How to get number of groups in a groupby object in pandas?

This would be useful so I know how many unique groups I have to perform calculations on. Thank you.

Suppose groupby object is called dfgroup.

[pandas >= 0.23] Simple, Fast, and Pandaic: ngroups

Newer versions of the groupby API provide this (undocumented) attribute which stores the number of groups in a GroupBy object.

# setup
df = pd.DataFrame({'A': list('aabbcccd')})
dfg = df.groupby('A')

# call `.ngroups` on the GroupBy object
dfg.ngroups
# 4

Note that this is different from GroupBy.groups which returns the actual groups themselves.

Why should I prefer this over len?

As noted in BrenBarn's answer, you could use len(dfg) to get the number of groups. But you shouldn't. Looking at the implementation of GroupBy.__len__ (which is what len() calls interally), we see that __len__ makes a call to GroupBy.groups, which returns a dictionary of grouped indices:

dfg.groups
{'a': Int64Index([0, 1], dtype='int64'),
 'b': Int64Index([2, 3], dtype='int64'),
 'c': Int64Index([4, 5, 6], dtype='int64'),
 'd': Int64Index([7], dtype='int64')}

Depending on the number of groups in your operation, generating the dictionary only to find its length is a wasteful step. ngroups on the other hand is a stored property that can be accessed in constant time.

This has been documented in GroupBy object attributes. The issue with len, however, is that for a GroupBy object with a lot of groups, this can take a lot longer

But what if I actually want the size of each group?

You're in luck. We have a function for that, it's called GroupBy.size. But please note that size counts NaNs as well. If you don't want NaNs counted, use GroupBy.count instead.

As documented, you can get the number of groups with len(dfgroup).