Beautiful Soup using with lambda expressions

This recipe explains the use of lambda expressions in the beautiful soup as the external attributes and how to pass lambda expressions inside the functions.

Recipe Objective - Beautiful Soup with lambda expressions?

lambda expression:- A lambda function is a small incognito function. A lambda function can take any number of arguments, but can only have one expression. 

We can pass the "lambda" expression inside the find() and find_all() function to give the extra detail of a given tag or element.

Explore Fascinating Image Processing Project Ideas With Source Code

Steps:-

  1. Import necessary modules.
  2. Load an HTML document.
  3. Pass the HTML document into the Beautifulsoup() function.
  4. Now "find/find_all()" function and use lambda expression inside the function. e.g :- find_all(lambda tag: len(tag.attrs)==5)

For more related projects:-

https://www.projectpro.io/projects/data-science-projects/data-science-projects-in-python
https://www.projectpro.io/projects/data-science-projects/machine-learning-projects-in-python

Example:-

import requests
from bs4 import BeautifulSoup as bs

# load the projectpro webpage content
r = requests.get('https://www.projectpro.io/')

# convert to beautiful soup
soup = bs(r.content)

# printing our web page
print(soup.prettify())

# beautiful soup with lambda expression:-
lmbd_tag = soup.find_all(lambda tag: len(tag.attrs)==5)
lmbd_tag

What Users are saying..

profile image

Ed Godalle

Director Data Analytics at EY / EY Tech
linkedin profile url

I am the Director of Data Analytics with over 10+ years of IT experience. I have a background in SQL, Python, and Big Data working with Accenture, IBM, and Infosys. I am looking to enhance my skills... Read More

Relevant Projects

AWS MLOps Project to Deploy Multiple Linear Regression Model
Build and Deploy a Multiple Linear Regression Model in Python on AWS

Linear Regression Model Project in Python for Beginners Part 1
Machine Learning Linear Regression Project in Python to build a simple linear regression model and master the fundamentals of regression for beginners.

Model Deployment on GCP using Streamlit for Resume Parsing
Perform model deployment on GCP for resume parsing model using Streamlit App.

Deep Learning Project- Real-Time Fruit Detection using YOLOv4
In this deep learning project, you will learn to build an accurate, fast, and reliable real-time fruit detection system using the YOLOv4 object detection model for robotic harvesting platforms.

Census Income Data Set Project-Predict Adult Census Income
Use the Adult Income dataset to predict whether income exceeds 50K yr based oncensus data.

Build an Image Segmentation Model using Amazon SageMaker
In this Machine Learning Project, you will learn to implement the UNet Architecture and build an Image Segmentation Model using Amazon SageMaker

Time Series Analysis with Facebook Prophet Python and Cesium
Time Series Analysis Project - Use the Facebook Prophet and Cesium Open Source Library for Time Series Forecasting in Python

Skip Gram Model Python Implementation for Word Embeddings
Skip-Gram Model word2vec Example -Learn how to implement the skip gram algorithm in NLP for word embeddings on a set of documents.

Learn Object Tracking (SOT, MOT) using OpenCV and Python
Get Started with Object Tracking using OpenCV and Python - Learn to implement Multiple Instance Learning Tracker (MIL) algorithm, Generic Object Tracking Using Regression Networks Tracker (GOTURN) algorithm, Kernelized Correlation Filters Tracker (KCF) algorithm, Tracking, Learning, Detection Tracker (TLD) algorithm for single and multiple object tracking from various video clips.

Build OCR from Scratch Python using YOLO and Tesseract
In this deep learning project, you will learn how to build your custom OCR (optical character recognition) from scratch by using Google Tesseract and YOLO to read the text from any images.