Beautiful Soup & Requests for Data Science 2025

Learn to install and import BeautifulSoup and requests, fetch HTML from a sample book site, and create a soup object to parse data like titles, authors, and categories.

Master BeautifulSoup's find and find_all to retrieve the first matching element from HTML. Capture data by tag, class, and id, practice text extraction, and compare methods with practical scraping examples.

Use Beautiful Soup find with the a keyword and attrs to access elements and extract the first category from a tag.

Explore how to use the find_all function to collect all matching elements, extract book titles and prices with a simple for loop, and count results.

Master data extraction with css selectors in BeautifulSoup by using select_one and select to target first elements or all matches, with class and id syntax to retrieve text.

Navigate the dom with BeautifulSoup to locate next and previous siblings, find parents, and extract attributes using get() and attrs(), including hrefs for book links.

Learn to scrape book entries using Beautiful Soup and requests in a beginner project, then export the data to Excel and JSON files.

Learn to scrape the first book listing with Beautiful Soup, extracting name, rating, author, category, and price, then prepare data for Excel and JSON storage.

Extract data for all 30 book listings using list comprehension and for loops, storing results in a list for export to pandas data frames, Excel, and JSON.

Learn to organize data in a pandas dataframe with books, ratings, authors, category, and price, then save to Excel and JSON with index false and orient=records.

learn to fetch a real estate site with requests, parse HTML with BeautifulSoup, and extract 15 listings per page across five pages, using a get request and building the soup.

Master extracting the first listing data with Beautiful Soup, capturing name, street, state, zip, price, bedrooms, bathrooms, size, and garage, then prepare for pagination across five pages.

Extract bedrooms, bathrooms, size in square meters, and garage data from listing using Beautiful Soup; then expand to 15 listings on the first page and five pages via list comprehension.

Use a list comprehension to loop through results, extract listing names, streets, prices, beds, baths, sizes, and garages, and export to a pandas DataFrame and Excel/JSON for five pages scraping.

Create a pandas data frame by appending lists into columns: name, street, state, zip code, price, beds, baths, size, garage, for the first page, then export to excel and json.

Scrape five pages with requests and BeautifulSoup, handle index.html on page one, extract 75 listings, build a pandas data frame, and export to Excel and JSON.

Scrape five pages into a pandas data frame, inspect with head and tail, perform a zip–state split for cleaning, and save results to Excel and JSON.

Learn to scrape restaurant listings by loading results page and following detail pages via relative URLs with requests and BeautifulSoup, storing data in a pandas dataframe for export to Excel.

Learn to build absolute URLs by combining a base URL with relative paths and to extract data from restaurant listing detail pages using anchor href attributes, loops, and list comprehension.

Learn to extract nine data points: restaurant name, category, phone, website, email, address, distance from the city, payment methods, and price range—from the first page using Beautiful Soup and requests.

Learn to scrape eight restaurant entries using requests and BeautifulSoup, with try-except blocks to handle missing data, accumulate results into a pandas DataFrame, and export to Excel and JSON.

Expand the existing pagination logic to scrape all 40 listings across five pages using Beautiful Soup and Requests, generating URLs and storing results in Panda's data frame, Excel, and JSON.

Loop through 40 restaurant detail pages with requests and Beautiful Soup, extract data, and assemble it into a pandas DataFrame, then export to Excel and JSON.