How to merge two dataframes using index location and backfill or forward fill for missing rows

Question

df = 2018-01-03  0.034
     2019-05-01  0.095
     2020-05-28  0.110
     ......
     2020-05-29  0.697

df.iloc[:,0] = pd.to_datetime(df.iloc[:,0])
df.shape()
(605, 2)

s = pd.Series(pd.date_range(str(df.iloc[:,0].min()), str(df.iloc[:,0].max()), freq='D'),
          name= None)
s = s[s.dt.dayofweek.lt(5)]
df2 = s.to_frame()
df2 = 2018-01-03
      2018-01-04
      2018-01-05
      .......
      2020-05-28
      2020-05-29
df2.shape()
(628, 1)

I am trying to merge the above two dataframes by using index location. Df columns are Date and rates. Shape of df is (605,2). Df2 has only one column, which is the date and has a shape of 628,1. I want to merge df with df2 and forward or backfill the missing rates. My expected output shape should be 628,2. Can you please suggest how it can be possible by using above code.

Asked By: Rahul

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Source

Answer 1

You can do reindex

df=df.set_index(df.columns[0]).reindex(s, method = 'ffill').reset_index()

Answered By: BENY

How to merge two dataframes using index location and backfill or forward fill for missing rows

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