{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Quandl\n",
    "More info:\n",
    "https://www.quandl.com/tools/python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import quandl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Make a Basic Data Call\n",
    "This call gets the WTI Crude Oil price from the US Department of Energy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydata = quandl.get(\"EIA/PET_RWTC_D\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1986-01-02</th>\n",
       "      <td>25.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-03</th>\n",
       "      <td>26.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-06</th>\n",
       "      <td>26.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-07</th>\n",
       "      <td>25.85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1986-01-08</th>\n",
       "      <td>25.87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Value\n",
       "Date             \n",
       "1986-01-02  25.56\n",
       "1986-01-03  26.00\n",
       "1986-01-06  26.53\n",
       "1986-01-07  25.85\n",
       "1986-01-08  25.87"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ae067cab70>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kIiKiLGezmP+61wbQ2ZJJVqbTAcChVicsIvFMcirGWp80ssLw+NrdjVE93+3x4ttPrMQr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae06810b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mydata.plot(figsize=(12,6))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that you need to know the \"Quandl code\" of each dataset you download. In the above example, it is \"EIA/PET_RWTC_D\"."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Change Formats\n",
    "You can get the same data in a NumPy array:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mydata = quandl.get(\"EIA/PET_RWTC_D\", returns=\"numpy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Specifying Data\n",
    "\n",
    "To set start and end dates:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mydata = quandl.get(\"FRED/GDP\", start_date=\"2001-12-31\", end_date=\"2005-12-31\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2002-01-01</th>\n",
       "      <td>10834.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-04-01</th>\n",
       "      <td>10934.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-07-01</th>\n",
       "      <td>11037.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002-10-01</th>\n",
       "      <td>11103.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003-01-01</th>\n",
       "      <td>11230.1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Value\n",
       "Date               \n",
       "2002-01-01  10834.4\n",
       "2002-04-01  10934.8\n",
       "2002-07-01  11037.1\n",
       "2002-10-01  11103.8\n",
       "2003-01-01  11230.1"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mydata = quandl.get([\"NSE/OIL.1\", \"WIKI/AAPL.4\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>NSE/OIL - Open</th>\n",
       "      <th>WIKI/AAPL - Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1980-12-12</th>\n",
       "      <td>NaN</td>\n",
       "      <td>28.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-15</th>\n",
       "      <td>NaN</td>\n",
       "      <td>27.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-16</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-17</th>\n",
       "      <td>NaN</td>\n",
       "      <td>25.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1980-12-18</th>\n",
       "      <td>NaN</td>\n",
       "      <td>26.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            NSE/OIL - Open  WIKI/AAPL - Close\n",
       "Date                                         \n",
       "1980-12-12             NaN              28.75\n",
       "1980-12-15             NaN              27.25\n",
       "1980-12-16             NaN              25.25\n",
       "1980-12-17             NaN              25.87\n",
       "1980-12-18             NaN              26.63"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Usage Limits\n",
    "The Quandl Python module is free. If you would like to make more than 50 calls a day, however, you will need to create a free Quandl account and set your API key:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# EXAMPLE\n",
    "quandl.ApiConfig.api_key = \"YOUR_KEY_HERE\"\n",
    "mydata = quandl.get(\"FRED/GDP\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Database Codes\n",
    "\n",
    "Each database on Quandl has a short (3-to-6 character) database ID.  For example:\n",
    "\n",
    "* CFTC Commitment of Traders Data: CFTC\n",
    "* Core US Stock Fundamentals: SF1\n",
    "* Federal Reserve Economic Data: FRED\n",
    "\n",
    "Each database contains many datasets.  Datasets have their own IDs which are appended to their parent database ID, like this:\n",
    "\n",
    "* Commitment of traders for wheat:  CFTC/W_F_ALL\n",
    "* Market capitalization for Apple:  SF1/AAPL_MARKETCAP\n",
    "* US civilian unemployment rate:  FRED/UNRATE\n",
    "\n",
    "You can download all dataset codes in a database in a single API call, by appending  /codes to your database request.  The call will return a ZIP file containing a CSV.\n",
    "\n",
    "### Databases\n",
    "\n",
    "\n",
    "Every Quandl code has 2 parts: the database code (“WIKI”) which specifies where the data comes from, and the dataset code (“FB”) which identifies the specific time series you want.\n",
    "\n",
    "You can find Quandl codes on their website, using their data browser.\n",
    "\n",
    "https://www.quandl.com/search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# FOR STOCKS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mydata = quandl.get('WIKI/FB',start_date='2015-01-01',end_date='2017-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Ex-Dividend</th>\n",
       "      <th>Split Ratio</th>\n",
       "      <th>Adj. Open</th>\n",
       "      <th>Adj. High</th>\n",
       "      <th>Adj. Low</th>\n",
       "      <th>Adj. Close</th>\n",
       "      <th>Adj. Volume</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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>78.58</td>\n",
       "      <td>78.9300</td>\n",
       "      <td>77.700</td>\n",
       "      <td>78.450</td>\n",
       "      <td>18177475.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>78.58</td>\n",
       "      <td>78.9300</td>\n",
       "      <td>77.700</td>\n",
       "      <td>78.450</td>\n",
       "      <td>18177475.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>77.98</td>\n",
       "      <td>79.2455</td>\n",
       "      <td>76.860</td>\n",
       "      <td>77.190</td>\n",
       "      <td>26452191.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>77.98</td>\n",
       "      <td>79.2455</td>\n",
       "      <td>76.860</td>\n",
       "      <td>77.190</td>\n",
       "      <td>26452191.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>77.23</td>\n",
       "      <td>77.5900</td>\n",
       "      <td>75.365</td>\n",
       "      <td>76.150</td>\n",
       "      <td>27399288.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>77.23</td>\n",
       "      <td>77.5900</td>\n",
       "      <td>75.365</td>\n",
       "      <td>76.150</td>\n",
       "      <td>27399288.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>76.76</td>\n",
       "      <td>77.3600</td>\n",
       "      <td>75.820</td>\n",
       "      <td>76.150</td>\n",
       "      <td>22045333.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.76</td>\n",
       "      <td>77.3600</td>\n",
       "      <td>75.820</td>\n",
       "      <td>76.150</td>\n",
       "      <td>22045333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>76.74</td>\n",
       "      <td>78.2300</td>\n",
       "      <td>76.080</td>\n",
       "      <td>78.175</td>\n",
       "      <td>23960953.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>76.74</td>\n",
       "      <td>78.2300</td>\n",
       "      <td>76.080</td>\n",
       "      <td>78.175</td>\n",
       "      <td>23960953.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open     High     Low   Close      Volume  Ex-Dividend  \\\n",
       "Date                                                                  \n",
       "2015-01-02  78.58  78.9300  77.700  78.450  18177475.0          0.0   \n",
       "2015-01-05  77.98  79.2455  76.860  77.190  26452191.0          0.0   \n",
       "2015-01-06  77.23  77.5900  75.365  76.150  27399288.0          0.0   \n",
       "2015-01-07  76.76  77.3600  75.820  76.150  22045333.0          0.0   \n",
       "2015-01-08  76.74  78.2300  76.080  78.175  23960953.0          0.0   \n",
       "\n",
       "            Split Ratio  Adj. Open  Adj. High  Adj. Low  Adj. Close  \\\n",
       "Date                                                                  \n",
       "2015-01-02          1.0      78.58    78.9300    77.700      78.450   \n",
       "2015-01-05          1.0      77.98    79.2455    76.860      77.190   \n",
       "2015-01-06          1.0      77.23    77.5900    75.365      76.150   \n",
       "2015-01-07          1.0      76.76    77.3600    75.820      76.150   \n",
       "2015-01-08          1.0      76.74    78.2300    76.080      78.175   \n",
       "\n",
       "            Adj. Volume  \n",
       "Date                     \n",
       "2015-01-02   18177475.0  \n",
       "2015-01-05   26452191.0  \n",
       "2015-01-06   27399288.0  \n",
       "2015-01-07   22045333.0  \n",
       "2015-01-08   23960953.0  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mydata = quandl.get('WIKI/FB.1',start_date='2015-01-01',end_date='2017-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "    }\n",
       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>78.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>77.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>77.23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>76.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>76.74</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open\n",
       "Date             \n",
       "2015-01-02  78.58\n",
       "2015-01-05  77.98\n",
       "2015-01-06  77.23\n",
       "2015-01-07  76.76\n",
       "2015-01-08  76.74"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "mydata = quandl.get('WIKI/FB.7',start_date='2015-01-01',end_date='2017-01-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Split Ratio</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-01-02</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-05</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-06</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-07</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-01-08</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Split Ratio\n",
       "Date                   \n",
       "2015-01-02          1.0\n",
       "2015-01-05          1.0\n",
       "2015-01-06          1.0\n",
       "2015-01-07          1.0\n",
       "2015-01-08          1.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Housing Price Example\n",
    "\n",
    "**Zillow Home Value Index (Metro): Zillow Rental Index - All Homes - San Francisco, CA**\n",
    "\n",
    "The Zillow Home Value Index is Zillow's estimate of the median market value of zillow rental index - all homes within the metro of San Francisco, CA. This data is calculated by Zillow Real Estate Research (www.zillow.com/research) using their database of 110 million homes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "houses = quandl.get('ZILLOW/M11_ZRIAH')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "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>Value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2010-11-30</th>\n",
       "      <td>2240.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010-12-31</th>\n",
       "      <td>2253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-01-31</th>\n",
       "      <td>2275.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-02-28</th>\n",
       "      <td>2304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-03-31</th>\n",
       "      <td>2333.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Value\n",
       "Date              \n",
       "2010-11-30  2240.0\n",
       "2010-12-31  2253.0\n",
       "2011-01-31  2275.0\n",
       "2011-02-28  2304.0\n",
       "2011-03-31  2333.0"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "houses.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2ae07d12dd8>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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H5FczVmIGd1+gSeJimZKAiBy1gnXbeWV+MePPzKVr2/RIhyPHQUlARI7Khm2V3PanufTq\n0Irbzzns7UISI5QERKTRyiurGT9xDnXuPHfTCNqma5K4WKckICKNsr+mjm/+uYCi7XuZcH0+eVmt\nIx2ShEFj5g4SkQTn7tz78iJmrdnO4187mRF5HSIdkoSJrgREpEGPzVjJS/OK+N75/TRBXJxREhCR\nI3p85kp+/Y9Cvpbfg7vOU0dwvFESEJHDevLtVTw+cxXXDM/hv68aormB4pCSgIgc0lP/WMVjM1Zy\n1Sndeejqk2ihKaLjkjqGReRz3J2n/lHI/8xYyZXDuvPINUP1jIA4piQgIp+pqq7lvpcX8fKnxVw5\nrDuPflUJIN4pCYgIAJvLq/jmnwpYUBR6TvCd5/ZRH0ACUBIQEeZt2ME3/zSXyn01TLh+OBcO6hLp\nkKSZKAmIJLhP1m3nuj/MpmvbNJ7/xmn066xpoROJkoBIAtu4vZJv/mkuOe3SeenbZ9C+dWqkQ5Jm\npiGiIgmqYl8N35hUQE1tHX+4MV8JIEHpSkAkAdXWOXdP/pTC0gom3TSC3tkZkQ5JIkRXAiIJ6OHp\ny5m5bCsPXDaQUX2zIh2ORJCSgEiCeXVBCb9/bw1fH9mTG07PjXQ4EmENJgEzSzOzOWa2wMyWmNlP\ng/IOZjbDzFYFX9vX2+deMys0sxVmdlG98uFmtihY96RpELJIsyreuZf7X1nE8F7teeCyQZEOR6JA\nY64E9gHnuvtQ4GTgYjMbCdwDvO3ufYG3g+8xs4HAWGAQcDHwWzNLCo71NHAr0Dd4XRzGuojIEdTV\nOf8+ZQF1dc6vrj2ZlCQ1BEgjkoCHVATfpgQvB8YAk4LyScAVwfIYYLK773P3tUAhMMLMugKZ7j7L\n3R34Y719RKSJPfvhWj5es43/vGwgPTu2inQ4EiUadSpgZklmNh/YCsxw99lAZ3ffFGyyGegcLHcH\nNtbbvSgo6x4sH1x+qJ93m5kVmFlBaWlpoysjIoe2YvNuHp6+gvNP7My1+T0iHY5EkUYlAXevdfeT\ngRxCZ/WDD1rvhK4OwsLdJ7h7vrvnZ2dnh+uwIglpX00td/91Pplpyfzyaj0TQD7vqBoF3X0n8A6h\ntvwtQRMPwdetwWbFQP1TjZygrDhYPrhcRJrQEzNXsWzTLn551UlkZbSMdDgSZRozOijbzNoFy+nA\nBcBy4FXgxmCzG4GpwfKrwFgza2lmeYQ6gOcETUe7zGxkMCrohnr7iEgTKNxawYT313DN8BzOH9i5\n4R0k4TTmjuGuwKRghE8LYIq7v2ZmHwNTzOwWYD1wLYC7LzGzKcBSoAa4w91rg2PdDkwE0oFpwUtE\nmoC787PXlpKeksQ9lwyIdDgSpRpMAu6+EBh2iPJtwHmH2edB4MFDlBcAg7+4h4iE29vLtvL+ylL+\n4ysD1Qwkh6WBwiJxqKq6lp+9tpQ+nTK44fRekQ5HopiSgEgceuaDtWzYXskDlw3UTWFyRPrrEIkz\nm8r38tQ/CrloUGfO6qsh1nJkSgIiceaX05ZT586Pvzww0qFIDFASEIkji4vLmTq/hNvO7k2PDpoa\nQhqmJCASR558exVt0pK59ezekQ5FYoSSgEicWFqyi7eWbuHmM/PITEuJdDgSI5QEROLEr/+xijYt\nk7n5zLxIhyIxRElAJA4s37yLaYs3c9OZubRtpasAaTwlAZE48Ou3C8lomczNo3QVIEdHSUAkxq3c\nsps3Fm9i/Bm5tGuVGulwJMYoCYjEuCffXkWrlCRu0VWAHAMlAZEYVrh1N68v2sSNZ+TSvrWuAuTo\nKQmIxLAn3i4kPSWJb5yl+wLk2CgJiMSolVt289rCEsafkUsHXQXIMVISEIlRT8xcRevUZG7VVYAc\nByUBkRi0bNMuXl+0iZvOVF+AHB8lAZEY9MTM0N3B3xilqwA5PkoCIjFmSUk5by7ZzM2j8nR3sBw3\nJQGRGPP4zNBMobo7WMJBSUAkhiwqKmfG0i3celZv2qbrKkCOn5KASAz53furyUxL5qYzcyMdisQJ\nJQGRGLF9z37eWrKZq4fn0EbPC5AwURIQiRFT5xdTXet87dQekQ5F4kiDScDMepjZO2a21MyWmNl3\ng/KTzWyWmc03swIzG1Fvn3vNrNDMVpjZRfXKh5vZomDdk2ZmTVMtkfji7vz1k42clNOWAV0yIx2O\nxJHGXAnUAD9w94HASOAOMxsIPAz81N1PBv4z+J5g3VhgEHAx8FszSwqO9TRwK9A3eF0cxrqIxK3F\nxbtYvnk3X83XVYCEV4NJwN03ufu8YHk3sAzoDjhw4JSkLVASLI8BJrv7PndfCxQCI8ysK5Dp7rPc\n3YE/AleEtTYiceqvBRtomdyCy4d2i3QoEmeSj2ZjM8sFhgGzgbuB6Wb2KKFkckawWXdgVr3dioKy\n6mD54HIROYKq6lqmzi/hksFdNCxUwq7RHcNmlgG8BNzt7ruAbwPfc/cewPeAZ8IVlJndFvQzFJSW\nlobrsCIx6c3Fm9ldVcO16hCWJtCoJGBmKYQSwPPu/nJQfCNwYPlvwIGO4WKg/l9rTlBWHCwfXP4F\n7j7B3fPdPT87O7sxIYrErSkFG+nRIZ2ReR0jHYrEocaMDjJCZ/nL3P2xeqtKgC8Fy+cCq4LlV4Gx\nZtbSzPIIdQDPcfdNwC4zGxkc8wZgapjqIRKXNm6v5KPV27h2eA9atNBgOgm/xvQJnAlcDywys/lB\n2X2ERvk8YWbJQBVwG4C7LzGzKcBSQiOL7nD32mC/24GJQDowLXiJyGH8rWAjZnD18JyGNxY5Bg0m\nAXf/ADjcKcjww+zzIPDgIcoLgMFHE6BIoqqurWNKQRFn9c2mW7v0SIcjcUp3DItEqb8vKGHzripu\nOiM30qFIHFMSEIlC7s6E99fQv3MbzumvwRHSdJQERKLQeytLWb55N7ed3RvNriJNSUlAJAr9/r01\ndG2bxmW6Q1iamJKASJRZsHEnH6/Zxs1n5pGarH9RaVr6CxOJMhPeX0ObtGTGjtAdwtL0lAREosi6\nsj1MW7yJr4/spQfHSLNQEhCJIn/4YA3JLVpoWKg0GyUBkSixdVcVfyso4qpTutMpMy3S4UiCUBIQ\niRIPvLoEB771pRMiHYokECUBkSjw5uLNTFu8me+e15fcrNaRDkcSiJKASISV763mP6cuZmDXTG47\nu3ekw5EEc1RPFhOR8PvvN5ZRVrGPZ248lZQknZdJ89JfnEgEfbS6jMmfbOTWs3ozJKdtpMORBKQk\nIBIhe/fXcu/Li+jVsRV3n98v0uFIglJzkEgErN+2hx++uJD12yr5y62nkZ6aFOmQJEEpCYg0o9o6\nZ+JH63hk+nJSWrTgkWtO4owTsiIdliQwJQGRZlK4dTf/76VFzF2/g9H9s/nFVUPo2lZPDJPIUhIQ\naWILNu7kd++t5s0lm8lMS+Gxa4dy5bDuek6ARAUlAZEm8lFhGU+9U8hHq7fRJi2Z2885gZvOzCMr\no2WkQxP5jJKASJhV19bxizeW8dyH6+ic2ZL7Lh3AuBE9NSuoRCUlAZEw2rKrijuen0fB+h3cdGYu\n91wygJbJGvkj0UtJQCRMPl69jTtfmEfl/lqeHDeMy/VoSIkBSgIiYfCnWev5yatLyO3YihduHUnf\nzm0iHZJIozR4x7CZ9TCzd8xsqZktMbPv1lt3p5ktD8ofrld+r5kVmtkKM7uoXvlwM1sUrHvSNDxC\nYpy788j05fzH/y3mnH7ZTP3OKCUAiSmNuRKoAX7g7vPMrA0w18xmAJ2BMcBQd99nZp0AzGwgMBYY\nBHQDZppZP3evBZ4GbgVmA28AFwPTwl0pkeZQXVvHPS8t4qV5RYwb0YP/GjOYZE0AJzGmwSTg7puA\nTcHybjNbBnQn9GH+S3ffF6zbGuwyBpgclK81s0JghJmtAzLdfRaAmf0RuAIlAYlBe/bV8O3n5/H+\nylK+f0E/7jy3j8b9S0w6qtMWM8sFhhE6k+8HnGVms83sPTM7NdisO7Cx3m5FQVn3YPngcpGYUr63\nmuv+MJsPC8t46Ooh3HVeXyUAiVmN7hg2swzgJeBud99lZslAB2AkcCowxczC8kQMM7sNuA2gZ8+e\n4TikSFjs2LOfrz8zm1VbKnj6ulO4cFCXSIckclwadSVgZimEEsDz7v5yUFwEvOwhc4A6IAsoBnrU\n2z0nKCsOlg8u/wJ3n+Du+e6en52dfTT1EWkypbv3Me5/Z1G4tYIJNwxXApC40JjRQQY8Ayxz98fq\nrfo/YHSwTT8gFSgDXgXGmllLM8sD+gJzgr6FXWY2MjjmDcDUsNZGpIls2VXF2Akfs27bHp4dfyrn\n9O8U6ZBEwqIxzUFnAtcDi8xsflB2H/As8KyZLQb2Aze6uwNLzGwKsJTQyKI7gpFBALcDE4F0Qh3C\n6hSWqLdxeyVff2Y2Zbv3MemmEZzWu2OkQxIJGwt9bkev/Px8LygoiHQYkqBWbdnN15+Zzd79tUy8\neQSn9Gwf6ZBEGsXM5rp7fkPb6Y5hkcNYsHEnNz43h5SkFkz51ukM6JIZ6ZBEwk5JQOQQPlpdxq2T\nCuiQkcqfbzmNXh1bRzokkSahJCBykLeWbOY7L3xKbsdW/OmW0+icmRbpkESajJKASD0vzS3iRy8t\nZHD3tkwcfyrtW6dGOiSRJqUkIBJ49oO1/Oy1pZzZpyMTrs+ndUv9e0j801+5JDx35/GZq3ji7VVc\nNKgzT44bpgfBSMJQEpCE9+hbK/jNO6u5ZngOv7xqiGYClYSiJCAJ7Y8fr+M376xm3IgePHjFEFq0\n0ERwklh0yiMJ683Fm3ng1SWcf2In/mvMYCUASUhKApKQCtZt57uTP2VoTjt+Pe4UNQFJwtJfviSc\nwq27uWVSAd3apfPs+FNJT1UnsCQuJQFJKPM27GDc/84mJcmYdNMIOug+AElwSgKSMP5WsJGxv59F\nWkoL/nLrSHp2bBXpkEQiTqODJO7V1Nbx4BvLeO7DdZzZpyNPjTtFdwKLBJQEJG5V19bxQWEZv39v\nNbPWbOemM3O5/9IT1QksUo+SgMSVPftqWFxczt8XlvD6wk3sqKwmMy2Zh685iWvzezR8AJEEoyQg\nUcvdWVO2h8XF5cFrF8s276KFGe1bpdChdSrtW6VS507xzipKdu6lfG81AGkpLTj/xM6MObk7Z/fL\n0jQQIoehJCBRpaa2jjnrtjNj6RZmLN1C0Y69AKQmt+DErplcMrgrSS1gx55qtu3Zx7pte2hhRvd2\n6eT3ak+3dunkdmzF2f2yNQGcSCPov0Siws7K/Tz97mr+WrCRnZXVpCa34Oy+Wdwxug/DerbjhOwM\nUtSWLxJ2SgISUVXVtUz8aB2/faeQ3ftq+PKQrnzlpK6c1Vdn8iLNQf9lEhG1dc4rnxbz2FsrKCmv\nYnT/bP7fJQP0HF+RZqYkIM3K3Xl3ZSkPTVvO8s27OSmnLY9eO5QzTsiKdGgiCUlJQJrNoqJy/nva\nMj5avY2eHVrx63HD+PKQrpq9UySClATiWF2d8/qiTby5eDPb9+xnR+V+tu/ZT+X+Wk7p1Z4LB3bm\ngoGdm/xB6ktLdvH4zJW8tXQLHVqn8pPLBvJvp/UiNVkdvSKRZu4e6RiOKD8/3wsKCiIdRsz5aHUZ\nv5y2nIVF5XRtm0ZO+3TatwqNq09OMj4oLGP9tkoAhvZox9fye3DN8JywfjAv27SLJ2au4s0lm2mT\nlswto/K4ZVQebdJSwvYzROTQzGyuu+c3tF2DVwJm1gP4I9AZcGCCuz9Rb/0PgEeBbHcvC8ruBW4B\naoG73H16UD4cmAikA28A3/Voz0IxZnFxOY9MX8F7K0vp1jaNR786lCuHdSfpoCYXd2fV1gpmLN3C\n6ws3cd8ri/jNO4V859w+XDM855iHY64pDR3zraVbmLt+B21aJnPXeX25ZVQebdP14S8SbRq8EjCz\nrkBXd59nZm2AucAV7r40SBB/AAYAw929zMwGAi8AI4BuwEygn7vXmtkc4C5gNqEk8KS7TzvSz9eV\nQMNqauuYvmQLkz5ax5x128lMS+aO0X248Yxc0lIavlPW3XlvZSm/mrmKBRt3ktM+nVtG5XHugE70\n6tj6kNtvKq9i4/ZKSsr3UrKziqIde/lk3XYKt1YAMKhbJpcM7sL1I3Np20of/iLNLWxXAu6+CdgU\nLO82s2VzkuUaAAANhElEQVRAd2Ap8CvgR8DUeruMASa7+z5grZkVAiPMbB2Q6e6zggD/CFwBHDEJ\nyKHV1TlLN+3iH8u38sKcDWwqryKnfTr3XTqAr+X3PKoPXjPjnP6d+FK/bN5dUcqvZq7kp39fyk//\nvpQeHdI5q282J3Vvy9qyPSwp2cXiknJ2VlZ/7hgdWqdyYtc2fP20npw/sDM57TVNs0gsOKqOYTPL\nBYYBs81sDFDs7gvMPtfU0B2YVe/7oqCsOlg+uPxQP+c24DaAnj17Hk2Icamuzimt2Efxzr0Ubq3g\ng1VlfFhYxrY9+wEY1SeLn40ZzLkDOn2h2edomBmjB3TinP7ZrC3bwz9XlfHPVWVM/bSYv8zeQGpS\nC/p3acPFg7owqFsmuVmt6dYunW5t0/V0LpEY1egkYGYZwEvA3UANcB9wYVME5e4TgAkQag5qip8R\nae7O2rI9rNpawerSClZv3cPasgr2Vtd9bruKfdVsLq+iuvZfv4asjJac3S+bUX2yGNU3K+yje8yM\n3tkZ9M7O4MYzcqmuraNox166t0vXiB6RONOoJGBmKYQSwPPu/rKZDQHygANXATnAPDMbARQD9efs\nzQnKioPlg8sTxoHJ0d5aEpocrXjn3s/Wdc5sSV5WazpmtPzcPq1TM+jWLp2u7dLp3i6Nnh1acUJ2\nBgddfTWplKQW5GV9sW9ARGJfY0YHGfAMsMzdHwNw90VAp3rbrAPyg47hV4G/mNljhDqG+wJzgo7h\nXWY2klDH8A3Ar8NdoWi0v6aO375byHMfrqN8b2hytLP6hCZHG9Qtk97ZrTVsUkQiojFXAmcC1wOL\nzGx+UHafu79xqI3dfYmZTSHUcVwD3OHutcHq2/nXENFpRFmn8P6aOhaXlLNg405Sk1t81t7drV3a\nMX9ILyoq54cvLmD55t1cNKgzVw7rrsnRRCRqJPzNYhu2VfK3uRuZs3Y78zfuZF9N3SG3y8pIZWC3\ntgzulsmgbm0Z3D2Tnh1aHbZZZl9NLU/MXMXv319DVkYqD14xhPMHdm6yeoiI1Be2IaLxanFxOb97\nbzVvLNqEmTGoWybXndaL/Nz2nNKzPY5TsjM0Br54515Wb61gcckuJry/hpq6UOJsk5bMoG6ZDO7W\nln5d2rCtYj+rSytYU1rBqq0V7K6q4avDc/jxVwbqRikRiUoJlwQWFZXz8PTl/HNVGRktk7n1rN7c\nPCrvkCNsurZNZ3ivz5dVVdeyaksFi4rLWVJSzpKSXfxp1vrPriA6tWnJCdkZXD60G5cM7sqovpod\nU0SiV8Ikgbo655kP1vLQm8tp1yqFH13cn+tO63XUZ+hpKUkMyWnLkJy2n5XV1NaxccdesjJS1cEr\nIjElIZLA9j37+cGU+byzopQLB3bm4WtOol2r1LAdP1lDKEUkRsV9Epi9Zht3Tf6UHXuq+enlg7jh\n9F7NOsZeRCSaxXUS+GTddq5/dg457dJ5dvypDOrWtuGdREQSSNwmgcKtu/nGpAJy2qfz0rfOoH3r\n8DX/iIjEi7icCGbLripufPYTUpJaMOmmEUoAIiKHEXdJYHdVNeOf+4SdlfuZeNOp9OigKY1FRA4n\nrpqD9tfU8e0/z2PVlt08O/5UBndXH4CIyJHETRKoq3N+9OICPigs49GvDuXsftmRDklEJOrFTXPQ\nw9NX8H/zS/jhRf25ZnhOwzuIiEh8JIFJH63jd++t5usje3L7OSdEOhwRkZgR80ngzcWb+Mnfl3DB\nwM789PLBuhFMROQoxHQS+LCwjLsmz2dYj3Y8OXbYcT1fV0QkEcVsEphSsJEbn51DbsdWPHPjqXrQ\nuYjIMYi50UF1dc4jb63g6XdXc1bfLJ76t1M0V7+IyDGKqSRQVV3L96fM541Fmxk3oic/GzOIlKSY\nvZgREYm4mEgCdXXO64s28fjMlawp28P9l57IN87KUyewiMhxivokUL63moufeJ+VWyro2ymDZ8ef\nyuj+nSIdlohIXIj6JLBheyU965wnxw3jy0O6agSQiEgYRX0S6NG+FW9970v68BcRaQJR36varlWK\nEoCISBOJ+iQgIiJNp8EkYGY9zOwdM1tqZkvM7LtB+SNmttzMFprZK2bWrt4+95pZoZmtMLOL6pUP\nN7NFwbonTcN7REQiqjFXAjXAD9x9IDASuMPMBgIzgMHufhKwErgXIFg3FhgEXAz81swO3M77NHAr\n0Dd4XRzGuoiIyFFqMAm4+yZ3nxcs7waWAd3d/S13rwk2mwUcmL95DDDZ3fe5+1qgEBhhZl2BTHef\n5e4O/BG4Isz1ERGRo3BUfQJmlgsMA2YftOpmYFqw3B3YWG9dUVDWPVg+uFxERCKk0UnAzDKAl4C7\n3X1XvfL7CTUZPR+uoMzsNjMrMLOC0tLScB1WREQO0qgkYGYphBLA8+7+cr3y8cBXgOuCJh6AYqBH\nvd1zgrJi/tVkVL/8C9x9grvnu3t+drYeEyki0lQaMzrIgGeAZe7+WL3yi4EfAZe7e2W9XV4FxppZ\nSzPLI9QBPMfdNwG7zGxkcMwbgKlhrIuIiBwl+9cJ/GE2MBsF/BNYBNQFxfcBTwItgW1B2Sx3/1aw\nz/2E+glqCDUfTQvK84GJQDqhPoQ7vYEAzKwUWH+ETXoCG45YiS9qC5RH6T7RWp94qsux7hOt9Ymn\nuhzrPtFan0jWpZe7N9yU4u4x/QJKj2GfCVG8T1TWJ57qEm/1iae6xFt9orkuB17xcMfwzmPY5+9R\nvE+01iee6nKs+0RrfeKpLse6T7TWJ5rrAjSiOSjamVmBu+dHOo5wiaf6xFNdIL7qE091gfiqT3PX\nJR6uBCZEOoAwi6f6xFNdIL7qE091gfiqT7PWJeavBERE5NjFw5WAiIgco6hLAkeYtbSDmc0ws1XB\n1/ZBecdg+woze+qgYz1oZhvNrCISdQliCEt9zKyVmb0ezNy6xMx+Gat1Cda9aWYLguP8rt4kgzFZ\nn3rHfNXMFjdnPYKfG8735t1gBuD5wavZn+ca5vqkmtkEM1sZ/P9cHYt1MbM29d6T+WZWZmaPH3eA\nxzqsqKleQFfglGC5DaEZSgcCDwP3BOX3AA8Fy62BUcC3gKcOOtbI4HgVsV4foBUwOlhOJXTvxiWx\nWJdgXWbw1QjdjT42Vt+bese7CvgLsDiW6wK8C+Q3dx2asD4/BX4eLLcAsmK1Lgcddy5w9nHHF8k3\nupG/wKnABcAKoGu9X+qKg7Ybf7hfGBFMAk1Rn2D9E8CtsV4XIIXQ8LavxfJ7A2QAHwT/3M2eBMJc\nl4gngTDXZyPQOtJ1CEdd6q3rF9TLjjeeqGsOqs8+P2tpZw9NPQGwGegcobCOWbjqY6EH+FwGvB3m\nEBstHHUxs+nAVmA38GL4o2y8MNTnv4D/ASob2rCphenvbFLQ5PAfZpF9+NPx1Mf+9bCr/zKzeWb2\nNzOL2GdHGD/TxgJ/9SAjHI+oTQJ2mFlLAYKKx9SwpnDVx8ySgReAJ919TdgDbVwMYamLu19E6Ayo\nJXBuuONsrOOtj5mdDJzg7q80XZSNE6b35jp3HwScFbyuD3ugjRSG+iQTmqzyI3c/BfgYeLQpYm1I\nmD/TxhL6HDhuUZkE7NCzlm6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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2ae07d12860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "houses.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}