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Territorial Authority Data

Here is some data from each of the Territorial Authorities:





In [44]:



    


import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
% matplotlib inline

import seaborn
seaborn.set()



    











In [10]:



    


mean_rents = pd.read_csv('..\\data\\Detailed Geomean Rents.csv')



    


















    






C:\Users\caleb\AppData\Local\conda\conda\envs\tensorflow-env\lib\site-packages\IPython\core\interactiveshell.py:2717: DtypeWarning: Columns (0) have mixed types. Specify dtype option on import or set low_memory=False.
  interactivity=interactivity, compiler=compiler, result=result)

















In [13]:



    


mean_rents.head()



    


















    
Out[13]:










  
    

      
      SAU
      Property_Type
      Bedrooms
      1993-03-01
      1993-06-01
      1993-09-01
      1993-12-01
      1994-03-01
      1994-06-01
      1994-09-01
      ...
      2015-03-01
      2015-06-01
      2015-09-01
      2015-12-01
      2016-03-01
      2016-06-01
      2016-09-01
      2016-12-01
      2017-03-01
      2017-06-01
    

  
  
    

      0
      0
      Flat or Apartment
      Bedroooms Total
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      418.0
    

    

      1
      0
      House
      3
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      441.0
    

    

      2
      0
      House
      4
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      605.0
    

    

      3
      0
      House
      Bedroooms Total
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      522.0
    

    

      4
      0
      Property Type Total
      3
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      ...
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      NaN
      451.0
    

  



5 rows × 101 columns



















In [40]:



    


print(list(mean_rents.columns))



    


















    






['SAU', 'Property_Type', 'Bedrooms', '1993-03-01', '1993-06-01', '1993-09-01', '1993-12-01', '1994-03-01', '1994-06-01', '1994-09-01', '1994-12-01', '1995-03-01', '1995-06-01', '1995-09-01', '1995-12-01', '1996-03-01', '1996-06-01', '1996-09-01', '1996-12-01', '1997-03-01', '1997-06-01', '1997-09-01', '1997-12-01', '1998-03-01', '1998-06-01', '1998-09-01', '1998-12-01', '1999-03-01', '1999-06-01', '1999-09-01', '1999-12-01', '2000-03-01', '2000-06-01', '2000-09-01', '2000-12-01', '2001-03-01', '2001-06-01', '2001-09-01', '2001-12-01', '2002-03-01', '2002-06-01', '2002-09-01', '2002-12-01', '2003-03-01', '2003-06-01', '2003-09-01', '2003-12-01', '2004-03-01', '2004-06-01', '2004-09-01', '2004-12-01', '2005-03-01', '2005-06-01', '2005-09-01', '2005-12-01', '2006-03-01', '2006-06-01', '2006-09-01', '2006-12-01', '2007-03-01', '2007-06-01', '2007-09-01', '2007-12-01', '2008-03-01', '2008-06-01', '2008-09-01', '2008-12-01', '2009-03-01', '2009-06-01', '2009-09-01', '2009-12-01', '2010-03-01', '2010-06-01', '2010-09-01', '2010-12-01', '2011-03-01', '2011-06-01', '2011-09-01', '2011-12-01', '2012-03-01', '2012-06-01', '2012-09-01', '2012-12-01', '2013-03-01', '2013-06-01', '2013-09-01', '2013-12-01', '2014-03-01', '2014-06-01', '2014-09-01', '2014-12-01', '2015-03-01', '2015-06-01', '2015-09-01', '2015-12-01', '2016-03-01', '2016-06-01', '2016-09-01', '2016-12-01', '2017-03-01', '2017-06-01']

















In [50]:



    


date_cols = ['1993-03-01', '1993-06-01', '1993-09-01', '1993-12-01', '1994-03-01', '1994-06-01', '1994-09-01', '1994-12-01', '1995-03-01', '1995-06-01', '1995-09-01', '1995-12-01', '1996-03-01', '1996-06-01', '1996-09-01', '1996-12-01', '1997-03-01', '1997-06-01', '1997-09-01', '1997-12-01', '1998-03-01', '1998-06-01', '1998-09-01', '1998-12-01', '1999-03-01', '1999-06-01', '1999-09-01', '1999-12-01', '2000-03-01', '2000-06-01', '2000-09-01', '2000-12-01', '2001-03-01', '2001-06-01', '2001-09-01', '2001-12-01', '2002-03-01', '2002-06-01', '2002-09-01', '2002-12-01', '2003-03-01', '2003-06-01', '2003-09-01', '2003-12-01', '2004-03-01', '2004-06-01', '2004-09-01', '2004-12-01', '2005-03-01', '2005-06-01', '2005-09-01', '2005-12-01', '2006-03-01', '2006-06-01', '2006-09-01', '2006-12-01', '2007-03-01', '2007-06-01', '2007-09-01', '2007-12-01', '2008-03-01', '2008-06-01', '2008-09-01', '2008-12-01', '2009-03-01', '2009-06-01', '2009-09-01', '2009-12-01', '2010-03-01', '2010-06-01', '2010-09-01', '2010-12-01', '2011-03-01', '2011-06-01', '2011-09-01', '2011-12-01', '2012-03-01', '2012-06-01', '2012-09-01', '2012-12-01', '2013-03-01', '2013-06-01', '2013-09-01', '2013-12-01', '2014-03-01', '2014-06-01', '2014-09-01', '2014-12-01', '2015-03-01', '2015-06-01', '2015-09-01', '2015-12-01', '2016-03-01', '2016-06-01', '2016-09-01', '2016-12-01', '2017-03-01', '2017-06-01']

mean_rents.fillna(0)\
    .groupby("SAU")[date_cols]\
    .sum()\
    .transpose()\
    .rolling(window=15).mean()\
    .transpose()



    











In [17]:



    


mean_rents.columns[3:]



    


















    
Out[17]:








Index(['1993-03-01', '1993-06-01', '1993-09-01', '1993-12-01', '1994-03-01',
       '1994-06-01', '1994-09-01', '1994-12-01', '1995-03-01', '1995-06-01',
       '1995-09-01', '1995-12-01', '1996-03-01', '1996-06-01', '1996-09-01',
       '1996-12-01', '1997-03-01', '1997-06-01', '1997-09-01', '1997-12-01',
       '1998-03-01', '1998-06-01', '1998-09-01', '1998-12-01', '1999-03-01',
       '1999-06-01', '1999-09-01', '1999-12-01', '2000-03-01', '2000-06-01',
       '2000-09-01', '2000-12-01', '2001-03-01', '2001-06-01', '2001-09-01',
       '2001-12-01', '2002-03-01', '2002-06-01', '2002-09-01', '2002-12-01',
       '2003-03-01', '2003-06-01', '2003-09-01', '2003-12-01', '2004-03-01',
       '2004-06-01', '2004-09-01', '2004-12-01', '2005-03-01', '2005-06-01',
       '2005-09-01', '2005-12-01', '2006-03-01', '2006-06-01', '2006-09-01',
       '2006-12-01', '2007-03-01', '2007-06-01', '2007-09-01', '2007-12-01',
       '2008-03-01', '2008-06-01', '2008-09-01', '2008-12-01', '2009-03-01',
       '2009-06-01', '2009-09-01', '2009-12-01', '2010-03-01', '2010-06-01',
       '2010-09-01', '2010-12-01', '2011-03-01', '2011-06-01', '2011-09-01',
       '2011-12-01', '2012-03-01', '2012-06-01', '2012-09-01', '2012-12-01',
       '2013-03-01', '2013-06-01', '2013-09-01', '2013-12-01', '2014-03-01',
       '2014-06-01', '2014-09-01', '2014-12-01', '2015-03-01', '2015-06-01',
       '2015-09-01', '2015-12-01', '2016-03-01', '2016-06-01', '2016-09-01',
       '2016-12-01', '2017-03-01', '2017-06-01'],
      dtype='object')

















In [53]:



    


mean_rents.ix[mean_rents.SAU == '574702', :]



    


















    
Out[53]:










  
    

      
      SAU
      Property_Type
      Bedrooms
      1993-03-01
      1993-06-01
      1993-09-01
      1993-12-01
      1994-03-01
      1994-06-01
      1994-09-01
      ...
      2015-03-01
      2015-06-01
      2015-09-01
      2015-12-01
      2016-03-01
      2016-06-01
      2016-09-01
      2016-12-01
      2017-03-01
      2017-06-01
    

  
  
  



0 rows × 101 columns

Content source: whereswelly-govhack/wheres-welly

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