I have a pandas MultiIndex dataframe similar to the following:
import pandas as pd
rows = [('One', 'One', 'One', '20120105', 1, 'Text1'),
('One', 'One', 'One', '20120107', 2, 'Text2'),
('One', 'One', 'One', '20120110', 3, 'Text3'),
('One', 'One', 'Two', '20120104', 4, 'Text4'),
('One', 'Two', 'One', '20120109', 5, 'Text5'),
('Two', 'Three', 'Four', '20120111', 6, 'Text6')]
cols = ['Type', 'Subtype', 'Subsubtype', 'Date', 'Number', 'Text']
df = pd.DataFrame.from_records(rows, columns=cols)
df['Date'] = pd.to_datetime(df['Date'])
df = df.set_index(['Type', 'Subtype', 'Subsubtype'])
end_date = max(df['Date'])
print(df)
Date Number Text
Type Subtype Subsubtype
One One One 2012-01-05 1 Text1
One 2012-01-07 2 Text2
One 2012-01-10 3 Text3
Two 2012-01-04 4 Text4
Two One 2012-01-09 5 Text5
Two Three Four 2012-01-11 6 Text6
I would like to upsample the data so that each combination of the Type-Subtype-Subsubtype indexes gets daily date data: from the minimum date for which data is available to end_date = max(df['Date']).
An example of what I want:
Date Number Text
Type Subtype Subsubtype
One One One 2012-01-05 1 Text1
One 2012-01-06 1 Text2
One 2012-01-07 2 Text2
One 2012-01-08 2 Text2
One 2012-01-09 2 Text2
One 2012-01-10 3 Text3
One 2012-01-11 3 Text3
Two 2012-01-04 4 Text4
Two 2012-01-05 4 Text4
Two 2012-01-06 4 Text4
Two 2012-01-07 4 Text4
Two 2012-01-08 4 Text4
Two 2012-01-09 4 Text4
Two 2012-01-10 4 Text4
Two 2012-01-11 4 Text4
Two One 2012-01-09 5 Text5
One 2012-01-10 5 Text5
One 2012-01-11 5 Text5
Two Three Four 2012-01-11 6 Text6
Looking through similar questions I haven't been able to find anything that I could make work. Any help is greatly appreciated.
