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 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 to True or False. Some examples:
- Discard data that belong to groups with only a few members.
- Filter out data based on the group sum or mean.

Many of these operations are defined on GroupBy objects. These operations are similar to those of the aggregating API, window API, and resample API.

It is possible that a given operation does not fall into one of these categories or is some combination of them. In such a case, it may be possible to compute the operation using GroupBy’s apply method. This method will examine the results of the apply step and try to sensibly combine them into a single result if it doesn’t fit into either of the above three categories.

Note

An operation that is split into multiple steps using built-in GroupBy operations will be more efficient than using the apply method with a user-defined Python function.

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:

SELECTColumn1,Column2,mean(Column3),sum(Column4)
FROMSomeTable
GROUPBYColumn1,Column2

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

See the cookbook for some advanced strategies.

Splitting an object into groups#

The abstract definition of grouping is to provide a mapping of labels to group names. To create a GroupBy object (more on what the GroupBy object is later), you may do the following:

In [1]: speeds = 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]: speeds
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
In [3]: grouped = speeds.groupby("class")
In [4]: grouped = speeds.groupby(["class", "order"])

The mapping can be specified many different ways:

A Python function, to be called on each of the index labels.
A list or NumPy array of the same length as the index.
A dict or Series, providing a label -> group name mapping.
For DataFrame objects, a string indicating either a column name or an index level name 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:

Note

A string passed to groupby may refer to either a column or an index level. If a string matches both a column name and an index level name, a ValueError will be raised.

In [5]: 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 [6]: df
Out[6]: 
 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(). This method returns a pandas.api.typing.DataFrameGroupBy instance. We could naturally group by either the A or B columns, or both:

In [7]: grouped = df.groupby("A")
In [8]: grouped = df.groupby("B")
In [9]: grouped = df.groupby(["A", "B"])

Note

df.groupby('A') is just syntactic sugar for df.groupby(df['A']).

If we also have a MultiIndex on columns A and B, we can group by all the columns except the one we specify:

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

The above GroupBy will split the DataFrame on its index (rows). To split by columns, first do a transpose:

In [13]: defget_letter_type(letter):
 ....:  if letter.lower() in 'aeiou':
 ....:  return 'vowel'
 ....:  else:
 ....:  return 'consonant'
 ....: 
In [14]: grouped = df.T.groupby(get_letter_type)

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

In [15]: index = [1, 2, 3, 1, 2, 3]
In [16]: s = pd.Series([1, 2, 3, 10, 20, 30], index=index)
In [17]: s
Out[17]: 
1 1
2 2
3 3
1 10
2 20
3 30
dtype: int64
In [18]: grouped = s.groupby(level=0)
In [19]: grouped.first()
Out[19]: 
1 1
2 2
3 3
dtype: int64
In [20]: grouped.last()
Out[20]: 
1 10
2 20
3 30
dtype: int64
In [21]: grouped.sum()
Out[21]: 
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.

Note

Many kinds of complicated data manipulations can be expressed in terms of GroupBy operations (though it can’t be guaranteed to be the most efficient implementation). 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. With sort=False the order among group-keys follows the order of appearance of the keys in the original dataframe:

In [22]: df2 = pd.DataFrame({"X": ["B", "B", "A", "A"], "Y": [1, 2, 3, 4]})
In [23]: df2.groupby(["X"]).sum()
Out[23]: 
 Y
X 
A 7
B 3
In [24]: df2.groupby(["X"], sort=False).sum()
Out[24]: 
 Y
X 
B 3
A 7

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

In [25]: df3 = pd.DataFrame({"X": ["A", "B", "A", "B"], "Y": [1, 4, 3, 2]})
In [26]: df3.groupby("X").get_group("A")
Out[26]: 
 X Y
0 A 1
2 A 3
In [27]: df3.groupby(["X"]).get_group(("B",))
Out[27]: 
 X Y
1 B 4
3 B 2

GroupBy dropna#

By default NA values are excluded from group keys during the groupby operation. However, in case you want to include NA values in group keys, you could pass dropna=False to achieve it.

In [28]: df_list = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]]
In [29]: df_dropna = pd.DataFrame(df_list, columns=["a", "b", "c"])
In [30]: df_dropna
Out[30]: 
 a b c
0 1 2.0 3
1 1 NaN 4
2 2 1.0 3
3 1 2.0 2

# Default ``dropna`` is set to True, which will exclude NaNs in keys
In [31]: df_dropna.groupby(by=["b"], dropna=True).sum()
Out[31]: 
 a c
b 
1.0 2 3
2.0 2 5
# In order to allow NaN in keys, set ``dropna`` to False
In [32]: df_dropna.groupby(by=["b"], dropna=False).sum()
Out[32]: 
 a c
b 
1.0 2 3
2.0 2 5
NaN 1 4

The default setting of dropna argument is True which means NA are not included in group keys.

GroupBy object attributes#

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

In [33]: df.groupby("A").groups
Out[33]: {'bar': [1, 3, 5], 'foo': [0, 2, 4, 6, 7]}
In [34]: df.T.groupby(get_letter_type).groups
Out[34]: {'consonant': ['B', 'C', 'D'], 'vowel': ['A']}

Calling the standard Python len function on the GroupBy object returns the number of groups, which is the same as the length of the groups dictionary:

In [35]: grouped = df.groupby(["A", "B"])
In [36]: grouped.groups
Out[36]: {('bar', 'one'): [1], ('bar', 'three'): [3], ('bar', 'two'): [5], ('foo', 'one'): [0, 6], ('foo', 'three'): [7], ('foo', 'two'): [2, 4]}
In [37]: len(grouped)
Out[37]: 6

GroupBy will tab complete column names, GroupBy operations, and other attributes:

In [38]: n = 10
In [39]: weight = np.random.normal(166, 20, size=n)
In [40]: height = np.random.normal(60, 10, size=n)
In [41]: time = pd.date_range("1/1/2000", periods=n)
In [42]: gender = np.random.choice(["male", "female"], size=n)
In [43]: df = pd.DataFrame(
 ....:  {"height": height, "weight": weight, "gender": gender}, index=time
 ....: )
 ....: 
In [44]: df
Out[44]: 
 height weight gender
2000年01月01日 42.849980 157.500553 male
2000年01月02日 49.607315 177.340407 male
2000年01月03日 56.293531 171.524640 male
2000年01月04日 48.421077 144.251986 female
2000年01月05日 46.556882 152.526206 male
2000年01月06日 68.448851 168.272968 female
2000年01月07日 70.757698 136.431469 male
2000年01月08日 58.909500 176.499753 female
2000年01月09日 76.435631 174.094104 female
2000年01月10日 45.306120 177.540920 male
In [45]: gb = df.groupby("gender")

In [46]: gb.<TAB> # noqa: E225, E999
gb.agg gb.boxplot gb.cummin gb.describe gb.filter gb.get_group gb.height gb.last gb.median gb.ngroups gb.plot gb.rank gb.std gb.transform
gb.aggregate gb.count gb.cumprod gb.dtype gb.first gb.groups gb.hist gb.max gb.min gb.nth gb.prod gb.resample gb.sum gb.var
gb.apply gb.cummax gb.cumsum gb.fillna gb.gender gb.head gb.indices gb.mean gb.name gb.ohlc gb.quantile gb.size gb.tail gb.weight

GroupBy with MultiIndex#

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

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

In [47]: arrays = [
 ....:  ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
 ....:  ["one", "two", "one", "two", "one", "two", "one", "two"],
 ....: ]
 ....: 
In [48]: index = pd.MultiIndex.from_arrays(arrays, names=["first", "second"])
In [49]: s = pd.Series(np.random.randn(8), index=index)
In [50]: s
Out[50]: 
first second
bar one -0.919854
 two -0.042379
baz one 1.247642
 two -0.009920
foo one 0.290213
 two 0.495767
qux one 0.362949
 two 1.548106
dtype: float64

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

In [51]: grouped = s.groupby(level=0)
In [52]: grouped.sum()
Out[52]: 
first
bar -0.962232
baz 1.237723
foo 0.785980
qux 1.911055
dtype: float64

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

In [53]: s.groupby(level="second").sum()
Out[53]: 
second
one 0.980950
two 1.991575
dtype: float64

Grouping with multiple levels is supported.

In [54]: arrays = [
 ....:  ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
 ....:  ["doo", "doo", "bee", "bee", "bop", "bop", "bop", "bop"],
 ....:  ["one", "two", "one", "two", "one", "two", "one", "two"],
 ....: ]
 ....: 
In [55]: index = pd.MultiIndex.from_arrays(arrays, names=["first", "second", "third"])
In [56]: s = pd.Series(np.random.randn(8), index=index)
In [57]: s
Out[57]: 
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 [58]: s.groupby(level=["first", "second"]).sum()
Out[58]: 
first second
bar doo -1.220674
baz bee -0.608004
foo bop 1.023898
qux bop 0.000895
dtype: float64

Index level names may be supplied as keys.

In [59]: s.groupby(["first", "second"]).sum()
Out[59]: 
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. You can specify both column and index names, or use a Grouper.

Let’s first create a DataFrame with a MultiIndex:

In [60]: arrays = [
 ....:  ["bar", "bar", "baz", "baz", "foo", "foo", "qux", "qux"],
 ....:  ["one", "two", "one", "two", "one", "two", "one", "two"],
 ....: ]
 ....: 
In [61]: index = pd.MultiIndex.from_arrays(arrays, names=["first", "second"])
In [62]: df = pd.DataFrame({"A": [1, 1, 1, 1, 2, 2, 3, 3], "B": np.arange(8)}, index=index)
In [63]: df
Out[63]: 
 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

Then we group df by the second index level and the A column.

In [64]: df.groupby([pd.Grouper(level=1), "A"]).sum()
Out[64]: 
 B
second A 
one 1 2
 2 4
 3 6
two 1 4
 2 5
 3 7

Index levels may also be specified by name.

In [65]: df.groupby([pd.Grouper(level="second"), "A"]).sum()
Out[65]: 
 B
second A 
one 1 2
 2 4
 3 6
two 1 4
 2 5
 3 7

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

In [66]: df.groupby(["second", "A"]).sum()
Out[66]: 
 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, by using [] on the GroupBy object in a similar way as the one used to get a column from a DataFrame, you can do:

In [67]: 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 [68]: df
Out[68]: 
 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 [69]: grouped = df.groupby(["A"])
In [70]: grouped_C = grouped["C"]
In [71]: grouped_D = grouped["D"]

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

In [72]: df["C"].groupby(df["A"])
Out[72]: <pandas.core.groupby.generic.SeriesGroupBy object at 0x7fa3c5858340>

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

You can also include the grouping columns if you want to operate on them.

In [73]: grouped[["A", "B"]].sum()
Out[73]: 
 A B
A 
bar barbarbar onethreetwo
foo foofoofoofoofoo onetwotwoonethree

Iterating through groups#

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

In [74]: grouped = df.groupby('A')
In [75]: for name, group in grouped:
 ....:  print(name)
 ....:  print(group)
 ....: 
bar
 A B C D
1 bar one 0.254161 1.511763
3 bar three 0.215897 -0.990582
5 bar two -0.077118 1.211526
foo
 A B C D
0 foo one -0.575247 1.346061
2 foo two -1.143704 1.627081
4 foo two 1.193555 -0.441652
6 foo one -0.408530 0.268520
7 foo three -0.862495 0.024580

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

In [76]: for name, group in df.groupby(['A', 'B']):
 ....:  print(name)
 ....:  print(group)
 ....: 
('bar', 'one')
 A B C D
1 bar one 0.254161 1.511763
('bar', 'three')
 A B C D
3 bar three 0.215897 -0.990582
('bar', 'two')
 A B C D
5 bar two -0.077118 1.211526
('foo', 'one')
 A B C D
0 foo one -0.575247 1.346061
6 foo one -0.408530 0.268520
('foo', 'three')
 A B C D
7 foo three -0.862495 0.02458
('foo', 'two')
 A B C D
2 foo two -1.143704 1.627081
4 foo two 1.193555 -0.441652

See Iterating through groups.

Selecting a group#

A single group can be selected using DataFrameGroupBy.get_group():

In [77]: grouped.get_group("bar")
Out[77]: 
 A B C D
1 bar one 0.254161 1.511763
3 bar three 0.215897 -0.990582
5 bar two -0.077118 1.211526

Or for an object grouped on multiple columns:

In [78]: df.groupby(["A", "B"]).get_group(("bar", "one"))
Out[78]: 
 A B C D
1 bar one 0.254161 1.511763

Aggregation#

An aggregation is a GroupBy operation that reduces the dimension of the grouping object. The result of an aggregation is, or at least is treated as, a scalar value for each column in a group. For example, producing the sum of each column in a group of values.

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").sum()
Out[81]: 
 height weight
kind 
cat 18.6 17.8
dog 40.0 205.5

In the result, the keys of the groups appear in the index by default. They can be instead included in the columns by passing as_index=False.

In [82]: animals.groupby("kind", as_index=False).sum()
Out[82]: 
 kind height weight
0 cat 18.6 17.8
1 dog 40.0 205.5

Built-in aggregation methods#

Many common aggregations are built-in to GroupBy objects as methods. Of the methods listed below, those with a * do not have an efficient, GroupBy-specific, implementation.

Method	Description
`any()`	Compute whether any of the values in the groups are truthy
`all()`	Compute whether all of the values in the groups are truthy
`count()`	Compute the number of non-NA values in the groups
`cov()` *	Compute the covariance of the groups
`first()`	Compute the first occurring value in each group
`idxmax()`	Compute the index of the maximum value in each group
`idxmin()`	Compute the index of the minimum value in each group
`last()`	Compute the last occurring value in each group
`max()`	Compute the maximum value in each group
`mean()`	Compute the mean of each group
`median()`	Compute the median of each group
`min()`	Compute the minimum value in each group
`nunique()`	Compute the number of unique values in each group
`prod()`	Compute the product of the values in each group
`quantile()`	Compute a given quantile of the values in each group
`sem()`	Compute the standard error of the mean of the values in each group
`size()`	Compute the number of values in each group
`skew()` *	Compute the skew of the values in each group
`std()`	Compute the standard deviation of the values in each group
`sum()`	Compute the sum of the values in each group
`var()`	Compute the variance of the values in each group

Some examples:

In [83]: df.groupby("A")[["C", "D"]].max()
Out[83]: 
 C D
A 
bar 0.254161 1.511763
foo 1.193555 1.627081
In [84]: df.groupby(["A", "B"]).mean()
Out[84]: 
 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

Another 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 consists of the group names and the values are the sizes of each group.

In [85]: grouped = df.groupby(["A", "B"])
In [86]: grouped.size()
Out[86]: 
A B 
bar one 1
 three 1
 two 1
foo one 2
 three 1
 two 2
dtype: int64

While the DataFrameGroupBy.describe() method is not itself a reducer, it can be used to conveniently produce a collection of summary statistics about each of the groups.

In [87]: grouped.describe()
Out[87]: 
 C ... D 
 count mean std ... 50% 75% max
A B ... 
bar one 1.0 0.254161 NaN ... 1.511763 1.511763 1.511763
 three 1.0 0.215897 NaN ... -0.990582 -0.990582 -0.990582
 two 1.0 -0.077118 NaN ... 1.211526 1.211526 1.211526
foo one 2.0 -0.491888 0.117887 ... 0.807291 1.076676 1.346061
 three 1.0 -0.862495 NaN ... 0.024580 0.024580 0.024580
 two 2.0 0.024925 1.652692 ... 0.592714 1.109898 1.627081
[6 rows x 16 columns]

Another aggregation example is to compute the number of unique values of each group. This is similar to the DataFrameGroupBy.value_counts() function, except that it only counts the number of unique values.

In [88]: ll = [['foo', 1], ['foo', 2], ['foo', 2], ['bar', 1], ['bar', 1]]
In [89]: df4 = pd.DataFrame(ll, columns=["A", "B"])
In [90]: df4
Out[90]: 
 A B
0 foo 1
1 foo 2
2 foo 2
3 bar 1
4 bar 1
In [91]: df4.groupby("A")["B"].nunique()
Out[91]: 
A
bar 1
foo 2
Name: B, dtype: int64

Note

Aggregation functions will not return the groups that you are aggregating over as 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 as named columns, regardless if they are named indices or columns in the inputs.

The `aggregate()` method#

Note

The aggregate() method can accept many different types of inputs. This section details using string aliases for various GroupBy methods; other inputs are detailed in the sections below.

Any reduction method that pandas implements can be passed as a string to aggregate(). Users are encouraged to use the shorthand, agg. It will operate as if the corresponding method was called.

In [92]: grouped = df.groupby("A")
In [93]: grouped[["C", "D"]].aggregate("sum")
Out[93]: 
 C D
A 
bar 0.392940 1.732707
foo -1.796421 2.824590
In [94]: grouped = df.groupby(["A", "B"])
In [95]: grouped.agg("sum")
Out[95]: 
 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

The result of the aggregation will have the group names as the new index. In the case of multiple keys, the result is a MultiIndex by default. As mentioned above, this can be changed by using the as_index option:

In [96]: grouped = df.groupby(["A", "B"], as_index=False)
In [97]: grouped.agg("sum")
Out[97]: 
 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 [98]: df.groupby("A", as_index=False)[["C", "D"]].agg("sum")
Out[98]: 
 A C D
0 bar 0.392940 1.732707
1 foo -1.796421 2.824590

Note that you could use the DataFrame.reset_index() DataFrame function to achieve the same result as the column names are stored in the resulting MultiIndex, although this will make an extra copy.

In [99]: df.groupby(["A", "B"]).agg("sum").reset_index()
Out[99]: 
 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

Aggregation with User-Defined Functions#

Users can also provide their own User-Defined Functions (UDFs) for custom aggregations.

Warning

When aggregating with a UDF, the UDF should not mutate the provided Series. See Mutating with User Defined Function (UDF) methods for more information.

Note

Aggregating with a UDF is often less performant than using the pandas built-in methods on GroupBy. Consider breaking up a complex operation into a chain of operations that utilize the built-in methods.

In [100]: animals
Out[100]: 
 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 [101]: animals.groupby("kind")[["height"]].agg(lambda x: set(x))
Out[101]: 
 height
kind 
cat {9.1, 9.5}
dog {34.0, 6.0}

The resulting dtype will reflect that of the aggregating function. If the results from different groups have different dtypes, then a common dtype will be determined in the same way as DataFrame construction.

In [102]: animals.groupby("kind")[["height"]].agg(lambda x: x.astype(int).sum())
Out[102]: 
 height
kind 
cat 18
dog 40

Applying multiple functions at once#

On a grouped Series, you can pass a list or dict of functions to SeriesGroupBy.agg(), outputting a DataFrame:

In [103]: grouped = df.groupby("A")
In [104]: grouped["C"].agg(["sum", "mean", "std"])
Out[104]: 
 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 DataFrameGroupBy.agg() to aggregate each column, which produces an aggregated result with a hierarchical column index:

In [105]: grouped[["C", "D"]].agg(["sum", "mean", "std"])
Out[105]: 
 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 after the functions themselves. If you need to rename, then you can add in a chained operation for a Series like this:

In [106]: (
 .....:  grouped["C"]
 .....:  .agg(["sum", "mean", "std"])
 .....:  .rename(columns={"sum": "foo", "mean": "bar", "std": "baz"})
 .....: )
 .....: 
Out[106]: 
 foo bar baz
A 
bar 0.392940 0.130980 0.181231
foo -1.796421 -0.359284 0.912265

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

In [107]: (
 .....:  grouped[["C", "D"]].agg(["sum", "mean", "std"]).rename(
 .....:  columns={"sum": "foo", "mean": "bar", "std": "baz"}
 .....:  )
 .....: )
 .....: 
Out[107]: 
 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

Note

In general, the output column names should be unique, but pandas will allow you apply to the same function (or two functions with the same name) to the same column.

In [108]: grouped["C"].agg(["sum", "sum"])
Out[108]: 
 sum sum
A 
bar 0.392940 0.392940
foo -1.796421 -1.796421

pandas also allows you to provide multiple lambdas. In this case, pandas will mangle the name of the (nameless) lambda functions, appending _<i> to each subsequent lambda.

In [109]: grouped["C"].agg([lambda x: x.max() - x.min(), lambda x: x.median() - x.mean()])
Out[109]: 
 <lambda_0> <lambda_1>
A 
bar 0.331279 0.084917
foo 2.337259 -0.215962

Named aggregation#

To support column-specific aggregation with control over the output column names, pandas accepts the special syntax in DataFrameGroupBy.agg() and SeriesGroupBy.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 NamedAgg namedtuple with the fields ['column', 'aggfunc'] to make it clearer what the arguments are. As usual, the aggregation can be a callable or a string alias.

In [110]: animals
Out[110]: 
 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 [111]: 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="mean"),
 .....: )
 .....: 
Out[111]: 
 min_height max_height average_weight
kind 
cat 9.1 9.5 8.90
dog 6.0 34.0 102.75

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

In [112]: animals.groupby("kind").agg(
 .....:  min_height=("height", "min"),
 .....:  max_height=("height", "max"),
 .....:  average_weight=("weight", "mean"),
 .....: )
 .....: 
Out[112]: 
 min_height max_height average_weight
kind 
cat 9.1 9.5 8.90
dog 6.0 34.0 102.75

If the column names you want are not valid Python keywords, construct a dictionary and unpack the keyword arguments

In [113]: animals.groupby("kind").agg(
 .....:  **{
 .....:  "total weight": pd.NamedAgg(column="weight", aggfunc="sum")
 .....:  }
 .....: )
 .....: 
Out[113]: 
 total weight
kind 
cat 17.8
dog 205.5

When using named aggregation, 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 require additional arguments, apply them partially with functools.partial().

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

In [114]: animals.groupby("kind").height.agg(
 .....:  min_height="min",
 .....:  max_height="max",
 .....: )
 .....: 
Out[114]: 
 min_height max_height
kind 
cat 9.1 9.5
dog 6.0 34.0

Applying different functions to DataFrame columns#

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

In [115]: grouped.agg({"C": "sum", "D": lambda x: np.std(x, ddof=1)})
Out[115]: 
 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 implemented on GroupBy:

In [116]: grouped.agg({"C": "sum", "D": "std"})
Out[116]: 
 C D
A 
bar 0.392940 1.366330
foo -1.796421 0.884785

Transformation#

A transformation is a GroupBy operation whose result is indexed the same as the one being grouped. Common examples include cumsum() and diff().

In [117]: speeds
Out[117]: 
 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
In [118]: grouped = speeds.groupby("class")["max_speed"]
In [119]: grouped.cumsum()
Out[119]: 
falcon 389.0
parrot 413.0
lion 80.2
monkey NaN
leopard 138.2
Name: max_speed, dtype: float64
In [120]: grouped.diff()
Out[120]: 
falcon NaN
parrot -365.0
lion NaN
monkey NaN
leopard NaN
Name: max_speed, dtype: float64

Unlike aggregations, the groupings that are used to split the original object are not included in the result.

Note

Since transformations do not include the groupings that are used to split the result, the arguments as_index and sort in DataFrame.groupby() and Series.groupby() have no effect.

A common use of a transformation is to add the result back into the original DataFrame.

In [121]: result = speeds.copy()
In [122]: result["cumsum"] = grouped.cumsum()
In [123]: result["diff"] = grouped.diff()
In [124]: result
Out[124]: 
 class order max_speed cumsum diff
falcon bird Falconiformes 389.0 389.0 NaN
parrot bird Psittaciformes 24.0 413.0 -365.0
lion mammal Carnivora 80.2 80.2 NaN
monkey mammal Primates NaN NaN NaN
leopard mammal Carnivora 58.0 138.2 NaN

Built-in transformation methods#

The following methods on GroupBy act as transformations.

Method	Description
`bfill()`	Back fill NA values within each group
`cumcount()`	Compute the cumulative count within each group
`cummax()`	Compute the cumulative max within each group
`cummin()`	Compute the cumulative min within each group
`cumprod()`	Compute the cumulative product within each group
`cumsum()`	Compute the cumulative sum within each group
`diff()`	Compute the difference between adjacent values within each group
`ffill()`	Forward fill NA values within each group
`pct_change()`	Compute the percent change between adjacent values within each group
`rank()`	Compute the rank of each value within each group
`shift()`	Shift values up or down within each group

In addition, passing any built-in aggregation method as a string to transform() (see the next section) will broadcast the result across the group, producing a transformed result. If the aggregation method has an efficient implementation, this will be performant as well.

The `transform()` method#

Similar to the aggregation method, the transform() method can accept string aliases to the built-in transformation methods in the previous section. It can also accept string aliases to the built-in aggregation methods. When an aggregation method is provided, the result will be broadcast across the group.

In [125]: speeds
Out[125]: 
 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
In [126]: grouped = speeds.groupby("class")[["max_speed"]]
In [127]: grouped.transform("cumsum")
Out[127]: 
 max_speed
falcon 389.0
parrot 413.0
lion 80.2
monkey NaN
leopard 138.2
In [128]: grouped.transform("sum")
Out[128]: 
 max_speed
falcon 413.0
parrot 413.0
lion 138.2
monkey 138.2
leopard 138.2

In addition to string aliases, the transform() method can also accept User-Defined Functions (UDFs). The UDF 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. See Mutating with User Defined Function (UDF) methods for more information.
(Optionally) operates on all columns of the entire group chunk at once. If this is supported, a fast path is used starting from the second chunk.

Note

Transforming by supplying transform with a UDF is often less performant than using the built-in methods on GroupBy. Consider breaking up a complex operation into a chain of operations that utilize the built-in methods.

All of the examples in this section can be made more performant by calling built-in methods instead of using UDFs. See below for examples.

Changed in version 2.0.0: When using .transform on a grouped DataFrame and the transformation function returns a DataFrame, pandas now aligns the result’s index with the input’s index. You can call .to_numpy() within the transformation function to avoid alignment.

Similar to The aggregate() method, the resulting dtype will reflect that of the transformation function. If the results from different groups have different dtypes, then a common dtype will be determined in the same way as DataFrame construction.

Suppose we wish to standardize the data within each group:

In [129]: index = pd.date_range("10/1/1999", periods=1100)
In [130]: ts = pd.Series(np.random.normal(0.5, 2, 1100), index)
In [131]: ts = ts.rolling(window=100, min_periods=100).mean().dropna()
In [132]: ts.head()
Out[132]: 
2000年01月08日 0.779333
2000年01月09日 0.778852
2000年01月10日 0.786476
2000年01月11日 0.782797
2000年01月12日 0.798110
Freq: D, dtype: float64
In [133]: ts.tail()
Out[133]: 
2002年09月30日 0.660294
2002年10月01日 0.631095
2002年10月02日 0.673601
2002年10月03日 0.709213
2002年10月04日 0.719369
Freq: D, dtype: float64
In [134]: 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 (up to floating-point error), which we can easily check:

# Original Data
In [135]: grouped = ts.groupby(lambda x: x.year)
In [136]: grouped.mean()
Out[136]: 
2000 0.442441
2001 0.526246
2002 0.459365
dtype: float64
In [137]: grouped.std()
Out[137]: 
2000 0.131752
2001 0.210945
2002 0.128753
dtype: float64
# Transformed Data
In [138]: grouped_trans = transformed.groupby(lambda x: x.year)
In [139]: grouped_trans.mean()
Out[139]: 
2000 -4.870756e-16
2001 -1.545187e-16
2002 4.136282e-16
dtype: float64
In [140]: grouped_trans.std()
Out[140]: 
2000 1.0
2001 1.0
2002 1.0
dtype: float64

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

In [141]: compare = pd.DataFrame({"Original": ts, "Transformed": transformed})
In [142]: compare.plot()
Out[142]: <Axes: >

../_images/groupby_transform_plot.png

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

In [143]: ts.groupby(lambda x: x.year).transform(lambda x: x.max() - x.min())
Out[143]: 
2000年01月08日 0.623893
2000年01月09日 0.623893
2000年01月10日 0.623893
2000年01月11日 0.623893
2000年01月12日 0.623893
 ... 
2002年09月30日 0.558275
2002年10月01日 0.558275
2002年10月02日 0.558275
2002年10月03日 0.558275
2002年10月04日 0.558275
Freq: D, Length: 1001, dtype: float64

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

In [144]: cols = ["A", "B", "C"]
In [145]: values = np.random.randn(1000, 3)
In [146]: values[np.random.randint(0, 1000, 100), 0] = np.nan
In [147]: values[np.random.randint(0, 1000, 50), 1] = np.nan
In [148]: values[np.random.randint(0, 1000, 200), 2] = np.nan
In [149]: data_df = pd.DataFrame(values, columns=cols)
In [150]: data_df
Out[150]: 
 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 [151]: countries = np.array(["US", "UK", "GR", "JP"])
In [152]: key = countries[np.random.randint(0, 4, 1000)]
In [153]: grouped = data_df.groupby(key)
# Non-NA count in each group
In [154]: grouped.count()
Out[154]: 
 A B C
GR 209 217 189
JP 240 255 217
UK 216 231 193
US 239 250 217
In [155]: transformed = grouped.transform(lambda x: x.fillna(x.mean()))

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

In [156]: grouped_trans = transformed.groupby(key)
In [157]: grouped.mean() # original group means
Out[157]: 
 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 [158]: grouped_trans.mean() # transformation did not change group means
Out[158]: 
 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 [159]: grouped.count() # original has some missing data points
Out[159]: 
 A B C
GR 209 217 189
JP 240 255 217
UK 216 231 193
US 239 250 217
In [160]: grouped_trans.count() # counts after transformation
Out[160]: 
 A B C
GR 228 228 228
JP 267 267 267
UK 247 247 247
US 258 258 258
In [161]: grouped_trans.size() # Verify non-NA count equals group size
Out[161]: 
GR 228
JP 267
UK 247
US 258
dtype: int64

As mentioned in the note above, each of the examples in this section can be computed more efficiently using built-in methods. In the code below, the inefficient way using a UDF is commented out and the faster alternative appears below.

# result = ts.groupby(lambda x: x.year).transform(
# lambda x: (x - x.mean()) / x.std()
# )
In [162]: grouped = ts.groupby(lambda x: x.year)
In [163]: result = (ts - grouped.transform("mean")) / grouped.transform("std")
# result = ts.groupby(lambda x: x.year).transform(lambda x: x.max() - x.min())
In [164]: grouped = ts.groupby(lambda x: x.year)
In [165]: result = grouped.transform("max") - grouped.transform("min")
# grouped = data_df.groupby(key)
# result = grouped.transform(lambda x: x.fillna(x.mean()))
In [166]: grouped = data_df.groupby(key)
In [167]: result = data_df.fillna(grouped.transform("mean"))

Window and resample operations#

It is possible to use resample(), expanding() and rolling() as methods on groupbys.

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

In [168]: df_re = pd.DataFrame({"A": [1] * 10 + [5] * 10, "B": np.arange(20)})
In [169]: df_re
Out[169]: 
 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 [170]: df_re.groupby("A").rolling(4).B.mean()
Out[170]: 
A 
1 0 NaN
 1 NaN
 2 NaN
 3 1.5
 4 2.5
 ... 
5 15 13.5
 16 14.5
 17 15.5
 18 16.5
 19 17.5
Name: B, Length: 20, dtype: float64

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

In [171]: df_re.groupby("A").expanding().sum()
Out[171]: 
 B
A 
1 0 0.0
 1 1.0
 2 3.0
 3 6.0
 4 10.0
... ...
5 15 75.0
 16 91.0
 17 108.0
 18 126.0
 19 145.0
[20 rows x 1 columns]

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

In [172]: df_re = pd.DataFrame(
 .....:  {
 .....:  "date": pd.date_range(start="2016年01月01日", periods=4, freq="W"),
 .....:  "group": [1, 1, 2, 2],
 .....:  "val": [5, 6, 7, 8],
 .....:  }
 .....: ).set_index("date")
 .....: 
In [173]: df_re
Out[173]: 
 group val
date 
2016年01月03日 1 5
2016年01月10日 1 6
2016年01月17日 2 7
2016年01月24日 2 8
In [174]: df_re.groupby("group").resample("1D", include_groups=False).ffill()
Out[174]: 
 val
group date 
1 2016年01月03日 5
 2016年01月04日 5
 2016年01月05日 5
 2016年01月06日 5
 2016年01月07日 5
... ...
2 2016年01月20日 7
 2016年01月21日 7
 2016年01月22日 7
 2016年01月23日 7
 2016年01月24日 8
[16 rows x 1 columns]

Filtration#

A filtration is a GroupBy operation that subsets the original grouping object. It may either filter out entire groups, part of groups, or both. Filtrations return a filtered version of the calling object, including the grouping columns when provided. In the following example, class is included in the result.

In [175]: speeds
Out[175]: 
 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
In [176]: speeds.groupby("class").nth(1)
Out[176]: 
 class order max_speed
parrot bird Psittaciformes 24.0
monkey mammal Primates NaN

Note

Unlike aggregations, filtrations do not add the group keys to the index of the result. Because of this, passing as_index=False or sort=True will not affect these methods.

Filtrations will respect subsetting the columns of the GroupBy object.

In [177]: speeds.groupby("class")[["order", "max_speed"]].nth(1)
Out[177]: 
 order max_speed
parrot Psittaciformes 24.0
monkey Primates NaN

Built-in filtrations#

The following methods on GroupBy act as filtrations. All these methods have an efficient, GroupBy-specific, implementation.

Method	Description
`head()`	Select the top row(s) of each group
`nth()`	Select the nth row(s) of each group
`tail()`	Select the bottom row(s) of each group

Users can also use transformations along with Boolean indexing to construct complex filtrations within groups. For example, suppose we are given groups of products and their volumes, and we wish to subset the data to only the largest products capturing no more than 90% of the total volume within each group.

In [178]: product_volumes = pd.DataFrame(
 .....:  {
 .....:  "group": list("xxxxyyy"),
 .....:  "product": list("abcdefg"),
 .....:  "volume": [10, 30, 20, 15, 40, 10, 20],
 .....:  }
 .....: )
 .....: 
In [179]: product_volumes
Out[179]: 
 group product volume
0 x a 10
1 x b 30
2 x c 20
3 x d 15
4 y e 40
5 y f 10
6 y g 20
# Sort by volume to select the largest products first
In [180]: product_volumes = product_volumes.sort_values("volume", ascending=False)
In [181]: grouped = product_volumes.groupby("group")["volume"]
In [182]: cumpct = grouped.cumsum() / grouped.transform("sum")
In [183]: cumpct
Out[183]: 
4 0.571429
1 0.400000
2 0.666667
6 0.857143
3 0.866667
0 1.000000
5 1.000000
Name: volume, dtype: float64
In [184]: significant_products = product_volumes[cumpct <= 0.9]
In [185]: significant_products.sort_values(["group", "product"])
Out[185]: 
 group product volume
1 x b 30
2 x c 20
3 x d 15
4 y e 40
6 y g 20

The `filter` method#

Note

Filtering by supplying filter with a User-Defined Function (UDF) is often less performant than using the built-in methods on GroupBy. Consider breaking up a complex operation into a chain of operations that utilize the built-in methods.

The filter method takes a User-Defined Function (UDF) that, when applied to an entire group, returns either True or False. The result of the filter method is then the subset of groups for which the UDF returned True.

Suppose we want to take only elements that belong to groups with a group sum greater than 2.

In [186]: sf = pd.Series([1, 1, 2, 3, 3, 3])
In [187]: sf.groupby(sf).filter(lambda x: x.sum() > 2)
Out[187]: 
3 3
4 3
5 3
dtype: int64

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

In [188]: dff = pd.DataFrame({"A": np.arange(8), "B": list("aabbbbcc")})
In [189]: dff.groupby("B").filter(lambda x: len(x) > 2)
Out[189]: 
 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.

In [190]: dff.groupby("B").filter(lambda x: len(x) > 2, dropna=False)
Out[190]: 
 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.

In [191]: dff["C"] = np.arange(8)
In [192]: dff.groupby("B").filter(lambda x: len(x["C"]) > 2)
Out[192]: 
 A B C
2 2 b 2
3 3 b 3
4 4 b 4
5 5 b 5

Flexible `apply`#

Some operations on the grouped data might not fit into the aggregation, transformation, or filtration categories. For these, you can use the apply function.

Warning

apply has to try to infer from the result whether it should act as a reducer, transformer, or filter, depending on exactly what is passed to it. Thus the grouped column(s) may be included in the output or not. While it tries to intelligently guess how to behave, it can sometimes guess wrong.

Note

All of the examples in this section can be more reliably, and more efficiently, computed using other pandas functionality.

In [193]: df
Out[193]: 
 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 [194]: grouped = df.groupby("A")
# could also just call .describe()
In [195]: grouped["C"].apply(lambda x: x.describe())
Out[195]: 
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:

In [196]: grouped = df.groupby('A')['C']
In [197]: deff(group):
 .....:  return pd.DataFrame({'original': group,
 .....:  'demeaned': group - group.mean()})
 .....: 
In [198]: grouped.apply(f)
Out[198]: 
 original demeaned
A 
bar 1 0.254161 0.123181
 3 0.215897 0.084917
 5 -0.077118 -0.208098
foo 0 -0.575247 -0.215962
 2 -1.143704 -0.784420
 4 1.193555 1.552839
 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:

In [199]: deff(x):
 .....:  return pd.Series([x, x ** 2], index=["x", "x^2"])
 .....: 
In [200]: s = pd.Series(np.random.rand(5))
In [201]: s
Out[201]: 
0 0.582898
1 0.098352
2 0.001438
3 0.009420
4 0.815826
dtype: float64
In [202]: s.apply(f)
Out[202]: 
 x x^2
0 0.582898 0.339770
1 0.098352 0.009673
2 0.001438 0.000002
3 0.009420 0.000089
4 0.815826 0.665572

Similar to The aggregate() method, the resulting dtype will reflect that of the apply function. If the results from different groups have different dtypes, then a common dtype will be determined in the same way as DataFrame construction.

Control grouped column(s) placement with `group_keys`#

To control whether the grouped column(s) are included in the indices, you can use the argument group_keys which defaults to True. Compare

In [203]: df.groupby("A", group_keys=True).apply(lambda x: x, include_groups=False)
Out[203]: 
 B C D
A 
bar 1 one 0.254161 1.511763
 3 three 0.215897 -0.990582
 5 two -0.077118 1.211526
foo 0 one -0.575247 1.346061
 2 two -1.143704 1.627081
 4 two 1.193555 -0.441652
 6 one -0.408530 0.268520
 7 three -0.862495 0.024580

with

In [204]: df.groupby("A", group_keys=False).apply(lambda x: x, include_groups=False)
Out[204]: 
 B C D
0 one -0.575247 1.346061
1 one 0.254161 1.511763
2 two -1.143704 1.627081
3 three 0.215897 -0.990582
4 two 1.193555 -0.441652
5 two -0.077118 1.211526
6 one -0.408530 0.268520
7 three -0.862495 0.024580

Numba Accelerated Routines#

Added in version 1.1.

If Numba is installed as an optional dependency, the transform and aggregate methods support engine='numba' and engine_kwargs arguments. See enhancing performance with Numba for general usage of the arguments and performance considerations.

The function signature must start with values, index exactly as the data belonging to each group will be passed into values, and the group index will be passed into index.

Warning

When using engine='numba', there will be no "fall back" behavior internally. The group data and group index will be passed as NumPy arrays to the JITed user defined function, and no alternative execution attempts will be tried.

Other useful features#

Exclusion of non-numeric columns#

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

In [205]: df
Out[205]: 
 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 because it is not numeric. You can avoid non-numeric columns by specifying numeric_only=True:

In [206]: df.groupby("A").std(numeric_only=True)
Out[206]: 
 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 needed over one column (here colname), it may be filtered before applying the aggregation function.

In [207]: fromdecimalimport Decimal
In [208]: 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"),
 .....:  ],
 .....:  }
 .....: )
 .....: 
In [209]: df_dec.groupby(["id"])[["dec_column"]].sum()
Out[209]: 
 dec_column
id 
1 0.75
2 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:

In [210]: pd.Series([1, 1, 1]).groupby(
 .....:  pd.Categorical(["a", "a", "a"], categories=["a", "b"]), observed=False
 .....: ).count()
 .....: 
Out[210]: 
a 3
b 0
dtype: int64

Show only the observed values:

In [211]: pd.Series([1, 1, 1]).groupby(
 .....:  pd.Categorical(["a", "a", "a"], categories=["a", "b"]), observed=True
 .....: ).count()
 .....: 
Out[211]: 
a 3
dtype: int64

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

In [212]: s = (
 .....:  pd.Series([1, 1, 1])
 .....:  .groupby(pd.Categorical(["a", "a", "a"], categories=["a", "b"]), observed=True)
 .....:  .count()
 .....: )
 .....: 
In [213]: s.index.dtype
Out[213]: CategoricalDtype(categories=['a', 'b'], ordered=False, categories_dtype=object)

NA group handling#

By NA, we are referring to any NA values, including NA, NaN, NaT, and None. If there are any NA values in the grouping key, by default these will be excluded. In other words, any "NA group" will be dropped. You can include NA groups by specifying dropna=False.

In [214]: df = pd.DataFrame({"key": [1.0, 1.0, np.nan, 2.0, np.nan], "A": [1, 2, 3, 4, 5]})
In [215]: df
Out[215]: 
 key A
0 1.0 1
1 1.0 2
2 NaN 3
3 2.0 4
4 NaN 5
In [216]: df.groupby("key", dropna=True).sum()
Out[216]: 
 A
key 
1.0 3
2.0 4
In [217]: df.groupby("key", dropna=False).sum()
Out[217]: 
 A
key 
1.0 3
2.0 4
NaN 8

Grouping with ordered factors#

Categorical variables represented as instances of pandas’s Categorical class can be used as group keys. If so, the order of the levels will be preserved. When observed=False and sort=False, any unobserved categories will be at the end of the result in order.

In [218]: days = pd.Categorical(
 .....:  values=["Wed", "Mon", "Thu", "Mon", "Wed", "Sat"],
 .....:  categories=["Mon", "Tue", "Wed", "Thu", "Fri", "Sat", "Sun"],
 .....: )
 .....: 
In [219]: data = pd.DataFrame(
 .....:  {
 .....:  "day": days,
 .....:  "workers": [3, 4, 1, 4, 2, 2],
 .....:  }
 .....: )
 .....: 
In [220]: data
Out[220]: 
 day workers
0 Wed 3
1 Mon 4
2 Thu 1
3 Mon 4
4 Wed 2
5 Sat 2
In [221]: data.groupby("day", observed=False, sort=True).sum()
Out[221]: 
 workers
day 
Mon 8
Tue 0
Wed 5
Thu 1
Fri 0
Sat 2
Sun 0
In [222]: data.groupby("day", observed=False, sort=False).sum()
Out[222]: 
 workers
day 
Wed 5
Mon 8
Thu 1
Sat 2
Tue 0
Fri 0
Sun 0

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.

In [223]: importdatetime
In [224]: 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 [225]: df
Out[225]: 
 Branch Buyer Quantity Date
0 A Carl 1 2013年01月01日 13:00:00
1 A Mark 3 2013年01月01日 13:05:00
2 A Carl 5 2013年10月01日 20:00:00
3 A Carl 1 2013年10月02日 10:00:00
4 A Joe 8 2013年10月01日 20:00:00
5 A Joe 1 2013年10月02日 10:00:00
6 A Joe 9 2013年12月02日 12:00:00
7 B Carl 3 2013年12月02日 14:00:00

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

In [226]: df.groupby([pd.Grouper(freq="1ME", key="Date"), "Buyer"])[["Quantity"]].sum()
Out[226]: 
 Quantity
Date Buyer 
2013年01月31日 Carl 1
 Mark 3
2013年10月31日 Carl 6
 Joe 9
2013年12月31日 Carl 3
 Joe 9

When freq is specified, the object returned by pd.Grouper will be an instance of pandas.api.typing.TimeGrouper. When there is a column and index with the same name, you can use key to group by the column and level to group by the index.

In [227]: df = df.set_index("Date")
In [228]: df["Date"] = df.index + pd.offsets.MonthEnd(2)
In [229]: df.groupby([pd.Grouper(freq="6ME", key="Date"), "Buyer"])[["Quantity"]].sum()
Out[229]: 
 Quantity
Date Buyer 
2013年02月28日 Carl 1
 Mark 3
2014年02月28日 Carl 9
 Joe 18
In [230]: df.groupby([pd.Grouper(freq="6ME", level="Date"), "Buyer"])[["Quantity"]].sum()
Out[230]: 
 Quantity
Date Buyer 
2013年01月31日 Carl 1
 Mark 3
2014年01月31日 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:

In [231]: df = pd.DataFrame([[1, 2], [1, 4], [5, 6]], columns=["A", "B"])
In [232]: df
Out[232]: 
 A B
0 1 2
1 1 4
2 5 6
In [233]: g = df.groupby("A")
In [234]: g.head(1)
Out[234]: 
 A B
0 1 2
2 5 6
In [235]: g.tail(1)
Out[235]: 
 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 the nth item from each group, use DataFrameGroupBy.nth() or SeriesGroupBy.nth(). Arguments supplied can be any integer, lists of integers, slices, or lists of slices; see below for examples. When the nth element of a group does not exist an error is not raised; instead no corresponding rows are returned.

In general this operation acts as a filtration. In certain cases it will also return one row per group, making it also a reduction. However because in general it can return zero or multiple rows per group, pandas treats it as a filtration in all cases.

In [236]: df = pd.DataFrame([[1, np.nan], [1, 4], [5, 6]], columns=["A", "B"])
In [237]: g = df.groupby("A")
In [238]: g.nth(0)
Out[238]: 
 A B
0 1 NaN
2 5 6.0
In [239]: g.nth(-1)
Out[239]: 
 A B
1 1 4.0
2 5 6.0
In [240]: g.nth(1)
Out[240]: 
 A B
1 1 4.0

If the nth element of a group does not exist, then no corresponding row is included in the result. In particular, if the specified n is larger than any group, the result will be an empty DataFrame.

In [241]: g.nth(5)
Out[241]: 
Empty DataFrame
Columns: [A, B]
Index: []

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:

# nth(0) is the same as g.first()
In [242]: g.nth(0, dropna="any")
Out[242]: 
 A B
1 1 4.0
2 5 6.0
In [243]: g.first()
Out[243]: 
 B
A 
1 4.0
5 6.0
# nth(-1) is the same as g.last()
In [244]: g.nth(-1, dropna="any")
Out[244]: 
 A B
1 1 4.0
2 5 6.0
In [245]: g.last()
Out[245]: 
 B
A 
1 4.0
5 6.0
In [246]: g.B.nth(0, dropna="all")
Out[246]: 
1 4.0
2 6.0
Name: B, dtype: float64

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

In [247]: business_dates = pd.date_range(start="4/1/2014", end="6/30/2014", freq="B")
In [248]: df = pd.DataFrame(1, index=business_dates, columns=["a", "b"])
# get the first, 4th, and last date index for each month
In [249]: df.groupby([df.index.year, df.index.month]).nth([0, 3, -1])
Out[249]: 
 a b
2014年04月01日 1 1
2014年04月04日 1 1
2014年04月30日 1 1
2014年05月01日 1 1
2014年05月06日 1 1
2014年05月30日 1 1
2014年06月02日 1 1
2014年06月05日 1 1
2014年06月30日 1 1

You may also use slices or lists of slices.

In [250]: df.groupby([df.index.year, df.index.month]).nth[1:]
Out[250]: 
 a b
2014年04月02日 1 1
2014年04月03日 1 1
2014年04月04日 1 1
2014年04月07日 1 1
2014年04月08日 1 1
... .. ..
2014年06月24日 1 1
2014年06月25日 1 1
2014年06月26日 1 1
2014年06月27日 1 1
2014年06月30日 1 1
[62 rows x 2 columns]
In [251]: df.groupby([df.index.year, df.index.month]).nth[1:, :-1]
Out[251]: 
 a b
2014年04月01日 1 1
2014年04月02日 1 1
2014年04月03日 1 1
2014年04月04日 1 1
2014年04月07日 1 1
... .. ..
2014年06月24日 1 1
2014年06月25日 1 1
2014年06月26日 1 1
2014年06月27日 1 1
2014年06月30日 1 1
[65 rows x 2 columns]

Enumerate group items#

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

In [252]: dfg = pd.DataFrame(list("aaabba"), columns=["A"])
In [253]: dfg
Out[253]: 
 A
0 a
1 a
2 a
3 b
4 b
5 a
In [254]: dfg.groupby("A").cumcount()
Out[254]: 
0 0
1 1
2 2
3 0
4 1
5 3
dtype: int64
In [255]: dfg.groupby("A").cumcount(ascending=False)
Out[255]: 
0 3
1 2
2 1
3 1
4 0
5 0
dtype: int64

Enumerate groups#

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

In [256]: dfg = pd.DataFrame(list("aaabba"), columns=["A"])
In [257]: dfg
Out[257]: 
 A
0 a
1 a
2 a
3 b
4 b
5 a
In [258]: dfg.groupby("A").ngroup()
Out[258]: 
0 0
1 0
2 0
3 1
4 1
5 0
dtype: int64
In [259]: dfg.groupby("A").ngroup(ascending=False)
Out[259]: 
0 1
1 1
2 1
3 0
4 0
5 1
dtype: int64

Plotting#

Groupby also works with some plotting methods. In this case, suppose we suspect that the values in column 1 are 3 times higher on average in group "B".

In [260]: np.random.seed(1234)
In [261]: df = pd.DataFrame(np.random.randn(50, 2))
In [262]: df["g"] = np.random.choice(["A", "B"], size=50)
In [263]: df.loc[df["g"] == "B", 1] += 3

We can easily visualize this with a boxplot:

In [264]: df.groupby("g").boxplot()
Out[264]: 
A Axes(0.1,0.15;0.363636x0.75)
B Axes(0.536364,0.15;0.363636x0.75)
dtype: object

../_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 for more.

Warning

For historical reasons, df.groupby("g").boxplot() is not equivalent to df.boxplot(by="g"). See here for an explanation.

Piping function calls#

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.

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:

In [265]: n = 1000
In [266]: 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 [267]: df.head(2)
Out[267]: 
 Store Product Revenue Quantity
0 Store_2 Product_1 26.12 1
1 Store_2 Product_1 28.86 1

We now find the prices per store/product.

In [268]: (
 .....:  df.groupby(["Store", "Product"])
 .....:  .pipe(lambda grp: grp.Revenue.sum() / grp.Quantity.sum())
 .....:  .unstack()
 .....:  .round(2)
 .....: )
 .....: 
Out[268]: 
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:

In [269]: defmean(groupby):
 .....:  return groupby.mean()
 .....: 
In [270]: df.groupby(["Store", "Product"]).pipe(mean)
Out[270]: 
 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

Here 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#

Multi-column factorization#

By using DataFrameGroupBy.ngroup(), we can extract information about the groups in a way similar to factorize() (as described further in the reshaping API) 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 and the API documentation.)

In [271]: dfg = pd.DataFrame({"A": [1, 1, 2, 3, 2], "B": list("aaaba")})
In [272]: dfg
Out[272]: 
 A B
0 1 a
1 1 a
2 2 a
3 3 b
4 2 a
In [273]: dfg.groupby(["A", "B"]).ngroup()
Out[273]: 
0 0
1 0
2 1
3 2
4 1
dtype: int64
In [274]: dfg.groupby(["A", [0, 0, 0, 1, 1]]).ngroup()
Out[274]: 
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 for resample to work on indices that are non-datetimelike, the following procedure can be utilized.

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

Note

The example below shows how we can downsample by consolidation of samples into fewer ones. 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.

In [275]: df = pd.DataFrame(np.random.randn(10, 2))
In [276]: df
Out[276]: 
 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 [277]: df.index // 5
Out[277]: Index([0, 0, 0, 0, 0, 1, 1, 1, 1, 1], dtype='int64')
In [278]: df.groupby(df.index // 5).std()
Out[278]: 
 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:

In [279]: 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 [280]: defcompute_metrics(x):
 .....:  result = {"b_sum": x["b"].sum(), "c_mean": x["c"].mean()}
 .....:  return pd.Series(result, name="metrics")
 .....: 
In [281]: result = df.groupby("a").apply(compute_metrics, include_groups=False)
In [282]: result
Out[282]: 
metrics b_sum c_mean
a 
0 2.0 0.5
1 2.0 0.5
2 2.0 0.5
In [283]: result.stack(future_stack=True)
Out[283]: 
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

Group by: split-apply-combine#

Splitting an object into groups#

GroupBy sorting#

GroupBy dropna#

GroupBy object attributes#

GroupBy with MultiIndex#

Grouping DataFrame with Index levels and columns#

DataFrame column selection in GroupBy#

Iterating through groups#

Selecting a group#

Aggregation#

Built-in aggregation methods#

The aggregate() method#

Aggregation with User-Defined Functions#

Applying multiple functions at once#

Named aggregation#

Applying different functions to DataFrame columns#

Transformation#

Built-in transformation methods#

The transform() method#

Window and resample operations#

Filtration#

Built-in filtrations#

The filter method#

Flexible apply#

Control grouped column(s) placement with group_keys#

Numba Accelerated Routines#

Other useful features#

Exclusion of non-numeric columns#

Handling of (un)observed Categorical values#

NA group handling#

Grouping with ordered factors#

Grouping with a grouper specification#

Taking the first rows of each group#

Taking the nth row of each group#

Enumerate group items#

Enumerate groups#

Plotting#

Piping function calls#

Examples#

Multi-column factorization#

Groupby by indexer to ‘resample’ data#

Returning a Series to propagate names#

The `aggregate()` method#

The `transform()` method#

The `filter` method#

Flexible `apply`#

Control grouped column(s) placement with `group_keys`#