aggregate data by quarter - pandas

Question

I have a pivot pandas data frame (sales by region) that got created from another pandas data frame (sales by store) using the pivot_table method.
As an example:
df = pd.DataFrame(
{'store':['A','B','C','D','E']*7,
'region':['NW','NW','SW','NE','NE']*7,
'date':['2017-03-30']*5+['2017-04-05']*5+['2017-04-07']*5+['2017-04-12']*5+['2017-04-13']*5+['2017-04-17']*5+['2017-04-20']*5,
'sales':[30,1,133,9,1,30,3,135,9,11,30,1,140,15,15,25,10,137,9,3,29,10,137,9,11,30,19,145,20,10,30,8,141,25,25]
})
df['date'] = pd.to_datetime(df['date'])
df_sales = df.pivot_table(index = ['region'], columns = ['date'], aggfunc = [np.sum], margins = True)
df_sales = df_sales.ix[:,range(0, df_sales.shape[1]-1)]
My goal is to do the following to the sales data frame, df_sales.
Create a new dataframe that summarizes sales by quarter. I could use the original dataframe df, or the sales_df.
As of quarter here we only have only two quarters (USA fiscal calendar year) so the quarterly aggregated data frame would look like:
2017Q1 2017Q2
10 27
31 37.5
133 139.17
I take the average for all days in Q1, and same for Q2. Thus, for example for the North east region, 'NE', the Q1 is the average of only one day 2017-03-30, i.e., 10, and for the Q2 is the average across 2017-04-05 to 2017-04-20, i.e.,
(20+30+12+20+30+50)/6=27
Any suggestions?
ADDITIONAL NOTE: I would ideally do the quarter aggregations on the df_sales pivoted table since it's a much smaller dataframe to keep in memory. The current solution does it on the original df, but I am still seeking a way to do it in the df_sales dataframe.

Answer 1

UPDATE:
Setup:
df.date = pd.to_datetime(df.date)
df_sales = df.pivot_table(index='region', columns='date', values='sales', aggfunc='sum')
In [318]: df_sales
Out[318]:
date 2017-03-30 2017-04-05 2017-04-07 2017-04-12 2017-04-13 2017-04-17 2017-04-20
region
NE 10 20 30 12 20 30 50
NW 31 33 31 35 39 49 38
SW 133 135 140 137 137 145 141
Solution:
In [319]: (df_sales.groupby(pd.PeriodIndex(df_sales.columns, freq='Q'), axis=1)
...: .apply(lambda x: x.sum(axis=1)/x.shape[1])
...: )
Out[319]:
date 2017Q1 2017Q2
region
NE 10.0 27.000000
NW 31.0 37.500000
SW 133.0 139.166667
Solution based on the original DF:
In [253]: (df.groupby(['region', pd.PeriodIndex(df.date, freq='Q-DEC')])
...: .apply(lambda x: x['sales'].sum()/x['date'].nunique())
...: .to_frame('avg').unstack('date')
...: )
...:
Out[253]:
avg
date 2017Q1 2017Q2
region
NE 10.0 27.000000
NW 31.0 37.500000
SW 133.0 139.166667
NOTE: df - is the original DF (before "pivoting")