Python Web Scraping with Scrapy Course

Lesson 1 • Virtual Environments for Python Projects

Create isolated virtual environments to manage project dependencies cleanly. Isolation prevents package conflicts across multiple projects.

Lesson 2 • Code Editor Setup for Scrapy Projects

Configure a code editor with Python support, linting, and syntax highlighting. Proper tooling accelerates development and reduces syntax errors.

Lesson 3 • Installing Scrapy and Its Dependencies

Install Scrapy via pip and resolve common dependency issues. A clean install confirms the environment is ready for spider development.

Lesson 1 • Browser DevTools for Scraper Research

Use browser developer tools to inspect elements, monitor network traffic, and copy selectors. This research workflow directly informs spider design.

Lesson 2 • XPath Expressions for Advanced Selection

Write XPath expressions to navigate the DOM with greater flexibility than CSS alone. XPath handles complex traversal patterns that CSS cannot express.

Lesson 3 • How HTTP Requests and Responses Work

Trace the lifecycle of an HTTP request from browser to server and back. This model explains what Scrapy replicates when fetching pages.

Lesson 4 • CSS Selectors for Data Targeting

Use CSS selector syntax to pinpoint specific elements within an HTML document. These selectors are the primary targeting tool in Scrapy spiders.

Lesson 1 • Running and Debugging Spiders

Execute spiders with the scrapy crawl command and interpret log output. Debugging skills reduce time spent on broken spiders.

Lesson 2 • Anatomy of a Scrapy Spider Class

Examine the Spider base class and its required attributes and methods. Every spider inherits this structure, so mastering it is foundational.

Lesson 3 • Extracting Data with Selectors

Apply CSS and XPath selectors inside the parse method to extract text and attributes. Accurate extraction is the core skill of any spider.

Lesson 1 • Data Cleaning with Processors

Write custom processor functions to strip, cast, and normalise field values. Clean data at the loader level reduces downstream pipeline complexity.

Lesson 2 • Defining Scrapy Items

Declare Item classes with typed fields to represent scraped records. Items enforce a consistent schema across all spiders in a project.

Lesson 3 • Using ItemLoaders for Clean Data

Apply ItemLoaders to automate input and output processing on field values. Loaders separate extraction logic from cleaning logic for maintainability.

Lesson 1 • Following Links with Scrapy Requests

Yield scrapy.Request objects from parse methods to instruct the crawler to fetch additional URLs. Link-following is the mechanism behind full-site crawls.

Lesson 2 • Using CrawlSpider for Rule-Based Crawling

Leverage the CrawlSpider subclass and Rule objects to define link-following patterns declaratively. Rules reduce boilerplate for large, structured sites.

Lesson 3 • Managing Crawl Depth and Scope

Configure depth limits and URL filters to keep crawls focused and efficient. Scope control prevents runaway crawls that waste resources.

Lesson 4 • Pagination Strategies

Detect and follow next-page links across numbered and cursor-based pagination schemes. Handling pagination unlocks complete dataset extraction.

Lesson 1 • Exporting Data to CSV and JSON

Use Scrapy's built-in feed exporters to write items to flat files. Flat-file exports are the fastest path from spider to usable data.

Lesson 2 • Writing Custom Pipeline Components

Build pipeline classes that clean, deduplicate, or enrich items before storage. Custom pipelines handle logic that exporters cannot express.

Lesson 3 • Storing Data in MongoDB

Write a pipeline that inserts items into a MongoDB collection using PyMongo. Document storage suits scraped data with variable or nested fields.

Lesson 4 • Storing Data in a SQL Database

Connect a pipeline to a relational database and insert items row by row. Database storage enables querying and long-term data management.

Lesson 1 • Understanding Bot Detection Techniques

Catalogue the server-side signals used to identify automated traffic. Knowing detection methods informs the countermeasures applied in later sections.

Lesson 2 • Throttling Requests with AutoThrottle

Enable Scrapy's AutoThrottle extension to adapt request rates to server response times. Polite crawling reduces the risk of rate-limit bans.

Lesson 3 • Rotating User Agents and Headers

Configure middleware to randomise user-agent strings and HTTP headers per request. Header rotation reduces the fingerprint that triggers bot detection.

Lesson 4 • Using Proxy Rotation

Route requests through rotating proxy pools to distribute traffic across multiple IP addresses. Proxy rotation is the primary defence against IP-based bans.

Lesson 1 • Handling Infinite Scroll and Lazy Loading

Automate scroll actions and trigger lazy-load events to expose hidden content. These techniques complete dataset extraction on modern feed-style pages.

Lesson 2 • Identifying JavaScript-Rendered Content

Distinguish between server-rendered HTML and client-rendered content before choosing a scraping strategy. Correct identification prevents wasted effort on the wrong approach.

Lesson 3 • Performance Trade-offs of Browser Automation

Compare the speed, resource cost, and reliability of headless browsers against direct HTTP requests. Informed trade-off decisions keep projects maintainable and efficient.

Lesson 4 • Integrating Scrapy with Playwright

Use the scrapy-playwright library to render JavaScript inside Scrapy's request pipeline. Playwright integration handles sites where API interception is not feasible.