Are you delving into web scraping with Python but starting to feel overwhelmed? You’re not alone. It’s a complex process and can be challenging for those just starting out. This guide provides a simple, step-by-step approach to using Python for web scraping to give you all the information you need to clarify the process and successfully conduct your project.

How is Python used in web scraping?

Python is a high-level, interpreted programming language which supports various programming paradigms and is equipped with a large standard library. It is renowned for its simplicity and efficiency and has become a go-to language for web scraping due to its clear syntax and readability.

Generally, a programming language is merely a set of instructions used in the development of software programs. This allows the programmer to communicate with the computer to make the machine perform the task according to the given instructions.

Programmers develop algorithms, manage data, and develop various types of applications and systems through coding.

Code readability was, in fact, a key consideration when Python was designed, and its syntax allows programmers to express concepts in fewer lines of code than what would be possible in other programming languages such as C++ or Java.

So, how does web scraping with Python work?

Web scraping refers to automatically collecting data from websites. It is the automatic process of browsing the pages on the web, searching for and locating required information, collecting it, and then turning that unstructured web content into structured data.

Python can be used in web scraping as a script for navigating the way through web-browsing activity, to allow the content in web pages to be collected. Python’s various libraries and frameworks simplify these steps, making it a powerful tool for web scraping.

Data libraries and frameworks are collections of pre-written code that provide additional functionality for Python, allowing developers to perform complex tasks, like web scraping, more efficiently without having to write code from scratch.

For many web scraping projects, Python can be the best choice of programming language, given its user-friendliness, rich selection of libraries, and community support. But there’s more to it than just these standout features.

Some of the ways Python data libraries are used for web scraping are:

  1. Requesting Data: Libraries like requests allow Python to send HTTP requests to websites. This is the initial step where Python interacts with the web to fetch data.
  2. Parsing: Once the data is fetched, Python uses libraries like BeautifulSoup or lxml to parse the content. These libraries provide functionalities to navigate the structure of the web pages and extract the content.
  3. Data Extraction: Python’s ability to handle strings and its powerful regex module enable it to sift through text and extract the needed information efficiently.
  4. Storing Data: Python supports various data formats for storage. Libraries like pandas can be used to organize extracted data into structured formats like CSV or Excel files, which can then be used for further analysis.

Datamam, the global specialist data extraction company, works closely with customers to get exactly the data they need through developing and implementing bespoke web scraping solutions.

Sandro Shubladze, Founder & CEO of Datamam, says: “Consider that websites are not mere static entities but vibrant hubs where data is both created and consumed. From the spontaneous Tweets of an individual to the systematic transactions of e-commerce platforms, every interaction weaves into the rich tapestry of web data.”

What are some common Python data libraries?

Python is a powerhouse when it comes to data manipulation and web scraping, thanks to its wide array of specialized libraries. It is these libraries that make web data extraction, parsing, and analysis much easier, and thus, Python becomes one of the preferred languages for a data scientist or developer.

Here, we are going to discuss some of the major Python libraries that have some significance in the matter of web scraping, each with its unique strengths and limitations.


  • For making HTTP requests to web pages.
  • Pros: Simplicity: Requests is renowned for its user-friendly interface, making HTTP requests as straightforward as possible. Its simple API allows for easy sending of HTTP/1.1 requests without the need to manually add query strings to your URLs or form-encode your POST data.
  • Cons: Static Content: Primarily designed for dealing with static content, Requests cannot natively handle dynamic JavaScript content. This limitation can be a significant drawback when scraping modern web applications that rely heavily on JavaScript to render their pages.


  • For parsing HTML and XML documents.
  • Pros: Ease of Use: BeautifulSoup provides a straightforward way to parse HTML and XML documents. It allows you to search for specific elements, navigate the parse tree, and modify the tree, which is intuitive for beginners.
  • Cons: Dependent on Parsers: It doesn’t parse HTML content on its own and requires external parsers to work, which can sometimes lead to additional complexity in setting up a scraping environment.


  • Open-source data analysis and manipulation tool, built on top of the Python programming language.
  • Pros: Data Handling: Pandas excel in handling and manipulating structured data. It’s particularly effective for managing tabular data, like the kind you might extract during web scraping.
  • Cons: Memory Usage: Pandas can be memory-intensive, particularly when working with large datasets. This can sometimes lead to performance issues on machines with limited memory.


  • For automating web browsers, particularly useful for dynamic websites.
  • Pros: Dynamic Interaction: Selenium excels in interacting with web pages dynamically rendered with JavaScript. It can automate browser actions like clicking, scrolling, and filling out forms, mimicking human interaction.
  • Cons: Performance: Running a full browser in the background makes Selenium significantly slower compared to other libraries that operate at the HTTP protocol level. This can impact the efficiency of your scraping operations, especially at scale.

Sandro says: “Even though you can create scrapers in various programming languages, Python is the most popular. It enables faster coding, which is very helpful in keeping up with website changes.”

A step-by-step guide to web scraping with Python

Web scraping with Python can be a daunting journey, that’s a common sentiment amongst beginners to scraping. Most of the time, web scraping can be difficult; it requires an understanding that must be both focused on the web and programming.

This guide gives readers a detailed overview of the process, as it explains clearly, step by step, how Python can be used in web scraping. Helping people learn how to successfully navigate the difficulties and pull off their web scraping project.

1. Set up and planning

Before diving into the details of web scraping with Python, first things first: preparing your working environment. This involves setting the parameters and scale of the project, familiarizing yourself with the data you are looking to scrape, and getting your libraries up and running.

The first step is to install Python. To do this, the tool will need to be downloaded from the official website,, and installed by following the instructions. Python should be added to the system’s PATH so you can access it from your command line or terminal.

It’s best practice to create a virtual environment for Python projects, including web scraping. This isolated environment keeps a project’s dependencies separate from other Python projects. To set up a virtual environment, use the following commands:

python -m venv my_web_scraping_project

cd my_web_scraping_project

source bin/activate

With this environment set up, it’s time to choose and then install the Python libraries needed for web scraping. Libraries can be installed using the pip tool.

2. Write the Python Code and setting up GET Requests

Writing efficient Python code for web scraping involves understanding how to make requests to web pages, parse HTML to find the data you need, and use web developer tools to identify the HTML elements containing the data.

The first step in web scraping is to request data from a web page. This is typically done using a GET request, which retrieves data from a specified resource. In Python, the requests library makes this task straightforward.

import requests

url = ''

response = requests.get(url)

if response.status_code == 200:

    html_content = response.text


    print("Failed to retrieve the website")

This code snippet sends a GET request to ‘’ and stores the response. If the request is successful (status code 200), it stores the HTML content of the page.

3. Use ‘find’ and ‘find all’ Commands to find data

Once you have the HTML content, you can use BeautifulSoup to parse it and extract the necessary data. The find and find_all methods are particularly useful for this.

from bs4 import BeautifulSoup

soup = BeautifulSoup(html_content, 'lxml')

header = soup.find('h1').text


data_points = soup.find_all(class_='data-point')

for data_point in data_points:


In this example, find retrieves the first h1 tag, while find_all fetches all elements with the class data-point.

4. Open developer tools and finding the HTML elements that contain the desired data

To identify which HTML elements to target with your find and find_all commands, you’ll need to inspect the web page using Developer Tools, available in most modern web browsers. To do this,right-click on the webpage and select “Inspect” or press Ctrl+Shift+I to open Developer Tools.

Use the Elements tab to view the HTML structure of the page.

Then, hover over or click on elements in the Developer Tools to find the ones containing the data you want to scrape. Note the tags, ids, or classes of these elements, as you’ll use them to locate the data in your Python script.

Let’s combine these steps into an example:

import requests

from bs4 import BeautifulSoup

url = ''

response = requests.get(url)

soup = BeautifulSoup(response.text, 'html.parser')

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

print(f"Title: {title}")

data_list = soup.find_all(class_='item')

for item in data_list:


This code fetches the title of the webpage and prints out the text of each item in a list with the class item. From this, you can write up a Python code that scrapes information from web pages. This will turn into the structure of the web scraping script.

If you need additional information on web scraping, you can find it here.

5. Start web scraping

Once the environment is set up and the toolkit is selected, the next step is to start web scraping.

The first step to web scraping is to identify and extract the data you need. Most times, to get the data, you get to send a GET request to the website and write a parser to scrape through the HTML data for every data point you want to collect.

For further insights and a more detailed walkthrough, consider exploring the “How to Web Scrape” article, which provides a comprehensive guide to kick-starting your web scraping endeavors.

In the example below, we use Requests to fetch the web page and BeautifulSoup to parse and extract the data. It’s crucial to verify the extracted data to ensure it meets your expectations before proceeding.

import requests

from bs4 import BeautifulSoup

url = ""

response = requests.get(url)

soup = BeautifulSoup(response.text, 'lxml’)

data = soup.find('div', class_='data-class').text

if "expected value" in data:

    print("Data extraction is successful")


    print("Data extraction needs revision")

6.  Parse and store the data

After extraction, the next step is to parse this data into a structured format and save it for further analysis or use.

Python’s pandas library is particularly useful for this task, allowing you to save data in various formats, including CSV and Excel.

This code snippet demonstrates how scraped data can be converted into a DataFrame and then written into both CSV and Excel files. Adjusting the structure of the DataFrame according to your data is crucial for effective data storage and retrieval.

import pandas as pd

data_dict = {“Name”: [“John Doe”, “Jane Doe”], “Age”: [30, 25]}

df = pd.DataFrame(data_dict)

df.to_csv("scraped_data.csv", index=False)

df.to_excel("scraped_data.xlsx", index=False)

It’s worth noting that experts in the field emphasize the importance of clean and structured data extraction. Therefore, cleaning and validation of the imperative hours become redundant, provided there is an organized, verified, and continuous internal data flow of imperative hours from the point of extraction to the last phase.

It is a great approach to laying appropriate grounds for any form of analysis, or for that matter, for any other possibility of an application of the scraped data themselves.

Sandro says: “Web scraping process with Python requires a structured approach. The output can be biased if you make any mistake in the process or miss any step.”

“If you have the proper resources and are not intimidated by a large-scale project, then starting on your own is entirely doable.”

What are the benefits and challenges of using Python for web scraping?

Web scraping with Python can be a powerful tool. Some of the benefits of working with Python for web scraping include:

  • Community and Library Support: Python has a vast, active community and many choices of libraries, making web scraping more accessible. With libraries like BeautifulSoup, and lxml which simplifies HTML parsing, Python provides robust tools that cater to various scraping needs.
  • Cross-Platform Compatibility: Python’s cross-platform nature allows developers to write their scraping scripts once and run them on any operating system, be it Windows, macOS, or Linux. This universality ensures that Python’s web scraping solutions are versatile and adaptable to different environments.
  • Integration Capabilities: Python excels in its ability to integrate with other languages and tools. It can easily connect with databases, perform data analysis with libraries like pandas, and even integrate with data visualization tools, making the data scraped easily accessible as well as actionable.
  • Scalability: Python’s scalability is a significant benefit. As your data scraping needs grow, Python scripts can be scaled up with relative ease, making it a viable option for both small and large-scale scraping tasks.

Despite all the pros, Python is not without its challenges when being used in web scraping projects.Here we run through some of the potential pitfalls of using Python:

  • Rate Limiting and IP Blocking: Websites often have mechanisms to block scrapers, like rate limiting or IP bans. Python developers need to implement strategies to handle these, such as rotating user agents and IP addresses, which can complicate the scraping process.
  • Handling Ajax and JavaScript: Web pages heavily reliant on JavaScript for content rendering pose a challenge. While tools like Selenium can help Python interact with such pages, they introduce an additional layer of complexity and can slow down the scraping process.
  • Compliance with Legalities: Navigating the legal aspects of web scraping is crucial. Ensuring compliance with a website’s robots.txt file, terms of service, and various international data protection laws can be daunting and necessitates a thorough understanding of legal frameworks surrounding web data extraction.

Sandro says: “Ensuring the accuracy of extracted data can be challenging, especially when dealing with complex web page structures or inconsistent data formats. Developers must create robust parsing logic to handle these variances, which can require significant time and effort.”

Companies such as Datamam provide specialized services in navigating this complex landscape of web scraping. By leveraging Datamam’s expertise, you can avoid common pitfalls, ensuring your web scraping efforts are successful.

If you are interested in hearing more about the custom web scraping route, experts at Datamam are happy to help. Contact us today to find out more