Python Web Scraping: Data Extraction with Beautiful Soup

Install Anaconda on your machine, explore its inbuilt libraries and Jupyter notebook, and learn how an interpreter differs from a compiler during practical data science workflows.

install and set up Anaconda on macOS or Windows, choosing the macOS Intel or M1 version and completing the installer, then launch Navigator to use Jupyter Notebook.

Learn how to interpret an HTML document to extract data from websites using Beautiful Soup, focusing on the document declaration, doctype, and the HTML five structure of web pages.

Explore the head tag and its non-displayed information—title, meta data, and links to CSS and JavaScript—and understand how these elements configure a web page.

Discover how the body tag determines what appears in the browser, explore common elements like h1, p, and anchor tags, and learn where the data you scrape is typically found.

Explore how to identify heading tags (h1–h6) and the paragraph tag in HTML, understanding their structure, hierarchy, and default styling for effective web scraping with Beautiful Soup.

Identify list elements on web pages by distinguishing unordered lists with bullets from ordered lists with numbering, noting chronology, and inspecting elements to spot list tags.

Learn how HTML tables organize data into rows and columns using table, tr, th, and td tags, with headers in the first row and data in subsequent rows.

Learn how the anchor tag uses href to create hyperlinks for navigation between pages, such as home page or about page, and how default blue underlined links signal clickability.

Learn how grouping elements like header, section, div, and article structure web pages, enabling easier styling and data extraction, especially for scraping chapters with ids or classes.

Discover how to extract specific data from HTML pages using class selectors and dot notation with BeautifulSoup to target elements like div and h1.

Master the id selector by using the hash prefix to target elements with an id in Python web scraping, and distinguish it from the class selector using the dot prefix.

Explore the element name selector to extract all elements like div, a (anchor), and li, and learn when this approach is best for lists rather than single items.

Learn how attribute selectors in Beautiful Soup differentiate HTML elements by attributes, such as anchors with href, using [attribute=value] syntax to target specific links and extract results.

Understand how http requests work between a client and a server, and how the Python requests library streamlines get, post, put, and delete calls, handling response objects and status codes.

Learn how get requests retrieve data or resources from servers, with context on the other request types (post, put, delete) and when to use get for web scraping.

The post request submits data to the server, such as signup forms and comments, stores information with an id, and contrasts with get requests used to retrieve data.

Use the put request to update or replace an existing resource on the server by sending the resource ID and new data, enabling profile edits and password changes.

Delete requests remove a resource by ID from the server, yielding not found errors on subsequent get calls. In scraping, prioritize get requests, with post, put, and delete as secondary.

Learn to use the Python requests library to make http get requests, fetch a BBC page in a notebook, and inspect the response status 200 before moving to Beautiful Soup.

Explore http response codes for web scraping with BeautifulSoup, focusing on 2xx successes (200, 201, 204), 4xx client errors (400, 401, 403, 404), and 5xx server issues.

Explore Beautiful Soup, a Python library to retrieve data from HTML and XML, using a parser to navigate, search, and modify a parse tree.

Discover how to inspect a webpage using Chrome DevTools to view the HTML that renders the page and identify elements for scraping with Beautiful Soup.

Learn how to fetch a website's html code with the request library, pass it to BeautifulSoup, and extract the title tag using response.text.

Discover how to extract content by element name with BeautifulSoup in Python, inspect pages to identify tag names like title, h1, and h2, and retrieve text with dot string.

Explore how to use find and find_all in Beautiful Soup to extract elements, handle single versus multiple results, and work with tags, attributes, and css selectors.

Learn to scrape IMDb's top 20 most popular movies with BeautifulSoup in Python using css selectors (element or tag name) to build a movie recommendation list for a data workflow.

Learn how to use CSS selectors for id and class names to extract movie titles and ratings from a table using requests and Beautiful Soup.

Learn to use the css pseudo class selector to target an h1 inside a div with a specific id, navigating from the parent and using the child combinator for descendants.

Explore using CSS selectors for attributes in Beautiful Soup, including id, class, and href attributes on anchor tags; practice selecting elements with square brackets and combining methods to extract content.

Build an eBay price tracker using Python and Beautiful Soup for web scraping to monitor product prices, trigger notifications when the price drops below a user-specified budget.

Inspect the eBay page to locate the price of an iPhone 14 Pro Max, identifying the span with class ux-text-spans inside the div x-price-primary to build the price tracker.

Set up a Python web scraping workflow in a Jupyter notebook, fetch a page with requests, parse with BeautifulSoup (BS4) and lxml, and extract a price for budget checks.

Start the second project by scraping the Billboard top 200 songs from the target site and returning the list. Inspect the page to locate where the data is kept.

Inspect a website to locate title data for scraping, showing that title resides in an h3 with id 'title of a story' inside an unordered list, guiding navigation for extraction.

Scrape the Billboard top 20 songs using requests and BeautifulSoup in a Jupyter notebook, selecting h3 elements within lists, cleaning text, and rendering a numbered top-20 list.