pandas MultiIndex rolling mean

Preface: I'm newish, but have searched for hours here and in the pandas documentation without success. I've also read Wes's book. I am modeling stock market data for a hedge fund, and have a simple MultiIndexed-DataFrame with tickers, dates(daily), and fields. The sample here is from Bloomberg. 3 months - Dec. 2016 through Feb. 2017, 3 tickers(AAPL, IBM, MSFT). ``` import numpy as np import pandas as pd import os # get data from Excel curr_directory = os.getcwd() filename = 'Sample Data File.xlsx' filepath = os.path.join(curr_directory, filename) df = pd.read_excel(filepath, sheetname = 'Sheet1', index_col = [0,1], parse_cols = 'A:D') # sort df.sort_index(inplace=True) # sample of the data df.head(15) Out[4]: PX_LAST PX_VOLUME Security Name date AAPL US Equity 2016-12-01 109.49 37086862 2016-12-02 109.90 26527997 2016-12-05 109.11 34324540 2016-12-06 109.95 26195462 2016-12-07 111.03 29998719 2016-12-08 112.12 27068316 2016-12-09 113.95 34402627 2016-12-12 113.30 26374377 2016-12-13 115.19 43733811 2016-12-14 115.19 34031834 2016-12-15 115.82 46524544 2016-12-16 115.97 44351134 2016-12-19 116.64 27779423 2016-12-20 116.95 21424965 2016-12-21 117.06 23783165 df.tail(15) Out[5]: PX_LAST PX_VOLUME Security Name date MSFT US Equity 2017-02-07 63.43 20277226 2017-02-08 63.34 18096358 2017-02-09 64.06 22644443 2017-02-10 64.00 18170729 2017-02-13 64.72 22920101 2017-02-14 64.57 23108426 2017-02-15 64.53 17005157 2017-02-16 64.52 20546345 2017-02-17 64.62 21248818 2017-02-21 64.49 20655869 2017-02-22 64.36 19292651 2017-02-23 64.62 20273128 2017-02-24 64.62 21796800 2017-02-27 64.23 15871507 2017-02-28 63.98 23239825 ``` When I calculate daily price changes, like this, it seems to work, only the first day is NaN, as it should be: ``` df.head(5) Out[7]: PX_LAST PX_VOLUME px_change_% Security Name date AAPL US Equity 2016-12-01 109.49 37086862 NaN 2016-12-02 109.90 26527997 0.003745 2016-12-05 109.11 34324540 -0.007188 2016-12-06 109.95 26195462 0.007699 2016-12-07 111.03 29998719 0.009823 ``` But daily 30 Day Volume doesn't. It should only be NaN for the first 29 days, but is NaN for all of it: ``` # daily change from 30 day volume - doesn't work df['30_day_volume'] = df.groupby(level=0,group_keys=True)['PX_VOLUME'].rolling(window=30).mean() df['volume_change_%'] = (df['PX_VOLUME'] - df['30_day_volume']) / df['30_day_volume'] df.iloc[:,3:].tail(40) Out[12]: 30_day_volume volume_change_% Security Name date MSFT US Equity 2016-12-30 NaN NaN 2017-01-03 NaN NaN 2017-01-04 NaN NaN 2017-01-05 NaN NaN 2017-01-06 NaN NaN 2017-01-09 NaN NaN 2017-01-10 NaN NaN 2017-01-11 NaN NaN 2017-01-12 NaN NaN 2017-01-13 NaN NaN 2017-01-17 NaN NaN 2017-01-18 NaN NaN 2017-01-19 NaN NaN 2017-01-20 NaN NaN 2017-01-23 NaN NaN 2017-01-24 NaN NaN 2017-01-25 NaN NaN 2017-01-26 NaN NaN 2017-01-27 NaN NaN 2017-01-30 NaN NaN 2017-01-31 NaN NaN 2017-02-01 NaN NaN 2017-02-02 NaN NaN 2017-02-03 NaN NaN 2017-02-06 NaN NaN 2017-02-07 NaN NaN 2017-02-08 NaN NaN 2017-02-09 NaN NaN 2017-02-10 NaN NaN 2017-02-13 NaN NaN 2017-02-14 NaN NaN 2017-02-15 NaN NaN 2017-02-16 NaN NaN 2017-02-17 NaN NaN 2017-02-21 NaN NaN 2017-02-22 NaN NaN 2017-02-23 NaN NaN 2017-02-24 NaN NaN 2017-02-27 NaN NaN 2017-02-28 NaN NaN ``` As pandas seems to have been designed specifically for finance, I'm surprised this isn't straightforward. Edit: I've tried some other ways as well. - Tried converting it into a Panel (3D), but didn't find any built in functions for Windows except to convert to a DataFrame and back, so no advantage there. - Tried to create a pivot table, but couldn't find a way to reference just the first level of the MultiIndex. `df.index.levels[0]` or `...levels[1]` wasn't working. Thanks!