One can use the 'resample' method to add in the non-trading calendar days to a pandas series or dataframe. Something like this.

aapl_close_with_trading_days = prices(  
            assets=symbols('AAPL'),  
            start='2017-01-01',  
            end='2017-01-31'  
            )  
aaple_close_with_calendar_days = aapl_close_with_trading_days.resample('1D').last()  

See attached notebook. Good luck.

One may also use the resample() and ffill() methods to fill NaNs for non-trading days.

aaple_close_with_calendar_days = aapl_close.resample('1D').last().ffill()  

2017-01-03 00:00:00+00:00 116.14
2017-01-04 00:00:00+00:00 116.02
2017-01-05 00:00:00+00:00 116.61
2017-01-06 00:00:00+00:00 117.91
2017-01-07 00:00:00+00:00 117.91
2017-01-08 00:00:00+00:00 117.91
2017-01-09 00:00:00+00:00 119.00
2017-01-10 00:00:00+00:00 119.11
2017-01-11 00:00:00+00:00 119.74
2017-01-12 00:00:00+00:00 119.25
2017-01-13 00:00:00+00:00 119.04
2017-01-14 00:00:00+00:00 119.04
2017-01-15 00:00:00+00:00 119.04
2017-01-16 00:00:00+00:00 119.04
2017-01-17 00:00:00+00:00 119.99
2017-01-18 00:00:00+00:00 119.99
2017-01-19 00:00:00+00:00 119.77
2017-01-20 00:00:00+00:00 120.00
2017-01-21 00:00:00+00:00 120.00
2017-01-22 00:00:00+00:00 120.00
2017-01-23 00:00:00+00:00 120.09
2017-01-24 00:00:00+00:00 119.98
2017-01-25 00:00:00+00:00 121.90
2017-01-26 00:00:00+00:00 121.93
2017-01-27 00:00:00+00:00 121.72
2017-01-28 00:00:00+00:00 121.72
2017-01-29 00:00:00+00:00 121.72
2017-01-30 00:00:00+00:00 121.57
2017-01-31 00:00:00+00:00 121.30

Many thanks this is exactly what I wanted.