Adding a rows corresponding column to a new column

Question

I have a table constructed like so, I find with these types of normalized data it can be hard to quickly see which column has the highest value:

Test dataframe

The code is test = pd.DataFrame({'0 to 4': [0.031, 0.23, 0.13], '5 to 9':[0.32, 0.142, 0.532], '10 to 14': [0.24, 0.131, 0.564]}, index=['Barking and Dagenham', 'Barnet', 'Bexley'])

What I am trying to do is find the maximum value of each row and put the corresponding column name into a new column. Like so: Result dataframe

I've tried lambda and some other methods but I'm a bit stumped right now, any ideas?

Thanks

It's better to paste the text representation of a dataframe into your question than an image of the notebook graphic. — Bill, Sep 20 '21 at 16:17

score 1 · Answer 1 · answered Sep 20 '21 at 16:17

1

Sounds like a job for idxmax.

test['max'] = test.idxmax(axis='columns')

answered Sep 20 '21 at 16:17

Imre Kerr

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score 0 · Answer 2 · answered Sep 20 '21 at 16:21

0

Try this:

test['max'] = test.T.idxmax()

answered Sep 20 '21 at 16:21

Bill

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Actually, @Imre Kerr's answer is nicer. – Bill Sep 20 '21 at 16:22

Adding a rows corresponding column to a new column

2 Answers2