pandas.core.groupby.GroupBy.apply¶
- GroupBy.apply(func, *args, **kwargs)[source]¶
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
- funccallable
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.
- args, kwargstuple and dict
Optional positional and keyword arguments to pass to func.
- Returns
- appliedSeries or DataFrame
See also
pipeApply function to the full GroupBy object instead of to each group.
aggregateApply aggregate function to the GroupBy object.
transformApply function column-by-column to the GroupBy object.
Series.applyApply a function to a Series.
DataFrame.applyApply a function to each row or column of a DataFrame.