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

Not exactly the question since here you know that the second index is all days in January, but suppose you have another index say from another data frame df1, which might be disjoint and with a random frequency. Then you can do this:

ix = pd.DatetimeIndex(list(df2.index) + list(df1.index)).unique().sort_values()
df2.reindex(ix)

Converting indices to lists allows one to create a longer list in a natural way.