pandas join DataFrame force suffix?

You could force a suffix on the actual DataFrame:

In [11]: df_a = pd.DataFrame([[1], [2]], columns=['A'])

In [12]: df_b = pd.DataFrame([[3], [4]], columns=['B'])

In [13]: df_a.join(df_b)
Out[13]: 
   A  B
0  1  3
1  2  4

By appending to its column's names:

In [14]: df_a.columns = df_a.columns.map(lambda x: str(x) + '_a')

In [15]: df_a
Out[15]: 
   A_a
0    1
1    2

Now joins won't need the suffix correction, whether they collide or not:

In [16]: df_b.columns = df_b.columns.map(lambda x: str(x) + '_b')

In [17]: df_a.join(df_b)
Out[17]: 
   A_a  B_b
0    1    3
1    2    4

As of pandas version 0.24.2 you can add a suffix to column names on a DataFrame using the add_suffix method.

This makes a one-liner merge command with force-suffix more bearable, for example:

df_merged = df1.merge(df2.add_suffix('_2'))

Pandas merge will give the new columns a suffix when there is already a column with the same name, When i need to force the new columns with a suffix, i create an empty column with the name of the column that i want to join.

df["colName"] = "" #create empty column 
df.merge(right = "df1", suffixes = ("_a","_b"))

You can later drop the empty column.

You could do the same for more than one columns, or for every column in df.columns.values