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.

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

• Aggregation: compute a summary statistic (or statistics) for each group. Some examples:

  • Compute group sums or means.
  • Compute group sizes / counts.

• Transformation: perform some group-specific computations and return a like-indexed object. Some examples:

  • Standardize data (zscore) within a 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:

  • Discard data that belongs to groups with only a few members.
  • Filter 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 may do the following:

In [1]: df = pd.DataFrame([('bird', 'Falconiformes', 389.0),
   ...:                    ('bird', 'Psittaciformes', 24.0),
   ...:                    ('mammal', 'Carnivora', 80.2),
   ...:                    ('mammal', 'Primates', np.nan),
   ...:                    ('mammal', 'Carnivora', 58)],
   ...:                   index=['falcon', 'parrot', 'lion', 'monkey', 'leopard'],
   ...:                   columns=('class', 'order', 'max_speed'))
   ...: 
In [2]: df
Out[2]: 
          class           order  max_speed
falcon     bird   Falconiformes      389.0
parrot     bird  Psittaciformes       24.0
lion     mammal       Carnivora       80.2
monkey   mammal        Primates        NaN
leopard  mammal       Carnivora       58.0
# default is axis=0
In [3]: grouped = df.groupby('class')
In [4]: grouped = df.groupby('order', axis='columns')
In [5]: grouped = df.groupby(['class', 'order'])

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:

::: tip Note

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, a ValueError will be raised.

:::

In [6]: 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 [7]: df
Out[7]: 
     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

On a DataFrame, we obtain a GroupBy object by calling groupby(). We could naturally group by either the A or B columns, or both:

In [8]: grouped = df.groupby('A')
In [9]: grouped = df.groupby(['A', 'B'])

New in version 0.24.

If we also have a MultiIndex on columns A and B, we can group by all but the specified columns

In [10]: df2 = df.set_index(['A', 'B'])
In [11]: grouped = df2.groupby(level=df2.index.names.difference(['B']))
In [12]: grouped.sum()
Out[12]: 
            C         D
A                      
bar -1.591710 -1.739537
foo -0.752861 -1.402938

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

In [13]: def get_letter_type(letter):
   ....:     if letter.lower() in 'aeiou':
   ....:         return 'vowel'
   ....:     else:
   ....:         return 'consonant'
   ....: 
In [14]: grouped = df.groupby(get_letter_type, axis=1)

pandas Index objects 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 [15]: lst = [1, 2, 3, 1, 2, 3]
In [16]: s = pd.Series([1, 2, 3, 10, 20, 30], lst)
In [17]: grouped = s.groupby(level=0)
In [18]: grouped.first()
Out[18]: 
1    1
2    2
3    3
dtype: int64
In [19]: grouped.last()
Out[19]: 
1    10
2    20
3    30
dtype: int64
In [20]: grouped.sum()
Out[20]: 
1    11
2    22
3    33
dtype: int64

Note that no splitting occurs until it’s needed. Creating the GroupBy object only verifies that you’ve passed a valid mapping.

::: tip Note

Many kinds of complicated data manipulations can be expressed in terms of GroupBy operations (though can’t be guaranteed to be the most efficient). You can get quite creative with the label mapping functions.

:::

GroupBy sorting

By default the group keys are sorted during the groupby operation. You may however pass sort=False for potential speedups:

In [21]: df2 = pd.DataFrame({'X': ['B', 'B', 'A', 'A'], 'Y': [1, 2, 3, 4]})
In [22]: df2.groupby(['X']).sum()
Out[22]: 
   Y
X   
A  7
B  3
In [23]: df2.groupby(['X'], sort=False).sum()
Out[23]: 
   Y
X   
B  3
A  7

Note that groupby will preserve the order in which observations are sorted within each group. For example, the groups created by groupby() below are in the order they appeared in the original DataFrame:

In [24]: df3 = pd.DataFrame({'X': ['A', 'B', 'A', 'B'], 'Y': [1, 4, 3, 2]})
In [25]: df3.groupby(['X']).get_group('A')
Out[25]: 
   X  Y
0  A  1
2  A  3
In [26]: df3.groupby(['X']).get_group('B')
Out[26]: 
   X  Y
1  B  4
3  B  2

GroupBy object attributes

The groups attribute is a dict whose keys are the computed unique groups and corresponding values being the axis labels belonging to each group. In the above example we have:

In [27]: df.groupby('A').groups
Out[27]: 
{'bar': Int64Index([1, 3, 5], dtype='int64'),
 'foo': Int64Index([0, 2, 4, 6, 7], dtype='int64')}
In [28]: df.groupby(get_letter_type, axis=1).groups
Out[28]: 
{'consonant': Index(['B', 'C', 'D'], dtype='object'),
 'vowel': Index(['A'], dtype='object')}

Calling the standard Python len function on the GroupBy object just returns the length of the groups dict, so it is largely just a convenience:

In [29]: grouped = df.groupby(['A', 'B'])
In [30]: grouped.groups
Out[30]: 
{('bar', 'one'): Int64Index([1], dtype='int64'),
 ('bar', 'three'): Int64Index([3], dtype='int64'),
 ('bar', 'two'): Int64Index([5], dtype='int64'),
 ('foo', 'one'): Int64Index([0, 6], dtype='int64'),
 ('foo', 'three'): Int64Index([7], dtype='int64'),
 ('foo', 'two'): Int64Index([2, 4], dtype='int64')}
In [31]: len(grouped)
Out[31]: 6

GroupBy will tab complete column names (and other attributes):

In [32]: df
Out[32]: 
               height      weight  gender
2000-01-01  42.849980  157.500553    male
2000-01-02  49.607315  177.340407    male
2000-01-03  56.293531  171.524640    male
2000-01-04  48.421077  144.251986  female
2000-01-05  46.556882  152.526206    male
2000-01-06  68.448851  168.272968  female
2000-01-07  70.757698  136.431469    male
2000-01-08  58.909500  176.499753  female
2000-01-09  76.435631  174.094104  female
2000-01-10  45.306120  177.540920    male
In [33]: gb = df.groupby('gender')

In [34]: gb.<TAB>  # noqa: E225, E999
gb.agg        gb.boxplot    gb.cummin     gb.describe   gb.filter     gb.get_group  gb.height     gb.last       gb.median     gb.ngroups    gb.plot       gb.rank       gb.std        gb.transform
gb.aggregate  gb.count      gb.cumprod    gb.dtype      gb.first      gb.groups     gb.hist       gb.max        gb.min        gb.nth        gb.prod       gb.resample   gb.sum        gb.var
gb.apply      gb.cummax     gb.cumsum     gb.fillna     gb.gender     gb.head       gb.indices    gb.mean       gb.name       gb.ohlc       gb.quantile   gb.size       gb.tail       gb.weight

GroupBy with MultiIndex

With hierarchically-indexed data, it’s quite natural to group by one of the levels of the hierarchy.

Let’s create a Series with a two-level MultiIndex.

In [35]: arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
   ....:           ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
   ....: 
In [36]: index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second'])
In [37]: s = pd.Series(np.random.randn(8), index=index)
In [38]: s
Out[38]: 
first  second
bar    one      -0.919854
       two      -0.042379
baz    one       1.247642
       two      -0.009920
foo    one       0.290213
       two       0.495767
qux    one       0.362949
       two       1.548106
dtype: float64

We can then group by one of the levels in s.

In [39]: grouped = s.groupby(level=0)
In [40]: grouped.sum()
Out[40]: 
first
bar   -0.962232
baz    1.237723
foo    0.785980
qux    1.911055
dtype: float64

If the MultiIndex has names specified, these can be passed instead of the level number:

In [41]: s.groupby(level='second').sum()
Out[41]: 
second
one    0.980950
two    1.991575
dtype: float64

The aggregation functions such as sum will take the level parameter directly. Additionally, the resulting index will be named according to the chosen level:

In [42]: s.sum(level='second')
Out[42]: 
second
one    0.980950
two    1.991575
dtype: float64

Grouping with multiple levels is supported.

In [43]: s
Out[43]: 
first  second  third
bar    doo     one     -1.131345
               two     -0.089329
baz    bee     one      0.337863
               two     -0.945867
foo    bop     one     -0.932132
               two      1.956030
qux    bop     one      0.017587
               two     -0.016692
dtype: float64
In [44]: s.groupby(level=['first', 'second']).sum()
Out[44]: 
first  second
bar    doo      -1.220674
baz    bee      -0.608004
foo    bop       1.023898
qux    bop       0.000895
dtype: float64

New in version 0.20.

Index level names may be supplied as keys.

In [45]: s.groupby(['first', 'second']).sum()
Out[45]: 
first  second
bar    doo      -1.220674
baz    bee      -0.608004
foo    bop       1.023898
qux    bop       0.000895
dtype: float64

More on the sum function and aggregation later.

Grouping DataFrame with Index levels and columns

A DataFrame may be grouped by a combination of columns and index levels by specifying the column names as strings and the index levels as pd.Grouper objects.

In [46]: arrays = [['bar', 'bar', 'baz', 'baz', 'foo', 'foo', 'qux', 'qux'],
   ....:           ['one', 'two', 'one', 'two', 'one', 'two', 'one', 'two']]
   ....: 
In [47]: index = pd.MultiIndex.from_arrays(arrays, names=['first', 'second'])
In [48]: df = pd.DataFrame({'A': [1, 1, 1, 1, 2, 2, 3, 3],
   ....:                    'B': np.arange(8)},
   ....:                   index=index)
   ....: 
In [49]: df
Out[49]: 
              A  B
first second      
bar   one     1  0
      two     1  1
baz   one     1  2
      two     1  3
foo   one     2  4
      two     2  5
qux   one     3  6
      two     3  7

The following example groups df by the second index level and the A column.

In [50]: df.groupby([pd.Grouper(level=1), 'A']).sum()
Out[50]: 
          B
second A   
one    1  2
       2  4
       3  6
two    1  4
       2  5
       3  7

Index levels may also be specified by name.

In [51]: df.groupby([pd.Grouper(level='second'), 'A']).sum()
Out[51]: 
          B
second A   
one    1  2
       2  4
       3  6
two    1  4
       2  5
       3  7

New in version 0.20.

Index level names may be specified as keys directly to groupby.

In [52]: df.groupby(['second', 'A']).sum()
Out[52]: 
          B
second A   
one    1  2
       2  4
       3  6
two    1  4
       2  5
       3  7

DataFrame column selection in GroupBy

Once you have created the GroupBy object from a DataFrame, you might want to do something different for each of the columns. Thus, using [] similar to getting a column from a DataFrame, you can do:

In [53]: grouped = df.groupby(['A'])
In [54]: grouped_C = grouped['C']
In [55]: grouped_D = grouped['D']

This is mainly syntactic sugar for the alternative and much more verbose:

In [56]: df['C'].groupby(df['A'])
Out[56]: <pandas.core.groupby.generic.SeriesGroupBy object at 0x7f65f21ac518>

Additionally this method avoids recomputing the internal grouping information derived from the passed key.

Iterating through groups

With the GroupBy object in hand, iterating through the grouped data is very natural and functions similarly to itertools.groupby():

In [57]: grouped = df.groupby('A')
In [58]: for name, group in grouped:
   ....:     print(name)
   ....:     print(group)
   ....: 
bar
     A      B         C         D
1  bar    one  0.254161  1.511763
3  bar  three  0.215897 -0.990582
5  bar    two -0.077118  1.211526
foo
     A      B         C         D
0  foo    one -0.575247  1.346061
2  foo    two -1.143704  1.627081
4  foo    two  1.193555 -0.441652
6  foo    one -0.408530  0.268520
7  foo  three -0.862495  0.024580

In the case of grouping by multiple keys, the group name will be a tuple:

In [59]: for name, group in df.groupby(['A', 'B']):
   ....:     print(name)
   ....:     print(group)
   ....: 
('bar', 'one')
     A    B         C         D
1  bar  one  0.254161  1.511763
('bar', 'three')
     A      B         C         D
3  bar  three  0.215897 -0.990582
('bar', 'two')
     A    B         C         D
5  bar  two -0.077118  1.211526
('foo', 'one')
     A    B         C         D
0  foo  one -0.575247  1.346061
6  foo  one -0.408530  0.268520
('foo', 'three')
     A      B         C        D
7  foo  three -0.862495  0.02458
('foo', 'two')
     A    B         C         D
2  foo  two -1.143704  1.627081
4  foo  two  1.193555 -0.441652

See Iterating through groups.

Selecting a group

A single group can be selected using get_group():

In [60]: grouped.get_group('bar')
Out[60]: 
     A      B         C         D
1  bar    one  0.254161  1.511763
3  bar  three  0.215897 -0.990582
5  bar    two -0.077118  1.211526

Or for an object grouped on multiple columns:

In [61]: df.groupby(['A', 'B']).get_group(('bar', 'one'))
Out[61]: 
     A    B         C         D
1  bar  one  0.254161  1.511763

Aggregation

Once the GroupBy object has been created, several methods are available to perform a computation on the grouped data. These operations are similar to the aggregating API, window functions API, and resample API.

An obvious one is aggregation via the aggregate() or equivalently agg() method:

In [62]: grouped = df.groupby('A')
In [63]: grouped.aggregate(np.sum)
Out[63]: 
            C         D
A                      
bar  0.392940  1.732707
foo -1.796421  2.824590
In [64]: grouped = df.groupby(['A', 'B'])
In [65]: grouped.aggregate(np.sum)
Out[65]: 
                  C         D
A   B                        
bar one    0.254161  1.511763
    three  0.215897 -0.990582
    two   -0.077118  1.211526
foo one   -0.983776  1.614581
    three -0.862495  0.024580
    two    0.049851  1.185429

As you can see, the result of the aggregation will have the group names as the new index along the grouped axis. In the case of multiple keys, the result is a MultiIndex by default, though this can be changed by using the as_index option:

In [66]: grouped = df.groupby(['A', 'B'], as_index=False)
In [67]: grouped.aggregate(np.sum)
Out[67]: 
     A      B         C         D
0  bar    one  0.254161  1.511763
1  bar  three  0.215897 -0.990582
2  bar    two -0.077118  1.211526
3  foo    one -0.983776  1.614581
4  foo  three -0.862495  0.024580
5  foo    two  0.049851  1.185429
In [68]: df.groupby('A', as_index=False).sum()
Out[68]: 
     A         C         D
0  bar  0.392940  1.732707
1  foo -1.796421  2.824590

Note that you could use the reset_index DataFrame function to achieve the same result as the column names are stored in the resulting MultiIndex:

In [69]: df.groupby(['A', 'B']).sum().reset_index()
Out[69]: 
     A      B         C         D
0  bar    one  0.254161  1.511763
1  bar  three  0.215897 -0.990582
2  bar    two -0.077118  1.211526
3  foo    one -0.983776  1.614581
4  foo  three -0.862495  0.024580
5  foo    two  0.049851  1.185429

Another simple aggregation example is to compute the size of each group. This is included in GroupBy as the size method. It returns a Series whose index are the group names and whose values are the sizes of each group.

In [70]: grouped.size()
Out[70]: 
A    B    
bar  one      1
     three    1
     two      1
foo  one      2
     three    1
     two      2
dtype: int64

In [71]: grouped.describe()
Out[71]: 
      C                                                                           D                                                                      
  count      mean       std       min       25%       50%       75%       max count      mean       std       min       25%       50%       75%       max
0   1.0  0.254161       NaN  0.254161  0.254161  0.254161  0.254161  0.254161   1.0  1.511763       NaN  1.511763  1.511763  1.511763  1.511763  1.511763
1   1.0  0.215897       NaN  0.215897  0.215897  0.215897  0.215897  0.215897   1.0 -0.990582       NaN -0.990582 -0.990582 -0.990582 -0.990582 -0.990582
2   1.0 -0.077118       NaN -0.077118 -0.077118 -0.077118 -0.077118 -0.077118   1.0  1.211526       NaN  1.211526  1.211526  1.211526  1.211526  1.211526
3   2.0 -0.491888  0.117887 -0.575247 -0.533567 -0.491888 -0.450209 -0.408530   2.0  0.807291  0.761937  0.268520  0.537905  0.807291  1.076676  1.346061
4   1.0 -0.862495       NaN -0.862495 -0.862495 -0.862495 -0.862495 -0.862495   1.0  0.024580       NaN  0.024580  0.024580  0.024580  0.024580  0.024580
5   2.0  0.024925  1.652692 -1.143704 -0.559389  0.024925  0.609240  1.193555   2.0  0.592714  1.462816 -0.441652  0.075531  0.592714  1.109898  1.627081

::: tip Note

Aggregation functions will not return the groups that you are aggregating over if they are named columns, when as_index=True, the default. The grouped columns will be the indices of the returned object.

Passing as_index=False will return the groups that you are aggregating over, if they are named columns.

:::

Aggregating functions are the ones that reduce the dimension of the returned objects. Some common aggregating functions are tabulated below:

The aggregating functions above will exclude NA values. Any function which reduces a Series to a scalar value is an aggregation function and will work, a trivial example is df.groupby('A').agg(lambda ser: 1). Note that nth() can act as a reducer or a filter, see here.

Applying multiple functions at once

With grouped Series you can also pass a list or dict of functions to do aggregation with, outputting a DataFrame:

In [72]: grouped = df.groupby('A')
In [73]: grouped['C'].agg([np.sum, np.mean, np.std])
Out[73]: 
          sum      mean       std
A                                
bar  0.392940  0.130980  0.181231
foo -1.796421 -0.359284  0.912265

On a grouped DataFrame, you can pass a list of functions to apply to each column, which produces an aggregated result with a hierarchical index:

In [74]: grouped.agg([np.sum, np.mean, np.std])
Out[74]: 
            C                             D                    
          sum      mean       std       sum      mean       std
A                                                              
bar  0.392940  0.130980  0.181231  1.732707  0.577569  1.366330
foo -1.796421 -0.359284  0.912265  2.824590  0.564918  0.884785

The resulting aggregations are named for the functions themselves. If you need to rename, then you can add in a chained operation for a Series like this:

In [75]: (grouped['C'].agg([np.sum, np.mean, np.std])
   ....:              .rename(columns={'sum': 'foo',
   ....:                               'mean': 'bar',
   ....:                               'std': 'baz'}))
   ....: 
Out[75]: 
          foo       bar       baz
A                                
bar  0.392940  0.130980  0.181231
foo -1.796421 -0.359284  0.912265

For a grouped DataFrame, you can rename in a similar manner:

In [76]: (grouped.agg([np.sum, np.mean, np.std])
   ....:         .rename(columns={'sum': 'foo',
   ....:                          'mean': 'bar',
   ....:                          'std': 'baz'}))
   ....: 
Out[76]: 
            C                             D                    
          foo       bar       baz       foo       bar       baz
A                                                              
bar  0.392940  0.130980  0.181231  1.732707  0.577569  1.366330
foo -1.796421 -0.359284  0.912265  2.824590  0.564918  0.884785

::: tip Note

In general, the output column names should be unique. You can’t apply the same function (or two functions with the same name) to the same column.

In [77]: grouped['C'].agg(['sum', 'sum'])
---------------------------------------------------------------------------
SpecificationError                        Traceback (most recent call last)
<ipython-input-77-7be02859f395> in <module>
----> 1 grouped['C'].agg(['sum', 'sum'])
/pandas/pandas/core/groupby/generic.py in aggregate(self, func_or_funcs, *args, **kwargs)
    849             # but not the class list / tuple itself.
    850             func_or_funcs = _maybe_mangle_lambdas(func_or_funcs)
--> 851             ret = self._aggregate_multiple_funcs(func_or_funcs, (_level or 0) + 1)
    852             if relabeling:
    853                 ret.columns = columns
/pandas/pandas/core/groupby/generic.py in _aggregate_multiple_funcs(self, arg, _level)
    919                 raise SpecificationError(
    920                     "Function names must be unique, found multiple named "
--> 921                     "{}".format(name)
    922                 )
    923 
SpecificationError: Function names must be unique, found multiple named sum

Pandas does allow you to provide multiple lambdas. In this case, pandas will mangle the name of the (nameless) lambda functions, appending _ to each subsequent lambda.

In [78]: grouped['C'].agg([lambda x: x.max() - x.min(),
   ....:                   lambda x: x.median() - x.mean()])
   ....: 
Out[78]: 
     <lambda_0>  <lambda_1>
A                          
bar    0.331279    0.084917
foo    2.337259   -0.215962

:::

Named aggregation

New in version 0.25.0.

To support column-specific aggregation with control over the output column names, pandas accepts the special syntax in GroupBy.agg(), known as “named aggregation”, where

• The keywords are the output column names
• The values are tuples whose first element is the column to select and the second element is the aggregation to apply to that column. Pandas provides the pandas.NamedAgg namedtuple with the fields ['column', 'aggfunc'] to make it clearer what the arguments are. As usual, the aggregation can be a callable or a string alias.

In [79]: animals = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
   ....:                         'height': [9.1, 6.0, 9.5, 34.0],
   ....:                         'weight': [7.9, 7.5, 9.9, 198.0]})
   ....: 
In [80]: animals
Out[80]: 
  kind  height  weight
0  cat     9.1     7.9
1  dog     6.0     7.5
2  cat     9.5     9.9
3  dog    34.0   198.0
In [81]: animals.groupby("kind").agg(
   ....:     min_height=pd.NamedAgg(column='height', aggfunc='min'),
   ....:     max_height=pd.NamedAgg(column='height', aggfunc='max'),
   ....:     average_weight=pd.NamedAgg(column='weight', aggfunc=np.mean),
   ....: )
   ....: 
Out[81]: 
      min_height  max_height  average_weight
kind                                        
cat          9.1         9.5            8.90
dog          6.0        34.0          102.75

pandas.NamedAgg is just a namedtuple. Plain tuples are allowed as well.

In [82]: animals.groupby("kind").agg(
   ....:     min_height=('height', 'min'),
   ....:     max_height=('height', 'max'),
   ....:     average_weight=('weight', np.mean),
   ....: )
   ....: 
Out[82]: 
      min_height  max_height  average_weight
kind                                        
cat          9.1         9.5            8.90
dog          6.0        34.0          102.75

If your desired output column names are not valid python keywords, construct a dictionary and unpack the keyword arguments

In [83]: animals.groupby("kind").agg(**{
   ....:     'total weight': pd.NamedAgg(column='weight', aggfunc=sum),
   ....: })
   ....: 
Out[83]: 
      total weight
kind              
cat           17.8
dog          205.5

Additional keyword arguments are not passed through to the aggregation functions. Only pairs of (column, aggfunc) should be passed as **kwargs. If your aggregation functions requires additional arguments, partially apply them with functools.partial().

::: tip Note

For Python 3.5 and earlier, the order of **kwargs in a functions was not preserved. This means that the output column ordering would not be consistent. To ensure consistent ordering, the keys (and so output columns) will always be sorted for Python 3.5.

:::

Named aggregation is also valid for Series groupby aggregations. In this case there’s no column selection, so the values are just the functions.

In [84]: animals.groupby("kind").height.agg(
   ....:     min_height='min',
   ....:     max_height='max',
   ....: )
   ....: 
Out[84]: 
      min_height  max_height
kind                        
cat          9.1         9.5
dog          6.0        34.0

Applying different functions to DataFrame columns

By passing a dict to aggregate you can apply a different aggregation to the columns of a DataFrame:

In [85]: grouped.agg({'C': np.sum,
   ....:              'D': lambda x: np.std(x, ddof=1)})
   ....: 
Out[85]: 
            C         D
A                      
bar  0.392940  1.366330
foo -1.796421  0.884785

The function names can also be strings. In order for a string to be valid it must be either implemented on GroupBy or available via dispatching:

In [86]: grouped.agg({'C': 'sum', 'D': 'std'})
Out[86]: 
            C         D
A                      
bar  0.392940  1.366330
foo -1.796421  0.884785

Cython-optimized aggregation functions

Some common aggregations, currently only sum, mean, std, and sem, have optimized Cython implementations:

In [87]: df.groupby('A').sum()
Out[87]: 
            C         D
A                      
bar  0.392940  1.732707
foo -1.796421  2.824590
In [88]: df.groupby(['A', 'B']).mean()
Out[88]: 
                  C         D
A   B                        
bar one    0.254161  1.511763
    three  0.215897 -0.990582
    two   -0.077118  1.211526
foo one   -0.491888  0.807291
    three -0.862495  0.024580
    two    0.024925  0.592714

Of course sum and mean are implemented on pandas objects, so the above code would work even without the special versions via dispatching (see below).

Transformation

The transform method returns an object that is indexed the same (same size) as the one being grouped. The transform function must:

• Return a result that is either the same size as the group chunk or broadcastable to the size of the group chunk (e.g., a scalar, grouped.transform(lambda x: x.iloc[-1])).
• Operate column-by-column on the group chunk. The transform is applied to the first group chunk using chunk.apply.
• Not perform in-place operations on the group chunk. Group chunks should be treated as immutable, and changes to a group chunk may produce unexpected results. For example, when using fillna, inplace must be False (grouped.transform(lambda x: x.fillna(inplace=False))).
• (Optionally) operates on the entire group chunk. If this is supported, a fast path is used starting from the second chunk.

For example, suppose we wished to standardize the data within each group:

In [89]: index = pd.date_range('10/1/1999', periods=1100)
In [90]: ts = pd.Series(np.random.normal(0.5, 2, 1100), index)
In [91]: ts = ts.rolling(window=100, min_periods=100).mean().dropna()
In [92]: ts.head()
Out[92]: 
2000-01-08    0.779333
2000-01-09    0.778852
2000-01-10    0.786476
2000-01-11    0.782797
2000-01-12    0.798110
Freq: D, dtype: float64
In [93]: ts.tail()
Out[93]: 
2002-09-30    0.660294
2002-10-01    0.631095
2002-10-02    0.673601
2002-10-03    0.709213
2002-10-04    0.719369
Freq: D, dtype: float64
In [94]: transformed = (ts.groupby(lambda x: x.year)
   ....:                  .transform(lambda x: (x - x.mean()) / x.std()))
   ....:

We would expect the result to now have mean 0 and standard deviation 1 within each group, which we can easily check:

# Original Data
In [95]: grouped = ts.groupby(lambda x: x.year)
In [96]: grouped.mean()
Out[96]: 
2000    0.442441
2001    0.526246
2002    0.459365
dtype: float64
In [97]: grouped.std()
Out[97]: 
2000    0.131752
2001    0.210945
2002    0.128753
dtype: float64
# Transformed Data
In [98]: grouped_trans = transformed.groupby(lambda x: x.year)
In [99]: grouped_trans.mean()
Out[99]: 
2000    1.168208e-15
2001    1.454544e-15
2002    1.726657e-15
dtype: float64
In [100]: grouped_trans.std()
Out[100]: 
2000    1.0
2001    1.0
2002    1.0
dtype: float64

We can also visually compare the original and transformed data sets.

In [101]: compare = pd.DataFrame({'Original': ts, 'Transformed': transformed})
In [102]: compare.plot()
Out[102]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65f731cba8>

Transformation functions that have lower dimension outputs are broadcast to match the shape of the input array.

In [103]: ts.groupby(lambda x: x.year).transform(lambda x: x.max() - x.min())
Out[103]: 
2000-01-08    0.623893
2000-01-09    0.623893
2000-01-10    0.623893
2000-01-11    0.623893
2000-01-12    0.623893
                ...   
2002-09-30    0.558275
2002-10-01    0.558275
2002-10-02    0.558275
2002-10-03    0.558275
2002-10-04    0.558275
Freq: D, Length: 1001, dtype: float64

Alternatively, the built-in methods could be used to produce the same outputs.

In [104]: max = ts.groupby(lambda x: x.year).transform('max')
In [105]: min = ts.groupby(lambda x: x.year).transform('min')
In [106]: max - min
Out[106]: 
2000-01-08    0.623893
2000-01-09    0.623893
2000-01-10    0.623893
2000-01-11    0.623893
2000-01-12    0.623893
                ...   
2002-09-30    0.558275
2002-10-01    0.558275
2002-10-02    0.558275
2002-10-03    0.558275
2002-10-04    0.558275
Freq: D, Length: 1001, dtype: float64

Another common data transform is to replace missing data with the group mean.

In [107]: data_df
Out[107]: 
            A         B         C
0    1.539708 -1.166480  0.533026
1    1.302092 -0.505754       NaN
2   -0.371983  1.104803 -0.651520
3   -1.309622  1.118697 -1.161657
4   -1.924296  0.396437  0.812436
..        ...       ...       ...
995 -0.093110  0.683847 -0.774753
996 -0.185043  1.438572       NaN
997 -0.394469 -0.642343  0.011374
998 -1.174126  1.857148       NaN
999  0.234564  0.517098  0.393534
[1000 rows x 3 columns]
In [108]: countries = np.array(['US', 'UK', 'GR', 'JP'])
In [109]: key = countries[np.random.randint(0, 4, 1000)]
In [110]: grouped = data_df.groupby(key)
# Non-NA count in each group
In [111]: grouped.count()
Out[111]: 
      A    B    C
GR  209  217  189
JP  240  255  217
UK  216  231  193
US  239  250  217
In [112]: transformed = grouped.transform(lambda x: x.fillna(x.mean()))

We can verify that the group means have not changed in the transformed data and that the transformed data contains no NAs.

In [113]: grouped_trans = transformed.groupby(key)
In [114]: grouped.mean()  # original group means
Out[114]: 
           A         B         C
GR -0.098371 -0.015420  0.068053
JP  0.069025  0.023100 -0.077324
UK  0.034069 -0.052580 -0.116525
US  0.058664 -0.020399  0.028603
In [115]: grouped_trans.mean()  # transformation did not change group means
Out[115]: 
           A         B         C
GR -0.098371 -0.015420  0.068053
JP  0.069025  0.023100 -0.077324
UK  0.034069 -0.052580 -0.116525
US  0.058664 -0.020399  0.028603
In [116]: grouped.count()  # original has some missing data points
Out[116]: 
      A    B    C
GR  209  217  189
JP  240  255  217
UK  216  231  193
US  239  250  217
In [117]: grouped_trans.count()  # counts after transformation
Out[117]: 
      A    B    C
GR  228  228  228
JP  267  267  267
UK  247  247  247
US  258  258  258
In [118]: grouped_trans.size()  # Verify non-NA count equals group size
Out[118]: 
GR    228
JP    267
UK    247
US    258
dtype: int64

::: tip Note

Some functions will automatically transform the input when applied to a GroupBy object, but returning an object of the same shape as the original. Passing as_index=False will not affect these transformation methods.

For example: fillna, ffill, bfill, shift..

In [119]: grouped.ffill()
Out[119]: 
            A         B         C
0    1.539708 -1.166480  0.533026
1    1.302092 -0.505754  0.533026
2   -0.371983  1.104803 -0.651520
3   -1.309622  1.118697 -1.161657
4   -1.924296  0.396437  0.812436
..        ...       ...       ...
995 -0.093110  0.683847 -0.774753
996 -0.185043  1.438572 -0.774753
997 -0.394469 -0.642343  0.011374
998 -1.174126  1.857148 -0.774753
999  0.234564  0.517098  0.393534
[1000 rows x 3 columns]

:::

New syntax to window and resample operations

New in version 0.18.1.

Working with the resample, expanding or rolling operations on the groupby level used to require the application of helper functions. However, now it is possible to use resample(), expanding() and rolling() as methods on groupbys.

The example below will apply the rolling() method on the samples of the column B based on the groups of column A.

In [120]: df_re = pd.DataFrame({'A': [1] * 10 + [5] * 10,
   .....:                       'B': np.arange(20)})
   .....: 
In [121]: df_re
Out[121]: 
    A   B
0   1   0
1   1   1
2   1   2
3   1   3
4   1   4
.. ..  ..
15  5  15
16  5  16
17  5  17
18  5  18
19  5  19
[20 rows x 2 columns]
In [122]: df_re.groupby('A').rolling(4).B.mean()
Out[122]: 
A    
1  0      NaN
   1      NaN
   2      NaN
   3      1.5
   4      2.5
         ... 
5  15    13.5
   16    14.5
   17    15.5
   18    16.5
   19    17.5
Name: B, Length: 20, dtype: float64

The expanding() method will accumulate a given operation (sum() in the example) for all the members of each particular group.

In [123]: df_re.groupby('A').expanding().sum()
Out[123]: 
         A      B
A                
1 0    1.0    0.0
  1    2.0    1.0
  2    3.0    3.0
  3    4.0    6.0
  4    5.0   10.0
...    ...    ...
5 15  30.0   75.0
  16  35.0   91.0
  17  40.0  108.0
  18  45.0  126.0
  19  50.0  145.0
[20 rows x 2 columns]

Suppose you want to use the resample() method to get a daily frequency in each group of your dataframe and wish to complete the missing values with the ffill() method.

In [124]: df_re = pd.DataFrame({'date': pd.date_range(start='2016-01-01', periods=4,
   .....:                                             freq='W'),
   .....:                       'group': [1, 1, 2, 2],
   .....:                       'val': [5, 6, 7, 8]}).set_index('date')
   .....: 
In [125]: df_re
Out[125]: 
            group  val
date                  
2016-01-03      1    5
2016-01-10      1    6
2016-01-17      2    7
2016-01-24      2    8
In [126]: df_re.groupby('group').resample('1D').ffill()
Out[126]: 
                  group  val
group date                  
1     2016-01-03      1    5
      2016-01-04      1    5
      2016-01-05      1    5
      2016-01-06      1    5
      2016-01-07      1    5
...                 ...  ...
2     2016-01-20      2    7
      2016-01-21      2    7
      2016-01-22      2    7
      2016-01-23      2    7
      2016-01-24      2    8
[16 rows x 2 columns]

Filtration

The filter method returns a subset of the original object. Suppose we want to take only elements that belong to groups with a group sum greater than 2.

In [127]: sf = pd.Series([1, 1, 2, 3, 3, 3])
In [128]: sf.groupby(sf).filter(lambda x: x.sum() > 2)
Out[128]: 
3    3
4    3
5    3
dtype: int64