how to “rowise merge” pandas dataframes

Question

I want to merge 2 DataFrames, such that in the new DataFrame first row = row 1 from df1 and the second row is the first row from df2 etc.

A=pd.DataFrame({'A':[1,3,5], 'B': [7,9,11]}, index = ['bs','ss','db'])
B=pd.DataFrame({'A':[2,4,6], 'B': [8,10,12]}, index = ['bs','ss','db'])

Note that row and column labels are exactly the same and both dataframes have the same dimension.

So the desired output should look like

      A    B
bs_a  1    7
bs_b  2    8
ss_a  3    9
ss_b  4    10
db_a  5    11
db_b  6    12

Im not familiar with pandas merge, and after looking in the documentation i still dont know how to do this

Answer 1

You can do concat with keys , the merge the multiple index

s=pd.concat([A,B],keys=['a','b']).sort_index(level=1)
s.index=s.index.map('{0[1]}_{0[0]}'.format) 
s
Out[225]: 
      A   B
bs_a  1   7
bs_b  2   8
db_a  5  11
db_b  6  12
ss_a  3   9
ss_b  4  10

Answer 2

you could rename your dataframe's index and then append one to the other

df = A.rename('{}_a'.format).append(B.rename('{}_b'.format)).sort_index()

This gives

        A   B
bs_a    1   7
bs_b    2   8
db_a    5   11
db_b    6   12
ss_a    3   9
ss_b    4   10

Answer 3

try using pd.concat

A.reset_index(inplace=True)
B.reset_index(inplace=True)

A['index']=A['index']+'_a'
B['index']=B['index']+'_b'

A.set_index("index",inplace=True)
B.set_index("index",inplace=True)

final_df = pd.concat([A,B]).sort_values(['A'],ascending=[True])
final_df.index.name=''

        A   B
bs_a    1   7
bs_b    2   8
ss_a    3   9
ss_b    4   10
db_a    5   11
db_b    6   12