Developers spend more time thinking about the problem they're trying to solve, and less time thinking about language complexities or deciphering code left by others. Python is both popular and widely used, as the high rankings in surveys like the Tiobe Index and the large number of Git Hub projects using Python attest.
Python runs on every major operating system and platform, and most minor ones too.
However, over the past few years, Python has emerged as a first-class citizen in modern software development, infrastructure management, and data analysis.
It is no longer a back-room utility language, but a major force in web application development and systems management and a key driver behind the explosion in big data analytics and machine intelligence.
Sophisticated data analysis has become one of fastest moving areas of IT and one of Python's star use cases.
This allows Python to work as a highly efficient code generator, making it possible to write applications that manipulate their own functions and have the kind of extensibility that would be difficult or impossible to pull off in other languages. Python is often described as a "glue language," meaning it can allow disparate code (typically libraries with C language interfaces) to interoperate.
Its use in data science and machine learning is in this vein, but that's just one incarnation of the general idea.
It's also not ideal for situations that call for cross-platform standalone binaries.
You could build a standalone Python app for Windows, Mac, and Linux, but not elegantly or simply.