pandas.core.groupby.DataFrameGroupBy.take

DataFrameGroupBy.take

Return the elements in the given positional indices along an axis.

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.

Parameters:

indices : array-like

An array of ints indicating which positions to take.

axis : int, default 0

The axis on which to select elements. “0” means that we are selecting rows, “1” means that we are selecting columns, etc.

convert : bool, default True

Deprecated since version 0.21.0: In the future, negative indices will always be converted.

Whether to convert negative indices into positive ones. For example, -1 would map to the len(axis) - 1. The conversions are similar to the behavior of indexing a regular Python list.

is_copy : bool, default True

Whether to return a copy of the original object or not.

Returns:

taken : type of caller

An array-like containing the elements taken from the object.

See also

numpy.ndarray.take, numpy.take

Examples

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

Take elements at positions 0 and 3 along the axis 0 (default).

Note how the actual indices selected (0 and 1) do not correspond to our selected indices 0 and 3. That’s because we are selecting the 0th and 3rd rows, not rows whose indices equal 0 and 3.

>>> df.take([0, 3])
0  falcon    bird      389.0
1  monkey  mammal        NaN

Take elements at indices 1 and 2 along the axis 1 (column selection).

>>> df.take([1, 2], axis=1)
    class  max_speed
0    bird      389.0
2    bird       24.0
3  mammal       80.5
1  mammal        NaN

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

>>> df.take([-1, -2])
     name   class  max_speed
1  monkey  mammal        NaN
3    lion  mammal       80.5