Filling missing data in python timeseries

I am using the ‘SCIKITS.TIMESERIES‘ python library for time series analysis. Here is how to fill the missing dates and the default data in the time series. The version 0.91.3 has bug in its timeseries.fill_missing_dates() method. One of the arguments it takes is fill_value, this is the default value we want to set for the missing data. But it does not work as intended. In fact the missing data is masked. To fill in the required data one must use the timeseries.filled(fill_value) method. Here is an example:

>>>import scikits.timeseries as ts

>>> datarr = ts.date_array(['2009-01-01', '2009-01-05'], freq='D')
>>> datarr
DateArray([01-Jan-2009, 05-Jan-2009],
freq='D')

>>> sr1 = ts.time_series([3,4], datarr)
>>> sr1
timeseries([3 4],
dates = [01-Jan-2009 05-Jan-2009],
freq  = D)

>>> m1 = sr1.fill_missing_dates(fill_value=0)
>>> m1
timeseries([3 -- -- -- 4],
dates = [01-Jan-2009 ... 05-Jan-2009],
freq  = D)

>>> m1.filled(0)
timeseries([3 0 0 0 4],
dates = [01-Jan-2009 ... 05-Jan-2009],
freq  = D)

Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s