dpnp.compress
- dpnp.compress(condition, a, axis=None, out=None)[source]
Return selected slices of an array along given axis.
A slice of a is returned for each index along axis where condition is
True
.For full documentation refer to
numpy.choose
.- Parameters:
condition ({array_like, dpnp.ndarray, usm_ndarray}) -- Array that selects which entries to extract. If the length of condition is less than the size of a along axis, then the output is truncated to the length of condition.
a ({dpnp.ndarray, usm_ndarray}) -- Array to extract from.
axis ({None, int}, optional) -- Axis along which to extract slices. If
None
, works over the flattened array. Default:None
.out ({None, dpnp.ndarray, usm_ndarray}, optional) -- If provided, the result will be placed in this array. It should be of the appropriate shape and dtype. Default:
None
.
- Returns:
out -- A copy of the slices of a where condition is
True
.- Return type:
dpnp.ndarray
See also
dpnp.take
Take elements from an array along an axis.
dpnp.choose
Construct an array from an index array and a set of arrays to choose from.
dpnp.diag
Extract a diagonal or construct a diagonal array.
dpnp.diagonal
Return specified diagonals.
dpnp.select
Return an array drawn from elements in choicelist, depending on conditions.
dpnp.ndarray.compress
Equivalent method.
dpnp.extract
Equivalent function when working on 1-D arrays.
Examples
>>> import numpy as np >>> a = np.array([[1, 2], [3, 4], [5, 6]]) >>> a array([[1, 2], [3, 4], [5, 6]]) >>> np.compress([0, 1], a, axis=0) array([[3, 4]]) >>> np.compress([False, True, True], a, axis=0) array([[3, 4], [5, 6]]) >>> np.compress([False, True], a, axis=1) array([[2], [4], [6]])
Working on the flattened array does not return slices along an axis but selects elements.
>>> np.compress([False, True], a) array([2])