One day in the late 80s, a sudden thought dawned on Guido van Rossum: “Isn’t it crazy that all those people have computers, and very few of them learn to program?” Personal computers have been used for around 20 years, but coding wasn’t as standard among everyday users back then.
Inspired by his work with the ABC programming language, van Rossum set out to make coding more accessible. In 1989, after a few years of development, the first version of his own interpreted, open-source programming language was implemented. Van Rossum named it Python after the popular BBC show Monty Python’s Flying Circus.
Today, Python is the second most popular programming language on GitHub, ranking only behind JavaScript. It’s established itself as a potent, multi-purpose language in data science, data analysis and visualization, machine learning, and web development.
Python is also a perfect fit for RPA development, and its relative simplicity means even non-technical users can leverage it for basic automation. This article investigates its strengths and weaknesses, uses, and case studies featuring automation with Python.
Advantages of Python for business automation
For more than 30 years since its inception, Python has become one of the preferred programming languages for process automation. Why is that?
Easy to learn and read
Accessibility was the guiding principle for van Rossum. Python has a simple, logical syntax that resembles English, making it easy to understand. As a high-level language, Python doesn’t require an in-depth understanding of how computers work, although it helps.
Python is also an interpreted language, meaning each code line can be run and checked immediately. This is a plus for beginners and experienced programmers, too, as Python enables faster development and easier maintenance of automation scripts.
Flexibility
Unlike domain-specific languages such as HTML or SQL, Python wasn’t created to serve any single specific purpose. Instead, it’s a general-use language that can be applied to automate tasks like organizing data, web scraping, sending emails, etc.
Community support and documentation
As an open-source language with a relatively low learning curve, Python has garnered a large and thriving community that actively contributes to its development. With so many engaged users, it’s easy to find answers to questions regarding automation with Python or a fellow programmer willing to help.
A broad range of libraries and frameworks
Another benefit of Python’s large community is a vast ecosystem of user-created libraries and frameworks. They are designed for specific purposes, including various automation tasks, adding to Python’s great versatility. And considering its ease of use, there’s a good chance that even users with basic Python language knowledge can understand and reuse code made by others rather than writing it from scratch.
Platform independence
Python is also a cross-platform language, meaning that automation scripts developed in Python can run on multiple operating systems, including Windows, macOS, and Linux. Thanks to that, programs written in Python are easier to share and deploy at scale across various platforms.
Integration capabilities
Python provides excellent integration capabilities with other programming languages and systems. This includes many languages commonly used in enterprise-scale development, such as Java, PHP, and .NET, which is why Python is well-suited for large and diverse environments.
Rapid development and prototyping
Simplicity, a vast number of high-level libraries, and the fact that it’s an interpreted language all make Python the perfect prototyping tool. Automation allows programmers to quickly iterate, debug, and find the best way to expedite the process.
Performance in RPA
Python is a dynamically typed language, meaning developers don’t need to declare each variable’s type explicitly. Instead, the type of the variable is verified at runtime. While this feature further increases Python’s flexibility and ease of use, it takes its toll on execution time, making Python slower when compared to most general-purpose languages.
However, this isn’t an issue from the RPA perspective. Firstly, in terms of speed, Python is on par with other languages used specifically in RPA. Additionally, workloads can often be parallelized, i.e., divided into smaller parts, which allows for optimized efficiency. Python is also highly performant when it comes to tasks that involve data uploads and downloads through a network, e.g., making HTTP requests.