/pandas 0.20

Group By: split-apply-combine

By “group by” we are referring to a process involving one or more of the following steps

  • Splitting the data into groups based on some criteria
  • Applying a function to each group independently
  • Combining the results into a data structure

Of these, the split step is the most straightforward. In fact, in many situations you may wish to split the data set into groups and do something with those groups yourself. In the apply step, we might wish to one of the following:

  • Aggregation: computing a summary statistic (or statistics) about each group. Some examples:

    • Compute group sums or means
    • Compute group sizes / counts
  • Transformation: perform some group-specific computations and return a like-indexed. Some examples:

    • Standardizing data (zscore) within group
    • Filling NAs within groups with a value derived from each group
  • Filtration: discard some groups, according to a group-wise computation that evaluates True or False. Some examples:

    • Discarding data that belongs to groups with only a few members
    • Filtering out data based on the group sum or mean
  • Some combination of the above: GroupBy will examine the results of the apply step and try to return a sensibly combined result if it doesn’t fit into either of the above two categories

Since the set of object instance methods on pandas data structures are generally rich and expressive, we often simply want to invoke, say, a DataFrame function on each group. The name GroupBy should be quite familiar to those who have used a SQL-based tool (or itertools), in which you can write code like:

SELECT Column1, Column2, mean(Column3), sum(Column4)
FROM SomeTable
GROUP BY Column1, Column2

We aim to make operations like this natural and easy to express using pandas. We’ll address each area of GroupBy functionality then provide some non-trivial examples / use cases.

See the cookbook for some advanced strategies

Splitting an object into groups

pandas objects can be split on any of their axes. The abstract definition of grouping is to provide a mapping of labels to group names. To create a GroupBy object (more on what the GroupBy object is later), you do the following:

# default is axis=0
>>> grouped = obj.groupby(key)
>>> grouped = obj.groupby(key, axis=1)
>>> grouped = obj.groupby([key1, key2])

The mapping can be specified many different ways:

  • A Python function, to be called on each of the axis labels
  • A list or NumPy array of the same length as the selected axis
  • A dict or Series, providing a label -> group name mapping
  • For DataFrame objects, a string indicating a column to be used to group. Of course df.groupby('A') is just syntactic sugar for df.groupby(df['A']), but it makes life simpler
  • For DataFrame objects, a string indicating an index level to be used to group.
  • A list of any of the above things

Collectively we refer to the grouping objects as the keys. For example, consider the following DataFrame:


New in version 0.20.

A string passed to groupby may refer to either a column or an index level. If a string matches both a column name and an index level name then a warning is issued and the column takes precedence. This will result in an ambiguity error in a future version.

In [1]: df = pd.DataFrame({'A' : ['foo', 'bar', 'foo', 'bar',
   ...:                           'foo', 'bar', 'foo', 'foo'],
   ...:                    'B' : ['one', 'one', 'two', 'three',
   ...:                           'two', 'two', 'one', 'three'],
   ...:                    'C' : np.random.randn(8),
   ...:                    'D' : np.random.randn(8)})

In [2]: df
     A      B         C         D
0  foo    one  0.469112 -0.861849
1  bar    one -0.282863 -2.104569
2  foo    two -1.509059 -0.494929
3  bar  three -1.135632  1.071804
4  foo    two  1.212112  0.721555
5  bar    two -0.173215 -0.706771
6  foo    one  0.119209 -1.039575
7  foo  three -1.044236  0.271860

We could naturally group by either the A or B columns or both:

In [3]: grouped = df.groupby('A')

In [4]: grouped = df.groupby(['A', 'B'])

These will split the DataFrame on its index (rows). We could also split by the columns:

In [5]: def get_letter_type(letter):
   ...:     if letter.lower() in 'aeiou':
   ...:         return 'vowel'
   ...:     else:
   ...:         return 'consonant'

In [6]: grouped = df.groupby(get_letter_type, axis=1)

Starting with 0.8, pandas Index objects now support duplicate values. If a non-unique index is used as the group key in a groupby operation, all values for the same index value will be considered to be in one group and thus the output of aggregation functions will only contain unique index values:

In [7]: lst = [1, 2, 3, 1, 2, 3]

In [8]: s = pd.Series([1, 2, 3, 10, 20, 30], lst)

In [9]: grouped = s.groupby(level=0)

In [10]: grouped.first()
1    1
2    2
3    3
dtype: int64

In [11]: grouped.last()

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