As the demand for more diverse functionality in computers continued to rise in the 1980s—from word processing to gaming—developers needed to broaden their approach to creating these applications. To meet the evolving needs, developers needed programming languages with a wide range of possible functionality that were lean at their core.

These attitudes could be evidenced by the frustrations of developers like Guido van Rossum who envisioned a small base language that would be expandable thanks to a vast standard library and extensible interpreter, contrary to the ABC programming language.

In pursuit of a successor to this language, Rossum went to work on a new project called Python, and the resulting language was implemented starting December 1989. Rossum would go on to be the lead developer and chief decision-maker until 12th July 2018. As of May 2019, Python had gathered at least 8.2 million active developers around the world.

Python in test automation

As teams adopt efficient development models like Agile development, there’s an increasing need for people and tools that can deliver more robust testing. In order to test as fast as they develop, teams have utilized test automation to take over some of the more repetitive tasks.

For test automation, teams select a programming language that supports the processes they intend to carry out. Python is the ideal candidate for test automation partly because it supports a wide range of test frameworks. One of Python’s preferred test automation frameworks is the Robot Framework. Fundamentally speaking, test automation involves the simulation of interactions with various systems.

As it turns out, Robotic Process Automation (RPA) is quite similar to test automation. With this in mind, test automation framework can be useful in automating beyond just the test automation processes. Robot Framework was created for test automation, and the idea of bringing this to RPA was the basis for Robocorp’s founding. This is made possible largely by Robot Framework’s open and extensible nature, allowing for integration with an assortment of tools for greater flexibility.

Robocorp’s stack: RPA and Python

Robot Framework utilizes human-readable keywords and can be extended through various libraries. This is why Robocorp was built on top of this framework, so it could harness these features already created for test automation.

A common shortfall amongst many RPA platforms is their limited approach when it comes to building robots. Most RPA platforms offer drag-and-drop capabilities, but these leave little room for customization. In cases where developers can customize the robots, they still fail to achieve stability and easily fall apart. Finally, the robots on most platforms only interact with the graphical user interface (GUI), so if a program’s interface changes (as it often does), the robot breaks.

Robocorp threw out this old GUI-based, drag-and-drop playbook. With Robocorp, users can utilize Python to custom-build robots that function in the exact way you need them to. They can be defined using a simplified command language built on top of Python, courtesy of the Robot Framework. The robots can then be extended using Python libraries when necessary. Robocorp’s approach facilitates automation at layers beyond the GUI—through APIs and at the code level, for example—enabling scalability down the road.

Python and AAA: automation, analytics, and AI

The ability to track and report on business processes is critical. It has become increasingly important to have analytics tools that do more than just displaying metrics. Organizations have gone as far as incorporating artificial intelligence (AI) and machine learning (ML) into their analyses of large data volumes to detect patterns and use them to repeatedly refine the entire process.

However, due to the robust nature of these analysis processes, the AI tools require the right programming language. The language must meet the following criteria:

• Have easy syntax
• Be able to handle intricate processes
• Support numerous extensions where necessary.

This is where Python emerges as a top contender since it checks all these boxes and seamlessly combines automation, analytics, and AI—“AAA” for short.

For starters, Python supports a number of libraries that can aid AI-related processes. These include, among other AI-oriented libraries:

• Scikit-learn for basic ML algorithms
• Pandas for high-level data analysis
• Keras, Caffe and TensorFlow for deep learning
• NLTK for natural language processing.

Other libraries like Matplotlib also come in handy when trying to clearly represent data. They aid in the creation of charts, 2D plots, histograms and other visualizations that make it easier to digest data insights.

Python’s relatively low barrier to entry stems from its user-friendly syntax. The open source nature of offerings like the Robot Framework means that users don’t have to choke on licensing fees associated with some vendors’ products. Python also has a large and active online community from which users can get some helpful documentation and access other resources and discussions on a wide range of topics including AI, analytics and automation.

The future of Python: automation and beyond

There are many studies and reports, including one from StackOverflow, that show that Python adoption will continue to grow. This means that it’s going to become easier to access developer talent in the Python ecosystem. A quick look at job posts on sites like indeed.com shows that there are thousands of jobs for Python developers across business functions and in all corners of the globe.

AI and ML will also continue to penetrate different sectors and bring Python along with them. We are already seeing Python unsupervised ML algorithms being used in the travel industry to predict the characteristics surrounding new airplane routes.

AI is also being used in fintech and the broader financial services sector for fraud prevention, risk management, personalized banking, and others. Python has also permeated the Cryptocurrency space in the form of tools like Anaconda for market analysis, predictions and data visualization. Python-developed ML platforms are also being used in the transportation industry to facilitate online and offline predictions when solving daily tasks, with unprecedented scalability.

The healthcare industry has also seen natural language processing tools like Fathom assist in analyzing electronic health records, ultimately aiming to automate medical coding.

All-in-all, Python allows automation tools to strike the perfect balance of user-friendliness, extensible programming, and open source community, making it ideal for test automation and RPA. Or as we way at Robocorp, Python is the native language of automation.