Faster alternative to perform pandas groupby operation

Solution from pd.Series.mode

df.groupby('name').color.apply(pd.Series.mode).reset_index(level=1,drop=True)
Out[281]: 
name
Jerry    Black
John     White
Tom       Blue
Name: color, dtype: object

UPDATE

It must be hard to beat this (~10 times faster on the sample daraframe than any proposed pandas solution and 1.5 faster than the proposed numpy solution). The gist is to stay away from pandas and use itertools.groupby which is doing a much better job when it concerns non-numerical data.

from itertools import groupby
from collections import Counter

pd.Series({x: Counter(z[-1] for z in y).most_common(1)[0][0] for x,y 
          in groupby(sorted(df.values.tolist()), 
                            key=lambda x: x[0])})
# Jerry    Black
# John     White
# Tom       Blue

Old Answer

Here's another method. It is actually slower than the original one, but I'll keep it here:

data.groupby('name')['color']\
    .apply(pd.Series.value_counts)\
    .unstack().idxmax(axis=1)
# name
# Jerry    Black
# John     White
# Tom       Blue

Numpy's numpy.add.at and pandas.factorize

This is intended to be fast. However, I tried to organize it to be readable as well.

i, r = pd.factorize(df.name)
j, c = pd.factorize(df.color)
n, m = len(r), len(c)

b = np.zeros((n, m), dtype=np.int64)

np.add.at(b, (i, j), 1)
pd.Series(c[b.argmax(1)], r)

John     White
Tom       Blue
Jerry    Black
dtype: object

---

groupby, size, and idxmax

df.groupby(['name', 'color']).size().unstack().idxmax(1)

name
Jerry    Black
John     White
Tom       Blue
dtype: object

name
Jerry    Black
John     White
Tom       Blue
Name: color, dtype: object

---

Counter

¯\_(ツ)_/¯

from collections import Counter

df.groupby('name').color.apply(lambda c: Counter(c).most_common(1)[0][0])

name
Jerry    Black
John     White
Tom       Blue
Name: color, dtype: object