Replace values in a pandas dataframe from another

Question

I have a pandas data frame(df1) and I need to replace some of the df1 values with another data frame (df2). the df1 contains a time series from 1998-01-01 to 2002-12-31 and df1 contains time series from 1998-03-01 to 1998-07-31.

I want to replace the df1 values with df2 values for the time period of df2 (i.e. 1998-03-01 to 1998-07-31)

df1=

date          kc
1998-01-01    0
1998-01-02    0
1998-01-03    0
1998-01-04    0
1998-01-05    0
.
.
.
2002-12-30    0
2002-12-31    0

and df2=

date          kc
1998-03-01    0.3
1998-03-02    0.35
1998-03-03    0.4
1998-03-04    0.45
1998-03-05    0.4
.
.
.
1998-07-30    0.6
1998-07-31    0.7

Where the date column is set index for both of the dataframes. I tried the following:

df1.loc["1998-03-01":"1998-07-31","kc"]=df2

But it changes nothing, df1 remains same.

jezrael · Accepted Answer · 2020-04-18 06:00:39Z

2

Samples:

print (df1)
            kc
Date          
1998-01-01   0
1998-02-01   0
1998-03-01   0
1998-03-02   0
1998-03-03   0
2002-12-30   0
1998-12-31   0

print (df2)
              kc
date            
1998-02-01  0.30
1998-03-01  0.35
1998-03-02  0.40
1998-03-03  0.45
1998-03-04  0.40
2002-07-30  0.60
1998-07-31  0.70

You can use Series.combine_first

df1["kc"] = df2['kc'].combine_first(df1['kc'])

print (df1)
              kc
Date            
1998-01-01  0.00
1998-02-01  0.30
1998-03-01  0.35
1998-03-02  0.40
1998-03-03  0.45
2002-12-30  0.00
1998-12-31  0.00

Or Index.isin for new values by mask:

df1.loc[df1.index.isin(df2.index), "kc"]=df2['kc']

print (df1)
              kc
Date            
1998-01-01  0.00
1998-02-01  0.30
1998-03-01  0.35
1998-03-02  0.40
1998-03-03  0.45
2002-12-30  0.00
1998-12-31  0.00

edited Apr 18, 2020 at 6:00

answered Apr 18, 2020 at 5:21

jezrael

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