Python Language: What You Need To Know

Python is one of the world’s most popular computer languages, with over 8 million developers (this is according to research from SlashData).  The creator of Python is Guido van Rossum, a computer scientist and academic. Back in the late 1980s, he saw an opportunity to create a better language and also realized that the open source model would be ideal for bolstering innovation and adoption (by the way, the name for the language came from his favorite comedy, the Monty Python’s Flying Circus).

“Python is a high-level programming language, easy for beginners and advanced users to get started with,” said Jory Schwach, who is the CEO of “It’s forgiving in its usage, allowing coders to skip learning the nuances that are necessary in other, more structured languages like Java. Python was designed to be opinionated about how software should be created, so there’s often just a single appropriate way to write a piece of code, leaving developers with fewer design decisions to deliberate over.”

A way to get started with the language is to use a platform like Anaconda, which handles the configurations and installs various third-party modules. But there are cloud-based editors, such as REPL (I also have my own course on Python, which is focused on the fundamentals).

“Python has become the most popular language of choice for learning programming in school and university,” said Ben Finkel, who is a CBT Nuggets Trainer. “This is true not just in computer science departments, but also in other areas as programming has become more prevalent. Statistics, economics, physics, even traditionally non-technical fields such as sociology have all started introducing programming and data analysis into their curriculum.”

No doubt, a major catalyst for the growth of the language has been AI (Artificial Intelligence) and ML (Machine Learning), which rely on handling huge amounts of data and the use of sophisticated algorithms. 

“Because Python is easy to use and fast to iterate with, it was picked up early on by academics doing research in the ML/AI field,” said Mark Story, who is a principal developer at Sentry. “As a result, many libraries were created to build workflows in Python, including projects like TensorFlow and OpenAI.”

Although, Python has proven to be effective for a myriad of other areas, such as building websites and creating scripts for DevOps. Yet it is with AI/ML where the language has really shined.

“Analytics libraries such as NumPy, Pandas, SciPy, and several others have created an efficient way to build and test data models for use in analytics,” said Matt Ratliff, who is a Senior Data Science Mentor at NextUp Solutions. “In previous years, data scientists were confined to using proprietary platforms and C, and custom-building machine learning algorithms. But with Python libraries, data solutions can be built much faster and with more reliability. SciKit-Learn, for example, has built-in algorithms for classification, regression, clustering, and support for dimensionality reduction. Using Jupyter Notebooks, data scientists can add snippets of Python code to display calculations and visualizations, which can then be shared among colleagues and industry professionals.”

Granted, Python is certainly not perfect. No language is. 

“Due to its interpreted nature, Python does not have the most efficient runtime performance,” said Story. “A Python program will consume more memory than a similar program built in a compiled language like C++ would. Python is not well suited for mobile, or desktop application development as well.”

But despite all this, there are many more pros than cons–and Python is likely going to continue to grow. 

“Python is an excellent choice for most people to learn the basics of code, in the same way that everyone learns how to read and write,” said Tom Hatch, who is the CTO of SaltStack. “But the real beauty of Python is that it is also a language that can scale to large and complex software projects.”

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