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How to skip every Nth index of NumPy array ?
  • Last Updated : 21 Apr, 2021

In this article, we will see how to skip every Nth index of the NumPy array. There are various ways to access and skip elements of a NumPy array : 

Method 1: Naive Approach

A counter can be maintained to keep a count of the elements traversed so far, and then as soon as the Nth position is encountered, the element is skipped and the counter is reset to 0. All the elements are appended to a new list excluding the Nth index element encountered while traversal. The time required during this is equivalent to O(n), where n is the size of the numpy array. In case the elements need to be just printed and not stored, we can skip the declaration of creation of another array.

Example:

Python3






# importing required packages
import numpy as np
  
# declaring a numpy array
x = np.array([1.2, 3.0, 6.7, 8.7, 8.2,
              1.3, 4.5, 6.5, 1.2, 3.0,
              6.7, 8.7, 8.2, 1.3, 4.5
              6.5])
  
# skipping every 4th element
n = 4
  
# declaring new list
new_arr = []
  
# maintaining cntr
cntr = 0
  
# looping over array
for i in x:
    
    # checking if element is nth pos
    if(cntr % n != 0):
        new_arr.append(i)
          
    # incrementing counter
    cntr += 1
  
print("Array after skipping nth element")
print(new_arr)


Output:

Array after skipping nth element

[3.0, 6.7, 8.7, 1.3, 4.5, 6.5, 3.0, 6.7, 8.7, 1.3, 4.5, 6.5]

Method 2: Using NumPy modulus method

The array can first be arranged into chunks of evenly spaced intervals, using the numpy.arange() method.

Syntax: np.arange(start,stop,step)

Parameter:

  • start: Start of the interval
  • stop: End of the interval
  • step: Steps between the start and end interval

Then, the np.mod() method is applied over the list’s intervals obtained and each element’s modulo is then computed with the nth index. The elements of the original array whose modulo output is not 0, are returned as the final list. 



Example

Python3




# importing required packages
import numpy as np
  
# declaring a numpy array
x = np.array([0, 1, 2, 3, 2, 5, 2, 7, 2, 9])
  
print("Original Array")
print(x)
  
# skipping third element
new_arr = x[np.mod(np.arange(x.size), 3) != 0]
  
print("Array after skipping elements : ")
print(new_arr)


Output:

Original Array

[0 1 2 3 2 5 2 7 2 9]

Array after skipping elements :  

[1 2 2 5 7 2]

Method 3: NumPy Slicing 

NumPy slicing is basically data subsampling where we create a view of the original data, which incurs constant time. The changes are made to the original array and the entire original array is kept in memory. A copy of the data can also be made explicitly. 

Syntax:



arr[start:end:st]

Here, where the start is the starting index, the end is the stopping index, and st is the step, where the step is not equivalent to 0. And, it returns a sub-array that contains the elements belonging to the st index respectively. The indexes of the array are assumed to be starting at 0. 

Example

Python




# importing required packages
import numpy as np
  
# declaring a numpy array
x = np.array([0, 1, 2, 3, 2, 5, 2, 7, 2, 9])
  
# calculating length of array
length = len(x)
  
# accessing every third element 
# from the array
print("List after n=3rd element access")
print(x[0:length:3])


Output:

List after n=3rd element access

[0 3 2 9] 

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