python - Concatenate distinct columns in two dataframes using pandas (and append similar columns)

Question

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 dataframes have a column id, and some columns that are identical: Ex.

df1

id place name qty unit A 
1 NY    Tom   2  10   a
2 TK    Ron   3  15   a
3 Lon   Don   5  90   a
4 Hk    Sam   4  49   a

df2

id place name qty unit B 
1 NY    Tom   2  10   b
2 TK    Ron   3  15   b
3 Lon   Don   5  90   b
4 Hk    Sam   4  49   b

df3

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

Result:

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

The columns place, name, qty, and unit will always be part of the three dataframes, the names of columns that are different could vary (A,B,C,D in my example). The three dataframes have the same number of rows.

I have tried:

cols_to_use = df1.columns - df2.columns
dfNew = merge(df, df2[cols_to_use], left_index=True, right_index=True, how='outer')

The problem is that I get more rows than expected and columns renamed in the resulting dataframe (when using concat).

Answer 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