pandas.core.groupby.DataFrameGroupBy.aggregate¶
- DataFrameGroupBy.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 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 withvalues
andindex
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 settingcompute.use_numba
New in version 1.1.0.
- engine_kwargsdict, default None
For
'cython'
engine, there are no acceptedengine_kwargs
For
'numba'
engine, the engine can acceptnopython
,nogil
andparallel
dictionary keys. The values must either beTrue
orFalse
. The defaultengine_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
- DataFrame
See also
DataFrame.groupby.apply
Apply function func group-wise and combine the results together.
DataFrame.groupby.transform
Aggregate using one or more operations over the specified axis.
DataFrame.aggregate
Transforms the Series on each group based on the given function.
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
>>> 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 A 1 1 0.227877 2 3 -0.562860
Multiple aggregations
>>> df.groupby('A').agg(['min', 'max']) B C min max min max A 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 A 1 1 2 2 3 4
Different aggregations per column
>>> df.groupby('A').agg({'B': ['min', 'max'], 'C': 'sum'}) B C min max sum A 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 A 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()) B A 1 1.0 2 3.0