Web Scraping With Python: A Beginner’s Guide

Learn to perform web scraping with Python in order to gather data from multiple websites quickly, saving you both time, and effort.
header graphic - user at laptop using python for web scraping using a browser
Gal El Al of Bright Data
Gal El Al | Director of Support

Web scraping with Python allows you to efficiently collect relevant data points, providing you with the tools you need to get the job done.

6 reasons to use Python for Web Scraping

Python is one of the better-known coding languages, which makes it advantageous to many developers. It has many specific features that make it the preferred choice for data collection and web scraping automation, including:

#1: Simplicity – Python is a clear, straight forward coding language that does not include excessive non-alphabetical characters, unlike some other coding languages. The simplicity makes it easier for developers to learn and understand than other languages.

#2: Large libraries – Python has a large number of libraries at its disposal (NumPy, Matlpotlib, Pandas, etc.) which provide developers the ability to easily scrape and manipulate a wide variety of data sets.

#3: Timely typing – Python does not require developers to define or categorize the data types for variables. Instead, variables can be used directly whenever necessary, decreasing the possibility of confusion and saving time.

#4: Syntax is easily understood – Unlike other coding languages, Python syntax is very similar to reading English and therefore easy to understand. The indentations used in Python syntax can help developers discern different scopes and blocks in the code.

#5: Quick – Python allows developers to write simple code for complicated tasks. Developers do not want to spend an excessive amount of time writing code when the point of data scraping is to minimize unnecessary effort. Python allows you to do so.

#6: Familiarity – Python is one of the more commonly known coding languages. This creates a community of developers who can provide answers in case of any questions or road bumps that may come up throughout the process of writing the code.

How does Web Scraping with Python work

Once the code is written and run, a request for scraping is sent to your website of choice. If the request is approved, the server will send the desired data, allowing you to read the HTML or XML page. The code then automatically analyses the HTML or XML page, finds and parses the desired data.

The 5 basic steps of web scraping with Python:

Step 1: Choose the URL from which you would like to scrape

Step 2: Read the page and find the data you would like to collect.

Step 3: Write the code

Step 4: Run the code to extract the data

Step 5: Store the data in the necessary format

It is important to bear in mind that while certain sites allow web scraping freely, others may block you from doing so. In order to find out if a website blocks web scraping, you can check the website’s “robot.txt” file. You can find this file by adding “/robots.txt” to the URL of the website you wish to scrape. For example, if you would like to scrape data from kayak.com, you would type www.kayak.com/robot.txt into the address bar.

Using Python Libraries for Web Scraping

Python can be applied to a variety of different uses, each of which coincides with a different Python library. For web scraping purposes, you will use the following libraries:

Selenium: This is a web testing library used to automate browser activity.

Beautiful Soup: This is a library used for parsing HTML and XML documents. This library creates “parse trees,” allowing for easy data extraction.

Pandas: This is a library used for data manipulation and analysis. This library extracts and stores the data in your preferred format.

Inspecting the Site

Once you have chosen the website from which you would like to extract your desired data sets, your first step is to locate the links to the files that you would like to download. There are many layers of “tags” or code on any given site, and not all of this information is relevant to you. Inspecting the page allows us to figure out where the data you want to scrape is located.

To inspect the page, right-click on-site and then click ‘Inspect’ on the drop-down menu. Once you have clicked ‘Inspect’, you will see a box with raw code open.

right-clicking the mouse to get the pull down menu and then choose inspect to inspect the element, in this case the mesa infant car seat
Image source: snugglebugz.ca
Inspecting the product element to find the piece of code that will help you know where to start the parse
Image source: snugglebugz.ca

3 Steps to Writing Code in Python

Step 1: To start, you need to import the selenium library:

  • From Selenium import webdriver

Step 2: Set the credentials and settings to run Selenium:

  • Set the proxy credentials. In this case, we used Bright Data’s Proxy Manager
  • The path to the driver that will run Chrome.
  • Set the options of Selenium to use the proxy.
  • Set the target URL you want to scrape.

Note: You can send headers with the request to emulate more “human” behavior and avoid bot detection.

Step 3: Run your code. Selenium will open the target URL, store the page source to a variable, and then write it into a file called “output1.html”. After it’s done, the driver will close.

python script using we driver from selenium to scrape data

After extracting the data, you might want to store it in a specific format. This format varies depending on the purposes of your scraping activities. After changing the format, run the code again in its entirety. You can iterate through the data you scraped and extract the exact information you need.

Summing up

While web scraping with Python may seem complicated, this article was written to turn it into a quick and easy task for newcomers. Whether you are collecting pricing data, doing competitive research on your competitors, enforcing brand protection, or doing a host of other data-oriented tasks, web scraping with Python can be a powerful tool to get you the information you need in a straightforward and simple manner.


Is web scraping and crawling a part of data science?

Yes, web scraping and crawling are a part of the greater field of data science. Scraping/crawling serve as the foundation for all other by-products that can be derived from structured, and unstructured data. This includes analytics, algorithmic models/output, insights, and ‘applicable knowledge’. 

How do you scrape specific data from a website in Python?

Scraping data from a website using Python entails inspecting the page of your target URL, identifying the data you would like to extract, writing/running the data extraction code, and finally storing the data in your desired format.

How do you build a web scraper with Python?

The first step to building a web scraper with Python is utilizing string methods in order to parse website data, then parsing website data using an HTML parser, and finally interacting with necessary forms/website components.

Which is the best Python-based library for scraping or crawling?

You will want to work with Python’s Standard Library (with ‘urllib’ including Python tools to work with specific URLs such as ‘urlopen()’ allowing users to open target URLs within a desired program).

Gal El Al of Bright Data
Gal El Al | Director of Support

Head of Support at Bright Data with a demonstrated history of working in the computer and network security industry. Specializing in billing processes, technical support, quality assurance, account management, as well as helping customers streamline their data collection efforts while simultaneously improving cost efficiency.