Assignment-4.ipynb

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    "---\n",
    "\n",
    "_You are currently looking at **version 1.1** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-data-analysis/resources/0dhYG) course resource._\n",
    "\n",
    "---"
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  {
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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy.stats import ttest_ind"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 4 - Hypothesis Testing\n",
    "This assignment requires more individual learning than previous assignments - you are encouraged to check out the [pandas documentation](http://pandas.pydata.org/pandas-docs/stable/) to find functions or methods you might not have used yet, or ask questions on [Stack Overflow](http://stackoverflow.com/) and tag them as pandas and python related. And of course, the discussion forums are open for interaction with your peers and the course staff.\n",
    "\n",
    "Definitions:\n",
    "* A _quarter_ is a specific three month period, Q1 is January through March, Q2 is April through June, Q3 is July through September, Q4 is October through December.\n",
    "* A _recession_ is defined as starting with two consecutive quarters of GDP decline, and ending with two consecutive quarters of GDP growth.\n",
    "* A _recession bottom_ is the quarter within a recession which had the lowest GDP.\n",
    "* A _university town_ is a city which has a high percentage of university students compared to the total population of the city.\n",
    "\n",
    "**Hypothesis**: University towns have their mean housing prices less effected by recessions. Run a t-test to compare the ratio of the mean price of houses in university towns the quarter before the recession starts compared to the recession bottom. (`price_ratio=quarter_before_recession/recession_bottom`)\n",
    "\n",
    "The following data files are available for this assignment:\n",
    "* From the [Zillow research data site](http://www.zillow.com/research/data/) there is housing data for the United States. In particular the datafile for [all homes at a city level](http://files.zillowstatic.com/research/public/City/City_Zhvi_AllHomes.csv), ```City_Zhvi_AllHomes.csv```, has median home sale prices at a fine grained level.\n",
    "* From the Wikipedia page on college towns is a list of [university towns in the United States](https://en.wikipedia.org/wiki/List_of_college_towns#College_towns_in_the_United_States) which has been copy and pasted into the file ```university_towns.txt```.\n",
    "* From Bureau of Economic Analysis, US Department of Commerce, the [GDP over time](http://www.bea.gov/national/index.htm#gdp) of the United States in current dollars (use the chained value in 2009 dollars), in quarterly intervals, in the file ```gdplev.xls```. For this assignment, only look at GDP data from the first quarter of 2000 onward.\n",
    "\n",
    "Each function in this assignment below is worth 10%, with the exception of ```run_ttest()```, which is worth 50%."
   ]
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    "# Use this dictionary to map state names to two letter acronyms\n",
    "states = {'OH': 'Ohio', 'KY': 'Kentucky', 'AS': 'American Samoa', 'NV': 'Nevada', 'WY': 'Wyoming', 'NA': 'National', 'AL': 'Alabama', 'MD': 'Maryland', 'AK': 'Alaska', 'UT': 'Utah', 'OR': 'Oregon', 'MT': 'Montana', 'IL': 'Illinois', 'TN': 'Tennessee', 'DC': 'District of Columbia', 'VT': 'Vermont', 'ID': 'Idaho', 'AR': 'Arkansas', 'ME': 'Maine', 'WA': 'Washington', 'HI': 'Hawaii', 'WI': 'Wisconsin', 'MI': 'Michigan', 'IN': 'Indiana', 'NJ': 'New Jersey', 'AZ': 'Arizona', 'GU': 'Guam', 'MS': 'Mississippi', 'PR': 'Puerto Rico', 'NC': 'North Carolina', 'TX': 'Texas', 'SD': 'South Dakota', 'MP': 'Northern Mariana Islands', 'IA': 'Iowa', 'MO': 'Missouri', 'CT': 'Connecticut', 'WV': 'West Virginia', 'SC': 'South Carolina', 'LA': 'Louisiana', 'KS': 'Kansas', 'NY': 'New York', 'NE': 'Nebraska', 'OK': 'Oklahoma', 'FL': 'Florida', 'CA': 'California', 'CO': 'Colorado', 'PA': 'Pennsylvania', 'DE': 'Delaware', 'NM': 'New Mexico', 'RI': 'Rhode Island', 'MN': 'Minnesota', 'VI': 'Virgin Islands', 'NH': 'New Hampshire', 'MA': 'Massachusetts', 'GA': 'Georgia', 'ND': 'North Dakota', 'VA': 'Virginia'}"
   ]
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State</th>\n",
       "      <th>RegionName</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Auburn</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Florence</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jacksonville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Livingston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Montevallo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Troy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Tuscaloosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Tuskegee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>Fairbanks</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Flagstaff</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Tempe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>Tucson</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Arkadelphia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Conway</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Fayetteville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Jonesboro</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Magnolia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Monticello</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Russellville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>Searcy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>California</td>\n",
       "      <td>Angwin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>California</td>\n",
       "      <td>Arcata</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>California</td>\n",
       "      <td>Berkeley</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>California</td>\n",
       "      <td>Chico</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>California</td>\n",
       "      <td>Claremont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>California</td>\n",
       "      <td>Cotati</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>California</td>\n",
       "      <td>Davis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>California</td>\n",
       "      <td>Irvine</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>California</td>\n",
       "      <td>Isla Vista</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>California</td>\n",
       "      <td>University Park, Los Angeles</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>487</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Wise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>488</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>Chesapeake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>489</th>\n",
       "      <td>Washington</td>\n",
       "      <td>Bellingham</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>490</th>\n",
       "      <td>Washington</td>\n",
       "      <td>Cheney</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>491</th>\n",
       "      <td>Washington</td>\n",
       "      <td>Ellensburg</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>492</th>\n",
       "      <td>Washington</td>\n",
       "      <td>Pullman</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>493</th>\n",
       "      <td>Washington</td>\n",
       "      <td>University District, Seattle</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Athens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Buckhannon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Fairmont</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Glenville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Huntington</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Montgomery</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>500</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Morgantown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>501</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>Shepherdstown</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>502</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>West Liberty</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>503</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Appleton</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>504</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Eau Claire</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>505</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Green Bay</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>506</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>La Crosse</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>507</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Madison</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>508</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Menomonie</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>509</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Milwaukee</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>510</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Oshkosh</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>511</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Platteville</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>512</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>River Falls</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>513</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Stevens Point</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>514</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Waukesha</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>515</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Whitewater</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>516</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Laramie</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>517 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             State                    RegionName\n",
       "0          Alabama                        Auburn\n",
       "1          Alabama                      Florence\n",
       "2          Alabama                  Jacksonville\n",
       "3          Alabama                    Livingston\n",
       "4          Alabama                    Montevallo\n",
       "5          Alabama                          Troy\n",
       "6          Alabama                    Tuscaloosa\n",
       "7          Alabama                      Tuskegee\n",
       "8           Alaska                     Fairbanks\n",
       "9          Arizona                     Flagstaff\n",
       "10         Arizona                         Tempe\n",
       "11         Arizona                        Tucson\n",
       "12        Arkansas                   Arkadelphia\n",
       "13        Arkansas                        Conway\n",
       "14        Arkansas                  Fayetteville\n",
       "15        Arkansas                     Jonesboro\n",
       "16        Arkansas                      Magnolia\n",
       "17        Arkansas                    Monticello\n",
       "18        Arkansas                  Russellville\n",
       "19        Arkansas                        Searcy\n",
       "20      California                        Angwin\n",
       "21      California                        Arcata\n",
       "22      California                      Berkeley\n",
       "23      California                         Chico\n",
       "24      California                     Claremont\n",
       "25      California                        Cotati\n",
       "26      California                         Davis\n",
       "27      California                        Irvine\n",
       "28      California                    Isla Vista\n",
       "29      California  University Park, Los Angeles\n",
       "..             ...                           ...\n",
       "487       Virginia                          Wise\n",
       "488       Virginia                    Chesapeake\n",
       "489     Washington                    Bellingham\n",
       "490     Washington                        Cheney\n",
       "491     Washington                    Ellensburg\n",
       "492     Washington                       Pullman\n",
       "493     Washington  University District, Seattle\n",
       "494  West Virginia                        Athens\n",
       "495  West Virginia                    Buckhannon\n",
       "496  West Virginia                      Fairmont\n",
       "497  West Virginia                     Glenville\n",
       "498  West Virginia                    Huntington\n",
       "499  West Virginia                    Montgomery\n",
       "500  West Virginia                    Morgantown\n",
       "501  West Virginia                 Shepherdstown\n",
       "502  West Virginia                  West Liberty\n",
       "503      Wisconsin                      Appleton\n",
       "504      Wisconsin                    Eau Claire\n",
       "505      Wisconsin                     Green Bay\n",
       "506      Wisconsin                     La Crosse\n",
       "507      Wisconsin                       Madison\n",
       "508      Wisconsin                     Menomonie\n",
       "509      Wisconsin                     Milwaukee\n",
       "510      Wisconsin                       Oshkosh\n",
       "511      Wisconsin                   Platteville\n",
       "512      Wisconsin                   River Falls\n",
       "513      Wisconsin                 Stevens Point\n",
       "514      Wisconsin                      Waukesha\n",
       "515      Wisconsin                    Whitewater\n",
       "516        Wyoming                       Laramie\n",
       "\n",
       "[517 rows x 2 columns]"
      ]
     },
     "execution_count": 10,
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   "source": [
    "def get_list_of_university_towns():\n",
    "    '''Returns a DataFrame of towns and the states they are in from the \n",
    "    university_towns.txt list. The format of the DataFrame should be:\n",
    "    DataFrame( [ [\"Michigan\", \"Ann Arbor\"], [\"Michigan\", \"Yipsilanti\"] ], \n",
    "    columns=[\"State\", \"RegionName\"]  )\n",
    "    \n",
    "    The following cleaning needs to be done:\n",
    "\n",
    "    1. For \"State\", removing characters from \"[\" to the end.\n",
    "    2. For \"RegionName\", when applicable, removing every character from \" (\" to the end.\n",
    "    3. Depending on how you read the data, you may need to remove newline character '\\n'. '''\n",
    "    \n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "    import re\n",
    "    result = []\n",
    "    state = None\n",
    "    with open('university_towns.txt') as f:\n",
    "        for line in f:\n",
    "            line = line.strip() # remove '\\n'\n",
    "            if line.endswith('[edit]'):\n",
    "                # remember new state\n",
    "                state = line[:-6] # without `[edit]`\n",
    "            else:\n",
    "                city = re.sub(\"[\\(\\[].*\", \"\", line).strip()\n",
    "                result.append( [state, city] )\n",
    "    df = pd.DataFrame(result,columns=[\"State\",\"RegionName\"])\n",
    "    \n",
    "\n",
    "\n",
    "    return df\n",
    "get_list_of_university_towns()"
   ]
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   "source": [
    "def get_recession_start():\n",
    "    '''Returns the year and quarter of the recession start time as a \n",
    "    string value in a format such as 2005q3'''\n",
    "    import numpy as np\n",
    "    \n",
    "    df_gdp=pd.read_excel('gdplev.xls',skiprows=5,Header=0)\n",
    "    df_gdp=df_gdp.loc[df_gdp['Unnamed: 4'] >= '2000']\n",
    "    df_gdp=df_gdp[['Unnamed: 4','GDP in billions of current dollars.1','GDP in billions of chained 2009 dollars.1']]\n",
    "    df_gdp.rename(columns={'Unnamed: 4': 'Quarters', 'GDP in billions of current dollars.1': 'GDP in billions','GDP in billions of chained 2009 dollars.1':'Chained GDP in billions'}, inplace=True)\n",
    "    df_gdp['sign']=np.sign(df_gdp['Chained GDP in billions'].diff().fillna(0))\n",
    "    start=[]\n",
    "    for i in range(len(df_gdp['sign'])-1):\n",
    "        if (df_gdp['sign'].iloc[i] == -1.0) & (df_gdp['sign'].iloc[i+1] == -1.0):\n",
    "            start.append(df_gdp['Quarters'].iloc[i])\n",
    "    recession_start=start[0]\n",
    "\n",
    "    return str(recession_start)"
   ]
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    "def get_recession_end():\n",
    "    '''Returns the year and quarter of the recession end time as a \n",
    "    string value in a format such as 2005q3'''\n",
    "    import numpy as np\n",
    "    \n",
    "    df_gdp=pd.read_excel('gdplev.xls',skiprows=5,Header=0)\n",
    "    df_gdp=df_gdp.loc[df_gdp['Unnamed: 4'] >= '2000']\n",
    "    df_gdp=df_gdp[['Unnamed: 4','GDP in billions of current dollars.1','GDP in billions of chained 2009 dollars.1']]\n",
    "    df_gdp.rename(columns={'Unnamed: 4': 'Quarters', 'GDP in billions of current dollars.1': 'GDP in billions','GDP in billions of chained 2009 dollars.1':'Chained GDP in billions'}, inplace=True)\n",
    "    df_gdp['sign']=np.sign(df_gdp['Chained GDP in billions'].diff().fillna(0))\n",
    "    start=[]\n",
    "    for i in range(len(df_gdp['sign'])-1):\n",
    "        if (df_gdp['sign'].iloc[i] == -1.0) & (df_gdp['sign'].iloc[i+1] == -1.0):\n",
    "            start.append(df_gdp['Quarters'].iloc[i])\n",
    "    recession_start=start[0]\n",
    "    \n",
    "    end=[]\n",
    "    period1=df_gdp.where(df_gdp['Quarters'] > recession_start).dropna()\n",
    "    for i in range(len(period1['sign'])-1):\n",
    "        if (period1['sign'].iloc[i] == 1.0) & (period1['sign'].iloc[i+1] == 1.0):\n",
    "            end.append(period1['Quarters'].iloc[i])\n",
    "    recession_end=end[1]\n",
    "       \n",
    "    return str(recession_end)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "umich_part_id": "024",
    "umich_partlist_id": "004"
   },
   "outputs": [],
   "source": [
    "def get_recession_bottom():\n",
    "    '''Returns the year and quarter of the recession bottom time as a \n",
    "    string value in a format such as 2005q3'''\n",
    "    import numpy as np\n",
    "    \n",
    "    df_gdp=pd.read_excel('gdplev.xls',skiprows=5,Header=0)\n",
    "    df_gdp=df_gdp.loc[df_gdp['Unnamed: 4'] >= '2000']\n",
    "    df_gdp=df_gdp[['Unnamed: 4','GDP in billions of current dollars.1','GDP in billions of chained 2009 dollars.1']]\n",
    "    df_gdp.rename(columns={'Unnamed: 4': 'Quarters', 'GDP in billions of current dollars.1': 'GDP in billions','GDP in billions of chained 2009 dollars.1':'Chained GDP in billions'}, inplace=True)\n",
    "    df_gdp['sign']=np.sign(df_gdp['Chained GDP in billions'].diff().fillna(0))\n",
    "    start=[]\n",
    "    for i in range(len(df_gdp['sign'])-1):\n",
    "        if (df_gdp['sign'].iloc[i] == -1.0) & (df_gdp['sign'].iloc[i+1] == -1.0):\n",
    "            start.append(df_gdp['Quarters'].iloc[i])\n",
    "    recession_start=start[0]\n",
    "    \n",
    "    end=[]\n",
    "    period1=df_gdp.where(df_gdp['Quarters'] > recession_start).dropna()\n",
    "    for i in range(len(period1['sign'])-1):\n",
    "        if (period1['sign'].iloc[i] == 1.0) & (period1['sign'].iloc[i+1] == 1.0):\n",
    "            end.append(period1['Quarters'].iloc[i])\n",
    "    recession_end=end[0]\n",
    "    recession_period=df_gdp.where((df_gdp['Quarters'] >= recession_start) & (df_gdp['Quarters'] <= recession_end)).dropna()\n",
    "    min_gdp=recession_period['Chained GDP in billions'].min()\n",
    "    bottom=recession_period.loc[recession_period['Chained GDP in billions']==min_gdp,'Quarters'].values[0]\n",
    "    \n",
    "    return str(bottom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true,
    "umich_part_id": "025",
    "umich_partlist_id": "004"
   },
   "outputs": [],
   "source": [
    "def convert_housing_data_to_quarters():\n",
    "    '''Converts the housing data to quarters and returns it as mean \n",
    "    values in a dataframe. This dataframe should be a dataframe with\n",
    "    columns for 2000q1 through 2016q3, and should have a multi-index\n",
    "    in the shape of [\"State\",\"RegionName\"].\n",
    "    \n",
    "    Note: Quarters are defined in the assignment description, they are\n",
    "    not arbitrary three month periods.\n",
    "    \n",
    "    The resulting dataframe should have 67 columns, and 10,730 rows.\n",
    "    '''\n",
    "    states = {'OH': 'Ohio', 'KY': 'Kentucky', 'AS': 'American Samoa', 'NV': 'Nevada', 'WY': 'Wyoming', 'NA': 'National', 'AL': 'Alabama', 'MD': 'Maryland', 'AK': 'Alaska', 'UT': 'Utah', 'OR': 'Oregon', 'MT': 'Montana', 'IL': 'Illinois', 'TN': 'Tennessee', 'DC': 'District of Columbia', 'VT': 'Vermont', 'ID': 'Idaho', 'AR': 'Arkansas', 'ME': 'Maine', 'WA': 'Washington', 'HI': 'Hawaii', 'WI': 'Wisconsin', 'MI': 'Michigan', 'IN': 'Indiana', 'NJ': 'New Jersey', 'AZ': 'Arizona', 'GU': 'Guam', 'MS': 'Mississippi', 'PR': 'Puerto Rico', 'NC': 'North Carolina', 'TX': 'Texas', 'SD': 'South Dakota', 'MP': 'Northern Mariana Islands', 'IA': 'Iowa', 'MO': 'Missouri', 'CT': 'Connecticut', 'WV': 'West Virginia', 'SC': 'South Carolina', 'LA': 'Louisiana', 'KS': 'Kansas', 'NY': 'New York', 'NE': 'Nebraska', 'OK': 'Oklahoma', 'FL': 'Florida', 'CA': 'California', 'CO': 'Colorado', 'PA': 'Pennsylvania', 'DE': 'Delaware', 'NM': 'New Mexico', 'RI': 'Rhode Island', 'MN': 'Minnesota', 'VI': 'Virgin Islands', 'NH': 'New Hampshire', 'MA': 'Massachusetts', 'GA': 'Georgia', 'ND': 'North Dakota', 'VA': 'Virginia'}\n",
    "    acronym=pd.DataFrame.from_dict(states,orient='index')\n",
    "    acronym.reset_index(inplace=True)\n",
    "    acronym.rename(columns={'index': 'Stateabr', 0: 'State'}, inplace=True)\n",
    "    housing_df=pd.read_csv('City_Zhvi_AllHomes.csv')\n",
    "    housing_df=pd.merge(housing_df,acronym, how='inner',  left_on=['State'],right_on=['Stateabr'])\n",
    "    housing_df.rename(columns={'State_y':'State'},inplace=True)\n",
    "    housing_df.set_index(['State', 'RegionName'], inplace=True)\n",
    "    housing_df['2000q1']=housing_df[['2000-01','2000-02','2000-03']].mean(axis=1)\n",
    "    housing_df['2000q2']=housing_df[['2000-04','2000-05','2000-06']].mean(axis=1)\n",
    "    housing_df['2000q3']=housing_df[['2000-07','2000-08','2000-09']].mean(axis=1)\n",
    "    housing_df['2000q4']=housing_df[['2000-10','2000-11','2000-12']].mean(axis=1)\n",
    "    housing_df['2001q1']=housing_df[['2001-01','2001-02','2001-03']].mean(axis=1)\n",
    "    housing_df['2001q2']=housing_df[['2001-04','2001-05','2001-06']].mean(axis=1)\n",
    "    housing_df['2001q3']=housing_df[['2001-07','2001-08','2001-09']].mean(axis=1)\n",
    "    housing_df['2001q4']=housing_df[['2001-10','2001-11','2001-12']].mean(axis=1)\n",
    "    housing_df['2002q1']=housing_df[['2002-01','2002-02','2002-03']].mean(axis=1)\n",
    "    housing_df['2002q2']=housing_df[['2002-04','2002-05','2002-06']].mean(axis=1)\n",
    "    housing_df['2002q3']=housing_df[['2002-07','2002-08','2002-09']].mean(axis=1)\n",
    "    housing_df['2002q4']=housing_df[['2002-10','2002-11','2002-12']].mean(axis=1)\n",
    "    housing_df['2003q1']=housing_df[['2003-01','2003-02','2003-03']].mean(axis=1)\n",
    "    housing_df['2003q2']=housing_df[['2003-04','2003-05','2003-06']].mean(axis=1)\n",
    "    housing_df['2003q3']=housing_df[['2003-07','2003-08','2003-09']].mean(axis=1)\n",
    "    housing_df['2003q4']=housing_df[['2003-10','2003-11','2003-12']].mean(axis=1)\n",
    "    housing_df['2004q1']=housing_df[['2004-01','2004-02','2004-03']].mean(axis=1)\n",
    "    housing_df['2004q2']=housing_df[['2004-04','2004-05','2004-06']].mean(axis=1)\n",
    "    housing_df['2004q3']=housing_df[['2004-07','2004-08','2004-09']].mean(axis=1)\n",
    "    housing_df['2004q4']=housing_df[['2004-10','2004-11','2004-12']].mean(axis=1)\n",
    "    housing_df['2005q1']=housing_df[['2005-01','2005-02','2005-03']].mean(axis=1)\n",
    "    housing_df['2005q2']=housing_df[['2005-04','2005-05','2005-06']].mean(axis=1)\n",
    "    housing_df['2005q3']=housing_df[['2005-07','2005-08','2005-09']].mean(axis=1)\n",
    "    housing_df['2005q4']=housing_df[['2005-10','2005-11','2005-12']].mean(axis=1)\n",
    "    housing_df['2006q1']=housing_df[['2006-01','2006-02','2006-03']].mean(axis=1)\n",
    "    housing_df['2006q2']=housing_df[['2006-04','2006-05','2006-06']].mean(axis=1)\n",
    "    housing_df['2006q3']=housing_df[['2006-07','2006-08','2006-09']].mean(axis=1)\n",
    "    housing_df['2006q4']=housing_df[['2006-10','2006-11','2006-12']].mean(axis=1)\n",
    "    housing_df['2007q1']=housing_df[['2007-01','2007-02','2007-03']].mean(axis=1)\n",
    "    housing_df['2007q2']=housing_df[['2007-04','2007-05','2007-06']].mean(axis=1)\n",
    "    housing_df['2007q3']=housing_df[['2007-07','2007-08','2007-09']].mean(axis=1)\n",
    "    housing_df['2007q4']=housing_df[['2007-10','2007-11','2007-12']].mean(axis=1)\n",
    "    housing_df['2008q1']=housing_df[['2008-01','2008-02','2008-03']].mean(axis=1)\n",
    "    housing_df['2008q2']=housing_df[['2008-04','2008-05','2008-06']].mean(axis=1)\n",
    "    housing_df['2008q3']=housing_df[['2008-07','2008-08','2008-09']].mean(axis=1)\n",
    "    housing_df['2008q4']=housing_df[['2008-10','2008-11','2008-12']].mean(axis=1)\n",
    "    housing_df['2009q1']=housing_df[['2009-01','2009-02','2009-03']].mean(axis=1)\n",
    "    housing_df['2009q2']=housing_df[['2009-04','2009-05','2009-06']].mean(axis=1)\n",
    "    housing_df['2009q3']=housing_df[['2009-07','2009-08','2009-09']].mean(axis=1)\n",
    "    housing_df['2009q4']=housing_df[['2009-10','2009-11','2009-12']].mean(axis=1)\n",
    "    housing_df['2010q1']=housing_df[['2010-01','2010-02','2010-03']].mean(axis=1)\n",
    "    housing_df['2010q2']=housing_df[['2010-04','2010-05','2010-06']].mean(axis=1)\n",
    "    housing_df['2010q3']=housing_df[['2010-07','2010-08','2010-09']].mean(axis=1)\n",
    "    housing_df['2010q4']=housing_df[['2010-10','2010-11','2010-12']].mean(axis=1)\n",
    "    housing_df['2011q1']=housing_df[['2011-01','2011-02','2011-03']].mean(axis=1)\n",
    "    housing_df['2011q2']=housing_df[['2011-04','2011-05','2011-06']].mean(axis=1)\n",
    "    housing_df['2011q3']=housing_df[['2011-07','2011-08','2011-09']].mean(axis=1)\n",
    "    housing_df['2011q4']=housing_df[['2011-10','2011-11','2011-12']].mean(axis=1)\n",
    "    housing_df['2012q1']=housing_df[['2012-01','2012-02','2012-03']].mean(axis=1)\n",
    "    housing_df['2012q2']=housing_df[['2012-04','2012-05','2012-06']].mean(axis=1)\n",
    "    housing_df['2012q3']=housing_df[['2012-07','2012-08','2012-09']].mean(axis=1)\n",
    "    housing_df['2012q4']=housing_df[['2012-10','2012-11','2012-12']].mean(axis=1)\n",
    "    housing_df['2013q1']=housing_df[['2013-01','2013-02','2013-03']].mean(axis=1)\n",
    "    housing_df['2013q2']=housing_df[['2013-04','2013-05','2013-06']].mean(axis=1)\n",
    "    housing_df['2013q3']=housing_df[['2013-07','2013-08','2013-09']].mean(axis=1)\n",
    "    housing_df['2013q4']=housing_df[['2013-10','2013-11','2013-12']].mean(axis=1)\n",
    "    housing_df['2014q1']=housing_df[['2014-01','2014-02','2014-03']].mean(axis=1)\n",
    "    housing_df['2014q2']=housing_df[['2014-04','2014-05','2014-06']].mean(axis=1)\n",
    "    housing_df['2014q3']=housing_df[['2014-07','2014-08','2014-09']].mean(axis=1)\n",
    "    housing_df['2014q4']=housing_df[['2014-10','2014-11','2014-12']].mean(axis=1)\n",
    "    housing_df['2015q1']=housing_df[['2015-01','2015-02','2015-03']].mean(axis=1)\n",
    "    housing_df['2015q2']=housing_df[['2015-04','2015-05','2015-06']].mean(axis=1)\n",
    "    housing_df['2015q3']=housing_df[['2015-07','2015-08','2015-09']].mean(axis=1)\n",
    "    housing_df['2015q4']=housing_df[['2015-10','2015-11','2015-12']].mean(axis=1)\n",
    "    housing_df['2016q1']=housing_df[['2016-01','2016-02','2016-03']].mean(axis=1)\n",
    "    housing_df['2016q2']=housing_df[['2016-04','2016-05','2016-06']].mean(axis=1)\n",
    "    housing_df['2016q3']=housing_df[['2016-07','2016-08']].mean(axis=1)\n",
    "    \n",
    "    \n",
    "    housing_df=housing_df[['2000q1','2000q2','2000q3','2000q4','2001q1','2001q2','2001q3','2001q4','2002q1','2002q2','2002q3','2002q4','2003q1','2003q2','2003q3','2003q4','2004q1','2004q2','2004q3','2004q4','2005q1','2005q2','2005q3','2005q4','2006q1','2006q2','2006q3','2006q4','2007q1','2007q2','2007q3','2007q4','2008q1','2008q2','2008q3','2008q4','2009q1','2009q2','2009q3','2009q4','2010q1','2010q2','2010q3','2010q4','2011q1','2011q2','2011q3','2011q4','2012q1','2012q2','2012q3','2012q4','2013q1','2013q2','2013q3','2013q4','2014q1','2014q2','2014q3','2014q4','2015q1','2015q2','2015q3','2015q4','2016q1','2016q2','2016q3']]\n",
    "    return housing_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>2000q1</th>\n",
       "      <th>2000q2</th>\n",
       "      <th>2000q3</th>\n",
       "      <th>2000q4</th>\n",
       "      <th>2001q1</th>\n",
       "      <th>2001q2</th>\n",
       "      <th>2001q3</th>\n",
       "      <th>2001q4</th>\n",
       "      <th>2002q1</th>\n",
       "      <th>2002q2</th>\n",
       "      <th>...</th>\n",
       "      <th>2014q2</th>\n",
       "      <th>2014q3</th>\n",
       "      <th>2014q4</th>\n",
       "      <th>2015q1</th>\n",
       "      <th>2015q2</th>\n",
       "      <th>2015q3</th>\n",
       "      <th>2015q4</th>\n",
       "      <th>2016q1</th>\n",
       "      <th>2016q2</th>\n",
       "      <th>2016q3</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>State</th>\n",
       "      <th>RegionName</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <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 rowspan=\"30\" valign=\"top\">New York</th>\n",
       "      <th>New York</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>515466.666667</td>\n",
       "      <td>522800.000000</td>\n",
       "      <td>528066.666667</td>\n",
       "      <td>532266.666667</td>\n",
       "      <td>540800.000000</td>\n",
       "      <td>557200.000000</td>\n",
       "      <td>572833.333333</td>\n",
       "      <td>582866.666667</td>\n",
       "      <td>591633.333333</td>\n",
       "      <td>587200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rochester</th>\n",
       "      <td>53566.666667</td>\n",
       "      <td>53233.333333</td>\n",
       "      <td>53200.000000</td>\n",
       "      <td>53200.000000</td>\n",
       "      <td>53066.666667</td>\n",
       "      <td>52533.333333</td>\n",
       "      <td>52666.666667</td>\n",
       "      <td>53200.000000</td>\n",
       "      <td>53366.666667</td>\n",
       "      <td>53566.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>60100.000000</td>\n",
       "      <td>61266.666667</td>\n",
       "      <td>62800.000000</td>\n",
       "      <td>62900.000000</td>\n",
       "      <td>63966.666667</td>\n",
       "      <td>64800.000000</td>\n",
       "      <td>65333.333333</td>\n",
       "      <td>66833.333333</td>\n",
       "      <td>67000.000000</td>\n",
       "      <td>66900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Yonkers</th>\n",
       "      <td>222233.333333</td>\n",
       "      <td>230900.000000</td>\n",
       "      <td>245533.333333</td>\n",
       "      <td>254400.000000</td>\n",
       "      <td>259266.666667</td>\n",
       "      <td>263700.000000</td>\n",
       "      <td>271300.000000</td>\n",
       "      <td>282066.666667</td>\n",
       "      <td>292833.333333</td>\n",
       "      <td>303533.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>382033.333333</td>\n",
       "      <td>385766.666667</td>\n",
       "      <td>385333.333333</td>\n",
       "      <td>385866.666667</td>\n",
       "      <td>388800.000000</td>\n",
       "      <td>401233.333333</td>\n",
       "      <td>404433.333333</td>\n",
       "      <td>407066.666667</td>\n",
       "      <td>409800.000000</td>\n",
       "      <td>406550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Syracuse</th>\n",
       "      <td>56800.000000</td>\n",
       "      <td>57866.666667</td>\n",
       "      <td>57400.000000</td>\n",
       "      <td>57233.333333</td>\n",
       "      <td>57100.000000</td>\n",
       "      <td>56966.666667</td>\n",
       "      <td>57433.333333</td>\n",
       "      <td>58000.000000</td>\n",
       "      <td>59033.333333</td>\n",
       "      <td>60200.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>76533.333333</td>\n",
       "      <td>75733.333333</td>\n",
       "      <td>75133.333333</td>\n",
       "      <td>76966.666667</td>\n",
       "      <td>78533.333333</td>\n",
       "      <td>79200.000000</td>\n",
       "      <td>80400.000000</td>\n",
       "      <td>80566.666667</td>\n",
       "      <td>78733.333333</td>\n",
       "      <td>78350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town of Greece</th>\n",
       "      <td>95100.000000</td>\n",
       "      <td>95500.000000</td>\n",
       "      <td>96333.333333</td>\n",
       "      <td>96933.333333</td>\n",
       "      <td>98266.666667</td>\n",
       "      <td>99300.000000</td>\n",
       "      <td>100000.000000</td>\n",
       "      <td>101200.000000</td>\n",
       "      <td>102766.666667</td>\n",
       "      <td>103400.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>120000.000000</td>\n",
       "      <td>121133.333333</td>\n",
       "      <td>122433.333333</td>\n",
       "      <td>121633.333333</td>\n",
       "      <td>122800.000000</td>\n",
       "      <td>123966.666667</td>\n",
       "      <td>124466.666667</td>\n",
       "      <td>125933.333333</td>\n",
       "      <td>126633.333333</td>\n",
       "      <td>126050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Albany</th>\n",
       "      <td>91566.666667</td>\n",
       "      <td>92100.000000</td>\n",
       "      <td>91233.333333</td>\n",
       "      <td>91566.666667</td>\n",
       "      <td>92633.333333</td>\n",
       "      <td>94733.333333</td>\n",
       "      <td>97266.666667</td>\n",
       "      <td>99000.000000</td>\n",
       "      <td>99433.333333</td>\n",
       "      <td>101233.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>164266.666667</td>\n",
       "      <td>162100.000000</td>\n",
       "      <td>159700.000000</td>\n",
       "      <td>159433.333333</td>\n",
       "      <td>159066.666667</td>\n",
       "      <td>159500.000000</td>\n",
       "      <td>159533.333333</td>\n",
       "      <td>159166.666667</td>\n",
       "      <td>158800.000000</td>\n",
       "      <td>159000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town of Poughkeepsie</th>\n",
       "      <td>131966.666667</td>\n",
       "      <td>135433.333333</td>\n",
       "      <td>139566.666667</td>\n",
       "      <td>144366.666667</td>\n",
       "      <td>149366.666667</td>\n",
       "      <td>153833.333333</td>\n",
       "      <td>156966.666667</td>\n",
       "      <td>164200.000000</td>\n",
       "      <td>172800.000000</td>\n",
       "      <td>176933.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>191300.000000</td>\n",
       "      <td>191700.000000</td>\n",
       "      <td>193566.666667</td>\n",
       "      <td>194233.333333</td>\n",
       "      <td>194433.333333</td>\n",
       "      <td>194400.000000</td>\n",
       "      <td>193700.000000</td>\n",
       "      <td>193666.666667</td>\n",
       "      <td>196666.666667</td>\n",
       "      <td>199050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>New Rochelle</th>\n",
       "      <td>362766.666667</td>\n",
       "      <td>370866.666667</td>\n",
       "      <td>382233.333333</td>\n",
       "      <td>400066.666667</td>\n",
       "      <td>419166.666667</td>\n",
       "      <td>428066.666667</td>\n",
       "      <td>436633.333333</td>\n",
       "      <td>440400.000000</td>\n",
       "      <td>443966.666667</td>\n",
       "      <td>452133.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>577200.000000</td>\n",
       "      <td>596600.000000</td>\n",
       "      <td>610466.666667</td>\n",
       "      <td>604600.000000</td>\n",
       "      <td>601766.666667</td>\n",
       "      <td>611166.666667</td>\n",
       "      <td>614433.333333</td>\n",
       "      <td>627433.333333</td>\n",
       "      <td>632100.000000</td>\n",
       "      <td>611100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town of Colonie</th>\n",
       "      <td>110600.000000</td>\n",
       "      <td>112400.000000</td>\n",
       "      <td>113166.666667</td>\n",
       "      <td>114100.000000</td>\n",
       "      <td>115833.333333</td>\n",
       "      <td>118600.000000</td>\n",
       "      <td>120666.666667</td>\n",
       "      <td>122666.666667</td>\n",
       "      <td>124433.333333</td>\n",
       "      <td>126233.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>200966.666667</td>\n",
       "      <td>199533.333333</td>\n",
       "      <td>201100.000000</td>\n",
       "      <td>203200.000000</td>\n",
       "      <td>203933.333333</td>\n",
       "      <td>205766.666667</td>\n",
       "      <td>207400.000000</td>\n",
       "      <td>206400.000000</td>\n",
       "      <td>208000.000000</td>\n",
       "      <td>209650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mount Vernon</th>\n",
       "      <td>239333.333333</td>\n",
       "      <td>247300.000000</td>\n",
       "      <td>258633.333333</td>\n",
       "      <td>271700.000000</td>\n",
       "      <td>278833.333333</td>\n",
       "      <td>283466.666667</td>\n",
       "      <td>296700.000000</td>\n",
       "      <td>307133.333333</td>\n",
       "      <td>321633.333333</td>\n",
       "      <td>329700.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>347933.333333</td>\n",
       "      <td>354600.000000</td>\n",
       "      <td>364133.333333</td>\n",
       "      <td>377033.333333</td>\n",
       "      <td>390766.666667</td>\n",
       "      <td>399500.000000</td>\n",
       "      <td>395033.333333</td>\n",
       "      <td>395600.000000</td>\n",
       "      <td>393966.666667</td>\n",
       "      <td>379050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Utica</th>\n",
       "      <td>50700.000000</td>\n",
       "      <td>51233.333333</td>\n",
       "      <td>52600.000000</td>\n",
       "      <td>53000.000000</td>\n",
       "      <td>53300.000000</td>\n",
       "      <td>53933.333333</td>\n",
       "      <td>53833.333333</td>\n",
       "      <td>54200.000000</td>\n",
       "      <td>54266.666667</td>\n",
       "      <td>55166.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>78466.666667</td>\n",
       "      <td>78133.333333</td>\n",
       "      <td>78566.666667</td>\n",
       "      <td>79366.666667</td>\n",
       "      <td>78833.333333</td>\n",
       "      <td>78500.000000</td>\n",
       "      <td>79033.333333</td>\n",
       "      <td>81166.666667</td>\n",
       "      <td>82900.000000</td>\n",
       "      <td>82800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Valley Stream</th>\n",
       "      <td>223166.666667</td>\n",
       "      <td>225166.666667</td>\n",
       "      <td>229133.333333</td>\n",
       "      <td>237200.000000</td>\n",
       "      <td>243466.666667</td>\n",
       "      <td>250333.333333</td>\n",
       "      <td>256700.000000</td>\n",
       "      <td>264900.000000</td>\n",
       "      <td>272033.333333</td>\n",
       "      <td>279566.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>360666.666667</td>\n",
       "      <td>364600.000000</td>\n",
       "      <td>365300.000000</td>\n",
       "      <td>371066.666667</td>\n",
       "      <td>375466.666667</td>\n",
       "      <td>380733.333333</td>\n",
       "      <td>389033.333333</td>\n",
       "      <td>390200.000000</td>\n",
       "      <td>397533.333333</td>\n",
       "      <td>402550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Schenectady</th>\n",
       "      <td>72266.666667</td>\n",
       "      <td>73100.000000</td>\n",
       "      <td>73733.333333</td>\n",
       "      <td>73400.000000</td>\n",
       "      <td>75066.666667</td>\n",
       "      <td>76666.666667</td>\n",
       "      <td>77000.000000</td>\n",
       "      <td>78333.333333</td>\n",
       "      <td>80933.333333</td>\n",
       "      <td>83866.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>122733.333333</td>\n",
       "      <td>121233.333333</td>\n",
       "      <td>122266.666667</td>\n",
       "      <td>121966.666667</td>\n",
       "      <td>121333.333333</td>\n",
       "      <td>122566.666667</td>\n",
       "      <td>122100.000000</td>\n",
       "      <td>123000.000000</td>\n",
       "      <td>123966.666667</td>\n",
       "      <td>125100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White Plains</th>\n",
       "      <td>284333.333333</td>\n",
       "      <td>297833.333333</td>\n",
       "      <td>314366.666667</td>\n",
       "      <td>326633.333333</td>\n",
       "      <td>331266.666667</td>\n",
       "      <td>335600.000000</td>\n",
       "      <td>349566.666667</td>\n",
       "      <td>364366.666667</td>\n",
       "      <td>384966.666667</td>\n",
       "      <td>400700.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>500100.000000</td>\n",
       "      <td>510566.666667</td>\n",
       "      <td>515400.000000</td>\n",
       "      <td>518300.000000</td>\n",
       "      <td>526300.000000</td>\n",
       "      <td>534800.000000</td>\n",
       "      <td>534566.666667</td>\n",
       "      <td>537866.666667</td>\n",
       "      <td>539366.666667</td>\n",
       "      <td>533450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town of Hempstead</th>\n",
       "      <td>170166.666667</td>\n",
       "      <td>172266.666667</td>\n",
       "      <td>176800.000000</td>\n",
       "      <td>182333.333333</td>\n",
       "      <td>185000.000000</td>\n",
       "      <td>189400.000000</td>\n",
       "      <td>194433.333333</td>\n",
       "      <td>205200.000000</td>\n",
       "      <td>213333.333333</td>\n",
       "      <td>221200.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>265000.000000</td>\n",
       "      <td>265800.000000</td>\n",
       "      <td>265100.000000</td>\n",
       "      <td>269500.000000</td>\n",
       "      <td>271133.333333</td>\n",
       "      <td>275600.000000</td>\n",
       "      <td>282866.666667</td>\n",
       "      <td>286133.333333</td>\n",
       "      <td>294533.333333</td>\n",
       "      <td>299150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town of Binghamton</th>\n",
       "      <td>81633.333333</td>\n",
       "      <td>82500.000000</td>\n",
       "      <td>81700.000000</td>\n",
       "      <td>82466.666667</td>\n",
       "      <td>84266.666667</td>\n",
       "      <td>85300.000000</td>\n",
       "      <td>86333.333333</td>\n",
       "      <td>87766.666667</td>\n",
       "      <td>87500.000000</td>\n",
       "      <td>88666.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>123333.333333</td>\n",
       "      <td>121766.666667</td>\n",
       "      <td>118466.666667</td>\n",
       "      <td>118533.333333</td>\n",
       "      <td>119333.333333</td>\n",
       "      <td>119766.666667</td>\n",
       "      <td>119666.666667</td>\n",
       "      <td>115100.000000</td>\n",
       "      <td>112900.000000</td>\n",
       "      <td>115000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town of Newburgh</th>\n",
       "      <td>116000.000000</td>\n",
       "      <td>118400.000000</td>\n",
       "      <td>120233.333333</td>\n",
       "      <td>123333.333333</td>\n",
       "      <td>129900.000000</td>\n",
       "      <td>131633.333333</td>\n",
       "      <td>132666.666667</td>\n",
       "      <td>139533.333333</td>\n",
       "      <td>144066.666667</td>\n",
       "      <td>151233.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>179066.666667</td>\n",
       "      <td>179233.333333</td>\n",
       "      <td>177133.333333</td>\n",
       "      <td>175433.333333</td>\n",
       "      <td>175000.000000</td>\n",
       "      <td>174000.000000</td>\n",
       "      <td>173366.666667</td>\n",
       "      <td>172733.333333</td>\n",
       "      <td>171266.666667</td>\n",
       "      <td>171250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Troy</th>\n",
       "      <td>74733.333333</td>\n",
       "      <td>76533.333333</td>\n",
       "      <td>78866.666667</td>\n",
       "      <td>78800.000000</td>\n",
       "      <td>79000.000000</td>\n",
       "      <td>79333.333333</td>\n",
       "      <td>79266.666667</td>\n",
       "      <td>79500.000000</td>\n",
       "      <td>80733.333333</td>\n",
       "      <td>82600.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>129800.000000</td>\n",
       "      <td>129466.666667</td>\n",
       "      <td>128166.666667</td>\n",
       "      <td>126500.000000</td>\n",
       "      <td>127533.333333</td>\n",
       "      <td>129866.666667</td>\n",
       "      <td>128700.000000</td>\n",
       "      <td>128733.333333</td>\n",
       "      <td>128600.000000</td>\n",
       "      <td>127950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Niagara Falls</th>\n",
       "      <td>50300.000000</td>\n",
       "      <td>50133.333333</td>\n",
       "      <td>50433.333333</td>\n",
       "      <td>50200.000000</td>\n",
       "      <td>50133.333333</td>\n",
       "      <td>50133.333333</td>\n",
       "      <td>48866.666667</td>\n",
       "      <td>47900.000000</td>\n",
       "      <td>48033.333333</td>\n",
       "      <td>48666.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>60200.000000</td>\n",
       "      <td>59966.666667</td>\n",
       "      <td>60500.000000</td>\n",
       "      <td>61100.000000</td>\n",
       "      <td>60666.666667</td>\n",
       "      <td>61500.000000</td>\n",
       "      <td>63000.000000</td>\n",
       "      <td>63666.666667</td>\n",
       "      <td>63833.333333</td>\n",
       "      <td>63150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Irondequoit</th>\n",
       "      <td>83400.000000</td>\n",
       "      <td>83500.000000</td>\n",
       "      <td>84400.000000</td>\n",
       "      <td>85266.666667</td>\n",
       "      <td>85933.333333</td>\n",
       "      <td>86733.333333</td>\n",
       "      <td>87233.333333</td>\n",
       "      <td>88133.333333</td>\n",
       "      <td>89600.000000</td>\n",
       "      <td>91033.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>109466.666667</td>\n",
       "      <td>110300.000000</td>\n",
       "      <td>111966.666667</td>\n",
       "      <td>111466.666667</td>\n",
       "      <td>112466.666667</td>\n",
       "      <td>113800.000000</td>\n",
       "      <td>114600.000000</td>\n",
       "      <td>116333.333333</td>\n",
       "      <td>116400.000000</td>\n",
       "      <td>116050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Binghamton</th>\n",
       "      <td>51766.666667</td>\n",
       "      <td>52000.000000</td>\n",
       "      <td>53366.666667</td>\n",
       "      <td>53933.333333</td>\n",
       "      <td>54800.000000</td>\n",
       "      <td>56066.666667</td>\n",
       "      <td>56333.333333</td>\n",
       "      <td>57800.000000</td>\n",
       "      <td>57933.333333</td>\n",
       "      <td>57866.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>75766.666667</td>\n",
       "      <td>75633.333333</td>\n",
       "      <td>74200.000000</td>\n",
       "      <td>73633.333333</td>\n",
       "      <td>74300.000000</td>\n",
       "      <td>74466.666667</td>\n",
       "      <td>74233.333333</td>\n",
       "      <td>72400.000000</td>\n",
       "      <td>70200.000000</td>\n",
       "      <td>70200.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Massapequa</th>\n",
       "      <td>248833.333333</td>\n",
       "      <td>251233.333333</td>\n",
       "      <td>254500.000000</td>\n",
       "      <td>262900.000000</td>\n",
       "      <td>270766.666667</td>\n",
       "      <td>279133.333333</td>\n",
       "      <td>286833.333333</td>\n",
       "      <td>297500.000000</td>\n",
       "      <td>307533.333333</td>\n",
       "      <td>320066.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>420100.000000</td>\n",
       "      <td>424466.666667</td>\n",
       "      <td>423733.333333</td>\n",
       "      <td>427033.333333</td>\n",
       "      <td>430266.666667</td>\n",
       "      <td>429366.666667</td>\n",
       "      <td>432266.666667</td>\n",
       "      <td>432366.666667</td>\n",
       "      <td>435733.333333</td>\n",
       "      <td>438800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town of Elmira</th>\n",
       "      <td>51200.000000</td>\n",
       "      <td>51600.000000</td>\n",
       "      <td>51700.000000</td>\n",
       "      <td>51966.666667</td>\n",
       "      <td>51733.333333</td>\n",
       "      <td>52300.000000</td>\n",
       "      <td>52300.000000</td>\n",
       "      <td>52866.666667</td>\n",
       "      <td>53666.666667</td>\n",
       "      <td>54000.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>74633.333333</td>\n",
       "      <td>74233.333333</td>\n",
       "      <td>73166.666667</td>\n",
       "      <td>71600.000000</td>\n",
       "      <td>71066.666667</td>\n",
       "      <td>70166.666667</td>\n",
       "      <td>70000.000000</td>\n",
       "      <td>70366.666667</td>\n",
       "      <td>68733.333333</td>\n",
       "      <td>69900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Henrietta</th>\n",
       "      <td>94566.666667</td>\n",
       "      <td>94400.000000</td>\n",
       "      <td>95766.666667</td>\n",
       "      <td>97333.333333</td>\n",
       "      <td>98533.333333</td>\n",
       "      <td>99500.000000</td>\n",
       "      <td>99166.666667</td>\n",
       "      <td>99400.000000</td>\n",
       "      <td>99933.333333</td>\n",
       "      <td>100466.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>126300.000000</td>\n",
       "      <td>127300.000000</td>\n",
       "      <td>129933.333333</td>\n",
       "      <td>129433.333333</td>\n",
       "      <td>131766.666667</td>\n",
       "      <td>132833.333333</td>\n",
       "      <td>134133.333333</td>\n",
       "      <td>136200.000000</td>\n",
       "      <td>136100.000000</td>\n",
       "      <td>135950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town of Webster</th>\n",
       "      <td>126433.333333</td>\n",
       "      <td>128600.000000</td>\n",
       "      <td>130833.333333</td>\n",
       "      <td>132066.666667</td>\n",
       "      <td>133066.666667</td>\n",
       "      <td>134266.666667</td>\n",
       "      <td>134533.333333</td>\n",
       "      <td>135633.333333</td>\n",
       "      <td>137433.333333</td>\n",
       "      <td>138833.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>167966.666667</td>\n",
       "      <td>167500.000000</td>\n",
       "      <td>168933.333333</td>\n",
       "      <td>168266.666667</td>\n",
       "      <td>170333.333333</td>\n",
       "      <td>172166.666667</td>\n",
       "      <td>174300.000000</td>\n",
       "      <td>176500.000000</td>\n",
       "      <td>176766.666667</td>\n",
       "      <td>176900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Patchogue</th>\n",
       "      <td>134400.000000</td>\n",
       "      <td>135200.000000</td>\n",
       "      <td>141966.666667</td>\n",
       "      <td>148433.333333</td>\n",
       "      <td>162166.666667</td>\n",
       "      <td>169233.333333</td>\n",
       "      <td>177533.333333</td>\n",
       "      <td>185400.000000</td>\n",
       "      <td>190266.666667</td>\n",
       "      <td>195166.666667</td>\n",
       "      <td>...</td>\n",
       "      <td>253533.333333</td>\n",
       "      <td>256833.333333</td>\n",
       "      <td>257233.333333</td>\n",
       "      <td>264100.000000</td>\n",
       "      <td>266166.666667</td>\n",
       "      <td>267133.333333</td>\n",
       "      <td>264233.333333</td>\n",
       "      <td>258233.333333</td>\n",
       "      <td>255500.000000</td>\n",
       "      <td>254750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Clifton Park</th>\n",
       "      <td>140633.333333</td>\n",
       "      <td>142500.000000</td>\n",
       "      <td>143466.666667</td>\n",
       "      <td>144433.333333</td>\n",
       "      <td>146366.666667</td>\n",
       "      <td>149133.333333</td>\n",
       "      <td>154433.333333</td>\n",
       "      <td>157900.000000</td>\n",
       "      <td>160533.333333</td>\n",
       "      <td>163833.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>254466.666667</td>\n",
       "      <td>259066.666667</td>\n",
       "      <td>262600.000000</td>\n",
       "      <td>265233.333333</td>\n",
       "      <td>264333.333333</td>\n",
       "      <td>262366.666667</td>\n",
       "      <td>261266.666667</td>\n",
       "      <td>267266.666667</td>\n",
       "      <td>272766.666667</td>\n",
       "      <td>270850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Freeport</th>\n",
       "      <td>183833.333333</td>\n",
       "      <td>186433.333333</td>\n",
       "      <td>190400.000000</td>\n",
       "      <td>197466.666667</td>\n",
       "      <td>203633.333333</td>\n",
       "      <td>209366.666667</td>\n",
       "      <td>217000.000000</td>\n",
       "      <td>227700.000000</td>\n",
       "      <td>237066.666667</td>\n",
       "      <td>244133.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>297400.000000</td>\n",
       "      <td>299133.333333</td>\n",
       "      <td>302233.333333</td>\n",
       "      <td>308233.333333</td>\n",
       "      <td>310166.666667</td>\n",
       "      <td>315833.333333</td>\n",
       "      <td>318000.000000</td>\n",
       "      <td>316933.333333</td>\n",
       "      <td>323766.666667</td>\n",
       "      <td>323250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lindenhurst</th>\n",
       "      <td>168733.333333</td>\n",
       "      <td>169100.000000</td>\n",
       "      <td>173033.333333</td>\n",
       "      <td>180066.666667</td>\n",
       "      <td>186366.666667</td>\n",
       "      <td>193833.333333</td>\n",
       "      <td>202066.666667</td>\n",
       "      <td>212666.666667</td>\n",
       "      <td>219500.000000</td>\n",
       "      <td>227533.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>284233.333333</td>\n",
       "      <td>289200.000000</td>\n",
       "      <td>291300.000000</td>\n",
       "      <td>292800.000000</td>\n",
       "      <td>293700.000000</td>\n",
       "      <td>291333.333333</td>\n",
       "      <td>292233.333333</td>\n",
       "      <td>294566.666667</td>\n",
       "      <td>295733.333333</td>\n",
       "      <td>295850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Levittown</th>\n",
       "      <td>189966.666667</td>\n",
       "      <td>193366.666667</td>\n",
       "      <td>197866.666667</td>\n",
       "      <td>204200.000000</td>\n",
       "      <td>210700.000000</td>\n",
       "      <td>215833.333333</td>\n",
       "      <td>223200.000000</td>\n",
       "      <td>234100.000000</td>\n",
       "      <td>241400.000000</td>\n",
       "      <td>250300.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>333800.000000</td>\n",
       "      <td>336566.666667</td>\n",
       "      <td>335166.666667</td>\n",
       "      <td>339533.333333</td>\n",
       "      <td>347366.666667</td>\n",
       "      <td>353733.333333</td>\n",
       "      <td>361633.333333</td>\n",
       "      <td>360733.333333</td>\n",
       "      <td>361833.333333</td>\n",
       "      <td>362500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"29\" valign=\"top\">Vermont</th>\n",
       "      <th>Plainfield</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>223000.000000</td>\n",
       "      <td>228966.666667</td>\n",
       "      <td>226200.000000</td>\n",
       "      <td>227233.333333</td>\n",
       "      <td>230566.666667</td>\n",
       "      <td>228033.333333</td>\n",
       "      <td>221400.000000</td>\n",
       "      <td>218233.333333</td>\n",
       "      <td>212733.333333</td>\n",
       "      <td>204950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>West Brattleboro</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>190000.000000</td>\n",
       "      <td>188833.333333</td>\n",
       "      <td>186433.333333</td>\n",
       "      <td>188200.000000</td>\n",
       "      <td>191633.333333</td>\n",
       "      <td>194733.333333</td>\n",
       "      <td>195100.000000</td>\n",
       "      <td>189933.333333</td>\n",
       "      <td>188333.333333</td>\n",
       "      <td>187000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Duxbury</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>244033.333333</td>\n",
       "      <td>237800.000000</td>\n",
       "      <td>230866.666667</td>\n",
       "      <td>230766.666667</td>\n",
       "      <td>233066.666667</td>\n",
       "      <td>235733.333333</td>\n",
       "      <td>234066.666667</td>\n",
       "      <td>239200.000000</td>\n",
       "      <td>252100.000000</td>\n",
       "      <td>263250.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rochester</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>199600.000000</td>\n",
       "      <td>205700.000000</td>\n",
       "      <td>208700.000000</td>\n",
       "      <td>212300.000000</td>\n",
       "      <td>210266.666667</td>\n",
       "      <td>207400.000000</td>\n",
       "      <td>203166.666667</td>\n",
       "      <td>194433.333333</td>\n",
       "      <td>187100.000000</td>\n",
       "      <td>187550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sharon</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>202933.333333</td>\n",
       "      <td>211633.333333</td>\n",
       "      <td>217633.333333</td>\n",
       "      <td>222333.333333</td>\n",
       "      <td>225233.333333</td>\n",
       "      <td>213433.333333</td>\n",
       "      <td>209066.666667</td>\n",
       "      <td>205266.666667</td>\n",
       "      <td>204433.333333</td>\n",
       "      <td>209800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>White River Junction</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>154133.333333</td>\n",
       "      <td>158233.333333</td>\n",
       "      <td>161033.333333</td>\n",
       "      <td>164333.333333</td>\n",
       "      <td>171466.666667</td>\n",
       "      <td>173866.666667</td>\n",
       "      <td>173066.666667</td>\n",
       "      <td>172033.333333</td>\n",
       "      <td>163566.666667</td>\n",
       "      <td>165550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Irasburg</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>139133.333333</td>\n",
       "      <td>137133.333333</td>\n",
       "      <td>133866.666667</td>\n",
       "      <td>131433.333333</td>\n",
       "      <td>132933.333333</td>\n",
       "      <td>139733.333333</td>\n",
       "      <td>145700.000000</td>\n",
       "      <td>147933.333333</td>\n",
       "      <td>147966.666667</td>\n",
       "      <td>153900.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mt Holly</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>206633.333333</td>\n",
       "      <td>205766.666667</td>\n",
       "      <td>202533.333333</td>\n",
       "      <td>202233.333333</td>\n",
       "      <td>204066.666667</td>\n",
       "      <td>201600.000000</td>\n",
       "      <td>200033.333333</td>\n",
       "      <td>195600.000000</td>\n",
       "      <td>194433.333333</td>\n",
       "      <td>195750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ferrisburgh</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>144000.000000</td>\n",
       "      <td>156300.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>288700.000000</td>\n",
       "      <td>287400.000000</td>\n",
       "      <td>274533.333333</td>\n",
       "      <td>271166.666667</td>\n",
       "      <td>272666.666667</td>\n",
       "      <td>278366.666667</td>\n",
       "      <td>270800.000000</td>\n",
       "      <td>257733.333333</td>\n",
       "      <td>263233.333333</td>\n",
       "      <td>266300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lincoln</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>236933.333333</td>\n",
       "      <td>240400.000000</td>\n",
       "      <td>236333.333333</td>\n",
       "      <td>235800.000000</td>\n",
       "      <td>236066.666667</td>\n",
       "      <td>241900.000000</td>\n",
       "      <td>244333.333333</td>\n",
       "      <td>243766.666667</td>\n",
       "      <td>243766.666667</td>\n",
       "      <td>242550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Franklin</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>175333.333333</td>\n",
       "      <td>176300.000000</td>\n",
       "      <td>172766.666667</td>\n",
       "      <td>171633.333333</td>\n",
       "      <td>173366.666667</td>\n",
       "      <td>172566.666667</td>\n",
       "      <td>176566.666667</td>\n",
       "      <td>175800.000000</td>\n",
       "      <td>175600.000000</td>\n",
       "      <td>176050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Bridport</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>228333.333333</td>\n",
       "      <td>225733.333333</td>\n",
       "      <td>223533.333333</td>\n",
       "      <td>219266.666667</td>\n",
       "      <td>222300.000000</td>\n",
       "      <td>230266.666667</td>\n",
       "      <td>227733.333333</td>\n",
       "      <td>223433.333333</td>\n",
       "      <td>225966.666667</td>\n",
       "      <td>226300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fayston</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>317233.333333</td>\n",
       "      <td>311666.666667</td>\n",
       "      <td>301800.000000</td>\n",
       "      <td>305533.333333</td>\n",
       "      <td>305500.000000</td>\n",
       "      <td>308633.333333</td>\n",
       "      <td>312366.666667</td>\n",
       "      <td>320133.333333</td>\n",
       "      <td>324666.666667</td>\n",
       "      <td>325300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Tunbridge</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>210433.333333</td>\n",
       "      <td>207433.333333</td>\n",
       "      <td>201333.333333</td>\n",
       "      <td>194200.000000</td>\n",
       "      <td>190166.666667</td>\n",
       "      <td>185933.333333</td>\n",
       "      <td>188633.333333</td>\n",
       "      <td>190966.666667</td>\n",
       "      <td>201000.000000</td>\n",
       "      <td>201350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Ryegate</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>160900.000000</td>\n",
       "      <td>155000.000000</td>\n",
       "      <td>152800.000000</td>\n",
       "      <td>150000.000000</td>\n",
       "      <td>150733.333333</td>\n",
       "      <td>146666.666667</td>\n",
       "      <td>139833.333333</td>\n",
       "      <td>141666.666667</td>\n",
       "      <td>143666.666667</td>\n",
       "      <td>150800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shaftsbury</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>231666.666667</td>\n",
       "      <td>228966.666667</td>\n",
       "      <td>216166.666667</td>\n",
       "      <td>213033.333333</td>\n",
       "      <td>207433.333333</td>\n",
       "      <td>205366.666667</td>\n",
       "      <td>201133.333333</td>\n",
       "      <td>200400.000000</td>\n",
       "      <td>205900.000000</td>\n",
       "      <td>209650.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Shoreham</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>231500.000000</td>\n",
       "      <td>222833.333333</td>\n",
       "      <td>216100.000000</td>\n",
       "      <td>207400.000000</td>\n",
       "      <td>206800.000000</td>\n",
       "      <td>207533.333333</td>\n",
       "      <td>201433.333333</td>\n",
       "      <td>199666.666667</td>\n",
       "      <td>205766.666667</td>\n",
       "      <td>218050.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fairlee</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>175533.333333</td>\n",
       "      <td>180266.666667</td>\n",
       "      <td>178400.000000</td>\n",
       "      <td>178800.000000</td>\n",
       "      <td>180500.000000</td>\n",
       "      <td>179866.666667</td>\n",
       "      <td>180866.666667</td>\n",
       "      <td>182033.333333</td>\n",
       "      <td>182266.666667</td>\n",
       "      <td>180300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Corinth</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>160800.000000</td>\n",
       "      <td>162600.000000</td>\n",
       "      <td>160666.666667</td>\n",
       "      <td>159233.333333</td>\n",
       "      <td>157866.666667</td>\n",
       "      <td>153000.000000</td>\n",
       "      <td>152900.000000</td>\n",
       "      <td>154666.666667</td>\n",
       "      <td>153133.333333</td>\n",
       "      <td>152350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Brookfield</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>217033.333333</td>\n",
       "      <td>214333.333333</td>\n",
       "      <td>204866.666667</td>\n",
       "      <td>195566.666667</td>\n",
       "      <td>195633.333333</td>\n",
       "      <td>193833.333333</td>\n",
       "      <td>192466.666667</td>\n",
       "      <td>191333.333333</td>\n",
       "      <td>191866.666667</td>\n",
       "      <td>194750.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Orwell</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>222166.666667</td>\n",
       "      <td>219866.666667</td>\n",
       "      <td>220233.333333</td>\n",
       "      <td>214100.000000</td>\n",
       "      <td>214733.333333</td>\n",
       "      <td>221033.333333</td>\n",
       "      <td>218233.333333</td>\n",
       "      <td>210766.666667</td>\n",
       "      <td>220266.666667</td>\n",
       "      <td>229950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town of Wallingford</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>177033.333333</td>\n",
       "      <td>174300.000000</td>\n",
       "      <td>172000.000000</td>\n",
       "      <td>174233.333333</td>\n",
       "      <td>178800.000000</td>\n",
       "      <td>181366.666667</td>\n",
       "      <td>179766.666667</td>\n",
       "      <td>175266.666667</td>\n",
       "      <td>173500.000000</td>\n",
       "      <td>172700.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Stamford</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>170733.333333</td>\n",
       "      <td>173266.666667</td>\n",
       "      <td>178066.666667</td>\n",
       "      <td>183600.000000</td>\n",
       "      <td>180466.666667</td>\n",
       "      <td>178333.333333</td>\n",
       "      <td>171966.666667</td>\n",
       "      <td>169966.666667</td>\n",
       "      <td>169133.333333</td>\n",
       "      <td>169550.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Mendon</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>207133.333333</td>\n",
       "      <td>214500.000000</td>\n",
       "      <td>209033.333333</td>\n",
       "      <td>209400.000000</td>\n",
       "      <td>207100.000000</td>\n",
       "      <td>205766.666667</td>\n",
       "      <td>207000.000000</td>\n",
       "      <td>206533.333333</td>\n",
       "      <td>206500.000000</td>\n",
       "      <td>203500.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Morrisville</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>172466.666667</td>\n",
       "      <td>172466.666667</td>\n",
       "      <td>172833.333333</td>\n",
       "      <td>173300.000000</td>\n",
       "      <td>167400.000000</td>\n",
       "      <td>168166.666667</td>\n",
       "      <td>170466.666667</td>\n",
       "      <td>171900.000000</td>\n",
       "      <td>175166.666667</td>\n",
       "      <td>174850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town of Marshfield</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>189266.666667</td>\n",
       "      <td>199633.333333</td>\n",
       "      <td>199600.000000</td>\n",
       "      <td>199300.000000</td>\n",
       "      <td>199100.000000</td>\n",
       "      <td>198166.666667</td>\n",
       "      <td>191533.333333</td>\n",
       "      <td>189500.000000</td>\n",
       "      <td>184900.000000</td>\n",
       "      <td>182300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Graniteville-East Barre</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>131433.333333</td>\n",
       "      <td>129600.000000</td>\n",
       "      <td>125866.666667</td>\n",
       "      <td>125300.000000</td>\n",
       "      <td>124166.666667</td>\n",
       "      <td>129600.000000</td>\n",
       "      <td>133633.333333</td>\n",
       "      <td>132866.666667</td>\n",
       "      <td>139633.333333</td>\n",
       "      <td>145350.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Town of Manchester</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>277300.000000</td>\n",
       "      <td>272000.000000</td>\n",
       "      <td>273733.333333</td>\n",
       "      <td>272466.666667</td>\n",
       "      <td>271266.666667</td>\n",
       "      <td>274333.333333</td>\n",
       "      <td>278200.000000</td>\n",
       "      <td>270033.333333</td>\n",
       "      <td>267900.000000</td>\n",
       "      <td>265850.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>East Montpelier</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>265366.666667</td>\n",
       "      <td>263533.333333</td>\n",
       "      <td>258633.333333</td>\n",
       "      <td>261733.333333</td>\n",
       "      <td>266633.333333</td>\n",
       "      <td>269200.000000</td>\n",
       "      <td>266333.333333</td>\n",
       "      <td>260200.000000</td>\n",
       "      <td>260300.000000</td>\n",
       "      <td>257950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Maine</th>\n",
       "      <th>Town of Dixfield</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>37966.666667</td>\n",
       "      <td>37766.666667</td>\n",
       "      <td>37333.333333</td>\n",
       "      <td>38066.666667</td>\n",
       "      <td>39766.666667</td>\n",
       "      <td>43966.666667</td>\n",
       "      <td>48433.333333</td>\n",
       "      <td>50866.666667</td>\n",
       "      <td>52000.000000</td>\n",
       "      <td>50500.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10730 rows × 67 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         2000q1         2000q2         2000q3  \\\n",
       "State    RegionName                                                             \n",
       "New York New York                           NaN            NaN            NaN   \n",
       "         Rochester                 53566.666667   53233.333333   53200.000000   \n",
       "         Yonkers                  222233.333333  230900.000000  245533.333333   \n",
       "         Syracuse                  56800.000000   57866.666667   57400.000000   \n",
       "         Town of Greece            95100.000000   95500.000000   96333.333333   \n",
       "         Albany                    91566.666667   92100.000000   91233.333333   \n",
       "         Town of Poughkeepsie     131966.666667  135433.333333  139566.666667   \n",
       "         New Rochelle             362766.666667  370866.666667  382233.333333   \n",
       "         Town of Colonie          110600.000000  112400.000000  113166.666667   \n",
       "         Mount Vernon             239333.333333  247300.000000  258633.333333   \n",
       "         Utica                     50700.000000   51233.333333   52600.000000   \n",
       "         Valley Stream            223166.666667  225166.666667  229133.333333   \n",
       "         Schenectady               72266.666667   73100.000000   73733.333333   \n",
       "         White Plains             284333.333333  297833.333333  314366.666667   \n",
       "         Town of Hempstead        170166.666667  172266.666667  176800.000000   \n",
       "         Town of Binghamton        81633.333333   82500.000000   81700.000000   \n",
       "         Town of Newburgh         116000.000000  118400.000000  120233.333333   \n",
       "         Troy                      74733.333333   76533.333333   78866.666667   \n",
       "         Niagara Falls             50300.000000   50133.333333   50433.333333   \n",
       "         Irondequoit               83400.000000   83500.000000   84400.000000   \n",
       "         Binghamton                51766.666667   52000.000000   53366.666667   \n",
       "         Massapequa               248833.333333  251233.333333  254500.000000   \n",
       "         Town of Elmira            51200.000000   51600.000000   51700.000000   \n",
       "         Henrietta                 94566.666667   94400.000000   95766.666667   \n",
       "         Town of Webster          126433.333333  128600.000000  130833.333333   \n",
       "         Patchogue                134400.000000  135200.000000  141966.666667   \n",
       "         Clifton Park             140633.333333  142500.000000  143466.666667   \n",
       "         Freeport                 183833.333333  186433.333333  190400.000000   \n",
       "         Lindenhurst              168733.333333  169100.000000  173033.333333   \n",
       "         Levittown                189966.666667  193366.666667  197866.666667   \n",
       "...                                         ...            ...            ...   \n",
       "Vermont  Plainfield                         NaN            NaN            NaN   \n",
       "         West Brattleboro                   NaN            NaN            NaN   \n",
       "         Duxbury                            NaN            NaN            NaN   \n",
       "         Rochester                          NaN            NaN            NaN   \n",
       "         Sharon                             NaN            NaN            NaN   \n",
       "         White River Junction               NaN            NaN            NaN   \n",
       "         Irasburg                           NaN            NaN            NaN   \n",
       "         Mt Holly                           NaN            NaN            NaN   \n",
       "         Ferrisburgh                        NaN            NaN            NaN   \n",
       "         Lincoln                            NaN            NaN            NaN   \n",
       "         Franklin                           NaN            NaN            NaN   \n",
       "         Bridport                           NaN            NaN            NaN   \n",
       "         Fayston                            NaN            NaN            NaN   \n",
       "         Tunbridge                          NaN            NaN            NaN   \n",
       "         Ryegate                            NaN            NaN            NaN   \n",
       "         Shaftsbury                         NaN            NaN            NaN   \n",
       "         Shoreham                           NaN            NaN            NaN   \n",
       "         Fairlee                            NaN            NaN            NaN   \n",
       "         Corinth                            NaN            NaN            NaN   \n",
       "         Brookfield                         NaN            NaN            NaN   \n",
       "         Orwell                             NaN            NaN            NaN   \n",
       "         Town of Wallingford                NaN            NaN            NaN   \n",
       "         Stamford                           NaN            NaN            NaN   \n",
       "         Mendon                             NaN            NaN            NaN   \n",
       "         Morrisville                        NaN            NaN            NaN   \n",
       "         Town of Marshfield                 NaN            NaN            NaN   \n",
       "         Graniteville-East Barre            NaN            NaN            NaN   \n",
       "         Town of Manchester                 NaN            NaN            NaN   \n",
       "         East Montpelier                    NaN            NaN            NaN   \n",
       "Maine    Town of Dixfield                   NaN            NaN            NaN   \n",
       "\n",
       "                                         2000q4         2001q1         2001q2  \\\n",
       "State    RegionName                                                             \n",
       "New York New York                           NaN            NaN            NaN   \n",
       "         Rochester                 53200.000000   53066.666667   52533.333333   \n",
       "         Yonkers                  254400.000000  259266.666667  263700.000000   \n",
       "         Syracuse                  57233.333333   57100.000000   56966.666667   \n",
       "         Town of Greece            96933.333333   98266.666667   99300.000000   \n",
       "         Albany                    91566.666667   92633.333333   94733.333333   \n",
       "         Town of Poughkeepsie     144366.666667  149366.666667  153833.333333   \n",
       "         New Rochelle             400066.666667  419166.666667  428066.666667   \n",
       "         Town of Colonie          114100.000000  115833.333333  118600.000000   \n",
       "         Mount Vernon             271700.000000  278833.333333  283466.666667   \n",
       "         Utica                     53000.000000   53300.000000   53933.333333   \n",
       "         Valley Stream            237200.000000  243466.666667  250333.333333   \n",
       "         Schenectady               73400.000000   75066.666667   76666.666667   \n",
       "         White Plains             326633.333333  331266.666667  335600.000000   \n",
       "         Town of Hempstead        182333.333333  185000.000000  189400.000000   \n",
       "         Town of Binghamton        82466.666667   84266.666667   85300.000000   \n",
       "         Town of Newburgh         123333.333333  129900.000000  131633.333333   \n",
       "         Troy                      78800.000000   79000.000000   79333.333333   \n",
       "         Niagara Falls             50200.000000   50133.333333   50133.333333   \n",
       "         Irondequoit               85266.666667   85933.333333   86733.333333   \n",
       "         Binghamton                53933.333333   54800.000000   56066.666667   \n",
       "         Massapequa               262900.000000  270766.666667  279133.333333   \n",
       "         Town of Elmira            51966.666667   51733.333333   52300.000000   \n",
       "         Henrietta                 97333.333333   98533.333333   99500.000000   \n",
       "         Town of Webster          132066.666667  133066.666667  134266.666667   \n",
       "         Patchogue                148433.333333  162166.666667  169233.333333   \n",
       "         Clifton Park             144433.333333  146366.666667  149133.333333   \n",
       "         Freeport                 197466.666667  203633.333333  209366.666667   \n",
       "         Lindenhurst              180066.666667  186366.666667  193833.333333   \n",
       "         Levittown                204200.000000  210700.000000  215833.333333   \n",
       "...                                         ...            ...            ...   \n",
       "Vermont  Plainfield                         NaN            NaN            NaN   \n",
       "         West Brattleboro                   NaN            NaN            NaN   \n",
       "         Duxbury                            NaN            NaN            NaN   \n",
       "         Rochester                          NaN            NaN            NaN   \n",
       "         Sharon                             NaN            NaN            NaN   \n",
       "         White River Junction               NaN            NaN            NaN   \n",
       "         Irasburg                           NaN            NaN            NaN   \n",
       "         Mt Holly                           NaN            NaN            NaN   \n",
       "         Ferrisburgh                        NaN            NaN            NaN   \n",
       "         Lincoln                            NaN            NaN            NaN   \n",
       "         Franklin                           NaN            NaN            NaN   \n",
       "         Bridport                           NaN            NaN            NaN   \n",
       "         Fayston                            NaN            NaN            NaN   \n",
       "         Tunbridge                          NaN            NaN            NaN   \n",
       "         Ryegate                            NaN            NaN            NaN   \n",
       "         Shaftsbury                         NaN            NaN            NaN   \n",
       "         Shoreham                           NaN            NaN            NaN   \n",
       "         Fairlee                            NaN            NaN            NaN   \n",
       "         Corinth                            NaN            NaN            NaN   \n",
       "         Brookfield                         NaN            NaN            NaN   \n",
       "         Orwell                             NaN            NaN            NaN   \n",
       "         Town of Wallingford                NaN            NaN            NaN   \n",
       "         Stamford                           NaN            NaN            NaN   \n",
       "         Mendon                             NaN            NaN            NaN   \n",
       "         Morrisville                        NaN            NaN            NaN   \n",
       "         Town of Marshfield                 NaN            NaN            NaN   \n",
       "         Graniteville-East Barre            NaN            NaN            NaN   \n",
       "         Town of Manchester                 NaN            NaN            NaN   \n",
       "         East Montpelier                    NaN            NaN            NaN   \n",
       "Maine    Town of Dixfield                   NaN            NaN            NaN   \n",
       "\n",
       "                                         2001q3         2001q4         2002q1  \\\n",
       "State    RegionName                                                             \n",
       "New York New York                           NaN            NaN            NaN   \n",
       "         Rochester                 52666.666667   53200.000000   53366.666667   \n",
       "         Yonkers                  271300.000000  282066.666667  292833.333333   \n",
       "         Syracuse                  57433.333333   58000.000000   59033.333333   \n",
       "         Town of Greece           100000.000000  101200.000000  102766.666667   \n",
       "         Albany                    97266.666667   99000.000000   99433.333333   \n",
       "         Town of Poughkeepsie     156966.666667  164200.000000  172800.000000   \n",
       "         New Rochelle             436633.333333  440400.000000  443966.666667   \n",
       "         Town of Colonie          120666.666667  122666.666667  124433.333333   \n",
       "         Mount Vernon             296700.000000  307133.333333  321633.333333   \n",
       "         Utica                     53833.333333   54200.000000   54266.666667   \n",
       "         Valley Stream            256700.000000  264900.000000  272033.333333   \n",
       "         Schenectady               77000.000000   78333.333333   80933.333333   \n",
       "         White Plains             349566.666667  364366.666667  384966.666667   \n",
       "         Town of Hempstead        194433.333333  205200.000000  213333.333333   \n",
       "         Town of Binghamton        86333.333333   87766.666667   87500.000000   \n",
       "         Town of Newburgh         132666.666667  139533.333333  144066.666667   \n",
       "         Troy                      79266.666667   79500.000000   80733.333333   \n",
       "         Niagara Falls             48866.666667   47900.000000   48033.333333   \n",
       "         Irondequoit               87233.333333   88133.333333   89600.000000   \n",
       "         Binghamton                56333.333333   57800.000000   57933.333333   \n",
       "         Massapequa               286833.333333  297500.000000  307533.333333   \n",
       "         Town of Elmira            52300.000000   52866.666667   53666.666667   \n",
       "         Henrietta                 99166.666667   99400.000000   99933.333333   \n",
       "         Town of Webster          134533.333333  135633.333333  137433.333333   \n",
       "         Patchogue                177533.333333  185400.000000  190266.666667   \n",
       "         Clifton Park             154433.333333  157900.000000  160533.333333   \n",
       "         Freeport                 217000.000000  227700.000000  237066.666667   \n",
       "         Lindenhurst              202066.666667  212666.666667  219500.000000   \n",
       "         Levittown                223200.000000  234100.000000  241400.000000   \n",
       "...                                         ...            ...            ...   \n",
       "Vermont  Plainfield                         NaN            NaN            NaN   \n",
       "         West Brattleboro                   NaN            NaN            NaN   \n",
       "         Duxbury                            NaN            NaN            NaN   \n",
       "         Rochester                          NaN            NaN            NaN   \n",
       "         Sharon                             NaN            NaN            NaN   \n",
       "         White River Junction               NaN            NaN            NaN   \n",
       "         Irasburg                           NaN            NaN            NaN   \n",
       "         Mt Holly                           NaN            NaN            NaN   \n",
       "         Ferrisburgh                        NaN            NaN  144000.000000   \n",
       "         Lincoln                            NaN            NaN            NaN   \n",
       "         Franklin                           NaN            NaN            NaN   \n",
       "         Bridport                           NaN            NaN            NaN   \n",
       "         Fayston                            NaN            NaN            NaN   \n",
       "         Tunbridge                          NaN            NaN            NaN   \n",
       "         Ryegate                            NaN            NaN            NaN   \n",
       "         Shaftsbury                         NaN            NaN            NaN   \n",
       "         Shoreham                           NaN            NaN            NaN   \n",
       "         Fairlee                            NaN            NaN            NaN   \n",
       "         Corinth                            NaN            NaN            NaN   \n",
       "         Brookfield                         NaN            NaN            NaN   \n",
       "         Orwell                             NaN            NaN            NaN   \n",
       "         Town of Wallingford                NaN            NaN            NaN   \n",
       "         Stamford                           NaN            NaN            NaN   \n",
       "         Mendon                             NaN            NaN            NaN   \n",
       "         Morrisville                        NaN            NaN            NaN   \n",
       "         Town of Marshfield                 NaN            NaN            NaN   \n",
       "         Graniteville-East Barre            NaN            NaN            NaN   \n",
       "         Town of Manchester                 NaN            NaN            NaN   \n",
       "         East Montpelier                    NaN            NaN            NaN   \n",
       "Maine    Town of Dixfield                   NaN            NaN            NaN   \n",
       "\n",
       "                                         2002q2    ...            2014q2  \\\n",
       "State    RegionName                                ...                     \n",
       "New York New York                           NaN    ...     515466.666667   \n",
       "         Rochester                 53566.666667    ...      60100.000000   \n",
       "         Yonkers                  303533.333333    ...     382033.333333   \n",
       "         Syracuse                  60200.000000    ...      76533.333333   \n",
       "         Town of Greece           103400.000000    ...     120000.000000   \n",
       "         Albany                   101233.333333    ...     164266.666667   \n",
       "         Town of Poughkeepsie     176933.333333    ...     191300.000000   \n",
       "         New Rochelle             452133.333333    ...     577200.000000   \n",
       "         Town of Colonie          126233.333333    ...     200966.666667   \n",
       "         Mount Vernon             329700.000000    ...     347933.333333   \n",
       "         Utica                     55166.666667    ...      78466.666667   \n",
       "         Valley Stream            279566.666667    ...     360666.666667   \n",
       "         Schenectady               83866.666667    ...     122733.333333   \n",
       "         White Plains             400700.000000    ...     500100.000000   \n",
       "         Town of Hempstead        221200.000000    ...     265000.000000   \n",
       "         Town of Binghamton        88666.666667    ...     123333.333333   \n",
       "         Town of Newburgh         151233.333333    ...     179066.666667   \n",
       "         Troy                      82600.000000    ...     129800.000000   \n",
       "         Niagara Falls             48666.666667    ...      60200.000000   \n",
       "         Irondequoit               91033.333333    ...     109466.666667   \n",
       "         Binghamton                57866.666667    ...      75766.666667   \n",
       "         Massapequa               320066.666667    ...     420100.000000   \n",
       "         Town of Elmira            54000.000000    ...      74633.333333   \n",
       "         Henrietta                100466.666667    ...     126300.000000   \n",
       "         Town of Webster          138833.333333    ...     167966.666667   \n",
       "         Patchogue                195166.666667    ...     253533.333333   \n",
       "         Clifton Park             163833.333333    ...     254466.666667   \n",
       "         Freeport                 244133.333333    ...     297400.000000   \n",
       "         Lindenhurst              227533.333333    ...     284233.333333   \n",
       "         Levittown                250300.000000    ...     333800.000000   \n",
       "...                                         ...    ...               ...   \n",
       "Vermont  Plainfield                         NaN    ...     223000.000000   \n",
       "         West Brattleboro                   NaN    ...     190000.000000   \n",
       "         Duxbury                            NaN    ...     244033.333333   \n",
       "         Rochester                          NaN    ...     199600.000000   \n",
       "         Sharon                             NaN    ...     202933.333333   \n",
       "         White River Junction               NaN    ...     154133.333333   \n",
       "         Irasburg                           NaN    ...     139133.333333   \n",
       "         Mt Holly                           NaN    ...     206633.333333   \n",
       "         Ferrisburgh              156300.000000    ...     288700.000000   \n",
       "         Lincoln                            NaN    ...     236933.333333   \n",
       "         Franklin                           NaN    ...     175333.333333   \n",
       "         Bridport                           NaN    ...     228333.333333   \n",
       "         Fayston                            NaN    ...     317233.333333   \n",
       "         Tunbridge                          NaN    ...     210433.333333   \n",
       "         Ryegate                            NaN    ...     160900.000000   \n",
       "         Shaftsbury                         NaN    ...     231666.666667   \n",
       "         Shoreham                           NaN    ...     231500.000000   \n",
       "         Fairlee                            NaN    ...     175533.333333   \n",
       "         Corinth                            NaN    ...     160800.000000   \n",
       "         Brookfield                         NaN    ...     217033.333333   \n",
       "         Orwell                             NaN    ...     222166.666667   \n",
       "         Town of Wallingford                NaN    ...     177033.333333   \n",
       "         Stamford                           NaN    ...     170733.333333   \n",
       "         Mendon                             NaN    ...     207133.333333   \n",
       "         Morrisville                        NaN    ...     172466.666667   \n",
       "         Town of Marshfield                 NaN    ...     189266.666667   \n",
       "         Graniteville-East Barre            NaN    ...     131433.333333   \n",
       "         Town of Manchester                 NaN    ...     277300.000000   \n",
       "         East Montpelier                    NaN    ...     265366.666667   \n",
       "Maine    Town of Dixfield                   NaN    ...      37966.666667   \n",
       "\n",
       "                                         2014q3         2014q4         2015q1  \\\n",
       "State    RegionName                                                             \n",
       "New York New York                 522800.000000  528066.666667  532266.666667   \n",
       "         Rochester                 61266.666667   62800.000000   62900.000000   \n",
       "         Yonkers                  385766.666667  385333.333333  385866.666667   \n",
       "         Syracuse                  75733.333333   75133.333333   76966.666667   \n",
       "         Town of Greece           121133.333333  122433.333333  121633.333333   \n",
       "         Albany                   162100.000000  159700.000000  159433.333333   \n",
       "         Town of Poughkeepsie     191700.000000  193566.666667  194233.333333   \n",
       "         New Rochelle             596600.000000  610466.666667  604600.000000   \n",
       "         Town of Colonie          199533.333333  201100.000000  203200.000000   \n",
       "         Mount Vernon             354600.000000  364133.333333  377033.333333   \n",
       "         Utica                     78133.333333   78566.666667   79366.666667   \n",
       "         Valley Stream            364600.000000  365300.000000  371066.666667   \n",
       "         Schenectady              121233.333333  122266.666667  121966.666667   \n",
       "         White Plains             510566.666667  515400.000000  518300.000000   \n",
       "         Town of Hempstead        265800.000000  265100.000000  269500.000000   \n",
       "         Town of Binghamton       121766.666667  118466.666667  118533.333333   \n",
       "         Town of Newburgh         179233.333333  177133.333333  175433.333333   \n",
       "         Troy                     129466.666667  128166.666667  126500.000000   \n",
       "         Niagara Falls             59966.666667   60500.000000   61100.000000   \n",
       "         Irondequoit              110300.000000  111966.666667  111466.666667   \n",
       "         Binghamton                75633.333333   74200.000000   73633.333333   \n",
       "         Massapequa               424466.666667  423733.333333  427033.333333   \n",
       "         Town of Elmira            74233.333333   73166.666667   71600.000000   \n",
       "         Henrietta                127300.000000  129933.333333  129433.333333   \n",
       "         Town of Webster          167500.000000  168933.333333  168266.666667   \n",
       "         Patchogue                256833.333333  257233.333333  264100.000000   \n",
       "         Clifton Park             259066.666667  262600.000000  265233.333333   \n",
       "         Freeport                 299133.333333  302233.333333  308233.333333   \n",
       "         Lindenhurst              289200.000000  291300.000000  292800.000000   \n",
       "         Levittown                336566.666667  335166.666667  339533.333333   \n",
       "...                                         ...            ...            ...   \n",
       "Vermont  Plainfield               228966.666667  226200.000000  227233.333333   \n",
       "         West Brattleboro         188833.333333  186433.333333  188200.000000   \n",
       "         Duxbury                  237800.000000  230866.666667  230766.666667   \n",
       "         Rochester                205700.000000  208700.000000  212300.000000   \n",
       "         Sharon                   211633.333333  217633.333333  222333.333333   \n",
       "         White River Junction     158233.333333  161033.333333  164333.333333   \n",
       "         Irasburg                 137133.333333  133866.666667  131433.333333   \n",
       "         Mt Holly                 205766.666667  202533.333333  202233.333333   \n",
       "         Ferrisburgh              287400.000000  274533.333333  271166.666667   \n",
       "         Lincoln                  240400.000000  236333.333333  235800.000000   \n",
       "         Franklin                 176300.000000  172766.666667  171633.333333   \n",
       "         Bridport                 225733.333333  223533.333333  219266.666667   \n",
       "         Fayston                  311666.666667  301800.000000  305533.333333   \n",
       "         Tunbridge                207433.333333  201333.333333  194200.000000   \n",
       "         Ryegate                  155000.000000  152800.000000  150000.000000   \n",
       "         Shaftsbury               228966.666667  216166.666667  213033.333333   \n",
       "         Shoreham                 222833.333333  216100.000000  207400.000000   \n",
       "         Fairlee                  180266.666667  178400.000000  178800.000000   \n",
       "         Corinth                  162600.000000  160666.666667  159233.333333   \n",
       "         Brookfield               214333.333333  204866.666667  195566.666667   \n",
       "         Orwell                   219866.666667  220233.333333  214100.000000   \n",
       "         Town of Wallingford      174300.000000  172000.000000  174233.333333   \n",
       "         Stamford                 173266.666667  178066.666667  183600.000000   \n",
       "         Mendon                   214500.000000  209033.333333  209400.000000   \n",
       "         Morrisville              172466.666667  172833.333333  173300.000000   \n",
       "         Town of Marshfield       199633.333333  199600.000000  199300.000000   \n",
       "         Graniteville-East Barre  129600.000000  125866.666667  125300.000000   \n",
       "         Town of Manchester       272000.000000  273733.333333  272466.666667   \n",
       "         East Montpelier          263533.333333  258633.333333  261733.333333   \n",
       "Maine    Town of Dixfield          37766.666667   37333.333333   38066.666667   \n",
       "\n",
       "                                         2015q2         2015q3         2015q4  \\\n",
       "State    RegionName                                                             \n",
       "New York New York                 540800.000000  557200.000000  572833.333333   \n",
       "         Rochester                 63966.666667   64800.000000   65333.333333   \n",
       "         Yonkers                  388800.000000  401233.333333  404433.333333   \n",
       "         Syracuse                  78533.333333   79200.000000   80400.000000   \n",
       "         Town of Greece           122800.000000  123966.666667  124466.666667   \n",
       "         Albany                   159066.666667  159500.000000  159533.333333   \n",
       "         Town of Poughkeepsie     194433.333333  194400.000000  193700.000000   \n",
       "         New Rochelle             601766.666667  611166.666667  614433.333333   \n",
       "         Town of Colonie          203933.333333  205766.666667  207400.000000   \n",
       "         Mount Vernon             390766.666667  399500.000000  395033.333333   \n",
       "         Utica                     78833.333333   78500.000000   79033.333333   \n",
       "         Valley Stream            375466.666667  380733.333333  389033.333333   \n",
       "         Schenectady              121333.333333  122566.666667  122100.000000   \n",
       "         White Plains             526300.000000  534800.000000  534566.666667   \n",
       "         Town of Hempstead        271133.333333  275600.000000  282866.666667   \n",
       "         Town of Binghamton       119333.333333  119766.666667  119666.666667   \n",
       "         Town of Newburgh         175000.000000  174000.000000  173366.666667   \n",
       "         Troy                     127533.333333  129866.666667  128700.000000   \n",
       "         Niagara Falls             60666.666667   61500.000000   63000.000000   \n",
       "         Irondequoit              112466.666667  113800.000000  114600.000000   \n",
       "         Binghamton                74300.000000   74466.666667   74233.333333   \n",
       "         Massapequa               430266.666667  429366.666667  432266.666667   \n",
       "         Town of Elmira            71066.666667   70166.666667   70000.000000   \n",
       "         Henrietta                131766.666667  132833.333333  134133.333333   \n",
       "         Town of Webster          170333.333333  172166.666667  174300.000000   \n",
       "         Patchogue                266166.666667  267133.333333  264233.333333   \n",
       "         Clifton Park             264333.333333  262366.666667  261266.666667   \n",
       "         Freeport                 310166.666667  315833.333333  318000.000000   \n",
       "         Lindenhurst              293700.000000  291333.333333  292233.333333   \n",
       "         Levittown                347366.666667  353733.333333  361633.333333   \n",
       "...                                         ...            ...            ...   \n",
       "Vermont  Plainfield               230566.666667  228033.333333  221400.000000   \n",
       "         West Brattleboro         191633.333333  194733.333333  195100.000000   \n",
       "         Duxbury                  233066.666667  235733.333333  234066.666667   \n",
       "         Rochester                210266.666667  207400.000000  203166.666667   \n",
       "         Sharon                   225233.333333  213433.333333  209066.666667   \n",
       "         White River Junction     171466.666667  173866.666667  173066.666667   \n",
       "         Irasburg                 132933.333333  139733.333333  145700.000000   \n",
       "         Mt Holly                 204066.666667  201600.000000  200033.333333   \n",
       "         Ferrisburgh              272666.666667  278366.666667  270800.000000   \n",
       "         Lincoln                  236066.666667  241900.000000  244333.333333   \n",
       "         Franklin                 173366.666667  172566.666667  176566.666667   \n",
       "         Bridport                 222300.000000  230266.666667  227733.333333   \n",
       "         Fayston                  305500.000000  308633.333333  312366.666667   \n",
       "         Tunbridge                190166.666667  185933.333333  188633.333333   \n",
       "         Ryegate                  150733.333333  146666.666667  139833.333333   \n",
       "         Shaftsbury               207433.333333  205366.666667  201133.333333   \n",
       "         Shoreham                 206800.000000  207533.333333  201433.333333   \n",
       "         Fairlee                  180500.000000  179866.666667  180866.666667   \n",
       "         Corinth                  157866.666667  153000.000000  152900.000000   \n",
       "         Brookfield               195633.333333  193833.333333  192466.666667   \n",
       "         Orwell                   214733.333333  221033.333333  218233.333333   \n",
       "         Town of Wallingford      178800.000000  181366.666667  179766.666667   \n",
       "         Stamford                 180466.666667  178333.333333  171966.666667   \n",
       "         Mendon                   207100.000000  205766.666667  207000.000000   \n",
       "         Morrisville              167400.000000  168166.666667  170466.666667   \n",
       "         Town of Marshfield       199100.000000  198166.666667  191533.333333   \n",
       "         Graniteville-East Barre  124166.666667  129600.000000  133633.333333   \n",
       "         Town of Manchester       271266.666667  274333.333333  278200.000000   \n",
       "         East Montpelier          266633.333333  269200.000000  266333.333333   \n",
       "Maine    Town of Dixfield          39766.666667   43966.666667   48433.333333   \n",
       "\n",
       "                                         2016q1         2016q2    2016q3  \n",
       "State    RegionName                                                       \n",
       "New York New York                 582866.666667  591633.333333  587200.0  \n",
       "         Rochester                 66833.333333   67000.000000   66900.0  \n",
       "         Yonkers                  407066.666667  409800.000000  406550.0  \n",
       "         Syracuse                  80566.666667   78733.333333   78350.0  \n",
       "         Town of Greece           125933.333333  126633.333333  126050.0  \n",
       "         Albany                   159166.666667  158800.000000  159000.0  \n",
       "         Town of Poughkeepsie     193666.666667  196666.666667  199050.0  \n",
       "         New Rochelle             627433.333333  632100.000000  611100.0  \n",
       "         Town of Colonie          206400.000000  208000.000000  209650.0  \n",
       "         Mount Vernon             395600.000000  393966.666667  379050.0  \n",
       "         Utica                     81166.666667   82900.000000   82800.0  \n",
       "         Valley Stream            390200.000000  397533.333333  402550.0  \n",
       "         Schenectady              123000.000000  123966.666667  125100.0  \n",
       "         White Plains             537866.666667  539366.666667  533450.0  \n",
       "         Town of Hempstead        286133.333333  294533.333333  299150.0  \n",
       "         Town of Binghamton       115100.000000  112900.000000  115000.0  \n",
       "         Town of Newburgh         172733.333333  171266.666667  171250.0  \n",
       "         Troy                     128733.333333  128600.000000  127950.0  \n",
       "         Niagara Falls             63666.666667   63833.333333   63150.0  \n",
       "         Irondequoit              116333.333333  116400.000000  116050.0  \n",
       "         Binghamton                72400.000000   70200.000000   70200.0  \n",
       "         Massapequa               432366.666667  435733.333333  438800.0  \n",
       "         Town of Elmira            70366.666667   68733.333333   69900.0  \n",
       "         Henrietta                136200.000000  136100.000000  135950.0  \n",
       "         Town of Webster          176500.000000  176766.666667  176900.0  \n",
       "         Patchogue                258233.333333  255500.000000  254750.0  \n",
       "         Clifton Park             267266.666667  272766.666667  270850.0  \n",
       "         Freeport                 316933.333333  323766.666667  323250.0  \n",
       "         Lindenhurst              294566.666667  295733.333333  295850.0  \n",
       "         Levittown                360733.333333  361833.333333  362500.0  \n",
       "...                                         ...            ...       ...  \n",
       "Vermont  Plainfield               218233.333333  212733.333333  204950.0  \n",
       "         West Brattleboro         189933.333333  188333.333333  187000.0  \n",
       "         Duxbury                  239200.000000  252100.000000  263250.0  \n",
       "         Rochester                194433.333333  187100.000000  187550.0  \n",
       "         Sharon                   205266.666667  204433.333333  209800.0  \n",
       "         White River Junction     172033.333333  163566.666667  165550.0  \n",
       "         Irasburg                 147933.333333  147966.666667  153900.0  \n",
       "         Mt Holly                 195600.000000  194433.333333  195750.0  \n",
       "         Ferrisburgh              257733.333333  263233.333333  266300.0  \n",
       "         Lincoln                  243766.666667  243766.666667  242550.0  \n",
       "         Franklin                 175800.000000  175600.000000  176050.0  \n",
       "         Bridport                 223433.333333  225966.666667  226300.0  \n",
       "         Fayston                  320133.333333  324666.666667  325300.0  \n",
       "         Tunbridge                190966.666667  201000.000000  201350.0  \n",
       "         Ryegate                  141666.666667  143666.666667  150800.0  \n",
       "         Shaftsbury               200400.000000  205900.000000  209650.0  \n",
       "         Shoreham                 199666.666667  205766.666667  218050.0  \n",
       "         Fairlee                  182033.333333  182266.666667  180300.0  \n",
       "         Corinth                  154666.666667  153133.333333  152350.0  \n",
       "         Brookfield               191333.333333  191866.666667  194750.0  \n",
       "         Orwell                   210766.666667  220266.666667  229950.0  \n",
       "         Town of Wallingford      175266.666667  173500.000000  172700.0  \n",
       "         Stamford                 169966.666667  169133.333333  169550.0  \n",
       "         Mendon                   206533.333333  206500.000000  203500.0  \n",
       "         Morrisville              171900.000000  175166.666667  174850.0  \n",
       "         Town of Marshfield       189500.000000  184900.000000  182300.0  \n",
       "         Graniteville-East Barre  132866.666667  139633.333333  145350.0  \n",
       "         Town of Manchester       270033.333333  267900.000000  265850.0  \n",
       "         East Montpelier          260200.000000  260300.000000  257950.0  \n",
       "Maine    Town of Dixfield          50866.666667   52000.000000   50500.0  \n",
       "\n",
       "[10730 rows x 67 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "convert_housing_data_to_quarters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2009q2'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_recession_bottom()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "umich_part_id": "026",
    "umich_partlist_id": "004"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, 0.0051648663279195324, 'university town')"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def run_ttest():\n",
    "    '''First creates new data showing the decline or growth of housing prices\n",
    "    between the recession start and the recession bottom. Then runs a ttest\n",
    "    comparing the university town values to the non-university towns values, \n",
    "    return whether the alternative hypothesis (that the two groups are the same)\n",
    "    is true or not as well as the p-value of the confidence. \n",
    "    \n",
    "    Return the tuple (different, p, better) where different=True if the t-test is\n",
    "    True at a p<0.01 (we reject the null hypothesis), or different=False if \n",
    "    otherwise (we cannot reject the null hypothesis). The variable p should\n",
    "    be equal to the exact p value returned from scipy.stats.ttest_ind(). The\n",
    "    value for better should be either \"university town\" or \"non-university town\"\n",
    "    depending on which has a lower mean price ratio (which is equivilent to a\n",
    "    reduced market loss).'''\n",
    "    \n",
    "    h=convert_housing_data_to_quarters()\n",
    "    ut=get_list_of_university_towns()\n",
    "    ut.set_index(['State','RegionName'],inplace=True)\n",
    "    hut=pd.merge(h,ut, how='inner', left_index=True, right_index=True).dropna()\n",
    "    hnut=h.drop(ut.index).dropna()\n",
    "    bef_rec=h.iloc[ :  ,h.columns.get_loc(get_recession_start())-1].name\n",
    "    hut['price_ratio']=hut[bef_rec]/hut[get_recession_bottom()]\n",
    "    hnut['price_ratio']=hnut[bef_rec]/hnut[get_recession_bottom()]\n",
    "    p = ttest_ind(hnut['price_ratio'].dropna(),hut['price_ratio'].dropna())[1]\n",
    "    if hut['price_ratio'].mean() > hnut['price_ratio'].mean():\n",
    "        better = \"non-university town\"\n",
    "    else:\n",
    "        better = \"university town\" \n",
    "    \n",
    "    \n",
    "    different = p<0.01\n",
    "    result  = (different, p, better)\n",
    "    \n",
    "    return result\n",
    "run_ttest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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