Modern Web Scraping Fundamentals with Python

Explore how http requests establish client–server communication for web scraping, focusing on get requests to fetch data from a url and introducing other methods like post, put, and delete.

Explore the three core components of a website—HTML, CSS, and JavaScript—through the DOM, and learn how classes, selectors, and XPath help web scraping retrieve data.

Build a basic HTML document using the example, covering doctype, opening and closing tags, inline styling, and inspect elements, attributes, classes, and IDs with the inspector tool for web scraping.

Explore the browser inspector to view page structure, identify elements and classes, and craft selectors for scraping. Use the tool for testing, debugging, and quality assurance across real sites.

Traverse the DOM by walking its tree of HTML elements, using the inspector to locate nodes and selectors, and extract specific texts like h1s for targeted scraping.

Robots.txt guides web crawlers on allowed and disallowed content for specific user agents, with a 2019 court case noting publicly available data is fair game.

Choose Python 3.7 for the virtual environment when installing Anaconda to ensure Scrapy compatibility, even though 3.8 is available. The update outlines future changes and invites questions.

Download and install the 64-bit Anaconda distribution with Python 3.7, choose the graphical installer, and set up the Spyder IDE for web scraping development.

Set up a dedicated python web scraping virtual environment to minimize dependency conflicts, using conda and anaconda navigator, with python 3.7 and activation from the terminal.

Advance into the practical web scraping sections with Python, learn to handle changing HTML structures and errors, share issues in the Q&A, and receive quick code updates.

Learn to set up a Python web scraping workflow using Scrapy, install it via Conda, manage environments, and launch Spyder in Anaconda Navigator for hands-on scraping projects.

Set up an anaconda environment, create a project folder, and initialize a Scrapy project named practical one, then open Spyder to configure spiders and items.py and test import Scrapy.

Build your first spider with Scrapy by defining a spider class, start URLs, and a parse method to convert unstructured page content into structured data for books to scrape.

Use the scrapy shell to debug spiders, test xpath and css expressions on a page, and inspect response objects from a url or local file.

Resolve common Scrapy scraping issues by updating the Scrapy version and refining XPath queries with text(), using response.xpath to extract h1 text in the Scrapy shell.

Explore selectors in web scraping using XPath and CSS selectors, and learn to extract data with Scrapy shell, response.xpath, and get or get all.

Create and use Scrapy items and item pipelines to extract data from web sources using XPath, and build a spider to collect book titles and prices for output to CSV.

Scrapy sends HTTP requests from spiders and returns response objects via the downloader, highlighting the core request–response cycle that powers efficient web scraping.

Traverse the html dom with xpath in scrapy to extract precise elements, such as price text by class, and test requests using scrapy shell, fetch, and view commands.

Set up your first Scrapy practical by crawling pages, sending requests, and extracting book data—title, price, category, and stock status—from four chosen items.

Build a scrapy spider to solve the challenge by extracting category, in stock, price, and title with xpath, then export results to a csv for verification.

Deploy your Scrapy spiders to the cloud with Scrapy Cloud using a free option, API key, and project ID. Run, monitor logs, and output to S3 for debugging and validation.

Install and configure Beautiful Soup, lxml, and requests in a conda environment to enable HTML and XML parsing for modern web scraping with Python.

Use the requests library to fetch web pages, check status codes, and inspect headers, encoding, and HTML text for debugging and basic web scraping workflows.

Use beautiful soup with python to parse html and extract data from web pages. Fetch pages with requests and traverse the html tree using soup methods like find_all and get_text.

Practice extracting quotes and tags with Beautiful Soup, using soup.find_all and span text, and explore navigating the HTML structure for a web scraping challenge.

Scrape amazon bestsellers using beautiful soup to retrieve product review counts, organize results in a pandas data frame, and respect headers, crawl rate, robots.txt guidelines and hourly updates.

Create a reusable amazon review-count scraper using requests, BeautifulSoup, and pandas; emulate a browser with headers, extract totals, and store results in a dataframe with timer-based runs.

Explore using Selenium for web scraping and automating browser actions. Install Selenium and the Chrome driver, set system paths across Mac, Windows, and Linux, and handle dynamic content in Python.

Automate browser tasks with Selenium for web scraping—locate elements by XPath, click buttons, and handle dynamic content. Parse HTML with BeautifulSoup and explore login, popups, and load more options.

Apply selenium to log into a site by entering email and password on the Super Data Science login page and submitting the form for scraping.

Explore dynamic content scraping with Selenium by navigating club.com, selecting learning paths and a career path, and extracting market text, career opportunities, and instructors; adapt to pop-ups and page changes.

Learn to build a Selenium-based web scraper in Python, importing Selenium WebDriver and BeautifulSoup, navigating pages, handling pop-ups, and using Chrome options to extract data from dynamic sites.

Demonstrates end-to-end web scraping workflow using Selenium to navigate pages, and BeautifulSoup with the lxml parser to extract text and instructor data, then organize results into pandas data frames.

Enhance your Selenium web scraping skills with a Python-based challenge. Receive actionable feedback through an optional GitHub classroom submission and build a portfolio by extending scraping to extra page data.

Explore practical web scraping with Scrapy, BeautifulSoup, and optionally Selenium to detect the word key and trigger a print or alert, with optional email alerts via smtplib.

Use requests and beautiful soup to fetch and parse a page with lxml, search for a key string, and print an alert when found, pacing requests with time.sleep.

Learn how to parse page text with BeautifulSoup instead of XPath, detect a key, and trigger an email alert using smtplib with Gmail port 587; includes safe handling of credentials.

upload your code or solution to GitHub to receive feedback on your web scraping approach, whether using Beautiful Soup, Scrapy, or Selenium for automation.

Compare Selenium and Puppeteer for taking web page screenshots in Python and Node, highlighting headless Chrome and setup, with use cases like monitoring changes, documentation, and automated tasks.