Adding rank column for every numerical column in pandas

Question

Here is an example of my dataframe (my actual dataframe has 20+ columns and 100+ rows)

df = pd.DataFrame([['Jim', 93, 87, 66], 
                   ['Bob', 88, 90, 65],
                   ['Joe', 72, 100, 70]],
                   columns=['Name', 'Score1', 'Score2', 'Score3'])


Name  Score1  Score2  Score3
Jim       93      87      66
Bob       88      90      65
Joe       72     100      70

I want to create a new table which shows the rank of each score in a column. For example, the desired output would be:

Name  Score1  Score2  Score3
Jim        1       3       2
Bob        2       2       3
Joe        3       1       1

Is it possible to achieve this in pandas by looping through every column?

Asked By: code_machine

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Source

Answer 1

You can use filter to get the Score columns, then rank with a dense method in descending order, finally combine_first the other columns:

out = (df.filter(like='Score')
         .rank(method='dense', ascending=False)
         .convert_dtypes()  # optional, to have integers
         .combine_first(df)
       )

Output:

  Name  Score1  Score2  Score3
0  Jim       1       3       2
1  Bob       2       2       3
2  Joe       3       1       1

Answered By: mozway

Answer 2

Yes, it is possible to achieve the desired output in Pandas without looping through every column. One approach is to use the rank method in Pandas to rank the values in each column separately.

# create a new dataframe to store the rankings
rank_df = pd.DataFrame(df['Name'])

# loop through each score column and rank the values
for col in df.columns[1:]:
    rank_df[col] = df[col].rank(ascending=False)

Answered By: David

Adding rank column for every numerical column in pandas

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