rentorbuy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import gc\n",
    "import altair as alt\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Data:\n",
    "\n",
    "- Explore the data.\n",
    "- Pick a starting point and create visualizations that might help understand the data better.\n",
    "- Come back and explore other parts of the data and create more visualizations and models.\n",
    "\n",
    "Quandl is a great place to start exploring datasets and has the Zillow Research Datasets(and many other datasets) that can be merged to create the customized dataset that might provide solutions to a specific problem.\n",
    "It also has an easy to use api. I started with the Zillow data because it was the first real estate dataset on the Quandl site and it contains a lot of metrics that look interesting. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import quandl\n",
    "\n",
    "quandl.ApiConfig.api_key = '############'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I downloaded the Zillow codes dataset: https://www.quandl.com/data/ZILLOW-Zillow-Real-Estate-Research/usage/export\n",
    "This was useful while exploring area specific codes and descriptions in Zillow Research Dataset which contains 1,318,489 datasets. One can use regular expressions among other tools during EDA."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "zillow_codes = pd.read_csv('input/ZILLOW-datasets-codes.csv',header=None)\n",
    "zillow_codes.columns = ['codes','description']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## API Reference:\n",
    "\n",
    "https://blog.quandl.com/api-for-housing-data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def cleanup_desc(df):\n",
    "    '''Function cleans up description column of Zillow codes dataframe.'''\n",
    "    df.description.values[:] = (df.loc[:,'description']).str.split(':').apply(lambda x: x[1].split('- San Francisco, CA')).apply(lambda x:x[0])\n",
    "    return df\n",
    "\n",
    "def get_df(df,col='code',exp='/M'):\n",
    "    ''' \n",
    "    Function takes in the zillow_codes dataframe and ouputs \n",
    "    a dataframe filtered by specified column and expression:\n",
    "    \n",
    "    Inputs:\n",
    "    col: 'code' or 'description'\n",
    "    exp: string Reference: https://blog.quandl.com/api-for-housing-data\n",
    "    \n",
    "    Ouputs:\n",
    "    pd.DataFrame\n",
    "    '''\n",
    "    indices = [i for i,val in enumerate(df[col].str.findall(exp)) if val != []]\n",
    "    print('Number of data points: {}'.format(len(indices)))\n",
    "    return df.iloc[indices,:]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def print_random_row(df):\n",
    "    randint = np.random.randint(df.shape[0])\n",
    "    print(df.codes.iloc[randint])\n",
    "    print(df.description.iloc[randint])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZILLOW/C12142_MSPFSF\n",
      "Zillow Home Value Index (City): Median Sold Price Per Square Foot - Single-Family Residence - Hoffman Estates, IL\n"
     ]
    }
   ],
   "source": [
    "print_random_row(zillow_codes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points: 6518\n"
     ]
    }
   ],
   "source": [
    "#Zip with Regex:\n",
    "zip_df = get_df(zillow_codes,col='codes',exp=r'/Z94[0-9][0-9][0-9]_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZILLOW/Z94123_ZHVI3B\n",
      "Zillow Home Value Index (Zip): Zillow Home Value Index - Three Bedrooms - 94123, San Francisco, CA\n"
     ]
    }
   ],
   "source": [
    "print_random_row(zip_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points: 700\n"
     ]
    }
   ],
   "source": [
    "#Metro Code: '/M'\n",
    "metro_df = get_df(zillow_codes,col='codes',exp='/M12')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZILLOW/M121_ZHVIBT\n",
      "Zillow Home Value Index (Metro): Zillow Home Value Index - Bottom Tier - Santa Cruz, CA\n"
     ]
    }
   ],
   "source": [
    "print_random_row(metro_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "\n",
    "## Percent of homes increasing in value:\n",
    "\n",
    "- Percent of homes increasing in value is a metric that may be useful while making the decision to buy or rent.\n",
    "- Using the code is \"/Z\" for getting data by zipcode.\n",
    "- Using the description to filter the data to Chicago zipcodes.\n",
    "- Make API call to Quandl and get the percent of homes increasing values for all homes by neighborhood in Chicago.(http://www.realestatedecoded.com/zillow-percent-homes-increasing-value/)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points: 204611\n",
      "Number of data points: 582471\n"
     ]
    }
   ],
   "source": [
    "#Getting neighborhood level information: '/N' \n",
    "#Getting metro level information: '/M'\n",
    "\n",
    "neighborhoods = get_df(zillow_codes,col='codes',exp='/N')\n",
    "zips = get_df(zillow_codes,col='codes',exp='/Z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points: 2769\n"
     ]
    }
   ],
   "source": [
    "zips_chicago = get_df(zips,col='description',exp='Chicago, IL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points: 5722\n"
     ]
    }
   ],
   "source": [
    "#  neighborhoods_sfo = get_df(neighborhoods,col='description',exp=' San Francisco, CA')\n",
    "neighborhoods_chicago = get_df(neighborhoods,col='description',exp='Chicago, IL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZILLOW/N552_MRPFMF\n",
      "Zillow Home Value Index (Neighborhood): Median Rental Price Per Square Foot - Multi-Family Residence - University Village - Little Italy, Chicago, IL\n"
     ]
    }
   ],
   "source": [
    "print_random_row(neighborhoods_chicago)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of data points: 165\n",
      "Number of data points: 56\n"
     ]
    }
   ],
   "source": [
    "#mspf = Median Sale Price per Square Foot\n",
    "# mspf_neighborhoods_sfo = get_df(neighborhoods_sfo,col='codes',exp='_MSPFAH')\n",
    "\n",
    "#prr = Price to rent ratio\n",
    "\n",
    "# prr_neighborhoods_sfo = get_df(neighborhoods_sfo,col='codes',exp='_PRRAH')\n",
    "\n",
    "#phi = Percent of Homes increasing in values - all homes\n",
    "phiv_neighborhoods_chicago = get_df(neighborhoods_chicago,col='codes',exp='_PHIVAH')\n",
    "phiv_zips_chicago = get_df(zips_chicago,col='codes',exp='_PHIVAH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ZILLOW/Z60640_PHIVAH\n",
      "Zillow Home Value Index (Zip): Percent Of Homes Increasing In Values - All Homes - 60640, Chicago, IL\n"
     ]
    }
   ],
   "source": [
    "print_random_row(phiv_zips_chicago)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Clearning up descriptions:\n",
    "\n",
    "neighborhood_names = phiv_neighborhoods_chicago.description.apply(lambda x: x.replace('Zillow Home Value Index (Neighborhood): Percent Of Homes Increasing In Values - All Homes - ',''))\n",
    "zips_names = phiv_zips_chicago.description.apply(lambda x:x.replace('Zillow Home Value Index (Zip): Percent Of Homes Increasing In Values - All Homes - ',''))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "931463    60657, Chicago, IL\n",
       "Name: description, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zips_names[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "177782    Logan Square, Chicago, IL\n",
       "Name: description, dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neighborhood_names[:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_quandl_data(df,names,filter_val=246):\n",
    "    quandl_get = [quandl.get(code) for i,code in enumerate(df['codes'])]\n",
    "    \n",
    "    #Cleaned up DF and DF columns\n",
    "    cleaned_up = pd.concat([val for i,val in enumerate(quandl_get) if val.shape[0]>=filter_val],axis=1)\n",
    "    cleaned_names = [names.iloc[i] for i,val in enumerate(quandl_get) if val.shape[0]>=filter_val]\n",
    "    cleaned_up.columns = cleaned_names\n",
    "    #Some time series have fewer than 246 data points, ignoring these points for the moment.\n",
    "    #Saving the indices and time series in a separate anomaly dict with {name:ts}\n",
    "    \n",
    "    anomaly_dict = {names.iloc[i]:val for i,val in enumerate(quandl_get) if val.shape[0]<filter_val}\n",
    "    return quandl_get,anomaly_dict,cleaned_up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Logan Square, Chicago, IL</th>\n",
       "      <th>Little Village, Chicago, IL</th>\n",
       "      <th>West Rogers Park, Chicago, IL</th>\n",
       "      <th>Albany Park, Chicago, IL</th>\n",
       "      <th>Rogers Park, Chicago, IL</th>\n",
       "      <th>South Austin, Chicago, IL</th>\n",
       "      <th>Archer Heights, Chicago, IL</th>\n",
       "      <th>Gresham, Chicago, IL</th>\n",
       "      <th>Lake View, Chicago, IL</th>\n",
       "      <th>Englewood, Chicago, IL</th>\n",
       "      <th>...</th>\n",
       "      <th>Printers Row, Chicago, IL</th>\n",
       "      <th>Pill Hill, Chicago, IL</th>\n",
       "      <th>LeClaire Courts, Chicago, IL</th>\n",
       "      <th>Chrysler Village, Chicago, IL</th>\n",
       "      <th>Roscoe Village, Chicago, IL</th>\n",
       "      <th>Forest Glen, Chicago, IL</th>\n",
       "      <th>Schorsch Forest View, Chicago, IL</th>\n",
       "      <th>Burnside, Chicago, IL</th>\n",
       "      <th>West Chatham, Chicago, IL</th>\n",
       "      <th>South Commons, Chicago, IL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-12-31</th>\n",
       "      <td>75.77</td>\n",
       "      <td>85.47</td>\n",
       "      <td>78.21</td>\n",
       "      <td>74.54</td>\n",
       "      <td>67.24</td>\n",
       "      <td>82.12</td>\n",
       "      <td>80.04</td>\n",
       "      <td>77.49</td>\n",
       "      <td>50.96</td>\n",
       "      <td>60.42</td>\n",
       "      <td>...</td>\n",
       "      <td>67.04</td>\n",
       "      <td>94.69</td>\n",
       "      <td>95.39</td>\n",
       "      <td>98.58</td>\n",
       "      <td>57.05</td>\n",
       "      <td>91.07</td>\n",
       "      <td>65.71</td>\n",
       "      <td>91.27</td>\n",
       "      <td>55.48</td>\n",
       "      <td>90.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-10-31</th>\n",
       "      <td>55.27</td>\n",
       "      <td>48.39</td>\n",
       "      <td>56.04</td>\n",
       "      <td>62.20</td>\n",
       "      <td>59.68</td>\n",
       "      <td>31.50</td>\n",
       "      <td>54.12</td>\n",
       "      <td>43.89</td>\n",
       "      <td>50.09</td>\n",
       "      <td>11.55</td>\n",
       "      <td>...</td>\n",
       "      <td>27.03</td>\n",
       "      <td>66.23</td>\n",
       "      <td>19.43</td>\n",
       "      <td>36.03</td>\n",
       "      <td>65.19</td>\n",
       "      <td>61.17</td>\n",
       "      <td>56.59</td>\n",
       "      <td>35.27</td>\n",
       "      <td>40.84</td>\n",
       "      <td>84.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>2 rows × 152 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Logan Square, Chicago, IL  Little Village, Chicago, IL  \\\n",
       "Date                                                                 \n",
       "2003-12-31                      75.77                        85.47   \n",
       "2015-10-31                      55.27                        48.39   \n",
       "\n",
       "            West Rogers Park, Chicago, IL  Albany Park, Chicago, IL  \\\n",
       "Date                                                                  \n",
       "2003-12-31                          78.21                     74.54   \n",
       "2015-10-31                          56.04                     62.20   \n",
       "\n",
       "            Rogers Park, Chicago, IL  South Austin, Chicago, IL  \\\n",
       "Date                                                              \n",
       "2003-12-31                     67.24                      82.12   \n",
       "2015-10-31                     59.68                      31.50   \n",
       "\n",
       "            Archer Heights, Chicago, IL  Gresham, Chicago, IL  \\\n",
       "Date                                                            \n",
       "2003-12-31                        80.04                 77.49   \n",
       "2015-10-31                        54.12                 43.89   \n",
       "\n",
       "            Lake View, Chicago, IL  Englewood, Chicago, IL  \\\n",
       "Date                                                         \n",
       "2003-12-31                   50.96                   60.42   \n",
       "2015-10-31                   50.09                   11.55   \n",
       "\n",
       "                       ...              Printers Row, Chicago, IL  \\\n",
       "Date                   ...                                          \n",
       "2003-12-31             ...                                  67.04   \n",
       "2015-10-31             ...                                  27.03   \n",
       "\n",
       "            Pill Hill, Chicago, IL  LeClaire Courts, Chicago, IL  \\\n",
       "Date                                                               \n",
       "2003-12-31                   94.69                         95.39   \n",
       "2015-10-31                   66.23                         19.43   \n",
       "\n",
       "            Chrysler Village, Chicago, IL  Roscoe Village, Chicago, IL  \\\n",
       "Date                                                                     \n",
       "2003-12-31                          98.58                        57.05   \n",
       "2015-10-31                          36.03                        65.19   \n",
       "\n",
       "            Forest Glen, Chicago, IL  Schorsch Forest View, Chicago, IL  \\\n",
       "Date                                                                      \n",
       "2003-12-31                     91.07                              65.71   \n",
       "2015-10-31                     61.17                              56.59   \n",
       "\n",
       "            Burnside, Chicago, IL  West Chatham, Chicago, IL  \\\n",
       "Date                                                           \n",
       "2003-12-31                  91.27                      55.48   \n",
       "2015-10-31                  35.27                      40.84   \n",
       "\n",
       "            South Commons, Chicago, IL  \n",
       "Date                                    \n",
       "2003-12-31                       90.77  \n",
       "2015-10-31                       84.80  \n",
       "\n",
       "[2 rows x 152 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  = get_quandl_data(phiv_neighborhoods_chicago)\n",
    "phiv_quandl_get_list,phiv_anomaly_dict,phiv_chicago_neighborhoods_df = get_quandl_data(phiv_neighborhoods_chicago,neighborhood_names)\n",
    "phiv_chicago_neighborhoods_df.sample(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "phiv_quandl_get_list,phiv_anomaly_dict,phiv_chicago_zips_df = get_quandl_data(phiv_zips_chicago,zips_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(246, 55)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phiv_chicago_zips_df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>60657, Chicago, IL</th>\n",
       "      <th>60614, Chicago, IL</th>\n",
       "      <th>60640, Chicago, IL</th>\n",
       "      <th>60647, Chicago, IL</th>\n",
       "      <th>60618, Chicago, IL</th>\n",
       "      <th>60613, Chicago, IL</th>\n",
       "      <th>60629, Chicago, IL</th>\n",
       "      <th>60625, Chicago, IL</th>\n",
       "      <th>60619, Chicago, IL</th>\n",
       "      <th>60617, Chicago, IL</th>\n",
       "      <th>...</th>\n",
       "      <th>60655, Chicago, IL</th>\n",
       "      <th>60636, Chicago, IL</th>\n",
       "      <th>60185, West Chicago, IL</th>\n",
       "      <th>60646, Chicago, IL</th>\n",
       "      <th>60601, Chicago, IL</th>\n",
       "      <th>60642, Chicago, IL</th>\n",
       "      <th>60661, Chicago, IL</th>\n",
       "      <th>60064, North Chicago, IL</th>\n",
       "      <th>60633, Chicago, IL</th>\n",
       "      <th>60606, Chicago, IL</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2003-09-30</th>\n",
       "      <td>53.06</td>\n",
       "      <td>54.24</td>\n",
       "      <td>77.92</td>\n",
       "      <td>77.87</td>\n",
       "      <td>77.69</td>\n",
       "      <td>62.93</td>\n",
       "      <td>88.88</td>\n",
       "      <td>85.37</td>\n",
       "      <td>72.32</td>\n",
       "      <td>66.50</td>\n",
       "      <td>...</td>\n",
       "      <td>88.36</td>\n",
       "      <td>81.39</td>\n",
       "      <td>84.86</td>\n",
       "      <td>75.74</td>\n",
       "      <td>36.68</td>\n",
       "      <td>75.07</td>\n",
       "      <td>74.89</td>\n",
       "      <td>68.93</td>\n",
       "      <td>67.12</td>\n",
       "      <td>49.77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017-07-31</th>\n",
       "      <td>64.12</td>\n",
       "      <td>58.70</td>\n",
       "      <td>63.33</td>\n",
       "      <td>75.78</td>\n",
       "      <td>65.79</td>\n",
       "      <td>55.26</td>\n",
       "      <td>56.91</td>\n",
       "      <td>63.14</td>\n",
       "      <td>65.59</td>\n",
       "      <td>50.40</td>\n",
       "      <td>...</td>\n",
       "      <td>77.07</td>\n",
       "      <td>66.72</td>\n",
       "      <td>90.02</td>\n",
       "      <td>78.04</td>\n",
       "      <td>68.10</td>\n",
       "      <td>74.40</td>\n",
       "      <td>84.47</td>\n",
       "      <td>83.48</td>\n",
       "      <td>82.97</td>\n",
       "      <td>80.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009-12-31</th>\n",
       "      <td>36.22</td>\n",
       "      <td>39.73</td>\n",
       "      <td>32.03</td>\n",
       "      <td>28.33</td>\n",
       "      <td>22.53</td>\n",
       "      <td>39.30</td>\n",
       "      <td>6.05</td>\n",
       "      <td>23.65</td>\n",
       "      <td>11.71</td>\n",
       "      <td>22.46</td>\n",
       "      <td>...</td>\n",
       "      <td>32.62</td>\n",
       "      <td>9.88</td>\n",
       "      <td>11.76</td>\n",
       "      <td>14.55</td>\n",
       "      <td>39.63</td>\n",
       "      <td>48.16</td>\n",
       "      <td>50.59</td>\n",
       "      <td>3.10</td>\n",
       "      <td>25.87</td>\n",
       "      <td>57.88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-08-31</th>\n",
       "      <td>58.20</td>\n",
       "      <td>60.07</td>\n",
       "      <td>82.81</td>\n",
       "      <td>78.70</td>\n",
       "      <td>79.69</td>\n",
       "      <td>65.94</td>\n",
       "      <td>90.96</td>\n",
       "      <td>84.88</td>\n",
       "      <td>72.53</td>\n",
       "      <td>67.15</td>\n",
       "      <td>...</td>\n",
       "      <td>88.12</td>\n",
       "      <td>81.00</td>\n",
       "      <td>86.45</td>\n",
       "      <td>78.34</td>\n",
       "      <td>42.15</td>\n",
       "      <td>83.31</td>\n",
       "      <td>70.09</td>\n",
       "      <td>65.37</td>\n",
       "      <td>70.07</td>\n",
       "      <td>40.83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998-04-30</th>\n",
       "      <td>72.93</td>\n",
       "      <td>56.27</td>\n",
       "      <td>72.68</td>\n",
       "      <td>44.37</td>\n",
       "      <td>70.09</td>\n",
       "      <td>70.42</td>\n",
       "      <td>62.59</td>\n",
       "      <td>81.66</td>\n",
       "      <td>46.70</td>\n",
       "      <td>60.74</td>\n",
       "      <td>...</td>\n",
       "      <td>50.90</td>\n",
       "      <td>38.52</td>\n",
       "      <td>66.41</td>\n",
       "      <td>66.75</td>\n",
       "      <td>34.75</td>\n",
       "      <td>56.08</td>\n",
       "      <td>48.82</td>\n",
       "      <td>84.37</td>\n",
       "      <td>57.14</td>\n",
       "      <td>29.40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012-11-30</th>\n",
       "      <td>37.20</td>\n",
       "      <td>46.31</td>\n",
       "      <td>30.58</td>\n",
       "      <td>51.10</td>\n",
       "      <td>46.25</td>\n",
       "      <td>36.69</td>\n",
       "      <td>46.21</td>\n",
       "      <td>31.94</td>\n",
       "      <td>40.58</td>\n",
       "      <td>24.62</td>\n",
       "      <td>...</td>\n",
       "      <td>38.87</td>\n",
       "      <td>54.47</td>\n",
       "      <td>40.19</td>\n",
       "      <td>45.88</td>\n",
       "      <td>25.05</td>\n",
       "      <td>49.02</td>\n",
       "      <td>46.35</td>\n",
       "      <td>42.92</td>\n",
       "      <td>9.32</td>\n",
       "      <td>34.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007-03-31</th>\n",
       "      <td>44.95</td>\n",
       "      <td>39.84</td>\n",
       "      <td>54.08</td>\n",
       "      <td>59.91</td>\n",
       "      <td>52.65</td>\n",
       "      <td>42.92</td>\n",
       "      <td>58.43</td>\n",
       "      <td>45.89</td>\n",
       "      <td>70.95</td>\n",
       "      <td>75.65</td>\n",
       "      <td>...</td>\n",
       "      <td>63.68</td>\n",
       "      <td>66.39</td>\n",
       "      <td>46.67</td>\n",
       "      <td>59.48</td>\n",
       "      <td>63.85</td>\n",
       "      <td>39.89</td>\n",
       "      <td>63.52</td>\n",
       "      <td>44.84</td>\n",
       "      <td>57.70</td>\n",
       "      <td>58.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006-02-28</th>\n",
       "      <td>77.12</td>\n",
       "      <td>77.93</td>\n",
       "      <td>73.34</td>\n",
       "      <td>89.50</td>\n",
       "      <td>84.37</td>\n",
       "      <td>61.48</td>\n",
       "      <td>97.12</td>\n",
       "      <td>79.69</td>\n",
       "      <td>92.34</td>\n",
       "      <td>92.46</td>\n",
       "      <td>...</td>\n",
       "      <td>93.66</td>\n",
       "      <td>95.03</td>\n",
       "      <td>93.05</td>\n",
       "      <td>93.37</td>\n",
       "      <td>88.29</td>\n",
       "      <td>87.04</td>\n",
       "      <td>66.96</td>\n",
       "      <td>95.92</td>\n",
       "      <td>94.40</td>\n",
       "      <td>51.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-07-31</th>\n",
       "      <td>11.99</td>\n",
       "      <td>20.31</td>\n",
       "      <td>7.80</td>\n",
       "      <td>14.65</td>\n",
       "      <td>13.66</td>\n",
       "      <td>8.65</td>\n",
       "      <td>3.59</td>\n",
       "      <td>8.26</td>\n",
       "      <td>6.56</td>\n",
       "      <td>8.34</td>\n",
       "      <td>...</td>\n",
       "      <td>3.87</td>\n",
       "      <td>7.68</td>\n",
       "      <td>3.06</td>\n",
       "      <td>8.98</td>\n",
       "      <td>48.92</td>\n",
       "      <td>10.94</td>\n",
       "      <td>4.00</td>\n",
       "      <td>5.21</td>\n",
       "      <td>7.54</td>\n",
       "      <td>9.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-04-30</th>\n",
       "      <td>15.27</td>\n",
       "      <td>17.02</td>\n",
       "      <td>10.94</td>\n",
       "      <td>15.18</td>\n",
       "      <td>13.73</td>\n",
       "      <td>7.36</td>\n",
       "      <td>5.14</td>\n",
       "      <td>9.44</td>\n",
       "      <td>14.29</td>\n",
       "      <td>6.96</td>\n",
       "      <td>...</td>\n",
       "      <td>8.63</td>\n",
       "      <td>16.39</td>\n",
       "      <td>5.71</td>\n",
       "      <td>16.49</td>\n",
       "      <td>45.80</td>\n",
       "      <td>10.09</td>\n",
       "      <td>3.43</td>\n",
       "      <td>3.44</td>\n",
       "      <td>13.74</td>\n",
       "      <td>4.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            60657, Chicago, IL  60614, Chicago, IL  60640, Chicago, IL  \\\n",
       "Date                                                                     \n",
       "2003-09-30               53.06               54.24               77.92   \n",
       "2017-07-31               64.12               58.70               63.33   \n",
       "2009-12-31               36.22               39.73               32.03   \n",
       "2003-08-31               58.20               60.07               82.81   \n",
       "1998-04-30               72.93               56.27               72.68   \n",
       "2012-11-30               37.20               46.31               30.58   \n",
       "2007-03-31               44.95               39.84               54.08   \n",
       "2006-02-28               77.12               77.93               73.34   \n",
       "2011-07-31               11.99               20.31                7.80   \n",
       "2011-04-30               15.27               17.02               10.94   \n",
       "\n",
       "            60647, Chicago, IL  60618, Chicago, IL  60613, Chicago, IL  \\\n",
       "Date                                                                     \n",
       "2003-09-30               77.87               77.69               62.93   \n",
       "2017-07-31               75.78               65.79               55.26   \n",
       "2009-12-31               28.33               22.53               39.30   \n",
       "2003-08-31               78.70               79.69               65.94   \n",
       "1998-04-30               44.37               70.09               70.42   \n",
       "2012-11-30               51.10               46.25               36.69   \n",
       "2007-03-31               59.91               52.65               42.92   \n",
       "2006-02-28               89.50               84.37               61.48   \n",
       "2011-07-31               14.65               13.66                8.65   \n",
       "2011-04-30               15.18               13.73                7.36   \n",
       "\n",
       "            60629, Chicago, IL  60625, Chicago, IL  60619, Chicago, IL  \\\n",
       "Date                                                                     \n",
       "2003-09-30               88.88               85.37               72.32   \n",
       "2017-07-31               56.91               63.14               65.59   \n",
       "2009-12-31                6.05               23.65               11.71   \n",
       "2003-08-31               90.96               84.88               72.53   \n",
       "1998-04-30               62.59               81.66               46.70   \n",
       "2012-11-30               46.21               31.94               40.58   \n",
       "2007-03-31               58.43               45.89               70.95   \n",
       "2006-02-28               97.12               79.69               92.34   \n",
       "2011-07-31                3.59                8.26                6.56   \n",
       "2011-04-30                5.14                9.44               14.29   \n",
       "\n",
       "            60617, Chicago, IL         ...          60655, Chicago, IL  \\\n",
       "Date                                   ...                               \n",
       "2003-09-30               66.50         ...                       88.36   \n",
       "2017-07-31               50.40         ...                       77.07   \n",
       "2009-12-31               22.46         ...                       32.62   \n",
       "2003-08-31               67.15         ...                       88.12   \n",
       "1998-04-30               60.74         ...                       50.90   \n",
       "2012-11-30               24.62         ...                       38.87   \n",
       "2007-03-31               75.65         ...                       63.68   \n",
       "2006-02-28               92.46         ...                       93.66   \n",
       "2011-07-31                8.34         ...                        3.87   \n",
       "2011-04-30                6.96         ...                        8.63   \n",
       "\n",
       "            60636, Chicago, IL  60185, West Chicago, IL  60646, Chicago, IL  \\\n",
       "Date                                                                          \n",
       "2003-09-30               81.39                    84.86               75.74   \n",
       "2017-07-31               66.72                    90.02               78.04   \n",
       "2009-12-31                9.88                    11.76               14.55   \n",
       "2003-08-31               81.00                    86.45               78.34   \n",
       "1998-04-30               38.52                    66.41               66.75   \n",
       "2012-11-30               54.47                    40.19               45.88   \n",
       "2007-03-31               66.39                    46.67               59.48   \n",
       "2006-02-28               95.03                    93.05               93.37   \n",
       "2011-07-31                7.68                     3.06                8.98   \n",
       "2011-04-30               16.39                     5.71               16.49   \n",
       "\n",
       "            60601, Chicago, IL  60642, Chicago, IL  60661, Chicago, IL  \\\n",
       "Date                                                                     \n",
       "2003-09-30               36.68               75.07               74.89   \n",
       "2017-07-31               68.10               74.40               84.47   \n",
       "2009-12-31               39.63               48.16               50.59   \n",
       "2003-08-31               42.15               83.31               70.09   \n",
       "1998-04-30               34.75               56.08               48.82   \n",
       "2012-11-30               25.05               49.02               46.35   \n",
       "2007-03-31               63.85               39.89               63.52   \n",
       "2006-02-28               88.29               87.04               66.96   \n",
       "2011-07-31               48.92               10.94                4.00   \n",
       "2011-04-30               45.80               10.09                3.43   \n",
       "\n",
       "            60064, North Chicago, IL  60633, Chicago, IL  60606, Chicago, IL  \n",
       "Date                                                                          \n",
       "2003-09-30                     68.93               67.12               49.77  \n",
       "2017-07-31                     83.48               82.97               80.23  \n",
       "2009-12-31                      3.10               25.87               57.88  \n",
       "2003-08-31                     65.37               70.07               40.83  \n",
       "1998-04-30                     84.37               57.14               29.40  \n",
       "2012-11-30                     42.92                9.32               34.03  \n",
       "2007-03-31                     44.84               57.70               58.25  \n",
       "2006-02-28                     95.92               94.40               51.53  \n",
       "2011-07-31                      5.21                7.54                9.73  \n",
       "2011-04-30                      3.44               13.74                4.71  \n",
       "\n",
       "[10 rows x 55 columns]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "phiv_chicago_zips_df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f9e773a6588>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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n/OKFSv2ThUutBLBZLVzWXcsLZ0YL9p6K7JkKZF7nBpImY4sU0RuBbLnDRpXb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\nzFFCr1AoFMscJfQKhUKxzPn/3MOXgaF8HloAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e5f3cac88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phiv_chicago_neighborhoods_df['Logan Square, Chicago, IL'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "phiv_chicago_neighborhoods_df.to_csv('input/phiv_chicago_neighborhoods_df.csv')\n",
    "phiv_chicago_zips_df.to_csv('input/phiv_zips_df.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# phiv_chicago_neighborhoods = pd.read_csv('input/phiv_chicago_neighborhoods_df.csv')\n",
    "phiv_chicago_fil_neigh_df = pd.read_csv('input/phiv_chicago_fil_neigh_df.csv')\n",
    "phiv_chicago_fil_neigh_df.set_index('Date',inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1287"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# phiv_chicago_neighborhoods_df.shape\n",
    "gc.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Using Prophet for time series forecasting:\n",
    "\n",
    "    \"Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers.\" -\n",
    "    \n",
    "https://facebookincubator.github.io/prophet/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from fbprophet import Prophet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "data = phiv_chicago_zips_df['60647, Chicago, IL']\n",
    "m = Prophet(mcmc_samples=200,interval_width=0.95,weekly_seasonality=False,changepoint_prior_scale=4,seasonality_prior_scale=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = phiv_chicago_neighborhoods_df['Logan Square, Chicago, IL']\n",
    "m = Prophet(mcmc_samples=200,interval_width=0.95,weekly_seasonality=False,changepoint_prior_scale=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>2006-11-30</td>\n",
       "      <td>64.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>2001-02-28</td>\n",
       "      <td>92.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108</th>\n",
       "      <td>2006-02-28</td>\n",
       "      <td>89.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>2004-01-31</td>\n",
       "      <td>74.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>2013-10-31</td>\n",
       "      <td>94.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>2001-09-30</td>\n",
       "      <td>86.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1997-08-31</td>\n",
       "      <td>50.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>2000-06-30</td>\n",
       "      <td>84.41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>168</th>\n",
       "      <td>2011-02-28</td>\n",
       "      <td>12.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>1998-04-30</td>\n",
       "      <td>36.86</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            ds      y\n",
       "117 2006-11-30  64.18\n",
       "48  2001-02-28  92.76\n",
       "108 2006-02-28  89.75\n",
       "83  2004-01-31  74.91\n",
       "200 2013-10-31  94.12\n",
       "55  2001-09-30  86.31\n",
       "6   1997-08-31  50.61\n",
       "40  2000-06-30  84.41\n",
       "168 2011-02-28  12.26\n",
       "14  1998-04-30  36.86"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# data = np.log(data)\n",
    "data = pd.DataFrame(data).reset_index()\n",
    "data.columns=['ds','y']\n",
    "# data = data[data['ds'].dt.year>2009]\n",
    "data.sample(10)\n",
    "\n",
    "# m.fit(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "params = dict(mcmc_samples=200,interval_width=0.98,weekly_seasonality=False,changepoint_prior_scale=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def prophet_forecast(data,params,periods=4,freq='BMS'):\n",
    "    m = Prophet(**params)\n",
    "    data = pd.DataFrame(data).reset_index()\n",
    "    data.columns=['ds','y']\n",
    "#     data = data[data['ds'].dt.year>2008]\n",
    "#     print(data.sample(10))\n",
    "    m.fit(data)\n",
    "    future = m.make_future_dataframe(periods=4,freq=freq)#'M')\n",
    "#     print(type(future))\n",
    "    forecast = m.predict(future)\n",
    "    return m,forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data = phiv_chicago_zips_df['60645, Chicago, IL']\n",
    "m,forecast = prophet_forecast(data,params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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1jY62ltwo6qkE3mJuOOfCrWz7xO34/YGilp0EfRrRtF3Uca9CCNEVN9CU/JNy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3fK5NW2BImsjQtDLhKrI/4Jn08h+HWVWMBXFAHy4Oswx/UIGfO/pvEU9+oWKdfz6MtYdCSzQ64g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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e613565c0>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1jY62ltwo6qkE3mJuOOfCrWz7xO34/YGilp0EfRrRtF3Uca9CCNEVN9CU/JNy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3fK5NW2BImsjQtDLhKrI/4Jn08h+HWVWMBXFAHy4Oswx/UIGfO/pvEU9+oWKdfz6MtYdCSzQ64g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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9e613565c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# forecast = m.predict()\n",
    "m.plot(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating a chart using Altair:\n",
    "This provides a pythonic interface to Vega-lite and makes it easy to create plots: https://altair-viz.github.io/\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def area_chart_create(fcst,cols,trend_name='PHIC(%)',title='60645'):\n",
    "    #Process data:\n",
    "    fcst = fcst[cols]\n",
    "#     fcst.loc[:,'ymin']=fcst[cols[2]]+fcst[cols[3]]#+fcst['trend']\n",
    "#     fcst.loc[:,'ymax']=fcst[cols[2]]+fcst[cols[4]]#+fcst['trend']\n",
    "    chart = alt.Chart(fcst).mark_area().encode(\n",
    "    x = alt.X(fcst.columns[0]+':T',title=title,\n",
    "        axis=alt.Axis(\n",
    "            ticks=20,\n",
    "            axisWidth=0.0,\n",
    "            format='%Y',\n",
    "            labelAngle=0.0,\n",
    "        ),\n",
    "        scale=alt.Scale(\n",
    "            nice='month',\n",
    "        ),\n",
    "        timeUnit='yearmonth',\n",
    "    ),\n",
    "    \n",
    "    y= alt.Y(fcst.columns[3]+':Q',title=trend_name),\n",
    "    y2=fcst.columns[4]+':Q')\n",
    "    return chart.configure_cell(height=200.0,width=700.0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# cols = ['ds','trend']+['yearly','yearly_lower','yearly_upper']\n",
    "cols = ['ds','trend']+['yhat','yhat_lower','yhat_upper']\n",
    "\n",
    "yhat_uncertainity = area_chart_create(forecast,cols=cols)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"710b771b-a341-4919-ad74-ba9cf3dca31f\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#710b771b-a341-4919-ad74-ba9cf3dca31f"
     },
     "output_type": "display_data"
    },
    {
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       "var selector = \"#710b771b-a341-4919-ad74-ba9cf3dca31f\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
       "    throw(err);\n",
       "  }\n",
       "});\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#710b771b-a341-4919-ad74-ba9cf3dca31f"
     },
     "output_type": "display_data"
    },
    {
     "data": {
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"
     },
     "metadata": {
      "jupyter-vega": "#710b771b-a341-4919-ad74-ba9cf3dca31f"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "yhat_uncertainity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def trend_chart_create(fcst,trend_name='PHIC(%)trend'):\n",
    "    chart = alt.Chart(fcst).mark_line().encode(\n",
    "        color= alt.Color(value='#000'),\n",
    "    x = alt.X(fcst.columns[0]+':T',title='Logan Sq',\n",
    "        axis=alt.Axis(ticks=10,\n",
    "            axisWidth=0.0,\n",
    "            format='%Y',\n",
    "            labelAngle=0.0,\n",
    "        ),\n",
    "        scale=alt.Scale(\n",
    "            nice='month',\n",
    "        ),\n",
    "        timeUnit='yearmonth',\n",
    "    ),\n",
    "    y=alt.Y(fcst.columns[2]+':Q',title=trend_name)\n",
    "    )\n",
    "    return chart.configure_cell(height=200.0,width=700.0,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "trend = trend_chart_create(forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div class=\"vega-embed\" id=\"0d08aec5-e62f-44cf-988b-b696d33c561c\"></div>\n",
       "\n",
       "<style>\n",
       ".vega-embed svg, .vega-embed canvas {\n",
       "  border: 1px dotted gray;\n",
       "}\n",
       "\n",
       ".vega-embed .vega-actions a {\n",
       "  margin-right: 6px;\n",
       "}\n",
       "</style>\n"
      ]
     },
     "metadata": {
      "jupyter-vega": "#0d08aec5-e62f-44cf-988b-b696d33c561c"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "var spec = {\"mark\": \"line\", \"config\": {\"cell\": {\"width\": 700.0, \"height\": 200.0}}, \"data\": {\"values\": [{\"yearly\": 0.2869155436255871, \"seasonal\": 0.2869155436255871, \"yhat_upper\": 79.54454515653046, \"seasonal_lower\": -4.825860624178761, \"yhat_lower\": 29.899592184232297, \"yhat\": 54.78632186784649, \"t\": 0.0, \"seasonal_upper\": 5.171149439003609, \"trend\": 54.4994063242209, \"trend_lower\": 46.40770702127463, \"ds\": \"1997-02-28\", \"yearly_lower\": -4.8258606241787625, \"yearly_upper\": 5.171149439003609, \"trend_upper\": 62.546499005457946}, {\"yearly\": -0.6126174338814665, \"seasonal\": -0.6126174338814665, \"yhat_upper\": 75.74603839967489, \"seasonal_lower\": -5.828915555287926, \"yhat_lower\": 31.20657876377587, \"yhat\": 54.491384645131085, \"t\": 0.004156610351300617, \"seasonal_upper\": 4.308742711735332, \"trend\": 55.10400207901255, \"trend_lower\": 47.33487532970029, \"ds\": \"1997-03-31\", \"yearly_lower\": -5.828915555287928, \"yearly_upper\": 4.308742711735332, \"trend_upper\": 62.7243335066079}, {\"yearly\": 0.19444368728401107, \"seasonal\": 0.19444368728401107, \"yhat_upper\": 83.71203428498477, \"seasonal_lower\": -4.680025156493293, \"yhat_lower\": 34.330842746335385, \"yhat\": 55.88353843222397, \"t\": 0.008179136497720568, \"seasonal_upper\": 5.554534856704496, \"trend\": 55.68909474493996, \"trend_lower\": 48.1731931249637, \"ds\": \"1997-04-30\", \"yearly_lower\": -4.680025156493295, \"yearly_upper\": 5.554534856704496, \"trend_upper\": 62.92358191570881}, {\"yearly\": 0.33466864045110584, \"seasonal\": 0.33466864045110584, \"yhat_upper\": 80.43288598555812, \"seasonal_lower\": -5.193846051428874, \"yhat_lower\": 31.5009020449946, \"yhat\": 56.6283591401827, \"t\": 0.012335746849021185, \"seasonal_upper\": 6.086238090439742, \"trend\": 56.293690499731596, \"trend_lower\": 48.972642000718075, \"ds\": \"1997-05-31\", \"yearly_lower\": -5.193846051428874, \"yearly_upper\": 6.086238090439742, \"trend_upper\": 63.16145024537774}, {\"yearly\": -2.54475349726568, \"seasonal\": -2.54475349726568, \"yhat_upper\": 78.27981643166859, \"seasonal_lower\": -7.908024651246263, \"yhat_lower\": 29.896419341562048, \"yhat\": 54.33402966839331, \"t\": 0.016358272995441136, \"seasonal_upper\": 2.392392969798489, \"trend\": 56.878783165658994, \"trend_lower\": 49.98377683445788, \"ds\": \"1997-06-30\", \"yearly_lower\": -7.908024651246264, \"yearly_upper\": 2.392392969798489, \"trend_upper\": 63.38742252462858}, {\"yearly\": 0.8467470480702325, \"seasonal\": 0.8467470480702325, \"yhat_upper\": 82.27500364081645, \"seasonal_lower\": -5.176135213285203, \"yhat_lower\": 33.16807527539915, \"yhat\": 58.33012596852088, \"t\": 0.020514883346741754, \"seasonal_upper\": 5.889120597382466, \"trend\": 57.48337892045065, \"trend_lower\": 50.97928983013596, \"ds\": \"1997-07-31\", \"yearly_lower\": -5.176135213285205, \"yearly_upper\": 5.889120597382466, \"trend_upper\": 63.62437092196513}, {\"yearly\": 2.342197572097938, \"seasonal\": 2.342197572097938, \"yhat_upper\": 83.65110231244813, \"seasonal_lower\": -3.6622407766328045, \"yhat_lower\": 36.7159336924032, \"yhat\": 60.43017224734023, \"t\": 0.02467149369804237, \"seasonal_upper\": 8.050613482078079, \"trend\": 58.08797467524229, \"trend_lower\": 51.9073669384131, \"ds\": \"1997-08-31\", \"yearly_lower\": -3.6622407766328062, \"yearly_upper\": 8.050613482078079, \"trend_upper\": 63.61526607599487}, {\"yearly\": 1.7418477741991518, \"seasonal\": 1.7418477741991518, \"yhat_upper\": 83.2192362366427, \"seasonal_lower\": -3.4901776438102963, \"yhat_lower\": 36.95140382216224, \"yhat\": 60.41491511536884, \"t\": 0.02869401984446232, \"seasonal_upper\": 7.816480491998594, \"trend\": 58.67306734116969, \"trend_lower\": 52.80385383888472, \"ds\": \"1997-09-30\", \"yearly_lower\": -3.4901776438103, \"yearly_upper\": 7.816480491998594, \"trend_upper\": 64.09102879268295}, {\"yearly\": 1.3933708878037543, \"seasonal\": 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     },
     "metadata": {
      "jupyter-vega": "#0d08aec5-e62f-44cf-988b-b696d33c561c"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "trend"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Layered Vega-lite chart created using Altair:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "layers = [yhat_uncertainity,trend]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "lchart = alt.LayeredChart(forecast,layers = layers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cols = ['ds','trend']+['yhat','yhat_lower','yhat_upper']\n",
    "def create_unc_chart(fcst,cols=cols,tsname='% of Homes increasing in value',title='Logan Sq'):\n",
    "    ''' \n",
    "    Create Chart showing the trends and uncertainity in forecasts.\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    yhat_uncertainity = area_chart_create(fcst,cols=cols,trend_name=tsname,title=title)\n",
    "    trend = trend_chart_create(fcst,trend_name=tsname)\n",
    "    \n",
    "    layers = [yhat_uncertainity,trend]\n",
    "    \n",
    "    unc_chart = alt.LayeredChart(fcst,layers=layers)\n",
    "    return unc_chart\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "unc_chart = create_unc_chart(forecast,title='60645')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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       "var selector = \"#8ca52655-5c9d-4a71-9715-30217286184c\";\n",
       "var type = \"vega-lite\";\n",
       "\n",
       "var output_area = this;\n",
       "require(['nbextensions/jupyter-vega/index'], function(vega) {\n",
       "  vega.render(selector, spec, type, output_area);\n",
       "}, function (err) {\n",
       "  if (err.requireType !== 'scripterror') {\n",
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"
     },
     "metadata": {
      "jupyter-vega": "#8ca52655-5c9d-4a71-9715-30217286184c"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unc_chart"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## Geopy: \n",
    "\n",
    "- Geopy is great for geocoding and geolocation: \n",
    "- It provides a geocoder wrapper class for the OpenStreetMap Nominatim class.\n",
    "- It is convenient and can be used for getting latitude and longitude information from addresses.\n",
    "- https://github.com/geopy/geopy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from geopy.geocoders import Nominatim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "geolocator = Nominatim()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Location(Chicago, Cook County, Illinois, 60647, United States of America, (41.9173964264724, -87.6993218600089, 0.0))"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Example:\n",
    "location = geolocator.geocode(\"60647\")\n",
    "location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting the geocoder locations from addresses:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from time import sleep\n",
    "\n",
    "def get_lat_lon(location):\n",
    "        \n",
    "        lat = location.latitude\n",
    "        lon = location.longitude\n",
    "        return lat,lon\n",
    "    \n",
    "def get_locations_list(address_list,geolocator,wait_time=np.arange(10,20,5)):\n",
    "    '''\n",
    "    Function returns the geocoded locations of addresses in address_list.\n",
    "    \n",
    "    Input:\n",
    "    address_list : Python list\n",
    "    \n",
    "    Output:\n",
    "    locations: Python list containing geocoded location objects.\n",
    "    '''\n",
    "    \n",
    "    locations = []\n",
    "    for i,addr in enumerate(address_list):\n",
    "#         print(addr)\n",
    "        sleep(5)\n",
    "        loc = geolocator.geocode(addr)\n",
    "        lat = loc.latitude\n",
    "        lon = loc.longitude    \n",
    "        locations.append((addr,lat,lon))\n",
    "#         print(lat,lon)\n",
    "        sleep(1)\n",
    "    return locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zip_list = phiv_chicago_zips_df.columns.tolist()\n",
    "zip_locations= get_locations_list(zip_list,geolocator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('60657, Chicago, IL', 41.9397553561784, -87.6577783611167),\n",
       " ('60614, Chicago, IL', 41.9223100964062, -87.651617455372)]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zip_locations[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "zips_lat_lon = pd.DataFrame(zip_locations)\n",
    "zips_lat_lon.columns=['zip','lat','lon']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>zip</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>60620, Chicago, IL</td>\n",
       "      <td>41.76498</td>\n",
       "      <td>-87.655622</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>60064, North Chicago, IL</td>\n",
       "      <td>42.32626</td>\n",
       "      <td>-87.855495</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         zip       lat        lon\n",
       "12        60620, Chicago, IL  41.76498 -87.655622\n",
       "52  60064, North Chicago, IL  42.32626 -87.855495"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zips_lat_lon.sample(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Folium plots:\n",
    "- Using openstreetmaps(https://www.openstreetmap.org) to create a map with popup forecasts on zip code markers.\n",
    "- Folium makes it easy to plot data on interactive maps. \n",
    "- It provides an interface to the Leaflet.js library.\n",
    "- Open .html file in browser to view the interactive map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import folium\n",
    "from folium import plugins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "params = dict(mcmc_samples=20,interval_width=0.95,weekly_seasonality=False,changepoint_prior_scale=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "map_name='CHI_zips_cluster_phiv_forecast.html'\n",
    "map_osm = folium.Map(location=[41.8755546,-87.6244212],zoom_start=10)\n",
    "marker_cluster = plugins.MarkerCluster().add_to(map_osm)\n",
    "\n",
    "for name,row in zips_lat_lon[:].iterrows():\n",
    "    address = row['zip']\n",
    "    if pd.isnull(address):\n",
    "        continue\n",
    "    data = phiv_chicago_zips_df[address]\n",
    "    m,forecast = prophet_forecast(data,params)\n",
    "    unc_chart = create_unc_chart(forecast,title=address)\n",
    "\n",
    "    unc_chart = unc_chart.to_json()\n",
    "        \n",
    "    popchart = folium.VegaLite(unc_chart)\n",
    "    popup = folium.Popup(max_width=800).add_child(popchart)\n",
    "\n",
    "    lat = row['lat']\n",
    "    lon = row['lon']\n",
    "\n",
    "    folium.Marker(location=(lat,lon),popup=popup).add_to(marker_cluster)\n",
    "map_osm.save(map_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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