numpy.compress#

numpy.compress(condition, a, axis=None, out=None)[source]#

Return selected slices of an array along given axis.

When working along a given axis, a slice along that axis is returned in output for each index where condition evaluates to True. When working on a 1-D array, compress is equivalent to extract.

Parameters

condition1-D array of bools: Array that selects which entries to return. If len(condition) is less than the size of a along the given axis, then output is truncated to the length of the condition array.
aarray_like: Array from which to extract a part.
axisint, optional: Axis along which to take slices. If None (default), work on the flattened array.
outndarray, optional: Output array. Its type is preserved and it must be of the right shape to hold the output.

Returns

compressed_arrayndarray: A copy of a without the slices along axis for which condition is false.

See also

take, choose, diag, diagonal, select
ndarray.compress: Equivalent method in ndarray
extract: Equivalent method when working on 1-D arrays
Output type determination

Examples

>>> 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])

numpy.choose

numpy.diag