pandas.core.groupby.SeriesGroupBy.aggregate

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

Aggregate using one or more operations over the specified axis.

Parameters
funcfunction, str, list or dict

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.

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.

*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.

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

New in version 1.1.0.

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

New in version 1.1.0.

**kwargs

Keyword arguments to be passed into func.

Returns
Series

See also

Series.groupby.apply
Series.groupby.transform
Series.aggregate

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.

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