What Is a Web Crawler? Basic Environment Setup and Python Scraping Workflow

This article explains what a web crawler is, describes the basic environment and tools needed for Python crawling, outlines the typical scraping workflow, and presents three implementation styles—basic, function‑encapsulated, and concurrent—illustrated with diagrams and practical guidance.

Python Programming Learning Circle
Python Programming Learning Circle
Python Programming Learning Circle
What Is a Web Crawler? Basic Environment Setup and Python Scraping Workflow

What Is a Crawler?

If we compare the Internet to a large spider web, data resides at the nodes, and a crawler is like a small spider that moves along the network to capture its prey (data).

A crawler is a program that sends requests to websites, obtains resources, then analyzes and extracts useful data.

Technically, it simulates browser requests to a site, fetches the returned HTML, JSON, or binary data (images, videos) to the local machine, extracts the needed information, and stores it for later use.

Basic Environment Configuration

Version: Python 3

System: Windows

IDE: PyCharm

Tools Required for Crawling

Request libraries: requests, selenium (can drive a browser to render CSS and JS but has performance drawbacks because it loads all page resources).

Parsing libraries: regular expressions, beautifulsoup, pyquery.

Storage options: files, MySQL, MongoDB, Redis.

Python Crawler Basic Workflow

Basic Version

Function‑Encapsulated Version

Concurrent Version

(If you need to crawl 30 videos, launching 30 threads will make the total time equal to the slowest thread's duration.)

Now that you understand the basic Python crawling process, doesn’t it seem surprisingly simple when you compare it with the actual code?

END

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Python Programming Learning Circle
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Python Programming Learning Circle

A global community of Chinese Python developers offering technical articles, columns, original video tutorials, and problem sets. Topics include web full‑stack development, web scraping, data analysis, natural language processing, image processing, machine learning, automated testing, DevOps automation, and big data.

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