Every language has been developed to solve a certain type of problem that developers face while trying to build software for a specific domain. Python, being a dynamically typed, interpreted language, also has a set of use cases where it excels.

These use cases involve the automation of repetitive and boring tasks, quick prototyping of applications, and small applications focusing on accomplishing a specific goal, such as the installation of software, the setting up of a development environment, performing cleanup, and so on.

But is that all? Is Python good only for doing small tasks? The answer to this is no. Python as a language is much more powerful and can easily accomplish a large amount of increasingly complex tasks, such as running a website that scales to cope with millions of users using it in a very short span of time, processing large sets of incoming files, or training a machine learning model for an image-recognition system.

We are talking about achieving increasingly complex tasks using Python, but isn't Python slow compared to our traditional compile-time languages, such as C++, Java, and .NET? Well, that completely depends upon the context in which a person wants to use Python. If your aim is to run a Python program on an embedded device with only a limited amount of processing power, then yes, Python might be inadequate because of the sheer extra load that its interpreter will have on the processing environment. But if you are planning to use Python to run a web application on decently configured modern hardware, you might never experience any slowdowns while using Python. Rather, you might well feel a bit more productive while using it because of the sheer simplicity of its syntax and the ease of performing operations without writing hundreds of lines to achieve simple tasks.

So, let's see how Python fares in the enterprise environment.