Series.
groupby
Group DataFrame or Series using one or more columns.
A groupby operation involves some combination of splitting the object, applying a function, and combining the results. This can be used to group large amounts of data and compute operations on these groups.
Used to determine the groups for the groupby. If Series is passed, the Series or dict VALUES will be used to determine the groups. A label or list of labels may be passed to group by the columns in self.
self
Can only be set to 0 now.
For aggregated output, return object with group labels as the index. Only relevant for DataFrame input. as_index=False is effectively “SQL-style” grouped output.
If True, and if group keys contain NA values, NA values together with row/column will be dropped. If False, NA values will also be treated as the key in groups.
Depends on the calling object and returns groupby object that contains information about the groups.
See also
pyspark.pandas.groupby.GroupBy
Examples
>>> df = ps.DataFrame({'Animal': ['Falcon', 'Falcon', ... 'Parrot', 'Parrot'], ... 'Max Speed': [380., 370., 24., 26.]}, ... columns=['Animal', 'Max Speed']) >>> df Animal Max Speed 0 Falcon 380.0 1 Falcon 370.0 2 Parrot 24.0 3 Parrot 26.0
>>> df.groupby(['Animal']).mean().sort_index() Max Speed Animal Falcon 375.0 Parrot 25.0
>>> df.groupby(['Animal'], as_index=False).mean().sort_values('Animal') ... Animal Max Speed ...Falcon 375.0 ...Parrot 25.0
We can also choose to include NA in group keys or not by setting dropna parameter, the default setting is True:
>>> l = [[1, 2, 3], [1, None, 4], [2, 1, 3], [1, 2, 2]] >>> df = ps.DataFrame(l, columns=["a", "b", "c"]) >>> df.groupby(by=["b"]).sum().sort_index() a c b 1.0 2 3 2.0 2 5
>>> df.groupby(by=["b"], dropna=False).sum().sort_index() a c b 1.0 2 3 2.0 2 5 NaN 1 4