pandas.core.groupby.SeriesGroupBy.aggregate#

SeriesGroupBy.aggregate(func=None, *args, engine=None, engine_kwargs=None, **kwargs)[source]#

Aggregate using one or more operations.

The aggregate method enables flexible and efficient aggregation of grouped data using a variety of functions, including built-in, user-defined, and optimized JIT-compiled functions.

Parameters:
funcfunction, str, list, dict or None

Function to use for aggregating the data. If a function, must either work when passed a Series or when passed to Series.apply.

Accepted combinations are:

  • function

  • string function name

  • list of functions and/or function names, e.g. [np.sum, 'mean']

  • None, in which case **kwargs are used with Named Aggregation. Here the output has one column for each element in **kwargs. The name of the column is keyword, whereas the value determines the aggregation used to compute the values in the column.

    Can also accept a Numba JIT function with engine='numba' specified. Only passing a single function is supported with this engine.

    If the 'numba' engine is chosen, the function must be a user defined function with values and index as the first and second arguments respectively in the function signature. Each group’s index will be passed to the user defined function and optionally available for use.

Deprecated since version 2.1.0: Passing a dictionary is deprecated and will raise in a future version of pandas. Pass a list of aggregations instead.

*args

Positional arguments to pass to func.

enginestr, default None

  • 'cython' : Runs the function through C-extensions from cython.

  • 'numba' : Runs the function through JIT compiled code from numba.

  • NoneDefaults to 'cython' or globally setting

    compute.use_numba

engine_kwargsdict, default None

  • For 'cython' engine, there are no accepted engine_kwargs

  • For 'numba' engine, the engine can accept nopython, nogil and parallel dictionary keys. The values must either be True or False. The default engine_kwargs for the 'numba' engine is {'nopython': True, 'nogil': False, 'parallel': False} and will be applied to the function

**kwargs

  • If func is None, **kwargs are used to define the output names and aggregations via Named Aggregation. See func entry.

  • Otherwise, keyword arguments to be passed into func.

Returns:
Series

Aggregated Series based on the grouping and the applied aggregation functions.

See also

SeriesGroupBy.apply

Apply function func group-wise and combine the results together.

SeriesGroupBy.transform

Transforms the Series on each group based on the given function.

Series.aggregate

Aggregate using one or more operations.

Notes

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.

Functions that mutate the passed object can produce unexpected behavior or errors and are not supported. See Mutating with User Defined Function (UDF) methods for more details.

Changed in version 1.3.0: The resulting dtype will reflect the return value of the passed func, see the examples below.

Examples

>>> s = pd.Series([1, 2, 3, 4])

>>> s
0    1
1    2
2    3
3    4
dtype: int64

>>> s.groupby([1, 1, 2, 2]).min()
1    1
2    3
dtype: int64

>>> s.groupby([1, 1, 2, 2]).agg("min")
1    1
2    3
dtype: int64

>>> s.groupby([1, 1, 2, 2]).agg(["min", "max"])
   min  max
1    1    2
2    3    4

The output column names can be controlled by passing the desired column names and aggregations as keyword arguments.

>>> s.groupby([1, 1, 2, 2]).agg(
...     minimum="min",
...     maximum="max",
... )
   minimum  maximum
1        1        2
2        3        4

Changed in version 1.3.0: The resulting dtype will reflect the return value of the aggregating function.

>>> s.groupby([1, 1, 2, 2]).agg(lambda x: x.astype(float).min())
1    1.0
2    3.0
dtype: float64
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