numpy.clip#

numpy.clip(a, a_min=<no value>, a_max=<no value>, out=None, *, min=<no value>, max=<no value>, **kwargs)[source]#

Clip (limit) the values in an array.

Given an interval, values outside the interval are clipped to the interval edges. For example, if an interval of [0, 1] is specified, values smaller than 0 become 0, and values larger than 1 become 1.

Equivalent to but faster than np.minimum(a_max, np.maximum(a, a_min)).

No check is performed to ensure a_min < a_max.

Parameters:

aarray_like: Array containing elements to clip.
a_min, a_maxarray_like or None: Minimum and maximum value. If None, clipping is not performed on the corresponding edge. If both a_min and a_max are None, the elements of the returned array stay the same. Both are broadcasted against a.
outndarray, optional: The results will be placed in this array. It may be the input array for in-place clipping. out must be of the right shape to hold the output. Its type is preserved.
min, maxarray_like or None: Array API compatible alternatives for a_min and a_max arguments. Either a_min and a_max or min and max can be passed at the same time. Default: None.

New in version 2.1.0.
**kwargs: For other keyword-only arguments, see the ufunc docs.

Returns:

clipped_arrayndarray: An array with the elements of a, but where values < a_min are replaced with a_min, and those > a_max with a_max.

See also

Output type determination

Notes

When a_min is greater than a_max, clip returns an array in which all values are equal to a_max, as shown in the second example.

Examples

>>> import numpy as np
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.clip(a, 1, 8)
array([1, 1, 2, 3, 4, 5, 6, 7, 8, 8])
>>> np.clip(a, 8, 1)
array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1])
>>> np.clip(a, 3, 6, out=a)
array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
>>> a
array([3, 3, 3, 3, 4, 5, 6, 6, 6, 6])
>>> a = np.arange(10)
>>> a
array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> np.clip(a, [3, 4, 1, 1, 1, 4, 4, 4, 4, 4], 8)
array([3, 4, 2, 3, 4, 5, 6, 7, 8, 8])