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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 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.
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:
SELECT Column1, Column2, mean(Column3), sum(Column4)
FROM SomeTable
GROUP BY Column1, Column2
We aim to make operations like this natural and easy to express using
pandas. We’ll address each area of GroupBy functionality, then provide some
non-trivial examples / use cases.
See the cookbook for some advanced strategies.
Splitting an object into groups#
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:
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"])
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
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]: def get_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.
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]:
In [24]: df2.groupby(["X"], sort=False).sum()
Out[24]:
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]:
0 A 1
2 A 3
In [27]: df3.groupby(["X"]).get_group(("B",))
Out[27]:
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]:
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]:
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
2000-01-01 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 [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]:
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]:
second A
one 1 2
two 1 4
Index levels may also be specified by name.
In [65]: df.groupby([pd.Grouper(level="second"), "A"]).sum()
Out[65]:
second A
one 1 2
two 1 4
Index level names may be specified as keys directly to groupby.
In [66]: df.groupby(["second", "A"]).sum()
Out[66]:
second A
one 1 2
two 1 4
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 0x7ff2cef1c730>
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
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)
....:
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
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
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
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.
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]:
0 foo 1
1 foo 2
2 foo 2
3 bar 1
4 bar 1
In [91]: df4.groupby("A")["B"].nunique()
Out[91]:
bar 1
foo 2
Name: B, dtype: int64
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#
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
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.
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
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
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
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
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
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
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
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
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>
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
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
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
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
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
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
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.
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.
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.
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]:
2000-01-08 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 [133]: ts.tail()
Out[133]:
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 [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: