pandas.core.groupby.GroupBy.apply — pandas 0.24.0rc1 documentation (original) (raw)
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: | 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. args, kwargs : tuple and dict Optional positional and keyword arguments to pass to func. |
|---|---|
| Returns: | applied : Series or DataFrame |
See also
Apply function to the full GroupBy object instead of to each group.
Apply aggregate function to the GroupBy object.
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.