pandas.core.groupby.SeriesGroupBy.take

SeriesGroupBy.take(indices, **kwargs)

Return the elements in the given positional indices in each group.

This means that we are not indexing according to actual values in the index attribute of the object. We are indexing according to the actual position of the element in the object.

If a requested index does not exist for some group, this method will raise. To get similar behavior that ignores indices that don’t exist, see SeriesGroupBy.nth().

Parameters:

indicesarray-like

An array of ints indicating which positions to take in each group.

**kwargs

For compatibility with numpy.take(). Has no effect on the output.

Returns:

Series

A Series containing the elements taken from each group.

See also

Series.take

Take elements from a Series along an axis.

Series.loc

Select a subset of a DataFrame by labels.

Series.iloc

Select a subset of a DataFrame by positions.

numpy.take

Take elements from an array along an axis.

SeriesGroupBy.nth

Similar to take, won’t raise if indices don’t exist.

Examples

>>> df = pd.DataFrame(
...     [
...         ("falcon", "bird", 389.0),
...         ("parrot", "bird", 24.0),
...         ("lion", "mammal", 80.5),
...         ("monkey", "mammal", np.nan),
...         ("rabbit", "mammal", 15.0),
...     ],
...     columns=["name", "class", "max_speed"],
...     index=[4, 3, 2, 1, 0],
... )
>>> df
     name   class  max_speed
4  falcon    bird      389.0
3  parrot    bird       24.0
2    lion  mammal       80.5
1  monkey  mammal        NaN
0  rabbit  mammal       15.0
>>> gb = df["name"].groupby([1, 1, 2, 2, 2])

Take elements at rows 0 and 1 in each group.

>>> gb.take([0, 1])
1  4    falcon
   3    parrot
2  2      lion
   1    monkey
Name: name, dtype: object

We may take elements using negative integers for positive indices, starting from the end of the object, just like with Python lists.

>>> gb.take([-1, -2])
1  3    parrot
   4    falcon
2  0    rabbit
   1    monkey
Name: name, dtype: object