At the specified interval, values ​​outside the interval are trimmed to the edges of the interval. For example, if an interval of [0, 1] is specified, values ​​less than 0 become 0, and values ​​greater than 1 become 1.

Syntax: numpy.clip (a, a_min , a_max, out = None)

Parameters:
a: Array containing elements to clip.
a_min : Minimum value.
 – & gt; If None, clipping is not performed on lower interval edge. Not more than one of a_min and a_max may be None.
a_max: Maximum value.
 – & gt; If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None.
 – & gt; If a_min or a_max are array_like, then the three arrays will be broadcasted to match their shapes.
out: 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.

Return: clipped_array

Code # 1:

# Python3 code demonstrates the clip () function   # importing NumPy import numpy as np   in_array = [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 ] print ( "Input array:" , in_array)   out_array = np.clip (in_array, a_min = 2 , a_max = 6 ) print ( " Output array: " , out_array)

Output:

 Input array: [1, 2, 3, 4, 5, 6, 7, 8] Output array: [2 2 3 4 5 6 6 6] 

Code # 2:

# Python3 code demonstrates the clip () function   # NumPy import import numpy as np   in_array = [ 1 , 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 ] print ( "Input array:" , in_array)   out_array = np.clip (in_array, a_min = [ 3 , 4 , 1 , 1 , 1 , 4 , 4 , 4 , 4 , 4 ],   a_max = 9 ) print ( "Output array:" , out_array)

Output:

 Input array: [1, 2, 3, 4, 5, 6, 7, 8, 9 , 10] Output array: [3 4 3 4 5 6 7 8 9 9]