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# 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
```
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