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

Aggregate using one or more operations over the specified axis.

funcfunction, str, list or dict

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


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.


Keyword arguments to be passed into func.


See also


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


Aggregate using one or more operations over the specified axis.


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


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.


>>> df = pd.DataFrame(
...     {
...         "A": [1, 1, 2, 2],
...         "B": [1, 2, 3, 4],
...         "C": [0.362838, 0.227877, 1.267767, -0.562860],
...     }
... )
>>> df
   A  B         C
0  1  1  0.362838
1  1  2  0.227877
2  2  3  1.267767
3  2  4 -0.562860

The aggregation is for each column.

>>> df.groupby('A').agg('min')
   B         C
1  1  0.227877
2  3 -0.562860

Multiple aggregations

>>> df.groupby('A').agg(['min', 'max'])
    B             C
  min max       min       max
1   1   2  0.227877  0.362838
2   3   4 -0.562860  1.267767

Select a column for aggregation

>>> df.groupby('A').B.agg(['min', 'max'])
   min  max
1    1    2
2    3    4

Different aggregations per column

>>> df.groupby('A').agg({'B': ['min', 'max'], 'C': 'sum'})
    B             C
  min max       sum
1   1   2  0.590715
2   3   4  0.704907

To control the output names with different aggregations per column, pandas supports “named aggregation”

>>> df.groupby("A").agg(
...     b_min=pd.NamedAgg(column="B", aggfunc="min"),
...     c_sum=pd.NamedAgg(column="C", aggfunc="sum"))
   b_min     c_sum
1      1  0.590715
2      3  0.704907
  • 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 pandas.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.

See Named aggregation for more.

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

>>> df.groupby("A")[["B"]].agg(lambda x: x.astype(float).min())
1   1.0
2   3.0