Resampling a multi-index DataFrame

pandas, python

Solution

The example unstacks a non-numerical column 'NAME' which is silently dropped but causes problems during re-stacking. The code below worked for me

print df[['VAR1']].unstack('ID').resample('W-THU').stack('ID')
Params         VAR1
DATE       ID
2013-01-03 A   69.0
           B   69.0
2013-01-10 A   76.0
           B   73.5

Problem

I want to resample a DataFrame with a multi-index containing both a datetime column and some other key. The Dataframe looks like: ``` import pandas as pd from StringIO import StringIO csv = StringIO("""ID,NAME,DATE,VAR1 1,a,03-JAN-2013,69 1,a,04-JAN-2013,77 1,a,05-JAN-2013,75 2,b,03-JAN-2013,69 2,b,04-JAN-2013,75 2,b,05-JAN-2013,72""") df = pd.read_csv(csv, index_col=['DATE', 'ID'], parse_dates=['DATE']) df.columns.name = 'Params' ``` Because resampling is only allowed on datatime indexes, i thought unstacking the other index column would help. And indeed it does, but i cant stack it again afterwards. ``` print df.unstack('ID').resample('W-THU') Params VAR1 ID 1 2 DATE 2013-01-03 69 69.0 2013-01-10 76 73.5 ``` But then stacking 'ID' again results in an index-error: ``` print df.unstack('ID').resample('W-THU').stack('ID') IndexError: index 0 is out of bounds for axis 0 with size 0 ``` Strangely enough, i can stack the other column level with both: ``` print df.unstack('ID').resample('W-THU').stack(0) ``` and ``` print df.unstack('ID').resample('W-THU').stack('Params') ``` The index-error also occurs if i reorder (swap) both column levels. Does anyone know how to overcome this issue?

Original source