import pandas as pd

# df1 = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/big_df_10.csv')
df1 = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/big_df_100.csv')
df2 = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/big_df_1000.csv')
df3 = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/big_df_2000.csv')
df4 = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/big_df_4000.csv')
df5 = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/big_df_6000.csv')
df6 = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/big_df_8000.csv')
df7 = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/big_df_10000.csv')
df8 = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/big_df_12000.csv')
df9 = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/big_df_13900.csv')
df10 = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/big_df_-1.csv')

df_all = pd.concat([df1, df2, df3, df4, df5, df6, df7, df8, df9, df10], axis=1)
df = df_all.copy()
df = df.loc[:,~df.columns.duplicated()]
dates = [i for i in range(1997,2030)]
df['dates'] = dates
columns = df.columns.tolist()
columns = columns[-1:] + columns[:-1]
df = df[columns]
df.drop('Unnamed: 0', axis=1, inplace=True)
# df.to_csv('big_df_all.csv', index=False)
df_t = df.set_index('dates').T
# df_t = df.T
# df_t.to_csv('big_df_all_t.csv', index=False)

df_t.reset_index(inplace=True)

df_t.rename(columns={"index": "RegionName"}, inplace=True) 
df_t

# df_t['roi_28_18'] = df_t[2028] - df_t[2018]
# df_t.to_csv('big_df_all_t.csv', index=False)
# three_zips_csv = three_zips.head(3)
# three_zips_csv.to_csv('three_zips_roi_28_18.csv')
# files.download('three_zips_roi_28_18.csv')
# three_zips = df_t.sort_values(by='roi_28_18', ascending=False)
# df_t['roi_19_18'] = df_t[2019] - df_t[2018]
# df_t.sort_values(by='roi_19_18', ascending=False)

df_t.index

Index(['60657', '77494', '60614', '77449', '77084', '79936', '60640', '11226',
       '10467', '78660',
       ...
       '43523', '81225', '21405', '45816', '41101', '47986', '89155', '55144',
       '4033', '86343'],
      dtype='object', length=13915)

og_df = pd.read_csv('https://raw.githubusercontent.com/danielcaraway/data/master/Zip_Zhvi_SingleFamilyResidence.csv', encoding='latin')

og_df.set_index('RegionName', inplace=True)

# new_df = pd.merge(df_t, og_df, left_index=True, right_index=True)

df_t.reset_indexa

new_df = df_t.join(og_df)

test = df_t.copy()
test.reset_index(drop=False, inplace=True)

test.rename(columns={"index": "RegionName"}, inplace=True)

test

test.set_index('RegionName', inplace=True)

test

df_t['RegionName'] = df_t['RegionName'].astype(int)

new_df = pd.merge(df_t, og_df, on="RegionName", how="left")

new_df

new_df.to_csv('zips_merged.csv')

from google.colab import files
files.download('zips_merged.csv')

list(new_df.columns)

['RegionName',
 1997,
 1998,
 1999,
 2000,
 2001,
 2002,
 2003,
 2004,
 2005,
 2006,
 2007,
 2008,
 2009,
 2010,
 2011,
 2012,
 2013,
 2014,
 2015,
 2016,
 2017,
 2018,
 2019,
 2020,
 2021,
 2022,
 2023,
 2024,
 2025,
 2026,
 2027,
 2028,
 2029,
 'RegionID',
 'City',
 'State',
 'Metro',
 'CountyName',
 'SizeRank',
 '1996-04',
 '1996-05',
 '1996-06',
 '1996-07',
 '1996-08',
 '1996-09',
 '1996-10',
 '1996-11',
 '1996-12',
 '1997-01',
 '1997-02',
 '1997-03',
 '1997-04',
 '1997-05',
 '1997-06',
 '1997-07',
 '1997-08',
 '1997-09',
 '1997-10',
 '1997-11',
 '1997-12',
 '1998-01',
 '1998-02',
 '1998-03',
 '1998-04',
 '1998-05',
 '1998-06',
 '1998-07',
 '1998-08',
 '1998-09',
 '1998-10',
 '1998-11',
 '1998-12',
 '1999-01',
 '1999-02',
 '1999-03',
 '1999-04',
 '1999-05',
 '1999-06',
 '1999-07',
 '1999-08',
 '1999-09',
 '1999-10',
 '1999-11',
 '1999-12',
 '2000-01',
 '2000-02',
 '2000-03',
 '2000-04',
 '2000-05',
 '2000-06',
 '2000-07',
 '2000-08',
 '2000-09',
 '2000-10',
 '2000-11',
 '2000-12',
 '2001-01',
 '2001-02',
 '2001-03',
 '2001-04',
 '2001-05',
 '2001-06',
 '2001-07',
 '2001-08',
 '2001-09',
 '2001-10',
 '2001-11',
 '2001-12',
 '2002-01',
 '2002-02',
 '2002-03',
 '2002-04',
 '2002-05',
 '2002-06',
 '2002-07',
 '2002-08',
 '2002-09',
 '2002-10',
 '2002-11',
 '2002-12',
 '2003-01',
 '2003-02',
 '2003-03',
 '2003-04',
 '2003-05',
 '2003-06',
 '2003-07',
 '2003-08',
 '2003-09',
 '2003-10',
 '2003-11',
 '2003-12',
 '2004-01',
 '2004-02',
 '2004-03',
 '2004-04',
 '2004-05',
 '2004-06',
 '2004-07',
 '2004-08',
 '2004-09',
 '2004-10',
 '2004-11',
 '2004-12',
 '2005-01',
 '2005-02',
 '2005-03',
 '2005-04',
 '2005-05',
 '2005-06',
 '2005-07',
 '2005-08',
 '2005-09',
 '2005-10',
 '2005-11',
 '2005-12',
 '2006-01',
 '2006-02',
 '2006-03',
 '2006-04',
 '2006-05',
 '2006-06',
 '2006-07',
 '2006-08',
 '2006-09',
 '2006-10',
 '2006-11',
 '2006-12',
 '2007-01',
 '2007-02',
 '2007-03',
 '2007-04',
 '2007-05',
 '2007-06',
 '2007-07',
 '2007-08',
 '2007-09',
 '2007-10',
 '2007-11',
 '2007-12',
 '2008-01',
 '2008-02',
 '2008-03',
 '2008-04',
 '2008-05',
 '2008-06',
 '2008-07',
 '2008-08',
 '2008-09',
 '2008-10',
 '2008-11',
 '2008-12',
 '2009-01',
 '2009-02',
 '2009-03',
 '2009-04',
 '2009-05',
 '2009-06',
 '2009-07',
 '2009-08',
 '2009-09',
 '2009-10',
 '2009-11',
 '2009-12',
 '2010-01',
 '2010-02',
 '2010-03',
 '2010-04',
 '2010-05',
 '2010-06',
 '2010-07',
 '2010-08',
 '2010-09',
 '2010-10',
 '2010-11',
 '2010-12',
 '2011-01',
 '2011-02',
 '2011-03',
 '2011-04',
 '2011-05',
 '2011-06',
 '2011-07',
 '2011-08',
 '2011-09',
 '2011-10',
 '2011-11',
 '2011-12',
 '2012-01',
 '2012-02',
 '2012-03',
 '2012-04',
 '2012-05',
 '2012-06',
 '2012-07',
 '2012-08',
 '2012-09',
 '2012-10',
 '2012-11',
 '2012-12',
 '2013-01',
 '2013-02',
 '2013-03',
 '2013-04',
 '2013-05',
 '2013-06',
 '2013-07',
 '2013-08',
 '2013-09',
 '2013-10',
 '2013-11',
 '2013-12',
 '2014-01',
 '2014-02',
 '2014-03',
 '2014-04',
 '2014-05',
 '2014-06',
 '2014-07',
 '2014-08',
 '2014-09',
 '2014-10',
 '2014-11',
 '2014-12',
 '2015-01',
 '2015-02',
 '2015-03',
 '2015-04',
 '2015-05',
 '2015-06',
 '2015-07',
 '2015-08',
 '2015-09',
 '2015-10',
 '2015-11',
 '2015-12',
 '2016-01',
 '2016-02',
 '2016-03',
 '2016-04',
 '2016-05',
 '2016-06',
 '2016-07',
 '2016-08',
 '2016-09',
 '2016-10',
 '2016-11',
 '2016-12',
 '2017-01',
 '2017-02',
 '2017-03',
 '2017-04',
 '2017-05',
 '2017-06',
 '2017-07',
 '2017-08',
 '2017-09',
 '2017-10',
 '2017-11',
 '2017-12',
 '2018-01',
 '2018-02',
 '2018-03',
 '2018-04',
 '2018-05',
 '2018-06',
 '2018-07',
 '2018-08',
 '2018-09',
 '2018-10',
 '2018-11',
 '2018-12',
 '2019-01',
 '2019-02',
 '2019-03',
 '2019-04',
 '2019-05',
 '2019-06',
 '2019-07',
 '2019-08',
 '2019-09',
 '2019-10',
 '2019-11',
 '2019-12']

columns = ['RegionName','RegionID',
 'City',
 'State',
 'Metro',
 'CountyName',
 'SizeRank',
  1997,
 1998,
 1999,
 2000,
 2001,
 2002,
 2003,
 2004,
 2005,
 2006,
 2007,
 2008,
 2009,
 2010,
 2011,
 2012,
 2013,
 2014,
 2015,
 2016,
 2017,
 2018,
 2019,
 2020,
 2021,
 2022,
 2023,
 2024,
 2025,
 2026,
 2027,
 2028,
 2029,
 '1996-04',
 '1996-05',
 '1996-06',
 '1996-07',
 '1996-08',
 '1996-09',
 '1996-10',
 '1996-11',
 '1996-12',
 '1997-01',
 '1997-02',
 '1997-03',
 '1997-04',
 '1997-05',
 '1997-06',
 '1997-07',
 '1997-08',
 '1997-09',
 '1997-10',
 '1997-11',
 '1997-12',
 '1998-01',
 '1998-02',
 '1998-03',
 '1998-04',
 '1998-05',
 '1998-06',
 '1998-07',
 '1998-08',
 '1998-09',
 '1998-10',
 '1998-11',
 '1998-12',
 '1999-01',
 '1999-02',
 '1999-03',
 '1999-04',
 '1999-05',
 '1999-06',
 '1999-07',
 '1999-08',
 '1999-09',
 '1999-10',
 '1999-11',
 '1999-12',
 '2000-01',
 '2000-02',
 '2000-03',
 '2000-04',
 '2000-05',
 '2000-06',
 '2000-07',
 '2000-08',
 '2000-09',
 '2000-10',
 '2000-11',
 '2000-12',
 '2001-01',
 '2001-02',
 '2001-03',
 '2001-04',
 '2001-05',
 '2001-06',
 '2001-07',
 '2001-08',
 '2001-09',
 '2001-10',
 '2001-11',
 '2001-12',
 '2002-01',
 '2002-02',
 '2002-03',
 '2002-04',
 '2002-05',
 '2002-06',
 '2002-07',
 '2002-08',
 '2002-09',
 '2002-10',
 '2002-11',
 '2002-12',
 '2003-01',
 '2003-02',
 '2003-03',
 '2003-04',
 '2003-05',
 '2003-06',
 '2003-07',
 '2003-08',
 '2003-09',
 '2003-10',
 '2003-11',
 '2003-12',
 '2004-01',
 '2004-02',
 '2004-03',
 '2004-04',
 '2004-05',
 '2004-06',
 '2004-07',
 '2004-08',
 '2004-09',
 '2004-10',
 '2004-11',
 '2004-12',
 '2005-01',
 '2005-02',
 '2005-03',
 '2005-04',
 '2005-05',
 '2005-06',
 '2005-07',
 '2005-08',
 '2005-09',
 '2005-10',
 '2005-11',
 '2005-12',
 '2006-01',
 '2006-02',
 '2006-03',
 '2006-04',
 '2006-05',
 '2006-06',
 '2006-07',
 '2006-08',
 '2006-09',
 '2006-10',
 '2006-11',
 '2006-12',
 '2007-01',
 '2007-02',
 '2007-03',
 '2007-04',
 '2007-05',
 '2007-06',
 '2007-07',
 '2007-08',
 '2007-09',
 '2007-10',
 '2007-11',
 '2007-12',
 '2008-01',
 '2008-02',
 '2008-03',
 '2008-04',
 '2008-05',
 '2008-06',
 '2008-07',
 '2008-08',
 '2008-09',
 '2008-10',
 '2008-11',
 '2008-12',
 '2009-01',
 '2009-02',
 '2009-03',
 '2009-04',
 '2009-05',
 '2009-06',
 '2009-07',
 '2009-08',
 '2009-09',
 '2009-10',
 '2009-11',
 '2009-12',
 '2010-01',
 '2010-02',
 '2010-03',
 '2010-04',
 '2010-05',
 '2010-06',
 '2010-07',
 '2010-08',
 '2010-09',
 '2010-10',
 '2010-11',
 '2010-12',
 '2011-01',
 '2011-02',
 '2011-03',
 '2011-04',
 '2011-05',
 '2011-06',
 '2011-07',
 '2011-08',
 '2011-09',
 '2011-10',
 '2011-11',
 '2011-12',
 '2012-01',
 '2012-02',
 '2012-03',
 '2012-04',
 '2012-05',
 '2012-06',
 '2012-07',
 '2012-08',
 '2012-09',
 '2012-10',
 '2012-11',
 '2012-12',
 '2013-01',
 '2013-02',
 '2013-03',
 '2013-04',
 '2013-05',
 '2013-06',
 '2013-07',
 '2013-08',
 '2013-09',
 '2013-10',
 '2013-11',
 '2013-12',
 '2014-01',
 '2014-02',
 '2014-03',
 '2014-04',
 '2014-05',
 '2014-06',
 '2014-07',
 '2014-08',
 '2014-09',
 '2014-10',
 '2014-11',
 '2014-12',
 '2015-01',
 '2015-02',
 '2015-03',
 '2015-04',
 '2015-05',
 '2015-06',
 '2015-07',
 '2015-08',
 '2015-09',
 '2015-10',
 '2015-11',
 '2015-12',
 '2016-01',
 '2016-02',
 '2016-03',
 '2016-04',
 '2016-05',
 '2016-06',
 '2016-07',
 '2016-08',
 '2016-09',
 '2016-10',
 '2016-11',
 '2016-12',
 '2017-01',
 '2017-02',
 '2017-03',
 '2017-04',
 '2017-05',
 '2017-06',
 '2017-07',
 '2017-08',
 '2017-09',
 '2017-10',
 '2017-11',
 '2017-12',
 '2018-01',
 '2018-02',
 '2018-03',
 '2018-04',
 '2018-05',
 '2018-06',
 '2018-07',
 '2018-08',
 '2018-09',
 '2018-10',
 '2018-11',
 '2018-12',
 '2019-01',
 '2019-02',
 '2019-03',
 '2019-04',
 '2019-05',
 '2019-06',
 '2019-07',
 '2019-08',
 '2019-09',
 '2019-10',
 '2019-11',
 '2019-12']

new_df = new_df[columns]

new_df.sort_values(by="SizeRank")

columns2 = ['RegionName',
'RegionID',
 'City',
 'State',
 'Metro',
 'CountyName',
 'SizeRank',
 1997,
 1998,
 1999,
 2000,
 2001,
 2002,
 2003,
 2004,
 2005,
 2006,
 2007,
 2008,
 2009,
 2010,
 2011,
 2012,
 2013,
 2014,
 2015,
 2016,
 2017,
 2018,
 2019,
 2020,
 2021,
 2022,
 2023,
 2024,
 2025,
 2026,
 2027,
 2028,
 2029]

new_df_sm = new_df[columns2]

new_df_sm

new_df_sm.to_csv('zip_predictions_2029.csv')

files.download('zip_predictions_2029.csv')

df = new_df_sm.copy()

df

df['roi_28_18'] = df[2028] - df[2018]

df

roi_28_18 = df.sort_values(by="roi_28_18", ascending = False)

df['roi_19_18'] = df[2019] - df[2018]
roi_19_18 = df.sort_values(by="roi_19_18", ascending = False)

df['roi_19_97'] = df[2019] - df[1997]
roi_19_97 = df.sort_values(by="roi_19_97", ascending = False)

roi_19_97

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from datetime import datetime

def prep_data_for_graph(df, first_col, last_col):
  top = df.head(10)
  print(top)

  def make_df(y, ds):
    row_df = pd.DataFrame({ "y": y.values, "ds": ds })
    row_df["ds"] = pd.to_datetime(row_df["ds"])
    return row_df

  def get_all(x):
    string = "" + x['City'] +", "+  x['State'] +", "+ str(x['RegionName'])
    return string


  i = list(top.columns).index(first_col)
  t = list(top.columns).index(last_col)
  # ds = top.columns[i:t+1]
  # # cols = top.columns[i:t+1]
  ds = top.columns[i:t+1]
  top['df'] = top.apply(lambda x: make_df(x[first_col:last_col], ds), axis=1) 
  # top['df'] = top.apply(lambda x: make_df(x['1997':'2019'], ds), axis=1) 
  top['combined'] = top.apply(lambda x: get_all(x), axis=1)
  return top

def graph_top(top, title):
  colors = sns.color_palette("Blues_d", n_colors=10)
  with sns.plotting_context("talk"):
    plt.figure(figsize=(8, 6))
    plt.plot('ds', 'y', data = top.iloc[0]['df'], label = ''+ top.iloc[0]['combined'] + '', linewidth=8, color= colors[0])
    plt.plot('ds', 'y', data = top.iloc[1]['df'], label = ''+ top.iloc[1]['combined'] + '', linewidth=8, color= colors[1])
    plt.plot('ds', 'y', data = top.iloc[2]['df'], label = ''+ top.iloc[2]['combined'] + '', linewidth=8, color= colors[2])
    plt.plot('ds', 'y', data = top.iloc[3]['df'], label = ''+ top.iloc[3]['combined'] + '', linestyle="dotted", linewidth=4, color= colors[3])
    plt.plot('ds', 'y', data = top.iloc[4]['df'], label = ''+ top.iloc[4]['combined'] + '', linestyle="dotted", linewidth=4, color= colors[4])
    plt.plot('ds', 'y', data = top.iloc[5]['df'], label = ''+ top.iloc[5]['combined'] + '', linestyle="dotted", linewidth=4, color= colors[5])
    plt.plot('ds', 'y', data = top.iloc[6]['df'], label = ''+ top.iloc[6]['combined'] + '', linestyle="dotted", linewidth=4, color= colors[6])
    plt.plot('ds', 'y', data = top.iloc[7]['df'], label = ''+ top.iloc[7]['combined'] + '', linestyle="dotted", linewidth=4, color= colors[7])
    plt.plot('ds', 'y', data = top.iloc[8]['df'], label = ''+ top.iloc[8]['combined'] + '', linestyle="dotted", linewidth=4, color= colors[8])
    plt.plot('ds', 'y', data = top.iloc[9]['df'], label = ''+ top.iloc[9]['combined'] + '', linestyle="dotted", linewidth=4, color= colors[9])
    # plt.title(title)
    plt.xlabel('Date')
    plt.ylabel('Average Home Price')
    
    plt.title("Best One Year Investment \n", loc='left', fontsize=24)
    plt.title("Predicted Housing Prices for greatest ROI 2019 - 2018", loc='right', fontsize=16, color='grey')
    
    plt.legend(loc = "center left", bbox_to_anchor = (1,.5), title = "City" )

df = new_df_sm.copy()
df.columns = [str(c) for c in df.columns]

df['roi_29_18'] = df['2029'] - df['2018']
df = df.sort_values(by='roi_29_18', ascending=False)

top = prep_data_for_graph(df, '1997', '2028')
graph_top(top, 'Predicted Housing Prices for greatest ROI 2019 - 2018')

       RegionName  RegionID  ...          2029     roi_29_18
6467        81611     93816  ...  7.407312e+06  2.421466e+06
8557        94027     97518  ...  6.213545e+06  1.858716e+06
275         10021     61635  ...  8.972140e+06  1.768008e+06
8933         2108     58622  ...  6.141112e+06  1.587729e+06
11972       11962     62309  ...  5.770525e+06  1.517469e+06
4107        33480     72636  ...  5.571661e+06  1.403712e+06
4182        90210     96086  ...  4.086202e+06  1.276086e+06
9333        11976     62321  ...  4.467197e+06  1.237088e+06
14          11201     62012  ...  4.297462e+06  1.221647e+06
8405        94028     97519  ...  4.339992e+06  1.221215e+06

[10 rows x 41 columns]

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:25: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:27: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:5: RuntimeWarning: Second argument 'y' is ambiguous: could be a color spec but is in data; using as data.  Either rename the entry in data or use three arguments to plot.
  """
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:6: RuntimeWarning: Second argument 'y' is ambiguous: could be a color spec but is in data; using as data.  Either rename the entry in data or use three arguments to plot.
  
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:7: RuntimeWarning: Second argument 'y' is ambiguous: could be a color spec but is in data; using as data.  Either rename the entry in data or use three arguments to plot.
  import sys
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:8: RuntimeWarning: Second argument 'y' is ambiguous: could be a color spec but is in data; using as data.  Either rename the entry in data or use three arguments to plot.
  
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:9: RuntimeWarning: Second argument 'y' is ambiguous: could be a color spec but is in data; using as data.  Either rename the entry in data or use three arguments to plot.
  if __name__ == '__main__':
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:10: RuntimeWarning: Second argument 'y' is ambiguous: could be a color spec but is in data; using as data.  Either rename the entry in data or use three arguments to plot.
  # Remove the CWD from sys.path while we load stuff.
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:11: RuntimeWarning: Second argument 'y' is ambiguous: could be a color spec but is in data; using as data.  Either rename the entry in data or use three arguments to plot.
  # This is added back by InteractiveShellApp.init_path()
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:12: RuntimeWarning: Second argument 'y' is ambiguous: could be a color spec but is in data; using as data.  Either rename the entry in data or use three arguments to plot.
  if sys.path[0] == '':
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:13: RuntimeWarning: Second argument 'y' is ambiguous: could be a color spec but is in data; using as data.  Either rename the entry in data or use three arguments to plot.
  del sys.path[0]
/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:14: RuntimeWarning: Second argument 'y' is ambiguous: could be a color spec but is in data; using as data.  Either rename the entry in data or use three arguments to plot.

ddf.columns

Index(['RegionName', 'RegionID', 'City', 'State', 'Metro', 'CountyName',
       'SizeRank', '1997', '1998', '1999', '2000', '2001', '2002', '2003',
       '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011', '2012',
       '2013', '2014', '2015', '2016', '2017', '2018', '2019', '2020', '2021',
       '2022', '2023', '2024', '2025', '2026', '2027', '2028', '2029'],
      dtype='object')

# ``
# i = list(roi_19_97.columns).index(1997)
# t = list(roi_19_97.columns).index(2029)
# ds = roi_19_97.columns[i:t+1]

  File "<ipython-input-139-5e893c5e7243>", line 1
    ``
    ^
SyntaxError: invalid syntax

ds

Index([1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008,
       2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020,
       2021, 2022, 2023, 2024, 2025, 2026, 2027, 2028, 2029],
      dtype='object')

df[1997:2029].values

array([[32503, 72025, 'Pensacola', ..., 13415.145938048838,
        2439.324944505439, 68073.58877023548],
       [10301, 61777, 'New York', ..., 139537.9941916227,
        16253.88012530515, 350376.28513985587],
       [85257, 94849, 'Scottsdale', ..., 41429.55038492585,
        5098.252208032587, 161753.37711471418],
       ...,
       [80031, 93219, 'Westminster', ..., 46122.08569086075,
        6283.624252735055, 168055.68983238138],
       [97404, 99254, 'Eugene', ..., 25696.133263382595,
        5023.897008099768, 121967.92344510881],
       [40165, 75548, 'Shepherdsville', ..., 22101.061717464123,
        3302.4932674675365, 59729.98015002094]], dtype=object)

df

df.dtypes

RegionName      int64
RegionID        int64
City           object
State          object
Metro          object
CountyName     object
SizeRank        int64
1997          float64
1998          float64
1999          float64
2000          float64
2001          float64
2002          float64
2003          float64
2004          float64
2005          float64
2006          float64
2007          float64
2008          float64
2009          float64
2010          float64
2011          float64
2012          float64
2013          float64
2014          float64
2015          float64
2016          float64
2017          float64
2018          float64
2019          float64
2020          float64
2021          float64
2022          float64
2023          float64
2024          float64
2025          float64
2026          float64
2027          float64
2028          float64
2029          float64
dtype: object

df

mine = df[df['RegionName'] == 90042]

mine['roi'] = mine['2029'] - mine['2020']

/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: 
A value is trying to be set on a copy of a slice from a DataFrame.
Try using .loc[row_indexer,col_indexer] = value instead

See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy
  """Entry point for launching an IPython kernel.

mine

blues = sns.palplot(sns.color_palette("Blues_d", n_colors=10))

sns.palplot(sns.color_palette("Oranges_d", n_colors=10)[1])

sns.palplot(sns.color_palette("Oranges_d", n_colors=10))

blues[1]

---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-286-3ec727638696> in <module>()
----> 1 blues[1]

TypeError: 'NoneType' object is not subscriptable

blues = (sns.color_palette("Blues_d", n_colors=10)

  File "<ipython-input-287-d2e351711367>", line 1
    blues = (sns.color_palette("Blues_d", n_colors=10)
                                                      ^
SyntaxError: unexpected EOF while parsing

blues = sns.color_palette("Blues_d", n_colors=10)

blues[0]

(0.20282968089196465, 0.25942329873125725, 0.29998205818275026)

dates	RegionName	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026	2027	2028	2029
0	60657	471939.146986	496487.087257	522374.359072	549597.800846	564426.006323	588973.946599	614861.218411	642080.732846	656903.847279	681444.165086	707317.035488	7.345223e+05	7.493324e+05	7.738621e+05	7.997312e+05	8.269365e+05	8.417464e+05	8.662762e+05	8.921453e+05	9.193505e+05	9.341605e+05	9.586903e+05	9.845594e+05	1.041382e+06	1.040300e+06	1.070158e+06	1.101328e+06	1.133797e+06	1.132714e+06	1.162573e+06	1.193742e+06	1.226211e+06	1.225129e+06
1	77494	202222.028612	207762.918144	212961.066732	217816.842570	223657.943941	229198.833509	234396.982068	239252.757916	245093.859298	250638.201717	255840.224392	2.607071e+05	2.665659e+05	2.721364e+05	2.773679e+05	2.850586e+05	2.937513e+05	3.021387e+05	3.101834e+05	3.178857e+05	3.265812e+05	3.349686e+05	3.430133e+05	3.452969e+05	3.580764e+05	3.650972e+05	3.717798e+05	3.781268e+05	3.909063e+05	3.979271e+05	4.046097e+05	4.109567e+05	4.237362e+05
2	60614	796920.397585	824024.164447	849791.908981	874214.243009	869097.909573	896201.676216	921969.420977	946391.754876	941275.421352	968379.178964	994146.907946	1.018569e+06	1.013452e+06	1.040555e+06	1.066322e+06	1.090743e+06	1.085626e+06	1.112729e+06	1.138496e+06	1.162917e+06	1.157800e+06	1.184903e+06	1.210669e+06	1.247033e+06	1.258160e+06	1.279870e+06	1.300220e+06	1.319207e+06	1.330333e+06	1.352044e+06	1.372393e+06	1.391380e+06	1.402507e+06
3	77449	91824.749488	95747.158288	98706.117626	100701.913820	104819.786172	108742.194970	111701.154312	113696.950513	117814.822871	121737.231672	124696.191015	1.266926e+05	1.308134e+05	1.347436e+05	1.377112e+05	1.397210e+05	1.438554e+05	1.477966e+05	1.507758e+05	1.527918e+05	1.569300e+05	1.608726e+05	1.638518e+05	1.512876e+05	1.669226e+05	1.670198e+05	1.661645e+05	1.643636e+05	1.799987e+05	1.800958e+05	1.792405e+05	1.774397e+05	1.930747e+05
4	77084	91459.742472	95429.981924	98406.642481	100389.968950	104448.220793	108418.460261	111395.120826	113378.447305	117437.724067	121408.985719	124386.668479	1.263710e+05	1.304339e+05	1.344147e+05	1.374060e+05	1.394107e+05	1.434962e+05	1.474937e+05	1.504996e+05	1.525123e+05	1.565999e+05	1.605995e+05	1.636055e+05	1.506939e+05	1.666394e+05	1.666729e+05	1.657240e+05	1.637997e+05	1.797452e+05	1.797787e+05	1.788298e+05	1.769055e+05	1.928510e+05
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13910	47986	66037.989375	66339.088058	66628.620749	66905.727509	64451.358133	64752.456813	65041.989476	65319.096199	62864.709569	63165.685591	63454.504079	6.373083e+04	6.127555e+04	6.157573e+04	6.186435e+04	6.214054e+04	5.968526e+04	5.998544e+04	6.027406e+04	6.055025e+04	5.809497e+04	5.839515e+04	5.868377e+04	6.151348e+04	5.922287e+04	5.947162e+04	5.970535e+04	5.992320e+04	5.763258e+04	5.788133e+04	5.811506e+04	5.833291e+04	5.604229e+04
13911	89155	250691.446764	255028.623188	258459.285007	260983.330300	264084.518904	268421.695325	271852.357148	274376.402445	277477.591049	281814.767023	285245.428393	2.877695e+05	2.908707e+05	2.952078e+05	2.986385e+05	3.011625e+05	3.042637e+05	3.086009e+05	3.120316e+05	3.145556e+05	3.176568e+05	3.219940e+05	3.254246e+05	3.156357e+05	3.295675e+05	3.302840e+05	3.301024e+05	3.290288e+05	3.429606e+05	3.436770e+05	3.434955e+05	3.424218e+05	3.563536e+05
13912	55144	76857.478471	82633.187309	87967.752763	92860.388688	95856.195633	101631.904482	106966.469947	111859.105861	114854.912788	120630.621620	125965.187071	1.308578e+05	1.338536e+05	1.396293e+05	1.449639e+05	1.498565e+05	1.528523e+05	1.586281e+05	1.639626e+05	1.688553e+05	1.718511e+05	1.776268e+05	1.829613e+05	1.840168e+05	1.923046e+05	1.963140e+05	1.998836e+05	2.030155e+05	2.113034e+05	2.153127e+05	2.188823e+05	2.220142e+05	2.303021e+05
13913	4033	444447.599617	466985.532392	489652.665437	512441.900874	511143.936786	533681.869435	556349.002322	579134.788063	577833.185287	600363.857043	623023.102601	6.458045e+05	6.444986e+05	6.670286e+05	6.896879e+05	7.124692e+05	7.111633e+05	7.336934e+05	7.563526e+05	7.791340e+05	7.778281e+05	8.003581e+05	8.230174e+05	8.713253e+05	8.686947e+05	8.916974e+05	9.147987e+05	9.379900e+05	9.353594e+05	9.583622e+05	9.814635e+05	1.004655e+06	1.002024e+06
13914	86343	91454.193554	94682.526922	97183.095405	98956.171309	102173.960151	105402.293517	107902.861999	109675.937901	112893.726742	116122.060107	118622.628589	1.203957e+05	1.236135e+05	1.268418e+05	1.293424e+05	1.311155e+05	1.343333e+05	1.375616e+05	1.400622e+05	1.418352e+05	1.450530e+05	1.482814e+05	1.507819e+05	1.417013e+05	1.536021e+05	1.539259e+05	1.535305e+05	1.524210e+05	1.643219e+05	1.646457e+05	1.642503e+05	1.631408e+05	1.750416e+05

dates	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026	2027	2028	2029
60657	471939.146986	496487.087257	522374.359072	549597.800846	564426.006323	588973.946599	614861.218411	642080.732846	656903.847279	681444.165086	707317.035488	7.345223e+05	7.493324e+05	7.738621e+05	7.997312e+05	8.269365e+05	8.417464e+05	8.662762e+05	8.921453e+05	9.193505e+05	9.341605e+05	9.586903e+05	9.845594e+05	1.041382e+06	1.040300e+06	1.070158e+06	1.101328e+06	1.133797e+06	1.132714e+06	1.162573e+06	1.193742e+06	1.226211e+06	1.225129e+06
77494	202222.028612	207762.918144	212961.066732	217816.842570	223657.943941	229198.833509	234396.982068	239252.757916	245093.859298	250638.201717	255840.224392	2.607071e+05	2.665659e+05	2.721364e+05	2.773679e+05	2.850586e+05	2.937513e+05	3.021387e+05	3.101834e+05	3.178857e+05	3.265812e+05	3.349686e+05	3.430133e+05	3.452969e+05	3.580764e+05	3.650972e+05	3.717798e+05	3.781268e+05	3.909063e+05	3.979271e+05	4.046097e+05	4.109567e+05	4.237362e+05
60614	796920.397585	824024.164447	849791.908981	874214.243009	869097.909573	896201.676216	921969.420977	946391.754876	941275.421352	968379.178964	994146.907946	1.018569e+06	1.013452e+06	1.040555e+06	1.066322e+06	1.090743e+06	1.085626e+06	1.112729e+06	1.138496e+06	1.162917e+06	1.157800e+06	1.184903e+06	1.210669e+06	1.247033e+06	1.258160e+06	1.279870e+06	1.300220e+06	1.319207e+06	1.330333e+06	1.352044e+06	1.372393e+06	1.391380e+06	1.402507e+06
77449	91824.749488	95747.158288	98706.117626	100701.913820	104819.786172	108742.194970	111701.154312	113696.950513	117814.822871	121737.231672	124696.191015	1.266926e+05	1.308134e+05	1.347436e+05	1.377112e+05	1.397210e+05	1.438554e+05	1.477966e+05	1.507758e+05	1.527918e+05	1.569300e+05	1.608726e+05	1.638518e+05	1.512876e+05	1.669226e+05	1.670198e+05	1.661645e+05	1.643636e+05	1.799987e+05	1.800958e+05	1.792405e+05	1.774397e+05	1.930747e+05
77084	91459.742472	95429.981924	98406.642481	100389.968950	104448.220793	108418.460261	111395.120826	113378.447305	117437.724067	121408.985719	124386.668479	1.263710e+05	1.304339e+05	1.344147e+05	1.374060e+05	1.394107e+05	1.434962e+05	1.474937e+05	1.504996e+05	1.525123e+05	1.565999e+05	1.605995e+05	1.636055e+05	1.506939e+05	1.666394e+05	1.666729e+05	1.657240e+05	1.637997e+05	1.797452e+05	1.797787e+05	1.788298e+05	1.769055e+05	1.928510e+05
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
47986	66037.989375	66339.088058	66628.620749	66905.727509	64451.358133	64752.456813	65041.989476	65319.096199	62864.709569	63165.685591	63454.504079	6.373083e+04	6.127555e+04	6.157573e+04	6.186435e+04	6.214054e+04	5.968526e+04	5.998544e+04	6.027406e+04	6.055025e+04	5.809497e+04	5.839515e+04	5.868377e+04	6.151348e+04	5.922287e+04	5.947162e+04	5.970535e+04	5.992320e+04	5.763258e+04	5.788133e+04	5.811506e+04	5.833291e+04	5.604229e+04
89155	250691.446764	255028.623188	258459.285007	260983.330300	264084.518904	268421.695325	271852.357148	274376.402445	277477.591049	281814.767023	285245.428393	2.877695e+05	2.908707e+05	2.952078e+05	2.986385e+05	3.011625e+05	3.042637e+05	3.086009e+05	3.120316e+05	3.145556e+05	3.176568e+05	3.219940e+05	3.254246e+05	3.156357e+05	3.295675e+05	3.302840e+05	3.301024e+05	3.290288e+05	3.429606e+05	3.436770e+05	3.434955e+05	3.424218e+05	3.563536e+05
55144	76857.478471	82633.187309	87967.752763	92860.388688	95856.195633	101631.904482	106966.469947	111859.105861	114854.912788	120630.621620	125965.187071	1.308578e+05	1.338536e+05	1.396293e+05	1.449639e+05	1.498565e+05	1.528523e+05	1.586281e+05	1.639626e+05	1.688553e+05	1.718511e+05	1.776268e+05	1.829613e+05	1.840168e+05	1.923046e+05	1.963140e+05	1.998836e+05	2.030155e+05	2.113034e+05	2.153127e+05	2.188823e+05	2.220142e+05	2.303021e+05
4033	444447.599617	466985.532392	489652.665437	512441.900874	511143.936786	533681.869435	556349.002322	579134.788063	577833.185287	600363.857043	623023.102601	6.458045e+05	6.444986e+05	6.670286e+05	6.896879e+05	7.124692e+05	7.111633e+05	7.336934e+05	7.563526e+05	7.791340e+05	7.778281e+05	8.003581e+05	8.230174e+05	8.713253e+05	8.686947e+05	8.916974e+05	9.147987e+05	9.379900e+05	9.353594e+05	9.583622e+05	9.814635e+05	1.004655e+06	1.002024e+06
86343	91454.193554	94682.526922	97183.095405	98956.171309	102173.960151	105402.293517	107902.861999	109675.937901	112893.726742	116122.060107	118622.628589	1.203957e+05	1.236135e+05	1.268418e+05	1.293424e+05	1.311155e+05	1.343333e+05	1.375616e+05	1.400622e+05	1.418352e+05	1.450530e+05	1.482814e+05	1.507819e+05	1.417013e+05	1.536021e+05	1.539259e+05	1.535305e+05	1.524210e+05	1.643219e+05	1.646457e+05	1.642503e+05	1.631408e+05	1.750416e+05

dates	RegionName	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026	2027	2028	2029
0	60657	471939.146986	496487.087257	522374.359072	549597.800846	564426.006323	588973.946599	614861.218411	642080.732846	656903.847279	681444.165086	707317.035488	7.345223e+05	7.493324e+05	7.738621e+05	7.997312e+05	8.269365e+05	8.417464e+05	8.662762e+05	8.921453e+05	9.193505e+05	9.341605e+05	9.586903e+05	9.845594e+05	1.041382e+06	1.040300e+06	1.070158e+06	1.101328e+06	1.133797e+06	1.132714e+06	1.162573e+06	1.193742e+06	1.226211e+06	1.225129e+06
1	77494	202222.028612	207762.918144	212961.066732	217816.842570	223657.943941	229198.833509	234396.982068	239252.757916	245093.859298	250638.201717	255840.224392	2.607071e+05	2.665659e+05	2.721364e+05	2.773679e+05	2.850586e+05	2.937513e+05	3.021387e+05	3.101834e+05	3.178857e+05	3.265812e+05	3.349686e+05	3.430133e+05	3.452969e+05	3.580764e+05	3.650972e+05	3.717798e+05	3.781268e+05	3.909063e+05	3.979271e+05	4.046097e+05	4.109567e+05	4.237362e+05
2	60614	796920.397585	824024.164447	849791.908981	874214.243009	869097.909573	896201.676216	921969.420977	946391.754876	941275.421352	968379.178964	994146.907946	1.018569e+06	1.013452e+06	1.040555e+06	1.066322e+06	1.090743e+06	1.085626e+06	1.112729e+06	1.138496e+06	1.162917e+06	1.157800e+06	1.184903e+06	1.210669e+06	1.247033e+06	1.258160e+06	1.279870e+06	1.300220e+06	1.319207e+06	1.330333e+06	1.352044e+06	1.372393e+06	1.391380e+06	1.402507e+06
3	77449	91824.749488	95747.158288	98706.117626	100701.913820	104819.786172	108742.194970	111701.154312	113696.950513	117814.822871	121737.231672	124696.191015	1.266926e+05	1.308134e+05	1.347436e+05	1.377112e+05	1.397210e+05	1.438554e+05	1.477966e+05	1.507758e+05	1.527918e+05	1.569300e+05	1.608726e+05	1.638518e+05	1.512876e+05	1.669226e+05	1.670198e+05	1.661645e+05	1.643636e+05	1.799987e+05	1.800958e+05	1.792405e+05	1.774397e+05	1.930747e+05
4	77084	91459.742472	95429.981924	98406.642481	100389.968950	104448.220793	108418.460261	111395.120826	113378.447305	117437.724067	121408.985719	124386.668479	1.263710e+05	1.304339e+05	1.344147e+05	1.374060e+05	1.394107e+05	1.434962e+05	1.474937e+05	1.504996e+05	1.525123e+05	1.565999e+05	1.605995e+05	1.636055e+05	1.506939e+05	1.666394e+05	1.666729e+05	1.657240e+05	1.637997e+05	1.797452e+05	1.797787e+05	1.788298e+05	1.769055e+05	1.928510e+05
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13910	47986	66037.989375	66339.088058	66628.620749	66905.727509	64451.358133	64752.456813	65041.989476	65319.096199	62864.709569	63165.685591	63454.504079	6.373083e+04	6.127555e+04	6.157573e+04	6.186435e+04	6.214054e+04	5.968526e+04	5.998544e+04	6.027406e+04	6.055025e+04	5.809497e+04	5.839515e+04	5.868377e+04	6.151348e+04	5.922287e+04	5.947162e+04	5.970535e+04	5.992320e+04	5.763258e+04	5.788133e+04	5.811506e+04	5.833291e+04	5.604229e+04
13911	89155	250691.446764	255028.623188	258459.285007	260983.330300	264084.518904	268421.695325	271852.357148	274376.402445	277477.591049	281814.767023	285245.428393	2.877695e+05	2.908707e+05	2.952078e+05	2.986385e+05	3.011625e+05	3.042637e+05	3.086009e+05	3.120316e+05	3.145556e+05	3.176568e+05	3.219940e+05	3.254246e+05	3.156357e+05	3.295675e+05	3.302840e+05	3.301024e+05	3.290288e+05	3.429606e+05	3.436770e+05	3.434955e+05	3.424218e+05	3.563536e+05
13912	55144	76857.478471	82633.187309	87967.752763	92860.388688	95856.195633	101631.904482	106966.469947	111859.105861	114854.912788	120630.621620	125965.187071	1.308578e+05	1.338536e+05	1.396293e+05	1.449639e+05	1.498565e+05	1.528523e+05	1.586281e+05	1.639626e+05	1.688553e+05	1.718511e+05	1.776268e+05	1.829613e+05	1.840168e+05	1.923046e+05	1.963140e+05	1.998836e+05	2.030155e+05	2.113034e+05	2.153127e+05	2.188823e+05	2.220142e+05	2.303021e+05
13913	4033	444447.599617	466985.532392	489652.665437	512441.900874	511143.936786	533681.869435	556349.002322	579134.788063	577833.185287	600363.857043	623023.102601	6.458045e+05	6.444986e+05	6.670286e+05	6.896879e+05	7.124692e+05	7.111633e+05	7.336934e+05	7.563526e+05	7.791340e+05	7.778281e+05	8.003581e+05	8.230174e+05	8.713253e+05	8.686947e+05	8.916974e+05	9.147987e+05	9.379900e+05	9.353594e+05	9.583622e+05	9.814635e+05	1.004655e+06	1.002024e+06
13914	86343	91454.193554	94682.526922	97183.095405	98956.171309	102173.960151	105402.293517	107902.861999	109675.937901	112893.726742	116122.060107	118622.628589	1.203957e+05	1.236135e+05	1.268418e+05	1.293424e+05	1.311155e+05	1.343333e+05	1.375616e+05	1.400622e+05	1.418352e+05	1.450530e+05	1.482814e+05	1.507819e+05	1.417013e+05	1.536021e+05	1.539259e+05	1.535305e+05	1.524210e+05	1.643219e+05	1.646457e+05	1.642503e+05	1.631408e+05	1.750416e+05

dates	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026	2027	2028	2029
RegionName
60657	471939.146986	496487.087257	522374.359072	549597.800846	564426.006323	588973.946599	614861.218411	642080.732846	656903.847279	681444.165086	707317.035488	7.345223e+05	7.493324e+05	7.738621e+05	7.997312e+05	8.269365e+05	8.417464e+05	8.662762e+05	8.921453e+05	9.193505e+05	9.341605e+05	9.586903e+05	9.845594e+05	1.041382e+06	1.040300e+06	1.070158e+06	1.101328e+06	1.133797e+06	1.132714e+06	1.162573e+06	1.193742e+06	1.226211e+06	1.225129e+06
77494	202222.028612	207762.918144	212961.066732	217816.842570	223657.943941	229198.833509	234396.982068	239252.757916	245093.859298	250638.201717	255840.224392	2.607071e+05	2.665659e+05	2.721364e+05	2.773679e+05	2.850586e+05	2.937513e+05	3.021387e+05	3.101834e+05	3.178857e+05	3.265812e+05	3.349686e+05	3.430133e+05	3.452969e+05	3.580764e+05	3.650972e+05	3.717798e+05	3.781268e+05	3.909063e+05	3.979271e+05	4.046097e+05	4.109567e+05	4.237362e+05
60614	796920.397585	824024.164447	849791.908981	874214.243009	869097.909573	896201.676216	921969.420977	946391.754876	941275.421352	968379.178964	994146.907946	1.018569e+06	1.013452e+06	1.040555e+06	1.066322e+06	1.090743e+06	1.085626e+06	1.112729e+06	1.138496e+06	1.162917e+06	1.157800e+06	1.184903e+06	1.210669e+06	1.247033e+06	1.258160e+06	1.279870e+06	1.300220e+06	1.319207e+06	1.330333e+06	1.352044e+06	1.372393e+06	1.391380e+06	1.402507e+06
77449	91824.749488	95747.158288	98706.117626	100701.913820	104819.786172	108742.194970	111701.154312	113696.950513	117814.822871	121737.231672	124696.191015	1.266926e+05	1.308134e+05	1.347436e+05	1.377112e+05	1.397210e+05	1.438554e+05	1.477966e+05	1.507758e+05	1.527918e+05	1.569300e+05	1.608726e+05	1.638518e+05	1.512876e+05	1.669226e+05	1.670198e+05	1.661645e+05	1.643636e+05	1.799987e+05	1.800958e+05	1.792405e+05	1.774397e+05	1.930747e+05
77084	91459.742472	95429.981924	98406.642481	100389.968950	104448.220793	108418.460261	111395.120826	113378.447305	117437.724067	121408.985719	124386.668479	1.263710e+05	1.304339e+05	1.344147e+05	1.374060e+05	1.394107e+05	1.434962e+05	1.474937e+05	1.504996e+05	1.525123e+05	1.565999e+05	1.605995e+05	1.636055e+05	1.506939e+05	1.666394e+05	1.666729e+05	1.657240e+05	1.637997e+05	1.797452e+05	1.797787e+05	1.788298e+05	1.769055e+05	1.928510e+05
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
47986	66037.989375	66339.088058	66628.620749	66905.727509	64451.358133	64752.456813	65041.989476	65319.096199	62864.709569	63165.685591	63454.504079	6.373083e+04	6.127555e+04	6.157573e+04	6.186435e+04	6.214054e+04	5.968526e+04	5.998544e+04	6.027406e+04	6.055025e+04	5.809497e+04	5.839515e+04	5.868377e+04	6.151348e+04	5.922287e+04	5.947162e+04	5.970535e+04	5.992320e+04	5.763258e+04	5.788133e+04	5.811506e+04	5.833291e+04	5.604229e+04
89155	250691.446764	255028.623188	258459.285007	260983.330300	264084.518904	268421.695325	271852.357148	274376.402445	277477.591049	281814.767023	285245.428393	2.877695e+05	2.908707e+05	2.952078e+05	2.986385e+05	3.011625e+05	3.042637e+05	3.086009e+05	3.120316e+05	3.145556e+05	3.176568e+05	3.219940e+05	3.254246e+05	3.156357e+05	3.295675e+05	3.302840e+05	3.301024e+05	3.290288e+05	3.429606e+05	3.436770e+05	3.434955e+05	3.424218e+05	3.563536e+05
55144	76857.478471	82633.187309	87967.752763	92860.388688	95856.195633	101631.904482	106966.469947	111859.105861	114854.912788	120630.621620	125965.187071	1.308578e+05	1.338536e+05	1.396293e+05	1.449639e+05	1.498565e+05	1.528523e+05	1.586281e+05	1.639626e+05	1.688553e+05	1.718511e+05	1.776268e+05	1.829613e+05	1.840168e+05	1.923046e+05	1.963140e+05	1.998836e+05	2.030155e+05	2.113034e+05	2.153127e+05	2.188823e+05	2.220142e+05	2.303021e+05
4033	444447.599617	466985.532392	489652.665437	512441.900874	511143.936786	533681.869435	556349.002322	579134.788063	577833.185287	600363.857043	623023.102601	6.458045e+05	6.444986e+05	6.670286e+05	6.896879e+05	7.124692e+05	7.111633e+05	7.336934e+05	7.563526e+05	7.791340e+05	7.778281e+05	8.003581e+05	8.230174e+05	8.713253e+05	8.686947e+05	8.916974e+05	9.147987e+05	9.379900e+05	9.353594e+05	9.583622e+05	9.814635e+05	1.004655e+06	1.002024e+06
86343	91454.193554	94682.526922	97183.095405	98956.171309	102173.960151	105402.293517	107902.861999	109675.937901	112893.726742	116122.060107	118622.628589	1.203957e+05	1.236135e+05	1.268418e+05	1.293424e+05	1.311155e+05	1.343333e+05	1.375616e+05	1.400622e+05	1.418352e+05	1.450530e+05	1.482814e+05	1.507819e+05	1.417013e+05	1.536021e+05	1.539259e+05	1.535305e+05	1.524210e+05	1.643219e+05	1.646457e+05	1.642503e+05	1.631408e+05	1.750416e+05

	RegionName	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026	2027	2028	2029	RegionID	City	State	Metro	CountyName	SizeRank	...	2016-09	2016-10	2016-11	2016-12	2017-01	2017-02	2017-03	2017-04	2017-05	2017-06	2017-07	2017-08	2017-09	2017-10	2017-11	2017-12	2018-01	2018-02	2018-03	2018-04	2018-05	2018-06	2018-07	2018-08	2018-09	2018-10	2018-11	2018-12	2019-01	2019-02	2019-03	2019-04	2019-05	2019-06	2019-07	2019-08	2019-09	2019-10	2019-11	2019-12
0	60657	471939.146986	496487.087257	522374.359072	549597.800846	564426.006323	588973.946599	614861.218411	642080.732846	656903.847279	681444.165086	707317.035488	7.345223e+05	7.493324e+05	7.738621e+05	7.997312e+05	8.269365e+05	8.417464e+05	8.662762e+05	8.921453e+05	9.193505e+05	9.341605e+05	9.586903e+05	9.845594e+05	1.041382e+06	1.040300e+06	1.070158e+06	1.101328e+06	1.133797e+06	1.132714e+06	1.162573e+06	1.193742e+06	1.226211e+06	1.225129e+06	84654	Chicago	IL	Chicago-Naperville-Elgin	Cook County	2	...	926166.0	926066.0	929987.0	931155.0	933542.0	935872.0	945765.0	950893.0	950923.0	945971.0	940106.0	937789.0	937761.0	935120.0	931248	932154	941705	953981	959260	960954	959673	959082	954912	950660	948543	948906	951721	954743	957441	961651	966123	967557	965155	960225	956709	953095	950684	948136	946838	945928
1	77494	202222.028612	207762.918144	212961.066732	217816.842570	223657.943941	229198.833509	234396.982068	239252.757916	245093.859298	250638.201717	255840.224392	2.607071e+05	2.665659e+05	2.721364e+05	2.773679e+05	2.850586e+05	2.937513e+05	3.021387e+05	3.101834e+05	3.178857e+05	3.265812e+05	3.349686e+05	3.430133e+05	3.452969e+05	3.580764e+05	3.650972e+05	3.717798e+05	3.781268e+05	3.909063e+05	3.979271e+05	4.046097e+05	4.109567e+05	4.237362e+05	91982	Katy	TX	Houston-The Woodlands-Sugar Land	Harris County	4	...	329476.0	328831.0	328912.0	329142.0	329312.0	330345.0	331281.0	331534.0	330699.0	330184.0	330013.0	329488.0	329015.0	328902.0	329123	330287	331699	332214	332264	332737	333805	334293	333806	333609	333839	334129	334753	334497	335272	335363	335789	335035	334542	334176	334363	334127	334458	334460	334679	334309
2	60614	796920.397585	824024.164447	849791.908981	874214.243009	869097.909573	896201.676216	921969.420977	946391.754876	941275.421352	968379.178964	994146.907946	1.018569e+06	1.013452e+06	1.040555e+06	1.066322e+06	1.090743e+06	1.085626e+06	1.112729e+06	1.138496e+06	1.162917e+06	1.157800e+06	1.184903e+06	1.210669e+06	1.247033e+06	1.258160e+06	1.279870e+06	1.300220e+06	1.319207e+06	1.330333e+06	1.352044e+06	1.372393e+06	1.391380e+06	1.402507e+06	84616	Chicago	IL	Chicago-Naperville-Elgin	Cook County	5	...	1141840.0	1140892.0	1144867.0	1147812.0	1153703.0	1159757.0	1174693.0	1185995.0	1191061.0	1187382.0	1179120.0	1174446.0	1174467.0	1173676.0	1172203	1176623	1186741	1197817	1201566	1204015	1204371	1201598	1195307	1189358	1186838	1185950	1187917	1190385	1191138	1193324	1198898	1203423	1204840	1199747	1194591	1188702	1183500	1179024	1175407	1174008
3	77449	91824.749488	95747.158288	98706.117626	100701.913820	104819.786172	108742.194970	111701.154312	113696.950513	117814.822871	121737.231672	124696.191015	1.266926e+05	1.308134e+05	1.347436e+05	1.377112e+05	1.397210e+05	1.438554e+05	1.477966e+05	1.507758e+05	1.527918e+05	1.569300e+05	1.608726e+05	1.638518e+05	1.512876e+05	1.669226e+05	1.670198e+05	1.661645e+05	1.643636e+05	1.799987e+05	1.800958e+05	1.792405e+05	1.774397e+05	1.930747e+05	91940	Katy	TX	Houston-The Woodlands-Sugar Land	Harris County	6	...	165953.0	165940.0	166732.0	167576.0	168021.0	168313.0	168786.0	169533.0	169947.0	170295.0	170306.0	170515.0	170809.0	171490.0	172012	172450	172949	173412	174031	175131	176228	177072	177465	178115	178774	179453	179904	180418	181290	181995	182559	182569	182940	183549	184481	185254	185518	186051	187010	187930
4	77084	91459.742472	95429.981924	98406.642481	100389.968950	104448.220793	108418.460261	111395.120826	113378.447305	117437.724067	121408.985719	124386.668479	1.263710e+05	1.304339e+05	1.344147e+05	1.374060e+05	1.394107e+05	1.434962e+05	1.474937e+05	1.504996e+05	1.525123e+05	1.565999e+05	1.605995e+05	1.636055e+05	1.506939e+05	1.666394e+05	1.666729e+05	1.657240e+05	1.637997e+05	1.797452e+05	1.797787e+05	1.788298e+05	1.769055e+05	1.928510e+05	91733	Houston	TX	Houston-The Woodlands-Sugar Land	Harris County	8	...	162716.0	162580.0	163419.0	164403.0	164907.0	165349.0	165907.0	166788.0	167307.0	167773.0	167875.0	168002.0	168128.0	168474.0	168793	168958	169280	169682	170375	171702	173079	174161	174860	175710	176572	177375	177998	178631	179539	180236	180757	180839	181191	181836	182590	183239	183408	184045	184837	185617
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
13910	47986	66037.989375	66339.088058	66628.620749	66905.727509	64451.358133	64752.456813	65041.989476	65319.096199	62864.709569	63165.685591	63454.504079	6.373083e+04	6.127555e+04	6.157573e+04	6.186435e+04	6.214054e+04	5.968526e+04	5.998544e+04	6.027406e+04	6.055025e+04	5.809497e+04	5.839515e+04	5.868377e+04	6.151348e+04	5.922287e+04	5.947162e+04	5.970535e+04	5.992320e+04	5.763258e+04	5.788133e+04	5.811506e+04	5.833291e+04	5.604229e+04	78883	Oxford	IN	Lafayette-West Lafayette	Benton County	30383	...	56253.0	56093.0	55167.0	54256.0	53854.0	54025.0	54160.0	54376.0	54282.0	54237.0	54842.0	55061.0	55166.0	55054.0	55362	55204	55192	55402	55810	56980	57107	58199	58360	59118	59511	60555	61862	62540	62753	62798	63227	63885	64907	65864	65798	65606	65327	65812	66459	66003
13911	89155	250691.446764	255028.623188	258459.285007	260983.330300	264084.518904	268421.695325	271852.357148	274376.402445	277477.591049	281814.767023	285245.428393	2.877695e+05	2.908707e+05	2.952078e+05	2.986385e+05	3.011625e+05	3.042637e+05	3.086009e+05	3.120316e+05	3.145556e+05	3.176568e+05	3.219940e+05	3.254246e+05	3.156357e+05	3.295675e+05	3.302840e+05	3.301024e+05	3.290288e+05	3.429606e+05	3.436770e+05	3.434955e+05	3.424218e+05	3.563536e+05	95851	Las Vegas	NV	Las Vegas-Henderson-Paradise	Clark County	30386	...	308128.0	307701.0	308713.0	306061.0	306130.0	306704.0	311002.0	313877.0	316316.0	320577.0	325170.0	328515.0	331595.0	335796.0	341168	345463	348375	352607	353869	355394	355965	359950	363843	367509	369608	370912	371608	373182	374463	376432	376454	377298	377540	377759	376302	374252	372875	373871	377652	381297
13912	55144	76857.478471	82633.187309	87967.752763	92860.388688	95856.195633	101631.904482	106966.469947	111859.105861	114854.912788	120630.621620	125965.187071	1.308578e+05	1.338536e+05	1.396293e+05	1.449639e+05	1.498565e+05	1.528523e+05	1.586281e+05	1.639626e+05	1.688553e+05	1.718511e+05	1.776268e+05	1.829613e+05	1.840168e+05	1.923046e+05	1.963140e+05	1.998836e+05	2.030155e+05	2.113034e+05	2.153127e+05	2.188823e+05	2.220142e+05	2.303021e+05	82132	North Saint Paul	MN	Minneapolis-St. Paul-Bloomington	Ramsey County	30394	...	173187.0	175760.0	177509.0	177564.0	178435.0	179381.0	180957.0	182640.0	184693.0	187146.0	188633.0	188982.0	187779.0	187085.0	186458	187445	187328	188565	190869	194636	196252	195963	195094	194027	192301	190185	190655	191777	194809	196492	199233	200192	202521	203196	205309	207089	210780	212704	214513	215048
13913	4033	444447.599617	466985.532392	489652.665437	512441.900874	511143.936786	533681.869435	556349.002322	579134.788063	577833.185287	600363.857043	623023.102601	6.458045e+05	6.444986e+05	6.670286e+05	6.896879e+05	7.124692e+05	7.111633e+05	7.336934e+05	7.563526e+05	7.791340e+05	7.778281e+05	8.003581e+05	8.230174e+05	8.713253e+05	8.686947e+05	8.916974e+05	9.147987e+05	9.379900e+05	9.353594e+05	9.583622e+05	9.814635e+05	1.004655e+06	1.002024e+06	59316	Cumberland	ME	Portland-South Portland	Cumberland County	30406	...	754909.0	758036.0	761435.0	768094.0	766578.0	762320.0	752773.0	748461.0	747821.0	757038.0	763469.0	769171.0	767781.0	768697.0	765186	762276	767842	782234	798384	809019	816325	822235	828279	835550	836361	828778	819612	816921	815432	818015	817141	821568	822967	827644	829819	829813	831954	834711	835468	835442
13914	86343	91454.193554	94682.526922	97183.095405	98956.171309	102173.960151	105402.293517	107902.861999	109675.937901	112893.726742	116122.060107	118622.628589	1.203957e+05	1.236135e+05	1.268418e+05	1.293424e+05	1.311155e+05	1.343333e+05	1.375616e+05	1.400622e+05	1.418352e+05	1.450530e+05	1.482814e+05	1.507819e+05	1.417013e+05	1.536021e+05	1.539259e+05	1.535305e+05	1.524210e+05	1.643219e+05	1.646457e+05	1.642503e+05	1.631408e+05	1.750416e+05	95186	Crown King	AZ	Prescott	Yavapai County	30409	...	148504.0	148728.0	148675.0	148071.0	148040.0	148237.0	150278.0	152458.0	154095.0	155395.0	155581.0	156679.0	158061.0	161172.0	163807	164597	164659	164551	164266	164466	164931	165918	164952	163705	161726	161421	160435	161620	161055	163640	162246	161795	159728	161152	162761	163262	163061	162006	162935	163710

	RegionName	RegionID	City	State	Metro	CountyName	SizeRank	1997	1998	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008	2009	2010	2011	2012	2013	2014	2015	2016	2017	2018	2019	2020	2021	2022	2023	2024	2025	2026	2027	2028	2029	roi_28_18	roi_19_18	roi_19_97
8557	94027	97518	Atherton	CA	San Francisco-Oakland-Hayward	San Mateo County	12336	5.304558e+05	7.045466e+05	8.576227e+05	9.897193e+05	1.260114e+06	1.434205e+06	1.587281e+06	1.719378e+06	1.989773e+06	2.163864e+06	2.316940e+06	2.449038e+06	2.719438e+06	2.893589e+06	3.046801e+06	3.179076e+06	3.449700e+06	3.624067e+06	3.777419e+06	3.909791e+06	4.180463e+06	4.354830e+06	4.508182e+06	4.231466e+06	4.752019e+06	4.842684e+06	4.912694e+06	4.962229e+06	5.482782e+06	5.573447e+06	5.643457e+06	5.692992e+06	6.213545e+06	1.338163e+06	153352.015236	3.977726e+06
6467	81611	93816	Aspen	CO	Glenwood Springs	Pitkin County	8618	1.783877e+06	1.910365e+06	2.033188e+06	2.152338e+06	2.256877e+06	2.383366e+06	2.506189e+06	2.625338e+06	2.729877e+06	2.856366e+06	2.979189e+06	3.098338e+06	3.202878e+06	3.329368e+06	3.452276e+06	3.672239e+06	3.878162e+06	4.106218e+06	4.330629e+06	4.551366e+06	4.757771e+06	4.985847e+06	5.210257e+06	5.397618e+06	5.648056e+06	5.861444e+06	6.071170e+06	6.277247e+06	6.527684e+06	6.741073e+06	6.950798e+06	7.156875e+06	7.407312e+06	2.171028e+06	224410.496749	3.426380e+06
8933	2108	58622	Boston	MA	Boston-Cambridge-Newton	Suffolk County	13039	1.374792e+06	1.533864e+06	1.676708e+06	1.803336e+06	1.978696e+06	2.137767e+06	2.280612e+06	2.407239e+06	2.582599e+06	2.741670e+06	2.884515e+06	3.011143e+06	3.186503e+06	3.345574e+06	3.488419e+06	3.615046e+06	3.790407e+06	3.949478e+06	4.092323e+06	4.218951e+06	4.394311e+06	4.553383e+06	4.696227e+06	4.564449e+06	4.933303e+06	5.027636e+06	5.105946e+06	5.168354e+06	5.537207e+06	5.631540e+06	5.709850e+06	5.772258e+06	6.141112e+06	1.218875e+06	142844.889036	3.321435e+06
275	10021	61635	New York	NY	New York-Newark-Jersey City	New York County	326	4.123239e+06	4.360921e+06	4.584946e+06	4.795214e+06	4.691881e+06	4.929563e+06	5.153588e+06	5.363856e+06	5.260523e+06	5.498205e+06	5.722230e+06	5.932498e+06	5.829165e+06	6.066847e+06	6.290872e+06	6.501140e+06	6.397807e+06	6.635489e+06	6.859514e+06	7.069782e+06	6.966449e+06	7.204131e+06	7.428156e+06	7.772056e+06	7.834856e+06	8.017396e+06	8.186014e+06	8.340698e+06	8.403498e+06	8.586039e+06	8.754656e+06	8.909340e+06	8.972140e+06	1.705208e+06	224025.034311	3.304917e+06
11972	11962	62309	Sagaponack	NY	New York-Newark-Jersey City	Suffolk County	20617	1.116978e+06	1.289223e+06	1.430647e+06	1.541256e+06	1.709738e+06	1.881984e+06	2.023407e+06	2.134016e+06	2.302498e+06	2.474744e+06	2.616167e+06	2.726777e+06	2.895258e+06	3.067504e+06	3.208927e+06	3.319537e+06	3.488024e+06	3.660275e+06	3.801704e+06	3.912318e+06	4.080805e+06	4.253056e+06	4.394485e+06	4.048675e+06	4.584962e+06	4.634189e+06	4.652946e+06	4.641456e+06	5.177744e+06	5.226970e+06	5.245728e+06	5.234238e+06	5.770525e+06	9.811816e+05	141428.616564	3.277507e+06
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
11205	89158	418163	Las Vegas	NV	Las Vegas-Henderson-Paradise	Clark County	18265	5.702911e+05	5.720521e+05	5.729065e+05	5.728481e+05	5.525652e+05	5.543261e+05	5.551806e+05	5.551221e+05	5.348392e+05	5.366002e+05	5.374535e+05	5.373940e+05	5.171099e+05	5.188698e+05	5.197232e+05	5.196636e+05	4.993796e+05	5.011394e+05	5.019928e+05	5.019332e+05	4.816492e+05	4.834091e+05	4.842624e+05	4.924859e+05	4.832253e+05	4.813262e+05	4.785032e+05	4.747555e+05	4.654949e+05	4.635958e+05	4.607728e+05	4.570251e+05	4.477646e+05	-2.638395e+04	853.356576	-8.602867e+04
5439	38112	74650	Memphis	TN	Memphis	Shelby County	7101	1.323592e+05	1.301804e+05	1.270570e+05	1.229880e+05	1.161575e+05	1.139787e+05	1.108553e+05	1.067863e+05	9.995582e+04	9.777699e+04	9.465364e+04	9.058459e+04	8.375414e+04	8.157531e+04	7.845196e+04	7.438291e+04	6.755245e+04	6.537363e+04	6.225027e+04	5.818122e+04	5.135077e+04	4.917194e+04	4.604859e+04	3.248057e+04	3.696522e+04	3.100765e+04	2.411047e+04	1.627889e+04	2.076353e+04	1.480597e+04	7.908787e+03	7.720745e+01	4.561849e+03	-4.909473e+04	-3123.352480	-8.631060e+04
6897	36105	73749	Montgomery	AL	Montgomery	Montgomery County	9352	1.132547e+05	1.102328e+05	1.058050e+05	9.997140e+04	9.567297e+04	9.265106e+04	8.822332e+04	8.238970e+04	7.809127e+04	7.506936e+04	7.064161e+04	6.480799e+04	6.050957e+04	5.748771e+04	5.306063e+04	4.722910e+04	4.293539e+04	3.991966e+04	3.550085e+04	2.967616e+04	2.538670e+04	2.237372e+04	1.795491e+04	-7.597921e+03	4.902032e+03	-3.724897e+03	-1.374338e+04	-2.514386e+04	-1.264390e+04	-2.127083e+04	-3.128932e+04	-4.268980e+04	-3.018984e+04	-6.506352e+04	-4418.807737	-9.529976e+04
12025	73014	89851	Calumet	OK	Oklahoma City	Canadian County	20781	2.438768e+05	2.417816e+05	2.382480e+05	2.332725e+05	2.188139e+05	2.167188e+05	2.131852e+05	2.082097e+05	1.937511e+05	1.916560e+05	1.881224e+05	1.831469e+05	1.686883e+05	1.665932e+05	1.630596e+05	1.580841e+05	1.436255e+05	1.415304e+05	1.379968e+05	1.330213e+05	1.185627e+05	1.164676e+05	1.129340e+05	9.871627e+04	1.015401e+05	9.368015e+04	8.438253e+04	7.365347e+04	7.647728e+04	6.861734e+04	5.931972e+04	4.859066e+04	5.141447e+04	-6.787693e+04	-3533.588225	-1.309428e+05
13640	44049	76963	Kipton	OH	Cleveland-Elyria	Lorain County	28264	2.298177e+05	2.235728e+05	2.165064e+05	2.086180e+05	2.010576e+05	1.948127e+05	1.877463e+05	1.798579e+05	1.722975e+05	1.660526e+05	1.589862e+05	1.510978e+05	1.435373e+05	1.372923e+05	1.302259e+05	1.223374e+05	1.147769e+05	1.085320e+05	1.014655e+05	9.357710e+04	8.601661e+04	7.977166e+04	7.270520e+04	5.384215e+04	5.610730e+04	4.657902e+04	3.623555e+04	2.508183e+04	2.734697e+04	1.781870e+04	7.475225e+03	-3.678496e+03	-1.413354e+03	-8.345016e+04	-7066.463140	-1.571125e+05