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.
Python's success revolves around several advantages it provides for beginners and experts alike: Python is easy to learn.
Most modern object types - Unicode strings, for instance - are built directly into the language.
Data structures - like lists, dictionaries (i.e., hashmaps), tuples (for storing immutable collections of objects), and sets (for storing collections of unique objects) - are available as standard-issue items.
You could build a standalone Python app for Windows, Mac, and Linux, but not elegantly or simply.
Finally, Python is not the best choice when speed is an absolute priority in every aspect of the application.
Its use in data science and machine learning is in this vein, but that's just one incarnation of the general idea.
Also worth noting are the sorts of tasks Python is not well-suited for.
However, over the past few years, Python has emerged as a first-class citizen in modern software development, infrastructure management, and data analysis.
Python isn't just a replacement for shell scripts or batch files, but is also used to automate interactions with web browsers or application GUIs or system provisioning and configuration in tools such as Ansible and Salt.
But scripting and automation represent only the tip of the iceberg with Python.
In this case, the object in question is loop in Python, much as you would in another language.
The point is that Python has a way to economically express things like loops that iterate over multiple objects and perform some simple operation on each element in the loop, or work with things that require explicit instantiation and disposal.