How can I combine Pandas DataFrames by column and index?

Question

I have four Pandas DataFrames with numeric columns and indexes:

A = pd.DataFrame(data={"435000": [9.792, 9.795], "435002": [9.825, 9.812]}, index=[119000, 119002])
B = pd.DataFrame(data={"435004": [9.805, 9.783], "435006": [9.785, 9.78]}, index=[119000, 119002])
C = pd.DataFrame(data={"435000": [9.778, 9.743], "435002": [9.75, 9.743]}, index=[119004, 119006])
D = pd.DataFrame(data={"435004": [9.743, 9.743], "435006": [9.762, 9.738]}, index=[119004, 119006])

I want to combine them into a single DataFrame similar to this, matching column names and indexes:

If I try to execute pd.concatfour dfs, they add up (top and bottom or to the side, depending on axis), and I get the values NaNin df:

result = pd.concat([A, B, C, D], axis=0)

How can I use pd.concat(or merge, joinetc.) to get the correct result?

Answer 1

You need to concat in pairs:

result = pd.concat([pd.concat([A, C], axis=0), pd.concat([B, D], axis=0)], axis=1)
print (result)
        435000  435002  435004  435006
119000   9.792   9.825   9.805   9.785
119002   9.795   9.812   9.783   9.780
119004   9.778   9.750   9.743   9.762
119006   9.743   9.743   9.743   9.738

Better stack+ concat+ unstack:

result = pd.concat([A.stack(), B.stack(), C.stack(), D.stack()], axis=0).unstack()
print (result)
        435000  435002  435004  435006
119000   9.792   9.825   9.805   9.785
119002   9.795   9.812   9.783   9.780
119004   9.778   9.750   9.743   9.762
119006   9.743   9.743   9.743   9.738

Additional dynamics:

dfs = [A,B,C,D]
result = pd.concat([df.stack() for df in dfs], axis=0).unstack()
print (result)
        435000  435002  435004  435006
119000   9.792   9.825   9.805   9.785
119002   9.795   9.812   9.783   9.780
119004   9.778   9.750   9.743   9.762
119006   9.743   9.743   9.743   9.738

Answer 2

:

pd.concat((A.join(B), C.join(D)))
Out: 
        435000  435002  435004  435006
119000   9.792   9.825   9.805   9.785
119002   9.795   9.812   9.783   9.780
119004   9.778   9.750   9.743   9.762
119006   9.743   9.743   9.743   9.738