Elaborating from this question, I am trying to match two Pandas dataframes.
The matching condition is:
(left_df.column_to_match == right_df.first_column) | (left_df.column_to_match == right_df.second_column )
or, in words, the column to match in the left dataframe should be equal to either first or second column in the right dataframe - thus the OR condition.
I can make a workaround using pd.merge and inputting lists such as
left_df.merge(right_df, left_on=['to_match', 'to_match'], right_on=['first_column', 'second_column'])
but this, in turn, gives me only the AND condition result when both columns match. That is, the two columns in right_df have the same value.
This is an example of input data
// left df // right df
To Match First Second
0 TCNU4843483 0 ASDREF TCNU4843483
1 MA18219 1 MA18219 Null
2 MA81192 2 Null Null
3 MFREIGHT 3 HROB789 NESU6748392
and this of the expected output
To Match First Second
0 TCNU4843483 ASDREF TCNU4843483
1 MA18219 MA18219 Null
2 MA81192 Null Null
3 MFREIGHT Null Null
4 Null HROB789 NESU6748392
Any idea about whether Pandas support this, or I have to write my own function?
