzavNOfTOEMLB5cc9CSFkswGXlbbVqeO+A2wgvrlflQqwhThh/CvwV6kTzyAeCVgELEua+6ViXJfe\nOK8H1qRCfRY4HrifePTjQcTBdAvxDfbgVKCVhZwuT/931byWpG1Fym8jcFjK7frU3kNei4BFhT6b\nAvZJef2AeGSsal4/K+W1GVhW2IKT/ivn2em/y4g76uCk/9rM67vp//kO8ADDG4MbgR+k9sX3+uXA\n2iHmleu+oTgGN9fsv1z2YV7zGpV9Q528ymN0ivih/zXpuckaeZXH6Bbih6UvpLp6yav8HtsM7Jte\nf0D6v7rlVZwjy2PUa/+Vx0IALgFOJ55ZMtli/+WYl+bQfOYAzaGjOYfq+4mfzyC5zPWaQ9vNq9tn\nI095tpVXLnP9IPPKZa7ymlebc+hmtq0jrau8BjzsReiaC9b3Ay8mnt15NPFoxNHA84iTw2+IRw3e\nQhw4exCPznwN2JA07gB+lYr6gvS3VcCC9Hhf4unwtyada1LhFhCPJOyQijMB7JXabCnkdSLxCFTV\nvG4E5gN3JK3Vhby+Vch72HmV+2wzcEght4kaeS0o1PLE9PpzU/uzk7aH/ivn2em/Tm6bnPRfm3l1\n/t/nA4sZ3hjcCNxQ2Dd03usnEn+Won1Dvbw6Y/Bs4iRYp/9y2Yd5zWtU9g118iqP0U3Aw1JudxE/\nyFXNqzxGNxC/PLw15bzJSV7l91gAdk3P75b6sFtea5h5jHrtv+nGQievh6e4rf7LNS/NoXnMAZpD\nR3MO1fcTP59BcpnrNYe2m1e3z0ae8mwrr1zm+kHmlctc5TWvNufQlcU8O/XvtQ19EbrORlypf34h\n/hnwyPT46lSYvYlHAzYWXnckcccykTpuEng68ZT8HxPPFN2c4uuIlz34WHruWuCfiD91WJS2u4k/\nXbgd+GfioPsC8ejD7ek1VfNam3KaIu50J4Gfpnb/L+XtJa9in21Jb8hObhfXyGs58Uj2ZelvvynV\ndbWT/ivnubX/0v+/xkn/tZnXPcT31BXE99WwxuAq4k9bLgQ+RHyvd/rjzCHmleu+YesYTPp1+i+n\nfZjXvEZh31AnrzPZ/j22hnikfg/gJ53cKuZVHqPFvD5OPLDmIa/ye+x64v70itSvt/fIa+vnH/54\njHrtv2nHQvr/f0Qcg231X7Z5aQ7NYg7QHDqac6i+n/j5DJLLXK85tN28un028pRnW3nlMtcPMq+c\n5iqvebU1h1493eNeW+7XsH4KsD6EsCjFBxM7DuDUEMJX098PBB4PPBY4MoTw6oLGM4mr/LsQjy5c\nG0KYSs8dCYQQwjVm9kjg3cDOwL+EEK4ws72J1+v5L2w73X4ecDDxNPfbCnn9gXg0ompejweOSLl9\ncYB59dVfhT57HXAl8SjYtSGEqdnmFUK4v4G6NtV/fdW1if4bQl1PIV676xLgyQOo1azGIHHH+ULi\ndZLmpJzmhRDuT++hfsdgU/sGd+/tcRqD47Rv9ZpXeYx26hjiDUfmdnKrmNfzKI3RAeS1PoSwqNNf\nIYRNg8grPVfM7XXE/en1wPuJ1z/sOT6TTmN5Dqqu6f/oNkbPBfZpo/9q7tO85qU5VHOo5lB9PxnX\n7yfPI5O5XnNoe3lRwmueLde17lzv9TPISO5rx3kOnS3ZLlib2T4AIYSVdeN+2k4Xi8ExyNqors2i\nMTiaaAyODk2NOdVxuLS1b1Vdm0Vz6GiiOXQ0UV1HB82ho8mw5lDVdbBoXzua9NO3WS1Yp6MF5xFP\nm19F/PnOAenpu4lHN2aK7yGeGr8b8eLgq4CHVGw7XbyFeFfTHwLvCiEsLuR5YwjhyeXH/cZVXku8\nXszHiXf0fCzxdP6HE38yczTxFP8DidekeTHw0VnG+xN/JlBV+9SU1xSxbuuAk9K/fw18GDiE+LOE\ne4kXfZ9Nbfqtc6W6luOm68z2dX008Scds61FP3U9CfgIcQzuSqyVqzHY9phrcQz2W+fiGNwLODRp\n3pz68wWpj+9KdurUovUxqH3rjPvW1cBLU78uI94IYxBjrm5dZ7UvLceDrjPd96WDHnN16nwMcf6b\nbo48lXjzk05dy2N0kPvWXloDqWs5HkCdbyWe5VSlrk3WuVddi+Oz7r5Wc6jmUM2h+n4yrt9P6ta5\n22cjzaH5zqF1Phs1OYeWte4j3ojOc10vJ97YsRO3PYcW44cRLy2xhXh946tTLTSHzhAzGnPocuL6\n0R/1bTdyW7D+OfGC4ZeEELak+JPEN9rZQOgSfw74S+DfgZcDnyfu5Kq0nS4+j/jzhSOBPyPeeZP0\n/F8DF6THbyReX4aKcaftbLUWAz8nXlfmvBS/DngF8c6z5xKvZ3Ml8U31oVnGvwSW1tDeKb0mAF8m\nXtfmncBRxBtTXJD+PTH15RtnWZt+61ys60nAtwdYm37qvJhtdf0w8dpFs61FP3XdBTgH+BLxIv0T\nLdamGP8d8eco5TE4jDHX1hjst87FMfhV4BPECemTwO7Aa9l2Y4UH1ahFk2NQ+9b6+9bFwP8g1vIr\nwBv6qEU/de22L21yDPbSWszM+9JBj7k6dT4LeA3Tz5FvTq/t1LU8Rge5b+2lVaeubdb5b4k/raxS\n1ybr3Kuui5m5jppDNYdqDh3+HKrvJz6/n9Stc7fPRppD851D63w2anIOLWt9AHgvw69rOe7U9bfA\nscTLNrxjQLXpt863A+cD7yMukp6B5tBuWovJfw49BzgeeCVwTgjh2VQhVLzYtYcNWDRTnIrXLZ4s\ntZ2s0Xa6eBNx0e6iVKBFha0YbyIecaga96t1X8rpIuIRolcDNwGPJE4+ncfzgSV9xOtrai8uxBPA\ndYX+nCrVZqKP2vRb52JdVw+4Nv1oFeu6ps9a9FPXZaV4WGMwFOpUHoNtj7m2xmC/dV5ciKcKfXkb\n24+5unVtcgxq31p/31rsz4k+a9FPXbvtS5scg720uu1LBz3mZqtdniPLdS2P0UHuW3tp1alr23Wu\nWtcm69yrrt3qqDlUc+gg6qw5VN9PRvH7Sd06l+uqOXQ05tA6n42anEPLWsU5dZh1nU77osI21VBt\n+tFajObQcZlD5xf7uvhcty23M6wvBlYSV++XAp8h3pEU4ur+VJf4SOJRgKuJK/7HEY9SVGk7XXwo\n8XT45xKPvhwVQliQ8pwEnhZCWGBmS4k/TTijYry17Sy1HgCeHkKYMLNNxDse/ynxKM0hwEuATxN/\nJrESePss40Nqaq8gHl35LPFnGUtCCIcnjxuIg69T11uJd92dTW36rXOxrvsRB9+gatNPnYt17cSz\nrUU/df0R8ajnEam/ft5ibYrxscQbXawnHpHdOgaHMObaGoP91rk4Bg8LIeyQ/J5FvMzL14hj8MvE\nMVhnf9jUGNS+tf6+9XK2zZNXEs8wGMSYq1vXYh3L+9Imx2AvrW770kGPubp1PoHp58g/pH7v1LU8\nRge5b+2lVaeubdZ5E7BHxbo2WededS2Oz7r7Ws2hmkM1h+r7ybh+P6lb526fjTSH5juH1vls1OQc\nWtY6mXiW6bDrWo631rVQ52HNodtphRDmmtmxxLlzD7btazWHju4c+kPiZ84zgP1CCKdSgdwWrHcC\n/oJ4raIDiafIW3q6Y2SmGOK1PHciXkNlVeHvvdpOF+9K7PhvEAfVbSGEO1OefwlcEUK408z+a3rt\nkorx1raz1Hou8ejFj8zsfODS9PgI4GLiz8MfIP6U4HvEIyOziS8G3lBD+xLiTygfXYoPIP704xZi\nXQ8jXg9n+SxrA/3VuVjXLwLPZHC16afOxbpujWdZi37q+mLiGHw1cDjxKNswxuCuSWMxcBmFMTiE\nMdfWGOy3zsUx+FXgmSGEtWb2KLaNwdOAJxDPpK+zPyzGMLgxqH1r/X1rZ4x2armxj1oUY6hX1277\n0ibHYC+tbvvSQY+5OnX+auqj6ebINxN/Xlesa3GM1q1Nt7iXVp26tlnn4pjsVdcm69yrruXxWWdf\n26s23dpqDtUcqjlU309y/n5St869PhtpDs1zDq3z2ajJObSstRG4k+HXtRxvrWvSPh+4dEC16afO\nxTn0COI1kL+F5tBxmEN/Rfzc+cUQwkYqkNWCtRBCCCGEEEIIIYQQQojRZc6wE+gXM5s/27iftm1q\ne9XKJU959qOVS57y7Fd7HLRyyVOe/Wp71colT69aueQpz361x0Erlzzl2a+2V61c8vSqlUue8uxX\nexy02syzCtkvWMN2p//Xjftp26a2V60mtb1qNak9DlpNanvValLbq1aT2uOg1aS2V60mtb1qNant\nVatJ7XHQalLbq1aT2l61mtQeB60mtb1qNantVatJba9aTWqPg1aT2l61mtT2qtWk9jhoNaldfq4n\no7Bg/d0+4n7atqntVatJba9aTWqPg1aT2l61mtT2qtWk9jhoNantVatJba9aTWp71WpSexy0mtT2\nqtWktletJrXHQatJba9aTWp71WpS26tWk9rjoNWktletJrW9ajWpPQ5aTWqXn+uJrmEthBDijzCz\np4UQ5g8iltZwtcXoYWZ7Em9wckcI4f5iDGyZ6bm68SC1csmzTa1+3wfCL2a2Xwjh3kHE0hq+NnEs\nbwkhPJD+tvds437atqntVWsA2vsAhBBWluNuz9WNB6mVS57D1BJCjCghhJHYgBtnG/fTtk1tr1oD\n0L6VeAfSHwPvKcU3A4fViH8L/FtBa14fWk1q56r1JOBy4tGxK4D/C2wi3oH3+B7xXwC/T/ENwJU1\n2uailUueZa0fAmuABcDLgP8OLCferfnPa8bvKcXvlVar2h9ItT0COB/4AXA/8a7Mt6XHPyPebfrm\nGvFHB6jVpLZXrX617yaO3Z8BrwPWAT8ClgA3ppr/B/G9MFF47sOl1/aKB6nVpHauWluAu9JzrwRW\nAl8AjiH+TLJbXOe1uWrlkmf5uROA3wE/Ie57lxDn143AG2vGnwImgQ3E98lyaQ1N+53E/e4WIAAr\ngLXEcd0Zy3XitcBU2uq2bVPbq1a/2uvStghYnJ6bAO4kfgaeSv8uLTz3u9Jre8WD1GpSe1S0bgPu\nIX5XPdTDuohXLed5Ftd+lqH1mlFZ77oZmFuo87dneg902yovCHvYiAspxe1jaTsPWN0jXjdDXKVt\nm9petZrU3gR8Fngr8D3iJPU24KnED4erasSTwO3A0cCngc19aDWpnavWauKi2GnptbcA/w34OPHL\nQbd4S6r3acQPF+tqtM1FK5c8y1qBuBD2G+IX8ZBec3/a6sSB+D7ZMou246g1aO01qd5XEfetbyBe\n/uun6e9zgFOIX+KX1IgHqdWktletfrV/X4hXAbemz0X7EefMQ1P8S2Ci8Ny60mt7xYPUalI7V63b\ngTcDvyC+B+4HPkF8D9zVI95EHNc3VHhtrlq55FnWWgH8GXAkcB/xC9/j098214xDQWsVcWFGWsPR\nngLOSmP4b4ife19EPPh0BXFxrWp8H/BNYLf079o+tJrU9qrVr/YfgNOB/wV8Dvh5qvFpxM9NrwLm\npr+tKTx3Tem1veJBajWpPSpaLwNeAfxvYCF5rrHksl7TpFZnLehtxM/DWq8ZjfWu1cSDEacTx+od\n6d+XAytGdcF6E/Al4KK0TRGPki5Kz3WLQ+FxOe7Vtk1tr1pN53kR29f1JuCRwHziF/eq8Xrg1YV4\nYx9aTWrnqrW4EE8A1xXGZ694qvD4NmB9jba5aOWSZ1nrDuJZgCekOr+c+IHhhPSaynF6/CNgWd22\n46jVgPZ84HfpcfE9ckMp3gDMrxFPDVCrSW2vWv1q3wTsmeK1pXgK2Cs9/glxvHeeW1d6ba94kFpN\naueqVazrb4mLnfOJ++ALesT3EM/47Mzba2q0zUUrlzy7aT1QqvNkzbi4b1jK9vsGaQ0pz2m0by1p\n94ongF8PSKtJba9a/WpPlOJFxbqX6jxZeLyo9Npe8SC1mtQeFa3i2tBq8lxjGaRWLnlOp3VR2tag\n9ZpRWe8KwNXEA/6XEA80bq1zcax323YkL34DfCKEsADAzA4HzgghLDCzpcQPkDPFk8AphbZb4wpt\n29T2qtWk9ibgTSGEiaT1auDtxJ8g7EY847NqPJc4IJaleE4fWk1q56q1gngm/DziDmwHtrGyR7yl\n8Pj8tFVtm4tWLnmWtc4CXgx8iDjBXEt8DxxvZq8jHimtFAMHEX9We42ZfbNO23HUakD7YGCumX0a\n2NnMLiB+YNgP2MnMPkX8CfuOwEE1YhugVpPaXrX61f4wcJWZ/R/iAvYa4AYzu4u4SLbYzL4F7EUc\n753nbiJ+SagaD1KrSe1ctR5jZmcAzwe+A7wghPA0M3sc8ayye7rEV4UQzgPOK8RV2+ailUueZa3v\nAf9EnD9PB/Yws3OJlwfZVDMOZvYN4tmCEPcV0hpenv8K/ANwDrDZ4neUq4B9gDU14jnAFjM7kHiG\n2VQfWk1qe9XqV3uFmX0G+DLxu86NZnZJeo+sMLMvERdWjkpx57lbknbVeJBaTWqPitYG4nfS56Z/\nH0l+ayy5rNc0qbV1LcjMjiWemJHDusgoaDWpvZm4xnAA8Qz6yRDCa1PNj6UiuS1Yn0M8c2G6+BRg\n1y7xWaW2Z9Vo26a2V60mtT8NPIt4VmEnXk+8vuN5xC8CVeOLgWeFEP7DzDrxbLWa1M5V6xLiT7C2\nxma2O3FH9A3gli7x1Wa2ewhhLfHneT+t0TYXrVzyLGs9JoSw1uJi2EOJH+h3DyH8rZk9bRbxhcQv\nfh+VVuvaTwIOJF4X+YvERbL1xDMYTgR+TVxI+z7bxn6V+PoBajWp7VWrX+3vEw8knUP8MLiC+JO9\nuenxnsDTifPq4vTv3LTtWHhtr3iQWk1q56q1L/Bs4OshhHlmdj5ACOFW4ANmttdMsZl9nUQxrtI2\nF61c8pxG62jgfcQDUE8F3gScSzyw9CLg2BrxqcQDkccSz1a6VFpDzfN9xMvA3Ee8DMGriPeWuCX9\nP++sGM8nLtjMI17X893AS2ap1aS2V61+tV9DPDHjI8R99qOIGPEn7Mel5ydT/Lj0XGAbVeJBajWp\nPSpauxIvr/ZN4mfeZ5LfGksu6zVNahXXgk4hHoDIYV1kFLSa1P4c8EA6SNF5vsMpVMRCCL1fJYQQ\nYmwwMwP2CNvuqD7rWFrD1RZCCCGEEEIIIXIjuwVrM3shcDLxDDKIp6lD/Elmr/ghwB7Eo+YrarZt\nU9urVi55yrMfrVzylGd5HqZWLnnKszzX1colT69aueQpz/I8TK1c8pTn4Xq+C7g0hPB9CpjZ/wwh\nfHC6uNtzdeNBauWSp1etXPKUZ7/a46A1CG3iTcY7a7ePIV6m6Y/2w93IasHazP6RaPQrwO+BtwCP\nTk/fRvxpyEzxbsSfc/2S+HORfYk3wKnStk1tr1q55CnPfrRyyVOe5Vn9J8/yLM/etHLJU57lWf0n\nz949f5J4j5DTiTfuOzs9h5ndGUI4eLq423N140Fq5ZKnV61c8pRnv9rjoDUA7QeINyDvrN1+Czib\nafbDXQkV787oYQN+O10MGPEakN3i4l1mt8YV27ap7VUrlzzl2Y9WLnnKszyr/+RZnuXZm1Yuecqz\nPKv/5Nm75wcK2xbigrY2bdq0aWt26+x31wCbi/vlzj6815bbTRcnzOwZIYRrizHR9AQQusRbCm2f\n0Ykrtm1T26tWLnnKsx+tXPKUZ3lW/8mzPMuzN61c8pRneVb/ybN3z6vS6w4h3pxvL+AZIYTlZrYZ\nODCEsBygGJvZUiAUXtsrHqRWk9rjoJVLnvIsz6Paf5PAMWk/TXoe4r54gorktmB9JnChme1BPK18\nHXA1sWNuSK+ZKV4EXGlmuwIbUly1bZvaXrVyyVOe/Wjlkqc8y7P6T57lWZ69aeWSpzzLs/pPnr17\n3g24BlhOXE84hbh4vZx4WZHOY0rx14BNNeJBajWpPQ5aueQpz/I8qv33NeCCwtrtZjO7BVhN3A9X\nIqtrWHcwswPYdtPFu9K/VeNJYKdZtm1T26tWLnnKsx+tXPKU5/60csnTq1Yuecpzf1q55CnPfrRy\nyVOe+9PKJU+vWrnkKc/9afWlHUJYhhBCiNYor93W3g+HitcO8boBfzfbuJ+2bWp71colT3n2o5VL\nnvLsV3sctHLJU579anvVyiVPr1q55CnPfrXHQSuXPOXZr7ZXrVzy9KqVS57y7Fd7HLTazLPKVuvF\nHjdg/mzjftq2qe1VK5c85dmPVi55yrNf7XHQyiVPefar7VUrlzy9auWSpzz71R4HrVzylGe/2l61\ncsnTq1YuecqzX+1x0GozzyrbHPLH+oj7adumtletJrW9ajWpPQ5aTWp71WpS26tWk9rjoNWktlet\nJrW9ajWp7VWrSe1x0GpS26tWk9petZrUHgetJrW9ajWp7VWrSW2vWk1qj4NWk9petZrU9qrVpPY4\naDWpXX6uJ1lew7qImc0JIUzNJu6nbZvaXrVyyVOe/Wjlkqc8+9UeB61c8pRnv9petXLJ06tWLnnK\ns1/tcdDKJU959qvtVSuXPL1q5ZKnPPvVHgethvO0EOotQGe1YG1m+4UQ7i3EfwU8AVgAfB54Y5f4\n34EXAc9M8ZwabdvU9qqVS57y7EcrlzzlWZ7Vf/Isz/LsTSuXPOVZntV/8izP8uxNK5c85VmeR7X/\nVgAPBZ4ILAaeChwB3Ay8LYTwe6oQal5DZJgb21//5H3AA8AZwDeBn/aI1wLzUrwA2FijbZvaXrVy\nyVOe/Wjlkqc8y7P6T57lWZ69aeWSpzzLs/pPnuVZnr1p5ZKnPMvzqPbf+oLWUuD/AwcBZwJXVl4D\nHvYidM0F6+uKi9fADenxXGBDj3gK2K3QdkONtm1qe9XKJU959qOVS57yLM/qP3mWZ3n2ppVLnvIs\nz+o/eZZnefamlUue8izP49B/vwZuDGHrWu71nce9th3Ji13N7AjiqelzgbmFGGDnHvFjzKzT1mq2\nbVPbq1YuecqzH61c8pRneVb/ybM8y7M3rVzylGd5Vv/JszzLszetXPKUZ3kexf6bAs41s48Sz65+\nGVHw+cBqKpLbNayvKv1pJ2AS2BF4CnBjl/hI4BeF+Ajg2opt29T2qpVLnvLsRyuXPOVZntV/8izP\n8uxNK5c85Vme1X/yLM/y7E0rlzzlWZ5Htf+eAnwOOJW4gH0g8ZIj3wHeFUK4kypUPRXb8wbsADxo\nNnE/bdvU9qqVS57y7Ecrlzzl2a/2OGjlkqc8+9X2qpVLnl61cslTnv1qj4NWLnnKs19tr1q55OlV\nK5c85dmv9jhoNaC9N3BQJ66zdU7dzpoQwhbg4NnE/bRtU9urVi55yrMfrVzylGe/2uOglUue8uxX\n26tWLnl61colT3n2qz0OWrnkKc9+tb1q5ZKnV61c8pRnv9rjoNWA9v3A7p3YzB5HVcIsVrk9bsCd\ns437adumtletXPKUZz9aueQpz361x0Erlzzl2a+2V61c8vSqlUue8uxXexy0cslTnv1qe9XKeQrn\nwgAAB8RJREFUJU+vWrnkKc9+tcdBq808u207khFm9qnSn47qPAXsb2bXd4kfPkNcpW2b2l61cslT\nnv1o5ZKnPMuz+k+e5VmevWnlkqc8y7P6T57lWZ69aeWSpzzL87j03/4W13MNeDAVyWrBGngt8DZg\nY4pfD1wMbAIeATy2S3w4cF16TCnu1bZNba9aueQpz360cslTnuVZ/SfP8izP3rRyyVOe5Vn9J8/y\nLM/etHLJU57leVz67xHAr9Pj06hK1VOxPWzAD4E/mS4Gftcj3lBqu6FG2za1vWrlkqc8+9HKJU95\nlmf1nzzLszx708olT3mWZ/WfPMuzPHvTyiVPeZbncem/3033uNc29EXoOhuwD9vfbbJy3E/bNrW9\nauWSpzz70colT3n2qz0OWrnkKc9+tb1q5ZKnV61c8pRnv9rjoJVLnvLsV9urVi55etXKJU959qs9\nDlpt5jnbzZKYEEIIIYQQQgghhBBCCDFUsrqGtZntBbwbOBl4aPrzFPHC3XOA0CPeJf07BUwAW2q0\nbVPbq1YuecqzH61c8pRneVb/ybM8y7M3rVzylGd5Vv/JszzLszetXPKUZ3keh/7bACwHLgX+PoSw\nigpkdYa1mc0jXhflyyGEZSn+ZXr6WcQOmil+E/Bx4HvAicA7gAsrtm1T26tWLnnKsx+tXPKUZ3lW\n/8mzPMuzN61c8pRneVb/ybM8y7M3rVzylGd5Hpf+OwY4HTgDOCaEcDxV6PeaIm1uwMKZYmBhj3iy\n1HayRts2tb1q5ZKnPPvRyiVPeZZn9Z88y7M8e9PKJU95lmf1nzzLszx708olT3mW53Hpv4XTPe61\nZXVJEGCJmb2TeIb18hR/kHjK+VJgqku8JrX9LnBSiqu2bVPbq1YuecqzH61c8pRneVb/ybM8y7M3\nrVzylGd5Vv/JszzLszetXPKUZ3kel/5bamb7A2em11Yit0uC7A28C3gp8RrWxvbXVKFLvAOwM9uu\nobIR2FyxbZvaXrVyyVOe/Wjlkqc8y7P6T57lWZ69aeWSpzzLs/pPnuVZnr1p5ZKnPMvzuPTfBLAM\nuAz4WAhhJRXIasFaCCGEEEIIIYQQQgghxOiS2yVBMLPHAQcC14QQ1hXi3UMIl3aLiddVORC4Bngu\nsLhq2za1vWrlkqc8+9HKJU95lmf1nzzLszx708olT3mWZ/WfPMuzPHvTyiVPeZbncem/EML3Aczs\nRZ3HPQkVL3btYQPekkx/O3Xc5wvxxh7xZuCuQtt7a7RtU9urVi55yrMfrVzylGd5Vv/JszzLszet\nXPKUZ3lW/8mzPMuzN61c8pRneR6X/ru9sK47v/Ia8LAXoWsuWN9IXLkHOBRYD7wjxTf1iCeA+cDZ\nqe1UjbZtanvVyiVPefajlUue8izP6j95lmd59qaVS57yLM/qP3mWZ3n2ppVLnvIsz+PSf+uBs9Nz\n14VQbQ04t0uCzAkhrAUIISw2syXAMWb2MGAT0C0OwFHAJcAhwGSNtm1qe9XKJU959qOVS57yLM/q\nP3mWZ3n2ppVLnvIsz+o/eZZnefamlUue8izP49J/twEnmNkhxJsyVmJO75e4YrmZPbUQ3w28F9gP\neHKPeBfgUcBJKd65Rts2tb1q5ZKnPPvRyiVPeZZn9Z88y7M8e9PKJU95lmf1nzzLszx708olT3mW\n53HpvycWHj+ZqlQ9FdvDBhwEHDBdDDynR3xyqe3JNdq2qe1VK5c85dmPVi55yrM8q//kWZ7l2ZtW\nLnnKszyr/+RZnuXZm1YuecqzPI9L/z1nuse9NksNhBBCCCGEEEIIIYQQQoihktslQYQQQgghhBBC\nCCGEEEKMKFqwFkIIIYQQQgghhBBCCOECLVgLIYQQQgghhBBCCCGEcIEWrIUQQgghhGgBM9tiZvPN\nbIGZXWdmbzUz69HmEDM7ra0chRBCCCGEGDZasBZCCCGEEKId1oUQnhZCeBJwHHAC8P4ebQ4D/rzx\nzIQQQgghhHCCFqyFEEIIIYRomRDCvcAbgDfD1jOprzazX6Xt2emlHwX+NJ2ZfbaZzTGz88zsF2Z2\nvZm9flgehBBCCCGEaAILIQw7ByGEEEIIIUYeM3sghLBn6W8rgccCa4CpEMKkmT0K+HoI4RlmdjTw\nthDCS9LrXw88JITwETPbCfgp8IoQwpJ23QghhBBCCNEMOw47ASGEEEIIIcaYzjWsdwIuMLOnAluA\nR8/w+uOBJ5vZK1O8Z3qtFqyFEEIIIcRIoAVrIYQQQgghhoCZPQLYHEJYYWbvB5aFEA43sx2ADTM1\nA/4mhHBla4kKIYQQQgjRIrqGtRBCCCGEEO1gWx+YPQS4EPh0+tNewN3p8enADunxGmCPgsY84Cwz\n2zHpPNrMdm0yaSGEEEIIIdpEZ1gLIYQQQgjRDruY2Xzi5T82AV8JIfxDeu4zwLfM7HTg+8C69Pff\nAFNmdh3wpRDCJ83sUGC+mRlwD3Byix6EEEIIIYRoFN10UQghhBBCCCGEEEIIIYQLdEkQIYQQQggh\nhBBCCCGEEC7QgrUQQgghhBBCCCGEEEIIF2jBWgghhBBCCCGEEEIIIYQLtGAthBBCCCGEEEIIIYQQ\nwgVasBZCCCGEEEIIIYQQQgjhAi1YCyGEEEIIIYQQQgghhHCBFqyFEEIIIYQQQgghhBBCuOA/AWZ1\nnk4O0DppAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x240ade10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Examine overall price difference trend\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(25, 4))\n",
    "final_sums.plot(kind='bar', ax=ax)\n",
    "ax.set_xticklabels(final_sums['datetime'])\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Avg Price Difference')\n",
    "ax.set_title('Days When List Price Exceeded Average List Price for Individual AirBnbs')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Subset the data for easier viewing \n",
    "\n",
    "fall = final_sums[(final_sums['datetime'] > '2015-09-22') & (final_sums['datetime'] < '2015-12-22')] \n",
    "winter = final_sums[(final_sums['datetime'] > '2015-12-21') | (final_sums['datetime'] < '2015-03-20')] \n",
    "spring = final_sums[(final_sums['datetime'] > '2015-03-19') & (final_sums['datetime'] < '2015-06-21')] \n",
    "summer = final_sums[(final_sums['datetime'] > '2015-06-20') & (final_sums['datetime'] < '2015-09-23')] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x30868400>"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+dFG3u+yy72u97P3Uz/Jn7Vn/vD3P233asft83xZ5uw+5ftZj68ttvu1+m9ts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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x3067ac50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# WINTER \n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(25, 4))\n",
    "winter.plot(kind='bar', ax=ax)\n",
    "ax.set_xticklabels(winter['datetime'])\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Avg Price Difference')\n",
    "ax.set_title('Average Price Difference in Winter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2f52b5f8>"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BH1eSJEmSJEmSNFn6OTP63+hMPs/aAfjXIT3/z4Hfz8yHAY8FfjciHjKkx5Yk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\nREREZOk4GS0iIiIiIiIiIiIiS+c/64MILjUfZvQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f7c3e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SUMMER \n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(25, 4))\n",
    "summer.plot(kind='bar', ax=ax)\n",
    "ax.set_xticklabels(summer['datetime'])\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Avg Price Difference')\n",
    "ax.set_title('Average Price Difference in Summer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2f78e4e0>"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Vl+ZY6Nhjj91vXenlzqReL86MBgAAAABYIy9+8Ytz2WWX5aqrrsppp51249rPyy2DcdJJ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\ndHcfk+Tk8XVnzXBbbW1tbW1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gPc322tra2huxvb//v727C7WsLuM4/nvGURtMDUdRyUaL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2eec04a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#FALL\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(25, 4))\n",
    "fall.plot(kind='bar', ax=ax)\n",
    "ax.set_xticklabels(fall['datetime'])\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Avg Price Difference')\n",
    "ax.set_title('Average Price Difference in Fall')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x2ee2b2e8>"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LTpKDkhye5OndfYZWq9VqtVqtVqvVarVarVar1Won247U3TNxSXJe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\nJEmSJEnSwrMYLUmSJEmSJElaeP8BLw5Z1oxcI5YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2e0b8278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# SPRING\n",
    "\n",
    "fig, ax = plt.subplots(1, 1, figsize=(25, 4))\n",
    "spring.plot(kind='bar', ax=ax)\n",
    "ax.set_xticklabels(spring['datetime'])\n",
    "ax.set_xlabel('Date')\n",
    "ax.set_ylabel('Avg Price Difference')\n",
    "ax.set_title('Average Price Difference in Spring')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}