beautifulsoup4 Selectors
import requests from bs4 import BeautifulSoup import pandas as pd import re html = """ <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title>Ultra Running Events</title></head> <body class="site-body"> <header class="site-header"> <h1 class="site-title">Ultra Running Events</h1> <nav class="main-nav"> <ul class="nav-list"> <li><a class="nav-link" href="#races-50">50 Mile Races</a></li> <li><a class="nav-link" href="#races-100">100 Mile Races</a></li></ul></nav></header> <section id="races-50" class="race-section race-50"> <h2 class="section-title-50">50 Mile Races</h2> <ul class="race-list-50"> <li class="race-item"> <h3 class="race-name"><a href="https://www.ryandataraces.com/rocky-mountain-50">Rocky Mountain 50</a></h3> <p class="race-date">Date: August 10, 2025</p> <p class="race-location">Location: Boulder, Colorado</p></li> <li class="race-item"> <h3 class="race-name"><a href="https://www.ryandataraces.com/desert-dash-50">Desert Dash 50</a></h3> <p class="race-date">Date: September 14, 2025</p> <p class="race-location">Location: Moab, Utah</p></li></ul></section> <section id="races-100" class="race-section race-100"> <h2 class="section-title-100">100 Mile Races</h2> <ul class="race-list-100"> <li class="race-item"> <h3 class="race-name"><a href="https://www.ryandataraces.com/mountain-madness-100">Mountain Madness 100</a></h3> <p class="race-date">Date: July 5, 2025</p> <p class="race-location">Location: Lake Tahoe, California</p></li> <li class="race-item"> <h3 class="race-name"><a href="https://www.ryandataraces.com/endurance-beast-100">Endurance Beast 100</a></h3> <p class="race-date">Date: October 3, 2025</p> <p class="race-location">Location: Asheville, North Carolina</p></li></ul></section> <section id="important-notes"> <h2>Important Notes</h2> <p><strong>All races start at 6:00 AM sharp.</strong></p> <p><strong>Mandatory pre-race check-in the evening before.</strong></p></section> <footer class="site-footer"> <p>© 2025 Ultra Running Events</p></footer><script id="wc-add-to-cart-js-extra">var wc_add_to_cart_params={"ajax_url":"\/wp-admin\/admin-ajax.php","wc_ajax_url":"\/?wc-ajax=%%endpoint%%","i18n_view_cart":"View cart","cart_url":"https:\/\/ryanandmattdatascience.com\/cart\/","is_cart":"","cart_redirect_after_add":"no"};</script>
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</body></html>""" soup_html = BeautifulSoup(html, 'html.parser') soup_html.select_one('h2') 
soup_html.select_one('h2').get_text() 
soup_html.select_one('strong').get_text() 
soup_html.select('p') #grabs all p tags 
soup_html.select('p')[0].get_text() 
soup_html.select('p')[1].get_text() 
all_p = soup_html.select('p') #grabs all p tags for p in all_p: print(p.get_text()) 
soup_html.select('a') 
a_element = soup_html.select('a') for link in a_element: print(link['href']) 
a_element[2]['href'] 
soup_html.select('#races-100') 
#Example 10 This selector finds any <a> descendant of an <h3> element with the class race-name #Descendant" means any level deep inside the h3, not just direct children soup_html.select('h3.race-name a') 
#Example 11 Direct Descendents #Let’s say you want to extract the <a> tag directly under each <h3 class="race-name"> (but only if it's a direct child) #It will not match nested links. #Omit > when you want any level of nesting soup_html.select('h3.race-name > a') 
#Example 12 After an Elements Siblings #Paragraphs after h3 tag #Have the same parent as an <h3>, and #Appear after that <h3> in the HTML, regardless of how many elements are in between #It's useful when you want to grab siblings after a specific element, but not necessarily immediately after soup_html.select("h3 ~p") 
#Example 13 Element in one of two classes (e.g., .race-50 OR .race-100) sections = soup_html.select('section.race-50, section.race-100') for section in sections: title = section.find('h2').text.strip() print(f"Section Title: {title}") 
#Example 14 Element in both classes (e.g., .race-section AND .race-50) (Order doesnt matter) race50_section = soup_html.select('section.race-section.race-50') race50_section_v2 = soup_html.select('section.race-50.race-section') for section in race50_section: print(f"Found section: {section['id']}") 
for section in race50_section_v2: print(f"Found section: {section['id']}") 
URL = "http://books.toscrape.com/" response = requests.get(URL) soup = BeautifulSoup(response.text, 'html.parser') #Example 15 Select category links from sidebar category_links = soup.select("ul.nav-list ul a") for a in category_links: print(a.text.strip(), URL + a['href']) 
books = soup.select("article.product_pod h3 a") books 
books = soup.select("article.product_pod h3 a") for book in books: print(book.get_text()) 
books = soup.select("article.product_pod") for book in books: title = book.select_one("h3 a")["title"] price = book.select_one(".price_color").text rating = book.select_one("p.star-rating")["class"][-1] # e.g. 'Three' book_data.append({"title": title, "price": price, "rating": rating}) df = pd.DataFrame(book_data) df.head(10) 
df['price_clean'] = df['price'].str.replace('£', '', regex=False).astype(float) #Convert GBP to USD (example rate: 1 GBP = 1.0737 USD) (CHECK THIS) exchange_rate = 1.0737 df['price_usd'] = df['price_clean'] * exchange_rate df['Price_usd'] = df['price_usd'].apply(lambda x: f"${x:.2f}") df_five_star = df.loc[df['rating'] == 'Five', ['title', 'Price_usd']] df_five_star 
df_five_star.to_csv('scrapped_book_data.csv') df_five_star.to_excel('scrapped_book_data.xlsx') 