xorbits.pandas.groupby.SeriesGroupBy.aggregate#

SeriesGroupBy.aggregate(func=None, method='auto', combine_size=None, *args, **kwargs)#

Aggregate using one or more operations over the specified axis.

Parameters

  • func (function, 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']

    • dict of axis labels -> functions, function names or list of such.

    • 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.

    Changed in version 1.1.0(pandas).

  • *args – Positional arguments to pass to func.

  • engine (str, default None (Not supported yet)) –

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

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

    • None : Defaults to 'cython' or globally setting compute.use_numba

    New in version 1.1.0(pandas).

  • engine_kwargs (dict, default None (Not supported yet)) –

    • 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

    New in version 1.1.0(pandas).

  • **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.

Return type

Series

See also

Series.groupby.apply

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

Series.groupby.transform

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

Series.aggregate

Aggregate using one or more operations over the specified axis.

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 gotchas.udf-mutation for more details.

Changed in version 1.3.0(pandas): 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(pandas): 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
Extra Parameters
----------------
groupby : Mars Groupby
    Groupby data.
method : {'auto', 'shuffle', 'tree'}, default 'auto'
    'tree' method provide a better performance, 'shuffle' is recommended
    if aggregated result is very large, 'auto' will use 'shuffle' method
    in distributed mode and use 'tree' in local mode.
combine_size : int
    The number of chunks to combine when method is 'tree'

This docstring was copied from pandas.core.groupby.generic.SeriesGroupBy.