import pandas as pd
from fbprophet import Prophet 
df = pd.read_csv('https://raw.githubusercontent.com/facebook/prophet/master/examples/example_wp_log_peyton_manning.csv')
df.head()

PRACTICING WITH PEYTON¶

m = Prophet()
m.fit(df)

INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

<fbprophet.forecaster.Prophet at 0x7f4f3ad41e80>

future = m.make_future_dataframe(periods=365)
future.tail()

forecast = m.predict(future)
forecast.tail()

fig1 = m.plot(forecast)

fig2 = m.plot_components(forecast)

PRACTICING WITH ZILLOW¶

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

CA = df[df.State == 'CA']
LA = CA[CA.CountyName == 'Los Angeles County']
mine = LA[LA.RegionName == 90039]

# df = mine.copy()
# df_t = df.loc[:, '1997-01':'2018-12'].T

df = mine.copy()
df_t = df.loc[:, '1997-01':'2018-12'].T
df = df_t.copy()
df.reset_index(inplace=True)
# df['ds'] = df[df.columns[1]] + '-01'
df.columns = ['ds','y']
# df['ds'] = df['ds'] + '-01'
df.head()

df.tail()

m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=365)
future.tail()

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

forecast = m.predict(future)
forecast.tail()

fig1 = m.plot(forecast)

# df = pd.read_csv('../examples/example_retail_sales.csv')
m = Prophet(seasonality_mode='multiplicative').fit(df)
future = m.make_future_dataframe(periods=48)
fcst = m.predict(future)
fig = m.plot(fcst)

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

fcst

len(df)

264

m = Prophet(seasonality_mode='multiplicative', mcmc_samples=264).fit(df)
future = m.make_future_dataframe(periods=120, freq='M')
fcst = m.predict(future)
fig = m.plot(fcst)

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
WARNING:pystan:Rhat above 1.1 or below 0.9 indicates that the chains very likely have not mixed
WARNING:pystan:182 of 528 iterations saturated the maximum tree depth of 10 (34.5 %)
WARNING:pystan:Run again with max_treedepth larger than 10 to avoid saturation

m = Prophet(seasonality_mode='multiplicative', mcmc_samples=264).fit(df)
future = m.make_future_dataframe(periods=12, freq='M')
fcst = m.predict(future)
fig = m.plot(fcst)

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
WARNING:pystan:Rhat above 1.1 or below 0.9 indicates that the chains very likely have not mixed
WARNING:pystan:177 of 528 iterations saturated the maximum tree depth of 10 (33.5 %)
WARNING:pystan:Run again with max_treedepth larger than 10 to avoid saturation

df

m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=12, freq='M')
future.tail()

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

forecast = m.predict(future)
forecast.tail()

fig1 = m.plot(forecast)

forecast.tail()

components_fig = m.plot_components(forecast)
axes = components_fig.get_axes()
axes[0].set_ylabel('TESTING TESTING')

Text(32.375, 0.5, 'TESTING TESTING')

CA

LA

# for 
# m = Prophet(seasonality_mode='multiplicative', mcmc_samples=264).fit(df)
# future = m.make_future_dataframe(periods=120, freq='M')
# fcst = m.predict(future)
# fig = m.plot(fcst)


def prophet_predict_prices_for(df, location_name):
  df_t = df.loc[:, '1997-01':'2017-12'].T
  df_t['avg'] = df_t.mean(numeric_only=True, axis=1)
  df_t.reset_index(inplace=True)
  columns = ['index', 'avg']
  df = pd.DataFrame(df_t, columns = columns)
  df = df.rename(index=str, columns={"avg": "y", "index": "ds"})
  # m = Prophet()
  # m.fit(df)
  m = Prophet(seasonality_mode='multiplicative', mcmc_samples=264).fit(df)
  future = m.make_future_dataframe(periods=120, freq='M')
  # future = m.make_future_dataframe(periods=12, freq='M')
  forecast = m.predict(future)
  print("====",location_name,"====")
  forcast_fig = m.plot(forecast)
  f_axes = forcast_fig.get_axes()
  f_axes[0].set_xlabel('DATE')
  f_axes[0].set_ylabel(location_name)

glendale = LA[LA.City == 'Glendale']

df = glendale.copy()
for zipcode in set(df['RegionName']):
  region_df = df[df['RegionName'] == zipcode]
  prophet_predict_prices_for(region_df, str(zipcode))

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
WARNING:pystan:Rhat above 1.1 or below 0.9 indicates that the chains very likely have not mixed
WARNING:pystan:468 of 528 iterations saturated the maximum tree depth of 10 (88.6 %)
WARNING:pystan:Run again with max_treedepth larger than 10 to avoid saturation
INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

==== 91201 ====

WARNING:pystan:Rhat above 1.1 or below 0.9 indicates that the chains very likely have not mixed
WARNING:pystan:496 of 528 iterations saturated the maximum tree depth of 10 (93.9 %)
WARNING:pystan:Run again with max_treedepth larger than 10 to avoid saturation
INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

==== 91202 ====

WARNING:pystan:Rhat above 1.1 or below 0.9 indicates that the chains very likely have not mixed
WARNING:pystan:402 of 528 iterations saturated the maximum tree depth of 10 (76.1 %)
WARNING:pystan:Run again with max_treedepth larger than 10 to avoid saturation
INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

==== 91203 ====

WARNING:pystan:Rhat above 1.1 or below 0.9 indicates that the chains very likely have not mixed
WARNING:pystan:444 of 528 iterations saturated the maximum tree depth of 10 (84.1 %)
WARNING:pystan:Run again with max_treedepth larger than 10 to avoid saturation
INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

==== 91204 ====

WARNING:pystan:Rhat above 1.1 or below 0.9 indicates that the chains very likely have not mixed
WARNING:pystan:477 of 528 iterations saturated the maximum tree depth of 10 (90.3 %)
WARNING:pystan:Run again with max_treedepth larger than 10 to avoid saturation
INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

==== 91205 ====

df = mine.copy()
df_t = df.loc[:, '1997-01':'2018-12'].T
df = df_t.copy()
df.reset_index(inplace=True)
# df['ds'] = df[df.columns[1]] + '-01'
df.columns = ['ds','y']
# df['ds'] = df['ds'] + '-01'
df.head()

m = Prophet()
m.fit(df)
future = m.make_future_dataframe(periods=120, freq='M')
fcst = m.predict(future)
fig = m.plot(fcst)

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

fcst

m = Prophet()
m.fit(df)
invest_price = df.tail(1)
future = m.make_future_dataframe(periods=60, freq='M')
fcst = m.predict(future)
sell_price = fcst.tail(1)
print(sell_price['trend'].values[0] - invest_price['y'].values[0])
fig = m.plot(fcst)

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

274366.1740152382

invest_price = df.tail(1)

sell_price = fcst.tail(1)

sell_price['trend'].values[0] - invest_price['y'].values[0]

274366.1740152382

type(sell_price['trend'])

pandas.core.series.Series

sell_price['trend'].values[0]

1188424.1740152382

sell_price

zip_table = []
def get_investment_difference(df, zip_name):
  df_t = df.loc[:, '1997-01':'2017-12'].T
  df_t['avg'] = df_t.mean(numeric_only=True, axis=1)
  df_t.reset_index(inplace=True)
  columns = ['index', 'avg']
  df = pd.DataFrame(df_t, columns = columns)
  df = df.rename(index=str, columns={"avg": "y", "index": "ds"})
  m = Prophet()
  m.fit(df)
  invest_price = df.tail(1)
  future = m.make_future_dataframe(periods=60, freq='M')
  fcst = m.predict(future)
  sell_price = fcst.tail(1)
  print('++++++++',zip_name)
  roi = sell_price['trend'].values[0] - invest_price['y'].values[0]
  print(roi)
  fig = m.plot(fcst)
  zip_table.append({ "zipcode": zip_name, "return": roi })

glendale = LA[LA.City == 'Glendale']
df = glendale.copy()
for zipcode in set(df['RegionName']):
  region_df = df[df['RegionName'] == zipcode]
  get_investment_difference(region_df, str(zipcode))

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

++++++++ 91201
236843.65637025773

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

++++++++ 91202
244537.5453543407

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

++++++++ 91203
218796.04965441872

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

++++++++ 91204
254380.26474907843

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

++++++++ 91205
259213.92290523078

zt = pd.DataFrame(zip_table)

# zt['zipcode'] = zt.zipcode.astype('category')

zt.plot.bar(x="zipcode", y="return")

<matplotlib.axes._subplots.AxesSubplot at 0x7f4f39263588>

import time
seconds = time.time()
print("Seconds since epoch =", seconds)

Seconds since epoch = 1581554423.2250054

# glendale = LA[LA.City == 'Glendale']
df = LA.copy()
for zipcode in set(df['RegionName']):
  # print(zipcode)
  region_df = df[df['RegionName'] == zipcode]
  get_investment_difference(region_df, str(zipcode))

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.
INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

++++++++ 90638
150273.77434461808

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

++++++++ 90640
152654.15745010017

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

++++++++ 90650
140374.58345710777

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

++++++++ 90660
139750.03100599477

INFO:fbprophet:Disabling weekly seasonality. Run prophet with weekly_seasonality=True to override this.
INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.

++++++++ 90670
138778.13180726685

zt = pd.DataFrame(zip_table)
zt.plot.bar(x="zipcode", y="return")

<matplotlib.axes._subplots.AxesSubplot at 0x7f4f346f1588>

zt_top = zt.sort_values(by = "return", ascending=False)

zt_top[:20].plot.bar(x="zipcode", y="return")

<matplotlib.axes._subplots.AxesSubplot at 0x7f4f319c99e8>

zip_table = []
def get_dream_spreadsheet(df, zip_name):

  # df_t = df.loc[:, '1997-01':'2017-12'].T
  # df_t['avg'] = df_t.mean(numeric_only=True, axis=1)
  # df_t.reset_index(inplace=True)
  # columns = ['index', 'avg']
  # df = pd.DataFrame(df_t, columns = columns)
  # df = df.rename(index=str, columns={"avg": "y", "index": "ds"})
  # m = Prophet()
  # m.fit(df)
  # invest_price = df.tail(1)
  # future = m.make_future_dataframe(periods=60, freq='M')
  # fcst = m.predict(future)
  # sell_price = fcst.tail(1)
  # print('++++++++',zip_name)
  # roi = sell_price['trend'].values[0] - invest_price['y'].values[0]
  # print(roi)
  # fig = m.plot(fcst)
  zip_table.append({ "region": zip_name, 
                    "return": roi, 
                    "prediction_2018": prediction_2018,
                    "actual_2018": actual_2018,
                    "prediction_2019": prediction_2019,
                    "actual_2019": actual_2019
                    })

glendale = LA[LA.City == 'Glendale']
# df = glendale.copy()
# for zipcode in set(df['RegionName']):
#   # print(zipcode)
#   region_df = df[df['RegionName'] == zipcode]
#   get_dream_spreadsheet(region_df, str(zipcode))

df = glendale.copy()
df_t = df.loc[:, '1997-01'::].T
df_t.reset_index(inplace=True)
# df_t['avg'] = df_t.mean(numeric_only=True, axis=1)

# columns = ['index', 'avg']
# df = pd.DataFrame(df_t, columns = columns)
# df = df.rename(index=str, columns={"avg": "y", "index": "ds"})

df_t['year'] = df_t.apply(lambda x: x['index'].split('-')[0], axis=1)

df_t

by_year = pd.DataFrame(df_t.groupby('year').mean())

by_year

	ds	y
0	2007-12-10	9.590761
1	2007-12-11	8.519590
2	2007-12-12	8.183677
3	2007-12-13	8.072467
4	2007-12-14	7.893572

	ds
3265	2017-01-15
3266	2017-01-16
3267	2017-01-17
3268	2017-01-18
3269	2017-01-19

	ds	trend	yhat_lower	yhat_upper	trend_lower	trend_upper	additive_terms	additive_terms_lower	additive_terms_upper	weekly	weekly_lower	weekly_upper	yearly	yearly_lower	yearly_upper	yhat
3265	2017-01-15	7.180847	7.484119	8.912103	6.837290	7.526652	1.018428	1.018428	1.018428	0.048295	0.048295	0.048295	0.970133	0.970133	0.970133	8.199274
3266	2017-01-16	7.179809	7.890993	9.241308	6.834516	7.526953	1.344435	1.344435	1.344435	0.352287	0.352287	0.352287	0.992148	0.992148	0.992148	8.524244
3267	2017-01-17	7.178771	7.619836	9.010513	6.831743	7.527199	1.132844	1.132844	1.132844	0.119624	0.119624	0.119624	1.013220	1.013220	1.013220	8.311615
3268	2017-01-18	7.177733	7.420357	8.872825	6.828543	7.527256	0.966499	0.966499	0.966499	-0.066647	-0.066647	-0.066647	1.033146	1.033146	1.033146	8.144232
3269	2017-01-19	7.176695	7.412110	8.864867	6.825897	7.527090	0.979396	0.979396	0.979396	-0.072284	-0.072284	-0.072284	1.051680	1.051680	1.051680	8.156091

	ds	y
0	1997-01	147935.0
1	1997-02	148799.0
2	1997-03	149280.0
3	1997-04	149552.0
4	1997-05	149320.0

	ds	y
259	2018-08	868462.0
260	2018-09	879218.0
261	2018-10	888896.0
262	2018-11	903051.0
263	2018-12	914058.0

	ds
624	2019-11-27
625	2019-11-28
626	2019-11-29
627	2019-11-30
628	2019-12-01

	ds	trend	yhat_lower	yhat_upper	trend_lower	trend_upper	multiplicative_terms	multiplicative_terms_lower	multiplicative_terms_upper	yearly	yearly_lower	yearly_upper	additive_terms	additive_terms_lower	additive_terms_upper	yhat
0	1997-01-01	140047.491970	122187.251125	156589.993286	140047.491970	140047.491970	-0.001828	-0.001828	-0.001828	-0.001828	-0.001828	-0.001828	0.0	0.0	0.0	139791.432687
1	1997-02-01	141835.867074	124226.306882	157792.323187	141835.867074	141835.867074	-0.000150	-0.000150	-0.000150	-0.000150	-0.000150	-0.000150	0.0	0.0	0.0	141814.531781
2	1997-03-01	143451.173619	128994.542548	160912.284827	143451.173619	143451.173619	0.001271	0.001271	0.001271	0.001271	0.001271	0.001271	0.0	0.0	0.0	143633.507654
3	1997-04-01	145239.548723	127215.506933	161422.985381	145239.548723	145239.548723	0.002450	0.002450	0.002450	0.002450	0.002450	0.002450	0.0	0.0	0.0	145595.329110
4	1997-05-01	146970.234308	130267.104554	164323.678412	146970.234308	146970.234308	0.003117	0.003117	0.003117	0.003117	0.003117	0.003117	0.0	0.0	0.0	147428.370071
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
307	2019-01-14	884823.065837	851367.278144	884940.626978	884823.065837	884823.065837	-0.018989	-0.018989	-0.018989	-0.018989	-0.018989	-0.018989	0.0	0.0	0.0	868020.766237
308	2019-01-15	884994.214243	851579.017831	885911.379416	884994.214243	884994.214243	-0.019171	-0.019171	-0.019171	-0.019171	-0.019171	-0.019171	0.0	0.0	0.0	868027.822692
309	2019-01-16	885165.362649	851019.984804	884712.932237	885165.362649	885165.362649	-0.019036	-0.019036	-0.019036	-0.019036	-0.019036	-0.019036	0.0	0.0	0.0	868315.223924
310	2019-01-17	885336.511055	851721.103683	887087.420135	885336.511055	885336.511055	-0.018594	-0.018594	-0.018594	-0.018594	-0.018594	-0.018594	0.0	0.0	0.0	868874.659050
311	2019-01-18	885507.659461	852089.311855	887635.061339	885507.659461	885507.659461	-0.017864	-0.017864	-0.017864	-0.017864	-0.017864	-0.017864	0.0	0.0	0.0	869689.121422

	ds
271	2019-07-31
272	2019-08-31
273	2019-09-30
274	2019-10-31
275	2019-11-30

	ds	trend	yhat_lower	yhat_upper	trend_lower	trend_upper	additive_terms	additive_terms_lower	additive_terms_upper	yearly	yearly_lower	yearly_upper	yhat
271	2019-07-31	918473.369818	902613.865705	938495.782575	913448.589808	922065.797615	2323.096416	2323.096416	2323.096416	2323.096416	2323.096416	2323.096416	920796.466234
272	2019-08-31	923759.835346	906457.330437	942717.089958	917444.302157	928700.069581	316.944131	316.944131	316.944131	316.944131	316.944131	316.944131	924076.779476
273	2019-09-30	928875.769727	903878.037350	942229.923960	921240.232748	935426.789393	-5867.834013	-5867.834013	-5867.834013	-5867.834013	-5867.834013	-5867.834013	923007.935714
274	2019-10-31	934162.235255	912278.901934	950962.684107	924779.022588	942742.578093	-1841.329851	-1841.329851	-1841.329851	-1841.329851	-1841.329851	-1841.329851	932320.905404
275	2019-11-30	939278.169636	923329.638442	962380.444046	928995.886814	949423.632385	3670.706646	3670.706646	3670.706646	3670.706646	3670.706646	3670.706646	942948.876282

	RegionID	RegionName	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	...	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
13	97564	94109	San Francisco	CA	San Francisco-Oakland-Hayward	San Francisco County	14	547676.0	547569.0	536539.0	536211.0	536288.0	544996.0	549733.0	548116.0	548056.0	556765.0	564078.0	568911.0	563733.0	567592.0	576927.0	590090.0	601843.0	607732.0	612399.0	618075.0	624078.0	619463.0	608251.0	598124.0	599915.0	605774.0	612060.0	619972.0	630063.0	639649.0	648839.0	660816.0	673743.0	...	2076723.0	2089536.0	2116069.0	2126269.0	2136544.0	2130549.0	2125238.0	2136355.0	2157234.0	2183082.0	2201513.0	2223619.0	2242654.0	2262404.0	2273661	2288114	2296814	2319273	2340441	2367999	2377043	2406408	2437497	2468207	2497009	2527010	2560823	2571444	2588508	2602537	2603723	2596306	2594652	2619794	2639943	2653455	2659471	2673801	2688969	2717489
21	96107	90250	Hawthorne	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	22	174520.0	174451.0	174208.0	174168.0	174272.0	174236.0	174011.0	173853.0	173880.0	173809.0	173430.0	172856.0	172239.0	171385.0	170904.0	171034.0	171937.0	172959.0	173708.0	174116.0	174672.0	176446.0	178775.0	180840.0	181787.0	182670.0	183373.0	183753.0	184170.0	185105.0	186263.0	187815.0	188620.0	...	542639.0	549684.0	554883.0	559601.0	565991.0	572281.0	577192.0	580694.0	585780.0	590338.0	592356.0	593805.0	595088.0	598773.0	604365	610036	616579	623096	630327	635907	639595	642727	642779	642257	642941	647773	652424	654329	654836	654122	653035	654325	656639	660217	657600	656051	656649	661550	665524	670833
39	97771	94565	Pittsburg	CA	San Francisco-Oakland-Hayward	Contra Costa County	40	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	519924.0	520437.0	520271.0	521608.0	526538.0	534053.0	540373.0	544590.0	546909.0	545765.0	545227.0	545969.0	550546.0	552338.0	555309	559167	570681	581688	589077	588331	588979	590588	593489	591277	592299	591549	590131	585682	587867	590192	591096	588183	586736	584946	584380	582565	581526	579674	579779	582825
43	96027	90046	Los Angeles	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	44	158703.0	158615.0	158990.0	159599.0	160228.0	161176.0	162302.0	163102.0	163659.0	164005.0	164238.0	165215.0	167196.0	168901.0	170638.0	171418.0	173336.0	174781.0	176926.0	178643.0	180776.0	183543.0	186489.0	188650.0	190057.0	192063.0	194619.0	196076.0	197877.0	200594.0	203909.0	206660.0	208435.0	...	1125483.0	1141019.0	1150330.0	1161896.0	1179904.0	1195337.0	1208427.0	1219575.0	1236950.0	1248887.0	1259457.0	1272009.0	1291359.0	1312526.0	1335419	1356609	1380975	1408153	1434319	1452009	1454944	1455943	1460135	1471859	1488243	1508333	1526004	1537515	1545734	1549924	1550051	1554922	1566579	1580344	1588563	1599202	1619582	1643242	1655745	1668029
87	97711	94501	Alameda	CA	San Francisco-Oakland-Hayward	Alameda County	88	164594.0	164065.0	163554.0	163687.0	164207.0	164802.0	164966.0	164895.0	165257.0	166068.0	167170.0	167651.0	167925.0	168804.0	170949.0	173937.0	176381.0	178295.0	179198.0	179969.0	181228.0	183009.0	185088.0	186707.0	188432.0	189955.0	191916.0	194937.0	198030.0	201294.0	202926.0	204314.0	204491.0	...	823270.0	832964.0	845602.0	855472.0	862741.0	869135.0	880611.0	888377.0	892645.0	898715.0	900458.0	901673.0	907587.0	917554.0	931512	947430	962747	973865	980028	988153	995688	997469	1003458	1013300	1019174	1023141	1024986	1021306	1013708	1011229	1019833	1029955	1038937	1043473	1051026	1051526	1054081	1050810	1050477	1058938
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
30278	98009	95141	San Jose	CA	San Jose-Sunnyvale-Santa Clara	Santa Clara County	30279	539482.0	532297.0	529040.0	519340.0	509995.0	507596.0	513339.0	521067.0	525147.0	530192.0	533211.0	535826.0	534342.0	538771.0	548248.0	558472.0	563311.0	562934.0	559622.0	551968.0	541129.0	529191.0	523018.0	515872.0	519279.0	523226.0	530816.0	531179.0	531116.0	536700.0	544999.0	555692.0	563530.0	...	1085551.0	1088423.0	1079231.0	1067071.0	1071790.0	1095731.0	1111667.0	1120839.0	1125443.0	1135403.0	1130279.0	1127394.0	1124116.0	1139792.0	1144735	1158909	1177372	1212086	1224549	1225179	1222547	1230885	1223823	1198951	1175508	1180208	1187717	1193934	1193500	1189134	1180906	1174429	1175094	1184333	1191616	1203807	1191099	1161763	1123331	1105216
30350	98059	95229	Vallecito	CA	NaN	Calaveras County	30351	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	212889.0	214869.0	217488.0	217801.0	217860.0	220071.0	222218.0	222219.0	218200.0	214591.0	216916.0	220715.0	220659.0	217588.0	213734	213025	211969	212102	213038	214920	215538	215920	216355	215913	216553	217207	219360	222307	224479	226993	226685	224245	220362	219113	219777	221118	220298	220130	219565	219973
30420	98400	95721	Twin Bridges	CA	Sacramento--Roseville--Arden-Arcade	El Dorado County	30421	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	221106.0	222405.0	224061.0	226694.0	229175.0	231062.0	231671.0	229550.0	229457.0	228371.0	231913.0	236513.0	241288.0	241506.0	241026	241606	242682	242755	243387	247102	250117	251559	251094	250609	251871	254712	260237	266705	272689	273374	274286	272306	277570	283606	293289	293416	288793	281784	277526	273191
30422	98152	95375	Strawberry	CA	Sonora	Tuolumne County	30423	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	229014.0	230258.0	232993.0	237994.0	242690.0	245030.0	246624.0	248738.0	251551.0	254167.0	256218	257939	257146	256979	255471	255027	254391	255432	258595	262077	266299	269269	270577	270238	271945	274057	276549	276636	276468	276629	274957	272186	267689	264456	263067	263347
30428	97209	93282	Tulare	CA	Visalia-Porterville	Tulare County	30429	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	148858.0	150809.0	152622.0	155571	156944	157868	160666	163506	164605	162217	161130	160962	161256	160592	159573	159334	158590	158014	155349	153531	153059	155689	156909	157209	156855	156706	156429	155899	157523

	ds	trend	yhat_lower	yhat_upper	trend_lower	trend_upper	additive_terms	additive_terms_lower	additive_terms_upper	yearly	yearly_lower	yearly_upper	multiplicative_terms	multiplicative_terms_lower	multiplicative_terms_upper	yhat
0	1997-01-01	1.403879e+05	1.224807e+05	1.550072e+05	1.403879e+05	1.403879e+05	-863.332396	-863.332396	-863.332396	-863.332396	-863.332396	-863.332396	0.0	0.0	0.0	1.395246e+05
1	1997-02-01	1.421767e+05	1.247782e+05	1.592319e+05	1.421767e+05	1.421767e+05	-719.485451	-719.485451	-719.485451	-719.485451	-719.485451	-719.485451	0.0	0.0	0.0	1.414573e+05
2	1997-03-01	1.437924e+05	1.271982e+05	1.610423e+05	1.437924e+05	1.437924e+05	66.933588	66.933588	66.933588	66.933588	66.933588	66.933588	0.0	0.0	0.0	1.438594e+05
3	1997-04-01	1.455813e+05	1.295119e+05	1.623841e+05	1.455813e+05	1.455813e+05	314.960898	314.960898	314.960898	314.960898	314.960898	314.960898	0.0	0.0	0.0	1.458962e+05
4	1997-05-01	1.473124e+05	1.322133e+05	1.647389e+05	1.473124e+05	1.473124e+05	687.660365	687.660365	687.660365	687.660365	687.660365	687.660365	0.0	0.0	0.0	1.480000e+05
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
379	2028-07-31	1.479180e+06	1.080462e+06	1.917491e+06	1.082229e+06	1.906088e+06	880.118140	880.118140	880.118140	880.118140	880.118140	880.118140	0.0	0.0	0.0	1.480060e+06
380	2028-08-31	1.484466e+06	1.077554e+06	1.922085e+06	1.084508e+06	1.918488e+06	-253.024316	-253.024316	-253.024316	-253.024316	-253.024316	-253.024316	0.0	0.0	0.0	1.484213e+06
381	2028-09-30	1.489582e+06	1.074314e+06	1.929589e+06	1.085996e+06	1.932973e+06	-3027.946570	-3027.946570	-3027.946570	-3027.946570	-3027.946570	-3027.946570	0.0	0.0	0.0	1.486554e+06
382	2028-10-31	1.494869e+06	1.078185e+06	1.943399e+06	1.082751e+06	1.947499e+06	-1111.523930	-1111.523930	-1111.523930	-1111.523930	-1111.523930	-1111.523930	0.0	0.0	0.0	1.493757e+06
383	2028-11-30	1.499985e+06	1.083240e+06	1.950976e+06	1.080330e+06	1.958310e+06	1934.287311	1934.287311	1934.287311	1934.287311	1934.287311	1934.287311	0.0	0.0	0.0	1.501919e+06

	index	2265	2870	4416	5550	5984	6732	7509	7636	9774	year
0	1997-01	173020.0	257838.0	227568.0	258328.0	216725.0	160115.0	152341.0	274405.0	274271.0	1997
1	1997-02	172674.0	257562.0	227792.0	259341.0	217767.0	159759.0	151976.0	275025.0	274607.0	1997
2	1997-03	172814.0	257241.0	227539.0	259643.0	218212.0	160256.0	151849.0	275636.0	274854.0	1997
3	1997-04	172926.0	257513.0	227637.0	260252.0	218474.0	160569.0	152066.0	276350.0	275453.0	1997
4	1997-05	172343.0	258530.0	228097.0	260695.0	218717.0	160404.0	151644.0	278411.0	276385.0	1997
...	...	...	...	...	...	...	...	...	...	...	...
271	2019-08	771304.0	1073071.0	859923.0	985045.0	887651.0	714339.0	742891.0	1055336.0	1141203.0	2019
272	2019-09	767841.0	1067276.0	856641.0	981461.0	888164.0	712101.0	739385.0	1050709.0	1134129.0	2019
273	2019-10	771114.0	1076505.0	864172.0	990496.0	896972.0	714871.0	741959.0	1061596.0	1145810.0	2019
274	2019-11	773473.0	1085502.0	871322.0	998189.0	902250.0	716775.0	743386.0	1071419.0	1154904.0	2019
275	2019-12	778740.0	1095430.0	879621.0	1008724.0	909889.0	721991.0	748912.0	1081694.0	1166973.0	2019

	2265	2870	4416	5550	5984	6732	7509	7636	9774
year
1997	172768.166667	2.639039e+05	231237.083333	264780.083333	223722.166667	162969.916667	152049.750000	2.849844e+05	2.836072e+05
1998	187354.416667	2.963575e+05	257472.333333	296590.000000	250661.250000	183295.416667	168269.083333	3.197392e+05	3.186489e+05
1999	207786.250000	3.265815e+05	283440.750000	328894.750000	280353.083333	204041.000000	186879.333333	3.536721e+05	3.534318e+05
2000	227273.833333	3.566712e+05	310521.166667	357385.500000	306163.916667	232179.500000	208283.833333	3.848035e+05	3.872744e+05
2001	255363.750000	3.856152e+05	339045.750000	387372.333333	334421.250000	262450.833333	237697.166667	4.117520e+05	4.212940e+05
2002	298994.666667	4.344504e+05	383171.333333	438663.833333	376128.083333	297946.500000	276428.416667	4.586022e+05	4.647454e+05
2003	357710.416667	5.087211e+05	451152.416667	515898.250000	449789.083333	350046.500000	337954.750000	5.330435e+05	5.428636e+05
2004	452482.666667	6.280597e+05	556570.833333	626480.166667	553849.416667	447883.000000	433349.750000	6.498427e+05	6.622456e+05
2005	560197.000000	7.454538e+05	653204.666667	751596.916667	662478.166667	550258.750000	532113.750000	7.692282e+05	7.828684e+05
2006	630509.333333	8.232512e+05	731567.416667	833215.750000	737293.000000	623196.750000	616347.083333	8.490031e+05	8.750714e+05
2007	619169.500000	8.024552e+05	713301.750000	808942.166667	722292.333333	617116.833333	620928.333333	8.253435e+05	8.537762e+05
2008	525260.416667	7.154249e+05	620488.833333	698687.416667	628798.166667	500328.500000	512686.916667	7.278043e+05	7.547115e+05
2009	445350.583333	6.403431e+05	559588.250000	631942.666667	553679.250000	428028.166667	439597.916667	6.606394e+05	6.781112e+05
2010	447419.916667	6.550454e+05	579278.250000	639387.916667	563971.416667	424797.000000	436365.500000	6.758532e+05	6.872358e+05
2011	424010.750000	6.296292e+05	537600.750000	603127.833333	532206.166667	400624.250000	410746.750000	6.487944e+05	6.641018e+05
2012	420977.166667	6.468948e+05	523895.500000	597407.500000	526248.333333	393790.750000	402718.750000	6.654203e+05	6.870866e+05
2013	501598.500000	7.428678e+05	611195.666667	689987.416667	614310.916667	467597.500000	475593.583333	7.658213e+05	7.772384e+05
2014	548321.083333	7.959747e+05	668561.333333	745389.916667	662074.166667	506995.666667	519693.500000	8.197484e+05	8.336465e+05
2015	598662.083333	8.480332e+05	707277.416667	791708.916667	707595.250000	544868.000000	566196.166667	8.621392e+05	8.890444e+05
2016	660454.250000	9.197026e+05	763850.333333	848721.166667	762834.833333	594442.000000	623339.000000	9.271847e+05	9.815308e+05
2017	707261.083333	9.807372e+05	811469.500000	895753.833333	811862.666667	645348.333333	676925.500000	9.807728e+05	1.079714e+06
2018	772401.750000	1.069244e+06	866051.250000	977417.833333	884419.833333	710418.666667	746119.083333	1.058458e+06	1.164706e+06
2019	778934.750000	1.085502e+06	867707.500000	996119.333333	896309.500000	719589.750000	747025.416667	1.069491e+06	1.160395e+06

	RegionID	RegionName	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	...	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
21	96107	90250	Hawthorne	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	22	174520.0	174451.0	174208.0	174168.0	174272.0	174236.0	174011.0	173853.0	173880.0	173809.0	173430.0	172856.0	172239.0	171385.0	170904.0	171034.0	171937.0	172959.0	173708.0	174116.0	174672.0	176446.0	178775.0	180840.0	181787.0	182670.0	183373.0	183753.0	184170.0	185105.0	186263.0	187815.0	188620.0	...	542639.0	549684.0	554883.0	559601.0	565991.0	572281.0	577192.0	580694.0	585780.0	590338.0	592356.0	593805.0	595088.0	598773.0	604365	610036	616579	623096	630327	635907	639595	642727	642779	642257	642941	647773	652424	654329	654836	654122	653035	654325	656639	660217	657600	656051	656649	661550	665524	670833
43	96027	90046	Los Angeles	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	44	158703.0	158615.0	158990.0	159599.0	160228.0	161176.0	162302.0	163102.0	163659.0	164005.0	164238.0	165215.0	167196.0	168901.0	170638.0	171418.0	173336.0	174781.0	176926.0	178643.0	180776.0	183543.0	186489.0	188650.0	190057.0	192063.0	194619.0	196076.0	197877.0	200594.0	203909.0	206660.0	208435.0	...	1125483.0	1141019.0	1150330.0	1161896.0	1179904.0	1195337.0	1208427.0	1219575.0	1236950.0	1248887.0	1259457.0	1272009.0	1291359.0	1312526.0	1335419	1356609	1380975	1408153	1434319	1452009	1454944	1455943	1460135	1471859	1488243	1508333	1526004	1537515	1545734	1549924	1550051	1554922	1566579	1580344	1588563	1599202	1619582	1643242	1655745	1668029
98	96015	90034	Los Angeles	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	99	249808.0	249482.0	250401.0	251056.0	251514.0	251397.0	250283.0	249968.0	250338.0	251984.0	252901.0	254188.0	254578.0	255822.0	256382.0	257680.0	260300.0	263348.0	266764.0	268164.0	270148.0	273509.0	278206.0	282508.0	285693.0	289000.0	292101.0	295544.0	299422.0	303572.0	307245.0	311989.0	316186.0	...	1098613.0	1116683.0	1129006.0	1141621.0	1155358.0	1168045.0	1176148.0	1183092.0	1192333.0	1199192.0	1190123.0	1184125.0	1188810.0	1214663.0	1247125	1270676	1288018	1305398	1324662	1344220	1351806	1356336	1349641	1345237	1341463	1349432	1353677	1355101	1356760	1357547	1354072	1354989	1360972	1371112	1357947	1349303	1345223	1364396	1377603	1394108
111	96239	90805	Long Beach	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	112	137817.0	137415.0	137248.0	137198.0	137274.0	137207.0	137069.0	136945.0	136878.0	136589.0	136326.0	135893.0	135554.0	134987.0	134640.0	134405.0	134764.0	135245.0	135914.0	136466.0	137104.0	138896.0	140825.0	142597.0	143056.0	143338.0	143329.0	143061.0	142901.0	143393.0	143855.0	144632.0	144637.0	...	401058.0	406339.0	409653.0	412212.0	415488.0	419026.0	421879.0	423376.0	426035.0	428631.0	430342.0	431923.0	433537.0	436756.0	442200	447966	452898	457703	462067	465877	467806	469763	470023	469687	468882	470758	472025	472819	472463	471784	470695	471657	472929	475373	474002	473754	474411	479144	483458	488025
113	96193	90650	Norwalk	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	114	147481.0	147182.0	146908.0	146755.0	146450.0	146097.0	145692.0	145414.0	145158.0	144947.0	144763.0	144611.0	144451.0	144117.0	143890.0	143867.0	144347.0	144856.0	145225.0	145311.0	145568.0	146854.0	148378.0	149777.0	150252.0	150679.0	151164.0	151188.0	151347.0	151833.0	152567.0	153598.0	154239.0	...	432422.0	436375.0	438859.0	440851.0	444237.0	447533.0	450426.0	452110.0	454994.0	457611.0	459918.0	461409.0	462772.0	464739.0	468851	473157	477343	482122	486469	489710	490805	492170	492651	492327	491356	492278	493275	493810	493767	492713	491619	492074	492861	493873	491980	491376	492376	495968	499044	503135
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
17380	96039	90058	Los Angeles	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	17381	118410.0	117991.0	120237.0	123068.0	127823.0	131013.0	131861.0	131357.0	131468.0	132496.0	132925.0	133579.0	134290.0	134600.0	132810.0	129681.0	127333.0	127130.0	128622.0	129086.0	129405.0	129024.0	129748.0	130886.0	131032.0	131435.0	131179.0	131632.0	131597.0	129796.0	126555.0	124734.0	125675.0	...	303186.0	309476.0	314694.0	317294.0	320170.0	321963.0	326344.0	327113.0	330430.0	330553.0	333203.0	335117.0	338396.0	342555.0	346213	350278	353972	359593	364425	370712	373992	377692	378990	380787	379822	378925	378148	379070	380114	380890	380543	381035	381737	384453	385104	386134	386642	389196	391344	395551
18217	97331	93553	Pearblossom	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	18218	87664.0	86997.0	86637.0	86620.0	87675.0	88586.0	90216.0	90976.0	92204.0	91754.0	91262.0	91141.0	92380.0	92480.0	90945.0	89145.0	88096.0	88948.0	89429.0	89987.0	89813.0	90803.0	91127.0	91897.0	91799.0	91988.0	93062.0	94090.0	95298.0	95325.0	95357.0	96074.0	97123.0	...	227607.0	231888.0	232896.0	232554.0	234151.0	235742.0	237003.0	238851.0	241674.0	246895.0	248675.0	249350.0	249220.0	251599.0	256143	258672	261423	262936	264584	267527	270417	274536	275100	275549	274737	274805	275723	276405	278666	278833	279837	280583	282099	284206	282677	281829	280265	281574	282796	283995
21032	97324	93544	Llano	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	21033	128101.0	126755.0	125668.0	125506.0	125855.0	127261.0	128071.0	129294.0	129966.0	130057.0	129332.0	128861.0	130453.0	132076.0	132959.0	132990.0	133831.0	136108.0	139067.0	140720.0	141234.0	139489.0	138698.0	140070.0	142403.0	143346.0	142333.0	140317.0	139114.0	139062.0	139361.0	139690.0	140228.0	...	226474.0	229057.0	230557.0	231687.0	234412.0	237353.0	240098.0	243184.0	245577.0	247074.0	245587.0	244377.0	245162.0	247782.0	252035	254843	256340	257642	259744	263473	267284	271525	272349	272932	272444	273310	274497	275870	279848	282579	284081	284885	285527	287123	285334	285154	285197	288154	289016	289770
22563	399675	91008	Bradbury	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	22564	299236.0	299338.0	297435.0	295414.0	291656.0	290623.0	292183.0	294484.0	297310.0	298133.0	302957.0	306087.0	310376.0	310012.0	310798.0	310823.0	312884.0	314229.0	315463.0	314338.0	314685.0	315376.0	318706.0	321743.0	325285.0	326808.0	327076.0	327002.0	328575.0	332404.0	336472.0	343414.0	352301.0	...	1184504.0	1193048.0	1198569.0	1203923.0	1209900.0	1215592.0	1217751.0	1223244.0	1228543.0	1234276.0	1220389.0	1209054.0	1199230.0	1210158.0	1221662	1232501	1242322	1258584	1272872	1285892	1286602	1289543	1292734	1298696	1304408	1306182	1313124	1321300	1333949	1340705	1340088	1339797	1341705	1347899	1336720	1322992	1310898	1316338	1319847	1322469
29616	97339	93563	Valyermo	CA	Los Angeles-Long Beach-Anaheim	Los Angeles County	29617	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	...	210391.0	208975.0	207852.0	206735.0	208193.0	210037.0	212655.0	212863.0	213990.0	214615.0	214177.0	214457.0	214366.0	216703.0	219395	220129	220932	220555	222510	226429	230833	237241	243458	248587	250603	250190	249598	249665	251241	252341	253674	253702	253460	251749	246804	244706	244372	246366	244359	239410