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Time Shifting

Sometimes you may need to shift all your data up or down along the time series index, in fact, a lot of pandas built-in methods do this under the hood. This isn't something we won't do often in the course, but its definitely good to know about this anyways!



In [1]:

    
import pandas as pd

import matplotlib.pyplot as plt
%matplotlib inline



In [2]:

    
df = pd.read_csv('time_data/walmart_stock.csv',
                 index_col = 'Date')
df.index = pd.to_datetime(df.index)



In [3]:

    
df.head()



In [4]:

    
df.tail()

.shift() forward



In [5]:

    
df.shift(1).head()



In [6]:

    
# You will lose that last piece of data that no longer has an index!
df.shift(1).tail()

shift() backwards



In [7]:

    
df.shift(-1).head()



In [8]:

    
df.shift(-1).tail()

Shifting based off Time String Code

Using tshift()



In [9]:

    
# Shift everything forward one month
df.tshift(periods = 1,
          freq = 'M').head()

That is it for now!

	Open	High	Low	Close	Volume	Adj Close
Date
2012-01-03	59.970001	61.060001	59.869999	60.330002	12668800	52.619235
2012-01-04	60.209999	60.349998	59.470001	59.709999	9593300	52.078475
2012-01-05	59.349998	59.619999	58.369999	59.419998	12768200	51.825539
2012-01-06	59.419998	59.450001	58.869999	59.000000	8069400	51.459220
2012-01-09	59.029999	59.549999	58.919998	59.180000	6679300	51.616215

	Open	High	Low	Close	Volume	Adj Close
Date
2016-12-23	69.430000	69.750000	69.360001	69.540001	4803900	69.032411
2016-12-27	69.300003	69.820000	69.250000	69.699997	4435700	69.191240
2016-12-28	69.940002	70.000000	69.260002	69.309998	4875700	68.804087
2016-12-29	69.209999	69.519997	69.120003	69.260002	4298400	68.754456
2016-12-30	69.120003	69.430000	68.830002	69.120003	6889500	68.615479

	Open	High	Low	Close	Volume	Adj Close
Date
2012-01-03	NaN	NaN	NaN	NaN	NaN	NaN
2012-01-04	59.970001	61.060001	59.869999	60.330002	12668800.0	52.619235
2012-01-05	60.209999	60.349998	59.470001	59.709999	9593300.0	52.078475
2012-01-06	59.349998	59.619999	58.369999	59.419998	12768200.0	51.825539
2012-01-09	59.419998	59.450001	58.869999	59.000000	8069400.0	51.459220

	Open	High	Low	Close	Volume	Adj Close
Date
2016-12-23	71.239998	71.239998	69.209999	69.589996	12106800.0	69.082042
2016-12-27	69.430000	69.750000	69.360001	69.540001	4803900.0	69.032411
2016-12-28	69.300003	69.820000	69.250000	69.699997	4435700.0	69.191240
2016-12-29	69.940002	70.000000	69.260002	69.309998	4875700.0	68.804087
2016-12-30	69.209999	69.519997	69.120003	69.260002	4298400.0	68.754456

	Open	High	Low	Close	Volume	Adj Close
Date
2012-01-03	60.209999	60.349998	59.470001	59.709999	9593300.0	52.078475
2012-01-04	59.349998	59.619999	58.369999	59.419998	12768200.0	51.825539
2012-01-05	59.419998	59.450001	58.869999	59.000000	8069400.0	51.459220
2012-01-06	59.029999	59.549999	58.919998	59.180000	6679300.0	51.616215
2012-01-09	59.430000	59.709999	58.980000	59.040001	6907300.0	51.494109

	Open	High	Low	Close	Volume	Adj Close
Date
2012-01-31	59.970001	61.060001	59.869999	60.330002	12668800	52.619235
2012-01-31	60.209999	60.349998	59.470001	59.709999	9593300	52.078475
2012-01-31	59.349998	59.619999	58.369999	59.419998	12768200	51.825539
2012-01-31	59.419998	59.450001	58.869999	59.000000	8069400	51.459220
2012-01-31	59.029999	59.549999	58.919998	59.180000	6679300	51.616215