numpy.take

numpy.take(a, indices, axis=None, out=None, mode='raise')

Take elements from an array along an axis.

This function does the same thing as “fancy” indexing (indexing arrays using arrays); however, it can be easier to use if you need elements along a given axis.

Parameters:

a : array_like

The source array.

indices : array_like

The indices of the values to extract.

New in version 1.8.0.

Also allow scalars for indices.

axis : int, optional

The axis over which to select values. By default, the flattened input array is used.

out : ndarray, optional

If provided, the result will be placed in this array. It should be of the appropriate shape and dtype.

mode : {‘raise’, ‘wrap’, ‘clip’}, optional

Specifies how out-of-bounds indices will behave.

• ‘raise’ – raise an error (default)
• ‘wrap’ – wrap around
• ‘clip’ – clip to the range

‘clip’ mode means that all indices that are too large are replaced by the index that addresses the last element along that axis. Note that this disables indexing with negative numbers.

Returns:

subarray : ndarray

The returned array has the same type as a.

See also

compress

Take elements using a boolean mask

ndarray.take

equivalent method

Examples

>>> a = [4, 3, 5, 7, 6, 8]
>>> indices = [0, 1, 4]
>>> np.take(a, indices)
array([4, 3, 6])

In this example if a is an ndarray, “fancy” indexing can be used.

>>> a = np.array(a)
>>> a[indices]
array([4, 3, 6])

If indices is not one dimensional, the output also has these dimensions.

>>> np.take(a, [[0, 1], [2, 3]])
array([[4, 3],
       [5, 7]])