Dataframe concat value columns of multiple tables with matching index columns

Question

I have following tables in a pandas dataframe:

ID1	ID2	Value1
Data1	Data2	Data11

ID1	ID2	Value2
Data1	Data2	Data12

ID1	ID2	Value3
Data1	Data2	Data13

My aim is a table of form:

ID1	ID2	Value1	Value2	Value3
Data1	Data2	Data11	Data12	Data13

What is the easiest way to do that with python?

yes I have three seperate dataframes for each table. The aim-table should be in a new dataframe. — Martin Kunze
– Martin Kunze, Commented Dec 4, 2021 at 21:15

user17242583 · Accepted Answer · 2021-12-04 21:17:26Z

2

new_df = pd.concat([df1, df2, df3]).groupby(['ID1', 'ID2'], as_index=False).first()

Output:

>>> new_df
     ID1    ID2  Value1  Value2  Value3
0  Data1  Data2  Data11  Data12  Data13

answered Dec 4, 2021 at 21:17

user17242583

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Comments

tdy · Accepted Answer · 2021-12-04 21:28:36Z

2

Perform multiple merges in a chain:

df1.merge(df2.merge(df3))

#      ID1    ID2  Value1  Value2  Value3
# 0  Data1  Data2  Data11  Data12  Data13

from functools import reduce
reduce(lambda L, R: L.merge(R, on=['ID1', 'ID2']), [df1, df2, df3])

answered Dec 4, 2021 at 21:23

tdy

42k42 gold badges124 silver badges125 bronze badges

lol. my answer: reduce(lambda dfL, dfR: dfL.merge(dfR, on=['ID1', 'ID2']), [df1, df2, df3])