This is documentation for an old release of NumPy (version 1.19). Read this page Search for this page in the documentation of the latest stable release (version 2.2).

numpy.squeeze

numpy.squeeze(a, axis=None)

Remove single-dimensional entries from the shape of an array.

Parameters

aarray_like

Input data.

axisNone or int or tuple of ints, optional

New in version 1.7.0.

Selects a subset of the single-dimensional entries in the shape. If an axis is selected with shape entry greater than one, an error is raised.

Returns

squeezedndarray

The input array, but with all or a subset of the dimensions of length 1 removed. This is always a itself or a view into a. Note that if all axes are squeezed, the result is a 0d array and not a scalar.

Raises

ValueError

If axis is not None, and an axis being squeezed is not of length 1

See also

expand_dims

The inverse operation, adding singleton dimensions

reshape

Insert, remove, and combine dimensions, and resize existing ones

Examples

>>>

>>> x = np.array([[[0], [1], [2]]])
>>> x.shape
(1, 3, 1)
>>> np.squeeze(x).shape
(3,)
>>> np.squeeze(x, axis=0).shape
(3, 1)
>>> np.squeeze(x, axis=1).shape
Traceback (most recent call last):
...
ValueError: cannot select an axis to squeeze out which has size not equal to one
>>> np.squeeze(x, axis=2).shape
(1, 3)
>>> x = np.array([[1234]])
>>> x.shape
(1, 1)
>>> np.squeeze(x)
array(1234)  # 0d array
>>> np.squeeze(x).shape
()
>>> np.squeeze(x)[()]
1234