Pandas TimeGrouper on multiindex

Question

I have a multiIndex pandas dataframe, where the first level index is a group and the second level index is time. What I want to do is, within each group, to resample to daily frequency taking the average of intraday observations.

import pandas as pd
import numpy as np

data = pd.concat([pd.DataFrame([['A']*72, list(pd.date_range('1/1/2011', periods=72, freq='H')), list(np.random.rand(72))], index = ['Group', 'Time', 'Value']).T,
                  pd.DataFrame([['B']*72, list(pd.date_range('1/1/2011', periods=72, freq='H')), list(np.random.rand(72))], index = ['Group', 'Time', 'Value']).T,
                  pd.DataFrame([['C']*72, list(pd.date_range('1/1/2011', periods=72, freq='H')), list(np.random.rand(72))], index = ['Group', 'Time', 'Value']).T],
                  axis = 0).set_index(['Group', 'Time'])

This is what I tried so far:

daily_counts = data.groupby(pd.TimeGrouper('D'), level = ['Time']).mean()

But I get the following error:

TypeError: Only valid with DatetimeIndex, TimedeltaIndex or PeriodIndex, but got an instance of 'MultiIndex'

Any idea how to solve this?

Answer 1

You need first cast column to float and then use Grouper:

data['Value'] = data['Value'].astype(float)
daily_counts = data.groupby([pd.Grouper(freq='D', level='Time'), 
                             pd.Grouper(level='Group')])['Value'].mean()

print (daily_counts) 
Time        Group
2011-01-01  A        0.548358
            B        0.612878
            C        0.544822
2011-01-02  A        0.529880
            B        0.437062
            C        0.388626
2011-01-03  A        0.563854
            B        0.479299
            C        0.557190
Name: Value, dtype: float64

Another solution:

data = data.reset_index(level='Group')
print (data.groupby('Group').resample('D')['Value'].mean())