Find the column name which has the maximum value for each row
You can use idxmax
with axis=1
to find the column with the greatest value on each row:
>>> df.idxmax(axis=1)
0 Communications
1 Business
2 Communications
3 Communications
4 Business
dtype: object
To create the new column 'Max', use df['Max'] = df.idxmax(axis=1)
.
To find the row index at which the maximum value occurs in each column, use df.idxmax()
(or equivalently df.idxmax(axis=0)
).
And if you want to produce a column containing the name of the column with the maximum value but considering only a subset of columns then you use a variation of @ajcr's answer:
df['Max'] = df[['Communications','Business']].idxmax(axis=1)
You could apply
on dataframe and get argmax()
of each row via axis=1
In [144]: df.apply(lambda x: x.argmax(), axis=1)
Out[144]:
0 Communications
1 Business
2 Communications
3 Communications
4 Business
dtype: object
Here's a benchmark to compare how slow apply
method is to idxmax()
for len(df) ~ 20K
In [146]: %timeit df.apply(lambda x: x.argmax(), axis=1)
1 loops, best of 3: 479 ms per loop
In [147]: %timeit df.idxmax(axis=1)
10 loops, best of 3: 47.3 ms per loop