numpy.clip() in Python

numpy.clip() in Python

Last Updated : 29 Nov, 2018

numpy.clip() function is used to 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.

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

# Python3 code demonstrate clip() function

# importing the 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

# Python3 code demonstrate clip() function

# importing the numpy
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]

numpy.clip() in Python

jana_sayantan

Improve

Article Tags :

Practice Tags :

python

Similar Reads

numpy.floor() in Python

The numpy.floor() function returns the largest integer less than or equal to each element in the input array. It effectively rounds numbers down to the nearest whole number. Let's understand with an example:Pythonimport numpy as np a = [0.5, 1.5, 2.5, 3, 4.5, 10.1] res = np.floor(a) print("Floored:"

numpy.compress() in Python

The numpy.compress() function returns selected slices of an array along mentioned axis, that satisfies an axis. Syntax: numpy.compress(condition, array, axis = None, out = None) Parameters : condition : [array_like]Condition on the basis of which user extract elements. Applying condition on input_ar

numpy.any() in Python

The numpy.any() function tests whether any array elements along the mentioned axis evaluate to True. Syntax :Â numpy.any(a, axis = None, out = None, keepdims = class numpy._globals._NoValue at 0x40ba726c) Parameters :Â array :[array_like]Input array or object whose elements, we need to test. axis :

numpy.argmin() in Python

The numpy.argmin() method returns indices of the min element of the array in a particular axis. Syntax : numpy.argmin(array, axis = None, out = None) Parameters : array : Input array to work on axis : [int, optional]Along a specified axis like 0 or 1 out : [array optional]Provides a feature to inser

numpy.put() in Python

The numpy.put() function replaces specific elements of an array with given values of p_array. Array indexed works on flattened array. Syntax: numpy.put(array, indices, p_array, mode = 'raise') Parameters : array : array_like, target array indices : index of the values to be fetched p_array : array_l

numpy.take() in Python

The numpy.take() function returns elements from array along the mentioned axis and indices. Syntax: numpy.take(array, indices, axis = None, out = None, mode ='raise') Parameters : array : array_like, input array indices : index of the values to be fetched axis : [int, optional] axis over which we ne

Numpy is a general-purpose array-processing package. It provides a high-performance multidimensional array object, and tools for working with these arrays. It is the fundamental package for scientific computing with Python.Besides its obvious scientific uses, Numpy can also be used as an efficient m

Numpy recarray.clip() function | Python

In numpy, arrays may have a data-types containing fields, analogous to columns in a spreadsheet. An example is [(a, int), (b, float)], where each entry in the array is a pair of (int, float). Normally, these attributes are accessed using dictionary lookups such as arr['a'] and arr['b']. Record array

numpy.where() in Python

We will explore the basics of numpy.where(), how it works, and practical use cases to illustrate its importance in data manipulation and analysis.Syntax of numpy.where()Syntax :numpy.where(condition[, x, y]) Parameters condition: A condition that tests elements of the array.x (optional): Values from

numpy.squeeze() in Python

The numpy.squeeze() is a useful Python function, which is utilized for the removal of single-dimensional elements from the shape of a NumPy array. It comes in very handy when you have to discard redundant dimensions (like a dimension with size 1) after operations that introduce extra dimensions.Basi