SeriesGroupBy.apply¶
Apply a function group-wise and combine results together.
The function must take a Series as the first argument and return a scalar, DataFrame or Series. Bodo DataFrames will fallback to pandas.core.groupby.SeriesGroupBy.apply if the function returns a dataFrame or Series.
Parameters
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__func : callable*__: A callable that takes a Series as its first argument. Bodo DataFrames supports scalar return types. If *func returns a Series or DataFrame, a fallback to pandas.core.groupby.SeriesGroupBy.apply will be triggered.
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__include_groups : bool, default False*__ Whether to include grouping keys in the input to *func. Note that the default value differs from Pandas. Bodo DataFrames only supports the value False, if include_groups, a fallback to pandas.core.groupby.SeriesGroupBy.apply will be triggered.
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args, kwargs Positional and keyword arguments to pass to func. Passing arguments to func is not supported and will trigger a fallback to pandas.core.groupby.SeriesGroupBy.apply.
Returns
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BodoSeries or pandas.DataFrame depending on the value of as_index.
Example
import bodo.pandas as bd
df = bd.DataFrame({'A': 'a a b'.split(),
'B': [1, 2, 3],
'C': [4, 6, 5]})
apply_res = df.groupby('A')['B'].apply(lambda x: x.min() - x.max())
print(apply_res)
Output: