How to Web Scrap Getting Started with Python and BrightData

The internet contains a vast ocean of data—if you know how to extract it. The demand for web scraping has thus exploded in recent years as organizations seek to leverage this abundance of publicly available information. And Python has emerged as the most popular programming language for scraping due to its simplicity yet versatility.

In this comprehensive 3500+ word guide, you’ll learn step-by-step how to build a robust web scraper in Python for extracting data from the web, using the BrightData proxy service to handle rotating IP addresses and bypass anti-bot protections. Let’s dig in!

What Exactly is Web Scraping?

Simply put, web scraping refers to the automated extraction or harvesting of data from websites. Say you copy and paste information from a site into an Excel sheet—that qualifies as a rudimentary form of web scraping. However, the term more commonly implies the use of intelligent bots—scripts that systematically crawl through web pages, analyze their content, and retrieve information.

The origins of web scraping date back to the early 2000s, when the first commercial scraping tools emerged, alongside the growth of the world wide web itself. Since then, the practice has exploded into a multi-billion dollar industry encompassing fields like business intelligence, price monitoring, marketing analytics, academic research, and more.

But web scraping also comes with important legal caveats. Many websites prohibit the wholesale copying and storage of their data without permission. Violating sites' terms of service agreements through excessively aggressive scraping can provoke lawsuits.

Nonetheless, if practiced ethically and minimized in scope, web scraping poses little risk for most personal or academic purposes. As you'll see, our tutorial scraper extracts only a handful of data points from the sample site.

Now that we've clarified key scraping concepts, let's overview our web scraping learning roadmap using Python and BrightData!

Why Use BrightData for Web Scraping?

As your web scraping endeavors expand in scale, you'll inevitably encounter anti-bot technologies implemented by sites to block scrapers and bots. The most common of these defenses include:

  • IP blocking – Sites ban scraper IP addresses
  • CAPTCHAs – Special images that only humans can recognize
  • IP rate limiting – Sites restrict how often requests come from the same IP

BrightData offers reliable residential proxies designed specifically to facilitate web scraping. Its proxies provide access to a large, constantly rotating pool of clean IP addresses located in real homes across the world.

By routing your scraper through BrightData proxies, your requests appear to target sites as normal human visitors, thereby bypassing anti-bot systems. BrightData also handles IP rotation and CAPTCHA solving automatically behind the scenes.

We'll integrate BrightData directly into our Python scraper in this tutorial to scrape data seamlessly at scale. Let's get started!

Learning Roadmap

Here are the key topics we’ll explore:

  • Inspecting and analyzing a target website
  • Using the Requests library to download web pages
  • Parsing downloaded HTML content with Beautiful Soup
  • Writing scraping logic to extract desired data points
  • Exporting scraped data to usable file formats like CSV
  • Leveraging BrightData to circumvent anti-bot defenses

By the end, you’ll possess the skills to build robust, production-grade web scrapers in Python capable of bypassing anti-bot protections using BrightData!

Set Up the Python Coding Environment

Before writing any scraper code, we need to set up an appropriate coding environment. Here are the core components required:

Python 3.7+

This tutorial utilizes the latest Python 3.11.2. Download and install it from the official website. Verify installation afterwards by checking your Python version at the command prompt:

python --version
# -> Python 3.11.2


PIP stands for “Pip Installs Packages” – it enables installing Python packages with one terminal command. If you have Python >3.4, PIP came pre-bundled with your installation. Confirm you have it using:

pip --version
# -> pip 22.3.1

Python IDE

An IDE (= Integrated Development Environment) facilitates smoother Python coding with features like auto-complete, built-in terminal, debugging tools, etc. Many options exist – PyCharm Community Edition is a robust free offering used here.

With those basics set up, install the BrightData SDK:

pip install brightdata

Excellent, our environment now has everything necessary to begin coding our Python web scraper!

Initialize the Python Scraper Project

Launch your installed Python IDE and create a new project called python-scraper.

Inside it, create a file named – that will contain our scraper code.

First import the BrightData module:

from brightdata.sdk import BrightData

Next instantiate a BrightData client object by passing your unique API key:

brightdata = BrightData('API_KEY_HERE')

Replace API_KEY_HERE with your actual BrightData API key.

We'll integrate BrightData further alongside constructing the scraper.

With setup complete, we're ready to inspect our target site!

Inspect and Analyze the Target Website

We'll build a scraper to extract data from the site – an example online bookstore created by ScraperWiki for web scraping practice.

Let's initially visit in a web browser to inspect its:

  • Content – products listings, prices, ratings etc.
  • Structure – layout, HTML tags
  • URL patterns – address formats

This upfront research is an indispensable first step before writing any scraper code.

The Books to Scrape homepage displaying book product listings

Based on our manual inspection, let's note few high-level details:


  • 1000s of book products containing:
    • Title, price, star rating
    • Image, description


  • Products arranged in listings by category
  • Each book encapsulated in a div class="product_pod”
  • 10 products per page


  • Homepage:
  • Category listing pages:
  • Individual book pages:

Equipped with a basic mental model of the target site, we can now commence scraper development!

Fetch Web Pages with the Requests Library

To extract a site's data into our Python program, we first need to download copies of its web pages. The Requests library will handle sending HTTP requests to retrieve pages.

Use PIP to install Requests:

pip install requests

Then import it:

import requests
from brightdata.sdk import BrightData

Fetching a page as raw HTML with Requests involves just one line:

page = requests.get('')

The page variable now contains the entire HTML content of We can print it out:


This will output all the raw HTML code comprising the homepage.

Let's add a BrightData proxy to our request flow to avoid blocks:

proxy = brightdata.get_proxy()
page = requests.get('', proxies=proxy)

Calling brightdata.get_proxy() supplies a rotating proxy IP address with each invocation. Requests made using these proxies appear to sites as coming from residential homes instead of our Python script.

We've now successfully downloaded using Requests! Next we'll parse its content to make extraction easier.

Parse Downloaded HTML with Beautiful Soup

While we could analyze raw HTML manually, it's complex and messy. We need to convert pages into structured Python objects traversable by code.

Beautiful Soup is a popular Python library that parses HTML and XML documents into an easily navigable hierarchy of Python objects representing each tag, attribute, and piece of text.

Install Beautiful Soup 4:

pip install beautifulsoup4

Then import it:

from bs4 import BeautifulSoup
import requests
from brightdata.sdk import BrightData

To parse a page's HTML, pass the raw content into a new BeautifulSoup object:

page = requests.get('', proxies=brightdata.get_proxy())
soup = BeautifulSoup(page.text, 'html.parser')

We now have a structured soup object containing the entire DOM tree of

Beautiful Soup enables easily isolating and manipulating elements by name, id, class, tag type etc. Now we can finally extract data!

Write Scraping Logic to Extract Target Data

Let's grab the first book's title:

first_book = soup.find('h3')
title = first_book.text

This prints something like “A Light in the Attic Special Edition”.

To extract all books, we first need to capture the HTML blocks containing data for each one.

Notice on the homepage that every book exists within a div tag carrying the product_pod CSS class.

We can grab all such blocks with Beautiful Soup using the bookstore site's built-in class name:

books = soup.find_all('div', {'class': 'product_pod'})

books now contains a list of HTML snippets representing each book. Let's iterate them to extract and print titles and prices:

for book in books:
    title = book.h3.text
    price = book.select_one('.price_color').text 
    print(title, price)

And we have successfully extracted all book titles and prices from the homepage!

With just these basic techniques, you can write scraping logic to gather virtually any data points a target site offers – ratings, descriptions, author names etc.

Now let's save extracted info to files.

Export Scraped Data to Usable File Formats

Collecting data is usually just the initial phase. We also need to store scraped content in accessible formats for easier post-processing and analysis in other programs.

Let's save our scraped book info into a CSV file:

import csv

headers = ['Title', 'Price']
book_data = []

for book in books:
    title = book.h3.text  
    price = book.select_one('.price_color').text
    book_data.append([title, price])
with open('scraped_books.csv', 'w', newline ='') as f:  
    writer = csv.writer(f)

This iterates each book's info into rows of a Python list book_data. We then write the CSV headers, and use writer.writerows() to dump the complete list to scraped_books.csv.

The created CSV will contain nicely organized records of all data extracted from the site. We could similarly export info as JSON documents and other formats.

In more advanced scraping pipelines, data usually gets written to databases rather than local files. But exporting to accessible file types suffices for now.

We have a working scraper extracting and saving target book info! But it could still face blocks without proxy rotation…

Leverage BrightData to Bypass Anti-Bot Defenses

While our Python scraper functions correctly, scraping any non-trivial site without precautions around usage thresholds and anti-bot systems is prone to breakages or blocking.

As highlighted earlier, BrightData facilitates scrapers to slip past defenses by providing clean residential IPs that perfectly mimic human visitors.

We only used BrightData in a minor capacity before by adding this single line:

page = requests.get('', proxies=brightdata.get_proxy())

The key benefit comes from brightdata.get_proxy() – it supplies a different residential IP on each call to route traffic through. This automatically rotates proxies with zero added effort.

By randomly distributing requests across IPs, scrapers bypass protections tracking usage thresholds and blocks. The scale of BrightData's proxy pool also minimizes repeats.

And BrightData handles CAPTCHAs transparently too – if one appears, the proxy will solve it behind the scenes without our script seeing any disruption.

With these safeguards integrated, you can expand the scraper horizontally to gather data from an entire site without worrying about blocks!

Final Thoughts and Next Steps

Phew, congratulations! We covered a lot of ground around building a robust web scraper in Python. Let's recap the key points:

  • Web scraping fundamentals
  • Analyzing target sites
  • Using Requests and Beautiful Soup libraries
  • Writing scraping logic
  • Exporting data
  • Leveraging BrightData residential proxies

You're now equipped to start scraping basic sites. To refine your abilities further, some recommended next steps include:

Scrape More Complex Sites

Practice web scraping tactics on intricate dynamic JavaScript-heavy sites. Learn to incorporate Selenium browsers for JS rendering.

Build a Production Pipeline

Create an enterprise-grade scraper that downloads pages in parallel, stores data in databases, and integrates with data science tooling.

Understand Legalities

Research nuances around copyright laws and fair use as they relate to web scraping.

Scraping opens up a world of possibilities for harnessing online data at scale! We encourage you to continue exploring with your newfound skills.

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