Concatenate 2 dataframes. I would like to combine duplicate columns - python-3.x

Question

The following code can be used as an example of the problem I'm having:
dic={'A':['1','2','3'], 'B':['10','11','12']}
df1=pd.DataFrame(dic)
df1.set_index('A', inplace=True)
dic2={'A':['4','5','6'], 'B':['10','11','12']}
df2=pd.DataFrame(dic2)
df2.set_index('A', inplace=True)
df3=pd.concat([df1,df2], axis=1)
print(df3)
The result I get from this concatenation is:
B B
1 10 NaN
2 11 NaN
3 12 NaN
4 NaN 10
5 NaN 11
6 NaN 12
I would like to have:
B
1 10
2 11
3 12
4 10
5 11
6 12
I know that I can concatenate along axis=0. Unfortunately, that only solves the problem for this little example. The actual code I'm working with is more complex. Concatenating along axis=0 causes the index to be duplicated. I don't want that either.
EDIT:
People have asked me to give a more complex example to describe why simply removing 'axis=1' doesn't work. Here is a more complex example, first with axis=1 INCLUDED:
dic={'A':['1','2','3'], 'B':['10','11','12']}
df1=pd.DataFrame(dic)
df1.set_index('A', inplace=True)
dic2={'A':['4','5','6'], 'B':['10','11','12']}
df2=pd.DataFrame(dic2)
df2.set_index('A', inplace=True)
df=pd.concat([df1,df2], axis=1)
dic3={'A':['1','2','3'], 'C':['20','21','22']}
df3=pd.DataFrame(dic3)
df3.set_index('A', inplace=True)
df4=pd.concat([df,df3], axis=1)
print(df4)
This gives me:
B B C
1 10 NaN 20
2 11 NaN 21
3 12 NaN 22
4 NaN 10 NaN
5 NaN 11 NaN
6 NaN 12 NaN
I would like to have:
B C
1 10 20
2 11 21
3 12 22
4 10 NaN
5 11 NaN
6 12 NaN
Now here is an example with axis=1 REMOVED:
dic={'A':['1','2','3'], 'B':['10','11','12']}
df1=pd.DataFrame(dic)
df1.set_index('A', inplace=True)
dic2={'A':['4','5','6'], 'B':['10','11','12']}
df2=pd.DataFrame(dic2)
df2.set_index('A', inplace=True)
df=pd.concat([df1,df2])
dic3={'A':['1','2','3'], 'C':['20','21','22']}
df3=pd.DataFrame(dic3)
df3.set_index('A', inplace=True)
df4=pd.concat([df,df3])
print(df4)
This gives me:
B C
A
1 10 NaN
2 11 NaN
3 12 NaN
4 10 NaN
5 11 NaN
6 12 NaN
1 NaN 20
2 NaN 21
3 NaN 22
I would like to have:
B C
1 10 20
2 11 21
3 12 22
4 10 NaN
5 11 NaN
6 12 NaN
Sorry it wasn't very clear. I hope this helps.

Answer 1

Here is a two step process, for the example provided after the 'EDIT' point. Start by creating the dictionaries:
import pandas as pd
dic = {'A':['1','2','3'], 'B':['10','11','12']}
dic2 = {'A':['4','5','6'], 'B':['10','11','12']}
dic3 = {'A':['1','2','3'], 'C':['20','21','22']}
Step 1: convert each dictionary to a data frame, with index 'A', and concatenate (along axis=0):
t = pd.concat([pd.DataFrame(dic).set_index('A'),
pd.DataFrame(dic2).set_index('A'),
pd.DataFrame(dic3).set_index('A')])
Step 2: concatenate non-null elements of col 'B' with non-null elements of col 'C' (you could put this in a list comprehension if there are more than two columns). Now we concatenate along axis=1:
result = pd.concat([
t.loc[ t['B'].notna(), 'B' ],
t.loc[ t['C'].notna(), 'C' ],
], axis=1)
print(result)
B C
1 10 20
2 11 21
3 12 22
4 10 NaN
5 11 NaN
6 12 NaN

Answer 2

Edited:
If two objects need to be added along axis=1, then the new columns will be appended.And with axis=0 or default same column will be appended with new values.
Refer Below Solution:
import pandas as pd
dic={'A':['1','2','3'], 'B':['10','11','12']}
df1=pd.DataFrame(dic)
df1.set_index('A', inplace=True)
dic2={'A':['4','5','6'], 'B':['10','11','12']}
df2=pd.DataFrame(dic2)
df2.set_index('A', inplace=True)
df=pd.concat([df1,df2])
dic3={'A':['1','2','3'], 'C':['20','21','22']}
df3=pd.DataFrame(dic3)
df3.set_index('A', inplace=True)
df4=pd.concat([df,df3],axis=1) #As here C is new new column so need to use axis=1
print(df4)
Output:
B C
1 10 20
2 11 21
3 12 22
4 10 NaN
5 11 NaN
6 12 NaN