{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Profitability Analysis of Real Estates in NYC"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ------------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Harish Siva Subramanian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Email: hsivasub@asu.edu  or  sharish2395@gmail.com"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Mobile: (480)-494-7314"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -----------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Problem Statement"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " As a part of this assignment, a data product was built to assist a real estate company to understand which zip codes are profitable for short term rentals within New York City. They have already decided to invest in Two Bedroom houses as they are found to be profitable. For this purpose, publicly available data from Zillow and AirBnB were used."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Zillow Data-  Contains the Time Series cost data for 2 Bedroom Apartment for different Zipcodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Airbnb Data- Information on the listing including location, and other relevant short term rental informations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assumptions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1.The investor will pay for the property in cash (i.e. no mortgage/interest rate will need to be accounted for).\n",
    "\n",
    " \n",
    "2.The time value of money discount rate is 0% (i.e 1 dollar today is worth the same 100 years from now).\n",
    "\n",
    "\n",
    "3.All properties and all square feet within each locale can be assumed to be homogeneous (i.e. a 1000 square foot property in a locale such as Bronx or Manhattan generates twice the revenue and costs twice as much as any other 500 square foot property within that same locale).\n",
    "\n",
    "\n",
    "4.The occupancy rate is assumed to be 75%.\n",
    "\n",
    "\n",
    "5.It was assumed that the real estate company wants to buy the property in April 2019, so I had performed time series forecasting to obtain the value of property in April 2019 considering the data from January 2010.\n",
    "\n",
    "\n",
    "6.Since the data before January 2010 were showing abnormal pattern and also since most of them had missing values, the data from January 2010 were used.\n",
    "\n",
    "\n",
    "7.Availability for 90 days was considered during the availability analysis oowing to the fact availability for 365 days were incorrect for some zipcodes.\n",
    "\n",
    "\n",
    "8.Time for calculating the Breakeven Period was taken as 365 days(neglecting leap year) and thus the resultant Breakeven Period will be in years.\n",
    "\n",
    "\n",
    "9.While performing time series analysis, models were not built step by step for each zipcodes because of obvious time constraints and considering the scope of the project. Thus AutoARIMA was used to do Model fitting and forecasting for all the individual zipcodes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quality Check\n",
    "\n",
    "The project addressed the following quality checks,\n",
    "\n",
    "\n",
    "•\tChecking for Duplicates\n",
    "\n",
    "•\tChecking for Negative values\n",
    "\n",
    "•\tChecking the Columns with Null Values \n",
    "\n",
    "### Data Munging\n",
    "\n",
    "The project addressed the following quality checks,\n",
    "\n",
    "•\tCleaning up the Zillow Dataset.\n",
    "\n",
    "•\tCleaning up the Airbnb dataset.\n",
    "\n",
    "•\tImputing the missing values with group mean.\n",
    "\n",
    "•\tImputing some of the 0 values with the group mean.\n",
    "\n",
    "•\tFiltering the Airbnb dataset for New York and 2 Bedrooms apartment.\n",
    "\n",
    "•\tGrouping the Airbnb data by zipcode and taking mean.\n",
    "\n",
    "•\tJoining the cleaned final Airbnb and Zillow dataset based on matching Zipcodes.\n",
    "\n",
    "\n",
    "### Additional KPIs added\n",
    "\n",
    "•\tCurrent Month Cost-April 2019(Calculated from Time series Forecasting).  Also the cost for other months preceding Current Month were also added but they were not used anywhere.\n",
    "\n",
    "•\tNeighbourhood group cleansed(Extracted from Airbnb data to add to final dataframe).\n",
    "\n",
    "•\tExpected Revenue(Calulated Field).\n",
    "\n",
    "•\tBreakeven Period(Calculated Field).\n",
    "\n",
    "\n",
    "### Time Series Analysis\n",
    "\n",
    "Here the Zillow dataset contains the Time series of cost data and the data from January 2010 was taken to fit the Time series model and forecast the cost values upto the Current Month Cost which is April 2019 for all the 21 Zipcodes.\n",
    "Here for this purpose the Pyamid ARIMA package was used to perform the AutoARIMA and get the forecast values.\n",
    "\n",
    "\n",
    "### Visual Analysis:\n",
    "\n",
    "The following plots and analysis was carried out in this project,\n",
    "\n",
    "•\tVisualizing the Zipcodes in Real Time.\n",
    "\n",
    "•\tPreliminary Review Analysis:\n",
    "\n",
    "    Review Accuracy vs Reviews Ratings\n",
    "\n",
    "    Reviews Ratings Vs No of Reviews Vs Neighbourhood\n",
    "\n",
    "•\tAnalysis of Availability:\n",
    "\n",
    "    Bar plots of Availability for all Zipcodes\n",
    "    \n",
    "    Availability- Revenue Analysis\n",
    "    \n",
    "    Availability- Cost Analysis\n",
    "    \n",
    "•\tCorrelation between Revenue, Cost, Reviews and Availability\n",
    "\n",
    "•\tDistribution of Revenue and Cost\n",
    "\n",
    "    Violin Plot of Revenue\n",
    "    \n",
    "    Violin Plot of Cost\n",
    "    \n",
    "•\tRevenue-Cost Analysis\n",
    "\n",
    "•\tBreak Even Analysis\n",
    "\n",
    "•\t3-D Scatter Plot Between Revenue, Cost and Breakeven period\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "#Importing the Libraries\n",
    "\n",
    "import pandas as pd                \n",
    "import numpy as np                   \n",
    "\n",
    "# Matplotlib is for plotting graphs\n",
    "import matplotlib.pyplot as plt    \n",
    "# matplotlib inline will display the graphs right below the cell\n",
    "%matplotlib inline                 \n",
    "\n",
    "# Plotly is used for Interactive Visualization\n",
    "import plotly.plotly as py         \n",
    "from plotly.offline import init_notebook_mode, iplot         \n",
    "init_notebook_mode(connected=True)                           \n",
    "import plotly.graph_objs as go   \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shari\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2698: DtypeWarning:\n",
      "\n",
      "Columns (43,88) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Importing the Respective Datasets\n",
    "\n",
    "airbnb = pd.read_csv(\"listings.csv\")\n",
    "zillow = pd.read_csv(\"Zip_Zhvi_2bedroom.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40753, 95)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8946, 262)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zillow.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality Check- Phase I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Since there are many columns in these datasets, I have created a function to display all the columns as a list. This helps us to see all the columns in the Jupyter Notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to return all the Columns in a List Format:\n",
    "\n",
    "def cols(dataset):\n",
    "    return(dataset.columns.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['id',\n",
       " 'listing_url',\n",
       " 'scrape_id',\n",
       " 'last_scraped',\n",
       " 'name',\n",
       " 'summary',\n",
       " 'space',\n",
       " 'description',\n",
       " 'experiences_offered',\n",
       " 'neighborhood_overview',\n",
       " 'notes',\n",
       " 'transit',\n",
       " 'access',\n",
       " 'interaction',\n",
       " 'house_rules',\n",
       " 'thumbnail_url',\n",
       " 'medium_url',\n",
       " 'picture_url',\n",
       " 'xl_picture_url',\n",
       " 'host_id',\n",
       " 'host_url',\n",
       " 'host_name',\n",
       " 'host_since',\n",
       " 'host_location',\n",
       " 'host_about',\n",
       " 'host_response_time',\n",
       " 'host_response_rate',\n",
       " 'host_acceptance_rate',\n",
       " 'host_is_superhost',\n",
       " 'host_thumbnail_url',\n",
       " 'host_picture_url',\n",
       " 'host_neighbourhood',\n",
       " 'host_listings_count',\n",
       " 'host_total_listings_count',\n",
       " 'host_verifications',\n",
       " 'host_has_profile_pic',\n",
       " 'host_identity_verified',\n",
       " 'street',\n",
       " 'neighbourhood',\n",
       " 'neighbourhood_cleansed',\n",
       " 'neighbourhood_group_cleansed',\n",
       " 'city',\n",
       " 'state',\n",
       " 'zipcode',\n",
       " 'market',\n",
       " 'smart_location',\n",
       " 'country_code',\n",
       " 'country',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'is_location_exact',\n",
       " 'property_type',\n",
       " 'room_type',\n",
       " 'accommodates',\n",
       " 'bathrooms',\n",
       " 'bedrooms',\n",
       " 'beds',\n",
       " 'bed_type',\n",
       " 'amenities',\n",
       " 'square_feet',\n",
       " 'price',\n",
       " 'weekly_price',\n",
       " 'monthly_price',\n",
       " 'security_deposit',\n",
       " 'cleaning_fee',\n",
       " 'guests_included',\n",
       " 'extra_people',\n",
       " 'minimum_nights',\n",
       " 'maximum_nights',\n",
       " 'calendar_updated',\n",
       " 'has_availability',\n",
       " 'availability_30',\n",
       " 'availability_60',\n",
       " 'availability_90',\n",
       " 'availability_365',\n",
       " 'calendar_last_scraped',\n",
       " 'number_of_reviews',\n",
       " 'first_review',\n",
       " 'last_review',\n",
       " 'review_scores_rating',\n",
       " 'review_scores_accuracy',\n",
       " 'review_scores_cleanliness',\n",
       " 'review_scores_checkin',\n",
       " 'review_scores_communication',\n",
       " 'review_scores_location',\n",
       " 'review_scores_value',\n",
       " 'requires_license',\n",
       " 'license',\n",
       " 'jurisdiction_names',\n",
       " 'instant_bookable',\n",
       " 'cancellation_policy',\n",
       " 'require_guest_profile_picture',\n",
       " 'require_guest_phone_verification',\n",
       " 'calculated_host_listings_count',\n",
       " 'reviews_per_month']"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols(airbnb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['RegionID',\n",
       " 'RegionName',\n",
       " 'City',\n",
       " 'State',\n",
       " 'Metro',\n",
       " 'CountyName',\n",
       " 'SizeRank',\n",
       " '1996-04',\n",
       " '1996-05',\n",
       " '1996-06',\n",
       " '1996-07',\n",
       " '1996-08',\n",
       " '1996-09',\n",
       " '1996-10',\n",
       " '1996-11',\n",
       " '1996-12',\n",
       " '1997-01',\n",
       " '1997-02',\n",
       " '1997-03',\n",
       " '1997-04',\n",
       " '1997-05',\n",
       " '1997-06',\n",
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       " '1997-10',\n",
       " '1997-11',\n",
       " '1997-12',\n",
       " '1998-01',\n",
       " '1998-02',\n",
       " '1998-03',\n",
       " '1998-04',\n",
       " '1998-05',\n",
       " '1998-06',\n",
       " '1998-07',\n",
       " '1998-08',\n",
       " '1998-09',\n",
       " '1998-10',\n",
       " '1998-11',\n",
       " '1998-12',\n",
       " '1999-01',\n",
       " '1999-02',\n",
       " '1999-03',\n",
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       " '1999-05',\n",
       " '1999-06',\n",
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       " '2012-12',\n",
       " '2013-01',\n",
       " '2013-02',\n",
       " '2013-03',\n",
       " '2013-04',\n",
       " '2013-05',\n",
       " '2013-06',\n",
       " '2013-07',\n",
       " '2013-08',\n",
       " '2013-09',\n",
       " '2013-10',\n",
       " '2013-11',\n",
       " '2013-12',\n",
       " '2014-01',\n",
       " '2014-02',\n",
       " '2014-03',\n",
       " '2014-04',\n",
       " '2014-05',\n",
       " '2014-06',\n",
       " '2014-07',\n",
       " '2014-08',\n",
       " '2014-09',\n",
       " '2014-10',\n",
       " '2014-11',\n",
       " '2014-12',\n",
       " '2015-01',\n",
       " '2015-02',\n",
       " '2015-03',\n",
       " '2015-04',\n",
       " '2015-05',\n",
       " '2015-06',\n",
       " '2015-07',\n",
       " '2015-08',\n",
       " '2015-09',\n",
       " '2015-10',\n",
       " '2015-11',\n",
       " '2015-12',\n",
       " '2016-01',\n",
       " '2016-02',\n",
       " '2016-03',\n",
       " '2016-04',\n",
       " '2016-05',\n",
       " '2016-06',\n",
       " '2016-07',\n",
       " '2016-08',\n",
       " '2016-09',\n",
       " '2016-10',\n",
       " '2016-11',\n",
       " '2016-12',\n",
       " '2017-01',\n",
       " '2017-02',\n",
       " '2017-03',\n",
       " '2017-04',\n",
       " '2017-05',\n",
       " '2017-06']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols(zillow)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to determine the Missing Value Percent in every Column of a Dataset\n",
    "\n",
    "def mis_value_percent(dataset):\n",
    "    return(100* dataset.isnull().sum()/ len(dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RegionID       0.000000\n",
       "RegionName     0.000000\n",
       "City           0.000000\n",
       "State          0.000000\n",
       "Metro          2.794545\n",
       "CountyName     0.000000\n",
       "SizeRank       0.000000\n",
       "1996-04       29.756316\n",
       "1996-05       28.862061\n",
       "1996-06       28.862061\n",
       "1996-07       28.806170\n",
       "1996-08       28.794992\n",
       "1996-09       28.794992\n",
       "1996-10       28.794992\n",
       "1996-11       28.683210\n",
       "1996-12       28.683210\n",
       "1997-01       28.414934\n",
       "1997-02       23.619495\n",
       "1997-03       23.395931\n",
       "1997-04       23.395931\n",
       "1997-05       23.395931\n",
       "1997-06       23.373575\n",
       "1997-07       23.373575\n",
       "1997-08       22.289291\n",
       "1997-09       22.255757\n",
       "1997-10       22.255757\n",
       "1997-11       22.222222\n",
       "1997-12       22.177510\n",
       "1998-01       21.987480\n",
       "1998-02       20.366644\n",
       "                ...    \n",
       "2015-01        0.000000\n",
       "2015-02        0.000000\n",
       "2015-03        0.000000\n",
       "2015-04        0.000000\n",
       "2015-05        0.000000\n",
       "2015-06        0.000000\n",
       "2015-07        0.000000\n",
       "2015-08        0.000000\n",
       "2015-09        0.000000\n",
       "2015-10        0.000000\n",
       "2015-11        0.000000\n",
       "2015-12        0.000000\n",
       "2016-01        0.000000\n",
       "2016-02        0.201207\n",
       "2016-03        0.201207\n",
       "2016-04        0.201207\n",
       "2016-05        0.201207\n",
       "2016-06        0.000000\n",
       "2016-07        0.000000\n",
       "2016-08        0.000000\n",
       "2016-09        0.000000\n",
       "2016-10        0.000000\n",
       "2016-11        0.000000\n",
       "2016-12        0.033535\n",
       "2017-01        0.000000\n",
       "2017-02        0.000000\n",
       "2017-03        0.000000\n",
       "2017-04        0.000000\n",
       "2017-05        0.000000\n",
       "2017-06        0.000000\n",
       "Length: 262, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mis_value_percent(zillow)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Most of the Missing values in zillow data are associated with the time series data which I had later neglected the missing columns alone while performing time series analysis for that."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for Duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40753, 95)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40753, 95)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb.drop_duplicates().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No duplicates are found in AirBnB data as the dimensions didn't change"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8946, 262)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zillow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(8946, 262)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zillow.drop_duplicates().shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly no duplicate rows are found in Zillow dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Check for Negative Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\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>scrape_id</th>\n",
       "      <th>last_scraped</th>\n",
       "      <th>name</th>\n",
       "      <th>summary</th>\n",
       "      <th>space</th>\n",
       "      <th>description</th>\n",
       "      <th>experiences_offered</th>\n",
       "      <th>neighborhood_overview</th>\n",
       "      <th>...</th>\n",
       "      <th>review_scores_value</th>\n",
       "      <th>requires_license</th>\n",
       "      <th>license</th>\n",
       "      <th>jurisdiction_names</th>\n",
       "      <th>instant_bookable</th>\n",
       "      <th>cancellation_policy</th>\n",
       "      <th>require_guest_profile_picture</th>\n",
       "      <th>require_guest_phone_verification</th>\n",
       "      <th>calculated_host_listings_count</th>\n",
       "      <th>reviews_per_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 95 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [id, listing_url, scrape_id, last_scraped, name, summary, space, description, experiences_offered, neighborhood_overview, notes, transit, access, interaction, house_rules, thumbnail_url, medium_url, picture_url, xl_picture_url, host_id, host_url, host_name, host_since, host_location, host_about, host_response_time, host_response_rate, host_acceptance_rate, host_is_superhost, host_thumbnail_url, host_picture_url, host_neighbourhood, host_listings_count, host_total_listings_count, host_verifications, host_has_profile_pic, host_identity_verified, street, neighbourhood, neighbourhood_cleansed, neighbourhood_group_cleansed, city, state, zipcode, market, smart_location, country_code, country, latitude, longitude, is_location_exact, property_type, room_type, accommodates, bathrooms, bedrooms, beds, bed_type, amenities, square_feet, price, weekly_price, monthly_price, security_deposit, cleaning_fee, guests_included, extra_people, minimum_nights, maximum_nights, calendar_updated, has_availability, availability_30, availability_60, availability_90, availability_365, calendar_last_scraped, number_of_reviews, first_review, last_review, review_scores_rating, review_scores_accuracy, review_scores_cleanliness, review_scores_checkin, review_scores_communication, review_scores_location, review_scores_value, requires_license, license, jurisdiction_names, instant_bookable, cancellation_policy, require_guest_profile_picture, require_guest_phone_verification, calculated_host_listings_count, reviews_per_month]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 95 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb[(airbnb < 0).all(1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>RegionID</th>\n",
       "      <th>RegionName</th>\n",
       "      <th>City</th>\n",
       "      <th>State</th>\n",
       "      <th>Metro</th>\n",
       "      <th>CountyName</th>\n",
       "      <th>SizeRank</th>\n",
       "      <th>1996-04</th>\n",
       "      <th>1996-05</th>\n",
       "      <th>1996-06</th>\n",
       "      <th>...</th>\n",
       "      <th>2016-09</th>\n",
       "      <th>2016-10</th>\n",
       "      <th>2016-11</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-01</th>\n",
       "      <th>2017-02</th>\n",
       "      <th>2017-03</th>\n",
       "      <th>2017-04</th>\n",
       "      <th>2017-05</th>\n",
       "      <th>2017-06</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>0 rows × 262 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [RegionID, RegionName, City, State, Metro, CountyName, SizeRank, 1996-04, 1996-05, 1996-06, 1996-07, 1996-08, 1996-09, 1996-10, 1996-11, 1996-12, 1997-01, 1997-02, 1997-03, 1997-04, 1997-05, 1997-06, 1997-07, 1997-08, 1997-09, 1997-10, 1997-11, 1997-12, 1998-01, 1998-02, 1998-03, 1998-04, 1998-05, 1998-06, 1998-07, 1998-08, 1998-09, 1998-10, 1998-11, 1998-12, 1999-01, 1999-02, 1999-03, 1999-04, 1999-05, 1999-06, 1999-07, 1999-08, 1999-09, 1999-10, 1999-11, 1999-12, 2000-01, 2000-02, 2000-03, 2000-04, 2000-05, 2000-06, 2000-07, 2000-08, 2000-09, 2000-10, 2000-11, 2000-12, 2001-01, 2001-02, 2001-03, 2001-04, 2001-05, 2001-06, 2001-07, 2001-08, 2001-09, 2001-10, 2001-11, 2001-12, 2002-01, 2002-02, 2002-03, 2002-04, 2002-05, 2002-06, 2002-07, 2002-08, 2002-09, 2002-10, 2002-11, 2002-12, 2003-01, 2003-02, 2003-03, 2003-04, 2003-05, 2003-06, 2003-07, 2003-08, 2003-09, 2003-10, 2003-11, 2003-12, ...]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 262 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zillow[(zillow < 0).all(1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No negative values are found in both the dataframes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Munging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['New York', 'Chicago', 'El Paso', ..., \"Hart'S Location\",\n",
       "       'Waterville Valley', 'Shelburne Falls'], dtype=object)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zillow.City.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       10025\n",
       "1       60657\n",
       "2       10023\n",
       "3       60614\n",
       "4       79936\n",
       "5       60640\n",
       "6       94109\n",
       "7       77494\n",
       "8       32162\n",
       "9       60647\n",
       "10      37211\n",
       "11      37013\n",
       "12      60618\n",
       "13      10128\n",
       "14      10011\n",
       "15      28269\n",
       "16      77573\n",
       "17      30349\n",
       "18      90046\n",
       "19      77584\n",
       "20      10003\n",
       "21      60613\n",
       "22      78660\n",
       "23      75052\n",
       "24      20009\n",
       "25      20002\n",
       "26      21234\n",
       "27      63376\n",
       "28      30044\n",
       "29      60629\n",
       "        ...  \n",
       "8916    47640\n",
       "8917    92333\n",
       "8918     3605\n",
       "8919    47968\n",
       "8920    47597\n",
       "8921    80515\n",
       "8922     3282\n",
       "8923    55781\n",
       "8924    96148\n",
       "8925     3457\n",
       "8926    80456\n",
       "8927    80510\n",
       "8928     1255\n",
       "8929    95497\n",
       "8930    55053\n",
       "8931    95728\n",
       "8932    97149\n",
       "8933     3279\n",
       "8934    12480\n",
       "8935    80481\n",
       "8936     1270\n",
       "8937     3812\n",
       "8938     3215\n",
       "8939    47965\n",
       "8940     3765\n",
       "8941    80532\n",
       "8942    12429\n",
       "8943    97028\n",
       "8944     1338\n",
       "8945     3293\n",
       "Name: RegionName, Length: 8946, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zillow.RegionName"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Cleaning the Zillow Data:\n",
    "\n",
    "def zillow_clean(dataset, city_of_interest):\n",
    "    \n",
    "    #Filtering Out the City Based on the Requirement\n",
    "    dataset=dataset[dataset.City==city_of_interest]\n",
    "    \n",
    "    # Renaming the Column Name \"RegionName\" to \"zipcode\"\n",
    "    dataset.rename(columns= {\"RegionName\": \"zipcode\"}, inplace= True)\n",
    "    \n",
    "    # Converting the zipcode column to integer format\n",
    "    dataset['zipcode']= dataset['zipcode'].astype(int)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shari\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py:2746: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n",
      "C:\\Users\\shari\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:12: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Using the Function and Storing it as zillow\n",
    "\n",
    "zillow=zillow_clean(zillow, \"New York\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 262)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zillow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                    0.000000\n",
       "listing_url                           0.000000\n",
       "scrape_id                             0.000000\n",
       "last_scraped                          0.000000\n",
       "name                                  0.085883\n",
       "summary                               3.962898\n",
       "space                                31.585405\n",
       "description                           0.046622\n",
       "experiences_offered                   0.000000\n",
       "neighborhood_overview                42.018992\n",
       "notes                                64.157240\n",
       "transit                              37.526072\n",
       "access                               42.136775\n",
       "interaction                          44.845778\n",
       "house_rules                          36.399774\n",
       "thumbnail_url                        21.142002\n",
       "medium_url                           21.142002\n",
       "picture_url                           0.000000\n",
       "xl_picture_url                       21.142002\n",
       "host_id                               0.000000\n",
       "host_url                              0.000000\n",
       "host_name                             0.615906\n",
       "host_since                            0.615906\n",
       "host_location                         1.020784\n",
       "host_about                           38.505141\n",
       "host_response_time                   32.247933\n",
       "host_response_rate                   32.247933\n",
       "host_acceptance_rate                100.000000\n",
       "host_is_superhost                     0.615906\n",
       "host_thumbnail_url                    0.615906\n",
       "                                       ...    \n",
       "guests_included                       0.000000\n",
       "extra_people                          0.000000\n",
       "minimum_nights                        0.000000\n",
       "maximum_nights                        0.000000\n",
       "calendar_updated                      0.000000\n",
       "has_availability                    100.000000\n",
       "availability_30                       0.000000\n",
       "availability_60                       0.000000\n",
       "availability_90                       0.000000\n",
       "availability_365                      0.000000\n",
       "calendar_last_scraped                 0.000000\n",
       "number_of_reviews                     0.000000\n",
       "first_review                         22.817952\n",
       "last_review                          22.538218\n",
       "review_scores_rating                 24.351581\n",
       "review_scores_accuracy               24.579786\n",
       "review_scores_cleanliness            24.501264\n",
       "review_scores_checkin                24.626408\n",
       "review_scores_communication          24.503718\n",
       "review_scores_location               24.628862\n",
       "review_scores_value                  24.633769\n",
       "requires_license                      0.000000\n",
       "license                             100.000000\n",
       "jurisdiction_names                   99.980370\n",
       "instant_bookable                      0.000000\n",
       "cancellation_policy                   0.000000\n",
       "require_guest_profile_picture         0.000000\n",
       "require_guest_phone_verification      0.000000\n",
       "calculated_host_listings_count        0.000000\n",
       "reviews_per_month                    22.817952\n",
       "Length: 95, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mis_value_percent(airbnb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40753, 95)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airbnb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to clean up the Airbnb Dataset and Filter it based on the Number of Bedrooms\n",
    "\n",
    "def airbnb_clean(dataset, number_of_bedrooms):\n",
    "    \n",
    "    \n",
    "    # Remove the Dollar Sign in the Price Column\n",
    "    dataset['price']=dataset['price'].replace( '[\\$,)]','', regex=True ).astype(float)\n",
    "    \n",
    "    # Some rows have their postal code along with the zip code and we will be needing only the Zipcode(5 numbers alone)\n",
    "    dataset['zipcode']= dataset['zipcode'].str[:5]\n",
    "    \n",
    "    # Removing the Nan values from Zipcodes\n",
    "    dataset['zipcode'] = pd.to_numeric(dataset['zipcode'], errors='coerce')\n",
    "    dataset.dropna(subset=['zipcode'], inplace=True)\n",
    "    dataset['zipcode']= dataset['zipcode'].astype(int)\n",
    "    \n",
    "    # Here data cleaning was performed on Reviews below since they are used further down the analyses\n",
    "    # Converting the 0 values in the number_of_reviews with their group(zipcode) mean values\n",
    "    dataset['number_of_reviews']=dataset.groupby('zipcode')['number_of_reviews'].transform(lambda x: x.replace(0,x.mean()))\n",
    "\n",
    "    # Removing the NA values in those 2 columns and replacing with their group(zipcode) mean values\n",
    "    dataset[['review_scores_rating', 'review_scores_accuracy']]=dataset.groupby('zipcode')['review_scores_rating', 'review_scores_accuracy'].transform(lambda x: x.fillna(x.mean()))\n",
    "    \n",
    "    # Filter by the Number of Bedrooms\n",
    "    dataset= dataset.loc[dataset.bedrooms ==number_of_bedrooms]\n",
    "    \n",
    "    \n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Using the above function to Filter out the 2 Bedrooms data and store it in airbnb\n",
    "airbnb= airbnb_clean(airbnb,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3882, 95)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Resulting Shape\n",
    "airbnb.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                    int64\n",
       "listing_url                          object\n",
       "scrape_id                           float64\n",
       "last_scraped                         object\n",
       "name                                 object\n",
       "summary                              object\n",
       "space                                object\n",
       "description                          object\n",
       "experiences_offered                  object\n",
       "neighborhood_overview                object\n",
       "notes                                object\n",
       "transit                              object\n",
       "access                               object\n",
       "interaction                          object\n",
       "house_rules                          object\n",
       "thumbnail_url                        object\n",
       "medium_url                           object\n",
       "picture_url                          object\n",
       "xl_picture_url                       object\n",
       "host_id                               int64\n",
       "host_url                             object\n",
       "host_name                            object\n",
       "host_since                           object\n",
       "host_location                        object\n",
       "host_about                           object\n",
       "host_response_time                   object\n",
       "host_response_rate                   object\n",
       "host_acceptance_rate                float64\n",
       "host_is_superhost                    object\n",
       "host_thumbnail_url                   object\n",
       "                                     ...   \n",
       "guests_included                       int64\n",
       "extra_people                         object\n",
       "minimum_nights                        int64\n",
       "maximum_nights                        int64\n",
       "calendar_updated                     object\n",
       "has_availability                    float64\n",
       "availability_30                       int64\n",
       "availability_60                       int64\n",
       "availability_90                       int64\n",
       "availability_365                      int64\n",
       "calendar_last_scraped                object\n",
       "number_of_reviews                   float64\n",
       "first_review                         object\n",
       "last_review                          object\n",
       "review_scores_rating                float64\n",
       "review_scores_accuracy              float64\n",
       "review_scores_cleanliness           float64\n",
       "review_scores_checkin               float64\n",
       "review_scores_communication         float64\n",
       "review_scores_location              float64\n",
       "review_scores_value                 float64\n",
       "requires_license                     object\n",
       "license                             float64\n",
       "jurisdiction_names                   object\n",
       "instant_bookable                     object\n",
       "cancellation_policy                  object\n",
       "require_guest_profile_picture        object\n",
       "require_guest_phone_verification     object\n",
       "calculated_host_listings_count        int64\n",
       "reviews_per_month                   float64\n",
       "Length: 95, dtype: object"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if zipcode is converted to Integer Format\n",
    "airbnb.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10462, 10469, 11102, 11105, 11370, 11417, 11416, 10458, 10305,\n",
       "       11692, 11103, 11106, 11101, 11356, 11228, 10006, 10004, 10280,\n",
       "       11209, 11220, 11360, 11233, 11221, 11216, 11213, 11238, 11205,\n",
       "       11206, 11237, 11694, 11001, 11426, 11214, 11204, 11201, 11217,\n",
       "       11218, 11219, 11235, 11224, 11225, 11226, 11203, 10455, 10454,\n",
       "       11212, 11207, 11385, 10002, 10013, 10038, 11236, 11231, 10011,\n",
       "       10010, 10001, 10003, 10014, 10036, 10473, 10304, 10451, 10452,\n",
       "       11368, 10465, 11208, 11369, 11210, 10029, 10026, 10035, 10037,\n",
       "       10128, 10027, 10459, 11373, 10012, 10009, 10016, 10475, 11691,\n",
       "       10270, 10005, 11230, 11234, 11358, 11354, 10018, 11109, 10463,\n",
       "       10019, 10022, 10017, 10306, 11378, 11377, 11379, 10025, 10467,\n",
       "       10301, 11215, 10461, 11374, 11418, 11693, 10044,  7310, 10312,\n",
       "       11422, 11229, 11223, 11420, 11434, 11412, 11104, 10460, 11232,\n",
       "       10111, 10007, 10028, 10065, 10021, 10075, 10162, 10024, 10023,\n",
       "       10069, 10466, 10032, 10033, 10040, 10310, 11211, 11249, 11222],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking if Zipcode is in correct format\n",
    "airbnb.zipcode.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                    0.000000\n",
       "listing_url                           0.000000\n",
       "scrape_id                             0.000000\n",
       "last_scraped                          0.000000\n",
       "name                                  0.025760\n",
       "summary                               4.070067\n",
       "space                                25.837197\n",
       "description                           0.025760\n",
       "experiences_offered                   0.000000\n",
       "neighborhood_overview                38.021638\n",
       "notes                                61.746522\n",
       "transit                              34.389490\n",
       "access                               40.340031\n",
       "interaction                          42.606904\n",
       "house_rules                          31.942298\n",
       "thumbnail_url                        24.343122\n",
       "medium_url                           24.343122\n",
       "picture_url                           0.000000\n",
       "xl_picture_url                       24.343122\n",
       "host_id                               0.000000\n",
       "host_url                              0.000000\n",
       "host_name                             0.283359\n",
       "host_since                            0.283359\n",
       "host_location                         0.489438\n",
       "host_about                           34.209171\n",
       "host_response_time                   27.408552\n",
       "host_response_rate                   27.408552\n",
       "host_acceptance_rate                100.000000\n",
       "host_is_superhost                     0.283359\n",
       "host_thumbnail_url                    0.283359\n",
       "                                       ...    \n",
       "guests_included                       0.000000\n",
       "extra_people                          0.000000\n",
       "minimum_nights                        0.000000\n",
       "maximum_nights                        0.000000\n",
       "calendar_updated                      0.000000\n",
       "has_availability                    100.000000\n",
       "availability_30                       0.000000\n",
       "availability_60                       0.000000\n",
       "availability_90                       0.000000\n",
       "availability_365                      0.000000\n",
       "calendar_last_scraped                 0.000000\n",
       "number_of_reviews                     0.000000\n",
       "first_review                         19.809377\n",
       "last_review                          19.500258\n",
       "review_scores_rating                  0.025760\n",
       "review_scores_accuracy                0.025760\n",
       "review_scores_cleanliness            20.942813\n",
       "review_scores_checkin                21.045853\n",
       "review_scores_communication          20.968573\n",
       "review_scores_location               21.045853\n",
       "review_scores_value                  21.045853\n",
       "requires_license                      0.000000\n",
       "license                             100.000000\n",
       "jurisdiction_names                   99.974240\n",
       "instant_bookable                      0.000000\n",
       "cancellation_policy                   0.000000\n",
       "require_guest_profile_picture         0.000000\n",
       "require_guest_phone_verification      0.000000\n",
       "calculated_host_listings_count        0.000000\n",
       "reviews_per_month                    19.809377\n",
       "Length: 95, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mis_value_percent(airbnb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Joining the Cleaned Zillow and Cleaned Airbnb Datasets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to basically group the Airbnb based on Zipcodes and then Join with the Zillow Data\n",
    "\n",
    "def group_join(x,y):\n",
    "    \n",
    "    # Since our Airbnb has lot of properties in each Zipcodes- Grouping by the Zipcode and taking the mean\n",
    "    airbnb_meandf=x.groupby('zipcode',as_index=False).mean()\n",
    "    \n",
    "    # Joining this groupped by Dataframe with the y dataframe which will be Zillow Dataframe\n",
    "    final=pd.merge(airbnb_meandf, y, on='zipcode')\n",
    "    \n",
    "    return final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>id</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>...</th>\n",
       "      <th>2016-09</th>\n",
       "      <th>2016-10</th>\n",
       "      <th>2016-11</th>\n",
       "      <th>2016-12</th>\n",
       "      <th>2017-01</th>\n",
       "      <th>2017-02</th>\n",
       "      <th>2017-03</th>\n",
       "      <th>2017-04</th>\n",
       "      <th>2017-05</th>\n",
       "      <th>2017-06</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003</td>\n",
       "      <td>9.124661e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.496037e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.646018</td>\n",
       "      <td>1.646018</td>\n",
       "      <td>40.728902</td>\n",
       "      <td>-73.988258</td>\n",
       "      <td>4.566372</td>\n",
       "      <td>...</td>\n",
       "      <td>1932800</td>\n",
       "      <td>1930400</td>\n",
       "      <td>1937500</td>\n",
       "      <td>1935100.0</td>\n",
       "      <td>1915700</td>\n",
       "      <td>1916500</td>\n",
       "      <td>1965700</td>\n",
       "      <td>2045300</td>\n",
       "      <td>2109100</td>\n",
       "      <td>2147000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10011</td>\n",
       "      <td>9.889107e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.005599e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.448276</td>\n",
       "      <td>1.448276</td>\n",
       "      <td>40.742371</td>\n",
       "      <td>-73.999752</td>\n",
       "      <td>4.517241</td>\n",
       "      <td>...</td>\n",
       "      <td>2354000</td>\n",
       "      <td>2355500</td>\n",
       "      <td>2352200</td>\n",
       "      <td>2332100.0</td>\n",
       "      <td>2313300</td>\n",
       "      <td>2319600</td>\n",
       "      <td>2342100</td>\n",
       "      <td>2365900</td>\n",
       "      <td>2419700</td>\n",
       "      <td>2480400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10013</td>\n",
       "      <td>1.054726e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.783653e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.412698</td>\n",
       "      <td>1.412698</td>\n",
       "      <td>40.719555</td>\n",
       "      <td>-74.004075</td>\n",
       "      <td>4.281250</td>\n",
       "      <td>...</td>\n",
       "      <td>3347100</td>\n",
       "      <td>3305500</td>\n",
       "      <td>3261100</td>\n",
       "      <td>3244000.0</td>\n",
       "      <td>3231400</td>\n",
       "      <td>3183300</td>\n",
       "      <td>3160200</td>\n",
       "      <td>3193500</td>\n",
       "      <td>3262200</td>\n",
       "      <td>3316500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10014</td>\n",
       "      <td>8.654217e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.054620e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.537634</td>\n",
       "      <td>1.537634</td>\n",
       "      <td>40.734209</td>\n",
       "      <td>-74.004240</td>\n",
       "      <td>4.344086</td>\n",
       "      <td>...</td>\n",
       "      <td>2480800</td>\n",
       "      <td>2443200</td>\n",
       "      <td>2430100</td>\n",
       "      <td>2452900.0</td>\n",
       "      <td>2451200</td>\n",
       "      <td>2441900</td>\n",
       "      <td>2460900</td>\n",
       "      <td>2494900</td>\n",
       "      <td>2498400</td>\n",
       "      <td>2491600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10021</td>\n",
       "      <td>1.022891e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.568867e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>40.768441</td>\n",
       "      <td>-73.956628</td>\n",
       "      <td>4.263158</td>\n",
       "      <td>...</td>\n",
       "      <td>1668300</td>\n",
       "      <td>1664800</td>\n",
       "      <td>1659500</td>\n",
       "      <td>1656000.0</td>\n",
       "      <td>1643700</td>\n",
       "      <td>1649800</td>\n",
       "      <td>1688500</td>\n",
       "      <td>1731400</td>\n",
       "      <td>1774100</td>\n",
       "      <td>1815600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10022</td>\n",
       "      <td>9.963782e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>4.129974e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.394737</td>\n",
       "      <td>1.394737</td>\n",
       "      <td>40.758886</td>\n",
       "      <td>-73.966700</td>\n",
       "      <td>5.447368</td>\n",
       "      <td>...</td>\n",
       "      <td>1773200</td>\n",
       "      <td>1760600</td>\n",
       "      <td>1762000</td>\n",
       "      <td>1776800.0</td>\n",
       "      <td>1775200</td>\n",
       "      <td>1774500</td>\n",
       "      <td>1812700</td>\n",
       "      <td>1914600</td>\n",
       "      <td>1997800</td>\n",
       "      <td>2031600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10023</td>\n",
       "      <td>8.424752e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.053048e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.888889</td>\n",
       "      <td>2.888889</td>\n",
       "      <td>40.777581</td>\n",
       "      <td>-73.981486</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>1993500</td>\n",
       "      <td>1980700</td>\n",
       "      <td>1960900</td>\n",
       "      <td>1951300.0</td>\n",
       "      <td>1937800</td>\n",
       "      <td>1929800</td>\n",
       "      <td>1955000</td>\n",
       "      <td>2022400</td>\n",
       "      <td>2095000</td>\n",
       "      <td>2142300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10025</td>\n",
       "      <td>7.849437e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.567103e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>40.798025</td>\n",
       "      <td>-73.966355</td>\n",
       "      <td>4.821429</td>\n",
       "      <td>...</td>\n",
       "      <td>1374400</td>\n",
       "      <td>1364100</td>\n",
       "      <td>1366300</td>\n",
       "      <td>1354800.0</td>\n",
       "      <td>1327500</td>\n",
       "      <td>1317300</td>\n",
       "      <td>1333700</td>\n",
       "      <td>1352100</td>\n",
       "      <td>1390000</td>\n",
       "      <td>1431000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10028</td>\n",
       "      <td>1.038785e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.790560e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>40.775367</td>\n",
       "      <td>-73.950934</td>\n",
       "      <td>4.281250</td>\n",
       "      <td>...</td>\n",
       "      <td>1814600</td>\n",
       "      <td>1820400</td>\n",
       "      <td>1810000</td>\n",
       "      <td>1786500.0</td>\n",
       "      <td>1787100</td>\n",
       "      <td>1801100</td>\n",
       "      <td>1847900</td>\n",
       "      <td>1922800</td>\n",
       "      <td>2015700</td>\n",
       "      <td>2083900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10036</td>\n",
       "      <td>8.401201e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.853206e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>40.754810</td>\n",
       "      <td>-73.987824</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>1766400</td>\n",
       "      <td>1749800</td>\n",
       "      <td>1744100</td>\n",
       "      <td>1737900.0</td>\n",
       "      <td>1722800</td>\n",
       "      <td>1736600</td>\n",
       "      <td>1750100</td>\n",
       "      <td>1735500</td>\n",
       "      <td>1717100</td>\n",
       "      <td>1712900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10128</td>\n",
       "      <td>1.079017e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.984589e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.321429</td>\n",
       "      <td>1.321429</td>\n",
       "      <td>40.781842</td>\n",
       "      <td>-73.949537</td>\n",
       "      <td>4.357143</td>\n",
       "      <td>...</td>\n",
       "      <td>1526000</td>\n",
       "      <td>1523700</td>\n",
       "      <td>1527200</td>\n",
       "      <td>1541600.0</td>\n",
       "      <td>1557800</td>\n",
       "      <td>1582900</td>\n",
       "      <td>1598900</td>\n",
       "      <td>1646100</td>\n",
       "      <td>1720500</td>\n",
       "      <td>1787100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10304</td>\n",
       "      <td>1.590640e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>7.534770e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.626721</td>\n",
       "      <td>-74.076721</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>284500</td>\n",
       "      <td>287100</td>\n",
       "      <td>290700</td>\n",
       "      <td>294700.0</td>\n",
       "      <td>299000</td>\n",
       "      <td>303800</td>\n",
       "      <td>308300</td>\n",
       "      <td>313800</td>\n",
       "      <td>321300</td>\n",
       "      <td>328300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10305</td>\n",
       "      <td>1.368595e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>5.535834e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>40.596360</td>\n",
       "      <td>-74.077371</td>\n",
       "      <td>4.555556</td>\n",
       "      <td>...</td>\n",
       "      <td>373500</td>\n",
       "      <td>378600</td>\n",
       "      <td>382700</td>\n",
       "      <td>385300.0</td>\n",
       "      <td>387900</td>\n",
       "      <td>394500</td>\n",
       "      <td>403800</td>\n",
       "      <td>412000</td>\n",
       "      <td>418900</td>\n",
       "      <td>425100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10306</td>\n",
       "      <td>8.328929e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>5.237069e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>40.568879</td>\n",
       "      <td>-74.111565</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>316100</td>\n",
       "      <td>318300</td>\n",
       "      <td>321200</td>\n",
       "      <td>322900.0</td>\n",
       "      <td>325400</td>\n",
       "      <td>331700</td>\n",
       "      <td>339000</td>\n",
       "      <td>343500</td>\n",
       "      <td>347800</td>\n",
       "      <td>352900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10312</td>\n",
       "      <td>1.800434e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>1.197988e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.546331</td>\n",
       "      <td>-74.183701</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>325400</td>\n",
       "      <td>326600</td>\n",
       "      <td>328400</td>\n",
       "      <td>330300.0</td>\n",
       "      <td>333600</td>\n",
       "      <td>339300</td>\n",
       "      <td>345000</td>\n",
       "      <td>348400</td>\n",
       "      <td>351700</td>\n",
       "      <td>355000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11201</td>\n",
       "      <td>9.298929e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.327364e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.451613</td>\n",
       "      <td>1.451613</td>\n",
       "      <td>40.692611</td>\n",
       "      <td>-73.990576</td>\n",
       "      <td>4.451613</td>\n",
       "      <td>...</td>\n",
       "      <td>1340200</td>\n",
       "      <td>1338700</td>\n",
       "      <td>1350600</td>\n",
       "      <td>1375600.0</td>\n",
       "      <td>1390200</td>\n",
       "      <td>1398100</td>\n",
       "      <td>1399900</td>\n",
       "      <td>1400500</td>\n",
       "      <td>1407300</td>\n",
       "      <td>1420700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11215</td>\n",
       "      <td>9.035422e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.460656e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.422764</td>\n",
       "      <td>1.422764</td>\n",
       "      <td>40.666241</td>\n",
       "      <td>-73.984527</td>\n",
       "      <td>4.266129</td>\n",
       "      <td>...</td>\n",
       "      <td>982200</td>\n",
       "      <td>992000</td>\n",
       "      <td>1002600</td>\n",
       "      <td>1021400.0</td>\n",
       "      <td>1039300</td>\n",
       "      <td>1040800</td>\n",
       "      <td>1035800</td>\n",
       "      <td>1050000</td>\n",
       "      <td>1066200</td>\n",
       "      <td>1070800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11217</td>\n",
       "      <td>6.866520e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>1.988437e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.283333</td>\n",
       "      <td>1.283333</td>\n",
       "      <td>40.681377</td>\n",
       "      <td>-73.979068</td>\n",
       "      <td>3.983333</td>\n",
       "      <td>...</td>\n",
       "      <td>1207600</td>\n",
       "      <td>1209800</td>\n",
       "      <td>1216200</td>\n",
       "      <td>1221400.0</td>\n",
       "      <td>1225600</td>\n",
       "      <td>1225000</td>\n",
       "      <td>1219600</td>\n",
       "      <td>1238100</td>\n",
       "      <td>1276400</td>\n",
       "      <td>1302300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11231</td>\n",
       "      <td>7.697324e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.171201e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.550725</td>\n",
       "      <td>1.550725</td>\n",
       "      <td>40.680632</td>\n",
       "      <td>-74.000049</td>\n",
       "      <td>4.391304</td>\n",
       "      <td>...</td>\n",
       "      <td>1128000</td>\n",
       "      <td>1152400</td>\n",
       "      <td>1166000</td>\n",
       "      <td>1166500.0</td>\n",
       "      <td>1166600</td>\n",
       "      <td>1182900</td>\n",
       "      <td>1202200</td>\n",
       "      <td>1213200</td>\n",
       "      <td>1211700</td>\n",
       "      <td>1202900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11234</td>\n",
       "      <td>1.066553e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.752097e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>40.623074</td>\n",
       "      <td>-73.925943</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>448400</td>\n",
       "      <td>452300</td>\n",
       "      <td>457200</td>\n",
       "      <td>461200.0</td>\n",
       "      <td>464400</td>\n",
       "      <td>466400</td>\n",
       "      <td>470900</td>\n",
       "      <td>475700</td>\n",
       "      <td>477100</td>\n",
       "      <td>476900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11434</td>\n",
       "      <td>9.374263e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>4.863844e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.662033</td>\n",
       "      <td>-73.769800</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>342100</td>\n",
       "      <td>348300</td>\n",
       "      <td>356100</td>\n",
       "      <td>358400.0</td>\n",
       "      <td>357500</td>\n",
       "      <td>361900</td>\n",
       "      <td>370800</td>\n",
       "      <td>377100</td>\n",
       "      <td>380200</td>\n",
       "      <td>382300</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 295 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    zipcode            id     scrape_id       host_id  host_acceptance_rate  \\\n",
       "0     10003  9.124661e+06  2.017050e+13  2.496037e+07                   NaN   \n",
       "1     10011  9.889107e+06  2.017050e+13  3.005599e+07                   NaN   \n",
       "2     10013  1.054726e+07  2.017050e+13  2.783653e+07                   NaN   \n",
       "3     10014  8.654217e+06  2.017050e+13  2.054620e+07                   NaN   \n",
       "4     10021  1.022891e+07  2.017050e+13  3.568867e+07                   NaN   \n",
       "5     10022  9.963782e+06  2.017050e+13  4.129974e+07                   NaN   \n",
       "6     10023  8.424752e+06  2.017050e+13  3.053048e+07                   NaN   \n",
       "7     10025  7.849437e+06  2.017050e+13  2.567103e+07                   NaN   \n",
       "8     10028  1.038785e+07  2.017050e+13  3.790560e+07                   NaN   \n",
       "9     10036  8.401201e+06  2.017050e+13  3.853206e+07                   NaN   \n",
       "10    10128  1.079017e+07  2.017050e+13  2.984589e+07                   NaN   \n",
       "11    10304  1.590640e+06  2.017050e+13  7.534770e+06                   NaN   \n",
       "12    10305  1.368595e+07  2.017050e+13  5.535834e+07                   NaN   \n",
       "13    10306  8.328929e+06  2.017050e+13  5.237069e+07                   NaN   \n",
       "14    10312  1.800434e+07  2.017050e+13  1.197988e+08                   NaN   \n",
       "15    11201  9.298929e+06  2.017050e+13  2.327364e+07                   NaN   \n",
       "16    11215  9.035422e+06  2.017050e+13  2.460656e+07                   NaN   \n",
       "17    11217  6.866520e+06  2.017050e+13  1.988437e+07                   NaN   \n",
       "18    11231  7.697324e+06  2.017050e+13  2.171201e+07                   NaN   \n",
       "19    11234  1.066553e+07  2.017050e+13  2.752097e+07                   NaN   \n",
       "20    11434  9.374263e+06  2.017050e+13  4.863844e+07                   NaN   \n",
       "\n",
       "    host_listings_count  host_total_listings_count   latitude  longitude  \\\n",
       "0              1.646018                   1.646018  40.728902 -73.988258   \n",
       "1              1.448276                   1.448276  40.742371 -73.999752   \n",
       "2              1.412698                   1.412698  40.719555 -74.004075   \n",
       "3              1.537634                   1.537634  40.734209 -74.004240   \n",
       "4              1.526316                   1.526316  40.768441 -73.956628   \n",
       "5              1.394737                   1.394737  40.758886 -73.966700   \n",
       "6              2.888889                   2.888889  40.777581 -73.981486   \n",
       "7              1.571429                   1.571429  40.798025 -73.966355   \n",
       "8              1.375000                   1.375000  40.775367 -73.950934   \n",
       "9             73.500000                  73.500000  40.754810 -73.987824   \n",
       "10             1.321429                   1.321429  40.781842 -73.949537   \n",
       "11             1.000000                   1.000000  40.626721 -74.076721   \n",
       "12             1.555556                   1.555556  40.596360 -74.077371   \n",
       "13             2.000000                   2.000000  40.568879 -74.111565   \n",
       "14             1.000000                   1.000000  40.546331 -74.183701   \n",
       "15             1.451613                   1.451613  40.692611 -73.990576   \n",
       "16             1.422764                   1.422764  40.666241 -73.984527   \n",
       "17             1.283333                   1.283333  40.681377 -73.979068   \n",
       "18             1.550725                   1.550725  40.680632 -74.000049   \n",
       "19             1.333333                   1.333333  40.623074 -73.925943   \n",
       "20             1.000000                   1.000000  40.662033 -73.769800   \n",
       "\n",
       "    accommodates   ...     2016-09  2016-10  2016-11    2016-12  2017-01  \\\n",
       "0       4.566372   ...     1932800  1930400  1937500  1935100.0  1915700   \n",
       "1       4.517241   ...     2354000  2355500  2352200  2332100.0  2313300   \n",
       "2       4.281250   ...     3347100  3305500  3261100  3244000.0  3231400   \n",
       "3       4.344086   ...     2480800  2443200  2430100  2452900.0  2451200   \n",
       "4       4.263158   ...     1668300  1664800  1659500  1656000.0  1643700   \n",
       "5       5.447368   ...     1773200  1760600  1762000  1776800.0  1775200   \n",
       "6       4.666667   ...     1993500  1980700  1960900  1951300.0  1937800   \n",
       "7       4.821429   ...     1374400  1364100  1366300  1354800.0  1327500   \n",
       "8       4.281250   ...     1814600  1820400  1810000  1786500.0  1787100   \n",
       "9       4.750000   ...     1766400  1749800  1744100  1737900.0  1722800   \n",
       "10      4.357143   ...     1526000  1523700  1527200  1541600.0  1557800   \n",
       "11      3.500000   ...      284500   287100   290700   294700.0   299000   \n",
       "12      4.555556   ...      373500   378600   382700   385300.0   387900   \n",
       "13      5.500000   ...      316100   318300   321200   322900.0   325400   \n",
       "14      2.000000   ...      325400   326600   328400   330300.0   333600   \n",
       "15      4.451613   ...     1340200  1338700  1350600  1375600.0  1390200   \n",
       "16      4.266129   ...      982200   992000  1002600  1021400.0  1039300   \n",
       "17      3.983333   ...     1207600  1209800  1216200  1221400.0  1225600   \n",
       "18      4.391304   ...     1128000  1152400  1166000  1166500.0  1166600   \n",
       "19      4.166667   ...      448400   452300   457200   461200.0   464400   \n",
       "20      4.000000   ...      342100   348300   356100   358400.0   357500   \n",
       "\n",
       "    2017-02  2017-03  2017-04  2017-05  2017-06  \n",
       "0   1916500  1965700  2045300  2109100  2147000  \n",
       "1   2319600  2342100  2365900  2419700  2480400  \n",
       "2   3183300  3160200  3193500  3262200  3316500  \n",
       "3   2441900  2460900  2494900  2498400  2491600  \n",
       "4   1649800  1688500  1731400  1774100  1815600  \n",
       "5   1774500  1812700  1914600  1997800  2031600  \n",
       "6   1929800  1955000  2022400  2095000  2142300  \n",
       "7   1317300  1333700  1352100  1390000  1431000  \n",
       "8   1801100  1847900  1922800  2015700  2083900  \n",
       "9   1736600  1750100  1735500  1717100  1712900  \n",
       "10  1582900  1598900  1646100  1720500  1787100  \n",
       "11   303800   308300   313800   321300   328300  \n",
       "12   394500   403800   412000   418900   425100  \n",
       "13   331700   339000   343500   347800   352900  \n",
       "14   339300   345000   348400   351700   355000  \n",
       "15  1398100  1399900  1400500  1407300  1420700  \n",
       "16  1040800  1035800  1050000  1066200  1070800  \n",
       "17  1225000  1219600  1238100  1276400  1302300  \n",
       "18  1182900  1202200  1213200  1211700  1202900  \n",
       "19   466400   470900   475700   477100   476900  \n",
       "20   361900   370800   377100   380200   382300  \n",
       "\n",
       "[21 rows x 295 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Storing it in a Variable Called \"df\"\n",
    "\n",
    "df= group_join(airbnb, zillow)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['zipcode',\n",
       " 'id',\n",
       " 'scrape_id',\n",
       " 'host_id',\n",
       " 'host_acceptance_rate',\n",
       " 'host_listings_count',\n",
       " 'host_total_listings_count',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'accommodates',\n",
       " 'bathrooms',\n",
       " 'bedrooms',\n",
       " 'beds',\n",
       " 'square_feet',\n",
       " 'price',\n",
       " 'guests_included',\n",
       " 'minimum_nights',\n",
       " 'maximum_nights',\n",
       " 'has_availability',\n",
       " 'availability_30',\n",
       " 'availability_60',\n",
       " 'availability_90',\n",
       " 'availability_365',\n",
       " 'number_of_reviews',\n",
       " 'review_scores_rating',\n",
       " 'review_scores_accuracy',\n",
       " 'review_scores_cleanliness',\n",
       " 'review_scores_checkin',\n",
       " 'review_scores_communication',\n",
       " 'review_scores_location',\n",
       " 'review_scores_value',\n",
       " 'license',\n",
       " 'calculated_host_listings_count',\n",
       " 'reviews_per_month',\n",
       " 'RegionID',\n",
       " 'City',\n",
       " 'State',\n",
       " 'Metro',\n",
       " 'CountyName',\n",
       " 'SizeRank',\n",
       " '1996-04',\n",
       " '1996-05',\n",
       " '1996-06',\n",
       " '1996-07',\n",
       " '1996-08',\n",
       " '1996-09',\n",
       " '1996-10',\n",
       " '1996-11',\n",
       " '1996-12',\n",
       " '1997-01',\n",
       " '1997-02',\n",
       " '1997-03',\n",
       " '1997-04',\n",
       " '1997-05',\n",
       " '1997-06',\n",
       " '1997-07',\n",
       " '1997-08',\n",
       " '1997-09',\n",
       " '1997-10',\n",
       " '1997-11',\n",
       " '1997-12',\n",
       " '1998-01',\n",
       " '1998-02',\n",
       " '1998-03',\n",
       " '1998-04',\n",
       " '1998-05',\n",
       " '1998-06',\n",
       " '1998-07',\n",
       " '1998-08',\n",
       " '1998-09',\n",
       " '1998-10',\n",
       " '1998-11',\n",
       " '1998-12',\n",
       " '1999-01',\n",
       " '1999-02',\n",
       " '1999-03',\n",
       " '1999-04',\n",
       " '1999-05',\n",
       " '1999-06',\n",
       " '1999-07',\n",
       " '1999-08',\n",
       " '1999-09',\n",
       " '1999-10',\n",
       " '1999-11',\n",
       " '1999-12',\n",
       " '2000-01',\n",
       " '2000-02',\n",
       " '2000-03',\n",
       " '2000-04',\n",
       " '2000-05',\n",
       " '2000-06',\n",
       " '2000-07',\n",
       " '2000-08',\n",
       " '2000-09',\n",
       " '2000-10',\n",
       " '2000-11',\n",
       " '2000-12',\n",
       " '2001-01',\n",
       " '2001-02',\n",
       " '2001-03',\n",
       " '2001-04',\n",
       " '2001-05',\n",
       " '2001-06',\n",
       " '2001-07',\n",
       " '2001-08',\n",
       " '2001-09',\n",
       " '2001-10',\n",
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       " '2017-06']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Series Analysis and Forecasting to determine the Current Month Cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shari\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:3: DeprecationWarning:\n",
      "\n",
      "\n",
      ".ix is deprecated. Please use\n",
      ".loc for label based indexing or\n",
      ".iloc for positional indexing\n",
      "\n",
      "See the documentation here:\n",
      "http://pandas.pydata.org/pandas-docs/stable/indexing.html#ix-indexer-is-deprecated\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(21, 255)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Zillow data has time series and thus extracting the time series data to perform Time Series analysis and Forecasting\n",
    "\n",
    "timeseriesdf= df.ix[:,40:]\n",
    "timeseriesdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
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       "      <th>1996-04</th>\n",
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       "      <td>3316500</td>\n",
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       "   1996-04  1996-05  1996-06  1996-07  1996-08  1996-09  1996-10  1996-11  \\\n",
       "0      NaN      NaN      NaN      NaN      NaN      NaN      NaN      NaN   \n",
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       "4      NaN      NaN   ...     1668300  1664800  1659500  1656000.0  1643700   \n",
       "\n",
       "   2017-02  2017-03  2017-04  2017-05  2017-06  \n",
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       "4  1649800  1688500  1731400  1774100  1815600  \n",
       "\n",
       "[5 rows x 255 columns]"
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     "execution_count": 33,
     "metadata": {},
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   "source": [
    "timeseriesdf.head()"
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  {
   "cell_type": "code",
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       "         0   1   2   3   4   5   6   7   8   9  ...        11        12  \\\n",
       "1996-04 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  117500.0  131900.0   \n",
       "1996-05 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  113700.0  131900.0   \n",
       "1996-06 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  111200.0  131400.0   \n",
       "1996-07 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  111100.0  130800.0   \n",
       "1996-08 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  110400.0  130200.0   \n",
       "\n",
       "               13        14  15  16  17  18  19  20  \n",
       "1996-04  138100.0  153000.0 NaN NaN NaN NaN NaN NaN  \n",
       "1996-05  136900.0  149800.0 NaN NaN NaN NaN NaN NaN  \n",
       "1996-06  135900.0  146900.0 NaN NaN NaN NaN NaN NaN  \n",
       "1996-07  134500.0  144800.0 NaN NaN NaN NaN NaN NaN  \n",
       "1996-08  132900.0  143000.0 NaN NaN NaN NaN NaN NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Transposing the Time Series Dataset to arrange it in sequential order so that it becomes easy to perform Time Series analysis\n",
    "\n",
    "timeseriesdf= timeseriesdf.T\n",
    "timeseriesdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(255, 21)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeseriesdf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Converting the Index to a DataTime Format\n",
    "\n",
    "timeseriesdf.index = pd.to_datetime(timeseriesdf.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "      <th>13</th>\n",
       "      <th>14</th>\n",
       "      <th>15</th>\n",
       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1996-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>117500.0</td>\n",
       "      <td>131900.0</td>\n",
       "      <td>138100.0</td>\n",
       "      <td>153000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-05-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>113700.0</td>\n",
       "      <td>131900.0</td>\n",
       "      <td>136900.0</td>\n",
       "      <td>149800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-06-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>111200.0</td>\n",
       "      <td>131400.0</td>\n",
       "      <td>135900.0</td>\n",
       "      <td>146900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-07-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>111100.0</td>\n",
       "      <td>130800.0</td>\n",
       "      <td>134500.0</td>\n",
       "      <td>144800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>110400.0</td>\n",
       "      <td>130200.0</td>\n",
       "      <td>132900.0</td>\n",
       "      <td>143000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            0   1   2   3   4   5   6   7   8   9  ...        11        12  \\\n",
       "1996-04-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  117500.0  131900.0   \n",
       "1996-05-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  113700.0  131900.0   \n",
       "1996-06-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  111200.0  131400.0   \n",
       "1996-07-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  111100.0  130800.0   \n",
       "1996-08-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  110400.0  130200.0   \n",
       "\n",
       "                  13        14  15  16  17  18  19  20  \n",
       "1996-04-01  138100.0  153000.0 NaN NaN NaN NaN NaN NaN  \n",
       "1996-05-01  136900.0  149800.0 NaN NaN NaN NaN NaN NaN  \n",
       "1996-06-01  135900.0  146900.0 NaN NaN NaN NaN NaN NaN  \n",
       "1996-07-01  134500.0  144800.0 NaN NaN NaN NaN NaN NaN  \n",
       "1996-08-01  132900.0  143000.0 NaN NaN NaN NaN NaN NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeseriesdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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e3fouHSt35KlmT2GUnItcFSWJhUBCQgLdunWjdu3a1KlThw0bNtgdkoiIiEiR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22nQW8fv91NbWUltbS3t7O9FolGg0iiD01pBTq9Wo1WoSEhLIyMggMzOTpKReQSPLMpIkIcty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\nkiRJktb9P3kXLN7uZk3oAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x156a426b320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizing the Time Series for all the 21 Zipcodes\n",
    "\n",
    "# Changing the Figure Size\n",
    "from pylab import rcParams\n",
    "rcParams['figure.figsize'] = 15, 7\n",
    "\n",
    "# Plotting the Time Series Plot\n",
    "timeseriesdf.plot()\n",
    "\n",
    "# marking the Red Dotted line on 2010-01-01\n",
    "plt.axvline(pd.to_datetime('2010-01-01'), color='r', linestyle='--', lw=2)\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plot, there are many irregular patterns and trends along with many missing values before the red vertical line. So the time series data after the vertical line was considered. \n",
    "\n",
    "Those Irregular patterns can be attributed to the recession during the period of 2008 and 2009 vitnessing a sudden drop in the costs.\n",
    "\n",
    "Also after the vertical line, the trend seems to be linearly increasing without any abrupt changes for all the Zipcodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Forecasting Current Month Cost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Sometimes, pyramid doesn't do Auto ARIMA and throws up an error. So in order for it to work efficiently, it is better to have these versions installed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.6.3 |Anaconda, Inc.| (default, Oct 15 2017, 03:27:45) [MSC v.1900 64 bit (AMD64)]\n",
      "1.14.2\n",
      "1.0.0\n",
      "0.19.1\n",
      "0.20.3\n",
      "0.9.0\n",
      "0.9.0\n",
      "19.0.3\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\shari\\Anaconda3\\lib\\site-packages\\pyramid\\__init__.py:68: UserWarning:\n",
      "\n",
      "\n",
      "    The 'pyramid' package will be migrating to a new namespace beginning in \n",
      "    version 1.0.0: 'pmdarima'. This is due to a package name collision with the\n",
      "    Pyramid web framework. For more information, see Issue #34:\n",
      "    \n",
      "        https://github.com/tgsmith61591/pyramid/issues/34\n",
      "        \n",
      "    The package will subsequently be installable via the name 'pmdarima'; the\n",
      "    only functional change to the user will be the import name. All imports\n",
      "    from 'pyramid' will change to 'pmdarima'.\n",
      "    \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Checking Versions\n",
    "\n",
    "import sys; print(sys.version)\n",
    "import numpy; print(numpy.__version__)\n",
    "import scipy; print(scipy.__version__)\n",
    "import sklearn; print(sklearn.__version__)\n",
    "import pandas; print(pandas.__version__)\n",
    "import statsmodels; print(statsmodels.__version__)\n",
    "import pyramid; print(pyramid.__version__)\n",
    "import pip; print(pip.__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>...</th>\n",
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       "      <th>16</th>\n",
       "      <th>17</th>\n",
       "      <th>18</th>\n",
       "      <th>19</th>\n",
       "      <th>20</th>\n",
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       "  <tbody>\n",
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       "      <th>1996-04-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>153000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1996-05-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>113700.0</td>\n",
       "      <td>131900.0</td>\n",
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       "      <td>149800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1996-06-01</th>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>111200.0</td>\n",
       "      <td>131400.0</td>\n",
       "      <td>135900.0</td>\n",
       "      <td>146900.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-07-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>111100.0</td>\n",
       "      <td>130800.0</td>\n",
       "      <td>134500.0</td>\n",
       "      <td>144800.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1996-08-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>110400.0</td>\n",
       "      <td>130200.0</td>\n",
       "      <td>132900.0</td>\n",
       "      <td>143000.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
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       "            0   1   2   3   4   5   6   7   8   9  ...        11        12  \\\n",
       "1996-04-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  117500.0  131900.0   \n",
       "1996-05-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  113700.0  131900.0   \n",
       "1996-06-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  111200.0  131400.0   \n",
       "1996-07-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  111100.0  130800.0   \n",
       "1996-08-01 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN ...  110400.0  130200.0   \n",
       "\n",
       "                  13        14  15  16  17  18  19  20  \n",
       "1996-04-01  138100.0  153000.0 NaN NaN NaN NaN NaN NaN  \n",
       "1996-05-01  136900.0  149800.0 NaN NaN NaN NaN NaN NaN  \n",
       "1996-06-01  135900.0  146900.0 NaN NaN NaN NaN NaN NaN  \n",
       "1996-07-01  134500.0  144800.0 NaN NaN NaN NaN NaN NaN  \n",
       "1996-08-01  132900.0  143000.0 NaN NaN NaN NaN NaN NaN  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeseriesdf.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Filtering out the Timeseries Data From 2010-01-01\n",
    "\n",
    "timeseriesdf=timeseriesdf[timeseriesdf.index>= '2010-01-01']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0.0\n",
       "1     0.0\n",
       "2     0.0\n",
       "3     0.0\n",
       "4     0.0\n",
       "5     0.0\n",
       "6     0.0\n",
       "7     0.0\n",
       "8     0.0\n",
       "9     0.0\n",
       "10    0.0\n",
       "11    0.0\n",
       "12    0.0\n",
       "13    0.0\n",
       "14    0.0\n",
       "15    0.0\n",
       "16    0.0\n",
       "17    0.0\n",
       "18    0.0\n",
       "19    0.0\n",
       "20    0.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for Missing Values\n",
    "\n",
    "mis_value_percent(timeseriesdf)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1227800.0</td>\n",
       "      <td>1378600.0</td>\n",
       "      <td>2268700.0</td>\n",
       "      <td>1451800.0</td>\n",
       "      <td>1163900.0</td>\n",
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       "      <td>1271900.0</td>\n",
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       "      <td>1126000.0</td>\n",
       "      <td>1238600.0</td>\n",
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       "      <td>266200.0</td>\n",
       "      <td>331100.0</td>\n",
       "      <td>297200.0</td>\n",
       "      <td>296200.0</td>\n",
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       "      <td>529300.0</td>\n",
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       "      <td>378700.0</td>\n",
       "      <td>315400.0</td>\n",
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       "      <td>821500.0</td>\n",
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       "      <td>330000.0</td>\n",
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       "      <td>589600.0</td>\n",
       "      <td>386600.0</td>\n",
       "      <td>320300.0</td>\n",
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       "      <th>2010-03-01</th>\n",
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       "      <td>1393100.0</td>\n",
       "      <td>2273600.0</td>\n",
       "      <td>1494500.0</td>\n",
       "      <td>1184500.0</td>\n",
       "      <td>1344600.0</td>\n",
       "      <td>1262300.0</td>\n",
       "      <td>831700.0</td>\n",
       "      <td>1116100.0</td>\n",
       "      <td>1230700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>264700.0</td>\n",
       "      <td>328100.0</td>\n",
       "      <td>296100.0</td>\n",
       "      <td>296900.0</td>\n",
       "      <td>750300.0</td>\n",
       "      <td>540900.0</td>\n",
       "      <td>612700.0</td>\n",
       "      <td>589500.0</td>\n",
       "      <td>391600.0</td>\n",
       "      <td>315800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-04-01</th>\n",
       "      <td>1220100.0</td>\n",
       "      <td>1430000.0</td>\n",
       "      <td>2261700.0</td>\n",
       "      <td>1542200.0</td>\n",
       "      <td>1192700.0</td>\n",
       "      <td>1348300.0</td>\n",
       "      <td>1279200.0</td>\n",
       "      <td>845100.0</td>\n",
       "      <td>1138500.0</td>\n",
       "      <td>1237300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>262500.0</td>\n",
       "      <td>327200.0</td>\n",
       "      <td>295200.0</td>\n",
       "      <td>296700.0</td>\n",
       "      <td>753800.0</td>\n",
       "      <td>538300.0</td>\n",
       "      <td>612800.0</td>\n",
       "      <td>589300.0</td>\n",
       "      <td>392900.0</td>\n",
       "      <td>314000.0</td>\n",
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       "      <th>2010-05-01</th>\n",
       "      <td>1252400.0</td>\n",
       "      <td>1462100.0</td>\n",
       "      <td>2282800.0</td>\n",
       "      <td>1587300.0</td>\n",
       "      <td>1203100.0</td>\n",
       "      <td>1350800.0</td>\n",
       "      <td>1309000.0</td>\n",
       "      <td>854500.0</td>\n",
       "      <td>1175900.0</td>\n",
       "      <td>1250400.0</td>\n",
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       "      <td>260800.0</td>\n",
       "      <td>324500.0</td>\n",
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       "      <td>758900.0</td>\n",
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       "      <td>590900.0</td>\n",
       "      <td>392900.0</td>\n",
       "      <td>311800.0</td>\n",
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       "      <th>2010-06-01</th>\n",
       "      <td>1285700.0</td>\n",
       "      <td>1476800.0</td>\n",
       "      <td>2288000.0</td>\n",
       "      <td>1616000.0</td>\n",
       "      <td>1203500.0</td>\n",
       "      <td>1365100.0</td>\n",
       "      <td>1335300.0</td>\n",
       "      <td>858900.0</td>\n",
       "      <td>1199700.0</td>\n",
       "      <td>1260500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>260700.0</td>\n",
       "      <td>321600.0</td>\n",
       "      <td>292700.0</td>\n",
       "      <td>293800.0</td>\n",
       "      <td>764600.0</td>\n",
       "      <td>539400.0</td>\n",
       "      <td>611300.0</td>\n",
       "      <td>595800.0</td>\n",
       "      <td>393800.0</td>\n",
       "      <td>312600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-07-01</th>\n",
       "      <td>1278300.0</td>\n",
       "      <td>1479000.0</td>\n",
       "      <td>2275700.0</td>\n",
       "      <td>1612300.0</td>\n",
       "      <td>1200400.0</td>\n",
       "      <td>1390900.0</td>\n",
       "      <td>1353800.0</td>\n",
       "      <td>859200.0</td>\n",
       "      <td>1198700.0</td>\n",
       "      <td>1260700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>260900.0</td>\n",
       "      <td>321000.0</td>\n",
       "      <td>293300.0</td>\n",
       "      <td>294500.0</td>\n",
       "      <td>767300.0</td>\n",
       "      <td>546600.0</td>\n",
       "      <td>605900.0</td>\n",
       "      <td>599000.0</td>\n",
       "      <td>392800.0</td>\n",
       "      <td>313100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-08-01</th>\n",
       "      <td>1279100.0</td>\n",
       "      <td>1476600.0</td>\n",
       "      <td>2281300.0</td>\n",
       "      <td>1605800.0</td>\n",
       "      <td>1202900.0</td>\n",
       "      <td>1402700.0</td>\n",
       "      <td>1366400.0</td>\n",
       "      <td>863500.0</td>\n",
       "      <td>1205000.0</td>\n",
       "      <td>1257300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>259900.0</td>\n",
       "      <td>320600.0</td>\n",
       "      <td>294400.0</td>\n",
       "      <td>295600.0</td>\n",
       "      <td>769400.0</td>\n",
       "      <td>553100.0</td>\n",
       "      <td>604400.0</td>\n",
       "      <td>600500.0</td>\n",
       "      <td>390800.0</td>\n",
       "      <td>307000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-09-01</th>\n",
       "      <td>1326700.0</td>\n",
       "      <td>1461200.0</td>\n",
       "      <td>2302800.0</td>\n",
       "      <td>1592500.0</td>\n",
       "      <td>1210100.0</td>\n",
       "      <td>1403700.0</td>\n",
       "      <td>1372100.0</td>\n",
       "      <td>876000.0</td>\n",
       "      <td>1237500.0</td>\n",
       "      <td>1249000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>259200.0</td>\n",
       "      <td>318900.0</td>\n",
       "      <td>294600.0</td>\n",
       "      <td>295300.0</td>\n",
       "      <td>772400.0</td>\n",
       "      <td>553900.0</td>\n",
       "      <td>610100.0</td>\n",
       "      <td>602700.0</td>\n",
       "      <td>390400.0</td>\n",
       "      <td>298200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-10-01</th>\n",
       "      <td>1376500.0</td>\n",
       "      <td>1448300.0</td>\n",
       "      <td>2290900.0</td>\n",
       "      <td>1584200.0</td>\n",
       "      <td>1210300.0</td>\n",
       "      <td>1404900.0</td>\n",
       "      <td>1381300.0</td>\n",
       "      <td>886100.0</td>\n",
       "      <td>1276200.0</td>\n",
       "      <td>1234500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>258000.0</td>\n",
       "      <td>316500.0</td>\n",
       "      <td>293100.0</td>\n",
       "      <td>292900.0</td>\n",
       "      <td>779100.0</td>\n",
       "      <td>559000.0</td>\n",
       "      <td>618000.0</td>\n",
       "      <td>607600.0</td>\n",
       "      <td>390000.0</td>\n",
       "      <td>293200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-11-01</th>\n",
       "      <td>1368900.0</td>\n",
       "      <td>1441600.0</td>\n",
       "      <td>2267800.0</td>\n",
       "      <td>1587600.0</td>\n",
       "      <td>1211700.0</td>\n",
       "      <td>1403500.0</td>\n",
       "      <td>1385000.0</td>\n",
       "      <td>890000.0</td>\n",
       "      <td>1304400.0</td>\n",
       "      <td>1231800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>256600.0</td>\n",
       "      <td>315700.0</td>\n",
       "      <td>291800.0</td>\n",
       "      <td>291900.0</td>\n",
       "      <td>785600.0</td>\n",
       "      <td>569100.0</td>\n",
       "      <td>624400.0</td>\n",
       "      <td>617100.0</td>\n",
       "      <td>389900.0</td>\n",
       "      <td>290400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-01</th>\n",
       "      <td>1366000.0</td>\n",
       "      <td>1444300.0</td>\n",
       "      <td>2253600.0</td>\n",
       "      <td>1594300.0</td>\n",
       "      <td>1219100.0</td>\n",
       "      <td>1390200.0</td>\n",
       "      <td>1388100.0</td>\n",
       "      <td>894200.0</td>\n",
       "      <td>1310800.0</td>\n",
       "      <td>1244100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>255100.0</td>\n",
       "      <td>316200.0</td>\n",
       "      <td>290800.0</td>\n",
       "      <td>292800.0</td>\n",
       "      <td>786700.0</td>\n",
       "      <td>573200.0</td>\n",
       "      <td>627300.0</td>\n",
       "      <td>632300.0</td>\n",
       "      <td>388400.0</td>\n",
       "      <td>286000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-01</th>\n",
       "      <td>1381300.0</td>\n",
       "      <td>1438600.0</td>\n",
       "      <td>2245300.0</td>\n",
       "      <td>1611700.0</td>\n",
       "      <td>1222500.0</td>\n",
       "      <td>1376600.0</td>\n",
       "      <td>1399100.0</td>\n",
       "      <td>901800.0</td>\n",
       "      <td>1310700.0</td>\n",
       "      <td>1255300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>254000.0</td>\n",
       "      <td>316400.0</td>\n",
       "      <td>289400.0</td>\n",
       "      <td>293900.0</td>\n",
       "      <td>782000.0</td>\n",
       "      <td>573100.0</td>\n",
       "      <td>628500.0</td>\n",
       "      <td>642900.0</td>\n",
       "      <td>387100.0</td>\n",
       "      <td>276800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-01</th>\n",
       "      <td>1380700.0</td>\n",
       "      <td>1434100.0</td>\n",
       "      <td>2264200.0</td>\n",
       "      <td>1631800.0</td>\n",
       "      <td>1230600.0</td>\n",
       "      <td>1372400.0</td>\n",
       "      <td>1399800.0</td>\n",
       "      <td>909500.0</td>\n",
       "      <td>1318200.0</td>\n",
       "      <td>1260500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>253700.0</td>\n",
       "      <td>315400.0</td>\n",
       "      <td>288400.0</td>\n",
       "      <td>293500.0</td>\n",
       "      <td>782700.0</td>\n",
       "      <td>579600.0</td>\n",
       "      <td>630900.0</td>\n",
       "      <td>642800.0</td>\n",
       "      <td>385300.0</td>\n",
       "      <td>263700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-01</th>\n",
       "      <td>1368500.0</td>\n",
       "      <td>1439300.0</td>\n",
       "      <td>2301000.0</td>\n",
       "      <td>1648800.0</td>\n",
       "      <td>1247400.0</td>\n",
       "      <td>1378100.0</td>\n",
       "      <td>1389300.0</td>\n",
       "      <td>913300.0</td>\n",
       "      <td>1321900.0</td>\n",
       "      <td>1257500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>253000.0</td>\n",
       "      <td>313000.0</td>\n",
       "      <td>288200.0</td>\n",
       "      <td>291700.0</td>\n",
       "      <td>785800.0</td>\n",
       "      <td>588800.0</td>\n",
       "      <td>635000.0</td>\n",
       "      <td>643000.0</td>\n",
       "      <td>380700.0</td>\n",
       "      <td>251400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-01</th>\n",
       "      <td>1372700.0</td>\n",
       "      <td>1437700.0</td>\n",
       "      <td>2328600.0</td>\n",
       "      <td>1670500.0</td>\n",
       "      <td>1258000.0</td>\n",
       "      <td>1384600.0</td>\n",
       "      <td>1384700.0</td>\n",
       "      <td>907400.0</td>\n",
       "      <td>1309200.0</td>\n",
       "      <td>1246800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>251400.0</td>\n",
       "      <td>308700.0</td>\n",
       "      <td>285700.0</td>\n",
       "      <td>289300.0</td>\n",
       "      <td>784600.0</td>\n",
       "      <td>592200.0</td>\n",
       "      <td>636700.0</td>\n",
       "      <td>646800.0</td>\n",
       "      <td>377000.0</td>\n",
       "      <td>245500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-05-01</th>\n",
       "      <td>1378000.0</td>\n",
       "      <td>1430300.0</td>\n",
       "      <td>2337000.0</td>\n",
       "      <td>1674300.0</td>\n",
       "      <td>1250800.0</td>\n",
       "      <td>1389600.0</td>\n",
       "      <td>1380900.0</td>\n",
       "      <td>900000.0</td>\n",
       "      <td>1298700.0</td>\n",
       "      <td>1231400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>250100.0</td>\n",
       "      <td>306000.0</td>\n",
       "      <td>281900.0</td>\n",
       "      <td>287400.0</td>\n",
       "      <td>785100.0</td>\n",
       "      <td>592700.0</td>\n",
       "      <td>635600.0</td>\n",
       "      <td>651700.0</td>\n",
       "      <td>377900.0</td>\n",
       "      <td>243900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-06-01</th>\n",
       "      <td>1361700.0</td>\n",
       "      <td>1426800.0</td>\n",
       "      <td>2341500.0</td>\n",
       "      <td>1657100.0</td>\n",
       "      <td>1251500.0</td>\n",
       "      <td>1390200.0</td>\n",
       "      <td>1367900.0</td>\n",
       "      <td>897700.0</td>\n",
       "      <td>1289000.0</td>\n",
       "      <td>1229400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>248900.0</td>\n",
       "      <td>305400.0</td>\n",
       "      <td>279900.0</td>\n",
       "      <td>285900.0</td>\n",
       "      <td>795300.0</td>\n",
       "      <td>600100.0</td>\n",
       "      <td>640300.0</td>\n",
       "      <td>656400.0</td>\n",
       "      <td>377200.0</td>\n",
       "      <td>242100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-01</th>\n",
       "      <td>1357800.0</td>\n",
       "      <td>1427800.0</td>\n",
       "      <td>2335200.0</td>\n",
       "      <td>1659200.0</td>\n",
       "      <td>1253100.0</td>\n",
       "      <td>1386600.0</td>\n",
       "      <td>1365400.0</td>\n",
       "      <td>896300.0</td>\n",
       "      <td>1258400.0</td>\n",
       "      <td>1247200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>247400.0</td>\n",
       "      <td>304900.0</td>\n",
       "      <td>280600.0</td>\n",
       "      <td>284700.0</td>\n",
       "      <td>799300.0</td>\n",
       "      <td>610400.0</td>\n",
       "      <td>648100.0</td>\n",
       "      <td>665800.0</td>\n",
       "      <td>373100.0</td>\n",
       "      <td>242000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-01</th>\n",
       "      <td>1364400.0</td>\n",
       "      <td>1424600.0</td>\n",
       "      <td>2316700.0</td>\n",
       "      <td>1661800.0</td>\n",
       "      <td>1252000.0</td>\n",
       "      <td>1393100.0</td>\n",
       "      <td>1375100.0</td>\n",
       "      <td>892300.0</td>\n",
       "      <td>1238900.0</td>\n",
       "      <td>1272200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>245700.0</td>\n",
       "      <td>304600.0</td>\n",
       "      <td>280700.0</td>\n",
       "      <td>283100.0</td>\n",
       "      <td>798000.0</td>\n",
       "      <td>616600.0</td>\n",
       "      <td>652400.0</td>\n",
       "      <td>671300.0</td>\n",
       "      <td>371400.0</td>\n",
       "      <td>245200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>1358000.0</td>\n",
       "      <td>1432800.0</td>\n",
       "      <td>2299900.0</td>\n",
       "      <td>1641600.0</td>\n",
       "      <td>1253800.0</td>\n",
       "      <td>1399900.0</td>\n",
       "      <td>1380400.0</td>\n",
       "      <td>890400.0</td>\n",
       "      <td>1247100.0</td>\n",
       "      <td>1287700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>242800.0</td>\n",
       "      <td>303000.0</td>\n",
       "      <td>279700.0</td>\n",
       "      <td>281600.0</td>\n",
       "      <td>801800.0</td>\n",
       "      <td>619800.0</td>\n",
       "      <td>654800.0</td>\n",
       "      <td>667600.0</td>\n",
       "      <td>370700.0</td>\n",
       "      <td>247100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-10-01</th>\n",
       "      <td>1329800.0</td>\n",
       "      <td>1456500.0</td>\n",
       "      <td>2318700.0</td>\n",
       "      <td>1637800.0</td>\n",
       "      <td>1257900.0</td>\n",
       "      <td>1381500.0</td>\n",
       "      <td>1377000.0</td>\n",
       "      <td>888600.0</td>\n",
       "      <td>1260600.0</td>\n",
       "      <td>1296700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>240400.0</td>\n",
       "      <td>300700.0</td>\n",
       "      <td>278800.0</td>\n",
       "      <td>281000.0</td>\n",
       "      <td>808000.0</td>\n",
       "      <td>619800.0</td>\n",
       "      <td>657100.0</td>\n",
       "      <td>656800.0</td>\n",
       "      <td>368500.0</td>\n",
       "      <td>245300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-11-01</th>\n",
       "      <td>1317800.0</td>\n",
       "      <td>1485100.0</td>\n",
       "      <td>2396400.0</td>\n",
       "      <td>1646600.0</td>\n",
       "      <td>1256600.0</td>\n",
       "      <td>1363900.0</td>\n",
       "      <td>1375100.0</td>\n",
       "      <td>891700.0</td>\n",
       "      <td>1255000.0</td>\n",
       "      <td>1302500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>238900.0</td>\n",
       "      <td>299700.0</td>\n",
       "      <td>277500.0</td>\n",
       "      <td>280100.0</td>\n",
       "      <td>808200.0</td>\n",
       "      <td>617100.0</td>\n",
       "      <td>655600.0</td>\n",
       "      <td>646100.0</td>\n",
       "      <td>366600.0</td>\n",
       "      <td>244300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-12-01</th>\n",
       "      <td>1333200.0</td>\n",
       "      <td>1500200.0</td>\n",
       "      <td>2479200.0</td>\n",
       "      <td>1652100.0</td>\n",
       "      <td>1253500.0</td>\n",
       "      <td>1362700.0</td>\n",
       "      <td>1379000.0</td>\n",
       "      <td>899500.0</td>\n",
       "      <td>1250200.0</td>\n",
       "      <td>1301800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>237600.0</td>\n",
       "      <td>299400.0</td>\n",
       "      <td>276400.0</td>\n",
       "      <td>278500.0</td>\n",
       "      <td>805600.0</td>\n",
       "      <td>611100.0</td>\n",
       "      <td>653600.0</td>\n",
       "      <td>638800.0</td>\n",
       "      <td>365500.0</td>\n",
       "      <td>244500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-01-01</th>\n",
       "      <td>1348500.0</td>\n",
       "      <td>1509600.0</td>\n",
       "      <td>2490300.0</td>\n",
       "      <td>1674800.0</td>\n",
       "      <td>1250500.0</td>\n",
       "      <td>1371500.0</td>\n",
       "      <td>1395200.0</td>\n",
       "      <td>904400.0</td>\n",
       "      <td>1249200.0</td>\n",
       "      <td>1298000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>235800.0</td>\n",
       "      <td>297300.0</td>\n",
       "      <td>275400.0</td>\n",
       "      <td>276700.0</td>\n",
       "      <td>805100.0</td>\n",
       "      <td>605800.0</td>\n",
       "      <td>650300.0</td>\n",
       "      <td>638300.0</td>\n",
       "      <td>362100.0</td>\n",
       "      <td>245000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-02-01</th>\n",
       "      <td>1349500.0</td>\n",
       "      <td>1518500.0</td>\n",
       "      <td>2466000.0</td>\n",
       "      <td>1714300.0</td>\n",
       "      <td>1247700.0</td>\n",
       "      <td>1372000.0</td>\n",
       "      <td>1414500.0</td>\n",
       "      <td>908200.0</td>\n",
       "      <td>1253400.0</td>\n",
       "      <td>1292300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>233900.0</td>\n",
       "      <td>295900.0</td>\n",
       "      <td>274100.0</td>\n",
       "      <td>275800.0</td>\n",
       "      <td>813000.0</td>\n",
       "      <td>606000.0</td>\n",
       "      <td>650300.0</td>\n",
       "      <td>648000.0</td>\n",
       "      <td>360400.0</td>\n",
       "      <td>247200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-03-01</th>\n",
       "      <td>1352200.0</td>\n",
       "      <td>1530800.0</td>\n",
       "      <td>2492400.0</td>\n",
       "      <td>1736800.0</td>\n",
       "      <td>1256800.0</td>\n",
       "      <td>1385500.0</td>\n",
       "      <td>1419000.0</td>\n",
       "      <td>914000.0</td>\n",
       "      <td>1275300.0</td>\n",
       "      <td>1288500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>232600.0</td>\n",
       "      <td>296700.0</td>\n",
       "      <td>273400.0</td>\n",
       "      <td>276600.0</td>\n",
       "      <td>825500.0</td>\n",
       "      <td>611200.0</td>\n",
       "      <td>652100.0</td>\n",
       "      <td>657000.0</td>\n",
       "      <td>361300.0</td>\n",
       "      <td>252100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-04-01</th>\n",
       "      <td>1354100.0</td>\n",
       "      <td>1538000.0</td>\n",
       "      <td>2542800.0</td>\n",
       "      <td>1736800.0</td>\n",
       "      <td>1273600.0</td>\n",
       "      <td>1402800.0</td>\n",
       "      <td>1403100.0</td>\n",
       "      <td>915100.0</td>\n",
       "      <td>1314200.0</td>\n",
       "      <td>1286600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>231600.0</td>\n",
       "      <td>297800.0</td>\n",
       "      <td>273600.0</td>\n",
       "      <td>277700.0</td>\n",
       "      <td>835700.0</td>\n",
       "      <td>615100.0</td>\n",
       "      <td>653000.0</td>\n",
       "      <td>661300.0</td>\n",
       "      <td>362500.0</td>\n",
       "      <td>254900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-05-01</th>\n",
       "      <td>1351900.0</td>\n",
       "      <td>1530500.0</td>\n",
       "      <td>2585500.0</td>\n",
       "      <td>1741200.0</td>\n",
       "      <td>1282200.0</td>\n",
       "      <td>1411600.0</td>\n",
       "      <td>1383200.0</td>\n",
       "      <td>912300.0</td>\n",
       "      <td>1329400.0</td>\n",
       "      <td>1288000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>230100.0</td>\n",
       "      <td>298600.0</td>\n",
       "      <td>273500.0</td>\n",
       "      <td>278300.0</td>\n",
       "      <td>839100.0</td>\n",
       "      <td>615000.0</td>\n",
       "      <td>654800.0</td>\n",
       "      <td>662900.0</td>\n",
       "      <td>363700.0</td>\n",
       "      <td>256000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-06-01</th>\n",
       "      <td>1364200.0</td>\n",
       "      <td>1524500.0</td>\n",
       "      <td>2618100.0</td>\n",
       "      <td>1757700.0</td>\n",
       "      <td>1278600.0</td>\n",
       "      <td>1397400.0</td>\n",
       "      <td>1376700.0</td>\n",
       "      <td>914000.0</td>\n",
       "      <td>1316900.0</td>\n",
       "      <td>1290900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>229200.0</td>\n",
       "      <td>299700.0</td>\n",
       "      <td>273800.0</td>\n",
       "      <td>279500.0</td>\n",
       "      <td>836700.0</td>\n",
       "      <td>616600.0</td>\n",
       "      <td>666300.0</td>\n",
       "      <td>669900.0</td>\n",
       "      <td>365900.0</td>\n",
       "      <td>257100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-01</th>\n",
       "      <td>1716300.0</td>\n",
       "      <td>1979200.0</td>\n",
       "      <td>3067600.0</td>\n",
       "      <td>2082800.0</td>\n",
       "      <td>1470700.0</td>\n",
       "      <td>1712000.0</td>\n",
       "      <td>1717700.0</td>\n",
       "      <td>1189700.0</td>\n",
       "      <td>1583200.0</td>\n",
       "      <td>1706000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>247700.0</td>\n",
       "      <td>316000.0</td>\n",
       "      <td>284400.0</td>\n",
       "      <td>305400.0</td>\n",
       "      <td>1253600.0</td>\n",
       "      <td>877100.0</td>\n",
       "      <td>948800.0</td>\n",
       "      <td>955400.0</td>\n",
       "      <td>392500.0</td>\n",
       "      <td>283800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-02-01</th>\n",
       "      <td>1720500.0</td>\n",
       "      <td>1982900.0</td>\n",
       "      <td>3047400.0</td>\n",
       "      <td>2107300.0</td>\n",
       "      <td>1471900.0</td>\n",
       "      <td>1706400.0</td>\n",
       "      <td>1700100.0</td>\n",
       "      <td>1193700.0</td>\n",
       "      <td>1553800.0</td>\n",
       "      <td>1691400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>249300.0</td>\n",
       "      <td>314500.0</td>\n",
       "      <td>283800.0</td>\n",
       "      <td>304900.0</td>\n",
       "      <td>1264500.0</td>\n",
       "      <td>890000.0</td>\n",
       "      <td>958200.0</td>\n",
       "      <td>959400.0</td>\n",
       "      <td>396200.0</td>\n",
       "      <td>285900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-03-01</th>\n",
       "      <td>1721800.0</td>\n",
       "      <td>2001600.0</td>\n",
       "      <td>3054500.0</td>\n",
       "      <td>2142400.0</td>\n",
       "      <td>1458700.0</td>\n",
       "      <td>1696900.0</td>\n",
       "      <td>1680400.0</td>\n",
       "      <td>1199900.0</td>\n",
       "      <td>1518000.0</td>\n",
       "      <td>1688700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>251800.0</td>\n",
       "      <td>312900.0</td>\n",
       "      <td>282200.0</td>\n",
       "      <td>304000.0</td>\n",
       "      <td>1270500.0</td>\n",
       "      <td>907400.0</td>\n",
       "      <td>978600.0</td>\n",
       "      <td>960700.0</td>\n",
       "      <td>401200.0</td>\n",
       "      <td>287300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-04-01</th>\n",
       "      <td>1741800.0</td>\n",
       "      <td>2014700.0</td>\n",
       "      <td>3071700.0</td>\n",
       "      <td>2167100.0</td>\n",
       "      <td>1442700.0</td>\n",
       "      <td>1672900.0</td>\n",
       "      <td>1676400.0</td>\n",
       "      <td>1201400.0</td>\n",
       "      <td>1508800.0</td>\n",
       "      <td>1694400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>254600.0</td>\n",
       "      <td>313100.0</td>\n",
       "      <td>283700.0</td>\n",
       "      <td>304600.0</td>\n",
       "      <td>1276300.0</td>\n",
       "      <td>920600.0</td>\n",
       "      <td>1013600.0</td>\n",
       "      <td>969000.0</td>\n",
       "      <td>404900.0</td>\n",
       "      <td>287900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-05-01</th>\n",
       "      <td>1775800.0</td>\n",
       "      <td>2023500.0</td>\n",
       "      <td>3078300.0</td>\n",
       "      <td>2189400.0</td>\n",
       "      <td>1436400.0</td>\n",
       "      <td>1656300.0</td>\n",
       "      <td>1685600.0</td>\n",
       "      <td>1202600.0</td>\n",
       "      <td>1515900.0</td>\n",
       "      <td>1703200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>257700.0</td>\n",
       "      <td>315500.0</td>\n",
       "      <td>287800.0</td>\n",
       "      <td>306600.0</td>\n",
       "      <td>1289600.0</td>\n",
       "      <td>925700.0</td>\n",
       "      <td>1028300.0</td>\n",
       "      <td>987700.0</td>\n",
       "      <td>409000.0</td>\n",
       "      <td>288800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>1796500.0</td>\n",
       "      <td>2055300.0</td>\n",
       "      <td>3073100.0</td>\n",
       "      <td>2220100.0</td>\n",
       "      <td>1445800.0</td>\n",
       "      <td>1670900.0</td>\n",
       "      <td>1708100.0</td>\n",
       "      <td>1214200.0</td>\n",
       "      <td>1532800.0</td>\n",
       "      <td>1709600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>260900.0</td>\n",
       "      <td>318700.0</td>\n",
       "      <td>291800.0</td>\n",
       "      <td>308700.0</td>\n",
       "      <td>1303800.0</td>\n",
       "      <td>936300.0</td>\n",
       "      <td>1020200.0</td>\n",
       "      <td>1004400.0</td>\n",
       "      <td>414500.0</td>\n",
       "      <td>289900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-01</th>\n",
       "      <td>1821500.0</td>\n",
       "      <td>2078300.0</td>\n",
       "      <td>3089400.0</td>\n",
       "      <td>2250300.0</td>\n",
       "      <td>1471800.0</td>\n",
       "      <td>1696900.0</td>\n",
       "      <td>1730400.0</td>\n",
       "      <td>1235200.0</td>\n",
       "      <td>1548500.0</td>\n",
       "      <td>1709900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>262400.0</td>\n",
       "      <td>322000.0</td>\n",
       "      <td>294500.0</td>\n",
       "      <td>310200.0</td>\n",
       "      <td>1305300.0</td>\n",
       "      <td>954000.0</td>\n",
       "      <td>1024200.0</td>\n",
       "      <td>1022900.0</td>\n",
       "      <td>419000.0</td>\n",
       "      <td>294300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-08-01</th>\n",
       "      <td>1870100.0</td>\n",
       "      <td>2083600.0</td>\n",
       "      <td>3121700.0</td>\n",
       "      <td>2275700.0</td>\n",
       "      <td>1493700.0</td>\n",
       "      <td>1713900.0</td>\n",
       "      <td>1751800.0</td>\n",
       "      <td>1258000.0</td>\n",
       "      <td>1550500.0</td>\n",
       "      <td>1703200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>262700.0</td>\n",
       "      <td>324900.0</td>\n",
       "      <td>297100.0</td>\n",
       "      <td>311300.0</td>\n",
       "      <td>1298900.0</td>\n",
       "      <td>969700.0</td>\n",
       "      <td>1052400.0</td>\n",
       "      <td>1044200.0</td>\n",
       "      <td>423300.0</td>\n",
       "      <td>302500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-09-01</th>\n",
       "      <td>1901000.0</td>\n",
       "      <td>2088800.0</td>\n",
       "      <td>3134200.0</td>\n",
       "      <td>2301600.0</td>\n",
       "      <td>1515100.0</td>\n",
       "      <td>1718600.0</td>\n",
       "      <td>1778300.0</td>\n",
       "      <td>1287700.0</td>\n",
       "      <td>1550200.0</td>\n",
       "      <td>1697700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>263300.0</td>\n",
       "      <td>326600.0</td>\n",
       "      <td>299300.0</td>\n",
       "      <td>312400.0</td>\n",
       "      <td>1301000.0</td>\n",
       "      <td>973200.0</td>\n",
       "      <td>1090500.0</td>\n",
       "      <td>1064800.0</td>\n",
       "      <td>428700.0</td>\n",
       "      <td>309300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-01</th>\n",
       "      <td>1904900.0</td>\n",
       "      <td>2110600.0</td>\n",
       "      <td>3152400.0</td>\n",
       "      <td>2314700.0</td>\n",
       "      <td>1542600.0</td>\n",
       "      <td>1721000.0</td>\n",
       "      <td>1810400.0</td>\n",
       "      <td>1307200.0</td>\n",
       "      <td>1560600.0</td>\n",
       "      <td>1693000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>264900.0</td>\n",
       "      <td>328800.0</td>\n",
       "      <td>301500.0</td>\n",
       "      <td>313800.0</td>\n",
       "      <td>1314200.0</td>\n",
       "      <td>971300.0</td>\n",
       "      <td>1113800.0</td>\n",
       "      <td>1090400.0</td>\n",
       "      <td>432500.0</td>\n",
       "      <td>311600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-11-01</th>\n",
       "      <td>1914000.0</td>\n",
       "      <td>2127500.0</td>\n",
       "      <td>3173600.0</td>\n",
       "      <td>2322000.0</td>\n",
       "      <td>1556500.0</td>\n",
       "      <td>1717000.0</td>\n",
       "      <td>1831600.0</td>\n",
       "      <td>1313900.0</td>\n",
       "      <td>1575700.0</td>\n",
       "      <td>1685400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>266200.0</td>\n",
       "      <td>332100.0</td>\n",
       "      <td>303600.0</td>\n",
       "      <td>315100.0</td>\n",
       "      <td>1322800.0</td>\n",
       "      <td>974100.0</td>\n",
       "      <td>1123100.0</td>\n",
       "      <td>1125400.0</td>\n",
       "      <td>433400.0</td>\n",
       "      <td>313100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-12-01</th>\n",
       "      <td>1926400.0</td>\n",
       "      <td>2168900.0</td>\n",
       "      <td>3195000.0</td>\n",
       "      <td>2334500.0</td>\n",
       "      <td>1551500.0</td>\n",
       "      <td>1721700.0</td>\n",
       "      <td>1844400.0</td>\n",
       "      <td>1317100.0</td>\n",
       "      <td>1587100.0</td>\n",
       "      <td>1694300.0</td>\n",
       "      <td>...</td>\n",
       "      <td>267000.0</td>\n",
       "      <td>336700.0</td>\n",
       "      <td>304400.0</td>\n",
       "      <td>315800.0</td>\n",
       "      <td>1320500.0</td>\n",
       "      <td>978100.0</td>\n",
       "      <td>1137000.0</td>\n",
       "      <td>1144400.0</td>\n",
       "      <td>435600.0</td>\n",
       "      <td>315500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01</th>\n",
       "      <td>1932200.0</td>\n",
       "      <td>2204700.0</td>\n",
       "      <td>3234600.0</td>\n",
       "      <td>2360300.0</td>\n",
       "      <td>1558800.0</td>\n",
       "      <td>1746200.0</td>\n",
       "      <td>1861600.0</td>\n",
       "      <td>1327400.0</td>\n",
       "      <td>1610900.0</td>\n",
       "      <td>1725400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>267700.0</td>\n",
       "      <td>341300.0</td>\n",
       "      <td>303800.0</td>\n",
       "      <td>316100.0</td>\n",
       "      <td>1318800.0</td>\n",
       "      <td>975400.0</td>\n",
       "      <td>1161200.0</td>\n",
       "      <td>1138100.0</td>\n",
       "      <td>437800.0</td>\n",
       "      <td>318200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-02-01</th>\n",
       "      <td>1936700.0</td>\n",
       "      <td>2216100.0</td>\n",
       "      <td>3302400.0</td>\n",
       "      <td>2384700.0</td>\n",
       "      <td>1577200.0</td>\n",
       "      <td>1763200.0</td>\n",
       "      <td>1889600.0</td>\n",
       "      <td>1338800.0</td>\n",
       "      <td>1646100.0</td>\n",
       "      <td>1742000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>268600.0</td>\n",
       "      <td>344700.0</td>\n",
       "      <td>304200.0</td>\n",
       "      <td>316300.0</td>\n",
       "      <td>1325600.0</td>\n",
       "      <td>967200.0</td>\n",
       "      <td>1179700.0</td>\n",
       "      <td>1121800.0</td>\n",
       "      <td>436200.0</td>\n",
       "      <td>320800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-03-01</th>\n",
       "      <td>1945200.0</td>\n",
       "      <td>2212500.0</td>\n",
       "      <td>3335800.0</td>\n",
       "      <td>2388200.0</td>\n",
       "      <td>1584500.0</td>\n",
       "      <td>1743800.0</td>\n",
       "      <td>1901500.0</td>\n",
       "      <td>1350400.0</td>\n",
       "      <td>1659500.0</td>\n",
       "      <td>1734500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>269600.0</td>\n",
       "      <td>347800.0</td>\n",
       "      <td>306200.0</td>\n",
       "      <td>316300.0</td>\n",
       "      <td>1333000.0</td>\n",
       "      <td>963800.0</td>\n",
       "      <td>1192500.0</td>\n",
       "      <td>1103900.0</td>\n",
       "      <td>434600.0</td>\n",
       "      <td>325400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-04-01</th>\n",
       "      <td>1935600.0</td>\n",
       "      <td>2222600.0</td>\n",
       "      <td>3324200.0</td>\n",
       "      <td>2358300.0</td>\n",
       "      <td>1587300.0</td>\n",
       "      <td>1709600.0</td>\n",
       "      <td>1895300.0</td>\n",
       "      <td>1356600.0</td>\n",
       "      <td>1648100.0</td>\n",
       "      <td>1729700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>271300.0</td>\n",
       "      <td>351700.0</td>\n",
       "      <td>307900.0</td>\n",
       "      <td>317000.0</td>\n",
       "      <td>1334800.0</td>\n",
       "      <td>962500.0</td>\n",
       "      <td>1194700.0</td>\n",
       "      <td>1082200.0</td>\n",
       "      <td>437600.0</td>\n",
       "      <td>329400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-05-01</th>\n",
       "      <td>1911200.0</td>\n",
       "      <td>2231900.0</td>\n",
       "      <td>3285100.0</td>\n",
       "      <td>2345800.0</td>\n",
       "      <td>1594100.0</td>\n",
       "      <td>1703500.0</td>\n",
       "      <td>1890200.0</td>\n",
       "      <td>1358500.0</td>\n",
       "      <td>1654800.0</td>\n",
       "      <td>1741800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>273000.0</td>\n",
       "      <td>356200.0</td>\n",
       "      <td>309600.0</td>\n",
       "      <td>318500.0</td>\n",
       "      <td>1333100.0</td>\n",
       "      <td>966100.0</td>\n",
       "      <td>1198400.0</td>\n",
       "      <td>1067000.0</td>\n",
       "      <td>440100.0</td>\n",
       "      <td>331700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-06-01</th>\n",
       "      <td>1918700.0</td>\n",
       "      <td>2250800.0</td>\n",
       "      <td>3274100.0</td>\n",
       "      <td>2381700.0</td>\n",
       "      <td>1613000.0</td>\n",
       "      <td>1731000.0</td>\n",
       "      <td>1898400.0</td>\n",
       "      <td>1364000.0</td>\n",
       "      <td>1693800.0</td>\n",
       "      <td>1764700.0</td>\n",
       "      <td>...</td>\n",
       "      <td>274600.0</td>\n",
       "      <td>359400.0</td>\n",
       "      <td>312700.0</td>\n",
       "      <td>320400.0</td>\n",
       "      <td>1334600.0</td>\n",
       "      <td>971500.0</td>\n",
       "      <td>1203700.0</td>\n",
       "      <td>1077200.0</td>\n",
       "      <td>437900.0</td>\n",
       "      <td>332700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-07-01</th>\n",
       "      <td>1947600.0</td>\n",
       "      <td>2285200.0</td>\n",
       "      <td>3298600.0</td>\n",
       "      <td>2439700.0</td>\n",
       "      <td>1636600.0</td>\n",
       "      <td>1762600.0</td>\n",
       "      <td>1924500.0</td>\n",
       "      <td>1373300.0</td>\n",
       "      <td>1745600.0</td>\n",
       "      <td>1777000.0</td>\n",
       "      <td>...</td>\n",
       "      <td>277600.0</td>\n",
       "      <td>363100.0</td>\n",
       "      <td>315800.0</td>\n",
       "      <td>322500.0</td>\n",
       "      <td>1339000.0</td>\n",
       "      <td>975300.0</td>\n",
       "      <td>1206000.0</td>\n",
       "      <td>1093200.0</td>\n",
       "      <td>438700.0</td>\n",
       "      <td>334900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-08-01</th>\n",
       "      <td>1951300.0</td>\n",
       "      <td>2329100.0</td>\n",
       "      <td>3341100.0</td>\n",
       "      <td>2483000.0</td>\n",
       "      <td>1657500.0</td>\n",
       "      <td>1778100.0</td>\n",
       "      <td>1967300.0</td>\n",
       "      <td>1382600.0</td>\n",
       "      <td>1790400.0</td>\n",
       "      <td>1776200.0</td>\n",
       "      <td>...</td>\n",
       "      <td>281400.0</td>\n",
       "      <td>367900.0</td>\n",
       "      <td>316400.0</td>\n",
       "      <td>324200.0</td>\n",
       "      <td>1343000.0</td>\n",
       "      <td>976200.0</td>\n",
       "      <td>1206100.0</td>\n",
       "      <td>1108300.0</td>\n",
       "      <td>443600.0</td>\n",
       "      <td>338000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-09-01</th>\n",
       "      <td>1932800.0</td>\n",
       "      <td>2354000.0</td>\n",
       "      <td>3347100.0</td>\n",
       "      <td>2480800.0</td>\n",
       "      <td>1668300.0</td>\n",
       "      <td>1773200.0</td>\n",
       "      <td>1993500.0</td>\n",
       "      <td>1374400.0</td>\n",
       "      <td>1814600.0</td>\n",
       "      <td>1766400.0</td>\n",
       "      <td>...</td>\n",
       "      <td>284500.0</td>\n",
       "      <td>373500.0</td>\n",
       "      <td>316100.0</td>\n",
       "      <td>325400.0</td>\n",
       "      <td>1340200.0</td>\n",
       "      <td>982200.0</td>\n",
       "      <td>1207600.0</td>\n",
       "      <td>1128000.0</td>\n",
       "      <td>448400.0</td>\n",
       "      <td>342100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-01</th>\n",
       "      <td>1930400.0</td>\n",
       "      <td>2355500.0</td>\n",
       "      <td>3305500.0</td>\n",
       "      <td>2443200.0</td>\n",
       "      <td>1664800.0</td>\n",
       "      <td>1760600.0</td>\n",
       "      <td>1980700.0</td>\n",
       "      <td>1364100.0</td>\n",
       "      <td>1820400.0</td>\n",
       "      <td>1749800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>287100.0</td>\n",
       "      <td>378600.0</td>\n",
       "      <td>318300.0</td>\n",
       "      <td>326600.0</td>\n",
       "      <td>1338700.0</td>\n",
       "      <td>992000.0</td>\n",
       "      <td>1209800.0</td>\n",
       "      <td>1152400.0</td>\n",
       "      <td>452300.0</td>\n",
       "      <td>348300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-11-01</th>\n",
       "      <td>1937500.0</td>\n",
       "      <td>2352200.0</td>\n",
       "      <td>3261100.0</td>\n",
       "      <td>2430100.0</td>\n",
       "      <td>1659500.0</td>\n",
       "      <td>1762000.0</td>\n",
       "      <td>1960900.0</td>\n",
       "      <td>1366300.0</td>\n",
       "      <td>1810000.0</td>\n",
       "      <td>1744100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>290700.0</td>\n",
       "      <td>382700.0</td>\n",
       "      <td>321200.0</td>\n",
       "      <td>328400.0</td>\n",
       "      <td>1350600.0</td>\n",
       "      <td>1002600.0</td>\n",
       "      <td>1216200.0</td>\n",
       "      <td>1166000.0</td>\n",
       "      <td>457200.0</td>\n",
       "      <td>356100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-12-01</th>\n",
       "      <td>1935100.0</td>\n",
       "      <td>2332100.0</td>\n",
       "      <td>3244000.0</td>\n",
       "      <td>2452900.0</td>\n",
       "      <td>1656000.0</td>\n",
       "      <td>1776800.0</td>\n",
       "      <td>1951300.0</td>\n",
       "      <td>1354800.0</td>\n",
       "      <td>1786500.0</td>\n",
       "      <td>1737900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>294700.0</td>\n",
       "      <td>385300.0</td>\n",
       "      <td>322900.0</td>\n",
       "      <td>330300.0</td>\n",
       "      <td>1375600.0</td>\n",
       "      <td>1021400.0</td>\n",
       "      <td>1221400.0</td>\n",
       "      <td>1166500.0</td>\n",
       "      <td>461200.0</td>\n",
       "      <td>358400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-01-01</th>\n",
       "      <td>1915700.0</td>\n",
       "      <td>2313300.0</td>\n",
       "      <td>3231400.0</td>\n",
       "      <td>2451200.0</td>\n",
       "      <td>1643700.0</td>\n",
       "      <td>1775200.0</td>\n",
       "      <td>1937800.0</td>\n",
       "      <td>1327500.0</td>\n",
       "      <td>1787100.0</td>\n",
       "      <td>1722800.0</td>\n",
       "      <td>...</td>\n",
       "      <td>299000.0</td>\n",
       "      <td>387900.0</td>\n",
       "      <td>325400.0</td>\n",
       "      <td>333600.0</td>\n",
       "      <td>1390200.0</td>\n",
       "      <td>1039300.0</td>\n",
       "      <td>1225600.0</td>\n",
       "      <td>1166600.0</td>\n",
       "      <td>464400.0</td>\n",
       "      <td>357500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-02-01</th>\n",
       "      <td>1916500.0</td>\n",
       "      <td>2319600.0</td>\n",
       "      <td>3183300.0</td>\n",
       "      <td>2441900.0</td>\n",
       "      <td>1649800.0</td>\n",
       "      <td>1774500.0</td>\n",
       "      <td>1929800.0</td>\n",
       "      <td>1317300.0</td>\n",
       "      <td>1801100.0</td>\n",
       "      <td>1736600.0</td>\n",
       "      <td>...</td>\n",
       "      <td>303800.0</td>\n",
       "      <td>394500.0</td>\n",
       "      <td>331700.0</td>\n",
       "      <td>339300.0</td>\n",
       "      <td>1398100.0</td>\n",
       "      <td>1040800.0</td>\n",
       "      <td>1225000.0</td>\n",
       "      <td>1182900.0</td>\n",
       "      <td>466400.0</td>\n",
       "      <td>361900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-03-01</th>\n",
       "      <td>1965700.0</td>\n",
       "      <td>2342100.0</td>\n",
       "      <td>3160200.0</td>\n",
       "      <td>2460900.0</td>\n",
       "      <td>1688500.0</td>\n",
       "      <td>1812700.0</td>\n",
       "      <td>1955000.0</td>\n",
       "      <td>1333700.0</td>\n",
       "      <td>1847900.0</td>\n",
       "      <td>1750100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>308300.0</td>\n",
       "      <td>403800.0</td>\n",
       "      <td>339000.0</td>\n",
       "      <td>345000.0</td>\n",
       "      <td>1399900.0</td>\n",
       "      <td>1035800.0</td>\n",
       "      <td>1219600.0</td>\n",
       "      <td>1202200.0</td>\n",
       "      <td>470900.0</td>\n",
       "      <td>370800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-04-01</th>\n",
       "      <td>2045300.0</td>\n",
       "      <td>2365900.0</td>\n",
       "      <td>3193500.0</td>\n",
       "      <td>2494900.0</td>\n",
       "      <td>1731400.0</td>\n",
       "      <td>1914600.0</td>\n",
       "      <td>2022400.0</td>\n",
       "      <td>1352100.0</td>\n",
       "      <td>1922800.0</td>\n",
       "      <td>1735500.0</td>\n",
       "      <td>...</td>\n",
       "      <td>313800.0</td>\n",
       "      <td>412000.0</td>\n",
       "      <td>343500.0</td>\n",
       "      <td>348400.0</td>\n",
       "      <td>1400500.0</td>\n",
       "      <td>1050000.0</td>\n",
       "      <td>1238100.0</td>\n",
       "      <td>1213200.0</td>\n",
       "      <td>475700.0</td>\n",
       "      <td>377100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-05-01</th>\n",
       "      <td>2109100.0</td>\n",
       "      <td>2419700.0</td>\n",
       "      <td>3262200.0</td>\n",
       "      <td>2498400.0</td>\n",
       "      <td>1774100.0</td>\n",
       "      <td>1997800.0</td>\n",
       "      <td>2095000.0</td>\n",
       "      <td>1390000.0</td>\n",
       "      <td>2015700.0</td>\n",
       "      <td>1717100.0</td>\n",
       "      <td>...</td>\n",
       "      <td>321300.0</td>\n",
       "      <td>418900.0</td>\n",
       "      <td>347800.0</td>\n",
       "      <td>351700.0</td>\n",
       "      <td>1407300.0</td>\n",
       "      <td>1066200.0</td>\n",
       "      <td>1276400.0</td>\n",
       "      <td>1211700.0</td>\n",
       "      <td>477100.0</td>\n",
       "      <td>380200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-06-01</th>\n",
       "      <td>2147000.0</td>\n",
       "      <td>2480400.0</td>\n",
       "      <td>3316500.0</td>\n",
       "      <td>2491600.0</td>\n",
       "      <td>1815600.0</td>\n",
       "      <td>2031600.0</td>\n",
       "      <td>2142300.0</td>\n",
       "      <td>1431000.0</td>\n",
       "      <td>2083900.0</td>\n",
       "      <td>1712900.0</td>\n",
       "      <td>...</td>\n",
       "      <td>328300.0</td>\n",
       "      <td>425100.0</td>\n",
       "      <td>352900.0</td>\n",
       "      <td>355000.0</td>\n",
       "      <td>1420700.0</td>\n",
       "      <td>1070800.0</td>\n",
       "      <td>1302300.0</td>\n",
       "      <td>1202900.0</td>\n",
       "      <td>476900.0</td>\n",
       "      <td>382300.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>90 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   0          1          2          3          4          5   \\\n",
       "2010-01-01  1227800.0  1378600.0  2268700.0  1451800.0  1163900.0  1323000.0   \n",
       "2010-02-01  1202600.0  1372300.0  2294100.0  1454400.0  1176400.0  1338500.0   \n",
       "2010-03-01  1207100.0  1393100.0  2273600.0  1494500.0  1184500.0  1344600.0   \n",
       "2010-04-01  1220100.0  1430000.0  2261700.0  1542200.0  1192700.0  1348300.0   \n",
       "2010-05-01  1252400.0  1462100.0  2282800.0  1587300.0  1203100.0  1350800.0   \n",
       "2010-06-01  1285700.0  1476800.0  2288000.0  1616000.0  1203500.0  1365100.0   \n",
       "2010-07-01  1278300.0  1479000.0  2275700.0  1612300.0  1200400.0  1390900.0   \n",
       "2010-08-01  1279100.0  1476600.0  2281300.0  1605800.0  1202900.0  1402700.0   \n",
       "2010-09-01  1326700.0  1461200.0  2302800.0  1592500.0  1210100.0  1403700.0   \n",
       "2010-10-01  1376500.0  1448300.0  2290900.0  1584200.0  1210300.0  1404900.0   \n",
       "2010-11-01  1368900.0  1441600.0  2267800.0  1587600.0  1211700.0  1403500.0   \n",
       "2010-12-01  1366000.0  1444300.0  2253600.0  1594300.0  1219100.0  1390200.0   \n",
       "2011-01-01  1381300.0  1438600.0  2245300.0  1611700.0  1222500.0  1376600.0   \n",
       "2011-02-01  1380700.0  1434100.0  2264200.0  1631800.0  1230600.0  1372400.0   \n",
       "2011-03-01  1368500.0  1439300.0  2301000.0  1648800.0  1247400.0  1378100.0   \n",
       "2011-04-01  1372700.0  1437700.0  2328600.0  1670500.0  1258000.0  1384600.0   \n",
       "2011-05-01  1378000.0  1430300.0  2337000.0  1674300.0  1250800.0  1389600.0   \n",
       "2011-06-01  1361700.0  1426800.0  2341500.0  1657100.0  1251500.0  1390200.0   \n",
       "2011-07-01  1357800.0  1427800.0  2335200.0  1659200.0  1253100.0  1386600.0   \n",
       "2011-08-01  1364400.0  1424600.0  2316700.0  1661800.0  1252000.0  1393100.0   \n",
       "2011-09-01  1358000.0  1432800.0  2299900.0  1641600.0  1253800.0  1399900.0   \n",
       "2011-10-01  1329800.0  1456500.0  2318700.0  1637800.0  1257900.0  1381500.0   \n",
       "2011-11-01  1317800.0  1485100.0  2396400.0  1646600.0  1256600.0  1363900.0   \n",
       "2011-12-01  1333200.0  1500200.0  2479200.0  1652100.0  1253500.0  1362700.0   \n",
       "2012-01-01  1348500.0  1509600.0  2490300.0  1674800.0  1250500.0  1371500.0   \n",
       "2012-02-01  1349500.0  1518500.0  2466000.0  1714300.0  1247700.0  1372000.0   \n",
       "2012-03-01  1352200.0  1530800.0  2492400.0  1736800.0  1256800.0  1385500.0   \n",
       "2012-04-01  1354100.0  1538000.0  2542800.0  1736800.0  1273600.0  1402800.0   \n",
       "2012-05-01  1351900.0  1530500.0  2585500.0  1741200.0  1282200.0  1411600.0   \n",
       "2012-06-01  1364200.0  1524500.0  2618100.0  1757700.0  1278600.0  1397400.0   \n",
       "...               ...        ...        ...        ...        ...        ...   \n",
       "2015-01-01  1716300.0  1979200.0  3067600.0  2082800.0  1470700.0  1712000.0   \n",
       "2015-02-01  1720500.0  1982900.0  3047400.0  2107300.0  1471900.0  1706400.0   \n",
       "2015-03-01  1721800.0  2001600.0  3054500.0  2142400.0  1458700.0  1696900.0   \n",
       "2015-04-01  1741800.0  2014700.0  3071700.0  2167100.0  1442700.0  1672900.0   \n",
       "2015-05-01  1775800.0  2023500.0  3078300.0  2189400.0  1436400.0  1656300.0   \n",
       "2015-06-01  1796500.0  2055300.0  3073100.0  2220100.0  1445800.0  1670900.0   \n",
       "2015-07-01  1821500.0  2078300.0  3089400.0  2250300.0  1471800.0  1696900.0   \n",
       "2015-08-01  1870100.0  2083600.0  3121700.0  2275700.0  1493700.0  1713900.0   \n",
       "2015-09-01  1901000.0  2088800.0  3134200.0  2301600.0  1515100.0  1718600.0   \n",
       "2015-10-01  1904900.0  2110600.0  3152400.0  2314700.0  1542600.0  1721000.0   \n",
       "2015-11-01  1914000.0  2127500.0  3173600.0  2322000.0  1556500.0  1717000.0   \n",
       "2015-12-01  1926400.0  2168900.0  3195000.0  2334500.0  1551500.0  1721700.0   \n",
       "2016-01-01  1932200.0  2204700.0  3234600.0  2360300.0  1558800.0  1746200.0   \n",
       "2016-02-01  1936700.0  2216100.0  3302400.0  2384700.0  1577200.0  1763200.0   \n",
       "2016-03-01  1945200.0  2212500.0  3335800.0  2388200.0  1584500.0  1743800.0   \n",
       "2016-04-01  1935600.0  2222600.0  3324200.0  2358300.0  1587300.0  1709600.0   \n",
       "2016-05-01  1911200.0  2231900.0  3285100.0  2345800.0  1594100.0  1703500.0   \n",
       "2016-06-01  1918700.0  2250800.0  3274100.0  2381700.0  1613000.0  1731000.0   \n",
       "2016-07-01  1947600.0  2285200.0  3298600.0  2439700.0  1636600.0  1762600.0   \n",
       "2016-08-01  1951300.0  2329100.0  3341100.0  2483000.0  1657500.0  1778100.0   \n",
       "2016-09-01  1932800.0  2354000.0  3347100.0  2480800.0  1668300.0  1773200.0   \n",
       "2016-10-01  1930400.0  2355500.0  3305500.0  2443200.0  1664800.0  1760600.0   \n",
       "2016-11-01  1937500.0  2352200.0  3261100.0  2430100.0  1659500.0  1762000.0   \n",
       "2016-12-01  1935100.0  2332100.0  3244000.0  2452900.0  1656000.0  1776800.0   \n",
       "2017-01-01  1915700.0  2313300.0  3231400.0  2451200.0  1643700.0  1775200.0   \n",
       "2017-02-01  1916500.0  2319600.0  3183300.0  2441900.0  1649800.0  1774500.0   \n",
       "2017-03-01  1965700.0  2342100.0  3160200.0  2460900.0  1688500.0  1812700.0   \n",
       "2017-04-01  2045300.0  2365900.0  3193500.0  2494900.0  1731400.0  1914600.0   \n",
       "2017-05-01  2109100.0  2419700.0  3262200.0  2498400.0  1774100.0  1997800.0   \n",
       "2017-06-01  2147000.0  2480400.0  3316500.0  2491600.0  1815600.0  2031600.0   \n",
       "\n",
       "                   6          7          8          9     ...           11  \\\n",
       "2010-01-01  1271900.0   813300.0  1126000.0  1238600.0    ...     266200.0   \n",
       "2010-02-01  1252300.0   821500.0  1107300.0  1227700.0    ...     266100.0   \n",
       "2010-03-01  1262300.0   831700.0  1116100.0  1230700.0    ...     264700.0   \n",
       "2010-04-01  1279200.0   845100.0  1138500.0  1237300.0    ...     262500.0   \n",
       "2010-05-01  1309000.0   854500.0  1175900.0  1250400.0    ...     260800.0   \n",
       "2010-06-01  1335300.0   858900.0  1199700.0  1260500.0    ...     260700.0   \n",
       "2010-07-01  1353800.0   859200.0  1198700.0  1260700.0    ...     260900.0   \n",
       "2010-08-01  1366400.0   863500.0  1205000.0  1257300.0    ...     259900.0   \n",
       "2010-09-01  1372100.0   876000.0  1237500.0  1249000.0    ...     259200.0   \n",
       "2010-10-01  1381300.0   886100.0  1276200.0  1234500.0    ...     258000.0   \n",
       "2010-11-01  1385000.0   890000.0  1304400.0  1231800.0    ...     256600.0   \n",
       "2010-12-01  1388100.0   894200.0  1310800.0  1244100.0    ...     255100.0   \n",
       "2011-01-01  1399100.0   901800.0  1310700.0  1255300.0    ...     254000.0   \n",
       "2011-02-01  1399800.0   909500.0  1318200.0  1260500.0    ...     253700.0   \n",
       "2011-03-01  1389300.0   913300.0  1321900.0  1257500.0    ...     253000.0   \n",
       "2011-04-01  1384700.0   907400.0  1309200.0  1246800.0    ...     251400.0   \n",
       "2011-05-01  1380900.0   900000.0  1298700.0  1231400.0    ...     250100.0   \n",
       "2011-06-01  1367900.0   897700.0  1289000.0  1229400.0    ...     248900.0   \n",
       "2011-07-01  1365400.0   896300.0  1258400.0  1247200.0    ...     247400.0   \n",
       "2011-08-01  1375100.0   892300.0  1238900.0  1272200.0    ...     245700.0   \n",
       "2011-09-01  1380400.0   890400.0  1247100.0  1287700.0    ...     242800.0   \n",
       "2011-10-01  1377000.0   888600.0  1260600.0  1296700.0    ...     240400.0   \n",
       "2011-11-01  1375100.0   891700.0  1255000.0  1302500.0    ...     238900.0   \n",
       "2011-12-01  1379000.0   899500.0  1250200.0  1301800.0    ...     237600.0   \n",
       "2012-01-01  1395200.0   904400.0  1249200.0  1298000.0    ...     235800.0   \n",
       "2012-02-01  1414500.0   908200.0  1253400.0  1292300.0    ...     233900.0   \n",
       "2012-03-01  1419000.0   914000.0  1275300.0  1288500.0    ...     232600.0   \n",
       "2012-04-01  1403100.0   915100.0  1314200.0  1286600.0    ...     231600.0   \n",
       "2012-05-01  1383200.0   912300.0  1329400.0  1288000.0    ...     230100.0   \n",
       "2012-06-01  1376700.0   914000.0  1316900.0  1290900.0    ...     229200.0   \n",
       "...               ...        ...        ...        ...    ...          ...   \n",
       "2015-01-01  1717700.0  1189700.0  1583200.0  1706000.0    ...     247700.0   \n",
       "2015-02-01  1700100.0  1193700.0  1553800.0  1691400.0    ...     249300.0   \n",
       "2015-03-01  1680400.0  1199900.0  1518000.0  1688700.0    ...     251800.0   \n",
       "2015-04-01  1676400.0  1201400.0  1508800.0  1694400.0    ...     254600.0   \n",
       "2015-05-01  1685600.0  1202600.0  1515900.0  1703200.0    ...     257700.0   \n",
       "2015-06-01  1708100.0  1214200.0  1532800.0  1709600.0    ...     260900.0   \n",
       "2015-07-01  1730400.0  1235200.0  1548500.0  1709900.0    ...     262400.0   \n",
       "2015-08-01  1751800.0  1258000.0  1550500.0  1703200.0    ...     262700.0   \n",
       "2015-09-01  1778300.0  1287700.0  1550200.0  1697700.0    ...     263300.0   \n",
       "2015-10-01  1810400.0  1307200.0  1560600.0  1693000.0    ...     264900.0   \n",
       "2015-11-01  1831600.0  1313900.0  1575700.0  1685400.0    ...     266200.0   \n",
       "2015-12-01  1844400.0  1317100.0  1587100.0  1694300.0    ...     267000.0   \n",
       "2016-01-01  1861600.0  1327400.0  1610900.0  1725400.0    ...     267700.0   \n",
       "2016-02-01  1889600.0  1338800.0  1646100.0  1742000.0    ...     268600.0   \n",
       "2016-03-01  1901500.0  1350400.0  1659500.0  1734500.0    ...     269600.0   \n",
       "2016-04-01  1895300.0  1356600.0  1648100.0  1729700.0    ...     271300.0   \n",
       "2016-05-01  1890200.0  1358500.0  1654800.0  1741800.0    ...     273000.0   \n",
       "2016-06-01  1898400.0  1364000.0  1693800.0  1764700.0    ...     274600.0   \n",
       "2016-07-01  1924500.0  1373300.0  1745600.0  1777000.0    ...     277600.0   \n",
       "2016-08-01  1967300.0  1382600.0  1790400.0  1776200.0    ...     281400.0   \n",
       "2016-09-01  1993500.0  1374400.0  1814600.0  1766400.0    ...     284500.0   \n",
       "2016-10-01  1980700.0  1364100.0  1820400.0  1749800.0    ...     287100.0   \n",
       "2016-11-01  1960900.0  1366300.0  1810000.0  1744100.0    ...     290700.0   \n",
       "2016-12-01  1951300.0  1354800.0  1786500.0  1737900.0    ...     294700.0   \n",
       "2017-01-01  1937800.0  1327500.0  1787100.0  1722800.0    ...     299000.0   \n",
       "2017-02-01  1929800.0  1317300.0  1801100.0  1736600.0    ...     303800.0   \n",
       "2017-03-01  1955000.0  1333700.0  1847900.0  1750100.0    ...     308300.0   \n",
       "2017-04-01  2022400.0  1352100.0  1922800.0  1735500.0    ...     313800.0   \n",
       "2017-05-01  2095000.0  1390000.0  2015700.0  1717100.0    ...     321300.0   \n",
       "2017-06-01  2142300.0  1431000.0  2083900.0  1712900.0    ...     328300.0   \n",
       "\n",
       "                  12        13        14         15         16         17  \\\n",
       "2010-01-01  331100.0  297200.0  296200.0   742800.0   529300.0   619000.0   \n",
       "2010-02-01  330000.0  296500.0  296400.0   747500.0   544800.0   619300.0   \n",
       "2010-03-01  328100.0  296100.0  296900.0   750300.0   540900.0   612700.0   \n",
       "2010-04-01  327200.0  295200.0  296700.0   753800.0   538300.0   612800.0   \n",
       "2010-05-01  324500.0  293600.0  295100.0   758900.0   536800.0   614500.0   \n",
       "2010-06-01  321600.0  292700.0  293800.0   764600.0   539400.0   611300.0   \n",
       "2010-07-01  321000.0  293300.0  294500.0   767300.0   546600.0   605900.0   \n",
       "2010-08-01  320600.0  294400.0  295600.0   769400.0   553100.0   604400.0   \n",
       "2010-09-01  318900.0  294600.0  295300.0   772400.0   553900.0   610100.0   \n",
       "2010-10-01  316500.0  293100.0  292900.0   779100.0   559000.0   618000.0   \n",
       "2010-11-01  315700.0  291800.0  291900.0   785600.0   569100.0   624400.0   \n",
       "2010-12-01  316200.0  290800.0  292800.0   786700.0   573200.0   627300.0   \n",
       "2011-01-01  316400.0  289400.0  293900.0   782000.0   573100.0   628500.0   \n",
       "2011-02-01  315400.0  288400.0  293500.0   782700.0   579600.0   630900.0   \n",
       "2011-03-01  313000.0  288200.0  291700.0   785800.0   588800.0   635000.0   \n",
       "2011-04-01  308700.0  285700.0  289300.0   784600.0   592200.0   636700.0   \n",
       "2011-05-01  306000.0  281900.0  287400.0   785100.0   592700.0   635600.0   \n",
       "2011-06-01  305400.0  279900.0  285900.0   795300.0   600100.0   640300.0   \n",
       "2011-07-01  304900.0  280600.0  284700.0   799300.0   610400.0   648100.0   \n",
       "2011-08-01  304600.0  280700.0  283100.0   798000.0   616600.0   652400.0   \n",
       "2011-09-01  303000.0  279700.0  281600.0   801800.0   619800.0   654800.0   \n",
       "2011-10-01  300700.0  278800.0  281000.0   808000.0   619800.0   657100.0   \n",
       "2011-11-01  299700.0  277500.0  280100.0   808200.0   617100.0   655600.0   \n",
       "2011-12-01  299400.0  276400.0  278500.0   805600.0   611100.0   653600.0   \n",
       "2012-01-01  297300.0  275400.0  276700.0   805100.0   605800.0   650300.0   \n",
       "2012-02-01  295900.0  274100.0  275800.0   813000.0   606000.0   650300.0   \n",
       "2012-03-01  296700.0  273400.0  276600.0   825500.0   611200.0   652100.0   \n",
       "2012-04-01  297800.0  273600.0  277700.0   835700.0   615100.0   653000.0   \n",
       "2012-05-01  298600.0  273500.0  278300.0   839100.0   615000.0   654800.0   \n",
       "2012-06-01  299700.0  273800.0  279500.0   836700.0   616600.0   666300.0   \n",
       "...              ...       ...       ...        ...        ...        ...   \n",
       "2015-01-01  316000.0  284400.0  305400.0  1253600.0   877100.0   948800.0   \n",
       "2015-02-01  314500.0  283800.0  304900.0  1264500.0   890000.0   958200.0   \n",
       "2015-03-01  312900.0  282200.0  304000.0  1270500.0   907400.0   978600.0   \n",
       "2015-04-01  313100.0  283700.0  304600.0  1276300.0   920600.0  1013600.0   \n",
       "2015-05-01  315500.0  287800.0  306600.0  1289600.0   925700.0  1028300.0   \n",
       "2015-06-01  318700.0  291800.0  308700.0  1303800.0   936300.0  1020200.0   \n",
       "2015-07-01  322000.0  294500.0  310200.0  1305300.0   954000.0  1024200.0   \n",
       "2015-08-01  324900.0  297100.0  311300.0  1298900.0   969700.0  1052400.0   \n",
       "2015-09-01  326600.0  299300.0  312400.0  1301000.0   973200.0  1090500.0   \n",
       "2015-10-01  328800.0  301500.0  313800.0  1314200.0   971300.0  1113800.0   \n",
       "2015-11-01  332100.0  303600.0  315100.0  1322800.0   974100.0  1123100.0   \n",
       "2015-12-01  336700.0  304400.0  315800.0  1320500.0   978100.0  1137000.0   \n",
       "2016-01-01  341300.0  303800.0  316100.0  1318800.0   975400.0  1161200.0   \n",
       "2016-02-01  344700.0  304200.0  316300.0  1325600.0   967200.0  1179700.0   \n",
       "2016-03-01  347800.0  306200.0  316300.0  1333000.0   963800.0  1192500.0   \n",
       "2016-04-01  351700.0  307900.0  317000.0  1334800.0   962500.0  1194700.0   \n",
       "2016-05-01  356200.0  309600.0  318500.0  1333100.0   966100.0  1198400.0   \n",
       "2016-06-01  359400.0  312700.0  320400.0  1334600.0   971500.0  1203700.0   \n",
       "2016-07-01  363100.0  315800.0  322500.0  1339000.0   975300.0  1206000.0   \n",
       "2016-08-01  367900.0  316400.0  324200.0  1343000.0   976200.0  1206100.0   \n",
       "2016-09-01  373500.0  316100.0  325400.0  1340200.0   982200.0  1207600.0   \n",
       "2016-10-01  378600.0  318300.0  326600.0  1338700.0   992000.0  1209800.0   \n",
       "2016-11-01  382700.0  321200.0  328400.0  1350600.0  1002600.0  1216200.0   \n",
       "2016-12-01  385300.0  322900.0  330300.0  1375600.0  1021400.0  1221400.0   \n",
       "2017-01-01  387900.0  325400.0  333600.0  1390200.0  1039300.0  1225600.0   \n",
       "2017-02-01  394500.0  331700.0  339300.0  1398100.0  1040800.0  1225000.0   \n",
       "2017-03-01  403800.0  339000.0  345000.0  1399900.0  1035800.0  1219600.0   \n",
       "2017-04-01  412000.0  343500.0  348400.0  1400500.0  1050000.0  1238100.0   \n",
       "2017-05-01  418900.0  347800.0  351700.0  1407300.0  1066200.0  1276400.0   \n",
       "2017-06-01  425100.0  352900.0  355000.0  1420700.0  1070800.0  1302300.0   \n",
       "\n",
       "                   18        19        20  \n",
       "2010-01-01   591300.0  378700.0  315400.0  \n",
       "2010-02-01   589600.0  386600.0  320300.0  \n",
       "2010-03-01   589500.0  391600.0  315800.0  \n",
       "2010-04-01   589300.0  392900.0  314000.0  \n",
       "2010-05-01   590900.0  392900.0  311800.0  \n",
       "2010-06-01   595800.0  393800.0  312600.0  \n",
       "2010-07-01   599000.0  392800.0  313100.0  \n",
       "2010-08-01   600500.0  390800.0  307000.0  \n",
       "2010-09-01   602700.0  390400.0  298200.0  \n",
       "2010-10-01   607600.0  390000.0  293200.0  \n",
       "2010-11-01   617100.0  389900.0  290400.0  \n",
       "2010-12-01   632300.0  388400.0  286000.0  \n",
       "2011-01-01   642900.0  387100.0  276800.0  \n",
       "2011-02-01   642800.0  385300.0  263700.0  \n",
       "2011-03-01   643000.0  380700.0  251400.0  \n",
       "2011-04-01   646800.0  377000.0  245500.0  \n",
       "2011-05-01   651700.0  377900.0  243900.0  \n",
       "2011-06-01   656400.0  377200.0  242100.0  \n",
       "2011-07-01   665800.0  373100.0  242000.0  \n",
       "2011-08-01   671300.0  371400.0  245200.0  \n",
       "2011-09-01   667600.0  370700.0  247100.0  \n",
       "2011-10-01   656800.0  368500.0  245300.0  \n",
       "2011-11-01   646100.0  366600.0  244300.0  \n",
       "2011-12-01   638800.0  365500.0  244500.0  \n",
       "2012-01-01   638300.0  362100.0  245000.0  \n",
       "2012-02-01   648000.0  360400.0  247200.0  \n",
       "2012-03-01   657000.0  361300.0  252100.0  \n",
       "2012-04-01   661300.0  362500.0  254900.0  \n",
       "2012-05-01   662900.0  363700.0  256000.0  \n",
       "2012-06-01   669900.0  365900.0  257100.0  \n",
       "...               ...       ...       ...  \n",
       "2015-01-01   955400.0  392500.0  283800.0  \n",
       "2015-02-01   959400.0  396200.0  285900.0  \n",
       "2015-03-01   960700.0  401200.0  287300.0  \n",
       "2015-04-01   969000.0  404900.0  287900.0  \n",
       "2015-05-01   987700.0  409000.0  288800.0  \n",
       "2015-06-01  1004400.0  414500.0  289900.0  \n",
       "2015-07-01  1022900.0  419000.0  294300.0  \n",
       "2015-08-01  1044200.0  423300.0  302500.0  \n",
       "2015-09-01  1064800.0  428700.0  309300.0  \n",
       "2015-10-01  1090400.0  432500.0  311600.0  \n",
       "2015-11-01  1125400.0  433400.0  313100.0  \n",
       "2015-12-01  1144400.0  435600.0  315500.0  \n",
       "2016-01-01  1138100.0  437800.0  318200.0  \n",
       "2016-02-01  1121800.0  436200.0  320800.0  \n",
       "2016-03-01  1103900.0  434600.0  325400.0  \n",
       "2016-04-01  1082200.0  437600.0  329400.0  \n",
       "2016-05-01  1067000.0  440100.0  331700.0  \n",
       "2016-06-01  1077200.0  437900.0  332700.0  \n",
       "2016-07-01  1093200.0  438700.0  334900.0  \n",
       "2016-08-01  1108300.0  443600.0  338000.0  \n",
       "2016-09-01  1128000.0  448400.0  342100.0  \n",
       "2016-10-01  1152400.0  452300.0  348300.0  \n",
       "2016-11-01  1166000.0  457200.0  356100.0  \n",
       "2016-12-01  1166500.0  461200.0  358400.0  \n",
       "2017-01-01  1166600.0  464400.0  357500.0  \n",
       "2017-02-01  1182900.0  466400.0  361900.0  \n",
       "2017-03-01  1202200.0  470900.0  370800.0  \n",
       "2017-04-01  1213200.0  475700.0  377100.0  \n",
       "2017-05-01  1211700.0  477100.0  380200.0  \n",
       "2017-06-01  1202900.0  476900.0  382300.0  \n",
       "\n",
       "[90 rows x 21 columns]"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "timeseriesdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Importing the Library for performing the Auto ARIMA:\n",
    "\n",
    "from pyramid.arima import auto_arima"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creating a Function to Perform Auto ARIMA\n",
    "\n",
    "def auto_arima_func(dataset):\n",
    "    stepwise_model = auto_arima(dataset, start_p=1, start_q=1,\n",
    "                               max_p=3, max_q=3, m=12,\n",
    "                                seasonal=True,\n",
    "                                trace=True,\n",
    "                               error_action='ignore',  \n",
    "                               suppress_warnings=True, \n",
    "                               stepwise=True)\n",
    "    # Here it is essential to forecast the values upto 22 periods to obtain the Current Cost (For April 2019)\n",
    "    # If the Current Month changes if using this in the future, the n_periods value below will change accordingly counting from July 2017.\n",
    "    \n",
    "    future_forecast = stepwise_model.predict(n_periods=22)\n",
    "    return future_forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=2073.591, BIC=2088.523, Fit time=0.441 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2024.471, BIC=2029.448, Fit time=0.010 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2048.337, BIC=2058.291, Fit time=0.291 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=2301.472, BIC=2311.426, Fit time=0.265 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2026.429, BIC=2033.895, Fit time=0.091 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=2026.435, BIC=2033.901, Fit time=0.031 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=2028.368, BIC=2038.323, Fit time=0.053 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2046.404, BIC=2053.870, Fit time=0.067 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=2310.301, BIC=2317.767, Fit time=0.067 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=2072.606, BIC=2082.561, Fit time=0.113 seconds\n",
      "Total fit time: 1.440 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=2049.476, BIC=2064.408, Fit time=0.361 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=1994.220, BIC=1999.197, Fit time=0.010 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2069.662, BIC=2079.616, Fit time=0.072 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=2013.937, BIC=2023.892, Fit time=0.145 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=1994.735, BIC=2002.201, Fit time=0.216 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=1994.615, BIC=2002.081, Fit time=0.045 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=1996.250, BIC=2006.205, Fit time=0.087 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2068.013, BIC=2075.479, Fit time=0.034 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=2013.788, BIC=2021.254, Fit time=0.076 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=2054.437, BIC=2064.392, Fit time=0.125 seconds\n",
      "Total fit time: 1.174 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2072.449, BIC=2077.426, Fit time=0.009 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2089.155, BIC=2099.110, Fit time=0.058 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2073.418, BIC=2080.884, Fit time=0.038 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=2073.356, BIC=2080.822, Fit time=0.038 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=2074.935, BIC=2084.890, Fit time=0.156 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2087.809, BIC=2095.275, Fit time=0.045 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Total fit time: 0.357 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=2153.670, BIC=2168.602, Fit time=0.430 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2072.641, BIC=2077.619, Fit time=0.009 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2113.746, BIC=2123.700, Fit time=0.059 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2074.542, BIC=2082.008, Fit time=0.092 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=2074.544, BIC=2082.010, Fit time=0.040 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=2076.540, BIC=2086.495, Fit time=0.209 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2111.802, BIC=2119.268, Fit time=0.078 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=2151.444, BIC=2161.399, Fit time=0.155 seconds\n",
      "Total fit time: 1.078 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=2029.024, BIC=2043.956, Fit time=0.426 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=1955.196, BIC=1960.173, Fit time=0.012 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2009.863, BIC=2019.817, Fit time=0.074 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=1956.970, BIC=1964.436, Fit time=0.028 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=1956.951, BIC=1964.417, Fit time=0.104 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=1958.811, BIC=1968.765, Fit time=0.229 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2007.865, BIC=2015.331, Fit time=0.035 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=1958.940, BIC=1968.894, Fit time=0.280 seconds\n",
      "Total fit time: 1.197 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=2099.666, BIC=2114.598, Fit time=0.506 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2023.957, BIC=2028.934, Fit time=0.011 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2049.482, BIC=2059.437, Fit time=0.062 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2024.348, BIC=2031.814, Fit time=0.224 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=2024.313, BIC=2031.779, Fit time=0.038 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=2026.232, BIC=2036.186, Fit time=0.202 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2049.004, BIC=2056.470, Fit time=0.045 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=2107.801, BIC=2117.756, Fit time=0.208 seconds\n",
      "Total fit time: 1.305 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=2124.294, BIC=2139.226, Fit time=0.475 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2000.033, BIC=2005.010, Fit time=0.010 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2076.713, BIC=2086.668, Fit time=0.074 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2002.008, BIC=2009.474, Fit time=0.034 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=2002.010, BIC=2009.476, Fit time=0.075 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=2003.999, BIC=2013.954, Fit time=0.184 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2075.098, BIC=2082.564, Fit time=0.037 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=2129.996, BIC=2139.951, Fit time=0.173 seconds\n",
      "Total fit time: 1.069 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=1958.550, BIC=1973.482, Fit time=0.474 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=1914.629, BIC=1919.606, Fit time=0.008 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=1967.119, BIC=1977.074, Fit time=0.068 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=1938.367, BIC=1948.322, Fit time=0.189 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=1916.621, BIC=1924.086, Fit time=0.039 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=1916.621, BIC=1924.087, Fit time=0.081 seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=1918.617, BIC=1928.571, Fit time=0.103 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=1965.819, BIC=1973.285, Fit time=0.032 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=1936.399, BIC=1943.865, Fit time=0.074 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=1958.698, BIC=1968.652, Fit time=0.145 seconds\n",
      "Total fit time: 1.215 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=2138.392, BIC=2153.324, Fit time=0.458 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2036.481, BIC=2041.458, Fit time=0.013 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2098.283, BIC=2108.237, Fit time=0.077 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=2038.117, BIC=2045.583, Fit time=0.031 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=2038.137, BIC=2045.603, Fit time=0.038 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=2040.066, BIC=2050.020, Fit time=0.224 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=2096.309, BIC=2103.775, Fit time=0.031 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=2138.882, BIC=2148.837, Fit time=0.215 seconds\n",
      "Total fit time: 1.098 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=1986.413, BIC=2001.345, Fit time=0.436 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=1941.134, BIC=1946.112, Fit time=0.009 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=1970.275, BIC=1980.230, Fit time=0.065 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=1942.996, BIC=1950.462, Fit time=0.040 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=1943.007, BIC=1950.473, Fit time=0.038 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=1944.801, BIC=1954.756, Fit time=0.179 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=1944.876, BIC=1952.342, Fit time=0.113 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=1985.697, BIC=1995.652, Fit time=0.171 seconds\n",
      "Total fit time: 1.057 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(1, 0, 1, 12); AIC=1900.633, BIC=1915.497, Fit time=0.450 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1894.129, BIC=1899.084, Fit time=0.010 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1897.932, BIC=1907.841, Fit time=0.118 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 1, 12); AIC=1898.675, BIC=1908.584, Fit time=0.089 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1895.925, BIC=1903.357, Fit time=0.048 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 1, 12); AIC=1895.920, BIC=1903.352, Fit time=0.050 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 0, 1, 12); AIC=1897.343, BIC=1907.252, Fit time=0.235 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1896.138, BIC=1903.570, Fit time=0.026 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1896.913, BIC=1904.345, Fit time=0.069 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1898.024, BIC=1907.933, Fit time=0.100 seconds\n",
      "Total fit time: 1.199 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(1, 0, 1, 12); AIC=1430.055, BIC=1444.919, Fit time=0.350 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1426.192, BIC=1431.147, Fit time=0.009 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1425.517, BIC=1435.426, Fit time=0.126 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 1, 12); AIC=1426.566, BIC=1436.475, Fit time=0.065 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1424.291, BIC=1431.723, Fit time=0.144 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 0, 1, 12); AIC=1425.512, BIC=1435.421, Fit time=0.124 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 0, 1, 12); AIC=1427.499, BIC=1439.886, Fit time=0.255 seconds\n",
      "Fit ARIMA: order=(2, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1425.368, BIC=1435.277, Fit time=0.188 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1427.004, BIC=1436.913, Fit time=0.107 seconds\n",
      "Fit ARIMA: order=(2, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1435.623, BIC=1448.009, Fit time=0.190 seconds\n",
      "Total fit time: 1.562 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(1, 0, 1, 12); AIC=1493.631, BIC=1508.495, Fit time=0.499 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1488.721, BIC=1493.676, Fit time=0.010 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1491.254, BIC=1501.163, Fit time=0.139 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 1, 12); AIC=1491.474, BIC=1501.383, Fit time=0.066 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1490.077, BIC=1497.509, Fit time=0.050 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 1, 12); AIC=1490.067, BIC=1497.499, Fit time=0.061 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 0, 1, 12); AIC=1491.472, BIC=1501.381, Fit time=0.056 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1489.928, BIC=1497.360, Fit time=0.052 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1490.198, BIC=1497.630, Fit time=0.065 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1490.968, BIC=1500.878, Fit time=0.245 seconds\n",
      "Total fit time: 1.248 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(1, 0, 1, 12); AIC=1514.459, BIC=1529.323, Fit time=0.099 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1506.264, BIC=1511.219, Fit time=0.009 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1510.116, BIC=1520.025, Fit time=0.138 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 1, 12); AIC=1510.352, BIC=1520.262, Fit time=0.059 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1508.104, BIC=1515.536, Fit time=0.033 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 1, 12); AIC=1508.104, BIC=1515.536, Fit time=0.035 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 0, 1, 12); AIC=1510.102, BIC=1520.011, Fit time=0.105 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1508.281, BIC=1515.713, Fit time=0.073 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1508.530, BIC=1515.962, Fit time=0.034 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1510.655, BIC=1520.565, Fit time=0.047 seconds\n",
      "Total fit time: 0.636 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(1, 0, 1, 12); AIC=1448.639, BIC=1463.503, Fit time=0.190 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1443.675, BIC=1448.630, Fit time=0.009 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1445.062, BIC=1454.972, Fit time=0.115 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 1, 12); AIC=1445.241, BIC=1455.151, Fit time=0.061 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1443.040, BIC=1450.472, Fit time=0.075 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 0, 1, 12); AIC=1444.481, BIC=1454.391, Fit time=0.206 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(2, 0, 1, 12); AIC=1445.108, BIC=1457.494, Fit time=0.719 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(1, 0, 0, 12); AIC=1445.299, BIC=1455.208, Fit time=0.137 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(1, 0, 0, 12); AIC=1447.307, BIC=1459.694, Fit time=0.086 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(2, 0, 0, 12); AIC=1443.580, BIC=1453.490, Fit time=0.253 seconds\n",
      "Total fit time: 1.857 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=1934.729, BIC=1949.661, Fit time=0.385 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=1861.785, BIC=1866.762, Fit time=0.009 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=1986.297, BIC=1996.251, Fit time=0.087 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=1862.165, BIC=1869.631, Fit time=0.180 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=1861.954, BIC=1869.419, Fit time=0.047 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=1862.995, BIC=1872.950, Fit time=0.227 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=1984.309, BIC=1991.775, Fit time=0.033 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=1865.780, BIC=1875.734, Fit time=0.322 seconds\n",
      "Total fit time: 1.297 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=1860.451, BIC=1875.382, Fit time=0.423 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=1824.686, BIC=1829.664, Fit time=0.014 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=1833.945, BIC=1843.900, Fit time=0.333 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=1849.111, BIC=1859.065, Fit time=0.193 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=1825.803, BIC=1833.269, Fit time=0.081 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=1825.650, BIC=1833.116, Fit time=0.055 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=1827.039, BIC=1836.994, Fit time=0.130 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=1874.773, BIC=1882.239, Fit time=0.058 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=1848.255, BIC=1855.721, Fit time=0.079 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=1881.852, BIC=1891.807, Fit time=0.129 seconds\n",
      "Total fit time: 1.497 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(1, 0, 1, 12); AIC=1835.562, BIC=1850.426, Fit time=0.457 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1829.614, BIC=1834.569, Fit time=0.010 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1832.050, BIC=1841.959, Fit time=0.180 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 1, 12); AIC=1832.283, BIC=1842.192, Fit time=0.134 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1831.401, BIC=1838.833, Fit time=0.053 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 1, 12); AIC=1831.400, BIC=1838.832, Fit time=0.062 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 0, 1, 12); AIC=1833.349, BIC=1843.258, Fit time=0.088 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1830.273, BIC=1837.705, Fit time=0.046 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1830.517, BIC=1837.949, Fit time=0.062 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1831.983, BIC=1841.892, Fit time=0.199 seconds\n",
      "Total fit time: 1.294 seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(1, 0, 1, 12); AIC=1927.213, BIC=1942.145, Fit time=0.671 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=1910.265, BIC=1915.242, Fit time=0.009 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=1993.788, BIC=2003.743, Fit time=0.072 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 1, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 0, 12); AIC=1912.182, BIC=1919.648, Fit time=0.040 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(0, 0, 1, 12); AIC=1912.203, BIC=1919.669, Fit time=0.036 seconds\n",
      "Fit ARIMA: order=(0, 1, 0) seasonal_order=(1, 0, 1, 12); AIC=1913.990, BIC=1923.944, Fit time=0.107 seconds\n",
      "Fit ARIMA: order=(1, 1, 0) seasonal_order=(0, 0, 0, 12); AIC=1920.071, BIC=1927.537, Fit time=0.105 seconds\n",
      "Fit ARIMA: order=(0, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=nan, BIC=nan, Fit time=nan seconds\n",
      "Fit ARIMA: order=(1, 1, 1) seasonal_order=(0, 0, 0, 12); AIC=1995.354, BIC=2005.308, Fit time=0.107 seconds\n",
      "Total fit time: 1.158 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(1, 0, 1, 12); AIC=1584.520, BIC=1599.384, Fit time=0.674 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1581.597, BIC=1586.552, Fit time=0.017 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1583.223, BIC=1593.132, Fit time=0.085 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 1, 12); AIC=1583.506, BIC=1593.415, Fit time=0.153 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1583.551, BIC=1590.983, Fit time=0.191 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 1, 12); AIC=1583.550, BIC=1590.982, Fit time=0.053 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(1, 0, 1, 12); AIC=1584.344, BIC=1594.253, Fit time=0.321 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1581.266, BIC=1588.698, Fit time=0.024 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1581.886, BIC=1591.796, Fit time=0.177 seconds\n",
      "Fit ARIMA: order=(2, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1609.935, BIC=1622.322, Fit time=0.309 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 0, 1, 12); AIC=1583.222, BIC=1593.131, Fit time=0.064 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 0, 1, 12); AIC=1584.088, BIC=1596.474, Fit time=0.273 seconds\n",
      "Fit ARIMA: order=(2, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1592.641, BIC=1602.551, Fit time=0.225 seconds\n",
      "Total fit time: 2.574 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(1, 0, 1, 12); AIC=1623.058, BIC=1637.922, Fit time=0.270 seconds\n",
      "Fit ARIMA: order=(0, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1633.266, BIC=1638.220, Fit time=0.012 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 0, 0, 12); AIC=1620.803, BIC=1630.713, Fit time=0.054 seconds\n",
      "Fit ARIMA: order=(0, 2, 1) seasonal_order=(0, 0, 1, 12); AIC=1621.313, BIC=1631.222, Fit time=0.134 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1618.816, BIC=1626.248, Fit time=0.042 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(0, 0, 1, 12); AIC=1620.803, BIC=1630.713, Fit time=0.062 seconds\n",
      "Fit ARIMA: order=(1, 2, 0) seasonal_order=(1, 0, 1, 12); AIC=1622.816, BIC=1635.202, Fit time=0.080 seconds\n",
      "Fit ARIMA: order=(2, 2, 0) seasonal_order=(0, 0, 0, 12); AIC=1625.331, BIC=1635.241, Fit time=0.038 seconds\n",
      "Fit ARIMA: order=(1, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1619.049, BIC=1628.958, Fit time=0.161 seconds\n",
      "Fit ARIMA: order=(2, 2, 1) seasonal_order=(0, 0, 0, 12); AIC=1639.944, BIC=1652.330, Fit time=0.133 seconds\n",
      "Total fit time: 0.987 seconds\n"
     ]
    }
   ],
   "source": [
    "final_forecast=[]\n",
    "\n",
    "# Here it is required to forecast for 21 Zipcodes. If the number of Zipcodes change in the future, the range in for loop also changes\n",
    "for i in range(0,21,1):\n",
    "    final_forecast.append(auto_arima_func(timeseriesdf[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def join_forecast_values(original_dataset, forecast_dataset):\n",
    "    \n",
    "    # Converting the Forecast values to a Dataframe\n",
    "    forecast = pd.DataFrame(forecast_dataset)\n",
    "    \n",
    "    # Changing the Column Names to Date\n",
    "    forecast.columns= pd.date_range(start= '2017-07-01', end='2019-04-01', freq='MS')\n",
    "    \n",
    "    #Concatenating the Forecasted Dataframe to the Original Dataframe\n",
    "    final_df=pd.concat([original_dataset,forecast], axis=1)\n",
    "    \n",
    "    # Converting the final month column to String and then Renaming\n",
    "    final_df.columns = final_df.columns.astype(str)\n",
    "    final_df.rename( columns={\"2019-04-01 00:00:00\": \"Current Month Cost\"}, inplace=True)\n",
    "    \n",
    "    return final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>id</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-07-01 00:00:00</th>\n",
       "      <th>2018-08-01 00:00:00</th>\n",
       "      <th>2018-09-01 00:00:00</th>\n",
       "      <th>2018-10-01 00:00:00</th>\n",
       "      <th>2018-11-01 00:00:00</th>\n",
       "      <th>2018-12-01 00:00:00</th>\n",
       "      <th>2019-01-01 00:00:00</th>\n",
       "      <th>2019-02-01 00:00:00</th>\n",
       "      <th>2019-03-01 00:00:00</th>\n",
       "      <th>Current Month Cost</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003</td>\n",
       "      <td>9.124661e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.496037e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.646018</td>\n",
       "      <td>1.646018</td>\n",
       "      <td>40.728902</td>\n",
       "      <td>-73.988258</td>\n",
       "      <td>4.566372</td>\n",
       "      <td>...</td>\n",
       "      <td>2.281265e+06</td>\n",
       "      <td>2.291593e+06</td>\n",
       "      <td>2.301921e+06</td>\n",
       "      <td>2.312249e+06</td>\n",
       "      <td>2.322578e+06</td>\n",
       "      <td>2.332906e+06</td>\n",
       "      <td>2.343234e+06</td>\n",
       "      <td>2.353562e+06</td>\n",
       "      <td>2.363890e+06</td>\n",
       "      <td>2.374218e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10011</td>\n",
       "      <td>9.889107e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.005599e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.448276</td>\n",
       "      <td>1.448276</td>\n",
       "      <td>40.742371</td>\n",
       "      <td>-73.999752</td>\n",
       "      <td>4.517241</td>\n",
       "      <td>...</td>\n",
       "      <td>2.641337e+06</td>\n",
       "      <td>2.653717e+06</td>\n",
       "      <td>2.666097e+06</td>\n",
       "      <td>2.678476e+06</td>\n",
       "      <td>2.690856e+06</td>\n",
       "      <td>2.703236e+06</td>\n",
       "      <td>2.715616e+06</td>\n",
       "      <td>2.727996e+06</td>\n",
       "      <td>2.740375e+06</td>\n",
       "      <td>2.752755e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10013</td>\n",
       "      <td>1.054726e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.783653e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.412698</td>\n",
       "      <td>1.412698</td>\n",
       "      <td>40.719555</td>\n",
       "      <td>-74.004075</td>\n",
       "      <td>4.281250</td>\n",
       "      <td>...</td>\n",
       "      <td>3.469549e+06</td>\n",
       "      <td>3.481322e+06</td>\n",
       "      <td>3.493096e+06</td>\n",
       "      <td>3.504869e+06</td>\n",
       "      <td>3.516642e+06</td>\n",
       "      <td>3.528415e+06</td>\n",
       "      <td>3.540188e+06</td>\n",
       "      <td>3.551961e+06</td>\n",
       "      <td>3.563734e+06</td>\n",
       "      <td>3.575507e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10014</td>\n",
       "      <td>8.654217e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.054620e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.537634</td>\n",
       "      <td>1.537634</td>\n",
       "      <td>40.734209</td>\n",
       "      <td>-74.004240</td>\n",
       "      <td>4.344086</td>\n",
       "      <td>...</td>\n",
       "      <td>2.643481e+06</td>\n",
       "      <td>2.655164e+06</td>\n",
       "      <td>2.666847e+06</td>\n",
       "      <td>2.678530e+06</td>\n",
       "      <td>2.690213e+06</td>\n",
       "      <td>2.701897e+06</td>\n",
       "      <td>2.713580e+06</td>\n",
       "      <td>2.725263e+06</td>\n",
       "      <td>2.736946e+06</td>\n",
       "      <td>2.748629e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10021</td>\n",
       "      <td>1.022891e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.568867e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>40.768441</td>\n",
       "      <td>-73.956628</td>\n",
       "      <td>4.263158</td>\n",
       "      <td>...</td>\n",
       "      <td>1.910792e+06</td>\n",
       "      <td>1.918115e+06</td>\n",
       "      <td>1.925437e+06</td>\n",
       "      <td>1.932760e+06</td>\n",
       "      <td>1.940082e+06</td>\n",
       "      <td>1.947404e+06</td>\n",
       "      <td>1.954727e+06</td>\n",
       "      <td>1.962049e+06</td>\n",
       "      <td>1.969372e+06</td>\n",
       "      <td>1.976694e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10022</td>\n",
       "      <td>9.963782e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>4.129974e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.394737</td>\n",
       "      <td>1.394737</td>\n",
       "      <td>40.758886</td>\n",
       "      <td>-73.966700</td>\n",
       "      <td>5.447368</td>\n",
       "      <td>...</td>\n",
       "      <td>2.135103e+06</td>\n",
       "      <td>2.143065e+06</td>\n",
       "      <td>2.151027e+06</td>\n",
       "      <td>2.158989e+06</td>\n",
       "      <td>2.166951e+06</td>\n",
       "      <td>2.174912e+06</td>\n",
       "      <td>2.182874e+06</td>\n",
       "      <td>2.190836e+06</td>\n",
       "      <td>2.198798e+06</td>\n",
       "      <td>2.206760e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10023</td>\n",
       "      <td>8.424752e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.053048e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.888889</td>\n",
       "      <td>2.888889</td>\n",
       "      <td>40.777581</td>\n",
       "      <td>-73.981486</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>2.269437e+06</td>\n",
       "      <td>2.279217e+06</td>\n",
       "      <td>2.288997e+06</td>\n",
       "      <td>2.298776e+06</td>\n",
       "      <td>2.308556e+06</td>\n",
       "      <td>2.318336e+06</td>\n",
       "      <td>2.328116e+06</td>\n",
       "      <td>2.337896e+06</td>\n",
       "      <td>2.347675e+06</td>\n",
       "      <td>2.357455e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10025</td>\n",
       "      <td>7.849437e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.567103e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>40.798025</td>\n",
       "      <td>-73.966355</td>\n",
       "      <td>4.821429</td>\n",
       "      <td>...</td>\n",
       "      <td>1.521226e+06</td>\n",
       "      <td>1.528166e+06</td>\n",
       "      <td>1.535107e+06</td>\n",
       "      <td>1.542047e+06</td>\n",
       "      <td>1.548988e+06</td>\n",
       "      <td>1.555928e+06</td>\n",
       "      <td>1.562869e+06</td>\n",
       "      <td>1.569809e+06</td>\n",
       "      <td>1.576749e+06</td>\n",
       "      <td>1.583690e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10028</td>\n",
       "      <td>1.038785e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.790560e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>40.775367</td>\n",
       "      <td>-73.950934</td>\n",
       "      <td>4.281250</td>\n",
       "      <td>...</td>\n",
       "      <td>2.223818e+06</td>\n",
       "      <td>2.234581e+06</td>\n",
       "      <td>2.245344e+06</td>\n",
       "      <td>2.256107e+06</td>\n",
       "      <td>2.266870e+06</td>\n",
       "      <td>2.277633e+06</td>\n",
       "      <td>2.288396e+06</td>\n",
       "      <td>2.299158e+06</td>\n",
       "      <td>2.309921e+06</td>\n",
       "      <td>2.320684e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10036</td>\n",
       "      <td>8.401201e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.853206e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>40.754810</td>\n",
       "      <td>-73.987824</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.782180e+06</td>\n",
       "      <td>1.787509e+06</td>\n",
       "      <td>1.792838e+06</td>\n",
       "      <td>1.798167e+06</td>\n",
       "      <td>1.803497e+06</td>\n",
       "      <td>1.808826e+06</td>\n",
       "      <td>1.814155e+06</td>\n",
       "      <td>1.819484e+06</td>\n",
       "      <td>1.824813e+06</td>\n",
       "      <td>1.830143e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10128</td>\n",
       "      <td>1.079017e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.984589e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.321429</td>\n",
       "      <td>1.321429</td>\n",
       "      <td>40.781842</td>\n",
       "      <td>-73.949537</td>\n",
       "      <td>4.357143</td>\n",
       "      <td>...</td>\n",
       "      <td>2.723528e+06</td>\n",
       "      <td>2.800994e+06</td>\n",
       "      <td>2.879236e+06</td>\n",
       "      <td>2.958255e+06</td>\n",
       "      <td>3.038049e+06</td>\n",
       "      <td>3.118619e+06</td>\n",
       "      <td>3.199966e+06</td>\n",
       "      <td>3.282089e+06</td>\n",
       "      <td>3.364987e+06</td>\n",
       "      <td>3.448662e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10304</td>\n",
       "      <td>1.590640e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>7.534770e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.626721</td>\n",
       "      <td>-74.076721</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.259331e+05</td>\n",
       "      <td>4.339654e+05</td>\n",
       "      <td>4.420723e+05</td>\n",
       "      <td>4.502538e+05</td>\n",
       "      <td>4.585099e+05</td>\n",
       "      <td>4.668406e+05</td>\n",
       "      <td>4.752459e+05</td>\n",
       "      <td>4.837257e+05</td>\n",
       "      <td>4.922802e+05</td>\n",
       "      <td>5.009092e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10305</td>\n",
       "      <td>1.368595e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>5.535834e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>40.596360</td>\n",
       "      <td>-74.077371</td>\n",
       "      <td>4.555556</td>\n",
       "      <td>...</td>\n",
       "      <td>5.132489e+05</td>\n",
       "      <td>5.206102e+05</td>\n",
       "      <td>5.280545e+05</td>\n",
       "      <td>5.355818e+05</td>\n",
       "      <td>5.431920e+05</td>\n",
       "      <td>5.508852e+05</td>\n",
       "      <td>5.586614e+05</td>\n",
       "      <td>5.665205e+05</td>\n",
       "      <td>5.744625e+05</td>\n",
       "      <td>5.824875e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10306</td>\n",
       "      <td>8.328929e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>5.237069e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>40.568879</td>\n",
       "      <td>-74.111565</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.251977e+05</td>\n",
       "      <td>4.312205e+05</td>\n",
       "      <td>4.373091e+05</td>\n",
       "      <td>4.434636e+05</td>\n",
       "      <td>4.496841e+05</td>\n",
       "      <td>4.559705e+05</td>\n",
       "      <td>4.623227e+05</td>\n",
       "      <td>4.687409e+05</td>\n",
       "      <td>4.752250e+05</td>\n",
       "      <td>4.817750e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10312</td>\n",
       "      <td>1.800434e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>1.197988e+08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.546331</td>\n",
       "      <td>-74.183701</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.012734e+05</td>\n",
       "      <td>4.050852e+05</td>\n",
       "      <td>4.089326e+05</td>\n",
       "      <td>4.128157e+05</td>\n",
       "      <td>4.167345e+05</td>\n",
       "      <td>4.206890e+05</td>\n",
       "      <td>4.246794e+05</td>\n",
       "      <td>4.287059e+05</td>\n",
       "      <td>4.327680e+05</td>\n",
       "      <td>4.368654e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11201</td>\n",
       "      <td>9.298929e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.327364e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.451613</td>\n",
       "      <td>1.451613</td>\n",
       "      <td>40.692611</td>\n",
       "      <td>-73.990576</td>\n",
       "      <td>4.451613</td>\n",
       "      <td>...</td>\n",
       "      <td>1.519719e+06</td>\n",
       "      <td>1.527336e+06</td>\n",
       "      <td>1.534953e+06</td>\n",
       "      <td>1.542570e+06</td>\n",
       "      <td>1.550187e+06</td>\n",
       "      <td>1.557803e+06</td>\n",
       "      <td>1.565420e+06</td>\n",
       "      <td>1.573037e+06</td>\n",
       "      <td>1.580654e+06</td>\n",
       "      <td>1.588271e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11215</td>\n",
       "      <td>9.035422e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.460656e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.422764</td>\n",
       "      <td>1.422764</td>\n",
       "      <td>40.666241</td>\n",
       "      <td>-73.984527</td>\n",
       "      <td>4.266129</td>\n",
       "      <td>...</td>\n",
       "      <td>1.149896e+06</td>\n",
       "      <td>1.155980e+06</td>\n",
       "      <td>1.162064e+06</td>\n",
       "      <td>1.168148e+06</td>\n",
       "      <td>1.174233e+06</td>\n",
       "      <td>1.180317e+06</td>\n",
       "      <td>1.186401e+06</td>\n",
       "      <td>1.192485e+06</td>\n",
       "      <td>1.198570e+06</td>\n",
       "      <td>1.204654e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11217</td>\n",
       "      <td>6.866520e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>1.988437e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.283333</td>\n",
       "      <td>1.283333</td>\n",
       "      <td>40.681377</td>\n",
       "      <td>-73.979068</td>\n",
       "      <td>3.983333</td>\n",
       "      <td>...</td>\n",
       "      <td>1.665473e+06</td>\n",
       "      <td>1.695445e+06</td>\n",
       "      <td>1.725709e+06</td>\n",
       "      <td>1.756264e+06</td>\n",
       "      <td>1.787109e+06</td>\n",
       "      <td>1.818245e+06</td>\n",
       "      <td>1.849673e+06</td>\n",
       "      <td>1.881391e+06</td>\n",
       "      <td>1.913400e+06</td>\n",
       "      <td>1.945700e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11231</td>\n",
       "      <td>7.697324e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.171201e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.550725</td>\n",
       "      <td>1.550725</td>\n",
       "      <td>40.680632</td>\n",
       "      <td>-74.000049</td>\n",
       "      <td>4.391304</td>\n",
       "      <td>...</td>\n",
       "      <td>1.292235e+06</td>\n",
       "      <td>1.299107e+06</td>\n",
       "      <td>1.305979e+06</td>\n",
       "      <td>1.312851e+06</td>\n",
       "      <td>1.319722e+06</td>\n",
       "      <td>1.326594e+06</td>\n",
       "      <td>1.333466e+06</td>\n",
       "      <td>1.340338e+06</td>\n",
       "      <td>1.347210e+06</td>\n",
       "      <td>1.354082e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11234</td>\n",
       "      <td>1.066553e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.752097e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>40.623074</td>\n",
       "      <td>-73.925943</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>...</td>\n",
       "      <td>4.688470e+05</td>\n",
       "      <td>4.678265e+05</td>\n",
       "      <td>4.667486e+05</td>\n",
       "      <td>4.656135e+05</td>\n",
       "      <td>4.644211e+05</td>\n",
       "      <td>4.631714e+05</td>\n",
       "      <td>4.618644e+05</td>\n",
       "      <td>4.605001e+05</td>\n",
       "      <td>4.590786e+05</td>\n",
       "      <td>4.575997e+05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11434</td>\n",
       "      <td>9.374263e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>4.863844e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.662033</td>\n",
       "      <td>-73.769800</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.165402e+05</td>\n",
       "      <td>4.197565e+05</td>\n",
       "      <td>4.230561e+05</td>\n",
       "      <td>4.264390e+05</td>\n",
       "      <td>4.299051e+05</td>\n",
       "      <td>4.334544e+05</td>\n",
       "      <td>4.370870e+05</td>\n",
       "      <td>4.408028e+05</td>\n",
       "      <td>4.446019e+05</td>\n",
       "      <td>4.484842e+05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 317 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    zipcode            id     scrape_id       host_id  host_acceptance_rate  \\\n",
       "0     10003  9.124661e+06  2.017050e+13  2.496037e+07                   NaN   \n",
       "1     10011  9.889107e+06  2.017050e+13  3.005599e+07                   NaN   \n",
       "2     10013  1.054726e+07  2.017050e+13  2.783653e+07                   NaN   \n",
       "3     10014  8.654217e+06  2.017050e+13  2.054620e+07                   NaN   \n",
       "4     10021  1.022891e+07  2.017050e+13  3.568867e+07                   NaN   \n",
       "5     10022  9.963782e+06  2.017050e+13  4.129974e+07                   NaN   \n",
       "6     10023  8.424752e+06  2.017050e+13  3.053048e+07                   NaN   \n",
       "7     10025  7.849437e+06  2.017050e+13  2.567103e+07                   NaN   \n",
       "8     10028  1.038785e+07  2.017050e+13  3.790560e+07                   NaN   \n",
       "9     10036  8.401201e+06  2.017050e+13  3.853206e+07                   NaN   \n",
       "10    10128  1.079017e+07  2.017050e+13  2.984589e+07                   NaN   \n",
       "11    10304  1.590640e+06  2.017050e+13  7.534770e+06                   NaN   \n",
       "12    10305  1.368595e+07  2.017050e+13  5.535834e+07                   NaN   \n",
       "13    10306  8.328929e+06  2.017050e+13  5.237069e+07                   NaN   \n",
       "14    10312  1.800434e+07  2.017050e+13  1.197988e+08                   NaN   \n",
       "15    11201  9.298929e+06  2.017050e+13  2.327364e+07                   NaN   \n",
       "16    11215  9.035422e+06  2.017050e+13  2.460656e+07                   NaN   \n",
       "17    11217  6.866520e+06  2.017050e+13  1.988437e+07                   NaN   \n",
       "18    11231  7.697324e+06  2.017050e+13  2.171201e+07                   NaN   \n",
       "19    11234  1.066553e+07  2.017050e+13  2.752097e+07                   NaN   \n",
       "20    11434  9.374263e+06  2.017050e+13  4.863844e+07                   NaN   \n",
       "\n",
       "    host_listings_count  host_total_listings_count   latitude  longitude  \\\n",
       "0              1.646018                   1.646018  40.728902 -73.988258   \n",
       "1              1.448276                   1.448276  40.742371 -73.999752   \n",
       "2              1.412698                   1.412698  40.719555 -74.004075   \n",
       "3              1.537634                   1.537634  40.734209 -74.004240   \n",
       "4              1.526316                   1.526316  40.768441 -73.956628   \n",
       "5              1.394737                   1.394737  40.758886 -73.966700   \n",
       "6              2.888889                   2.888889  40.777581 -73.981486   \n",
       "7              1.571429                   1.571429  40.798025 -73.966355   \n",
       "8              1.375000                   1.375000  40.775367 -73.950934   \n",
       "9             73.500000                  73.500000  40.754810 -73.987824   \n",
       "10             1.321429                   1.321429  40.781842 -73.949537   \n",
       "11             1.000000                   1.000000  40.626721 -74.076721   \n",
       "12             1.555556                   1.555556  40.596360 -74.077371   \n",
       "13             2.000000                   2.000000  40.568879 -74.111565   \n",
       "14             1.000000                   1.000000  40.546331 -74.183701   \n",
       "15             1.451613                   1.451613  40.692611 -73.990576   \n",
       "16             1.422764                   1.422764  40.666241 -73.984527   \n",
       "17             1.283333                   1.283333  40.681377 -73.979068   \n",
       "18             1.550725                   1.550725  40.680632 -74.000049   \n",
       "19             1.333333                   1.333333  40.623074 -73.925943   \n",
       "20             1.000000                   1.000000  40.662033 -73.769800   \n",
       "\n",
       "    accommodates         ...          2018-07-01 00:00:00  \\\n",
       "0       4.566372         ...                 2.281265e+06   \n",
       "1       4.517241         ...                 2.641337e+06   \n",
       "2       4.281250         ...                 3.469549e+06   \n",
       "3       4.344086         ...                 2.643481e+06   \n",
       "4       4.263158         ...                 1.910792e+06   \n",
       "5       5.447368         ...                 2.135103e+06   \n",
       "6       4.666667         ...                 2.269437e+06   \n",
       "7       4.821429         ...                 1.521226e+06   \n",
       "8       4.281250         ...                 2.223818e+06   \n",
       "9       4.750000         ...                 1.782180e+06   \n",
       "10      4.357143         ...                 2.723528e+06   \n",
       "11      3.500000         ...                 4.259331e+05   \n",
       "12      4.555556         ...                 5.132489e+05   \n",
       "13      5.500000         ...                 4.251977e+05   \n",
       "14      2.000000         ...                 4.012734e+05   \n",
       "15      4.451613         ...                 1.519719e+06   \n",
       "16      4.266129         ...                 1.149896e+06   \n",
       "17      3.983333         ...                 1.665473e+06   \n",
       "18      4.391304         ...                 1.292235e+06   \n",
       "19      4.166667         ...                 4.688470e+05   \n",
       "20      4.000000         ...                 4.165402e+05   \n",
       "\n",
       "    2018-08-01 00:00:00  2018-09-01 00:00:00  2018-10-01 00:00:00  \\\n",
       "0          2.291593e+06         2.301921e+06         2.312249e+06   \n",
       "1          2.653717e+06         2.666097e+06         2.678476e+06   \n",
       "2          3.481322e+06         3.493096e+06         3.504869e+06   \n",
       "3          2.655164e+06         2.666847e+06         2.678530e+06   \n",
       "4          1.918115e+06         1.925437e+06         1.932760e+06   \n",
       "5          2.143065e+06         2.151027e+06         2.158989e+06   \n",
       "6          2.279217e+06         2.288997e+06         2.298776e+06   \n",
       "7          1.528166e+06         1.535107e+06         1.542047e+06   \n",
       "8          2.234581e+06         2.245344e+06         2.256107e+06   \n",
       "9          1.787509e+06         1.792838e+06         1.798167e+06   \n",
       "10         2.800994e+06         2.879236e+06         2.958255e+06   \n",
       "11         4.339654e+05         4.420723e+05         4.502538e+05   \n",
       "12         5.206102e+05         5.280545e+05         5.355818e+05   \n",
       "13         4.312205e+05         4.373091e+05         4.434636e+05   \n",
       "14         4.050852e+05         4.089326e+05         4.128157e+05   \n",
       "15         1.527336e+06         1.534953e+06         1.542570e+06   \n",
       "16         1.155980e+06         1.162064e+06         1.168148e+06   \n",
       "17         1.695445e+06         1.725709e+06         1.756264e+06   \n",
       "18         1.299107e+06         1.305979e+06         1.312851e+06   \n",
       "19         4.678265e+05         4.667486e+05         4.656135e+05   \n",
       "20         4.197565e+05         4.230561e+05         4.264390e+05   \n",
       "\n",
       "    2018-11-01 00:00:00  2018-12-01 00:00:00  2019-01-01 00:00:00  \\\n",
       "0          2.322578e+06         2.332906e+06         2.343234e+06   \n",
       "1          2.690856e+06         2.703236e+06         2.715616e+06   \n",
       "2          3.516642e+06         3.528415e+06         3.540188e+06   \n",
       "3          2.690213e+06         2.701897e+06         2.713580e+06   \n",
       "4          1.940082e+06         1.947404e+06         1.954727e+06   \n",
       "5          2.166951e+06         2.174912e+06         2.182874e+06   \n",
       "6          2.308556e+06         2.318336e+06         2.328116e+06   \n",
       "7          1.548988e+06         1.555928e+06         1.562869e+06   \n",
       "8          2.266870e+06         2.277633e+06         2.288396e+06   \n",
       "9          1.803497e+06         1.808826e+06         1.814155e+06   \n",
       "10         3.038049e+06         3.118619e+06         3.199966e+06   \n",
       "11         4.585099e+05         4.668406e+05         4.752459e+05   \n",
       "12         5.431920e+05         5.508852e+05         5.586614e+05   \n",
       "13         4.496841e+05         4.559705e+05         4.623227e+05   \n",
       "14         4.167345e+05         4.206890e+05         4.246794e+05   \n",
       "15         1.550187e+06         1.557803e+06         1.565420e+06   \n",
       "16         1.174233e+06         1.180317e+06         1.186401e+06   \n",
       "17         1.787109e+06         1.818245e+06         1.849673e+06   \n",
       "18         1.319722e+06         1.326594e+06         1.333466e+06   \n",
       "19         4.644211e+05         4.631714e+05         4.618644e+05   \n",
       "20         4.299051e+05         4.334544e+05         4.370870e+05   \n",
       "\n",
       "    2019-02-01 00:00:00  2019-03-01 00:00:00  Current Month Cost  \n",
       "0          2.353562e+06         2.363890e+06        2.374218e+06  \n",
       "1          2.727996e+06         2.740375e+06        2.752755e+06  \n",
       "2          3.551961e+06         3.563734e+06        3.575507e+06  \n",
       "3          2.725263e+06         2.736946e+06        2.748629e+06  \n",
       "4          1.962049e+06         1.969372e+06        1.976694e+06  \n",
       "5          2.190836e+06         2.198798e+06        2.206760e+06  \n",
       "6          2.337896e+06         2.347675e+06        2.357455e+06  \n",
       "7          1.569809e+06         1.576749e+06        1.583690e+06  \n",
       "8          2.299158e+06         2.309921e+06        2.320684e+06  \n",
       "9          1.819484e+06         1.824813e+06        1.830143e+06  \n",
       "10         3.282089e+06         3.364987e+06        3.448662e+06  \n",
       "11         4.837257e+05         4.922802e+05        5.009092e+05  \n",
       "12         5.665205e+05         5.744625e+05        5.824875e+05  \n",
       "13         4.687409e+05         4.752250e+05        4.817750e+05  \n",
       "14         4.287059e+05         4.327680e+05        4.368654e+05  \n",
       "15         1.573037e+06         1.580654e+06        1.588271e+06  \n",
       "16         1.192485e+06         1.198570e+06        1.204654e+06  \n",
       "17         1.881391e+06         1.913400e+06        1.945700e+06  \n",
       "18         1.340338e+06         1.347210e+06        1.354082e+06  \n",
       "19         4.605001e+05         4.590786e+05        4.575997e+05  \n",
       "20         4.408028e+05         4.446019e+05        4.484842e+05  \n",
       "\n",
       "[21 rows x 317 columns]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Joining the Forecasted Data to the Actual Group Joined Data created before\n",
    "\n",
    "final_df=join_forecast_values(df, final_forecast)\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(21, 317)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE: Now since the Airbnb data was grouped by Zipcode and averaged, only the variables with numerical\n",
    "### values will be included in that. The same data was used for Joining with the Zillows Dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Thus some categorical string variables like \"neighbourhood_group_cleansed\" are missing here in final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to Extract the Neighbours\n",
    "\n",
    "def extract_neighbours(from_data, to_data):\n",
    "    \n",
    "    # Since a particular zipcode has only a particular \"neighbourhood_group_cleansed\", I am using the .first() method to \n",
    "    # extract the neighbourhood and convert them to a Dataframe\n",
    "    neighbours=from_data.groupby('zipcode').first()['neighbourhood_group_cleansed'].to_frame().reset_index()\n",
    "    \n",
    "    # Merging the Extracted data to the to_data\n",
    "    to_data=to_data.merge(neighbours, on='zipcode')\n",
    "    \n",
    "    return(to_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>id</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_acceptance_rate</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-08-01 00:00:00</th>\n",
       "      <th>2018-09-01 00:00:00</th>\n",
       "      <th>2018-10-01 00:00:00</th>\n",
       "      <th>2018-11-01 00:00:00</th>\n",
       "      <th>2018-12-01 00:00:00</th>\n",
       "      <th>2019-01-01 00:00:00</th>\n",
       "      <th>2019-02-01 00:00:00</th>\n",
       "      <th>2019-03-01 00:00:00</th>\n",
       "      <th>Current Month Cost</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003</td>\n",
       "      <td>9.124661e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.496037e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.646018</td>\n",
       "      <td>1.646018</td>\n",
       "      <td>40.728902</td>\n",
       "      <td>-73.988258</td>\n",
       "      <td>4.566372</td>\n",
       "      <td>...</td>\n",
       "      <td>2.291593e+06</td>\n",
       "      <td>2.301921e+06</td>\n",
       "      <td>2.312249e+06</td>\n",
       "      <td>2.322578e+06</td>\n",
       "      <td>2.332906e+06</td>\n",
       "      <td>2.343234e+06</td>\n",
       "      <td>2.353562e+06</td>\n",
       "      <td>2.363890e+06</td>\n",
       "      <td>2.374218e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10011</td>\n",
       "      <td>9.889107e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.005599e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.448276</td>\n",
       "      <td>1.448276</td>\n",
       "      <td>40.742371</td>\n",
       "      <td>-73.999752</td>\n",
       "      <td>4.517241</td>\n",
       "      <td>...</td>\n",
       "      <td>2.653717e+06</td>\n",
       "      <td>2.666097e+06</td>\n",
       "      <td>2.678476e+06</td>\n",
       "      <td>2.690856e+06</td>\n",
       "      <td>2.703236e+06</td>\n",
       "      <td>2.715616e+06</td>\n",
       "      <td>2.727996e+06</td>\n",
       "      <td>2.740375e+06</td>\n",
       "      <td>2.752755e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10013</td>\n",
       "      <td>1.054726e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.783653e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.412698</td>\n",
       "      <td>1.412698</td>\n",
       "      <td>40.719555</td>\n",
       "      <td>-74.004075</td>\n",
       "      <td>4.281250</td>\n",
       "      <td>...</td>\n",
       "      <td>3.481322e+06</td>\n",
       "      <td>3.493096e+06</td>\n",
       "      <td>3.504869e+06</td>\n",
       "      <td>3.516642e+06</td>\n",
       "      <td>3.528415e+06</td>\n",
       "      <td>3.540188e+06</td>\n",
       "      <td>3.551961e+06</td>\n",
       "      <td>3.563734e+06</td>\n",
       "      <td>3.575507e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10014</td>\n",
       "      <td>8.654217e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.054620e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.537634</td>\n",
       "      <td>1.537634</td>\n",
       "      <td>40.734209</td>\n",
       "      <td>-74.004240</td>\n",
       "      <td>4.344086</td>\n",
       "      <td>...</td>\n",
       "      <td>2.655164e+06</td>\n",
       "      <td>2.666847e+06</td>\n",
       "      <td>2.678530e+06</td>\n",
       "      <td>2.690213e+06</td>\n",
       "      <td>2.701897e+06</td>\n",
       "      <td>2.713580e+06</td>\n",
       "      <td>2.725263e+06</td>\n",
       "      <td>2.736946e+06</td>\n",
       "      <td>2.748629e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10021</td>\n",
       "      <td>1.022891e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.568867e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>40.768441</td>\n",
       "      <td>-73.956628</td>\n",
       "      <td>4.263158</td>\n",
       "      <td>...</td>\n",
       "      <td>1.918115e+06</td>\n",
       "      <td>1.925437e+06</td>\n",
       "      <td>1.932760e+06</td>\n",
       "      <td>1.940082e+06</td>\n",
       "      <td>1.947404e+06</td>\n",
       "      <td>1.954727e+06</td>\n",
       "      <td>1.962049e+06</td>\n",
       "      <td>1.969372e+06</td>\n",
       "      <td>1.976694e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 318 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   zipcode            id     scrape_id       host_id  host_acceptance_rate  \\\n",
       "0    10003  9.124661e+06  2.017050e+13  2.496037e+07                   NaN   \n",
       "1    10011  9.889107e+06  2.017050e+13  3.005599e+07                   NaN   \n",
       "2    10013  1.054726e+07  2.017050e+13  2.783653e+07                   NaN   \n",
       "3    10014  8.654217e+06  2.017050e+13  2.054620e+07                   NaN   \n",
       "4    10021  1.022891e+07  2.017050e+13  3.568867e+07                   NaN   \n",
       "\n",
       "   host_listings_count  host_total_listings_count   latitude  longitude  \\\n",
       "0             1.646018                   1.646018  40.728902 -73.988258   \n",
       "1             1.448276                   1.448276  40.742371 -73.999752   \n",
       "2             1.412698                   1.412698  40.719555 -74.004075   \n",
       "3             1.537634                   1.537634  40.734209 -74.004240   \n",
       "4             1.526316                   1.526316  40.768441 -73.956628   \n",
       "\n",
       "   accommodates              ...               2018-08-01 00:00:00  \\\n",
       "0      4.566372              ...                      2.291593e+06   \n",
       "1      4.517241              ...                      2.653717e+06   \n",
       "2      4.281250              ...                      3.481322e+06   \n",
       "3      4.344086              ...                      2.655164e+06   \n",
       "4      4.263158              ...                      1.918115e+06   \n",
       "\n",
       "   2018-09-01 00:00:00  2018-10-01 00:00:00  2018-11-01 00:00:00  \\\n",
       "0         2.301921e+06         2.312249e+06         2.322578e+06   \n",
       "1         2.666097e+06         2.678476e+06         2.690856e+06   \n",
       "2         3.493096e+06         3.504869e+06         3.516642e+06   \n",
       "3         2.666847e+06         2.678530e+06         2.690213e+06   \n",
       "4         1.925437e+06         1.932760e+06         1.940082e+06   \n",
       "\n",
       "   2018-12-01 00:00:00  2019-01-01 00:00:00  2019-02-01 00:00:00  \\\n",
       "0         2.332906e+06         2.343234e+06         2.353562e+06   \n",
       "1         2.703236e+06         2.715616e+06         2.727996e+06   \n",
       "2         3.528415e+06         3.540188e+06         3.551961e+06   \n",
       "3         2.701897e+06         2.713580e+06         2.725263e+06   \n",
       "4         1.947404e+06         1.954727e+06         1.962049e+06   \n",
       "\n",
       "   2019-03-01 00:00:00  Current Month Cost  neighbourhood_group_cleansed  \n",
       "0         2.363890e+06        2.374218e+06                     Manhattan  \n",
       "1         2.740375e+06        2.752755e+06                     Manhattan  \n",
       "2         3.563734e+06        3.575507e+06                     Manhattan  \n",
       "3         2.736946e+06        2.748629e+06                     Manhattan  \n",
       "4         1.969372e+06        1.976694e+06                     Manhattan  \n",
       "\n",
       "[5 rows x 318 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Storing it as \"final_df\"\n",
    "\n",
    "final_df= extract_neighbours(airbnb, final_df)\n",
    "final_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quality Check Phase II"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zipcode                           0.000000\n",
       "id                                0.000000\n",
       "scrape_id                         0.000000\n",
       "host_id                           0.000000\n",
       "host_acceptance_rate            100.000000\n",
       "host_listings_count               0.000000\n",
       "host_total_listings_count         0.000000\n",
       "latitude                          0.000000\n",
       "longitude                         0.000000\n",
       "accommodates                      0.000000\n",
       "bathrooms                         0.000000\n",
       "bedrooms                          0.000000\n",
       "beds                              0.000000\n",
       "square_feet                      47.619048\n",
       "price                             0.000000\n",
       "guests_included                   0.000000\n",
       "minimum_nights                    0.000000\n",
       "maximum_nights                    0.000000\n",
       "has_availability                100.000000\n",
       "availability_30                   0.000000\n",
       "availability_60                   0.000000\n",
       "availability_90                   0.000000\n",
       "availability_365                  0.000000\n",
       "number_of_reviews                 0.000000\n",
       "review_scores_rating              0.000000\n",
       "review_scores_accuracy            0.000000\n",
       "review_scores_cleanliness         4.761905\n",
       "review_scores_checkin             4.761905\n",
       "review_scores_communication       4.761905\n",
       "review_scores_location            4.761905\n",
       "                                   ...    \n",
       "2016-12                           0.000000\n",
       "2017-01                           0.000000\n",
       "2017-02                           0.000000\n",
       "2017-03                           0.000000\n",
       "2017-04                           0.000000\n",
       "2017-05                           0.000000\n",
       "2017-06                           0.000000\n",
       "2017-07-01 00:00:00               0.000000\n",
       "2017-08-01 00:00:00               0.000000\n",
       "2017-09-01 00:00:00               0.000000\n",
       "2017-10-01 00:00:00               0.000000\n",
       "2017-11-01 00:00:00               0.000000\n",
       "2017-12-01 00:00:00               0.000000\n",
       "2018-01-01 00:00:00               0.000000\n",
       "2018-02-01 00:00:00               0.000000\n",
       "2018-03-01 00:00:00               0.000000\n",
       "2018-04-01 00:00:00               0.000000\n",
       "2018-05-01 00:00:00               0.000000\n",
       "2018-06-01 00:00:00               0.000000\n",
       "2018-07-01 00:00:00               0.000000\n",
       "2018-08-01 00:00:00               0.000000\n",
       "2018-09-01 00:00:00               0.000000\n",
       "2018-10-01 00:00:00               0.000000\n",
       "2018-11-01 00:00:00               0.000000\n",
       "2018-12-01 00:00:00               0.000000\n",
       "2019-01-01 00:00:00               0.000000\n",
       "2019-02-01 00:00:00               0.000000\n",
       "2019-03-01 00:00:00               0.000000\n",
       "Current Month Cost                0.000000\n",
       "neighbourhood_group_cleansed      0.000000\n",
       "Length: 318, dtype: float64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mis_value_percent(final_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It is to be noted, that the Null values present here were not used for any analysis and thus they were removed instead of imputing with Mean."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_df=final_df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "zipcode                           0.0\n",
       "id                                0.0\n",
       "scrape_id                         0.0\n",
       "host_id                           0.0\n",
       "host_listings_count               0.0\n",
       "host_total_listings_count         0.0\n",
       "latitude                          0.0\n",
       "longitude                         0.0\n",
       "accommodates                      0.0\n",
       "bathrooms                         0.0\n",
       "bedrooms                          0.0\n",
       "beds                              0.0\n",
       "price                             0.0\n",
       "guests_included                   0.0\n",
       "minimum_nights                    0.0\n",
       "maximum_nights                    0.0\n",
       "availability_30                   0.0\n",
       "availability_60                   0.0\n",
       "availability_90                   0.0\n",
       "availability_365                  0.0\n",
       "number_of_reviews                 0.0\n",
       "review_scores_rating              0.0\n",
       "review_scores_accuracy            0.0\n",
       "calculated_host_listings_count    0.0\n",
       "RegionID                          0.0\n",
       "City                              0.0\n",
       "State                             0.0\n",
       "Metro                             0.0\n",
       "CountyName                        0.0\n",
       "SizeRank                          0.0\n",
       "                                 ... \n",
       "2016-12                           0.0\n",
       "2017-01                           0.0\n",
       "2017-02                           0.0\n",
       "2017-03                           0.0\n",
       "2017-04                           0.0\n",
       "2017-05                           0.0\n",
       "2017-06                           0.0\n",
       "2017-07-01 00:00:00               0.0\n",
       "2017-08-01 00:00:00               0.0\n",
       "2017-09-01 00:00:00               0.0\n",
       "2017-10-01 00:00:00               0.0\n",
       "2017-11-01 00:00:00               0.0\n",
       "2017-12-01 00:00:00               0.0\n",
       "2018-01-01 00:00:00               0.0\n",
       "2018-02-01 00:00:00               0.0\n",
       "2018-03-01 00:00:00               0.0\n",
       "2018-04-01 00:00:00               0.0\n",
       "2018-05-01 00:00:00               0.0\n",
       "2018-06-01 00:00:00               0.0\n",
       "2018-07-01 00:00:00               0.0\n",
       "2018-08-01 00:00:00               0.0\n",
       "2018-09-01 00:00:00               0.0\n",
       "2018-10-01 00:00:00               0.0\n",
       "2018-11-01 00:00:00               0.0\n",
       "2018-12-01 00:00:00               0.0\n",
       "2019-01-01 00:00:00               0.0\n",
       "2019-02-01 00:00:00               0.0\n",
       "2019-03-01 00:00:00               0.0\n",
       "Current Month Cost                0.0\n",
       "neighbourhood_group_cleansed      0.0\n",
       "Length: 174, dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mis_value_percent(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Renaming the Price column to Revenue to make it clear\n",
    "\n",
    "final_df.rename(columns={'price':'Revenue'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4     False\n",
       "5     False\n",
       "6     False\n",
       "7     False\n",
       "8     False\n",
       "9     False\n",
       "10    False\n",
       "11    False\n",
       "12    False\n",
       "13    False\n",
       "14    False\n",
       "15    False\n",
       "16    False\n",
       "17    False\n",
       "18    False\n",
       "19    False\n",
       "20    False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for duplicate values\n",
    "\n",
    "final_df.duplicated()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Now the data is clean and there are no more missing data values in the final data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ---------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visual Narrative"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing the Zipcodes in Map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "lat": [
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         "lon": [
          -73.9882578099115,
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         ],
         "marker": {
          "size": 9
         },
         "mode": "markers",
         "text": [
          "10003 Manhattan",
          "10011 Manhattan",
          "10013 Manhattan",
          "10014 Manhattan",
          "10021 Manhattan",
          "10022 Manhattan",
          "10023 Manhattan",
          "10025 Manhattan",
          "10028 Manhattan",
          "10036 Manhattan",
          "10128 Manhattan",
          "10304 Staten Island",
          "10305 Staten Island",
          "10306 Staten Island",
          "10312 Staten Island",
          "11201 Brooklyn",
          "11215 Brooklyn",
          "11217 Brooklyn",
          "11231 Brooklyn",
          "11234 Brooklyn",
          "11434 Queens"
         ],
         "type": "scattermapbox",
         "uid": "54d2a3ba-5c97-11e9-a75d-3cf862c2ea5b"
        }
       ],
       "layout": {
        "autosize": true,
        "hovermode": "closest",
        "mapbox": {
         "accesstoken": "pk.eyJ1IjoiaGFyaXNoMjMiLCJhIjoiY2p1N2h5bzliMXVrbDQwb2J3NmF1N3JoMSJ9.7BrKDWLpEdewUnq47GWOKA",
         "bearing": 0,
         "center": {
          "lat": 40.666,
          "lon": -73.98
         },
         "pitch": 0,
         "zoom": 9
        }
       }
      },
      "text/html": [
       "<div id=\"bf122a12-1cbf-49d7-bf4a-20c0553f3180\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"bf122a12-1cbf-49d7-bf4a-20c0553f3180\", [{\"lat\": [40.728901940796455, 40.742370868735634, 40.71955466156251, 40.73420932107527, 40.76844115578947, 40.75888597184211, 40.77758059492063, 40.798025117053555, 40.7753669890625, 40.754810299166664, 40.78184155374999, 40.626720555000006, 40.596360087777775, 40.568878725000005, 40.54633149, 40.69261064741934, 40.66624076870966, 40.68137690216667, 40.68063242840581, 40.62307418833334, 40.66203338], \"lon\": [-73.9882578099115, -73.99975193206895, -74.00407486906252, -74.0042401108602, -73.95662805578948, -73.9667001713158, -73.98148638841269, -73.96635482160715, -73.9509343671875, -73.98782434666667, -73.94953672053568, -74.07672088000001, -74.07737053222223, -74.11156492999999, -74.18370056, -73.99057626854841, -73.98452717411286, -73.97906801133335, -74.00004876927538, -73.925943205, -73.7698003], \"marker\": {\"size\": 9}, \"mode\": \"markers\", \"text\": [\"10003 Manhattan\", \"10011 Manhattan\", \"10013 Manhattan\", \"10014 Manhattan\", \"10021 Manhattan\", \"10022 Manhattan\", \"10023 Manhattan\", \"10025 Manhattan\", \"10028 Manhattan\", \"10036 Manhattan\", \"10128 Manhattan\", \"10304 Staten Island\", \"10305 Staten Island\", \"10306 Staten Island\", \"10312 Staten Island\", \"11201 Brooklyn\", \"11215 Brooklyn\", \"11217 Brooklyn\", \"11231 Brooklyn\", \"11234 Brooklyn\", \"11434 Queens\"], \"type\": \"scattermapbox\", \"uid\": \"54d2a3ba-5c97-11e9-a75d-3cf862c2ea5b\"}], {\"autosize\": true, \"hovermode\": \"closest\", \"mapbox\": {\"accesstoken\": \"pk.eyJ1IjoiaGFyaXNoMjMiLCJhIjoiY2p1N2h5bzliMXVrbDQwb2J3NmF1N3JoMSJ9.7BrKDWLpEdewUnq47GWOKA\", \"bearing\": 0, \"center\": {\"lat\": 40.666, \"lon\": -73.98}, \"pitch\": 0, \"zoom\": 9}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"bf122a12-1cbf-49d7-bf4a-20c0553f3180\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"bf122a12-1cbf-49d7-bf4a-20c0553f3180\", [{\"lat\": [40.728901940796455, 40.742370868735634, 40.71955466156251, 40.73420932107527, 40.76844115578947, 40.75888597184211, 40.77758059492063, 40.798025117053555, 40.7753669890625, 40.754810299166664, 40.78184155374999, 40.626720555000006, 40.596360087777775, 40.568878725000005, 40.54633149, 40.69261064741934, 40.66624076870966, 40.68137690216667, 40.68063242840581, 40.62307418833334, 40.66203338], \"lon\": [-73.9882578099115, -73.99975193206895, -74.00407486906252, -74.0042401108602, -73.95662805578948, -73.9667001713158, -73.98148638841269, -73.96635482160715, -73.9509343671875, -73.98782434666667, -73.94953672053568, -74.07672088000001, -74.07737053222223, -74.11156492999999, -74.18370056, -73.99057626854841, -73.98452717411286, -73.97906801133335, -74.00004876927538, -73.925943205, -73.7698003], \"marker\": {\"size\": 9}, \"mode\": \"markers\", \"text\": [\"10003 Manhattan\", \"10011 Manhattan\", \"10013 Manhattan\", \"10014 Manhattan\", \"10021 Manhattan\", \"10022 Manhattan\", \"10023 Manhattan\", \"10025 Manhattan\", \"10028 Manhattan\", \"10036 Manhattan\", \"10128 Manhattan\", \"10304 Staten Island\", \"10305 Staten Island\", \"10306 Staten Island\", \"10312 Staten Island\", \"11201 Brooklyn\", \"11215 Brooklyn\", \"11217 Brooklyn\", \"11231 Brooklyn\", \"11234 Brooklyn\", \"11434 Queens\"], \"type\": \"scattermapbox\", \"uid\": \"54d2a3ba-5c97-11e9-a75d-3cf862c2ea5b\"}], {\"autosize\": true, \"hovermode\": \"closest\", \"mapbox\": {\"accesstoken\": \"pk.eyJ1IjoiaGFyaXNoMjMiLCJhIjoiY2p1N2h5bzliMXVrbDQwb2J3NmF1N3JoMSJ9.7BrKDWLpEdewUnq47GWOKA\", \"bearing\": 0, \"center\": {\"lat\": 40.666, \"lon\": -73.98}, \"pitch\": 0, \"zoom\": 9}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# First an account in Mapbox was created and the Public access token from that was used.\n",
    "\n",
    "mapbox_access_token = 'pk.eyJ1IjoiaGFyaXNoMjMiLCJhIjoiY2p1N2h5bzliMXVrbDQwb2J3NmF1N3JoMSJ9.7BrKDWLpEdewUnq47GWOKA'\n",
    "\n",
    "# Inside the Scattermapbox, I have specified the longitude and the latitude column for the map\n",
    "data = [\n",
    "    go.Scattermapbox(\n",
    "        lat=final_df.latitude,\n",
    "        lon=final_df.longitude,\n",
    "        mode='markers',\n",
    "        marker=go.scattermapbox.Marker(\n",
    "            size=9\n",
    "        ),text= final_df.zipcode.map(str) + \" \" + final_df.neighbourhood_group_cleansed # To Display both the Zipcode and Neighbourhood \n",
    "        ,\n",
    "    )\n",
    "]\n",
    "\n",
    "\n",
    "# And then the access token defined above was assigned and then the center point of the map is specified\n",
    "layout = go.Layout(\n",
    "    autosize=True,\n",
    "    hovermode='closest',\n",
    "    mapbox=go.layout.Mapbox(\n",
    "        accesstoken=mapbox_access_token,\n",
    "        bearing=0,\n",
    "        center=go.layout.mapbox.Center(\n",
    "            lat=40.666,\n",
    "            lon=-73.98\n",
    "        ),\n",
    "        pitch=0,\n",
    "        zoom=9\n",
    "    ),\n",
    ")\n",
    "\n",
    "fig = go.Figure(data=data, layout=layout)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Preliminary Analysis- Review Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE: An important thing to note here is the review analysis won't be considered fully because some empty values are filled with their group mean in order to plot the graph. However, this review analysis will taken as a support for our conclusion."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Manhattan        11\n",
       "Brooklyn          5\n",
       "Staten Island     4\n",
       "Queens            1\n",
       "Name: neighbourhood_group_cleansed, dtype: int64"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.neighbourhood_group_cleansed.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     92.514445\n",
       "1     93.691141\n",
       "2     91.690927\n",
       "3     93.658104\n",
       "4     93.153286\n",
       "5     88.233652\n",
       "6     94.411979\n",
       "7     92.062500\n",
       "8     94.200047\n",
       "9     90.763228\n",
       "10    91.747068\n",
       "Name: review_scores_rating, dtype: float64"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[final_df['neighbourhood_group_cleansed']=='Manhattan']['review_scores_rating']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Function to Create a Scatterplot for Different Neighbours\n",
    "\n",
    "def scatterplot(xaxis, yaxis, title):\n",
    "\n",
    "    data=[]\n",
    "    neighbours=['Manhattan', 'Brooklyn', 'Staten Island', 'Queens']\n",
    "    \n",
    "    # Now looping over the Neighbours List\n",
    "    for i in neighbours:\n",
    "\n",
    "        trace = go.Scatter(x= final_df[final_df['neighbourhood_group_cleansed']==i][xaxis],\n",
    "             y = final_df[final_df['neighbourhood_group_cleansed']==i][yaxis],\n",
    "             mode= 'markers',\n",
    "            name= i,\n",
    "             marker = dict(symbol='circle', size=16),\n",
    "                           text= final_df[final_df['neighbourhood_group_cleansed']==i]['zipcode'])\n",
    "\n",
    "        data.append(trace)\n",
    "    # The above loop will create 4 traces for 4 different neighbourhoods and append it to the data list\n",
    "    \n",
    "    layout= go.Layout(title=title,\n",
    "                     hovermode= 'closest',\n",
    "                     xaxis= dict(\n",
    "            title= xaxis),\n",
    "        yaxis=dict(\n",
    "            title= yaxis\n",
    "        ))\n",
    "    fig =go.Figure(data=data, layout=layout)\n",
    "    iplot(fig)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "marker": {
          "size": 16,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "Manhattan",
         "text": [
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         "type": "scatter",
         "uid": "578a7d68-5c97-11e9-b6bc-3cf862c2ea5b",
         "x": [
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        {
         "marker": {
          "size": 16,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "Brooklyn",
         "text": [
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         "type": "scatter",
         "uid": "578a7d69-5c97-11e9-89aa-3cf862c2ea5b",
         "x": [
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        },
        {
         "marker": {
          "size": 16,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "Staten Island",
         "text": [
          10304,
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         "type": "scatter",
         "uid": "578a7d6a-5c97-11e9-96d7-3cf862c2ea5b",
         "x": [
          9,
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         "y": [
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        },
        {
         "marker": {
          "size": 16,
          "symbol": "circle"
         },
         "mode": "markers",
         "name": "Queens",
         "text": [
          11434
         ],
         "type": "scatter",
         "uid": "578a7d6b-5c97-11e9-8065-3cf862c2ea5b",
         "x": [
          10
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         "y": [
          95
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        }
       ],
       "layout": {
        "hovermode": "closest",
        "title": "Review Analysis",
        "xaxis": {
         "title": "review_scores_accuracy"
        },
        "yaxis": {
         "title": "review_scores_rating"
        }
       }
      },
      "text/html": [
       "<div id=\"4ee0b986-5ca9-4465-9dc5-18a310244f54\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"4ee0b986-5ca9-4465-9dc5-18a310244f54\", [{\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Manhattan\", \"text\": [10003.0, 10011.0, 10013.0, 10014.0, 10021.0, 10022.0, 10023.0, 10025.0, 10028.0, 10036.0, 10128.0], \"x\": [9.54045302701752, 9.504465799886944, 9.519472947761196, 9.600039100684263, 9.605263157894736, 8.956676626062976, 9.529610144341023, 9.398517829796546, 9.572679924242426, 9.164021164021165, 9.399700598802394], \"y\": [92.51444452570757, 93.69114133604228, 91.69092704460968, 93.65810361681334, 93.15328644191122, 88.23365231259969, 94.41197895588829, 92.06249999999999, 94.20004734848487, 90.7632275132275, 91.74706823027716], \"type\": \"scatter\", \"uid\": \"578a7d68-5c97-11e9-b6bc-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Brooklyn\", \"text\": [11201.0, 11215.0, 11217.0, 11231.0, 11234.0], \"x\": [9.723076923076922, 9.68092764716603, 9.61124497991968, 9.574980292280635, 9.666666666666666], \"y\": [95.51756823821339, 95.11184210526315, 94.70993333333334, 94.35398550724638, 88.0], \"type\": \"scatter\", \"uid\": \"578a7d69-5c97-11e9-89aa-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Staten Island\", \"text\": [10304.0, 10305.0, 10306.0, 10312.0], \"x\": [9.0, 9.333333333333334, 9.5, 10.0], \"y\": [80.0, 93.33333333333333, 95.5, 97.85714285714286], \"type\": \"scatter\", \"uid\": \"578a7d6a-5c97-11e9-96d7-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Queens\", \"text\": [11434.0], \"x\": [10.0], \"y\": [95.0], \"type\": \"scatter\", \"uid\": \"578a7d6b-5c97-11e9-8065-3cf862c2ea5b\"}], {\"hovermode\": \"closest\", \"title\": \"Review Analysis\", \"xaxis\": {\"title\": \"review_scores_accuracy\"}, \"yaxis\": {\"title\": \"review_scores_rating\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"4ee0b986-5ca9-4465-9dc5-18a310244f54\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"4ee0b986-5ca9-4465-9dc5-18a310244f54\", [{\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Manhattan\", \"text\": [10003.0, 10011.0, 10013.0, 10014.0, 10021.0, 10022.0, 10023.0, 10025.0, 10028.0, 10036.0, 10128.0], \"x\": [9.54045302701752, 9.504465799886944, 9.519472947761196, 9.600039100684263, 9.605263157894736, 8.956676626062976, 9.529610144341023, 9.398517829796546, 9.572679924242426, 9.164021164021165, 9.399700598802394], \"y\": [92.51444452570757, 93.69114133604228, 91.69092704460968, 93.65810361681334, 93.15328644191122, 88.23365231259969, 94.41197895588829, 92.06249999999999, 94.20004734848487, 90.7632275132275, 91.74706823027716], \"type\": \"scatter\", \"uid\": \"578a7d68-5c97-11e9-b6bc-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Brooklyn\", \"text\": [11201.0, 11215.0, 11217.0, 11231.0, 11234.0], \"x\": [9.723076923076922, 9.68092764716603, 9.61124497991968, 9.574980292280635, 9.666666666666666], \"y\": [95.51756823821339, 95.11184210526315, 94.70993333333334, 94.35398550724638, 88.0], \"type\": \"scatter\", \"uid\": \"578a7d69-5c97-11e9-89aa-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Staten Island\", \"text\": [10304.0, 10305.0, 10306.0, 10312.0], \"x\": [9.0, 9.333333333333334, 9.5, 10.0], \"y\": [80.0, 93.33333333333333, 95.5, 97.85714285714286], \"type\": \"scatter\", \"uid\": \"578a7d6a-5c97-11e9-96d7-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Queens\", \"text\": [11434.0], \"x\": [10.0], \"y\": [95.0], \"type\": \"scatter\", \"uid\": \"578a7d6b-5c97-11e9-8065-3cf862c2ea5b\"}], {\"hovermode\": \"closest\", \"title\": \"Review Analysis\", \"xaxis\": {\"title\": \"review_scores_accuracy\"}, \"yaxis\": {\"title\": \"review_scores_rating\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "scatterplot('review_scores_accuracy','review_scores_rating', 'Review Analysis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This is one basic analysis in which shows how accurate are the reviews. We see that Brooklyn in general has more review score accuracy and review rating value. Besides, 10312 and 11434 tops the graph with both high accuracy and 10312 tops with highest rating."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It is very important to know on an average how many reviews a zipcode gets and analyze the scores further"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>review_scores_rating</th>\n",
       "      <th>number_of_reviews</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003</td>\n",
       "      <td>92.514445</td>\n",
       "      <td>31.292486</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10011</td>\n",
       "      <td>93.691141</td>\n",
       "      <td>15.945257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10013</td>\n",
       "      <td>91.690927</td>\n",
       "      <td>18.906767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10014</td>\n",
       "      <td>93.658104</td>\n",
       "      <td>17.774297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10021</td>\n",
       "      <td>93.153286</td>\n",
       "      <td>18.810971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10022</td>\n",
       "      <td>88.233652</td>\n",
       "      <td>29.905702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10023</td>\n",
       "      <td>94.411979</td>\n",
       "      <td>16.877034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10025</td>\n",
       "      <td>92.062500</td>\n",
       "      <td>20.180494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10028</td>\n",
       "      <td>94.200047</td>\n",
       "      <td>11.622623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10036</td>\n",
       "      <td>90.763228</td>\n",
       "      <td>22.604651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10128</td>\n",
       "      <td>91.747068</td>\n",
       "      <td>9.917482</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10304</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10305</td>\n",
       "      <td>93.333333</td>\n",
       "      <td>8.261438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10306</td>\n",
       "      <td>95.500000</td>\n",
       "      <td>71.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10312</td>\n",
       "      <td>97.857143</td>\n",
       "      <td>23.222222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11201</td>\n",
       "      <td>95.517568</td>\n",
       "      <td>22.670683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11215</td>\n",
       "      <td>95.111842</td>\n",
       "      <td>16.446481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11217</td>\n",
       "      <td>94.709933</td>\n",
       "      <td>20.022059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11231</td>\n",
       "      <td>94.353986</td>\n",
       "      <td>19.016645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11234</td>\n",
       "      <td>88.000000</td>\n",
       "      <td>13.833333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11434</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>42.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    zipcode  review_scores_rating  number_of_reviews\n",
       "0     10003             92.514445          31.292486\n",
       "1     10011             93.691141          15.945257\n",
       "2     10013             91.690927          18.906767\n",
       "3     10014             93.658104          17.774297\n",
       "4     10021             93.153286          18.810971\n",
       "5     10022             88.233652          29.905702\n",
       "6     10023             94.411979          16.877034\n",
       "7     10025             92.062500          20.180494\n",
       "8     10028             94.200047          11.622623\n",
       "9     10036             90.763228          22.604651\n",
       "10    10128             91.747068           9.917482\n",
       "11    10304             80.000000           1.000000\n",
       "12    10305             93.333333           8.261438\n",
       "13    10306             95.500000          71.500000\n",
       "14    10312             97.857143          23.222222\n",
       "15    11201             95.517568          22.670683\n",
       "16    11215             95.111842          16.446481\n",
       "17    11217             94.709933          20.022059\n",
       "18    11231             94.353986          19.016645\n",
       "19    11234             88.000000          13.833333\n",
       "20    11434             95.000000          42.000000"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df[['zipcode','review_scores_rating', 'number_of_reviews']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# This function is used to create a plot with xaxis, yaxis and a variable whose size will change in the plot depending on the values\n",
    "\n",
    "def size_scatter(xaxis, yaxis, size):\n",
    "    \n",
    "    data=[]\n",
    "    neighbours=['Manhattan', 'Brooklyn', 'Staten Island', 'Queens']\n",
    "\n",
    "    for i in neighbours:\n",
    "\n",
    "        trace = go.Scatter(x= final_df[final_df['neighbourhood_group_cleansed']==i][xaxis],\n",
    "            y = final_df[final_df['neighbourhood_group_cleansed']==i][yaxis],\n",
    "            mode='markers',\n",
    "            marker=dict(\n",
    "                size=final_df[final_df['neighbourhood_group_cleansed']==i][size]\n",
    "            ),\n",
    "            text = \"%s\"%(size)+'='+ final_df[final_df['neighbourhood_group_cleansed']==i][size].map(str) + '</br> ' + \"Zipcode =\"+ final_df[final_df['neighbourhood_group_cleansed']==i]['zipcode'].map(str)\n",
    "        )\n",
    "        data.append(trace)\n",
    "\n",
    "    layout= go.Layout(\n",
    "        title= 'Scatter Plot with Size = %s' %(size),\n",
    "        hovermode= 'closest',\n",
    "        xaxis= dict(type='category',\n",
    "            ticklen= 5,\n",
    "            zeroline= False,\n",
    "            gridwidth= 2,\n",
    "        ),\n",
    "        yaxis=dict(\n",
    "            title= '%s'%(yaxis),\n",
    "            ticklen= 5,\n",
    "            gridwidth= 2,\n",
    "        ),\n",
    "        showlegend= False\n",
    "    )\n",
    "\n",
    "    fig = go.Figure(data=data, layout=layout)\n",
    "    iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
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           11.622623239436619,
           22.6046511627907,
           9.917482061317681
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         },
         "mode": "markers",
         "text": [
          "number_of_reviews=31.29248596316921</br> Zipcode =10003",
          "number_of_reviews=15.945256943512682</br> Zipcode =10011",
          "number_of_reviews=18.90676652892562</br> Zipcode =10013",
          "number_of_reviews=17.774296797793415</br> Zipcode =10014",
          "number_of_reviews=18.810971089696075</br> Zipcode =10021",
          "number_of_reviews=29.905701754385962</br> Zipcode =10022",
          "number_of_reviews=16.877033937703395</br> Zipcode =10023",
          "number_of_reviews=20.180494356833645</br> Zipcode =10025",
          "number_of_reviews=11.622623239436619</br> Zipcode =10028",
          "number_of_reviews=22.6046511627907</br> Zipcode =10036",
          "number_of_reviews=9.917482061317681</br> Zipcode =10128"
         ],
         "type": "scatter",
         "uid": "57d81a28-5c97-11e9-bb68-3cf862c2ea5b",
         "x": [
          "Manhattan",
          "Manhattan",
          "Manhattan",
          "Manhattan",
          "Manhattan",
          "Manhattan",
          "Manhattan",
          "Manhattan",
          "Manhattan",
          "Manhattan",
          "Manhattan"
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         },
         "mode": "markers",
         "text": [
          "number_of_reviews=22.67068288551852</br> Zipcode =11201",
          "number_of_reviews=16.446480938416418</br> Zipcode =11215",
          "number_of_reviews=20.022058823529406</br> Zipcode =11217",
          "number_of_reviews=19.016645142775143</br> Zipcode =11231",
          "number_of_reviews=13.833333333333334</br> Zipcode =11234"
         ],
         "type": "scatter",
         "uid": "57d81a29-5c97-11e9-9f16-3cf862c2ea5b",
         "x": [
          "Brooklyn",
          "Brooklyn",
          "Brooklyn",
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         "y": [
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          "size": [
           1,
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         },
         "mode": "markers",
         "text": [
          "number_of_reviews=1.0</br> Zipcode =10304",
          "number_of_reviews=8.261437908496731</br> Zipcode =10305",
          "number_of_reviews=71.5</br> Zipcode =10306",
          "number_of_reviews=23.22222222222222</br> Zipcode =10312"
         ],
         "type": "scatter",
         "uid": "57d81a2a-5c97-11e9-8dd0-3cf862c2ea5b",
         "x": [
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         "y": [
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        {
         "marker": {
          "size": [
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         },
         "mode": "markers",
         "text": [
          "number_of_reviews=42.0</br> Zipcode =11434"
         ],
         "type": "scatter",
         "uid": "57d81a2b-5c97-11e9-be5c-3cf862c2ea5b",
         "x": [
          "Queens"
         ],
         "y": [
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        }
       ],
       "layout": {
        "hovermode": "closest",
        "showlegend": false,
        "title": "Scatter Plot with Size = number_of_reviews",
        "xaxis": {
         "gridwidth": 2,
         "ticklen": 5,
         "type": "category",
         "zeroline": false
        },
        "yaxis": {
         "gridwidth": 2,
         "ticklen": 5,
         "title": "review_scores_rating"
        }
       }
      },
      "text/html": [
       "<div id=\"edd2d779-2fae-47d4-9554-a5dd35f0e759\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"edd2d779-2fae-47d4-9554-a5dd35f0e759\", [{\"marker\": {\"size\": [31.29248596316921, 15.945256943512682, 18.90676652892562, 17.774296797793415, 18.810971089696075, 29.905701754385962, 16.877033937703395, 20.180494356833645, 11.622623239436619, 22.6046511627907, 9.917482061317681]}, \"mode\": \"markers\", \"text\": [\"number_of_reviews=31.29248596316921</br> Zipcode =10003\", \"number_of_reviews=15.945256943512682</br> Zipcode =10011\", \"number_of_reviews=18.90676652892562</br> Zipcode =10013\", \"number_of_reviews=17.774296797793415</br> Zipcode =10014\", \"number_of_reviews=18.810971089696075</br> Zipcode =10021\", \"number_of_reviews=29.905701754385962</br> Zipcode =10022\", \"number_of_reviews=16.877033937703395</br> Zipcode =10023\", \"number_of_reviews=20.180494356833645</br> Zipcode =10025\", \"number_of_reviews=11.622623239436619</br> Zipcode =10028\", \"number_of_reviews=22.6046511627907</br> Zipcode =10036\", \"number_of_reviews=9.917482061317681</br> Zipcode =10128\"], \"x\": [\"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\"], \"y\": [92.51444452570757, 93.69114133604228, 91.69092704460968, 93.65810361681334, 93.15328644191122, 88.23365231259969, 94.41197895588829, 92.06249999999999, 94.20004734848487, 90.7632275132275, 91.74706823027716], \"type\": \"scatter\", \"uid\": \"57d81a28-5c97-11e9-bb68-3cf862c2ea5b\"}, {\"marker\": {\"size\": [22.67068288551852, 16.446480938416418, 20.022058823529406, 19.016645142775143, 13.833333333333334]}, \"mode\": \"markers\", \"text\": [\"number_of_reviews=22.67068288551852</br> Zipcode =11201\", \"number_of_reviews=16.446480938416418</br> Zipcode =11215\", \"number_of_reviews=20.022058823529406</br> Zipcode =11217\", \"number_of_reviews=19.016645142775143</br> Zipcode =11231\", \"number_of_reviews=13.833333333333334</br> Zipcode =11234\"], \"x\": [\"Brooklyn\", \"Brooklyn\", \"Brooklyn\", \"Brooklyn\", \"Brooklyn\"], \"y\": [95.51756823821339, 95.11184210526315, 94.70993333333334, 94.35398550724638, 88.0], \"type\": \"scatter\", \"uid\": \"57d81a29-5c97-11e9-9f16-3cf862c2ea5b\"}, {\"marker\": {\"size\": [1.0, 8.261437908496731, 71.5, 23.22222222222222]}, \"mode\": \"markers\", \"text\": [\"number_of_reviews=1.0</br> Zipcode =10304\", \"number_of_reviews=8.261437908496731</br> Zipcode =10305\", \"number_of_reviews=71.5</br> Zipcode =10306\", \"number_of_reviews=23.22222222222222</br> Zipcode =10312\"], \"x\": [\"Staten Island\", \"Staten Island\", \"Staten Island\", \"Staten Island\"], \"y\": [80.0, 93.33333333333333, 95.5, 97.85714285714286], \"type\": \"scatter\", \"uid\": \"57d81a2a-5c97-11e9-8dd0-3cf862c2ea5b\"}, {\"marker\": {\"size\": [42.0]}, \"mode\": \"markers\", \"text\": [\"number_of_reviews=42.0</br> Zipcode =11434\"], \"x\": [\"Queens\"], \"y\": [95.0], \"type\": \"scatter\", \"uid\": \"57d81a2b-5c97-11e9-be5c-3cf862c2ea5b\"}], {\"hovermode\": \"closest\", \"showlegend\": false, \"title\": \"Scatter Plot with Size = number_of_reviews\", \"xaxis\": {\"gridwidth\": 2, \"ticklen\": 5, \"type\": \"category\", \"zeroline\": false}, \"yaxis\": {\"gridwidth\": 2, \"ticklen\": 5, \"title\": \"review_scores_rating\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"edd2d779-2fae-47d4-9554-a5dd35f0e759\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"edd2d779-2fae-47d4-9554-a5dd35f0e759\", [{\"marker\": {\"size\": [31.29248596316921, 15.945256943512682, 18.90676652892562, 17.774296797793415, 18.810971089696075, 29.905701754385962, 16.877033937703395, 20.180494356833645, 11.622623239436619, 22.6046511627907, 9.917482061317681]}, \"mode\": \"markers\", \"text\": [\"number_of_reviews=31.29248596316921</br> Zipcode =10003\", \"number_of_reviews=15.945256943512682</br> Zipcode =10011\", \"number_of_reviews=18.90676652892562</br> Zipcode =10013\", \"number_of_reviews=17.774296797793415</br> Zipcode =10014\", \"number_of_reviews=18.810971089696075</br> Zipcode =10021\", \"number_of_reviews=29.905701754385962</br> Zipcode =10022\", \"number_of_reviews=16.877033937703395</br> Zipcode =10023\", \"number_of_reviews=20.180494356833645</br> Zipcode =10025\", \"number_of_reviews=11.622623239436619</br> Zipcode =10028\", \"number_of_reviews=22.6046511627907</br> Zipcode =10036\", \"number_of_reviews=9.917482061317681</br> Zipcode =10128\"], \"x\": [\"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\", \"Manhattan\"], \"y\": [92.51444452570757, 93.69114133604228, 91.69092704460968, 93.65810361681334, 93.15328644191122, 88.23365231259969, 94.41197895588829, 92.06249999999999, 94.20004734848487, 90.7632275132275, 91.74706823027716], \"type\": \"scatter\", \"uid\": \"57d81a28-5c97-11e9-bb68-3cf862c2ea5b\"}, {\"marker\": {\"size\": [22.67068288551852, 16.446480938416418, 20.022058823529406, 19.016645142775143, 13.833333333333334]}, \"mode\": \"markers\", \"text\": [\"number_of_reviews=22.67068288551852</br> Zipcode =11201\", \"number_of_reviews=16.446480938416418</br> Zipcode =11215\", \"number_of_reviews=20.022058823529406</br> Zipcode =11217\", \"number_of_reviews=19.016645142775143</br> Zipcode =11231\", \"number_of_reviews=13.833333333333334</br> Zipcode =11234\"], \"x\": [\"Brooklyn\", \"Brooklyn\", \"Brooklyn\", \"Brooklyn\", \"Brooklyn\"], \"y\": [95.51756823821339, 95.11184210526315, 94.70993333333334, 94.35398550724638, 88.0], \"type\": \"scatter\", \"uid\": \"57d81a29-5c97-11e9-9f16-3cf862c2ea5b\"}, {\"marker\": {\"size\": [1.0, 8.261437908496731, 71.5, 23.22222222222222]}, \"mode\": \"markers\", \"text\": [\"number_of_reviews=1.0</br> Zipcode =10304\", \"number_of_reviews=8.261437908496731</br> Zipcode =10305\", \"number_of_reviews=71.5</br> Zipcode =10306\", \"number_of_reviews=23.22222222222222</br> Zipcode =10312\"], \"x\": [\"Staten Island\", \"Staten Island\", \"Staten Island\", \"Staten Island\"], \"y\": [80.0, 93.33333333333333, 95.5, 97.85714285714286], \"type\": \"scatter\", \"uid\": \"57d81a2a-5c97-11e9-8dd0-3cf862c2ea5b\"}, {\"marker\": {\"size\": [42.0]}, \"mode\": \"markers\", \"text\": [\"number_of_reviews=42.0</br> Zipcode =11434\"], \"x\": [\"Queens\"], \"y\": [95.0], \"type\": \"scatter\", \"uid\": \"57d81a2b-5c97-11e9-be5c-3cf862c2ea5b\"}], {\"hovermode\": \"closest\", \"showlegend\": false, \"title\": \"Scatter Plot with Size = number_of_reviews\", \"xaxis\": {\"gridwidth\": 2, \"ticklen\": 5, \"type\": \"category\", \"zeroline\": false}, \"yaxis\": {\"gridwidth\": 2, \"ticklen\": 5, \"title\": \"review_scores_rating\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "size_scatter('neighbourhood_group_cleansed', 'review_scores_rating', 'number_of_reviews' )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One important trend to note here is that 10306 from Staten Island has both higher number of reviews and higher review score. Also 10312 from Staten Island has higher review score."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Also 11201 from Brooklyn and 11434 from Queens also show higher number of reviews with higher review scores."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Also it can be seen that Manhattan on an average receives more reviews for all their zipcodes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### However as discussed at the beginning of this section, review analysis will only be taken as a support."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### -----------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analysis of Availability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['zipcode',\n",
       " 'id',\n",
       " 'scrape_id',\n",
       " 'host_id',\n",
       " 'host_listings_count',\n",
       " 'host_total_listings_count',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'accommodates',\n",
       " 'bathrooms',\n",
       " 'bedrooms',\n",
       " 'beds',\n",
       " 'Revenue',\n",
       " 'guests_included',\n",
       " 'minimum_nights',\n",
       " 'maximum_nights',\n",
       " 'availability_30',\n",
       " 'availability_60',\n",
       " 'availability_90',\n",
       " 'availability_365',\n",
       " 'number_of_reviews',\n",
       " 'review_scores_rating',\n",
       " 'review_scores_accuracy',\n",
       " 'calculated_host_listings_count',\n",
       " 'RegionID',\n",
       " 'City',\n",
       " 'State',\n",
       " 'Metro',\n",
       " 'CountyName',\n",
       " 'SizeRank',\n",
       " '2007-06',\n",
       " '2007-07',\n",
       " '2007-08',\n",
       " '2007-09',\n",
       " '2007-10',\n",
       " '2007-11',\n",
       " '2007-12',\n",
       " '2008-01',\n",
       " '2008-02',\n",
       " '2008-03',\n",
       " '2008-04',\n",
       " '2008-05',\n",
       " '2008-06',\n",
       " '2008-07',\n",
       " '2008-08',\n",
       " '2008-09',\n",
       " '2008-10',\n",
       " '2008-11',\n",
       " '2008-12',\n",
       " '2009-01',\n",
       " '2009-02',\n",
       " '2009-03',\n",
       " '2009-04',\n",
       " '2009-05',\n",
       " '2009-06',\n",
       " '2009-07',\n",
       " '2009-08',\n",
       " '2009-09',\n",
       " '2009-10',\n",
       " '2009-11',\n",
       " '2009-12',\n",
       " '2010-01',\n",
       " '2010-02',\n",
       " '2010-03',\n",
       " '2010-04',\n",
       " '2010-05',\n",
       " '2010-06',\n",
       " '2010-07',\n",
       " '2010-08',\n",
       " '2010-09',\n",
       " '2010-10',\n",
       " '2010-11',\n",
       " '2010-12',\n",
       " '2011-01',\n",
       " '2011-02',\n",
       " '2011-03',\n",
       " '2011-04',\n",
       " '2011-05',\n",
       " '2011-06',\n",
       " '2011-07',\n",
       " '2011-08',\n",
       " '2011-09',\n",
       " '2011-10',\n",
       " '2011-11',\n",
       " '2011-12',\n",
       " '2012-01',\n",
       " '2012-02',\n",
       " '2012-03',\n",
       " '2012-04',\n",
       " '2012-05',\n",
       " '2012-06',\n",
       " '2012-07',\n",
       " '2012-08',\n",
       " '2012-09',\n",
       " '2012-10',\n",
       " '2012-11',\n",
       " '2012-12',\n",
       " '2013-01',\n",
       " '2013-02',\n",
       " '2013-03',\n",
       " '2013-04',\n",
       " '2013-05',\n",
       " '2013-06',\n",
       " '2013-07',\n",
       " '2013-08',\n",
       " '2013-09',\n",
       " '2013-10',\n",
       " '2013-11',\n",
       " '2013-12',\n",
       " '2014-01',\n",
       " '2014-02',\n",
       " '2014-03',\n",
       " '2014-04',\n",
       " '2014-05',\n",
       " '2014-06',\n",
       " '2014-07',\n",
       " '2014-08',\n",
       " '2014-09',\n",
       " '2014-10',\n",
       " '2014-11',\n",
       " '2014-12',\n",
       " '2015-01',\n",
       " '2015-02',\n",
       " '2015-03',\n",
       " '2015-04',\n",
       " '2015-05',\n",
       " '2015-06',\n",
       " '2015-07',\n",
       " '2015-08',\n",
       " '2015-09',\n",
       " '2015-10',\n",
       " '2015-11',\n",
       " '2015-12',\n",
       " '2016-01',\n",
       " '2016-02',\n",
       " '2016-03',\n",
       " '2016-04',\n",
       " '2016-05',\n",
       " '2016-06',\n",
       " '2016-07',\n",
       " '2016-08',\n",
       " '2016-09',\n",
       " '2016-10',\n",
       " '2016-11',\n",
       " '2016-12',\n",
       " '2017-01',\n",
       " '2017-02',\n",
       " '2017-03',\n",
       " '2017-04',\n",
       " '2017-05',\n",
       " '2017-06',\n",
       " '2017-07-01 00:00:00',\n",
       " '2017-08-01 00:00:00',\n",
       " '2017-09-01 00:00:00',\n",
       " '2017-10-01 00:00:00',\n",
       " '2017-11-01 00:00:00',\n",
       " '2017-12-01 00:00:00',\n",
       " '2018-01-01 00:00:00',\n",
       " '2018-02-01 00:00:00',\n",
       " '2018-03-01 00:00:00',\n",
       " '2018-04-01 00:00:00',\n",
       " '2018-05-01 00:00:00',\n",
       " '2018-06-01 00:00:00',\n",
       " '2018-07-01 00:00:00',\n",
       " '2018-08-01 00:00:00',\n",
       " '2018-09-01 00:00:00',\n",
       " '2018-10-01 00:00:00',\n",
       " '2018-11-01 00:00:00',\n",
       " '2018-12-01 00:00:00',\n",
       " '2019-01-01 00:00:00',\n",
       " '2019-02-01 00:00:00',\n",
       " '2019-03-01 00:00:00',\n",
       " 'Current Month Cost',\n",
       " 'neighbourhood_group_cleansed']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4.566372\n",
       "1     4.517241\n",
       "2     4.281250\n",
       "3     4.344086\n",
       "4     4.263158\n",
       "5     5.447368\n",
       "6     4.666667\n",
       "7     4.821429\n",
       "8     4.281250\n",
       "9     4.750000\n",
       "10    4.357143\n",
       "11    3.500000\n",
       "12    4.555556\n",
       "13    5.500000\n",
       "14    2.000000\n",
       "15    4.451613\n",
       "16    4.266129\n",
       "17    3.983333\n",
       "18    4.391304\n",
       "19    4.166667\n",
       "20    4.000000\n",
       "Name: accommodates, dtype: float64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df['accommodates']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to Create Barplots based on the Column Name Input\n",
    "\n",
    "def barplots_with_columnnames(columnlist, title):\n",
    "    data=[]\n",
    "    columns= columnlist\n",
    "    \n",
    "    # This loops over the column list which is specified while using the function and create a trace for each of them\n",
    "    \n",
    "    for i in columns:\n",
    "        trace= go.Bar(x= final_df.zipcode.astype(str).tolist(),\n",
    "                  y= final_df[i],\n",
    "                  name=i)\n",
    "        data.append(trace)\n",
    "    \n",
    "    # In the Layout, xaxis type='category' will convert the numerical Zipcodes in Xaxis as Categories\n",
    "    \n",
    "    layout=go.Layout(title='Bar Plot of %s'%(title) , xaxis=dict(type='category'), yaxis= dict( title='Days Available'))\n",
    "    fig= go.Figure(data=data, layout=layout)\n",
    "    iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "name": "availability_30",
         "type": "bar",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barplots_with_columnnames(['availability_30','availability_60','availability_90','availability_365'], 'Availability')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This chart shows that the zipcode 10312 is mostly available for the 30 day, 60 day and 90 day period. A data error is present for its availability for 365 day period. Since it is fully available during the 30,60 and 90 day period, it is not a good property to invest. This substantiates the above statement in the review analysis where 10312 seemes better during the review analysis but it is fully available. This shows that 10312 might have been with 0 reviews and have performed better in review analysis due to the fact that it is replaced with their group mean."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It is the same with 10304 where the 365 day period is an error. But 10304 has lesser availability during the 30, 60 and 90 day period making it a potential investment. But nothing can be concluded before performing the economic analysis."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Also 10021 and 10028 seems to have very less availability which could also be considered."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 11434 from Queens has higher availability and hence it is also not a potential investment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
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       "<div id=\"3edd4a34-818e-4c0d-ac1a-ba5fe2c5386b\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"3edd4a34-818e-4c0d-ac1a-ba5fe2c5386b\", [{\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Manhattan\", \"text\": [10003.0, 10011.0, 10013.0, 10014.0, 10021.0, 10022.0, 10023.0, 10025.0, 10028.0, 10036.0, 10128.0], \"x\": [28.8141592920354, 30.79310344827586, 41.03125, 24.537634408602152, 12.736842105263158, 28.42105263157895, 22.095238095238095, 21.571428571428573, 18.4375, 48.25, 21.678571428571427], \"y\": [310.8141592920354, 363.58620689655174, 389.84375, 325.2903225806452, 273.8421052631579, 367.2894736842105, 284.6984126984127, 286.0, 270.46875, 445.6666666666667, 225.375], \"type\": \"scatter\", \"uid\": \"5aac4222-5c97-11e9-bb48-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Brooklyn\", \"text\": [11201.0, 11215.0, 11217.0, 11231.0, 11234.0], \"x\": [20.161290322580644, 17.056451612903224, 13.366666666666667, 25.202898550724637, 58.833333333333336], \"y\": [208.6290322580645, 177.68548387096774, 203.11666666666667, 200.53623188405797, 107.33333333333333], \"type\": \"scatter\", \"uid\": \"5aac4223-5c97-11e9-a3a7-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Staten Island\", \"text\": [10304.0, 10305.0, 10306.0, 10312.0], \"x\": [30.5, 36.55555555555556, 44.0, 90.0], \"y\": [97.0, 111.22222222222223, 93.0, 215.0], \"type\": \"scatter\", \"uid\": \"5aac6914-5c97-11e9-8e01-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Queens\", \"text\": [11434.0], \"x\": [73.0], \"y\": [155.0], \"type\": \"scatter\", \"uid\": \"5aac6915-5c97-11e9-bcce-3cf862c2ea5b\"}], {\"hovermode\": \"closest\", \"title\": \"Availability-Revenue Analysis\", \"xaxis\": {\"title\": \"availability_90\"}, \"yaxis\": {\"title\": \"Revenue\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
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    }
   ],
   "source": [
    "# Using the scatterplot function create above to plot the graph between availability and revenue\n",
    "\n",
    "scatterplot('availability_90','Revenue', 'Availability-Revenue Analysis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This plot shows that houses which charge more have lesser availability compared to the houses which charge less."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### It is pleasing to know that people are ready to spent more on houses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One important trend to note here is that the revenue for Brooklyn is only slightly lesser than Manhattan but also has equally low availability as Manhattan. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
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        "xaxis": {
         "title": "availability_90"
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        "yaxis": {
         "title": "Current Month Cost"
        }
       }
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       "<div id=\"28867954-411b-4a26-85a6-93b68444755b\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"28867954-411b-4a26-85a6-93b68444755b\", [{\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Manhattan\", \"text\": [10003.0, 10011.0, 10013.0, 10014.0, 10021.0, 10022.0, 10023.0, 10025.0, 10028.0, 10036.0, 10128.0], \"x\": [28.8141592920354, 30.79310344827586, 41.03125, 24.537634408602152, 12.736842105263158, 28.42105263157895, 22.095238095238095, 21.571428571428573, 18.4375, 48.25, 21.678571428571427], \"y\": [2374217.9775280897, 2752755.056179776, 3575506.741573031, 2748629.2134831483, 1976694.3820224707, 2206759.550561795, 2357455.056179776, 1583689.8876404483, 2320684.2696629236, 1830142.6966292155, 3448662.4999999967], \"type\": \"scatter\", \"uid\": \"5b7fd92e-5c97-11e9-8149-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Brooklyn\", \"text\": [11201.0, 11215.0, 11217.0, 11231.0, 11234.0], \"x\": [20.161290322580644, 17.056451612903224, 13.366666666666667, 25.202898550724637, 58.833333333333336], \"y\": [1588270.7865168517, 1204653.932584269, 1945700.0000000016, 1354082.0224719103, 457599.69604294165], \"type\": \"scatter\", \"uid\": \"5b7fd92f-5c97-11e9-acd2-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Staten Island\", \"text\": [10304.0, 10305.0, 10306.0, 10312.0], \"x\": [30.5, 36.55555555555556, 44.0, 90.0], \"y\": [500909.2119367609, 582487.5000000007, 481775.0, 436865.44943987636], \"type\": \"scatter\", \"uid\": \"5b7fd930-5c97-11e9-b221-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Queens\", \"text\": [11434.0], \"x\": [73.0], \"y\": [448484.1523794591], \"type\": \"scatter\", \"uid\": \"5b7fd931-5c97-11e9-bf60-3cf862c2ea5b\"}], {\"hovermode\": \"closest\", \"title\": \"Availability-Cost Analysis\", \"xaxis\": {\"title\": \"availability_90\"}, \"yaxis\": {\"title\": \"Current Month Cost\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the scatterplot function create above to plot the graph between availability and Cost\n",
    "\n",
    "scatterplot('availability_90','Current Month Cost', 'Availability-Cost Analysis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###  Similar to the Revenue, the houses with higher cost has lesser availability. It also supports the logic that higher cost houses are more sophisticated and bring better living experience making it higher in demand."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### This shows that people are willing to rent houses which are more expensive. Thus this should be considered while deciding to invest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The above two plots also indiate that there could be some correlation between Price and Cost. Let's check that!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ------------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Correlation between Revenue, Cost, Reviews and Availability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>id</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-08-01 00:00:00</th>\n",
       "      <th>2018-09-01 00:00:00</th>\n",
       "      <th>2018-10-01 00:00:00</th>\n",
       "      <th>2018-11-01 00:00:00</th>\n",
       "      <th>2018-12-01 00:00:00</th>\n",
       "      <th>2019-01-01 00:00:00</th>\n",
       "      <th>2019-02-01 00:00:00</th>\n",
       "      <th>2019-03-01 00:00:00</th>\n",
       "      <th>Current Month Cost</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003</td>\n",
       "      <td>9.124661e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.496037e+07</td>\n",
       "      <td>1.646018</td>\n",
       "      <td>1.646018</td>\n",
       "      <td>40.728902</td>\n",
       "      <td>-73.988258</td>\n",
       "      <td>4.566372</td>\n",
       "      <td>1.225664</td>\n",
       "      <td>...</td>\n",
       "      <td>2.291593e+06</td>\n",
       "      <td>2.301921e+06</td>\n",
       "      <td>2.312249e+06</td>\n",
       "      <td>2.322578e+06</td>\n",
       "      <td>2.332906e+06</td>\n",
       "      <td>2.343234e+06</td>\n",
       "      <td>2.353562e+06</td>\n",
       "      <td>2.363890e+06</td>\n",
       "      <td>2.374218e+06</td>\n",
       "      <td>Manhattan</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10011</td>\n",
       "      <td>9.889107e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.005599e+07</td>\n",
       "      <td>1.448276</td>\n",
       "      <td>1.448276</td>\n",
       "      <td>40.742371</td>\n",
       "      <td>-73.999752</td>\n",
       "      <td>4.517241</td>\n",
       "      <td>1.337209</td>\n",
       "      <td>...</td>\n",
       "      <td>2.653717e+06</td>\n",
       "      <td>2.666097e+06</td>\n",
       "      <td>2.678476e+06</td>\n",
       "      <td>2.690856e+06</td>\n",
       "      <td>2.703236e+06</td>\n",
       "      <td>2.715616e+06</td>\n",
       "      <td>2.727996e+06</td>\n",
       "      <td>2.740375e+06</td>\n",
       "      <td>2.752755e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10013</td>\n",
       "      <td>1.054726e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.783653e+07</td>\n",
       "      <td>1.412698</td>\n",
       "      <td>1.412698</td>\n",
       "      <td>40.719555</td>\n",
       "      <td>-74.004075</td>\n",
       "      <td>4.281250</td>\n",
       "      <td>1.359375</td>\n",
       "      <td>...</td>\n",
       "      <td>3.481322e+06</td>\n",
       "      <td>3.493096e+06</td>\n",
       "      <td>3.504869e+06</td>\n",
       "      <td>3.516642e+06</td>\n",
       "      <td>3.528415e+06</td>\n",
       "      <td>3.540188e+06</td>\n",
       "      <td>3.551961e+06</td>\n",
       "      <td>3.563734e+06</td>\n",
       "      <td>3.575507e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10014</td>\n",
       "      <td>8.654217e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.054620e+07</td>\n",
       "      <td>1.537634</td>\n",
       "      <td>1.537634</td>\n",
       "      <td>40.734209</td>\n",
       "      <td>-74.004240</td>\n",
       "      <td>4.344086</td>\n",
       "      <td>1.182796</td>\n",
       "      <td>...</td>\n",
       "      <td>2.655164e+06</td>\n",
       "      <td>2.666847e+06</td>\n",
       "      <td>2.678530e+06</td>\n",
       "      <td>2.690213e+06</td>\n",
       "      <td>2.701897e+06</td>\n",
       "      <td>2.713580e+06</td>\n",
       "      <td>2.725263e+06</td>\n",
       "      <td>2.736946e+06</td>\n",
       "      <td>2.748629e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10021</td>\n",
       "      <td>1.022891e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.568867e+07</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>40.768441</td>\n",
       "      <td>-73.956628</td>\n",
       "      <td>4.263158</td>\n",
       "      <td>1.052632</td>\n",
       "      <td>...</td>\n",
       "      <td>1.918115e+06</td>\n",
       "      <td>1.925437e+06</td>\n",
       "      <td>1.932760e+06</td>\n",
       "      <td>1.940082e+06</td>\n",
       "      <td>1.947404e+06</td>\n",
       "      <td>1.954727e+06</td>\n",
       "      <td>1.962049e+06</td>\n",
       "      <td>1.969372e+06</td>\n",
       "      <td>1.976694e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 174 columns</p>\n",
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      "text/plain": [
       "   zipcode            id     scrape_id       host_id  host_listings_count  \\\n",
       "0    10003  9.124661e+06  2.017050e+13  2.496037e+07             1.646018   \n",
       "1    10011  9.889107e+06  2.017050e+13  3.005599e+07             1.448276   \n",
       "2    10013  1.054726e+07  2.017050e+13  2.783653e+07             1.412698   \n",
       "3    10014  8.654217e+06  2.017050e+13  2.054620e+07             1.537634   \n",
       "4    10021  1.022891e+07  2.017050e+13  3.568867e+07             1.526316   \n",
       "\n",
       "   host_total_listings_count   latitude  longitude  accommodates  bathrooms  \\\n",
       "0                   1.646018  40.728902 -73.988258      4.566372   1.225664   \n",
       "1                   1.448276  40.742371 -73.999752      4.517241   1.337209   \n",
       "2                   1.412698  40.719555 -74.004075      4.281250   1.359375   \n",
       "3                   1.537634  40.734209 -74.004240      4.344086   1.182796   \n",
       "4                   1.526316  40.768441 -73.956628      4.263158   1.052632   \n",
       "\n",
       "               ...               2018-08-01 00:00:00  2018-09-01 00:00:00  \\\n",
       "0              ...                      2.291593e+06         2.301921e+06   \n",
       "1              ...                      2.653717e+06         2.666097e+06   \n",
       "2              ...                      3.481322e+06         3.493096e+06   \n",
       "3              ...                      2.655164e+06         2.666847e+06   \n",
       "4              ...                      1.918115e+06         1.925437e+06   \n",
       "\n",
       "   2018-10-01 00:00:00  2018-11-01 00:00:00  2018-12-01 00:00:00  \\\n",
       "0         2.312249e+06         2.322578e+06         2.332906e+06   \n",
       "1         2.678476e+06         2.690856e+06         2.703236e+06   \n",
       "2         3.504869e+06         3.516642e+06         3.528415e+06   \n",
       "3         2.678530e+06         2.690213e+06         2.701897e+06   \n",
       "4         1.932760e+06         1.940082e+06         1.947404e+06   \n",
       "\n",
       "   2019-01-01 00:00:00  2019-02-01 00:00:00  2019-03-01 00:00:00  \\\n",
       "0         2.343234e+06         2.353562e+06         2.363890e+06   \n",
       "1         2.715616e+06         2.727996e+06         2.740375e+06   \n",
       "2         3.540188e+06         3.551961e+06         3.563734e+06   \n",
       "3         2.713580e+06         2.725263e+06         2.736946e+06   \n",
       "4         1.954727e+06         1.962049e+06         1.969372e+06   \n",
       "\n",
       "   Current Month Cost  neighbourhood_group_cleansed  \n",
       "0        2.374218e+06                     Manhattan  \n",
       "1        2.752755e+06                     Manhattan  \n",
       "2        3.575507e+06                     Manhattan  \n",
       "3        2.748629e+06                     Manhattan  \n",
       "4        1.976694e+06                     Manhattan  \n",
       "\n",
       "[5 rows x 174 columns]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# For plotting the Correlation, a function to normalize columns was created\n",
    "\n",
    "def normalize(dataset):\n",
    "    return((dataset- min(dataset))/ (max(dataset)- min(dataset)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Since all the variables are in different units, it will be essential to conver them to a range between 0-1 and then compare"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Normalizing the Revenue, Cost and Review:\n",
    "\n",
    "normalized_revenue= normalize(final_df['Revenue'])\n",
    "normalized_cost= normalize(final_df['Current Month Cost'])\n",
    "normalized_review= normalize(final_df['review_scores_rating'])\n",
    "normalized_availability= normalize(final_df['availability_90'])\n",
    "scatter_df = pd.concat([normalized_revenue, normalized_cost, normalized_review,normalized_availability], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the Scatterplot Matrix\n",
    "\n",
    "import plotly.figure_factory as ff\n",
    "\n",
    "# Here the Index is arranged so that the scale on the side starts from 1 and ends at 21 instead of the default (0 to 21)\n",
    "scatter_df['index'] = np.arange(1,len(scatter_df)+1)\n",
    "\n",
    "\n",
    "fig = ff.create_scatterplotmatrix(scatter_df, diag ='box', index = 'index' , colormap ='Portland',\n",
    "                                 colormap_type = 'seq', height = 900, width = 900)\n",
    "iplot(fig)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There seems to be a good positive correlation between the Cost and the Revenue indicating that when \n",
    "### the cost is high, the price host charge(revenue) is also high which is logical."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### There is no real correlation between the availability- review scores within each other and also among other variables "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ----------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribution of Revenue and Cost"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to Create Violin plots\n",
    "\n",
    "def violinplot(columnname):\n",
    "    data=[]\n",
    "    neighbours=['Manhattan', 'Brooklyn', 'Staten Island', 'Queens']\n",
    "    \n",
    "    # Loop to create 4 traces to plot 4 violin plots each for each neighbour.\n",
    "    for i in neighbours:\n",
    "        trace = {  \"type\": 'violin',\n",
    "                    \"y\": final_df[final_df['neighbourhood_group_cleansed']==i][columnname],\n",
    "                    \"name\": i,\n",
    "                    \"box\": {\n",
    "                        \"visible\": True\n",
    "                    },\n",
    "                    \"meanline\": {\n",
    "                        \"visible\": True\n",
    "                    }\n",
    "        }\n",
    "\n",
    "        data.append(trace)\n",
    "\n",
    "    fig = {\n",
    "        \"data\": data,\n",
    "        \"layout\" : {\n",
    "            \"title\": \"Violin plot of %s\" %columnname,\n",
    "            \"yaxis\": {\n",
    "                \"zeroline\": False,\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "\n",
    "    iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
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       "<div id=\"29a3f697-1ef3-4402-a069-baa14d3e2648\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"29a3f697-1ef3-4402-a069-baa14d3e2648\", [{\"box\": {\"visible\": true}, \"meanline\": {\"visible\": true}, \"name\": \"Manhattan\", \"y\": [310.8141592920354, 363.58620689655174, 389.84375, 325.2903225806452, 273.8421052631579, 367.2894736842105, 284.6984126984127, 286.0, 270.46875, 445.6666666666667, 225.375], \"type\": \"violin\", \"uid\": \"620a2e36-5c97-11e9-a022-3cf862c2ea5b\"}, {\"box\": {\"visible\": true}, \"meanline\": {\"visible\": true}, \"name\": \"Brooklyn\", \"y\": [208.6290322580645, 177.68548387096774, 203.11666666666667, 200.53623188405797, 107.33333333333333], \"type\": \"violin\", \"uid\": \"620a2e37-5c97-11e9-b39e-3cf862c2ea5b\"}, {\"box\": {\"visible\": true}, \"meanline\": {\"visible\": true}, \"name\": \"Staten Island\", \"y\": [97.0, 111.22222222222223, 93.0, 215.0], \"type\": \"violin\", \"uid\": \"620a2e38-5c97-11e9-a65f-3cf862c2ea5b\"}, {\"box\": {\"visible\": true}, \"meanline\": {\"visible\": true}, \"name\": \"Queens\", \"y\": [155.0], \"type\": \"violin\", \"uid\": \"620a2e39-5c97-11e9-b33a-3cf862c2ea5b\"}], {\"title\": \"Violin plot of Revenue\", \"yaxis\": {\"zeroline\": false}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Violin Plots of Revenue:\n",
    "\n",
    "violinplot('Revenue')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The above plot shows that Manhattan has a higher revenue in general and it is followed by Brooklyn, Staten Island and Queens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Also the revenue range is very broad in case of Manhattan and Staten Island. For queens, since it has just a single zipcode, it shows a line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div id=\"30488f8a-5102-4427-b21a-00407ef4d1a9\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"30488f8a-5102-4427-b21a-00407ef4d1a9\", [{\"box\": {\"visible\": true}, \"meanline\": {\"visible\": true}, \"name\": \"Manhattan\", \"y\": [2374217.9775280897, 2752755.056179776, 3575506.741573031, 2748629.2134831483, 1976694.3820224707, 2206759.550561795, 2357455.056179776, 1583689.8876404483, 2320684.2696629236, 1830142.6966292155, 3448662.4999999967], \"type\": \"violin\", \"uid\": \"6294b2d2-5c97-11e9-ae9f-3cf862c2ea5b\"}, {\"box\": {\"visible\": true}, \"meanline\": {\"visible\": true}, \"name\": \"Brooklyn\", \"y\": [1588270.7865168517, 1204653.932584269, 1945700.0000000016, 1354082.0224719103, 457599.69604294165], \"type\": \"violin\", \"uid\": \"6294b2d3-5c97-11e9-98b4-3cf862c2ea5b\"}, {\"box\": {\"visible\": true}, \"meanline\": {\"visible\": true}, \"name\": \"Staten Island\", \"y\": [500909.2119367609, 582487.5000000007, 481775.0, 436865.44943987636], \"type\": \"violin\", \"uid\": \"6294b2d4-5c97-11e9-a46f-3cf862c2ea5b\"}, {\"box\": {\"visible\": true}, \"meanline\": {\"visible\": true}, \"name\": \"Queens\", \"y\": [448484.1523794591], \"type\": \"violin\", \"uid\": \"6294b2d5-5c97-11e9-863e-3cf862c2ea5b\"}], {\"title\": \"Violin plot of Current Month Cost\", \"yaxis\": {\"zeroline\": false}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
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     "metadata": {},
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   ],
   "source": [
    "# Violin plots of Cost:\n",
    "\n",
    "violinplot('Current Month Cost')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In the Cost plot, it follow the same trend where Manhattan is the most expensive, and it is followed by Brooklyn, Staten island and Queens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### But the most important thing to note here is, the Cost difference between Brooklyn and Staten Island is very high which makes Staten Island to have a lower Breakeven Period and it is discussed in detail in further analyses below"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The distribution of cost for Manhattan is also wide similar to its revenue distribution."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The distribution of cost for Staten Island is very narrow compared to its revenue. This shows that people are buying properties within a narrow range of cost and leasing in Airbnb for a wide range of price. It is found below on further analyses of the prices they are charging for the lease."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ----------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Revenue- Cost Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
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       "<div id=\"759bdfce-39b6-4ad4-b12a-a19374a23391\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"759bdfce-39b6-4ad4-b12a-a19374a23391\", [{\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Manhattan\", \"text\": [10003.0, 10011.0, 10013.0, 10014.0, 10021.0, 10022.0, 10023.0, 10025.0, 10028.0, 10036.0, 10128.0], \"x\": [310.8141592920354, 363.58620689655174, 389.84375, 325.2903225806452, 273.8421052631579, 367.2894736842105, 284.6984126984127, 286.0, 270.46875, 445.6666666666667, 225.375], \"y\": [2374217.9775280897, 2752755.056179776, 3575506.741573031, 2748629.2134831483, 1976694.3820224707, 2206759.550561795, 2357455.056179776, 1583689.8876404483, 2320684.2696629236, 1830142.6966292155, 3448662.4999999967], \"type\": \"scatter\", \"uid\": \"636fac18-5c97-11e9-aacf-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Brooklyn\", \"text\": [11201.0, 11215.0, 11217.0, 11231.0, 11234.0], \"x\": [208.6290322580645, 177.68548387096774, 203.11666666666667, 200.53623188405797, 107.33333333333333], \"y\": [1588270.7865168517, 1204653.932584269, 1945700.0000000016, 1354082.0224719103, 457599.69604294165], \"type\": \"scatter\", \"uid\": \"636fac19-5c97-11e9-a41b-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Staten Island\", \"text\": [10304.0, 10305.0, 10306.0, 10312.0], \"x\": [97.0, 111.22222222222223, 93.0, 215.0], \"y\": [500909.2119367609, 582487.5000000007, 481775.0, 436865.44943987636], \"type\": \"scatter\", \"uid\": \"636fac1a-5c97-11e9-9732-3cf862c2ea5b\"}, {\"marker\": {\"size\": 16, \"symbol\": \"circle\"}, \"mode\": \"markers\", \"name\": \"Queens\", \"text\": [11434.0], \"x\": [155.0], \"y\": [448484.1523794591], \"type\": \"scatter\", \"uid\": \"636fac1b-5c97-11e9-b1c0-3cf862c2ea5b\"}], {\"hovermode\": \"closest\", \"title\": \"Revenue-Cost Analysis\", \"xaxis\": {\"title\": \"Revenue\"}, \"yaxis\": {\"title\": \"Current Month Cost\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatterplot between Revenue and Cost and using the function create above to plot the graph\n",
    "\n",
    "scatterplot('Revenue','Current Month Cost', 'Revenue-Cost Analysis')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Zipcode 10036 produces more revenue in the longer run and it can also be attributed to the fact that it is located near the Times Square(visible when zoomed in the above map) which has more demand and thus more revenue charged."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The property 10025 from Manhattan also seems to be great performer in the longer run considering its revenue and lower cost."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Also 10312 seems to be good place considering its lower cost and comparitively higher revenue. But again, since it has more availability, it is not advisable to invest in them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ---------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Break Even Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Expected Return = Revenue × Time × Occupancy Rate\n",
    "\n",
    "### Breakeven Period = Total Cost / Expected Return"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Occupancy Rate= 0.75 (Assumption Given)\n",
    "\n",
    "### Time= 365 days"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### So different columns separately for Expected Return, and BreakEven Period was created"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>id</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-08-01 00:00:00</th>\n",
       "      <th>2018-09-01 00:00:00</th>\n",
       "      <th>2018-10-01 00:00:00</th>\n",
       "      <th>2018-11-01 00:00:00</th>\n",
       "      <th>2018-12-01 00:00:00</th>\n",
       "      <th>2019-01-01 00:00:00</th>\n",
       "      <th>2019-02-01 00:00:00</th>\n",
       "      <th>2019-03-01 00:00:00</th>\n",
       "      <th>Current Month Cost</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003</td>\n",
       "      <td>9.124661e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.496037e+07</td>\n",
       "      <td>1.646018</td>\n",
       "      <td>1.646018</td>\n",
       "      <td>40.728902</td>\n",
       "      <td>-73.988258</td>\n",
       "      <td>4.566372</td>\n",
       "      <td>1.225664</td>\n",
       "      <td>...</td>\n",
       "      <td>2.291593e+06</td>\n",
       "      <td>2.301921e+06</td>\n",
       "      <td>2.312249e+06</td>\n",
       "      <td>2.322578e+06</td>\n",
       "      <td>2.332906e+06</td>\n",
       "      <td>2.343234e+06</td>\n",
       "      <td>2.353562e+06</td>\n",
       "      <td>2.363890e+06</td>\n",
       "      <td>2.374218e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10011</td>\n",
       "      <td>9.889107e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.005599e+07</td>\n",
       "      <td>1.448276</td>\n",
       "      <td>1.448276</td>\n",
       "      <td>40.742371</td>\n",
       "      <td>-73.999752</td>\n",
       "      <td>4.517241</td>\n",
       "      <td>1.337209</td>\n",
       "      <td>...</td>\n",
       "      <td>2.653717e+06</td>\n",
       "      <td>2.666097e+06</td>\n",
       "      <td>2.678476e+06</td>\n",
       "      <td>2.690856e+06</td>\n",
       "      <td>2.703236e+06</td>\n",
       "      <td>2.715616e+06</td>\n",
       "      <td>2.727996e+06</td>\n",
       "      <td>2.740375e+06</td>\n",
       "      <td>2.752755e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10013</td>\n",
       "      <td>1.054726e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.783653e+07</td>\n",
       "      <td>1.412698</td>\n",
       "      <td>1.412698</td>\n",
       "      <td>40.719555</td>\n",
       "      <td>-74.004075</td>\n",
       "      <td>4.281250</td>\n",
       "      <td>1.359375</td>\n",
       "      <td>...</td>\n",
       "      <td>3.481322e+06</td>\n",
       "      <td>3.493096e+06</td>\n",
       "      <td>3.504869e+06</td>\n",
       "      <td>3.516642e+06</td>\n",
       "      <td>3.528415e+06</td>\n",
       "      <td>3.540188e+06</td>\n",
       "      <td>3.551961e+06</td>\n",
       "      <td>3.563734e+06</td>\n",
       "      <td>3.575507e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10014</td>\n",
       "      <td>8.654217e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.054620e+07</td>\n",
       "      <td>1.537634</td>\n",
       "      <td>1.537634</td>\n",
       "      <td>40.734209</td>\n",
       "      <td>-74.004240</td>\n",
       "      <td>4.344086</td>\n",
       "      <td>1.182796</td>\n",
       "      <td>...</td>\n",
       "      <td>2.655164e+06</td>\n",
       "      <td>2.666847e+06</td>\n",
       "      <td>2.678530e+06</td>\n",
       "      <td>2.690213e+06</td>\n",
       "      <td>2.701897e+06</td>\n",
       "      <td>2.713580e+06</td>\n",
       "      <td>2.725263e+06</td>\n",
       "      <td>2.736946e+06</td>\n",
       "      <td>2.748629e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10021</td>\n",
       "      <td>1.022891e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.568867e+07</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>40.768441</td>\n",
       "      <td>-73.956628</td>\n",
       "      <td>4.263158</td>\n",
       "      <td>1.052632</td>\n",
       "      <td>...</td>\n",
       "      <td>1.918115e+06</td>\n",
       "      <td>1.925437e+06</td>\n",
       "      <td>1.932760e+06</td>\n",
       "      <td>1.940082e+06</td>\n",
       "      <td>1.947404e+06</td>\n",
       "      <td>1.954727e+06</td>\n",
       "      <td>1.962049e+06</td>\n",
       "      <td>1.969372e+06</td>\n",
       "      <td>1.976694e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 174 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   zipcode            id     scrape_id       host_id  host_listings_count  \\\n",
       "0    10003  9.124661e+06  2.017050e+13  2.496037e+07             1.646018   \n",
       "1    10011  9.889107e+06  2.017050e+13  3.005599e+07             1.448276   \n",
       "2    10013  1.054726e+07  2.017050e+13  2.783653e+07             1.412698   \n",
       "3    10014  8.654217e+06  2.017050e+13  2.054620e+07             1.537634   \n",
       "4    10021  1.022891e+07  2.017050e+13  3.568867e+07             1.526316   \n",
       "\n",
       "   host_total_listings_count   latitude  longitude  accommodates  bathrooms  \\\n",
       "0                   1.646018  40.728902 -73.988258      4.566372   1.225664   \n",
       "1                   1.448276  40.742371 -73.999752      4.517241   1.337209   \n",
       "2                   1.412698  40.719555 -74.004075      4.281250   1.359375   \n",
       "3                   1.537634  40.734209 -74.004240      4.344086   1.182796   \n",
       "4                   1.526316  40.768441 -73.956628      4.263158   1.052632   \n",
       "\n",
       "               ...               2018-08-01 00:00:00  2018-09-01 00:00:00  \\\n",
       "0              ...                      2.291593e+06         2.301921e+06   \n",
       "1              ...                      2.653717e+06         2.666097e+06   \n",
       "2              ...                      3.481322e+06         3.493096e+06   \n",
       "3              ...                      2.655164e+06         2.666847e+06   \n",
       "4              ...                      1.918115e+06         1.925437e+06   \n",
       "\n",
       "   2018-10-01 00:00:00  2018-11-01 00:00:00  2018-12-01 00:00:00  \\\n",
       "0         2.312249e+06         2.322578e+06         2.332906e+06   \n",
       "1         2.678476e+06         2.690856e+06         2.703236e+06   \n",
       "2         3.504869e+06         3.516642e+06         3.528415e+06   \n",
       "3         2.678530e+06         2.690213e+06         2.701897e+06   \n",
       "4         1.932760e+06         1.940082e+06         1.947404e+06   \n",
       "\n",
       "   2019-01-01 00:00:00  2019-02-01 00:00:00  2019-03-01 00:00:00  \\\n",
       "0         2.343234e+06         2.353562e+06         2.363890e+06   \n",
       "1         2.715616e+06         2.727996e+06         2.740375e+06   \n",
       "2         3.540188e+06         3.551961e+06         3.563734e+06   \n",
       "3         2.713580e+06         2.725263e+06         2.736946e+06   \n",
       "4         1.954727e+06         1.962049e+06         1.969372e+06   \n",
       "\n",
       "   Current Month Cost  neighbourhood_group_cleansed  \n",
       "0        2.374218e+06                     Manhattan  \n",
       "1        2.752755e+06                     Manhattan  \n",
       "2        3.575507e+06                     Manhattan  \n",
       "3        2.748629e+06                     Manhattan  \n",
       "4        1.976694e+06                     Manhattan  \n",
       "\n",
       "[5 rows x 174 columns]"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['zipcode',\n",
       " 'id',\n",
       " 'scrape_id',\n",
       " 'host_id',\n",
       " 'host_listings_count',\n",
       " 'host_total_listings_count',\n",
       " 'latitude',\n",
       " 'longitude',\n",
       " 'accommodates',\n",
       " 'bathrooms',\n",
       " 'bedrooms',\n",
       " 'beds',\n",
       " 'Revenue',\n",
       " 'guests_included',\n",
       " 'minimum_nights',\n",
       " 'maximum_nights',\n",
       " 'availability_30',\n",
       " 'availability_60',\n",
       " 'availability_90',\n",
       " 'availability_365',\n",
       " 'number_of_reviews',\n",
       " 'review_scores_rating',\n",
       " 'review_scores_accuracy',\n",
       " 'calculated_host_listings_count',\n",
       " 'RegionID',\n",
       " 'City',\n",
       " 'State',\n",
       " 'Metro',\n",
       " 'CountyName',\n",
       " 'SizeRank',\n",
       " '2007-06',\n",
       " '2007-07',\n",
       " '2007-08',\n",
       " '2007-09',\n",
       " '2007-10',\n",
       " '2007-11',\n",
       " '2007-12',\n",
       " '2008-01',\n",
       " '2008-02',\n",
       " '2008-03',\n",
       " '2008-04',\n",
       " '2008-05',\n",
       " '2008-06',\n",
       " '2008-07',\n",
       " '2008-08',\n",
       " '2008-09',\n",
       " '2008-10',\n",
       " '2008-11',\n",
       " '2008-12',\n",
       " '2009-01',\n",
       " '2009-02',\n",
       " '2009-03',\n",
       " '2009-04',\n",
       " '2009-05',\n",
       " '2009-06',\n",
       " '2009-07',\n",
       " '2009-08',\n",
       " '2009-09',\n",
       " '2009-10',\n",
       " '2009-11',\n",
       " '2009-12',\n",
       " '2010-01',\n",
       " '2010-02',\n",
       " '2010-03',\n",
       " '2010-04',\n",
       " '2010-05',\n",
       " '2010-06',\n",
       " '2010-07',\n",
       " '2010-08',\n",
       " '2010-09',\n",
       " '2010-10',\n",
       " '2010-11',\n",
       " '2010-12',\n",
       " '2011-01',\n",
       " '2011-02',\n",
       " '2011-03',\n",
       " '2011-04',\n",
       " '2011-05',\n",
       " '2011-06',\n",
       " '2011-07',\n",
       " '2011-08',\n",
       " '2011-09',\n",
       " '2011-10',\n",
       " '2011-11',\n",
       " '2011-12',\n",
       " '2012-01',\n",
       " '2012-02',\n",
       " '2012-03',\n",
       " '2012-04',\n",
       " '2012-05',\n",
       " '2012-06',\n",
       " '2012-07',\n",
       " '2012-08',\n",
       " '2012-09',\n",
       " '2012-10',\n",
       " '2012-11',\n",
       " '2012-12',\n",
       " '2013-01',\n",
       " '2013-02',\n",
       " '2013-03',\n",
       " '2013-04',\n",
       " '2013-05',\n",
       " '2013-06',\n",
       " '2013-07',\n",
       " '2013-08',\n",
       " '2013-09',\n",
       " '2013-10',\n",
       " '2013-11',\n",
       " '2013-12',\n",
       " '2014-01',\n",
       " '2014-02',\n",
       " '2014-03',\n",
       " '2014-04',\n",
       " '2014-05',\n",
       " '2014-06',\n",
       " '2014-07',\n",
       " '2014-08',\n",
       " '2014-09',\n",
       " '2014-10',\n",
       " '2014-11',\n",
       " '2014-12',\n",
       " '2015-01',\n",
       " '2015-02',\n",
       " '2015-03',\n",
       " '2015-04',\n",
       " '2015-05',\n",
       " '2015-06',\n",
       " '2015-07',\n",
       " '2015-08',\n",
       " '2015-09',\n",
       " '2015-10',\n",
       " '2015-11',\n",
       " '2015-12',\n",
       " '2016-01',\n",
       " '2016-02',\n",
       " '2016-03',\n",
       " '2016-04',\n",
       " '2016-05',\n",
       " '2016-06',\n",
       " '2016-07',\n",
       " '2016-08',\n",
       " '2016-09',\n",
       " '2016-10',\n",
       " '2016-11',\n",
       " '2016-12',\n",
       " '2017-01',\n",
       " '2017-02',\n",
       " '2017-03',\n",
       " '2017-04',\n",
       " '2017-05',\n",
       " '2017-06',\n",
       " '2017-07-01 00:00:00',\n",
       " '2017-08-01 00:00:00',\n",
       " '2017-09-01 00:00:00',\n",
       " '2017-10-01 00:00:00',\n",
       " '2017-11-01 00:00:00',\n",
       " '2017-12-01 00:00:00',\n",
       " '2018-01-01 00:00:00',\n",
       " '2018-02-01 00:00:00',\n",
       " '2018-03-01 00:00:00',\n",
       " '2018-04-01 00:00:00',\n",
       " '2018-05-01 00:00:00',\n",
       " '2018-06-01 00:00:00',\n",
       " '2018-07-01 00:00:00',\n",
       " '2018-08-01 00:00:00',\n",
       " '2018-09-01 00:00:00',\n",
       " '2018-10-01 00:00:00',\n",
       " '2018-11-01 00:00:00',\n",
       " '2018-12-01 00:00:00',\n",
       " '2019-01-01 00:00:00',\n",
       " '2019-02-01 00:00:00',\n",
       " '2019-03-01 00:00:00',\n",
       " 'Current Month Cost',\n",
       " 'neighbourhood_group_cleansed']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols(final_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### KPIs created for Breakeven period Calculation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to create KPIs:\n",
    "\n",
    "def breakeven_calc(dataset):\n",
    "    \n",
    "    #Calculating all the KPIs for Breakeven Analysis\n",
    "    dataset['Expected Return']= dataset['Revenue'] * 365 * 0.75\n",
    "    dataset['Breakeven Period']= dataset['Current Month Cost']/dataset['Expected Return'] \n",
    "    \n",
    "    return dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final_df=breakeven_calc(final_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zipcode</th>\n",
       "      <th>id</th>\n",
       "      <th>scrape_id</th>\n",
       "      <th>host_id</th>\n",
       "      <th>host_listings_count</th>\n",
       "      <th>host_total_listings_count</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>accommodates</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>...</th>\n",
       "      <th>2018-10-01 00:00:00</th>\n",
       "      <th>2018-11-01 00:00:00</th>\n",
       "      <th>2018-12-01 00:00:00</th>\n",
       "      <th>2019-01-01 00:00:00</th>\n",
       "      <th>2019-02-01 00:00:00</th>\n",
       "      <th>2019-03-01 00:00:00</th>\n",
       "      <th>Current Month Cost</th>\n",
       "      <th>neighbourhood_group_cleansed</th>\n",
       "      <th>Expected Return</th>\n",
       "      <th>Breakeven Period</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10003</td>\n",
       "      <td>9.124661e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.496037e+07</td>\n",
       "      <td>1.646018</td>\n",
       "      <td>1.646018</td>\n",
       "      <td>40.728902</td>\n",
       "      <td>-73.988258</td>\n",
       "      <td>4.566372</td>\n",
       "      <td>1.225664</td>\n",
       "      <td>...</td>\n",
       "      <td>2.312249e+06</td>\n",
       "      <td>2.322578e+06</td>\n",
       "      <td>2.332906e+06</td>\n",
       "      <td>2.343234e+06</td>\n",
       "      <td>2.353562e+06</td>\n",
       "      <td>2.363890e+06</td>\n",
       "      <td>2.374218e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>85085.376106</td>\n",
       "      <td>27.903949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10011</td>\n",
       "      <td>9.889107e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.005599e+07</td>\n",
       "      <td>1.448276</td>\n",
       "      <td>1.448276</td>\n",
       "      <td>40.742371</td>\n",
       "      <td>-73.999752</td>\n",
       "      <td>4.517241</td>\n",
       "      <td>1.337209</td>\n",
       "      <td>...</td>\n",
       "      <td>2.678476e+06</td>\n",
       "      <td>2.690856e+06</td>\n",
       "      <td>2.703236e+06</td>\n",
       "      <td>2.715616e+06</td>\n",
       "      <td>2.727996e+06</td>\n",
       "      <td>2.740375e+06</td>\n",
       "      <td>2.752755e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>99531.724138</td>\n",
       "      <td>27.657062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10013</td>\n",
       "      <td>1.054726e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.783653e+07</td>\n",
       "      <td>1.412698</td>\n",
       "      <td>1.412698</td>\n",
       "      <td>40.719555</td>\n",
       "      <td>-74.004075</td>\n",
       "      <td>4.281250</td>\n",
       "      <td>1.359375</td>\n",
       "      <td>...</td>\n",
       "      <td>3.504869e+06</td>\n",
       "      <td>3.516642e+06</td>\n",
       "      <td>3.528415e+06</td>\n",
       "      <td>3.540188e+06</td>\n",
       "      <td>3.551961e+06</td>\n",
       "      <td>3.563734e+06</td>\n",
       "      <td>3.575507e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>106719.726562</td>\n",
       "      <td>33.503710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10014</td>\n",
       "      <td>8.654217e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.054620e+07</td>\n",
       "      <td>1.537634</td>\n",
       "      <td>1.537634</td>\n",
       "      <td>40.734209</td>\n",
       "      <td>-74.004240</td>\n",
       "      <td>4.344086</td>\n",
       "      <td>1.182796</td>\n",
       "      <td>...</td>\n",
       "      <td>2.678530e+06</td>\n",
       "      <td>2.690213e+06</td>\n",
       "      <td>2.701897e+06</td>\n",
       "      <td>2.713580e+06</td>\n",
       "      <td>2.725263e+06</td>\n",
       "      <td>2.736946e+06</td>\n",
       "      <td>2.748629e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>89048.225806</td>\n",
       "      <td>30.866749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10021</td>\n",
       "      <td>1.022891e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.568867e+07</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>1.526316</td>\n",
       "      <td>40.768441</td>\n",
       "      <td>-73.956628</td>\n",
       "      <td>4.263158</td>\n",
       "      <td>1.052632</td>\n",
       "      <td>...</td>\n",
       "      <td>1.932760e+06</td>\n",
       "      <td>1.940082e+06</td>\n",
       "      <td>1.947404e+06</td>\n",
       "      <td>1.954727e+06</td>\n",
       "      <td>1.962049e+06</td>\n",
       "      <td>1.969372e+06</td>\n",
       "      <td>1.976694e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>74964.276316</td>\n",
       "      <td>26.368485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10022</td>\n",
       "      <td>9.963782e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>4.129974e+07</td>\n",
       "      <td>1.394737</td>\n",
       "      <td>1.394737</td>\n",
       "      <td>40.758886</td>\n",
       "      <td>-73.966700</td>\n",
       "      <td>5.447368</td>\n",
       "      <td>1.276316</td>\n",
       "      <td>...</td>\n",
       "      <td>2.158989e+06</td>\n",
       "      <td>2.166951e+06</td>\n",
       "      <td>2.174912e+06</td>\n",
       "      <td>2.182874e+06</td>\n",
       "      <td>2.190836e+06</td>\n",
       "      <td>2.198798e+06</td>\n",
       "      <td>2.206760e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>100545.493421</td>\n",
       "      <td>21.947871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10023</td>\n",
       "      <td>8.424752e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.053048e+07</td>\n",
       "      <td>2.888889</td>\n",
       "      <td>2.888889</td>\n",
       "      <td>40.777581</td>\n",
       "      <td>-73.981486</td>\n",
       "      <td>4.666667</td>\n",
       "      <td>1.349206</td>\n",
       "      <td>...</td>\n",
       "      <td>2.298776e+06</td>\n",
       "      <td>2.308556e+06</td>\n",
       "      <td>2.318336e+06</td>\n",
       "      <td>2.328116e+06</td>\n",
       "      <td>2.337896e+06</td>\n",
       "      <td>2.347675e+06</td>\n",
       "      <td>2.357455e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>77936.190476</td>\n",
       "      <td>30.248528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10025</td>\n",
       "      <td>7.849437e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.567103e+07</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>1.571429</td>\n",
       "      <td>40.798025</td>\n",
       "      <td>-73.966355</td>\n",
       "      <td>4.821429</td>\n",
       "      <td>1.232143</td>\n",
       "      <td>...</td>\n",
       "      <td>1.542047e+06</td>\n",
       "      <td>1.548988e+06</td>\n",
       "      <td>1.555928e+06</td>\n",
       "      <td>1.562869e+06</td>\n",
       "      <td>1.569809e+06</td>\n",
       "      <td>1.576749e+06</td>\n",
       "      <td>1.583690e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>78292.500000</td>\n",
       "      <td>20.227862</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10028</td>\n",
       "      <td>1.038785e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.790560e+07</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>1.375000</td>\n",
       "      <td>40.775367</td>\n",
       "      <td>-73.950934</td>\n",
       "      <td>4.281250</td>\n",
       "      <td>1.258065</td>\n",
       "      <td>...</td>\n",
       "      <td>2.256107e+06</td>\n",
       "      <td>2.266870e+06</td>\n",
       "      <td>2.277633e+06</td>\n",
       "      <td>2.288396e+06</td>\n",
       "      <td>2.299158e+06</td>\n",
       "      <td>2.309921e+06</td>\n",
       "      <td>2.320684e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>74040.820312</td>\n",
       "      <td>31.343308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10036</td>\n",
       "      <td>8.401201e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>3.853206e+07</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>73.500000</td>\n",
       "      <td>40.754810</td>\n",
       "      <td>-73.987824</td>\n",
       "      <td>4.750000</td>\n",
       "      <td>1.416667</td>\n",
       "      <td>...</td>\n",
       "      <td>1.798167e+06</td>\n",
       "      <td>1.803497e+06</td>\n",
       "      <td>1.808826e+06</td>\n",
       "      <td>1.814155e+06</td>\n",
       "      <td>1.819484e+06</td>\n",
       "      <td>1.824813e+06</td>\n",
       "      <td>1.830143e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>122001.250000</td>\n",
       "      <td>15.001016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10128</td>\n",
       "      <td>1.079017e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.984589e+07</td>\n",
       "      <td>1.321429</td>\n",
       "      <td>1.321429</td>\n",
       "      <td>40.781842</td>\n",
       "      <td>-73.949537</td>\n",
       "      <td>4.357143</td>\n",
       "      <td>1.232143</td>\n",
       "      <td>...</td>\n",
       "      <td>2.958255e+06</td>\n",
       "      <td>3.038049e+06</td>\n",
       "      <td>3.118619e+06</td>\n",
       "      <td>3.199966e+06</td>\n",
       "      <td>3.282089e+06</td>\n",
       "      <td>3.364987e+06</td>\n",
       "      <td>3.448662e+06</td>\n",
       "      <td>Manhattan</td>\n",
       "      <td>61696.406250</td>\n",
       "      <td>55.897300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10304</td>\n",
       "      <td>1.590640e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>7.534770e+06</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.626721</td>\n",
       "      <td>-74.076721</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.502538e+05</td>\n",
       "      <td>4.585099e+05</td>\n",
       "      <td>4.668406e+05</td>\n",
       "      <td>4.752459e+05</td>\n",
       "      <td>4.837257e+05</td>\n",
       "      <td>4.922802e+05</td>\n",
       "      <td>5.009092e+05</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>26553.750000</td>\n",
       "      <td>18.863973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10305</td>\n",
       "      <td>1.368595e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>5.535834e+07</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>1.555556</td>\n",
       "      <td>40.596360</td>\n",
       "      <td>-74.077371</td>\n",
       "      <td>4.555556</td>\n",
       "      <td>1.111111</td>\n",
       "      <td>...</td>\n",
       "      <td>5.355818e+05</td>\n",
       "      <td>5.431920e+05</td>\n",
       "      <td>5.508852e+05</td>\n",
       "      <td>5.586614e+05</td>\n",
       "      <td>5.665205e+05</td>\n",
       "      <td>5.744625e+05</td>\n",
       "      <td>5.824875e+05</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>30447.083333</td>\n",
       "      <td>19.131143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10306</td>\n",
       "      <td>8.328929e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>5.237069e+07</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>40.568879</td>\n",
       "      <td>-74.111565</td>\n",
       "      <td>5.500000</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.434636e+05</td>\n",
       "      <td>4.496841e+05</td>\n",
       "      <td>4.559705e+05</td>\n",
       "      <td>4.623227e+05</td>\n",
       "      <td>4.687409e+05</td>\n",
       "      <td>4.752250e+05</td>\n",
       "      <td>4.817750e+05</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>25458.750000</td>\n",
       "      <td>18.923749</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10312</td>\n",
       "      <td>1.800434e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>1.197988e+08</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.546331</td>\n",
       "      <td>-74.183701</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.128157e+05</td>\n",
       "      <td>4.167345e+05</td>\n",
       "      <td>4.206890e+05</td>\n",
       "      <td>4.246794e+05</td>\n",
       "      <td>4.287059e+05</td>\n",
       "      <td>4.327680e+05</td>\n",
       "      <td>4.368654e+05</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>58856.250000</td>\n",
       "      <td>7.422584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>11201</td>\n",
       "      <td>9.298929e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.327364e+07</td>\n",
       "      <td>1.451613</td>\n",
       "      <td>1.451613</td>\n",
       "      <td>40.692611</td>\n",
       "      <td>-73.990576</td>\n",
       "      <td>4.451613</td>\n",
       "      <td>1.250000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.542570e+06</td>\n",
       "      <td>1.550187e+06</td>\n",
       "      <td>1.557803e+06</td>\n",
       "      <td>1.565420e+06</td>\n",
       "      <td>1.573037e+06</td>\n",
       "      <td>1.580654e+06</td>\n",
       "      <td>1.588271e+06</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>57112.197581</td>\n",
       "      <td>27.809660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>11215</td>\n",
       "      <td>9.035422e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.460656e+07</td>\n",
       "      <td>1.422764</td>\n",
       "      <td>1.422764</td>\n",
       "      <td>40.666241</td>\n",
       "      <td>-73.984527</td>\n",
       "      <td>4.266129</td>\n",
       "      <td>1.189516</td>\n",
       "      <td>...</td>\n",
       "      <td>1.168148e+06</td>\n",
       "      <td>1.174233e+06</td>\n",
       "      <td>1.180317e+06</td>\n",
       "      <td>1.186401e+06</td>\n",
       "      <td>1.192485e+06</td>\n",
       "      <td>1.198570e+06</td>\n",
       "      <td>1.204654e+06</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>48641.401210</td>\n",
       "      <td>24.766020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>11217</td>\n",
       "      <td>6.866520e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>1.988437e+07</td>\n",
       "      <td>1.283333</td>\n",
       "      <td>1.283333</td>\n",
       "      <td>40.681377</td>\n",
       "      <td>-73.979068</td>\n",
       "      <td>3.983333</td>\n",
       "      <td>1.228814</td>\n",
       "      <td>...</td>\n",
       "      <td>1.756264e+06</td>\n",
       "      <td>1.787109e+06</td>\n",
       "      <td>1.818245e+06</td>\n",
       "      <td>1.849673e+06</td>\n",
       "      <td>1.881391e+06</td>\n",
       "      <td>1.913400e+06</td>\n",
       "      <td>1.945700e+06</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>55603.187500</td>\n",
       "      <td>34.992598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>11231</td>\n",
       "      <td>7.697324e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.171201e+07</td>\n",
       "      <td>1.550725</td>\n",
       "      <td>1.550725</td>\n",
       "      <td>40.680632</td>\n",
       "      <td>-74.000049</td>\n",
       "      <td>4.391304</td>\n",
       "      <td>1.176471</td>\n",
       "      <td>...</td>\n",
       "      <td>1.312851e+06</td>\n",
       "      <td>1.319722e+06</td>\n",
       "      <td>1.326594e+06</td>\n",
       "      <td>1.333466e+06</td>\n",
       "      <td>1.340338e+06</td>\n",
       "      <td>1.347210e+06</td>\n",
       "      <td>1.354082e+06</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>54896.793478</td>\n",
       "      <td>24.665958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>11234</td>\n",
       "      <td>1.066553e+07</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>2.752097e+07</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>1.333333</td>\n",
       "      <td>40.623074</td>\n",
       "      <td>-73.925943</td>\n",
       "      <td>4.166667</td>\n",
       "      <td>1.083333</td>\n",
       "      <td>...</td>\n",
       "      <td>4.656135e+05</td>\n",
       "      <td>4.644211e+05</td>\n",
       "      <td>4.631714e+05</td>\n",
       "      <td>4.618644e+05</td>\n",
       "      <td>4.605001e+05</td>\n",
       "      <td>4.590786e+05</td>\n",
       "      <td>4.575997e+05</td>\n",
       "      <td>Brooklyn</td>\n",
       "      <td>29382.500000</td>\n",
       "      <td>15.573886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>11434</td>\n",
       "      <td>9.374263e+06</td>\n",
       "      <td>2.017050e+13</td>\n",
       "      <td>4.863844e+07</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>40.662033</td>\n",
       "      <td>-73.769800</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>4.264390e+05</td>\n",
       "      <td>4.299051e+05</td>\n",
       "      <td>4.334544e+05</td>\n",
       "      <td>4.370870e+05</td>\n",
       "      <td>4.408028e+05</td>\n",
       "      <td>4.446019e+05</td>\n",
       "      <td>4.484842e+05</td>\n",
       "      <td>Queens</td>\n",
       "      <td>42431.250000</td>\n",
       "      <td>10.569666</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21 rows × 176 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    zipcode            id     scrape_id       host_id  host_listings_count  \\\n",
       "0     10003  9.124661e+06  2.017050e+13  2.496037e+07             1.646018   \n",
       "1     10011  9.889107e+06  2.017050e+13  3.005599e+07             1.448276   \n",
       "2     10013  1.054726e+07  2.017050e+13  2.783653e+07             1.412698   \n",
       "3     10014  8.654217e+06  2.017050e+13  2.054620e+07             1.537634   \n",
       "4     10021  1.022891e+07  2.017050e+13  3.568867e+07             1.526316   \n",
       "5     10022  9.963782e+06  2.017050e+13  4.129974e+07             1.394737   \n",
       "6     10023  8.424752e+06  2.017050e+13  3.053048e+07             2.888889   \n",
       "7     10025  7.849437e+06  2.017050e+13  2.567103e+07             1.571429   \n",
       "8     10028  1.038785e+07  2.017050e+13  3.790560e+07             1.375000   \n",
       "9     10036  8.401201e+06  2.017050e+13  3.853206e+07            73.500000   \n",
       "10    10128  1.079017e+07  2.017050e+13  2.984589e+07             1.321429   \n",
       "11    10304  1.590640e+06  2.017050e+13  7.534770e+06             1.000000   \n",
       "12    10305  1.368595e+07  2.017050e+13  5.535834e+07             1.555556   \n",
       "13    10306  8.328929e+06  2.017050e+13  5.237069e+07             2.000000   \n",
       "14    10312  1.800434e+07  2.017050e+13  1.197988e+08             1.000000   \n",
       "15    11201  9.298929e+06  2.017050e+13  2.327364e+07             1.451613   \n",
       "16    11215  9.035422e+06  2.017050e+13  2.460656e+07             1.422764   \n",
       "17    11217  6.866520e+06  2.017050e+13  1.988437e+07             1.283333   \n",
       "18    11231  7.697324e+06  2.017050e+13  2.171201e+07             1.550725   \n",
       "19    11234  1.066553e+07  2.017050e+13  2.752097e+07             1.333333   \n",
       "20    11434  9.374263e+06  2.017050e+13  4.863844e+07             1.000000   \n",
       "\n",
       "    host_total_listings_count   latitude  longitude  accommodates  bathrooms  \\\n",
       "0                    1.646018  40.728902 -73.988258      4.566372   1.225664   \n",
       "1                    1.448276  40.742371 -73.999752      4.517241   1.337209   \n",
       "2                    1.412698  40.719555 -74.004075      4.281250   1.359375   \n",
       "3                    1.537634  40.734209 -74.004240      4.344086   1.182796   \n",
       "4                    1.526316  40.768441 -73.956628      4.263158   1.052632   \n",
       "5                    1.394737  40.758886 -73.966700      5.447368   1.276316   \n",
       "6                    2.888889  40.777581 -73.981486      4.666667   1.349206   \n",
       "7                    1.571429  40.798025 -73.966355      4.821429   1.232143   \n",
       "8                    1.375000  40.775367 -73.950934      4.281250   1.258065   \n",
       "9                   73.500000  40.754810 -73.987824      4.750000   1.416667   \n",
       "10                   1.321429  40.781842 -73.949537      4.357143   1.232143   \n",
       "11                   1.000000  40.626721 -74.076721      3.500000   1.250000   \n",
       "12                   1.555556  40.596360 -74.077371      4.555556   1.111111   \n",
       "13                   2.000000  40.568879 -74.111565      5.500000   1.250000   \n",
       "14                   1.000000  40.546331 -74.183701      2.000000   2.000000   \n",
       "15                   1.451613  40.692611 -73.990576      4.451613   1.250000   \n",
       "16                   1.422764  40.666241 -73.984527      4.266129   1.189516   \n",
       "17                   1.283333  40.681377 -73.979068      3.983333   1.228814   \n",
       "18                   1.550725  40.680632 -74.000049      4.391304   1.176471   \n",
       "19                   1.333333  40.623074 -73.925943      4.166667   1.083333   \n",
       "20                   1.000000  40.662033 -73.769800      4.000000   1.000000   \n",
       "\n",
       "          ...         2018-10-01 00:00:00  2018-11-01 00:00:00  \\\n",
       "0         ...                2.312249e+06         2.322578e+06   \n",
       "1         ...                2.678476e+06         2.690856e+06   \n",
       "2         ...                3.504869e+06         3.516642e+06   \n",
       "3         ...                2.678530e+06         2.690213e+06   \n",
       "4         ...                1.932760e+06         1.940082e+06   \n",
       "5         ...                2.158989e+06         2.166951e+06   \n",
       "6         ...                2.298776e+06         2.308556e+06   \n",
       "7         ...                1.542047e+06         1.548988e+06   \n",
       "8         ...                2.256107e+06         2.266870e+06   \n",
       "9         ...                1.798167e+06         1.803497e+06   \n",
       "10        ...                2.958255e+06         3.038049e+06   \n",
       "11        ...                4.502538e+05         4.585099e+05   \n",
       "12        ...                5.355818e+05         5.431920e+05   \n",
       "13        ...                4.434636e+05         4.496841e+05   \n",
       "14        ...                4.128157e+05         4.167345e+05   \n",
       "15        ...                1.542570e+06         1.550187e+06   \n",
       "16        ...                1.168148e+06         1.174233e+06   \n",
       "17        ...                1.756264e+06         1.787109e+06   \n",
       "18        ...                1.312851e+06         1.319722e+06   \n",
       "19        ...                4.656135e+05         4.644211e+05   \n",
       "20        ...                4.264390e+05         4.299051e+05   \n",
       "\n",
       "    2018-12-01 00:00:00  2019-01-01 00:00:00  2019-02-01 00:00:00  \\\n",
       "0          2.332906e+06         2.343234e+06         2.353562e+06   \n",
       "1          2.703236e+06         2.715616e+06         2.727996e+06   \n",
       "2          3.528415e+06         3.540188e+06         3.551961e+06   \n",
       "3          2.701897e+06         2.713580e+06         2.725263e+06   \n",
       "4          1.947404e+06         1.954727e+06         1.962049e+06   \n",
       "5          2.174912e+06         2.182874e+06         2.190836e+06   \n",
       "6          2.318336e+06         2.328116e+06         2.337896e+06   \n",
       "7          1.555928e+06         1.562869e+06         1.569809e+06   \n",
       "8          2.277633e+06         2.288396e+06         2.299158e+06   \n",
       "9          1.808826e+06         1.814155e+06         1.819484e+06   \n",
       "10         3.118619e+06         3.199966e+06         3.282089e+06   \n",
       "11         4.668406e+05         4.752459e+05         4.837257e+05   \n",
       "12         5.508852e+05         5.586614e+05         5.665205e+05   \n",
       "13         4.559705e+05         4.623227e+05         4.687409e+05   \n",
       "14         4.206890e+05         4.246794e+05         4.287059e+05   \n",
       "15         1.557803e+06         1.565420e+06         1.573037e+06   \n",
       "16         1.180317e+06         1.186401e+06         1.192485e+06   \n",
       "17         1.818245e+06         1.849673e+06         1.881391e+06   \n",
       "18         1.326594e+06         1.333466e+06         1.340338e+06   \n",
       "19         4.631714e+05         4.618644e+05         4.605001e+05   \n",
       "20         4.334544e+05         4.370870e+05         4.408028e+05   \n",
       "\n",
       "    2019-03-01 00:00:00  Current Month Cost  neighbourhood_group_cleansed  \\\n",
       "0          2.363890e+06        2.374218e+06                     Manhattan   \n",
       "1          2.740375e+06        2.752755e+06                     Manhattan   \n",
       "2          3.563734e+06        3.575507e+06                     Manhattan   \n",
       "3          2.736946e+06        2.748629e+06                     Manhattan   \n",
       "4          1.969372e+06        1.976694e+06                     Manhattan   \n",
       "5          2.198798e+06        2.206760e+06                     Manhattan   \n",
       "6          2.347675e+06        2.357455e+06                     Manhattan   \n",
       "7          1.576749e+06        1.583690e+06                     Manhattan   \n",
       "8          2.309921e+06        2.320684e+06                     Manhattan   \n",
       "9          1.824813e+06        1.830143e+06                     Manhattan   \n",
       "10         3.364987e+06        3.448662e+06                     Manhattan   \n",
       "11         4.922802e+05        5.009092e+05                 Staten Island   \n",
       "12         5.744625e+05        5.824875e+05                 Staten Island   \n",
       "13         4.752250e+05        4.817750e+05                 Staten Island   \n",
       "14         4.327680e+05        4.368654e+05                 Staten Island   \n",
       "15         1.580654e+06        1.588271e+06                      Brooklyn   \n",
       "16         1.198570e+06        1.204654e+06                      Brooklyn   \n",
       "17         1.913400e+06        1.945700e+06                      Brooklyn   \n",
       "18         1.347210e+06        1.354082e+06                      Brooklyn   \n",
       "19         4.590786e+05        4.575997e+05                      Brooklyn   \n",
       "20         4.446019e+05        4.484842e+05                        Queens   \n",
       "\n",
       "    Expected Return  Breakeven Period  \n",
       "0      85085.376106         27.903949  \n",
       "1      99531.724138         27.657062  \n",
       "2     106719.726562         33.503710  \n",
       "3      89048.225806         30.866749  \n",
       "4      74964.276316         26.368485  \n",
       "5     100545.493421         21.947871  \n",
       "6      77936.190476         30.248528  \n",
       "7      78292.500000         20.227862  \n",
       "8      74040.820312         31.343308  \n",
       "9     122001.250000         15.001016  \n",
       "10     61696.406250         55.897300  \n",
       "11     26553.750000         18.863973  \n",
       "12     30447.083333         19.131143  \n",
       "13     25458.750000         18.923749  \n",
       "14     58856.250000          7.422584  \n",
       "15     57112.197581         27.809660  \n",
       "16     48641.401210         24.766020  \n",
       "17     55603.187500         34.992598  \n",
       "18     54896.793478         24.665958  \n",
       "19     29382.500000         15.573886  \n",
       "20     42431.250000         10.569666  \n",
       "\n",
       "[21 rows x 176 columns]"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to Create Barplots based on the Column Name Input\n",
    "\n",
    "def barplots_with_neighbourhoods(xaxis, yaxis, title):\n",
    "    data=[]\n",
    "    neighbours=['Manhattan', 'Brooklyn', 'Staten Island', 'Queens']\n",
    "\n",
    "    for i in neighbours:\n",
    "        trace= go.Bar(x= final_df[final_df['neighbourhood_group_cleansed']==i][xaxis],\n",
    "                  y= final_df[final_df['neighbourhood_group_cleansed']==i][yaxis],\n",
    "                  name=i)\n",
    "        data.append(trace)\n",
    "    \n",
    "    # In the Layout, xaxis type='category' will convert the numerical Zipcodes in Xaxis as Categories\n",
    "    \n",
    "    layout=go.Layout(title='Bar Plot of %s'%(title) , xaxis=dict(type='category'), yaxis= dict( title='Breakeven Period(in years)'))\n",
    "    fig= go.Figure(data=data, layout=layout)\n",
    "    iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "data": [
        {
         "name": "Manhattan",
         "type": "bar",
         "uid": "6537ebdc-5c97-11e9-834b-3cf862c2ea5b",
         "x": [
          10003,
          10011,
          10013,
          10014,
          10021,
          10022,
          10023,
          10025,
          10028,
          10036,
          10128
         ],
         "y": [
          27.90394878862425,
          27.657061906865074,
          33.503709733355144,
          30.866748759906322,
          26.368484819296867,
          21.947871311552333,
          30.24852820975358,
          20.22786202561482,
          31.343308459686693,
          15.001015945567898,
          55.897299528690084
         ]
        },
        {
         "name": "Brooklyn",
         "type": "bar",
         "uid": "6537ebdd-5c97-11e9-883e-3cf862c2ea5b",
         "x": [
          11201,
          11215,
          11217,
          11231,
          11234
         ],
         "y": [
          27.80965982396558,
          24.766020357665965,
          34.99259822110021,
          24.665958368007903,
          15.573885681713321
         ]
        },
        {
         "name": "Staten Island",
         "type": "bar",
         "uid": "6537ebde-5c97-11e9-8a60-3cf862c2ea5b",
         "x": [
          10304,
          10305,
          10306,
          10312
         ],
         "y": [
          18.863972581528444,
          19.131142829772987,
          18.92374920214072,
          7.422583828223449
         ]
        },
        {
         "name": "Queens",
         "type": "bar",
         "uid": "6537ebdf-5c97-11e9-be45-3cf862c2ea5b",
         "x": [
          11434
         ],
         "y": [
          10.56966628085336
         ]
        }
       ],
       "layout": {
        "title": "Bar Plot of Breakeven Period for Zipcodes",
        "xaxis": {
         "type": "category"
        },
        "yaxis": {
         "title": "Breakeven Period(in years)"
        }
       }
      },
      "text/html": [
       "<div id=\"1d4f17d0-74ab-4836-9590-dbfc84049d24\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"1d4f17d0-74ab-4836-9590-dbfc84049d24\", [{\"name\": \"Manhattan\", \"x\": [10003, 10011, 10013, 10014, 10021, 10022, 10023, 10025, 10028, 10036, 10128], \"y\": [27.90394878862425, 27.657061906865074, 33.503709733355144, 30.866748759906322, 26.368484819296867, 21.947871311552333, 30.24852820975358, 20.22786202561482, 31.343308459686693, 15.001015945567898, 55.897299528690084], \"type\": \"bar\", \"uid\": \"6537ebdc-5c97-11e9-834b-3cf862c2ea5b\"}, {\"name\": \"Brooklyn\", \"x\": [11201, 11215, 11217, 11231, 11234], \"y\": [27.80965982396558, 24.766020357665965, 34.99259822110021, 24.665958368007903, 15.573885681713321], \"type\": \"bar\", \"uid\": \"6537ebdd-5c97-11e9-883e-3cf862c2ea5b\"}, {\"name\": \"Staten Island\", \"x\": [10304, 10305, 10306, 10312], \"y\": [18.863972581528444, 19.131142829772987, 18.92374920214072, 7.422583828223449], \"type\": \"bar\", \"uid\": \"6537ebde-5c97-11e9-8a60-3cf862c2ea5b\"}, {\"name\": \"Queens\", \"x\": [11434], \"y\": [10.56966628085336], \"type\": \"bar\", \"uid\": \"6537ebdf-5c97-11e9-be45-3cf862c2ea5b\"}], {\"title\": \"Bar Plot of Breakeven Period for Zipcodes\", \"xaxis\": {\"type\": \"category\"}, \"yaxis\": {\"title\": \"Breakeven Period(in years)\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"1d4f17d0-74ab-4836-9590-dbfc84049d24\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"1d4f17d0-74ab-4836-9590-dbfc84049d24\", [{\"name\": \"Manhattan\", \"x\": [10003, 10011, 10013, 10014, 10021, 10022, 10023, 10025, 10028, 10036, 10128], \"y\": [27.90394878862425, 27.657061906865074, 33.503709733355144, 30.866748759906322, 26.368484819296867, 21.947871311552333, 30.24852820975358, 20.22786202561482, 31.343308459686693, 15.001015945567898, 55.897299528690084], \"type\": \"bar\", \"uid\": \"6537ebdc-5c97-11e9-834b-3cf862c2ea5b\"}, {\"name\": \"Brooklyn\", \"x\": [11201, 11215, 11217, 11231, 11234], \"y\": [27.80965982396558, 24.766020357665965, 34.99259822110021, 24.665958368007903, 15.573885681713321], \"type\": \"bar\", \"uid\": \"6537ebdd-5c97-11e9-883e-3cf862c2ea5b\"}, {\"name\": \"Staten Island\", \"x\": [10304, 10305, 10306, 10312], \"y\": [18.863972581528444, 19.131142829772987, 18.92374920214072, 7.422583828223449], \"type\": \"bar\", \"uid\": \"6537ebde-5c97-11e9-8a60-3cf862c2ea5b\"}, {\"name\": \"Queens\", \"x\": [11434], \"y\": [10.56966628085336], \"type\": \"bar\", \"uid\": \"6537ebdf-5c97-11e9-be45-3cf862c2ea5b\"}], {\"title\": \"Bar Plot of Breakeven Period for Zipcodes\", \"xaxis\": {\"type\": \"category\"}, \"yaxis\": {\"title\": \"Breakeven Period(in years)\"}}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using the above created function to plot the bar chart\n",
    "\n",
    "barplots_with_neighbourhoods('zipcode','Breakeven Period', 'Breakeven Period for Zipcodes')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### In general, the neighbourhood \"Staten Island\" has a lower break even period"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From the zipcodes analyzed, 10312 has the least breakdown period considering its ability to make profit quicker. But from the availability analyses conducted above, it is not rented even once. So it should not be considered for investing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Off the remaining, 10036 shows a promising trend with lower breakeven period. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The breakeven period for Brooklyn is also low and in that 11234 has a lower breakeven priod but it has higher availability. So it is not a potential investment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ----------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3-D Scatter Plot Between Revenue, Cost and Breakeven period"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Function to Create a 3-D Scatter plot:s\n",
    "\n",
    "def threedscatterplot(xaxis, yaxis, zaxis):\n",
    "    data=[]\n",
    "    neighbours=['Manhattan', 'Brooklyn', 'Staten Island', 'Queens']\n",
    "\n",
    "    for i in neighbours:\n",
    "        trace = go.Scatter3d(\n",
    "            x=final_df[final_df['neighbourhood_group_cleansed']==i][xaxis],\n",
    "            y=final_df[final_df['neighbourhood_group_cleansed']==i][yaxis],\n",
    "            z=final_df[final_df['neighbourhood_group_cleansed']==i][zaxis],\n",
    "            mode='markers',\n",
    "            name=i,\n",
    "            marker=dict(\n",
    "                size=10), \n",
    "            text= final_df[final_df['neighbourhood_group_cleansed']==i]['zipcode']\n",
    "        )\n",
    "        data.append(trace)\n",
    "    \n",
    "    layout= go.Layout(title='3D ScatterPlot Between X= %s, y= %s, and z= %s' %(xaxis,yaxis,zaxis),\n",
    "                     hovermode= 'closest')\n",
    "    \n",
    "    fig = go.Figure(data=data, layout=layout)\n",
    "    iplot(fig)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
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        },
        {
         "marker": {
          "size": 10
         },
         "mode": "markers",
         "name": "Brooklyn",
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         ],
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         "uid": "663fd541-5c97-11e9-ae2f-3cf862c2ea5b",
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         "z": [
          27.80965982396558,
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          34.99259822110021,
          24.665958368007903,
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         ]
        },
        {
         "marker": {
          "size": 10
         },
         "mode": "markers",
         "name": "Staten Island",
         "text": [
          10304,
          10305,
          10306,
          10312
         ],
         "type": "scatter3d",
         "uid": "663fd542-5c97-11e9-941b-3cf862c2ea5b",
         "x": [
          97,
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        {
         "marker": {
          "size": 10
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         "x": [
          155
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       "layout": {
        "hovermode": "closest",
        "title": "3D ScatterPlot Between X= Revenue, y= Current Month Cost, and z= Breakeven Period"
       }
      },
      "text/html": [
       "<div id=\"71d8620d-fe88-43c3-b49b-0d121bec395b\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"71d8620d-fe88-43c3-b49b-0d121bec395b\", [{\"marker\": {\"size\": 10}, \"mode\": \"markers\", \"name\": \"Manhattan\", \"text\": [10003.0, 10011.0, 10013.0, 10014.0, 10021.0, 10022.0, 10023.0, 10025.0, 10028.0, 10036.0, 10128.0], \"x\": [310.8141592920354, 363.58620689655174, 389.84375, 325.2903225806452, 273.8421052631579, 367.2894736842105, 284.6984126984127, 286.0, 270.46875, 445.6666666666667, 225.375], \"y\": [2374217.9775280897, 2752755.056179776, 3575506.741573031, 2748629.2134831483, 1976694.3820224707, 2206759.550561795, 2357455.056179776, 1583689.8876404483, 2320684.2696629236, 1830142.6966292155, 3448662.4999999967], \"z\": [27.90394878862425, 27.657061906865074, 33.503709733355144, 30.866748759906322, 26.368484819296867, 21.947871311552333, 30.24852820975358, 20.22786202561482, 31.343308459686693, 15.001015945567898, 55.897299528690084], \"type\": \"scatter3d\", \"uid\": \"663fd540-5c97-11e9-8baf-3cf862c2ea5b\"}, {\"marker\": {\"size\": 10}, \"mode\": \"markers\", \"name\": \"Brooklyn\", \"text\": [11201.0, 11215.0, 11217.0, 11231.0, 11234.0], \"x\": [208.6290322580645, 177.68548387096774, 203.11666666666667, 200.53623188405797, 107.33333333333333], \"y\": [1588270.7865168517, 1204653.932584269, 1945700.0000000016, 1354082.0224719103, 457599.69604294165], \"z\": [27.80965982396558, 24.766020357665965, 34.99259822110021, 24.665958368007903, 15.573885681713321], \"type\": \"scatter3d\", \"uid\": \"663fd541-5c97-11e9-ae2f-3cf862c2ea5b\"}, {\"marker\": {\"size\": 10}, \"mode\": \"markers\", \"name\": \"Staten Island\", \"text\": [10304.0, 10305.0, 10306.0, 10312.0], \"x\": [97.0, 111.22222222222223, 93.0, 215.0], \"y\": [500909.2119367609, 582487.5000000007, 481775.0, 436865.44943987636], \"z\": [18.863972581528444, 19.131142829772987, 18.92374920214072, 7.422583828223449], \"type\": \"scatter3d\", \"uid\": \"663fd542-5c97-11e9-941b-3cf862c2ea5b\"}, {\"marker\": {\"size\": 10}, \"mode\": \"markers\", \"name\": \"Queens\", \"text\": [11434.0], \"x\": [155.0], \"y\": [448484.1523794591], \"z\": [10.56966628085336], \"type\": \"scatter3d\", \"uid\": \"663fd543-5c97-11e9-8d31-3cf862c2ea5b\"}], {\"hovermode\": \"closest\", \"title\": \"3D ScatterPlot Between X= Revenue, y= Current Month Cost, and z= Breakeven Period\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<div id=\"71d8620d-fe88-43c3-b49b-0d121bec395b\" style=\"height: 525px; width: 100%;\" class=\"plotly-graph-div\"></div><script type=\"text/javascript\">require([\"plotly\"], function(Plotly) { window.PLOTLYENV=window.PLOTLYENV || {};window.PLOTLYENV.BASE_URL=\"https://plot.ly\";Plotly.newPlot(\"71d8620d-fe88-43c3-b49b-0d121bec395b\", [{\"marker\": {\"size\": 10}, \"mode\": \"markers\", \"name\": \"Manhattan\", \"text\": [10003.0, 10011.0, 10013.0, 10014.0, 10021.0, 10022.0, 10023.0, 10025.0, 10028.0, 10036.0, 10128.0], \"x\": [310.8141592920354, 363.58620689655174, 389.84375, 325.2903225806452, 273.8421052631579, 367.2894736842105, 284.6984126984127, 286.0, 270.46875, 445.6666666666667, 225.375], \"y\": [2374217.9775280897, 2752755.056179776, 3575506.741573031, 2748629.2134831483, 1976694.3820224707, 2206759.550561795, 2357455.056179776, 1583689.8876404483, 2320684.2696629236, 1830142.6966292155, 3448662.4999999967], \"z\": [27.90394878862425, 27.657061906865074, 33.503709733355144, 30.866748759906322, 26.368484819296867, 21.947871311552333, 30.24852820975358, 20.22786202561482, 31.343308459686693, 15.001015945567898, 55.897299528690084], \"type\": \"scatter3d\", \"uid\": \"663fd540-5c97-11e9-8baf-3cf862c2ea5b\"}, {\"marker\": {\"size\": 10}, \"mode\": \"markers\", \"name\": \"Brooklyn\", \"text\": [11201.0, 11215.0, 11217.0, 11231.0, 11234.0], \"x\": [208.6290322580645, 177.68548387096774, 203.11666666666667, 200.53623188405797, 107.33333333333333], \"y\": [1588270.7865168517, 1204653.932584269, 1945700.0000000016, 1354082.0224719103, 457599.69604294165], \"z\": [27.80965982396558, 24.766020357665965, 34.99259822110021, 24.665958368007903, 15.573885681713321], \"type\": \"scatter3d\", \"uid\": \"663fd541-5c97-11e9-ae2f-3cf862c2ea5b\"}, {\"marker\": {\"size\": 10}, \"mode\": \"markers\", \"name\": \"Staten Island\", \"text\": [10304.0, 10305.0, 10306.0, 10312.0], \"x\": [97.0, 111.22222222222223, 93.0, 215.0], \"y\": [500909.2119367609, 582487.5000000007, 481775.0, 436865.44943987636], \"z\": [18.863972581528444, 19.131142829772987, 18.92374920214072, 7.422583828223449], \"type\": \"scatter3d\", \"uid\": \"663fd542-5c97-11e9-941b-3cf862c2ea5b\"}, {\"marker\": {\"size\": 10}, \"mode\": \"markers\", \"name\": \"Queens\", \"text\": [11434.0], \"x\": [155.0], \"y\": [448484.1523794591], \"z\": [10.56966628085336], \"type\": \"scatter3d\", \"uid\": \"663fd543-5c97-11e9-8d31-3cf862c2ea5b\"}], {\"hovermode\": \"closest\", \"title\": \"3D ScatterPlot Between X= Revenue, y= Current Month Cost, and z= Breakeven Period\"}, {\"showLink\": true, \"linkText\": \"Export to plot.ly\"})});</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# threedscatterplot(xaxis, yaxis, zaxis)\n",
    "threedscatterplot('Revenue', 'Current Month Cost', 'Breakeven Period')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### From this plot, it is evident that 10036 has a higher x-value (Revenue), lower y- value( cost) and lower z-value (Breakeven Period). Also it had higher revenue- lower cost, located near the Times Square. So it  could be a potential Investment."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Also 10025 has lower breakeven period with lower cost than most."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ----------------------------------------------------------------------------------------------------------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### * If the Company wants to make profits sooner and willing to spend lesser, it is better to go with 10025, 11215, 11231 and 11201. It has lesser availability, lesser cost and lesser breakeven periods. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### *  If the Company wants to make profits sooner and ready to invest good amount of money, then it is better to invest in 10036 and 10022. This also is the best option in the longer run. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### These are the suggestions which could be drawn from the analysis and the final decisions should be made depending on the objective which the company is trying to achieve."
   ]
  }
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