How to resample data inside multiindex dataframe

If want grouping with resample first is necessary DatetimeIndex only, so added DataFrame.reset_index by all levels without first, then grouping and resample with custom function, because pct_change for resample is not implemented:

def percent_change(x):    return pd.Series(x).pct_change()

Another idea is use numpy solution for pct_change:

def percent_change(x):    return x / np.concatenate(([np.nan], x[:-1])) - 1

df1 = (df.reset_index(level=[1,2,3])        .groupby(['month', 'week'])['Adj Close']        .resample('W')        .apply(percent_change))

this way every month would yield 4 values for weekly change

So it seems there is no groupby, only necessary downsample like sum and chain Series.pct_change:

df2 = (df.reset_index(level=[1,2,3])        .resample('W')['Adj Close']        .sum()        .pct_change())

Drop unwanted indexes. The datetime index is enough for re-sampling / grouping

df.index = df.index.droplevel(['month', 'week', 'day'])

Re-sample by week, select the column needed, add a aggregation function and then calculate percentage change.

df.resample('W')['Adj Close'].mean().pct_change()