The Numpy library in Python comes with a number of useful methods and techniques to work with and manipulate data in arrays. In this tutorial, we will look at how to get the last N columns of a two-dimensional Numpy Array with the help of some examples.
How to get the last n columns of a 2D Numpy array?
You can use slicing to get the last N columns of a 2D array in Numpy. Here, we use column indices to specify the range of columns we’d like to slice. To get the last n columns, use the following slicing syntax –
# last n columns of numpy array ar[:, -n:]
It returns the array’s last n columns (including all the rows).
Steps to get the last n columns of 2D array
Let’s now look at a step-by-step example of using the above syntax on a 2D Numpy array.
Step 1 – Create a 2D Numpy array
First, we will create a 2D Numpy array that we’ll operate on.
import numpy as np # create a 2D array ar = np.array([ ['Tim', 181, 86], ['Peter', 170, 68], ['Isha', 158, 59], ['Rohan', 168, 81], ['Yuri', 171, 65], ['Emma', 166, 64], ['Michael', 175, 78], ['Jim', 190, 87], ['Pam', 168, 57], ['Dwight', 187, 84] ]) # display the array print(ar)
[['Tim' '181' '86'] ['Peter' '170' '68'] ['Isha' '158' '59'] ['Rohan' '168' '81'] ['Yuri' '171' '65'] ['Emma' '166' '64'] ['Michael' '175' '78'] ['Jim' '190' '87'] ['Pam' '168' '57'] ['Dwight' '187' '84']]
Here, we used the
numpy.array() function to create a 2D Numpy array with 10 rows and 3 columns. The array contains information on the height (in cm) and weight (in kg) of some employees in an office.
Step 2 – Slice the array to get the last n columns
To get the last n columns of the above array, slice the array starting from the nth last column up to the last column of the array. You can use negative indexing to get the index of the nth column from the end (which will be -n).
- Harvard University Data Science: Learn R Basics for Data Science
- Standford University Data Science: Introduction to Machine Learning
- UC Davis Data Science: Learn SQL Basics for Data Science
- IBM Data Science: Professional Certificate in Data Science
- IBM Data Analysis: Professional Certificate in Data Analytics
- Google Data Analysis: Professional Certificate in Data Analytics
- IBM Data Science: Professional Certificate in Python Data Science
- IBM Data Engineering Fundamentals: Python Basics for Data Science
- Harvard University Learning Python for Data Science: Introduction to Data Science with Python
- Harvard University Computer Science Courses: Using Python for Research
- IBM Python Data Science: Visualizing Data with Python
- DeepLearning.AI Data Science and Machine Learning: Deep Learning Specialization
- UC San Diego Data Science: Python for Data Science
- UC San Diego Data Science: Probability and Statistics in Data Science using Python
- Google Data Analysis: Professional Certificate in Advanced Data Analytics
- MIT Statistics and Data Science: Machine Learning with Python - from Linear Models to Deep Learning
- MIT Statistics and Data Science: MicroMasters® Program in Statistics and Data Science
🔎 Find Data Science Programs 👨💻 111,889 already enrolled
Disclaimer: Data Science Parichay is reader supported. When you purchase a course through a link on this site, we may earn a small commission at no additional cost to you. Earned commissions help support this website and its team of writers.
For example, you can use -5 as the index of the 5th column from the end.
For example, let’s get the last 2 columns from the array that we created in step 1.
# last 2 columns print(ar[:, -2:])
[['181' '86'] ['170' '68'] ['158' '59'] ['168' '81'] ['171' '65'] ['166' '64'] ['175' '78'] ['190' '87'] ['168' '57'] ['187' '84']]
We get the last 2 columns of the 2D array.
For more on slicing Numpy arrays, refer to its documentation.
In this tutorial, we looked at how to get the last n columns of a two-dimensional Numpy array using slicing. The following is a short summary of the steps mentioned in the tutorial.
- Create a 2D Numpy array (skip this step if you already have an array to operate on).
- Slice the array from the nth last column up to the last column of the array. Using a negative index can be helpful here.
You might also be interested in –
- Numpy – Get Max Value in Array
- Get the Median of Numpy Array – (With Examples)
- Pandas – Select first n rows of a DataFrame
Subscribe to our newsletter for more informative guides and tutorials.
We do not spam and you can opt out any time.