pandas.Series.take#
- Series.take(indices, axis=0, is_copy=None, **kwargs)[source]#
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
- indicesarray-like
An array of ints indicating which positions to take.
- axis{0 or ‘index’, 1 or ‘columns’, None}, default 0
The axis on which to select elements.
0
means that we are selecting rows,1
means that we are selecting columns. For Series this parameter is unused and defaults to 0.- is_copybool
Before pandas 1.0,
is_copy=False
can be specified to ensure that the return value is an actual copy. Starting with pandas 1.0,take
always returns a copy, and the keyword is therefore deprecated.Deprecated since version 1.0.0.
- **kwargs
For compatibility with
numpy.take()
. Has no effect on the output.
- Returns
- takensame type as caller
An array-like containing the elements taken from the object.
See also
DataFrame.loc
Select a subset of a DataFrame by labels.
DataFrame.iloc
Select a subset of a DataFrame by positions.
numpy.take
Take elements from an array along an axis.
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]) name class max_speed 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