Python Web Scraping and Data Extraction Training Course

Participants can create a reproducible Python project for web extraction work.

• Virtual environments isolate package dependencies for extraction projects
• Project folders separate source code, configuration, outputs and tests
• requirements.txt records pinned package versions for repeatable installation
• Environment variables protect tokens, credentials and local configuration values
• Logging configuration records requests, errors, warnings and run summaries
• Git commits capture meaningful changes to extraction code and documentation

Participants can retrieve web resources and inspect HTTP responses for extraction opportunities.

• HTTP methods, status codes, headers and cookies explained through requests
• Requests sessions preserve cookies and connection settings across calls
• Query parameters filter server responses for targeted record retrieval
• Response encoding and content types checked before parsing downloaded content
• Redirect chains, timeout settings and exceptions handled explicitly
• Developer tools reveal request URLs, payloads and response bodies

Participants can inspect page markup and write selectors that target required fields.

• HTML elements, attributes and document hierarchy inspected in browser tools
• CSS selectors target stable classes, attributes and structural relationships
• XPath expressions select nodes using axes, predicates and text conditions
• Selector specificity balanced against resilience to layout changes
• Repeated record containers identified before extracting individual fields
• Missing elements handled without failing complete extraction runs

Participants can extract structured records from static HTML pages using Beautiful Soup.

• Beautiful Soup parses HTML into searchable tag objects
• find, find_all and select retrieve targeted page elements
• Text extraction removes whitespace, nested tags and unwanted labels
• Attribute values capture links, identifiers, dates and image sources
• Record dictionaries preserve consistent field names across extracted items
• CSV and JSON exports store early extraction outputs for review

Participants can collect complete record sets across listing and detail pages.

• Pagination patterns include page numbers, offsets, cursors and load-more links
• Relative URLs converted into absolute links before follow-up requests
• Listing pages provide identifiers and detail-page collection targets
• URL deduplication prevents repeated retrieval of equivalent resources
• Stop conditions detect final pages and empty result sets
• Traversal logs record visited URLs, counts and failed detail requests

Participants can clean extracted values into analysis-ready data fields.

• pandas DataFrames organise extracted records for inspection and transformation
• Whitespace, Unicode characters and HTML entities cleaned consistently
• Dates parsed with explicit formats, locales and timezone assumptions
• Currency, percentages and quantities converted into numeric fields
• Categorical labels standardised through mapping tables and controlled vocabularies
• Duplicate records identified using keys, hashes and business rules

Participants can test web-derived datasets for completeness, validity and consistency.

• Schema checks confirm expected fields, types and required values
• Completeness metrics measure null rates across critical extracted fields
• Range rules identify implausible prices, dates and quantities
• Uniqueness tests detect repeated identifiers and duplicate records
• Source snapshots preserve evidence for disputed extracted values
• Quality exceptions logged for correction, exclusion or escalation

Participants can create Scrapy projects that organise crawling logic and extracted items.

• Scrapy project structure separates spiders, items, pipelines and settings
• Spider classes define start URLs, parsing methods and crawl rules
• Item definitions specify expected fields for collected records
• Request callbacks process responses and generate follow-up requests
• Feed exports write items to JSON, CSV and JSON Lines
• Scrapy shell tests selectors and responses before spider execution

Participants can build Scrapy spiders that collect, transform and export multi-page datasets.

• Pagination requests follow next-page links through callback methods
• Detail-page requests carry listing metadata using request meta values
• Item loaders apply field processors during extraction and cleaning
• Pipelines validate, transform and route completed scraped items
• Duplicate filters prevent identical requests during recursive crawling
• Spider settings control concurrency, delays, headers and output behaviour

Participants can identify where browser-rendered pages obtain their underlying data.

• Client-side rendering distinguished from server-rendered HTML responses
• Browser network panels identify XHR, fetch and GraphQL requests
• JSON response payloads inspected for records, metadata and pagination
• Request headers reveal required content types, origins and tokens
• Embedded JSON state extracted from script tags and page source
• Direct data endpoints preferred when authorised and technically suitable

Participants can automate browser interactions to extract authorised dynamic web content.

• Playwright browsers load JavaScript-rendered pages and user interactions
• Locator strategies target stable roles, labels and data attributes
• Explicit waits replace unreliable fixed delays during page loading
• Click, fill and select actions handle controlled user workflows
• Downloaded files captured and stored with source metadata
• Browser contexts isolate sessions, cookies and configuration settings

Participants can retrieve and normalise data from documented web APIs.

• REST endpoints use resources, parameters and HTTP response conventions
• API documentation identifies authentication, limits and pagination requirements
• Bearer tokens and API keys loaded from environment variables
• JSON payloads normalised into tabular records using pandas
• Cursor pagination retrieves complete result sets without duplication
• API errors classified using status codes and response messages

Participants can configure collection processes that minimise unnecessary load and access risk.

• Rate limiting spaces requests according to source capacity and policies
• Exponential backoff responds appropriately to temporary server failures
• Retry rules distinguish recoverable errors from permanent failures
• Caching reduces repeated downloads during development and testing
• Conditional requests use ETags and Last-Modified response headers
• Authentication boundaries respected without bypassing access restrictions

Participants can design operational controls for reliable recurring extraction jobs.

• Structured logs capture run identifiers, counts, errors and durations
• Assertions detect missing containers, changed labels and empty outputs
• Unit tests verify parsers against saved HTML response fixtures
• Data drift checks compare current outputs with historical distributions
• Alert thresholds flag failed jobs and material record-count changes
• Maintenance runbooks assign ownership, response steps and review frequency

Participants can prepare extracted data for recurring delivery to analytical users.

• CSV, Parquet and JSON formats selected by downstream analytical needs
• SQLite stores structured extraction outputs for local querying
• File naming conventions preserve source, date and run identifiers
• Scheduled jobs separate collection, transformation and publication steps
• Incremental loads append new records while preserving historical data
• Data dictionaries define fields, sources, transformations and refresh schedules

Participants can document an extraction solution and communicate its controls to stakeholders.

• Extraction specifications define purpose, sources, fields and acceptance criteria
• Data lineage records source URLs, transformations and output destinations
• Risk registers document legal, technical and data quality concerns
• Run metrics report volumes, failures, retries and completion times
• Change control records selector updates, source changes and approvals
• Stakeholder briefings explain collection limits, assumptions and refresh timing

Participants can build, review and present a complete documented Python web data extraction solution.

• Source assessment defines authorised scope, fields and collection constraints
• Python extractor collects records through appropriate HTTP or browser methods
• Selectors, pagination and error handling produce complete structured outputs
• Quality controls test completeness, validity, duplicates and data drift
• Operational plan specifies scheduling, logging, alerts and maintenance ownership
• Peer review evaluates code, documentation, ethics and business usefulness