pandas idxmax return all columns with maximum value

Question

I have a dataframe which looks like the following:

column_1    column_2
2           3
6           4
5           5
.
.
.

I would like to return for each row of the above dataframe the name(s) of the column(s) which have the maximum value in that row; for example for the first row I want to return ‘column_2’, and for the third row it will return both column names since they both have the same value.

I have tried to achieve it by using Pandas’s idxmax function; but this function returns only the first occurrence and ignores the rest of the possibilities; like in the example of the third row above, idxmax returns only ‘column_1’ instead of [‘column_1’, ‘column_2’].

Any idea how to solve this problem. Thanks in advance for your help.

Asked By: Aniss Chohra

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Source

Answer 1

For each row, you can check if the entry is equal to the maximum of that row; this will form a boolean frame. Then you can dot product it with the column names to choose those columns’ names that gave True for the rows:

is_max = df.eq(df.max(axis=1), axis=0)
result = is_max.dot(df.columns + " ")

where axis=1 of max says take the maximum of each row and axis=0 of eq says align the argument (i.e., df.max(axis=1)) to compare row-wise i.e., broadcast so),

to get

>>> is_max

   column_1  column_2
0     False      True
1      True     False
2      True      True

>>> result

0             column_2
1             column_1
2    column_1 column_2

Answered By: Mustafa Aydın

Answer 2

Aside from numpy dot, we can use numpy broadcasting

mask = df.eq(df.max(axis=1), axis=0)
df['max'] = ((df.columns * mask)[mask]
             .agg(lambda row: list(row.dropna()), axis=1))

print(df)

   column_1  column_2                   max
0         2         3            [column_2]
1         6         4            [column_1]
2         5         5  [column_1, column_2]

Answered By: Ynjxsjmh

pandas idxmax return all columns with maximum value

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