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Python for Automation: A Step-by-Step Guide for Beginners and Experts Alike

The knowledge of Python can greatly benefit a software testing company, not only by providing access to test automation tools but also by enhancing manual testing skills. As the tech industry continues to evolve, companies must stay prepared for market challenges by equipping their teams with the necessary competencies.

According to a recent survey by the DevOps Institute, Python is the most popular programming language for automation testing, with 57% of respondents indicating that they use it for testing purposes. This is likely due to the ease of use and flexibility of Python, making it an attractive choice for both novice and experienced programmers.

As tech specialist Matt Watson said, “Python is becoming the de facto language for test automation due to its simplicity, readability, and ease of learning.” Learning Python can be a valuable investment for QA specialists looking to stay ahead of the curve in the ever-changing tech landscape.


What is Python & Why is it popular?

Python is a widely used open-source programming language that has taken the tech industry by storm! In fact, according to a survey conducted by Stack Overflow in 2021, Python was the most widely used language among developers for the fourth year in a row. So, why is Python so popular?

One of the main reasons is its extensive collection of libraries and frameworks, which help to reduce development time and make coding a breeze! Plus, the simple and readable syntax makes it easy to learn for beginners, leading to a wider community of users.

In addition, the Python community is known for being welcoming and supportive. With so many resources available online, including forums and blogs, developers can quickly find solutions to their coding problems.

And did you know that Python is not just for coding? It can also be used for fun activities like creating games and even for controlling robots! In fact, there are several Python libraries and tools designed specifically for creating games, such as Pygame and Panda3D.

So, whether you’re a beginner or an experienced developer, Python has something for everyone! With its simplicity, versatility, and strong community, it’s no wonder that it’s the most popular language out there.

Why is learning Python a good idea?

Python’s popularity continues to soar and QA companies are taking notice due to its simplicity, readability, versatility, productivity, and supportive community. For instance, Python’s concise code allows for more efficient task-solving, while its vast libraries save time and effort in code creation.

Python is easy to learn:

For QA engineers, learning a new programming language can be a challenge while focusing on software testing. However, Python’s user-friendly syntax makes it an ideal language to learn from scratch. Additionally, ample online resources are available to assist in the learning process.

Example: Learning a new programming language can be a hurdle for QA engineers, but Python’s simplicity in syntax makes it an ideal language to begin with for software testing services. Moreover, Python has a wealth of online resources to assist in the learning process.

It has readable code:

Python code boasts readability, making it simple to comprehend. This feature makes it a favourable choice for scripting, aided by the support of various tools.

Example: The readability of Python’s code makes it an attractive option for scripting. The added support from a diverse range of tools enhances its functionality, ultimately leading to more efficient software testing services.

Python is a universal language (almost):

Python’s versatility as a general-purpose language enables it to tackle a wide range of tasks, making it a nearly universal language. Its diverse applications span from web and desktop app development to data analytics and scripting.

Example: Python’s status as a general-purpose language allows it to solve a multitude of tasks, solidifying its standing as a near-universal language. With applications in web and desktop app development, data analytics, scripting, and beyond, Python is a highly sought-after language for software testing services.

It enhances team productivity:

Python’s conciseness allows it to solve tasks with fewer lines of code, enabling higher team productivity as it leaves more time to focus on more complex tasks. In comparison to other languages like Java, Python’s streamlined syntax requires fewer lines of code, achieving the same results in less time.

Example: Python’s brevity when it comes to coding allows for improved team productivity, granting them additional time to work on more complex tasks. Unlike other languages, Python requires fewer lines of code due to its streamlined syntax, enabling faster problem resolution.

The strong community is your backup

Python’s extensive code libraries provide a supportive community that saves time by enabling the use of existing code rather than requiring the creation of new code. The availability of these pre-existing code resources makes Python a valuable asset to QA companies seeking to optimize their software testing services.

Example: Python’s thriving community provides a wealth of code libraries that QA companies can leverage to enhance their testing services. These existing code resources eliminate the need to create new code, freeing up time for QA engineers to tackle other essential tasks.

Python’s script automation capabilities offer an array of benefits to QA companies. By automating processes, Python enables companies to streamline their workflows and optimize productivity. From the deployment of the test environment to continuous integration, Python can automate various tasks and enhance the overall efficiency of the software testing services.

The pros of automation using Python:

  1. Increased efficiency: Python can automate repetitive tasks, freeing up valuable time for employees to focus on more important tasks.
  1. Cost-effective: Automating tasks with Python can significantly reduce labor costs, as businesses can save money by not having to pay employees to perform repetitive tasks.
  1. Faster turnaround times: Python automation can reduce turnaround times for tasks that would otherwise take longer to complete manually.
  1. Reduced errors: Automating tasks with Python can reduce the likelihood of errors, which can save businesses money and prevent potential issues down the line.
  1. Greater scalability: Python automation can be easily scaled to accommodate changing business needs, without the need for additional resources.
  1. Improved data analysis: Python’s data analysis capabilities make it an excellent tool for automating tasks related to data processing and analysis.
  1. Improved decision-making: By automating tasks with Python, businesses can make better, data-driven decisions faster.
  1. Better customer service: Python automation can help businesses respond to customer inquiries and complaints more quickly and efficiently, leading to greater customer satisfaction.

Overall, automation with Python can help businesses save time, reduce costs, improve efficiency and accuracy, and make better decisions.

How to learn Python:

Learning Python is not as difficult as it may seem. Here are some simple steps to help you get started:

  1. Set up your development environment: Download and install Python on your computer. You can download the latest version of Python from the official website.
  1. Learn the basics: Start with the fundamentals of programming, including variables, data types, operators, and control structures. Here is an example:

# Variables and data types

name = “John”

age = 25

height = 1.75

# Operators

result = 10 + 5

product = 10 * 5

quotient = 10 / 5

remainder = 10 % 3

# Control structures

if age >= 18:

  print(“You are an adult”)


  print(“You are a minor”)

  1. Practice with exercises and projects: To gain experience with Python, work on exercises and small projects. You can find plenty of resources online, such as Codecademy, HackerRank, and Project Euler.
  1. Join a community: Connect with other Python learners and professionals through online forums, social media, or local meetups. You can learn a lot from others and get valuable feedback on your code.
  1. Keep learning: Python is a versatile language with many applications. Keep exploring new topics and libraries, such as data analysis, web development, machine learning, and more.

Remember, learning Python takes time and practice. Don’t be afraid to make mistakes and keep trying until you succeed.

How to write and run automated tests with Python

To write and run automated tests using Python, you can use a testing framework such as pytest or unittest. Here are the general steps you can follow:

1. Install the testing framework of your choice by running the following command in your terminal:

pip install pytest

2. Create a new Python file in your project directory and define your test functions. For example, let’s say you want to test a function that calculates the sum of two numbers. Here’s how you could define your test function using pytest:

def test_sum():
assert sum(2, 3) == 5

3. Run your test file using the testing framework you installed. For example, if you’re using pytest, you can run your tests by running the following command in your terminal:


4. If all tests pass, you should see output similar to the following:

============================= test session starts =============================
collected 1 item . [100%]

============================== 1 passed in 0.01s ==============================

If any tests fail, you’ll see an output indicating which tests failed and why.

5. Continue adding test functions as needed and running them using your chosen testing framework. You can also use features of the framework, such as fixtures and parameterized tests, to write more complex tests.

Note: It’s a good practice to keep your test files separate from your application code files. You can create a separate directory for your test files and include them in your project directory.

Python automation frameworks

1. Python has several automation frameworks that can be used for different types of automation testing, including:

2. Pytest: Pytest is a popular testing framework for Python that is easy to use and supports fixtures, parameterized tests, and test discovery.

3. Robot Framework: Robot Framework is a generic test automation framework that is easy to learn and supports keyword-driven, data-driven, and behavior-driven testing.

4. Behave: Behave is a behavior-driven development (BDD) framework that uses natural language to describe test scenarios and supports test automation using Python.

5. unittest: unittest is a built-in Python testing framework that supports test discovery, fixtures, and test runners.

6. Selenium WebDriver: Selenium WebDriver is a popular web application testing framework that supports automation testing of web applications using Python.

7. Appium: Appium is an open-source automation framework that supports testing of mobile applications using Python.

8. PyAutoGUI: PyAutoGUI is a cross-platform automation framework that can be used to automate GUI applications using Python.

These frameworks can be used for different types of testing, including unit testing, integration testing, functional testing, and acceptance testing. Choose the framework that best fits your needs and project requirements.


Automating software testing with Python can significantly optimize QA resources, allowing teams to focus on more complex tasks and functionalities that require manual checks. By adopting automation practices, organizations can improve the efficiency and effectiveness of their testing processes, leading to faster time-to-market and better product quality. In addition, outsourcing QA tasks to expert teams can further enhance the benefits of automation, enabling organizations to leverage specialized skills and experience while freeing up internal resources. With the step-by-step guide provided in this blog, beginners and experts alike can get started with Python for automation and unlock the full potential of automated software testing.

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