Pandas create new date rows and forward fill column values

Question

I have a dataframe like this:

id     date       value
 1  12/01/2016      5 
 1  25/02/2016      7 
 1  10/03/2017      13 
 2  02/04/2016      0 
 2  06/07/         


        

Answer 1

Consider a groupby and merge approach:

import pandas as pd
from io import StringIO
from datetime import date

txt= """
id     date       value
 1  12/01/2016      5 
 1  25/02/2016      7 
 1  10/03/2017      13 
 2  02/04/2016      0 
 2  06/07/2016      1 
 2  18/04/2017      6 
"""

df = pd.read_table(StringIO(txt), sep="\s+", parse_dates=[1], dayfirst=True)

def expand_dates(ser):
    return pd.DataFrame({'date': pd.date_range(ser['date'].min(), date.today(), freq='D')})

newdf = df.groupby(['id']).apply(expand_dates).reset_index()\
          .merge(df, how='left')[['id', 'date', 'value']].ffill()