How to concatenate dataframe and series with different index

Question

if I have one dataframe and series like these

  bi            sm
0  A          0  a
1  B          1  b
2  C          2  C

I can concatenate like this

dfA['sm'] = dfB

  big  sm
0 A    a
1 B    b 
2 C    c

However I have one dataframe and seriese, each has the different index but same row numbers.

            bi            sm
2017-11-04  A          0  a
2017-11-03  B          1  b
2017-11-02  C          2  C

I would like to concatenate these two into this

            bi  sm
2017-11-04  A   a
2017-11-03  B   b
2017-11-02  C   C

How can I make it???

Possible duplicate of How to combine two pandas series which are differently indexed? — Bharath M Shetty
– Bharath M Shetty, Commented Aug 5, 2017 at 5:39

DYZ · Accepted Answer · 2017-08-05 03:45:50Z

9

Make both dataframes have the same index, then concatenate:

pd.concat([dfA, dfB.set_index(dfA.index)], axis=1)
#           bi sm
#2017-11-04  A  a
#2017-11-03  B  b
#2017-11-02  C  c

answered Aug 5, 2017 at 3:39

DYZ

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3 Comments

Thanks it works. Is it possible to change the name of column when concatenating?? sm -> md

@whitebear Use pd.concat([dfA, dfB.set_index(dfA.index)], axis=1).rename(columns={'sm' : 'md'})

THank you, change index before concatinating is the very understandable.

Zero · Accepted Answer · 2017-08-05 05:23:56Z

4

You can assign dfB.sm as values values

Option 1

In [209]: dfA['sm'] = dfB.sm.values

In [210]: dfA
Out[210]:
           bi sm
2017-11-04  A  a
2017-11-03  B  b
2017-11-02  C  C

Option 2

In [215]: dfA.assign(smm=dfB.sm.values)
Out[215]:
           bi smm
2017-11-04  A   a
2017-11-03  B   b
2017-11-02  C   C

Note: dfA, dfB are both dataframes.

answered Aug 5, 2017 at 5:23

Zero

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