pandas - Extend Index of a DataFrame setting all columns for new rows to NaN?

Question

I have time-indexed data:

df2 = pd.DataFrame({ \'day\': pd.Series([date(2012, 1, 1), date(2012, 1, 3)]), \'b\' : pd.Series([0.22, 0.3]) })
df2 = df2.set_index(\'         


        

Answer 1

You can resample passing day as frequency, without specifying a fill_method parameter missing values will be NaN filled as you desired

df3 = df2.asfreq('D')
df3

Out[16]:
               b
2012-01-01  0.22
2012-01-02   NaN
2012-01-03  0.30

To answer your second part, I can't think of a more elegant way at the moment:

df3 = DataFrame({ 'day': Series([date(2012, 1, 4), date(2012, 1, 31)])})
df3.set_index('day',inplace=True)
merged = df2.append(df3)
merged = merged.asfreq('D')
merged


Out[46]:
               b
2012-01-01  0.22
2012-01-02   NaN
2012-01-03  0.30
2012-01-04   NaN
2012-01-05   NaN
2012-01-06   NaN
2012-01-07   NaN
2012-01-08   NaN
2012-01-09   NaN
2012-01-10   NaN
2012-01-11   NaN
2012-01-12   NaN
2012-01-13   NaN
2012-01-14   NaN
2012-01-15   NaN
2012-01-16   NaN
2012-01-17   NaN
2012-01-18   NaN
2012-01-19   NaN
2012-01-20   NaN
2012-01-21   NaN
2012-01-22   NaN
2012-01-23   NaN
2012-01-24   NaN
2012-01-25   NaN
2012-01-26   NaN
2012-01-27   NaN
2012-01-28   NaN
2012-01-29   NaN
2012-01-30   NaN
2012-01-31   NaN

This constructs a second time series and then we just append and call asfreq('D') as before.