NumPy array indexing in python

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by shivanisanju03 dot icon Updated: May 9, 2023

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Indexing an array or indexing is accessing an array element. You can access an array element by guiding it to its index number. Indices in the NumPy array start with the first index value 0, meaning the first element has an index of 0. Then the second has index number 1. We can use comma-separated integers representing the element's dimension and index. It helps access elements from two-dimensional arrays. Another way to access elements from the NumPy array is using NumPy array slicing. Let's consider a two-dimensional array as a table with rows and columns. It is where a dimension represents a row, and an index represents a column. We can access the contents of a ndarray object and modify it by indexing or partitioning. Like Python's built-in container objects, the entries will follow a zero-based index. There are three indexing methods: array access, basic slice, and advanced indexing. We can index the ndarrays using the standard Python syntax x[obj], where x is an array and obj is a selection.  

Accessing items works more efficiently with a single square bracket. We can do it by accessing items in NumPy 2D arrays by accessing the 0th and 1st indexes. We can get access to the second item of the first array. We can traverse dimensions by passing a single index element containing two entries. This method is more efficient - the method works without first building a new temporary array. We can even combine it with negative indexing.  

The slicing operation creates a view of the original array, a method array data access. Standard sequential slicing rules apply to basic dimension-based slicing. We can use the Section objects in construction instead of writing. The basic slice syntax is where i is the start index, j is the end index, and k is the step. We can create a NumPy array from your list and then perform NumPy-style Boolean indexing on it:  

import NumPy as np x = ['a', 'b', 'c']  

mask = np.array ( [True, False, True] ) x_arr = np.asarray (x, dtype=object)  

output = x_arr [mask]  

# Get items x_arr [mask] = ['new', 'values']  

# Set items  

In this kit, we will see how to implement array indexing in Python using NumPy. We can perform indexing in different ways using the NumPy package of Python. We can perform the index array indexing using an array as an index indexing.  

The indexes in the NumPy array will start with zero. The element has an index of 0, and the element has an index of 1 and likewise.  

In the case of NumPy array slicing, we can return a view or shallow array copy. In the index, we can return the array copy of the original.  

Please check the code below to learn how to implement array indexing in Python using NumPy.  

Fig: Preview of the output that you will get on running this code from your IDE


In this solution we are using NumPy library


Follow the steps carefully to get the output easily.

  1. Install NumPy on your IDE(Any of your favorite IDE).
  2. Copy the snippet using the 'copy' and paste it in your IDE.
  3. Add print statement at end of the code: 'print(out)' (refer preview of the output).
  4. Run the file to generate the output.

I hope you found this useful. I have added the link to dependent library, version information in the following sections.

I found this code snippet by searching for 'indexing array with array on numpy' in kandi. You can try any such use case!

Environment tested

I tested this solution in the following versions. Be mindful of changes when working with other versions.

  1. The solution is created in PyCharm 2022.3.3 (Community Edition)
  2. The solution is tested on Python 3.11.1.
  3. NumPy version-1.24.1

Using this solution, we are able to understandhow to implement array indexing in python using NumPy with simple steps. This process also facilities an easy way to use, hassle-free method to create a hands-on working version of code which would help us how to implement array indexing in python using NumPy.

Dependent Libraries

numpyby numpy

Python doticonstar image 23755 doticonVersion:v1.25.0rc1doticon
License: Permissive (BSD-3-Clause)

The fundamental package for scientific computing with Python.


            numpyby numpy

            Python doticon star image 23755 doticonVersion:v1.25.0rc1doticon License: Permissive (BSD-3-Clause)

            The fundamental package for scientific computing with Python.

                      You can also search for any dependent libraries on kandi like 'numpy'


                      1. What is array indexing? How does it relate to Python's basic concept?  

                      Python arrays are variables that consist of more than one element. We use the array indexing method to access specific elements from an array. Indexing means searching for elements using an element index to retrieve information.


                      2. What is integer array indexing?  

                      Integer array indexing allows arbitrary item selection depending on their N-dimensional index. It works with individual indices or slices: If the index values are out of bounds, we can raise an Index Error. Each integer field represents a certain number of indexes in each dimension. We can allow the negative values in index fields.  

                      3. How to create a ndarray in Python?  

                      To create a ndarray, you can pass a list, tuple, or array-like object to the array () method. We can convert it to a ndarray: Example. Create a NumPy array using a tuple: import numpy as np.arr = np.array ((6, 7, 8, 9, 0)) print(arr)  

                      4. How to get the index of the largest value in a numpy array?  

                      To get the index of the largest value, translate the multidimensional array numpy. It helps {value:index} list and then sorts the list by value to get the index.  

                      5. What is an N-dimensional array in NumPy?  

                      An N-Dimensional array in NumPy Array is an array of elements indexed by a tuple of positive integers. The array shape is the array of integers specifying the array size with each dimension. In Numpy, the array rank is the number of dimensions of an array. 



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