maxframe.dataframe.Series.groupby#

Series.groupby(by=None, level=None, as_index=True, sort=True, group_keys=True, dropna=True)#

Group DataFrame using a mapper or by a Series of 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.

Parameters:

  • by (mapping, function, label, or list of labels) – Used to determine the groups for the groupby. If by is a function, it’s called on each value of the object’s index. If a dict or Series is passed, the Series or dict VALUES will be used to determine the groups (the Series’ values are first aligned; see .align() method). If an ndarray is passed, the values are used as-is to determine the groups. A label or list of labels may be passed to group by the columns in self. Notice that a tuple is interpreted as a (single) key.

  • as_index (bool, default True) – 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.

  • sort (bool, default True) – Sort group keys. Get better performance by turning this off. Note this does not influence the order of observations within each group. Groupby preserves the order of rows within each group.

  • group_keys (bool) – When calling apply, add group keys to index to identify pieces.

  • dropna (bool, default True) – If True (default), NA values in the grouping columns are excluded from the groups. If False, NA values are treated as a valid group.

Notes

MaxFrame only supports groupby with axis=0. Default value of group_keys will be decided given the version of local pandas library, which is True since pandas 2.0.

Returns:

Returns a groupby object that contains information about the groups.

Return type:

DataFrameGroupBy

See also

resample

Convenience method for frequency conversion and resampling of time series.

Examples

>>> import maxframe.dataframe as md
>>> df = md.DataFrame({'Animal': ['Falcon', 'Falcon',
...                               'Parrot', 'Parrot'],
...                    'Max Speed': [380., 370., 24., 26.]})
>>> df.execute()
   Animal  Max Speed
0  Falcon      380.0
1  Falcon      370.0
2  Parrot       24.0
3  Parrot       26.0
>>> df.groupby(['Animal']).mean().execute()
        Max Speed
Animal
Falcon      375.0
Parrot       25.0