How Python Started
In the late 1980s, Dutch programmer Guido van Rossum worked at a research center in Amsterdam. He was tired of complex programming languages that were widely used at the time. He wanted to create something easier to read and write, without all the unnecessary complexity of other languages. During a holiday in 1989, he started a new project, and in 1991, the first version of Python was officially released.
By the way, the name Python comes from Monty Python's Flying Circus, a British comedy show that Guido was a fan of. So, it has nothing to do with the snake (though the snake is often used as a logo).
Why is Python So Popular?
There are many different programming languages, but there are some reasons why python is so popular and I recommend to use python for your analyses.
1. Simple and Readable
Python almost looks like regular sentences. No complicated brackets or dollar signs like in other languages.
print("Hello, world!")
Of course, readability also depends on the programmer, but the language itself makes it easier.
2. Widely Applicable
Python is used everywhere: from web development and data analysis to artificial intelligence and machine learning. Whether you want to create a simple website, develop software, automate tasks, or analyze data, Python can do it.
3. Large Community
Since so many people use Python, help is always available. Whether you're stuck on an error message or looking for a useful library, someone has likely faced the same problem and shared a solution. Moreover, most GenAI tools handle Python and its modules/packages well.
4. Extensive Library Collection
Python has a massive collection of pre-built modules you can use. Want to analyze data? Use pandas. Want to create charts? Use matplotlib. Want to implement artificial intelligence? Use TensorFlow. There's no need to reinvent the wheel, and plenty of documentation is available for these popular modules.
Getting Started with Python?
The great thing is: you can start today. Install Python (free to download from python.org), open a text editor or a programming environment like Jupyter Notebook, and start coding. There are countless free courses and tutorials online.
Python for Audit Analytics
Python offers enormous benefits in the world of audit analytics. With powerful libraries like pandas and numpy, you can analyze large datasets, discover patterns, and detect anomalies that might otherwise remain hidden. Additionally, matplotlib and seaborn are handy tools for visualizing data. And with statsmodels and scipy, you can perform statistical analyses to better understand trends and deviations. Python enables complex analyses to be executed quickly and reproducibly, providing immense value in audit.
Conclusion
Python is not just a trend. It is a powerful, flexible, and yet simple programming language that is also useful in audit analytics and will continue to be in the coming years. With Python, you can efficiently process, analyze, and visualize large amounts of data. So if you work in audit and want to integrate data analysis more into your work, Python is an excellent choice to start with!
