Fastest way to count number of occurrences in Pandas

Question

What is the fastest way to compute the number of occurrences of elements within a Pandas series?

My current fastest solution involves .groupby(columnname).size(). Is there anything faster within Pandas? E.g. I want something like the following:

In [42]: df = DataFrame(['a', 'b', 'a'])

In [43]: df.groupby(0).size()
Out[43]: 
0
a    2
b    1
dtype: int64

Worrying about optimizations on this level seems like a waste of time, but you could try `value_counts`: it should have less overhead. — DSM, Apr 27 '14 at 00:26
possible duplicate of [what is the most efficient way of counting occurrences in pandas?](http://stackoverflow.com/questions/20076195/what-is-the-most-efficient-way-of-counting-occurrences-in-pandas) — Noah, Apr 27 '14 at 22:12

cwharland · Answer 1 · 2015-10-22T16:34:22.700

3

The value_counts() function in pandas does this exactly.

Use that function on the column you want. i.e.

df['column_i_want'].value_counts()

edited Oct 22 '15 at 16:34

answered Apr 27 '14 at 01:07

cwharland

1 Answers1