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