Where to Find Web Scraping Libraries in awesome-python: A Complete Guide

Web scraping libraries in awesome-python are located in two distinct sections of the README.md file: the "Data" category under "Web Crawlers & Scrapers" and the "Web" category under "Scraping-Related Libraries."

The awesome-python repository by dylanhogg is a curated catalogue of Python packages organized by functional domains. If you are searching for tools to extract data from websites, the repository structures these resources across the Data and Web categories within the single README.md file at the root of the project.

Locating Web Scraping Libraries in the awesome-python Repository

The repository maintains a flat structure where all listings reside in README.md. Web scraping tools appear in two primary locations based on their architectural approach and primary use case.

Data Category: Web Crawlers & Scrapers

Found within the Data section of the README (approximately lines 595-600 and 719-724), this subsection focuses on high-performance crawling frameworks and lightweight scraping utilities.

Key libraries in this section include:

• Scrapy – A fast, high-level web crawling and scraping framework designed for large-scale data extraction.
• Autoscraper – An automatic, fast, and lightweight scraper that learns extraction rules from provided examples.
• PySpider – A powerful spider (web-crawler) system with a web-based UI and task queue support.
• Trafilatura – A command-line tool and library for gathering text and metadata from web pages, optimized for article extraction.

Web Category: Scraping-Related Libraries

Located in the Web section (starting around line 5671, with specific entries near line 5703), this category contains modern, specialized, or LLM-augmented scraping tools.

Notable entries include:

• Scrapegraph-ai – A library that builds scraping pipelines using Large Language Models (LLMs) and graph logic to interpret page structure dynamically.
• Twint – A Twitter-scraping tool that operates without using the official Twitter API, enabling historical data extraction.

Getting Started with Popular Scraping Libraries

Below are minimal, runnable implementations demonstrating how to initialize and execute basic scraping tasks with three of the most prominent libraries listed in awesome-python.

Scrapy: Full-Featured Crawling Framework

Scrapy requires the scrapy package. This example creates an inline spider without generating a full project structure:

import scrapy
from scrapy.crawler import CrawlerProcess

class QuotesSpider(scrapy.Spider):
    name = "quotes"
    start_urls = ["http://quotes.toscrape.com/"]

    def parse(self, response):
        for quote in response.css("div.quote"):
            yield {
                "text": quote.css("span.text::text").get(),
                "author": quote.css("small.author::text").get(),
            }

# Run the spider without creating a full Scrapy project

process = CrawlerProcess(settings={"LOG_LEVEL": "ERROR"})
process.crawl(QuotesSpider)
process.start()

Autoscraper: Example-Based Rule Learning

Autoscraper learns extraction patterns from sample data. Install via pip install autoscraper:

from autoscraper import AutoScraper

url = "https://quotes.toscrape.com/"
wanted_list = ["Albert Einstein", "Mark Twain"]  # examples of data you want

scraper = AutoScraper()
result = scraper.build(url, wanted_list)

print(result)  # {'author': ['Albert Einstein', 'Mark Twain', ...]}

# Save the rules for later reuse

scraper.save("autoscraper_rules.json")

Scrapegraph-ai: LLM-Driven Extraction

This library requires scrapegraph-ai and an API key for an LLM provider such as OpenAI:

from scrapegraph_ai import ScrapegraphAI

# Initialise a graph with an LLM (e.g., OpenAI gpt-4o)

graph = ScrapegraphAI(
    model="gpt-4o-mini",
    api_key="YOUR_API_KEY",  # replace with your actual key

)

# Define the scraping pipeline in natural language

pipeline = """
1. Load the page https://quotes.toscrape.com/.
2. Extract all quote texts and their authors.
3. Return the data as a JSON list.
"""

result = graph.run(pipeline)
print(result)  # [{"quote": "...", "author": "..."}, ...]

Summary

• awesome-python organizes web scraping tools in README.md under two categories: Data (traditional crawlers) and Web (modern/LLM-based tools).
• The Data → Web Crawlers & Scrapers section contains Scrapy, Autoscraper, PySpider, and Trafilatura.
• The Web → Scraping-Related Libraries section includes Scrapegraph-ai and Twint.
• Each listing provides a direct link to the library's GitHub repository and a concise description of its capabilities.

Frequently Asked Questions

Where exactly in the README.md file are the web scraping libraries located?

Web scraping libraries appear in two distinct line ranges within README.md. The Data category entries (including Scrapy and Autoscraper) are located around lines 595-600 and 719-724, while the Web category entries (including Scrapegraph-ai) appear near line 5703, starting from line 5671.

What is the difference between the scraping libraries in the Data category versus the Web category?

The Data category focuses on traditional crawling frameworks and extraction utilities designed for high-volume data processing, such as Scrapy and Trafilatura. The Web category contains specialized or modern tools like Scrapegraph-ai, which leverage Large Language Models (LLMs) for dynamic content interpretation, and Twint, which targets specific social media platforms.

Does awesome-python include code examples for these scraping libraries?

The repository itself contains only curated lists and descriptions within README.md. However, each library entry includes a hyperlink to its official GitHub repository, where you can find comprehensive documentation, code examples, and installation instructions. The snippets provided in this guide demonstrate minimal implementations based on the libraries' official APIs.

How frequently is the awesome-python repository updated with new scraping tools?

As an actively maintained open-source project, awesome-python receives regular pull requests to add emerging libraries. New scraping tools—particularly those incorporating AI or LLM capabilities like Scrapegraph-ai—are typically added to the Web category once they demonstrate stable releases and community adoption. You can monitor recent changes via the repository's commit history or pull request queue.

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