Python is becoming the de facto standard for many scientific libraries

>Python is becoming the de facto standard for many scientific libraries
>I might end up having to learn Python
>I don't want to

Help me /g/.

Kill yourself code monkey

>implementing scientific algorithms
>""code monkey""
kys you stupid freshman

not even him
>>implementing scientific algorithms
>>implementing
>>not a code monkey

also I'm a grad student

>can't implement his own algorithms
>can't implement fellow researcher's algorithms
>calls himself a human
kys you dumb cunts.

Python is literally just plain English with indentation, what's wrong with you

I learned the basics in a week, and got more advanced as time went on. Its really easy actually.

t. stupid normie

Hellowworlds mean nothing.
Learning the frameworks and the extended libraries for anything takes time.

>A weekend learning the basics
>Spend the next month or two doing increasingly elaborate projects
>Google "How do I do X in Python" to fill in the blanks

It's pretty simple senpai, it's pretty much

Literally how to learn any programming language once you know the basics

Brainlet spotted.

you drunk?
Implementing is easy as fuck.
That's why you're a code monkey

How about not being an experiment monkey?

Thats what code monkey autists like you are for m8

wait until this baby gets version 1.0

Python is fucking easy, if you can't do it seriously kill yourself.

>using a language that is pronounced as 'jew liar'
kek

>Help me /g/.
>/g/
Why has no one pointed this out?

i understand OP desu. Python is easy but ugly a.f.

And what's the point of it if you need to re-write it all in C anyway when you want to run it in your local cluster to do serious simulations?

python is the best language for science prove me wrong? u cant

If you are coming from a more C-like language then it is harder than most people claim it is. Yes, the Syntax may be very easy to grasp and there is very little you can fuck up, but writing good, readable Python code is harder than most people know. Takes a lot of experience to know which abstractions in Python cost a lot of performance and which are basically for free. In the end you'll figure out it's best to just use as much numpy as you possibly can and just ignore 99% of all retarded abstractions that python offers. I saw so much Python code from overly enthusiastic undergrads that used all kinds of Python shit, lambda functions, iterators, generators etc, but in the end it was slow as fuck and simpler implementations using numpy were easily 100 times faster. I won't say that those abstractions are never useful, but you should always keep in mind that Python is not a real programming language, everything you implement directly in Python will be insanely slow.

Obviously you won't implement a fucking Monte Carlo simulation in Python. Look at Python as something like a way to organize code written in real languages. Everything that is somewhat performance critical should be written in C or something and then be used as a module imported into Python. You'll see pretty quickly that it this kind of workflow makes life a lot easier. You can focus your programming on real problems and let Python handle the simple shit that doesn't really need a lot of performance. You'll end up being a lot faster, a lot less debugging, a lot more readable code that is a lot easier to maintain. I had to maintain simulation software for a year that was written in fucking Fortran and holy shit, it took me almost two months to even understand what the hell was going on in that monolithic piece of shit code. Actually resolving tickets by fixing bugs instead of explaining why it's easier to rewrite it from scratch was a rarity.

>rust

kys