# 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:

``` sql
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](cookbook.html#cookbook-grouping) 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:

``` python
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.

:::

``` python
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()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html#pandas.DataFrame.groupby).
We could naturally group by either the ``A`` or ``B`` columns, or both:

``` python
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

``` python
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:

``` python
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``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Index.html#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:

``` python
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:

``` python
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``:

``` python
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:

``` python
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:

``` python
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):

``` python
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')
```

``` python
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](advanced.html#advanced-hierarchical), it’s quite
natural to group by one of the levels of the hierarchy.

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

``` python
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``.

``` python
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:

``` python
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:

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

Grouping with multiple levels is supported.

``` python
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.

``` python
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.

``` python
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.

``` python
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.

``` python
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``.

``` python
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:

``` python
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:

``` python
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()``](https://docs.python.org/3/library/itertools.html#itertools.groupby):

``` python
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:

``` python
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](timeseries.html#timeseries-iterating-label).

## Selecting a group

A single group can be selected using
``get_group()``:

``` python
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:

``` python
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](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-aggregate), [window functions API](computation.html#stats-aggregate),
and [resample API](timeseries.html#timeseries-aggregate).

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

``` python
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](advanced.html#advanced-hierarchical) by default, though this can be
changed by using the ``as_index`` option:

``` python
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``:

``` python
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.

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

``` python
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:

Function | Description
---|---
mean() | Compute mean of groups
sum() | Compute sum of group values
size() | Compute group sizes
count() | Compute count of group
std() | Standard deviation of groups
var() | Compute variance of groups
sem() | Standard error of the mean of groups
describe() | Generates descriptive statistics
first() | Compute first of group values
last() | Compute last of group values
nth() | Take nth value, or a subset if n is a list
min() | Compute min of group values
max() | Compute max of group values

The aggregating functions above will exclude NA values. Any function which
reduces a [``Series``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.Series.html#pandas.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](#groupby-nth).

### 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:

``` python
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:

``` python
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:

``` python
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:

``` python
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.

``` python
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.

``` python
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.

``` python
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.

``` python
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

``` python
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.

``` python
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:

``` python
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](#groupby-dispatch):

``` python
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:

``` python
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:

``` python
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:

``` python
# 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.

``` python
In [101]: compare = pd.DataFrame({'Original': ts, 'Transformed': transformed})

In [102]: compare.plot()
Out[102]: <matplotlib.axes._subplots.AxesSubplot at 0x7f65f731cba8>
```

![groupby_transform_plot](https://static.pypandas.cn/public/static/images/groupby_transform_plot.png)

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

``` python
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.

``` python
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.

``` python
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.

``` python
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.``.

``` python
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.

``` python
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.

``` python
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.

``` python
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.

``` python
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
```

The argument of ``filter`` must be a function that, applied to the group as a
whole, returns ``True`` or ``False``.

Another useful operation is filtering out elements that belong to groups
with only a couple members.

``` python
In [129]: dff = pd.DataFrame({'A': np.arange(8), 'B': list('aabbbbcc')})

In [130]: dff.groupby('B').filter(lambda x: len(x) > 2)
Out[130]: 
   A  B
2  2  b
3  3  b
4  4  b
5  5  b
```

Alternatively, instead of dropping the offending groups, we can return a
like-indexed objects where the groups that do not pass the filter are filled
with NaNs.

``` python
In [131]: dff.groupby('B').filter(lambda x: len(x) > 2, dropna=False)
Out[131]: 
     A    B
0  NaN  NaN
1  NaN  NaN
2  2.0    b
3  3.0    b
4  4.0    b
5  5.0    b
6  NaN  NaN
7  NaN  NaN
```

For DataFrames with multiple columns, filters should explicitly specify a column as the filter criterion.

``` python
In [132]: dff['C'] = np.arange(8)

In [133]: dff.groupby('B').filter(lambda x: len(x['C']) > 2)
Out[133]: 
   A  B  C
2  2  b  2
3  3  b  3
4  4  b  4
5  5  b  5
```

::: tip Note

Some functions when applied to a groupby object will act as a **filter** on the input, returning
a reduced shape of the original (and potentially eliminating groups), but with the index unchanged.
Passing ``as_index=False`` will not affect these transformation methods.

For example: ``head, tail``.

``` python
In [134]: dff.groupby('B').head(2)
Out[134]: 
   A  B  C
0  0  a  0
1  1  a  1
2  2  b  2
3  3  b  3
6  6  c  6
7  7  c  7
```

:::

## Dispatching to instance methods

When doing an aggregation or transformation, you might just want to call an
instance method on each data group. This is pretty easy to do by passing lambda
functions:

``` python
In [135]: grouped = df.groupby('A')

In [136]: grouped.agg(lambda x: x.std())
Out[136]: 
            C         D
A                      
bar  0.181231  1.366330
foo  0.912265  0.884785
```

But, it’s rather verbose and can be untidy if you need to pass additional
arguments. Using a bit of metaprogramming cleverness, GroupBy now has the
ability to “dispatch” method calls to the groups:

``` python
In [137]: grouped.std()
Out[137]: 
            C         D
A                      
bar  0.181231  1.366330
foo  0.912265  0.884785
```

What is actually happening here is that a function wrapper is being
generated. When invoked, it takes any passed arguments and invokes the function
with any arguments on each group (in the above example, the ``std``
function). The results are then combined together much in the style of ``agg``
and ``transform`` (it actually uses ``apply`` to infer the gluing, documented
next). This enables some operations to be carried out rather succinctly:

``` python
In [138]: tsdf = pd.DataFrame(np.random.randn(1000, 3),
   .....:                     index=pd.date_range('1/1/2000', periods=1000),
   .....:                     columns=['A', 'B', 'C'])
   .....: 

In [139]: tsdf.iloc[::2] = np.nan

In [140]: grouped = tsdf.groupby(lambda x: x.year)

In [141]: grouped.fillna(method='pad')
Out[141]: 
                   A         B         C
2000-01-01       NaN       NaN       NaN
2000-01-02 -0.353501 -0.080957 -0.876864
2000-01-03 -0.353501 -0.080957 -0.876864
2000-01-04  0.050976  0.044273 -0.559849
2000-01-05  0.050976  0.044273 -0.559849
...              ...       ...       ...
2002-09-22  0.005011  0.053897 -1.026922
2002-09-23  0.005011  0.053897 -1.026922
2002-09-24 -0.456542 -1.849051  1.559856
2002-09-25 -0.456542 -1.849051  1.559856
2002-09-26  1.123162  0.354660  1.128135

[1000 rows x 3 columns]
```

In this example, we chopped the collection of time series into yearly chunks
then independently called [fillna](missing_data.html#missing-data-fillna) on the
groups.

The ``nlargest`` and ``nsmallest`` methods work on ``Series`` style groupbys:

``` python
In [142]: s = pd.Series([9, 8, 7, 5, 19, 1, 4.2, 3.3])

In [143]: g = pd.Series(list('abababab'))

In [144]: gb = s.groupby(g)

In [145]: gb.nlargest(3)
Out[145]: 
a  4    19.0
   0     9.0
   2     7.0
b  1     8.0
   3     5.0
   7     3.3
dtype: float64

In [146]: gb.nsmallest(3)
Out[146]: 
a  6    4.2
   2    7.0
   0    9.0
b  5    1.0
   7    3.3
   3    5.0
dtype: float64
```

## Flexible ``apply``

Some operations on the grouped data might not fit into either the aggregate or
transform categories. Or, you may simply want GroupBy to infer how to combine
the results. For these, use the ``apply`` function, which can be substituted
for both ``aggregate`` and ``transform`` in many standard use cases. However,
``apply`` can handle some exceptional use cases, for example:

``` python
In [147]: df
Out[147]: 
     A      B         C         D
0  foo    one -0.575247  1.346061
1  bar    one  0.254161  1.511763
2  foo    two -1.143704  1.627081
3  bar  three  0.215897 -0.990582
4  foo    two  1.193555 -0.441652
5  bar    two -0.077118  1.211526
6  foo    one -0.408530  0.268520
7  foo  three -0.862495  0.024580

In [148]: grouped = df.groupby('A')

# could also just call .describe()
In [149]: grouped['C'].apply(lambda x: x.describe())
Out[149]: 
A         
bar  count    3.000000
     mean     0.130980
     std      0.181231
     min     -0.077118
     25%      0.069390
                ...   
foo  min     -1.143704
     25%     -0.862495
     50%     -0.575247
     75%     -0.408530
     max      1.193555
Name: C, Length: 16, dtype: float64
```

The dimension of the returned result can also change:

``` python
In [150]: grouped = df.groupby('A')['C']

In [151]: def f(group):
   .....:     return pd.DataFrame({'original': group,
   .....:                          'demeaned': group - group.mean()})
   .....: 

In [152]: grouped.apply(f)
Out[152]: 
   original  demeaned
0 -0.575247 -0.215962
1  0.254161  0.123181
2 -1.143704 -0.784420
3  0.215897  0.084917
4  1.193555  1.552839
5 -0.077118 -0.208098
6 -0.408530 -0.049245
7 -0.862495 -0.503211
```

``apply`` on a Series can operate on a returned value from the applied function,
that is itself a series, and possibly upcast the result to a DataFrame:

``` python
In [153]: def f(x):
   .....:     return pd.Series([x, x ** 2], index=['x', 'x^2'])
   .....: 

In [154]: s = pd.Series(np.random.rand(5))

In [155]: s
Out[155]: 
0    0.321438
1    0.493496
2    0.139505
3    0.910103
4    0.194158
dtype: float64

In [156]: s.apply(f)
Out[156]: 
          x       x^2
0  0.321438  0.103323
1  0.493496  0.243538
2  0.139505  0.019462
3  0.910103  0.828287
4  0.194158  0.037697
```

::: tip Note

``apply`` can act as a reducer, transformer, *or* filter function, depending on exactly what is passed to it.
So depending on the path taken, and exactly what you are grouping. Thus the grouped columns(s) may be included in
the output as well as set the indices.

:::

## Other useful features

### Automatic exclusion of “nuisance” columns

Again consider the example DataFrame we’ve been looking at:

``` python
In [157]: df
Out[157]: 
     A      B         C         D
0  foo    one -0.575247  1.346061
1  bar    one  0.254161  1.511763
2  foo    two -1.143704  1.627081
3  bar  three  0.215897 -0.990582
4  foo    two  1.193555 -0.441652
5  bar    two -0.077118  1.211526
6  foo    one -0.408530  0.268520
7  foo  three -0.862495  0.024580
```

Suppose we wish to compute the standard deviation grouped by the ``A``
column. There is a slight problem, namely that we don’t care about the data in
column ``B``. We refer to this as a “nuisance” column. If the passed
aggregation function can’t be applied to some columns, the troublesome columns
will be (silently) dropped. Thus, this does not pose any problems:

``` python
In [158]: df.groupby('A').std()
Out[158]: 
            C         D
A                      
bar  0.181231  1.366330
foo  0.912265  0.884785
```

Note that ``df.groupby('A').colname.std().`` is more efficient than
``df.groupby('A').std().colname``, so if the result of an aggregation function
is only interesting over one column (here ``colname``), it may be filtered
*before* applying the aggregation function.

::: tip Note

Any object column, also if it contains numerical values such as ``Decimal``
objects, is considered as a “nuisance” columns. They are excluded from
aggregate functions automatically in groupby.

If you do wish to include decimal or object columns in an aggregation with
other non-nuisance data types, you must do so explicitly.

:::

``` python
In [159]: from decimal import Decimal

In [160]: df_dec = pd.DataFrame(
   .....:     {'id': [1, 2, 1, 2],
   .....:      'int_column': [1, 2, 3, 4],
   .....:      'dec_column': [Decimal('0.50'), Decimal('0.15'),
   .....:                     Decimal('0.25'), Decimal('0.40')]
   .....:      }
   .....: )
   .....: 

# Decimal columns can be sum'd explicitly by themselves...
In [161]: df_dec.groupby(['id'])[['dec_column']].sum()
Out[161]: 
   dec_column
id           
1        0.75
2        0.55

# ...but cannot be combined with standard data types or they will be excluded
In [162]: df_dec.groupby(['id'])[['int_column', 'dec_column']].sum()
Out[162]: 
    int_column
id            
1            4
2            6

# Use .agg function to aggregate over standard and "nuisance" data types
# at the same time
In [163]: df_dec.groupby(['id']).agg({'int_column': 'sum', 'dec_column': 'sum'})
Out[163]: 
    int_column dec_column
id                       
1            4       0.75
2            6       0.55
```

### Handling of (un)observed Categorical values

When using a ``Categorical`` grouper (as a single grouper, or as part of multiple groupers), the ``observed`` keyword
controls whether to return a cartesian product of all possible groupers values (``observed=False``) or only those
that are observed groupers (``observed=True``).

Show all values:

``` python
In [164]: pd.Series([1, 1, 1]).groupby(pd.Categorical(['a', 'a', 'a'],
   .....:                                             categories=['a', 'b']),
   .....:                              observed=False).count()
   .....: 
Out[164]: 
a    3
b    0
dtype: int64
```

Show only the observed values:

``` python
In [165]: pd.Series([1, 1, 1]).groupby(pd.Categorical(['a', 'a', 'a'],
   .....:                                             categories=['a', 'b']),
   .....:                              observed=True).count()
   .....: 
Out[165]: 
a    3
dtype: int64
```

The returned dtype of the grouped will *always* include *all* of the categories that were grouped.

``` python
In [166]: s = pd.Series([1, 1, 1]).groupby(pd.Categorical(['a', 'a', 'a'],
   .....:                                                 categories=['a', 'b']),
   .....:                                  observed=False).count()
   .....: 

In [167]: s.index.dtype
Out[167]: CategoricalDtype(categories=['a', 'b'], ordered=False)
```

### NA and NaT group handling

If there are any NaN or NaT values in the grouping key, these will be
automatically excluded. In other words, there will never be an “NA group” or
“NaT group”. This was not the case in older versions of pandas, but users were
generally discarding the NA group anyway (and supporting it was an
implementation headache).

### Grouping with ordered factors

Categorical variables represented as instance of pandas’s ``Categorical`` class
can be used as group keys. If so, the order of the levels will be preserved:

``` python
In [168]: data = pd.Series(np.random.randn(100))

In [169]: factor = pd.qcut(data, [0, .25, .5, .75, 1.])

In [170]: data.groupby(factor).mean()
Out[170]: 
(-2.645, -0.523]   -1.362896
(-0.523, 0.0296]   -0.260266
(0.0296, 0.654]     0.361802
(0.654, 2.21]       1.073801
dtype: float64
```

### Grouping with a grouper specification

You may need to specify a bit more data to properly group. You can
use the ``pd.Grouper`` to provide this local control.

``` python
In [171]: import datetime

In [172]: df = pd.DataFrame({'Branch': 'A A A A A A A B'.split(),
   .....:                    'Buyer': 'Carl Mark Carl Carl Joe Joe Joe Carl'.split(),
   .....:                    'Quantity': [1, 3, 5, 1, 8, 1, 9, 3],
   .....:                    'Date': [
   .....:                        datetime.datetime(2013, 1, 1, 13, 0),
   .....:                        datetime.datetime(2013, 1, 1, 13, 5),
   .....:                        datetime.datetime(2013, 10, 1, 20, 0),
   .....:                        datetime.datetime(2013, 10, 2, 10, 0),
   .....:                        datetime.datetime(2013, 10, 1, 20, 0),
   .....:                        datetime.datetime(2013, 10, 2, 10, 0),
   .....:                        datetime.datetime(2013, 12, 2, 12, 0),
   .....:                        datetime.datetime(2013, 12, 2, 14, 0)]
   .....:                    })
   .....: 

In [173]: df
Out[173]: 
  Branch Buyer  Quantity                Date
0      A  Carl         1 2013-01-01 13:00:00
1      A  Mark         3 2013-01-01 13:05:00
2      A  Carl         5 2013-10-01 20:00:00
3      A  Carl         1 2013-10-02 10:00:00
4      A   Joe         8 2013-10-01 20:00:00
5      A   Joe         1 2013-10-02 10:00:00
6      A   Joe         9 2013-12-02 12:00:00
7      B  Carl         3 2013-12-02 14:00:00
```

Groupby a specific column with the desired frequency. This is like resampling.

``` python
In [174]: df.groupby([pd.Grouper(freq='1M', key='Date'), 'Buyer']).sum()
Out[174]: 
                  Quantity
Date       Buyer          
2013-01-31 Carl          1
           Mark          3
2013-10-31 Carl          6
           Joe           9
2013-12-31 Carl          3
           Joe           9
```

You have an ambiguous specification in that you have a named index and a column
that could be potential groupers.

``` python
In [175]: df = df.set_index('Date')

In [176]: df['Date'] = df.index + pd.offsets.MonthEnd(2)

In [177]: df.groupby([pd.Grouper(freq='6M', key='Date'), 'Buyer']).sum()
Out[177]: 
                  Quantity
Date       Buyer          
2013-02-28 Carl          1
           Mark          3
2014-02-28 Carl          9
           Joe          18

In [178]: df.groupby([pd.Grouper(freq='6M', level='Date'), 'Buyer']).sum()
Out[178]: 
                  Quantity
Date       Buyer          
2013-01-31 Carl          1
           Mark          3
2014-01-31 Carl          9
           Joe          18
```

### Taking the first rows of each group

Just like for a DataFrame or Series you can call head and tail on a groupby:

``` python
In [179]: df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=['A', 'B'])

In [180]: df
Out[180]: 
   A  B
0  1  2
1  1  4
2  5  6

In [181]: g = df.groupby('A')

In [182]: g.head(1)
Out[182]: 
   A  B
0  1  2
2  5  6

In [183]: g.tail(1)
Out[183]: 
   A  B
1  1  4
2  5  6
```

This shows the first or last n rows from each group.

### Taking the nth row of each group

To select from a DataFrame or Series the nth item, use
``nth()``. This is a reduction method, and
will return a single row (or no row) per group if you pass an int for n:

``` python
In [184]: df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])

In [185]: g = df.groupby('A')

In [186]: g.nth(0)
Out[186]: 
     B
A     
1  NaN
5  6.0

In [187]: g.nth(-1)
Out[187]: 
     B
A     
1  4.0
5  6.0

In [188]: g.nth(1)
Out[188]: 
     B
A     
1  4.0
```

If you want to select the nth not-null item, use the ``dropna`` kwarg. For a DataFrame this should be either ``'any'`` or ``'all'`` just like you would pass to dropna:

``` python
# nth(0) is the same as g.first()
In [189]: g.nth(0, dropna='any')
Out[189]: 
     B
A     
1  4.0
5  6.0

In [190]: g.first()
Out[190]: 
     B
A     
1  4.0
5  6.0

# nth(-1) is the same as g.last()
In [191]: g.nth(-1, dropna='any')  # NaNs denote group exhausted when using dropna
Out[191]: 
     B
A     
1  4.0
5  6.0

In [192]: g.last()
Out[192]: 
     B
A     
1  4.0
5  6.0

In [193]: g.B.nth(0, dropna='all')
Out[193]: 
A
1    4.0
5    6.0
Name: B, dtype: float64
```

As with other methods, passing ``as_index=False``, will achieve a filtration, which returns the grouped row.

``` python
In [194]: df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=['A', 'B'])

In [195]: g = df.groupby('A', as_index=False)

In [196]: g.nth(0)
Out[196]: 
   A    B
0  1  NaN
2  5  6.0

In [197]: g.nth(-1)
Out[197]: 
   A    B
1  1  4.0
2  5  6.0
```

You can also select multiple rows from each group by specifying multiple nth values as a list of ints.

``` python
In [198]: business_dates = pd.date_range(start='4/1/2014', end='6/30/2014', freq='B')

In [199]: df = pd.DataFrame(1, index=business_dates, columns=['a', 'b'])

# get the first, 4th, and last date index for each month
In [200]: df.groupby([df.index.year, df.index.month]).nth([0, 3, -1])
Out[200]: 
        a  b
2014 4  1  1
     4  1  1
     4  1  1
     5  1  1
     5  1  1
     5  1  1
     6  1  1
     6  1  1
     6  1  1
```

### Enumerate group items

To see the order in which each row appears within its group, use the
``cumcount`` method:

``` python
In [201]: dfg = pd.DataFrame(list('aaabba'), columns=['A'])

In [202]: dfg
Out[202]: 
   A
0  a
1  a
2  a
3  b
4  b
5  a

In [203]: dfg.groupby('A').cumcount()
Out[203]: 
0    0
1    1
2    2
3    0
4    1
5    3
dtype: int64

In [204]: dfg.groupby('A').cumcount(ascending=False)
Out[204]: 
0    3
1    2
2    1
3    1
4    0
5    0
dtype: int64
```

### Enumerate groups

*New in version 0.20.2.* 

To see the ordering of the groups (as opposed to the order of rows
within a group given by ``cumcount``) you can use
``ngroup()``.

Note that the numbers given to the groups match the order in which the
groups would be seen when iterating over the groupby object, not the
order they are first observed.

``` python
In [205]: dfg = pd.DataFrame(list('aaabba'), columns=['A'])

In [206]: dfg
Out[206]: 
   A
0  a
1  a
2  a
3  b
4  b
5  a

In [207]: dfg.groupby('A').ngroup()
Out[207]: 
0    0
1    0
2    0
3    1
4    1
5    0
dtype: int64

In [208]: dfg.groupby('A').ngroup(ascending=False)
Out[208]: 
0    1
1    1
2    1
3    0
4    0
5    1
dtype: int64
```

### Plotting

Groupby also works with some plotting methods.  For example, suppose we
suspect that some features in a DataFrame may differ by group, in this case,
the values in column 1 where the group is “B” are 3 higher on average.

``` python
In [209]: np.random.seed(1234)

In [210]: df = pd.DataFrame(np.random.randn(50, 2))

In [211]: df['g'] = np.random.choice(['A', 'B'], size=50)

In [212]: df.loc[df['g'] == 'B', 1] += 3
```

We can easily visualize this with a boxplot:

``` python
In [213]: df.groupby('g').boxplot()
Out[213]: 
A         AxesSubplot(0.1,0.15;0.363636x0.75)
B    AxesSubplot(0.536364,0.15;0.363636x0.75)
dtype: object
```

![groupby_boxplot](https://static.pypandas.cn/public/static/images/groupby_boxplot.png)

The result of calling ``boxplot`` is a dictionary whose keys are the values
of our grouping column ``g`` (“A” and “B”). The values of the resulting dictionary
can be controlled by the ``return_type`` keyword of ``boxplot``.
See the [visualization documentation](visualization.html#visualization-box) for more.

::: danger Warning

For historical reasons, ``df.groupby("g").boxplot()`` is not equivalent
to ``df.boxplot(by="g")``. See [here](visualization.html#visualization-box-return) for
an explanation.

:::

### Piping function calls

*New in version 0.21.0.* 

Similar to the functionality provided by ``DataFrame`` and ``Series``, functions
that take ``GroupBy`` objects can be chained together using a ``pipe`` method to
allow for a cleaner, more readable syntax. To read about ``.pipe`` in general terms,
see [here](https://pandas.pydata.org/pandas-docs/stable/getting_started/basics.html#basics-pipe).

Combining ``.groupby`` and ``.pipe`` is often useful when you need to reuse
GroupBy objects.

As an example, imagine having a DataFrame with columns for stores, products,
revenue and quantity sold. We’d like to do a groupwise calculation of *prices*
(i.e. revenue/quantity) per store and per product. We could do this in a
multi-step operation, but expressing it in terms of piping can make the
code more readable. First we set the data:

``` python
In [214]: n = 1000

In [215]: df = pd.DataFrame({'Store': np.random.choice(['Store_1', 'Store_2'], n),
   .....:                    'Product': np.random.choice(['Product_1',
   .....:                                                 'Product_2'], n),
   .....:                    'Revenue': (np.random.random(n) * 50 + 10).round(2),
   .....:                    'Quantity': np.random.randint(1, 10, size=n)})
   .....: 

In [216]: df.head(2)
Out[216]: 
     Store    Product  Revenue  Quantity
0  Store_2  Product_1    26.12         1
1  Store_2  Product_1    28.86         1
```

Now, to find prices per store/product, we can simply do:

``` python
In [217]: (df.groupby(['Store', 'Product'])
   .....:    .pipe(lambda grp: grp.Revenue.sum() / grp.Quantity.sum())
   .....:    .unstack().round(2))
   .....: 
Out[217]: 
Product  Product_1  Product_2
Store                        
Store_1       6.82       7.05
Store_2       6.30       6.64
```

Piping can also be expressive when you want to deliver a grouped object to some
arbitrary function, for example:

``` python
In [218]: def mean(groupby):
   .....:     return groupby.mean()
   .....: 

In [219]: df.groupby(['Store', 'Product']).pipe(mean)
Out[219]: 
                     Revenue  Quantity
Store   Product                       
Store_1 Product_1  34.622727  5.075758
        Product_2  35.482815  5.029630
Store_2 Product_1  32.972837  5.237589
        Product_2  34.684360  5.224000
```

where ``mean`` takes a GroupBy object and finds the mean of the Revenue and Quantity
columns respectively for each Store-Product combination. The ``mean`` function can
be any function that takes in a GroupBy object; the ``.pipe`` will pass the GroupBy
object as a parameter into the function you specify.

## Examples

### Regrouping by factor

Regroup columns of a DataFrame according to their sum, and sum the aggregated ones.

``` python
In [220]: df = pd.DataFrame({'a': [1, 0, 0], 'b': [0, 1, 0],
   .....:                    'c': [1, 0, 0], 'd': [2, 3, 4]})
   .....: 

In [221]: df
Out[221]: 
   a  b  c  d
0  1  0  1  2
1  0  1  0  3
2  0  0  0  4

In [222]: df.groupby(df.sum(), axis=1).sum()
Out[222]: 
   1  9
0  2  2
1  1  3
2  0  4
```

### Multi-column factorization

By using ``ngroup()``, we can extract
information about the groups in a way similar to [``factorize()``](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.factorize.html#pandas.factorize) (as described
further in the [reshaping API](reshaping.html#reshaping-factorize)) but which applies
naturally to multiple columns of mixed type and different
sources. This can be useful as an intermediate categorical-like step
in processing, when the relationships between the group rows are more
important than their content, or as input to an algorithm which only
accepts the integer encoding. (For more information about support in
pandas for full categorical data, see the [Categorical
introduction](categorical.html#categorical) and the
[API documentation](https://pandas.pydata.org/pandas-docs/stable/reference/arrays.html#api-arrays-categorical).)

``` python
In [223]: dfg = pd.DataFrame({"A": [1, 1, 2, 3, 2], "B": list("aaaba")})

In [224]: dfg
Out[224]: 
   A  B
0  1  a
1  1  a
2  2  a
3  3  b
4  2  a

In [225]: dfg.groupby(["A", "B"]).ngroup()
Out[225]: 
0    0
1    0
2    1
3    2
4    1
dtype: int64

In [226]: dfg.groupby(["A", [0, 0, 0, 1, 1]]).ngroup()
Out[226]: 
0    0
1    0
2    1
3    3
4    2
dtype: int64
```

### Groupby by indexer to ‘resample’ data

Resampling produces new hypothetical samples (resamples) from already existing observed data or from a model that generates data. These new samples are similar to the pre-existing samples.

In order to resample to work on indices that are non-datetimelike, the following procedure can be utilized.

In the following examples, **df.index // 5** returns a binary array which is used to determine what gets selected for the groupby operation.

::: tip Note

The below example shows how we can downsample by consolidation of samples into fewer samples. Here by using **df.index // 5**, we are aggregating the samples in bins. By applying **std()** function, we aggregate the information contained in many samples into a small subset of values which is their standard deviation thereby reducing the number of samples.

:::

``` python
In [227]: df = pd.DataFrame(np.random.randn(10, 2))

In [228]: df
Out[228]: 
          0         1
0 -0.793893  0.321153
1  0.342250  1.618906
2 -0.975807  1.918201
3 -0.810847 -1.405919
4 -1.977759  0.461659
5  0.730057 -1.316938
6 -0.751328  0.528290
7 -0.257759 -1.081009
8  0.505895 -1.701948
9 -1.006349  0.020208

In [229]: df.index // 5
Out[229]: Int64Index([0, 0, 0, 0, 0, 1, 1, 1, 1, 1], dtype='int64')

In [230]: df.groupby(df.index // 5).std()
Out[230]: 
          0         1
0  0.823647  1.312912
1  0.760109  0.942941
```

### Returning a Series to propagate names

Group DataFrame columns, compute a set of metrics and return a named Series.
The Series name is used as the name for the column index. This is especially
useful in conjunction with reshaping operations such as stacking in which the
column index name will be used as the name of the inserted column:

``` python
In [231]: df = pd.DataFrame({'a': [0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2],
   .....:                    'b': [0, 0, 1, 1, 0, 0, 1, 1, 0, 0, 1, 1],
   .....:                    'c': [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0],
   .....:                    'd': [0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0, 1]})
   .....: 

In [232]: def compute_metrics(x):
   .....:     result = {'b_sum': x['b'].sum(), 'c_mean': x['c'].mean()}
   .....:     return pd.Series(result, name='metrics')
   .....: 

In [233]: result = df.groupby('a').apply(compute_metrics)

In [234]: result
Out[234]: 
metrics  b_sum  c_mean
a                     
0          2.0     0.5
1          2.0     0.5
2          2.0     0.5

In [235]: result.stack()
Out[235]: 
a  metrics
0  b_sum      2.0
   c_mean     0.5
1  b_sum      2.0
   c_mean     0.5
2  b_sum      2.0
   c_mean     0.5
dtype: float64
```