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

Mark's answer seems to not be working anymore on pandas 1.1.1.

However, using the same idea, the following works:

from datetime import datetime
import pandas as pd


# get start and desired end dates
first_date = df['date'].min()
today = datetime.today()

# set index
df.set_index('date', inplace=True)

# and here is were the magic happens
idx = pd.date_range(first_date, today, freq='D')
df = df.reindex(idx)

EDIT: just found out that this exact use case is in the docs:

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.reindex.html#pandas.DataFrame.reindex