Python Tutorial: Web Scraping using Beautiful Soup

Web scraping is a very powerful tool to learn for any data professional. With web scraping, the entire internet becomes your database. In this python tutorial, we introduce the fundamentals of web scraping using the python library, beautifulsoup. We show you how to parse a web page into a data file (csv) using a Python package called BeautifulSoup.

There are many services out there that augment their business data or even build out their entire business by using web scraping. For example there is a steam sales website that tracks and ranks steam sales, updated hourly. Companies can also scrape product reviews from places like Amazon to stay up-to-date with what customers are saying about their products.

The accompanying material for this tutorial can be found here

More Data Science Material:
[Video] Learn how to web scrap in R
[Video] Python tutorial on setting up the language for data science

The Code

from bs4 import BeautifulSoup as soup  # HTML data structure
from urllib.request import urlopen as uReq  # Web client

# URl to web scrap from.
# in this example we web scrap graphics cards from
page_url = ""

# opens the connection and downloads html page from url
uClient = uReq(page_url)

# parses html into a soup data structure to traverse html
# as if it were a json data type.
page_soup = soup(, "html.parser")

# finds each product from the store page
containers = page_soup.findAll("div", {"class": "item-container"})

# name the output file to write to local disk
out_filename = "graphics_cards.csv"
# header of csv file to be written
headers = "brand,product_name,shipping\n"

# opens file, and writes headers
f = open(out_filename, "w")

# loops over each product and grabs attributes about
# each product
for container in containers:
    # Finds all link tags "a" from within the first div.
    make_rating_sp ="a")

    # Grabs the title from the image title attribute
    # Then does proper casing using .title()
    brand = make_rating_sp[0].img["title"].title()

    # Grabs the text within the second "(a)" tag from within
    # the list of queries.
    product_name ="a")[2].text

    # Grabs the product shipping information by searching
    # all lists with the class "price-ship".
    # Then cleans the text of white space with strip()
    # Cleans the strip of "Shipping $" if it exists to just get number
    shipping = container.findAll("li", {"class": "price-ship"})[0].text.strip().replace("$", "").replace(" Shipping", "")

    # prints the dataset to console
    print("brand: " + brand + "\n")
    print("product_name: " + product_name + "\n")
    print("shipping: " + shipping + "\n")

    # writes the dataset to file
    f.write(brand + ", " + product_name.replace(",", "|") + ", " + shipping + "\n")

f.close()  # Close the file


Phuc H Duong
About The Author
- Phuc holds a Bachelors degree in Business with a focus on Information Systems and Accounting from the University of Washington.

Start the discussion at