Concatenate Distinct Columns In Two Dataframes Using Pandas (and Append Similar Columns)

My question is closely related to Pandas Merge - How to avoid duplicating columns but not identical. I want to concatenate the columns that are different in three dataframes. The d

Solution 1:

Using reduce from functools

from functools import reduce
reduce(lambda left,right: pd.merge(left,right), [df1,df2,df3])
Out[725]: 
   id place name  qty  unit  A  B  C  D
0   1    NY  Tom    2    10  a  b  c  d
1   2    TK  Ron    3    15  a  b  c  d
2   3   Lon  Don    5    90  a  b  c  d
3   4    Hk  Sam    4    49  a  b  c  d

Solution 2:

You can use nested merge

merge_on = ['id','place','name','qty','unit']
df1.merge(df2, on = merge_on).merge(df3, on = merge_on)



    id  place   name    qty unit    A   B   C   D
0   1   NY      Tom     2   10      a   b   c   d
1   2   TK      Ron     3   15      a   b   c   d
2   3   Lon     Don     5   90      a   b   c   d
3   4   Hk      Sam     4   49      a   b   c   d

Solution 3:

Using concat with groupby and first:

pd.concat([df1, df2, df3], 1).groupby(level=0, axis=1).first()

   A  B  C  D  id name place  qty  unit
0  a  b  c  d   1  Tom    NY    2    10
1  a  b  c  d   2  Ron    TK    3    15
2  a  b  c  d   3  Don   Lon    5    90
3  a  b  c  d   4  Sam    Hk    4    49

Solution 4:

You can extract only those columns from df2 (and df3 similarly) which are not already present in df1. Then just use pd.concat to concatenate the data frames:

cols = [c for c in df2.columns if c not in df1.columns]
df = pd.concat([df1, df2[cols]], axis=1)