CODEWITHSUNDEEP
pythonpandas
Amelio Vazquez-Reina
Amelio Vazquez-Reina

Reputation: 96264

Accessing groups in Pandas

Say I have some random data frame:

> df
     A      B         C         D
0  foo    one  1.344866 -0.602697
1  bar    one  0.669491 -0.264758
2  foo    two  0.830100  0.381644
3  bar  three -0.756694 -0.382337
4  foo    two -0.267778  0.963123
5  bar    two  1.275177 -0.667924
6  foo    one  0.240863  0.321022
7  foo  three -1.431863 -0.333058

And I partition it according to:

groups =df.groupby(['A', 'B'])

What is the difference between the following two methods? They return group information in different formats.

Using key,value pairs:

for key, value in groups:
  print key
  print value

and

Using nth():

for group_ix in xrange(groups.ngroups)
  item = groups.nth(group_ix)

?

Upvotes: 2

Views: 94

Answers (1)

Andy Hayden
Andy Hayden

Reputation: 375435

These two things are quite different, nth takes the nth value in the group (currently with NaNs if the group has fewer than n items):

In [11]: groups.nth(n=0)  # the 0th items in each group
Out[11]:
                  C         D
A   B
bar one    0.669491 -0.264758
    three -0.756694 -0.382337
    two    1.275177 -0.667924
foo one    1.344866 -0.602697
    three -1.431863 -0.333058
    two    0.830100  0.381644

In [12]: groups.nth(n=1)  # the 1st items in each group, NaNs if <=1
Out[12]:
                  C         D
A   B
bar one         NaN       NaN
    three       NaN       NaN
    two         NaN       NaN
foo one    0.240863  0.321022
    three       NaN       NaN
    two   -0.267778  0.963123

Note: atm this isn't particularly well documented, there is an open issue to change that and tweak the behaviour of nth with a Series groupby (to be cumcount() == n).

When you iterate over groups, you get the keys (the mi) and the values (the subDataFrame for each group):

In [21]: for k, v in groups: print k  # the v are subDataFrames for each item
('bar', 'one')
('bar', 'three')
('bar', 'two')
('foo', 'one')
('foo', 'three')
('foo', 'two')

In [22]: groups.get_group(('foo' , 'one'))  # example v
Out[22]:
     A    B         C         D
0  foo  one  1.344866 -0.602697
6  foo  one  0.240863  0.321022

Upvotes: 3

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