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

Here's another option: First add a NaN record on the last day you want, then resample. This way resampling will fill the missing dates for you.

Starting Frame:

import pandas as pd
import numpy as np
from datetime import date

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

Out:
                  b
    day 
    2012-01-01  0.22
    2012-01-03  0.30

Filled Frame:

df2 = df2.set_value(date(2012,1,31),'b',np.float('nan'))
df2.asfreq('D')

Out:
                b
    day 
    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