How did Python become the king of programming languages?

How did Python become the king of programming languages?
Also, are people who put all of their eggs in R 10 years ago seething that Python won?

Attached: Py_vs_R.png (1096x348, 21K)

yes several of my colleagues are still furious that they have to now learn python after languishing in R

R fags, kek.

Attached: 7zKAG5sY.jpg (512x512, 40K)

>How did Python become the king of programming languages?
It became the king of data science OP, you dumb cocksucker. Most software engineers are not retards and still use other languages because python is a slow piece of shit for everything that doesn't require a brief and simple scripting language.

I'm hoping the next move will be to jvm - kotlin is actually very good for scientific programming.

seems like you have zero experience with python

>king of data science
That would still be Scala.

Python became popular amongst academics because it was very easy to learn (thanks to simple core syntax, and the design philosophy of "there should be only one way to do it".)

Then "data science" became popular thanks to the theft and sale of all of our data by every company everywhere. And the rest is history!

/thread

>How did Python become the king of programming languages?
Everyone else in the thread probably answered this already - it's just pretty easy to get into. From a data science perspective, there's literally one package that you need to really learn - numpy. It abstracts a lot of the low-level stuff around manipulating data, generating lists or arrays and keeping track of data types.

The problem is that these bolt-on packages totally ignore Python's core language strengths such as OOP, list manipulation and lambda expressions. This inevitably leads to a few things:
1. completely unoptimised spaghetti code that runs poorly
2. package dependency hell - you need X version of these 8 packages before your code can run

Also, most people who use it rely too much on the sandbox Jupyter notebooks. They're a pseudo web application that lets you write python and markdown in the same place and have the markdown run. While excellent at prototyping, the code then can't be easily integrated into a production environment and has to be completely rewritten.

>Also, are people who put all of their eggs in R 10 years ago seething that Python won?
I myself got into R very early at university and have been loving the language ever since. For data science there is a wealth of packages that implement specific algorithms you need. Moreover, the language is flexible enough that I've made complete production data pipelines and automated analyses using only R and an SQL database.

Its main strength is that all data manipulation tasks are included in the core language and are really well optimized. It's inherently vectorized which makes it easy to make your work multithreaded or even run on a GPU.

The bigger problem is that Python is so much more mainstream that it's hard to sell R projects either internally or to external clients because they have the perception that it's a niche unsupported fad, but it has an ecosystem just as robust and expansive as Python.