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Introduction
Web scraping is a technique used to extract data from web pages. While there are many tools and libraries in Python for this purpose, Python's built-in HTML Parser, a part of the html.parser module, is a versatile choice. This article will guide you through the wonders of the Python HTML Parser, helping you tap into the potential of web data without the weight of external dependencies.
Python’s built-in HTML parser is part of the standard library, so there's no need to install external packages. You can start using it right away.
Key Concepts
1. HTMLParser Class:
The html.parser module offers the HTMLParser class, which allows us to create custom subclasses to handle parsed data.
2. Methods and Overriding:
By overriding the built-in methods of HTMLParser, you can define custom behavior for different parsing events.
Dive into the Code
Basic Usage
Let's begin by creating a simple subclass of HTMLParser that extracts data from HTML tags:
from html.parser import HTMLParser
class MyHTMLParser(HTMLParser):
def handle_starttag(self, tag, attrs):
print(f"Encountered a start tag: {tag}")
def handle_endtag(self, tag):
print(f"Encountered an end tag: {tag}")
def handle_data(self, data):
print(f"Encountered some data: {data}")
# instantiate the parser and feed it some HTML
parser = MyHTMLParser()
parser.feed('<html><head><title>Python Rocks</title></head></html>')
You can also try this code with Online Python Compiler
Imagine you want to fetch all headlines from a news webpage. With the HTML Parser, you can navigate through the page's structure and extract relevant details.
from html.parser import HTMLParser # Import HTMLParser from correct module
class NewsParser(HTMLParser):
def __init__(self):
super().__init__()
self.in_title_tag = False
self.headlines = []
def handle_starttag(self, tag, attrs):
if tag == 'h1':
self.in_title_tag = True
def handle_data(self, data):
if self.in_title_tag:
self.headlines.append(data)
def handle_endtag(self, tag):
if tag == 'h1':
self.in_title_tag = False
# Imagine the below string is a simplified version of a news webpage
html_content = """
<h1>Breaking: Python becomes the most popular language</h1>
<p>Some content here...</p>
<h1>Coding Ninjas is best</h1>
"""
parser = NewsParser()
parser.feed(html_content)
print(parser.headlines)
You can also try this code with Online Python Compiler
It tries its best, but for heavily malformed HTML, using libraries like Beautiful Soup or lxml with robust parsers might be better.
Is Python’s HTML parser suitable for large-scale web scraping?
While it's lightweight and easy to use, for large-scale scraping or when needing advanced functionalities, specialized libraries like Scrapy are recommended.
Can I use this parser for XML documents?
While primarily for HTML, it can parse XML to an extent. For robust XML parsing, consider using the xml module in Python's standard library.
Conclusion
Python's built-in HTML Parser is a light, easy-to-use tool for basic web scraping tasks. While not as feature-rich as some external libraries, it offers a quick, dependency-free way to extract data from web pages. Whether you're stepping into web scraping or need a simple tool for a small project, the HTML parser might just be your perfect companion. Happy parsing!
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