Skip to content
Related Articles

Related Articles

numpy.insert() in Python
  • Last Updated : 23 Oct, 2020

The numpy.insert() function inserts values along the mentioned axis before the given indices.
Syntax :

numpy.insert(array, object, values, axis = None)

Parameters :

array   : [array_like]Input array. 
object  : [int, array of ints]Sub-array with the index or indices before 
     which values is inserted
values  : [array_like]values to be added in the arr. Values should be 
     shaped so that arr[...,obj,...] = values. If the type of values is different from 
     that of arr, values is converted to the type of arr
axis    : Axis along which we want to insert the values. By default, it 
     object is applied to flattened array    

Return :

An copy of array with values being inserted as per the mentioned object along a given axis. 

Code 1 : Deletion from 1D array




# Python Program illustrating
# numpy.insert()
  
import numpy as geek
  
#Working on 1D
arr = geek.arange(5)
print("1D arr : \n", arr)
print("Shape : ", arr.shape)
  
# value = 9
# index = 1   
# Insertion before first index
a = geek.insert(arr, 1, 9)
print("\nArray after insertion : ", a)
print("Shape : ", a.shape)
  
  
# Working on 2D array 
arr = geek.arange(12).reshape(3, 4)
print("\n\n2D arr : \n", arr)
print("Shape : ", arr.shape)
  
a = geek.insert(arr, 1, 9, axis = 1)
print("\nArray after insertion : \n", a)
print("Shape : ", a.shape)


Output :



1D arr : 
 [0 1 2 3 4]
Shape :  (5,)

Array after insertion :  [0 9 1 2 3 4]
Shape :  (6,)


2D arr : 
 [[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
Shape :  (3, 4)

Array after insertion : 
 [[ 0  9  1  2  3]
 [ 4  9  5  6  7]
 [ 8  9  9 10 11]]
Shape :  (3, 5)

Code 2 : Working with Scalars




# Python Program illustrating
# numpy.insert()
  
import numpy as geek
  
# Working on 2D array 
arr = geek.arange(12).reshape(3, 4)
print("2D arr : \n", arr)
print("Shape : ", arr.shape)
  
# Working with Scalars
a = geek.insert(arr, [1], [[6],[9],], axis = 0)
print("\nArray after insertion : \n", a)
print("Shape : ", a.shape)
  
# Working with Scalars
a = geek.insert(arr, [1], [[8],[7],[9]], axis = 1)
print("\nArray after insertion : \n", a)
print("Shape : ", a.shape)


Output :

2D arr : 
 [[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
Shape :  (3, 4)

Array after insertion : 
 [[ 0  1  2  3]
 [ 6  6  6  6]
 [ 9  9  9  9]
 [ 4  5  6  7]
 [ 8  9 10 11]]
Shape :  (5, 4)

Array after insertion : 
 [[ 0  8  1  2  3]
 [ 4  7  5  6  7]
 [ 8  9  9 10 11]]
Shape :  (3, 5)

Code 3 : Insertion at different points




# Python Program illustrating
# numpy.insert()
  
import numpy as geek
  
#Working on 1D
arr = geek.arange(6).reshape(2, 3)
print("1D arr : \n", arr)
print("Shape : ", arr.shape)
  
# value = 9
# index = 1   
# Insertion before first index
a = geek.insert(arr, (2, 4), 9)
print("\nInsertion at two points : ", a)
print("Shape : ", a.shape)
  
  
# Working on 2D array 
arr = geek.arange(12).reshape(3, 4)
print("\n\n2D arr : \n", arr)
print("Shape : ", arr.shape)
a = geek.insert(arr, (0, 3), 66, axis = 1)
print("\nInsertion at two points : \n", a)
print("Shape : ", a.shape)


Output :

1D arr : 
 [[0 1 2]
 [3 4 5]]
Shape :  (2, 3)

Insertion at two points :  [0 1 9 2 3 9 4 5]
Shape :  (8,)


2D arr : 
 [[ 0  1  2  3]
 [ 4  5  6  7]
 [ 8  9 10 11]]
Shape :  (3, 4)

Insertion at two points : 
 [[66  0  1  2 66  3]
 [66  4  5  6 66  7]
 [66  8  9 10 66 11]]
Shape :  (3, 6)

References :
https://docs.scipy.org/doc/numpy/reference/generated/numpy.insert.html#numpy.insert

Note :
These codes won’t run on online-ID. Please run them on your systems to explore the working.

This article is contributed by Mohit Gupta_OMG πŸ˜€. If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.

Attention geek! Strengthen your foundations with the Python Programming Foundation Course and learn the basics.

To begin with, your interview preparations Enhance your Data Structures concepts with the Python DS Course.

My Personal Notes arrow_drop_up
Recommended Articles
Page :