列名称不同时如何将数据框追加到另一个（具有相同列大小）

Question

Is there a way to merge two dataframes with the same number of columns but with different names? The concat , merge , and join methods do not seem to work for this task. For example, I want to combine these two:

#dataframe1:
columnA | columnB
15      | 23

#dataframe2:
columnC | columnD
12      | 29

Into this:

#dataframe3:
anyColumnName1 | anyColumnName1
15             | 23
12             | 29

Please note that I have 90 columns in my case.

Answer 1

You could rename the columns of the second dataframe first.

If they have the same number of columns then simply:

dataframe2.columns = dataframe1.columns

and then concat them.

Answer 2

You can use numpy.concatenate with DataFrame constructor:

print (np.concatenate([df1, df2]))
[[15 23]
 [12 29]]

df = pd.DataFrame(np.concatenate([df1, df2]), columns=['a','b'])
print (df)
    a   b
0  15  23
1  12  29

Answer 3

You could use append by renaming columns of second dataframe based on first.

In [304]: df1.append(df2.rename(columns=dict(zip(df2.columns, df1.columns))))
Out[304]:
   columnA  columnB
0       15       23
0       12       29

Details.

In [308]: df1
Out[308]:
   columnA  columnB
0       15       23

In [309]: df2
Out[309]:
   columnC  columnD
0       12       29

In [310]: dict(zip(df2.columns, df1.columns))
Out[310]: {'columnC': 'columnA', 'columnD': 'columnB'}

In [311]: df2.rename(columns=dict(zip(df2.columns, df1.columns)))
Out[311]:
   columnA  columnB
0       12       29