def paper_assignment(arr, reviewer_df, opt): body of the code return reviewer df["R1"] = df[["Author", "Theme", "Sub_dept", "R1", "R2"]].apply(paper_assignment, args=(reviewer_df, "R1"), axis=1)
As written, it raises
return should presumably be inside the
This creates a new dataframe containing the
"Author", "Theme", "Sub_dept", "R1", "R2" columns from
This applies the function
paper_assignment to the new dataframe. The function is called once per row in the dataframe, with each row passed to the function as a
pandas.Series with the column names as the index. Each time the function is called, the first argument is the Series corresponding to the dataframe row, the second argument is
reviewer_df, and the third is
The results from these function calls are put into another Series object, which is assigned to the
"R1" column of the original dataframe.
FWIW chatGPT does a pretty good job of answering this question (a good screenful of description).
Rather than copying the answer here I’ll just say it’s actually worth giving it this sort of question because it can answer it pretty well (though it complained a bit “Without knowing the specific implementation inside the
paper_assignment function, it is not possible to provide a detailed description of what the code does”) and you can then ask follow-on questions if you need more clarity on anything.
Keep in mind that row-wise function application is considered an antipattern in data analysis libraries like pandas and dplyr. It’s not needed in 99 percent of cases where it has been used, and for a rare few exceptions you’re better off using list comprehensions.