Over the last few years, Python has established itself as a market leader in the tech industry. This growth has primarily been thanks to the boon that machine learning, data science and artificial intelligence have encouraged. It has grown so fast and its reach gotten so wide that there is barely a niche that it cannot cover today. From the front end to the back end where it's used to create advanced APIs thanks to the number of features it comes built with, Python is the language to learn if you want to enjoy the safety of always having jobs that need you to fill.
let array1 = [1,2,3]; let array2 = [1,2,3]; console.log(array1===array2) //false
Such behaviour, and Python's own amazing API is what has necessitated the existence of transpilers.
Python also comes equipped with a heavy feature set that allows you to create development and production servers that can be deployed online. Essentially, a webserver is usually created to host an API. An API can simply be thought of as the middleman between the front-end and back-end features such as the database, analytics server by exposing various endpoints.
Python is especially advantageous for this since it can be used as a prototyping tool. Because Python is so easy to learn and basically everything has been done for you, when you need a quick and dirty API or server set up, a scripting language like Python (basically meaning it doesn't need to be compiled) is the tool for the job.It is an easy language to pick up and code in, as compared to more dated languages like Java. Getting a server up and running will be pretty easy if you simply want to showcase an idea rather than create a full working implementation of the project. This can later be optimized or transferred to a different language.
Another area that makes Python such an interesting language to learn is that it can be used to program hardware. Normally, this is a task meant for more low-level languages like C or C++.
This is because high-level languages like Java are bloated and will require a lot more configuration to get up and running. A high-level language is basically one that has a lot of the things you normally need to do yourself, especially memory management, done for you out of the box.
Don't expect this to be something you pick up at a basic Python training course, because despite how cool it is (it might be the beginning of your foray into the field of robotics!), it's also pretty difficult.
Projects such as Micropython and CircuitPython have been set up to enable a user to interact with GPIO pins on a Raspberry Pi, for example. However, do note that these still act as wrappers for later compiling to C before being run. Python by itself lacks the required features for implementing such programs alone.
If you're better off in research and working with numbers rather than building programs to run on the front-end, back-end or hardware, Python still has you covered. In fact, no other language is used close to as much as Python in the general science community. This is because Python comes with a lot of data science libraries and tools that are nearly impossible to find in any other language.
Of course, data science also includes machine learning and developing artificial intelligence tools. These run both on the server or can be compiled to low level languages like C to run on machines like autonomous vehicles or CNC machines.