Comprehensive List of Python Libraries for Web Crawling, Data Processing, and Web Development

Many people start learning Python with web crawling because of abundant resources and open‑source projects.

Web crawling in Python can be divided into three major parts: fetching, parsing, and storage.

When a URL is entered in a browser, the process involves domain lookup, sending a request to the server, receiving the response, and rendering the page.

Common libraries for fetching: urllib, requests, grab, pycurl, urllib3, httplib2, RoboBrowser, MechanicalSoup, mechanize, socket, Unirest, hyper, PySocks.

Crawling frameworks: grab, Scrapy, pyspider, cola, portia, restkit, demiurge.

HTML/XML parsers: lxml, cssselect, pyquery, BeautifulSoup, html5lib, feedparser, MarkupSafe, xmltodict, xhtml2pdf, untangle.

Cleaning tools: Bleach, sanitize.

Text processing: difflib, Levenshtein, fuzzywuzzy, esmre, ftfy.

Natural language processing: NLTK, Pattern, TextBlob, jieba, SnowNLP, loso.

Browser automation: selenium, Ghost.py, Spynner, Splinter.

Multiprocessing and async: threading, multiprocessing, celery, concurrent-futures, asyncio, Twisted, Tornado, pulsar, diesel, gevent, eventlet, Tomorrow.

Queues: celery, huey, mrq, RQ, simpleq, python-gearman.

Cloud execution: picloud, dominoup.com.

Web content extraction: newspaper, html2text, python-goose, lassie.

WebSocket libraries: Crossbar, AutobahnPython, WebSocket-for-Python.

DNS tools: dnsyo, pycares.

Computer vision: OpenCV, SimpleCV, mahotas.

Popular Python web frameworks include Django, Flask, Web2py, Tornado, and CherryPy, each with its own strengths and use cases.

When choosing a framework, avoid the pitfalls of chasing the “best” framework or over‑optimising performance for small projects; select the tool that best fits your team and requirements.