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# numpy.clip () in Python

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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.
-" 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.
-" If None, clipping is not performed on upper interval edge. Not more than one of a_min and a_max may be None.
-" 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] `