maxframe.dataframe.groupby.GroupBy.apply#

GroupBy.apply(func, *args, output_type=None, dtypes=None, dtype=None, name=None, index=None, skip_infer=None, prepend_index_group_keys=False, **kwargs)#

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

The function passed to apply must take a dataframe as its first argument and return a DataFrame, Series or scalar. apply will then take care of combining the results back together into a single dataframe or series. apply is therefore a highly flexible grouping method.

While apply is a very flexible method, its downside is that using it can be quite a bit slower than using more specific methods like agg or transform. Pandas offers a wide range of method that will be much faster than using apply for their specific purposes, so try to use them before reaching for apply.

Parameters:

  • func (callable) – A callable that takes a dataframe as its first argument, and returns a dataframe, a series or a scalar. In addition, the callable may take positional and keyword arguments.

  • output_type ({'dataframe', 'series'}, default None) – Specify type of returned object. See Notes for more details.

  • dtypes (Series, default None) – Specify dtypes of returned DataFrames. See Notes for more details.

  • dtype (numpy.dtype, default None) – Specify dtype of returned Series. See Notes for more details.

  • name (str, default None) – Specify name of returned Series. See Notes for more details.

  • index (Index, default None) – Specify index of returned object. See Notes for more details.

  • skip_infer (bool, default False) – Whether to infer dtypes when dtypes or output_type is not specified.

  • prepend_index_group_keys (bool, default False) –

    If True, the index of returned dataframe or series will automatically contain group keys if as_index=True, or group indexes if as_index=False, when group_keys=True.

    Note

    prepend_index_group_keys will be set to True by default in future releases, and a warning will be shown if the parameter is set to False. To make sure your code works in future releases, please set this to True and remove group indexes in index parameter or type annotation of func.

  • args (tuple and dict) – Optional positional and keyword arguments to pass to func.

  • kwargs (tuple and dict) – Optional positional and keyword arguments to pass to func.

Returns:

applied

Return type:

Series or DataFrame

See also

pipe

Apply function to the full GroupBy object instead of to each group.

aggregate

Apply aggregate function to the GroupBy object.

transform

Apply function column-by-column to the GroupBy object.

Series.apply

Apply a function to a Series.

DataFrame.apply

Apply a function to each row or column of a DataFrame.

Notes

When deciding output dtypes and shape of the return value, MaxFrame will try applying func onto a mock grouped object, and the apply call may fail. When this happens, you need to specify the type of apply call (DataFrame or Series) in output_type.

  • For DataFrame output, you need to specify a list or a pandas Series as dtypes of output DataFrame. index of output can also be specified.

  • For Series output, you need to specify dtype and name of output Series.