Pandas: groupby forward fill with datetime index

Question

I have a dataset that has two columns: company, and value.
It has a datetime index, which contains duplicates (on the same day, different companies have different values). The values have missing data, so I want to forward fill the missing data with the previous datapoint from the same company.

However, I can't seem to find a good way to do this without running into odd groupby errors, suggesting that I'm doing something wrong.

Toy data:

a = pd.DataFrame({'a': [1, 2, None], 'b': [12,None,14]})
a.index = pd.DatetimeIndex(['2010', '2011', '2012'])  
a = a.unstack() 
a = a.reset_index().set_index('level_1') 
a.columns = ['company', 'value'] 
a.sort_index(inplace=True)

Attempted solutions (didn't work: ValueError: cannot reindex from a duplicate axis):

a.groupby('company').ffill() 
a.groupby('company')['value'].ffill() 
a.groupby('company').fillna(method='ffill')

Hacky solution (that delivers the desired result, but is obviously just an ugly workaround):

a['value'] = a.reset_index().groupby(
    'company').fillna(method='ffill')['value'].values

There is probably a simple and elegant way to do this, how is this performed in Pandas?

Answer 1

One way is to use the transform function to fill the value column after group by:

import pandas as pd
a['value'] = a.groupby('company')['value'].transform(lambda v: v.ffill())

a
#          company  value
#level_1        
#2010-01-01      a    1.0
#2010-01-01      b   12.0
#2011-01-01      a    2.0
#2011-01-01      b   12.0
#2012-01-01      a    2.0
#2012-01-01      b   14.0

To compare, the original data frame looks like:

#            company    value
#level_1        
#2010-01-01        a      1.0
#2010-01-01        b     12.0
#2011-01-01        a      2.0
#2011-01-01        b      NaN
#2012-01-01        a      NaN
#2012-01-01        b     14.0