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('day') df2                b  day              2012-01-01  0.22 2012-01-03  0.30 

What is the best way to extend this data frame so that it has one row for every day in January 2012 (say), where all columns are set to NaN (here only b) where we don't have data?

So the desired result would be:

               b  day              2012-01-01  0.22 2012-01-02   NaN 2012-01-03  0.30 2012-01-04   NaN ... 2012-01-31   NaN 

Many thanks!

Answer 1

Use this (current as of pandas 1.1.3):

ix = pd.date_range(start=date(2012, 1, 1), end=date(2012, 1, 31), freq='D') df2.reindex(ix) 

Which gives:

               b 2012-01-01  0.22 2012-01-02   NaN 2012-01-03  0.30 2012-01-04   NaN 2012-01-05   NaN [...] 2012-01-29   NaN 2012-01-30   NaN 2012-01-31   NaN 

For older versions of pandas replace pd.date_range with pd.DatetimeIndex.