How to use the find next siblings Python function in BeautifulSoup?

This recipe explains using the find next siblings Python function in BeautifulSoup. It is used to find the succeeding sibling of a tag/element.

Beautiful Soup is a powerful tool for parsing HTML documents and extracting valuable information. In this guide, we'll delve into using the "find_next_sibling" Python function in BeautifulSoup to locate succeeding sibling elements. Let us get started!

Using the find next siblings Python function in  BeautifulSoup 

Here's the step-by-step guide on how to use the "find_next_sibling" Python function in BeautifulSoup to locate succeeding sibling elements, along with explanations for each step:

Step 1: Import Necessary Modules

To get started, you need to import the required Python modules, including "requests" for making HTTP requests and "BeautifulSoup" for parsing HTML documents. These modules will enable you to retrieve and parse web page content.

import requests

from bs4 import BeautifulSoup as bs

Step 2: Load an HTML Document

You'll need an HTML document to parse. In this example, we use the "requests" library to fetch the content of a web page. Replace the URL with the one you want to scrape.

# Load the content of the projectpro webpage

r = requests.get('https://www.projectpro.io/')

Step 3: Create a BeautifulSoup Object

Create a BeautifulSoup object by passing the content retrieved from the web page and specifying the parser you want to use. "html.parser" is a commonly used parser for HTML documents.

# Create a BeautifulSoup object

soup = bs(r.content, 'html.parser')

Step 4: Print the Formatted HTML Content

This step is optional but useful for understanding the structure of the HTML document. Printing the prettified content helps you identify the elements you want to work with.

# Print the formatted HTML content

print(soup.prettify())

Step 5: Locate a Tag with Multiple Siblings

Identify an HTML tag within the document that has multiple sibling elements. In this example, we're finding the first "link" tag.

# Find the first "link" tag

first_link = soup.find("link")

Step 6: Use find_next_sibling

Apply the ".find_next_sibling" method to the tag you found in the previous step. This method locates the next sibling element with the specified tag.

# Use find_next_sibling to find the next "link" tag

first_link.find_next_sibling("link")

By following these steps, you can effectively use the "find_next_sibling" function in BeautifulSoup to navigate and extract data from HTML documents. This technique is essential for web scraping and data analysis tasks.

Learn more about BeautifulSoup with ProjectPro!

Mastering the "find_next_sibling" function in BeautifulSoup is a valuable skill for web scraping and data extraction. It allows you to efficiently navigate HTML documents and locate succeeding sibling elements. All these skills will come handy when you start exploring practical projects related to the domain of Data science or Big Data. And if you are looking for a one stop solution for building your data science and big daa skills, consider exploring the wide range of projects available on ProjectPro. It contains a comprehensive list of projects in the two domains that will prepare you for solving real-world data-related problems across various industries.

What Users are saying..

profile image

Abhinav Agarwal

Graduate Student at Northwestern University
linkedin profile url

I come from Northwestern University, which is ranked 9th in the US. Although the high-quality academics at school taught me all the basics I needed, obtaining practical experience was a challenge.... Read More

Relevant Projects

Avocado Machine Learning Project Python for Price Prediction
In this ML Project, you will use the Avocado dataset to build a machine learning model to predict the average price of avocado which is continuous in nature based on region and varieties of avocado.

MLOps Project to Deploy Resume Parser Model on Paperspace
In this MLOps project, you will learn how to deploy a Resume Parser Streamlit Application on Paperspace Private Cloud.

Build a Graph Based Recommendation System in Python-Part 2
In this Graph Based Recommender System Project, you will build a recommender system project for eCommerce platforms and learn to use FAISS for efficient similarity search.

Build a Customer Churn Prediction Model using Decision Trees
Develop a customer churn prediction model using decision tree machine learning algorithms and data science on streaming service data.

Build CNN for Image Colorization using Deep Transfer Learning
Image Processing Project -Train a model for colorization to make grayscale images colorful using convolutional autoencoders.

Demand prediction of driver availability using multistep time series analysis
In this supervised learning machine learning project, you will predict the availability of a driver in a specific area by using multi step time series analysis.

House Price Prediction Project using Machine Learning in Python
Use the Zillow Zestimate Dataset to build a machine learning model for house price prediction.

Time Series Python Project using Greykite and Neural Prophet
In this time series project, you will forecast Walmart sales over time using the powerful, fast, and flexible time series forecasting library Greykite that helps automate time series problems.

ML Model Deployment on AWS for Customer Churn Prediction
MLOps Project-Deploy Machine Learning Model to Production Python on AWS for Customer Churn Prediction

NLP Project for Beginners on Text Processing and Classification
This Project Explains the Basic Text Preprocessing and How to Build a Classification Model in Python