An Introduction to Python: A Language for the Ages
For anyone just getting into software programming, one of your best friends will be Python. Why? Python is very simple to learn and easy to implement. Even better, what you can do with this language grows as you learn more. You can start with very simple text-based applications and migrate to GUI applications and much more. And because Python is supported by most major operating systems (Linux, macOS, and Windows), you can begin your journey, regardless of platform.
Python includes support for features such as lists, tuples, functions, variables, JSON, and ranges. But where did Python come from and why is it still so important today? Let’s dig in and find out. To follow our series of introductory Python tutorials, start here.
The History of Python
Python is a dynamically typed programming language that doesn’t require applications to be first compiled before they’re run. That alone helps to simplify the language, by removing what can often be a time- and resource-consuming process of compilation. And since the very philosophy behind Python is code reliability, you can be sure that learning this language is far simpler than many other languages.
Python began its life in the 1980s, created by Guido van Rossum as a successor to the ABC language. The original goal was to create a language that was capable of exception handling and interfacing with the now-defunct Amoeba operating system. This work began in December of 1989 and Rossum served as the primary developer until July 12, 2018, when he announced he was going on “permanent vacation” as Python’s “benevolent dictator for life” (a title given to him by the Python community).
In January 2019, the remaining Python core developers elected a Steering Council of five members that would serve to lead the project into the future.
The next major milestones in the Python timeline include:
- October 16, 2000 – the release of Python 2.0
- December 3, 2008 – the release of Python 3
- January 1, 2020 – the End of Life for Python 2.0
With the release of Python 3 came a utility, named 2to3, which automates the translation of Python 2 code to Python 3 code. Given how many projects and users were still working with Python 2, that automation tool became an absolute necessity.
For me, the main reason why I believe Python is so popular is that it’s the ideal language to learn the craft of software engineering. With a very easy-to-understand syntax, widespread support, cross-platform usability, and no need for compilation make Python so easy to get up to speed with. And given how flexible the language is, the sky’s the limit with what you can make it do.
The Differences Between Python 2 and 3
If you’re wondering what the differences are between Python 2 and 3, here’s a quick summary:
|Strings are stored as ASCII.
|Strings are stored as UNICODE.
|Results in an integer value.
|Results in a floating-point value.
|Enclosed by notations.
|Enclosed by parentheses.
|Values can change if in a for-loop.
|Values can never change.
|Achieved with xrange()
|Achieved with Range()
|More complicated syntax.
|Python 2 can be ported to Python 3.
|Python 3 cannot be ported to Python 2.
|Rules of ordering comparisons
Python and Today’s Needs
Python has been enjoying a bit of a renaissance of late, thanks to the astronomical rise of machine learning and artificial intelligence. With access to several open-source libraries — such as Scikit-learn, Tensorflow, PyTorch, Apache MXNet, XGBoost, Paddle Paddle, and ONNX — Python is an ideal partner for machine learning.
Machine Learning has been put into use by a vast majority of enterprise businesses for things like
- Fraud detection
- Algorithmic trading
- Finance and portfolio management
- Email monitoring
- Customer Journey Optimization
- Autonomous vehicles
- Behavior prediction
- Process automation
Machine Learning has become critical for so many businesses looking to remain relevant in a hyper-competitive world. And Python is at the heart of that, thanks to so many libraries and tools available to make the integration of Machine Learning into your processes and workflows easier than you might think.
But the resurgence of Python isn’t only about machine learning. There are other reasons why the language is enjoying so much popularity these days, such as
- Maturity and widespread support from an active community.
- Corporate sponsorship and support.
- Numerous Python libraries and frameworks.
- Reliability and speed.
- Big Data and Cloud-native development.
The last two entries alone should help make it clear why Python is still a very relevant language. Python is still a shining start in data analysis, with enterprise businesses depending on the language for data processing workloads everywhere.
Because Python is so adept at manipulating and processing large data sets, it’s an ideal language to meet the needs of cloud computing. Python also enjoys major support from all major cloud providers, such as Azure 2.x runtime support and Amazon Web Services‘ SDK for Python.
Python and the Future
Although the future of technology has become quite challenging to predict, it’s pretty safe to say that data analysis, machine learning, and cloud computing aren’t going anywhere.
According to Technavio, the data analytics field is expected to increase by $196.47 billion from 2021 to 2026, and the market’s growth momentum will accelerate at a CAGR of 13.54%. Those figures alone make it clear that Python’s future is pretty secure and more and more businesses will continue to depend on the language for the foreseeable future.
So, what are you waiting for? It’s time to hop on the Python train so you can help elevate your business into an extremely competitive present and the possibilities of the future.