CODEWITHSUNDEEP
pythonpandas
RafG
RafG

Reputation: 1516

Find all indices of maximum in Pandas DataFrame

I need to find all indices where the maximum value (per row) is obtained in a Pandas DataFrame. For instance, if I have a dataFrame like this:

   cat1  cat2  cat3
0     0     2     2
1     3     0     1
2     1     1     0

then the method I am looking for would yield a result like:

[['cat2', 'cat3'],
 ['cat1'],
 ['cat1', 'cat2']]

This is a list of lists, but some other data structure is also okay.

I cannot use df.idxmax(axis=1), because it only yields the first maximum.

Upvotes: 13

Views: 6320

Answers (2)

Zero
Zero

Reputation: 77027

You could do

In [2560]: cols = df.columns.values

In [2561]: vals = df.values

In [2562]: [cols[v].tolist() for v in vals == vals.max(1)[:, None]]
Out[2562]: [['cat2', 'cat3'], 
            ['cat1'], 
            ['cat1', 'cat2']]

Update

Here is a full example:

import pandas as pd
import numpy as np

np.random.seed(400)

df = pd.DataFrame({
    'a': np.random.randint(0,3,size=10), 
    'b': np.random.randint(0,3,size=10), 
    'c': np.random.randint(0,5,size=10),
})

print(df)

out = [df.columns[i].tolist() for i in df.values == df.max(axis=1)[:,None]]

for i in out:
    print(i)

Returning from print(df):

   a  b  c
0  0  1  4
1  2  2  4
2  1  1  1
3  0  1  3
4  2  2  1
5  1  1  1
6  0  2  4
7  2  0  2
8  2  1  3
9  2  2  4

And from print(out):

['c']
['c']
['a', 'b', 'c']
['c']
['a', 'b']
['a', 'b', 'c']
['c']
['a', 'c']
['c']
['c']

Upvotes: 1

unutbu
unutbu

Reputation: 880927

Here is the information, in a different data structure:

In [8]: df = pd.DataFrame({'cat1':[0,3,1], 'cat2':[2,0,1], 'cat3':[2,1,0]})

In [9]: df
Out[9]: 
   cat1  cat2  cat3
0     0     2     2
1     3     0     1
2     1     1     0

[3 rows x 3 columns]

In [10]: rowmax = df.max(axis=1)

The max values are indicated by True values:

In [82]: df.values == rowmax[:,None]
Out[82]: 
array([[False,  True,  True],
       [ True, False, False],
       [ True,  True, False]], dtype=bool)

np.where returns the indices where the DataFrame above is True. 

In [84]: np.where(df.values == rowmax[:,None])
Out[84]: (array([0, 0, 1, 2, 2]), array([1, 2, 0, 0, 1]))

The first array indicates index values for axis=0, the second array for axis=1. There are 5 values in each array since there are five locations that are True.

You could use itertools.groupby to build the list of lists you posted, though perhaps you don't need this given the data structures above:

In [46]: import itertools as IT

In [47]: import operator

In [48]: idx = np.where(df.values == rowmax[:,None])

In [49]: groups = IT.groupby(zip(*idx), key=operator.itemgetter(0))

In [50]: [[df.columns[j] for i, j in grp] for k, grp in groups]
Out[50]: [['cat1', 'cat1'], ['cat2'], ['cat3', 'cat3']]

Upvotes: 4

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