maxframe.dataframe.groupby.GroupBy.apply#

GroupBy.apply(func, *args, output_type=None, dtypes=None, dtype=None, name=None, index=None, skip_infer=None, **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 infer dtypes when dtypes or output_type is not specified.

  • 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.