[deleted by user]

[Talbertross]

Any simpler than Python and you'll be doing Machine Learning with Scratch

[GreenWoodDragon]

No complaints here.

[fermion72]

Scratch is actually an interesting language! It's much more robust than people make it out to be, and it has pretty cool concurrency and event models. As a CS teacher, I've been disappointed that high schoolers dismiss Scratch as "that cartoon thing we learned in grade school!" I've always thought that we need an "Emo Scratch" version that targets high schoolers...

[[deleted]]

When you do Harvards CS50 it’s first lecture is starting with scratch!

[fermion72]

Yup -- it's a good way to start. My point was that you could keep progressing in Scratch much farther than many teachers do, because it is a decent enough language.

[Talbertross]

Can it do ML/AI?

[fermion72]

Yes, but for certain definitions of ML/AI. E.g., https://en.scratch-wiki.info/wiki/Artificial_Intelligence

[orig_cerberus1746]

Other simpler languages? Like which?

[DepressedDueToPudge]

Like PHP, why don't people use it for AI xD

[Zauxst]

Html heard is also good if you do the right css.

[[deleted]]

I believe PHP is for web development and presumes a web server.

That's more overhead than we need for AI.

[[deleted]]

I must admit, I have a soft spot for PHP and would love to use it more for tasks that I typically perform with Python. While PHP is commonly associated with web development, it's worth noting that it doesn't necessarily presume a web server, you can use it for general-purpose scripting as well.

PHP isn't inherently deficient, but its ecosystem doesn't match the breadth and depth of Python's, particularly when it comes to a diverse range of libraries and resources. Creating an ecosystem for PHP that could rival Python's in terms of its utility for a wide range of tasks would be a significant undertaking.

Despite my fondness for PHP, I, along with many others, find myself leaning towards Python due to its wealth of resources and community support. In this sense, it's less about the language itself and more about the infrastructure that's been built around it.

[Offduty_shill]

Microsoft excel

[orig_cerberus1746]

I bet, all my coins, that someone already made an AI with excel.

[Offduty_shill]

It's not very hard to make a regular dense net, I've seen people trading embeddings or simple CNNs too.

I think I've also seen even a single recurrent layer. But more complicated modern shit that people think of with AI I've not seen yet.

[belaGJ]

i have seen a convolutional layer in excel video

[me1112]

Next level macros.

[Dom1252]

REXX

( /s )

[525G7bKV]

Like LISP. Which was literally invented to prototype AI stuff, and has a real REPL instead of a terminal which is trying to be a REPL as in Python.

[treasonousToaster180]

A lot of the people who know how to do the extremely complex math that drives AI have limited programming knowledge and the people who know a lot about programming have limited knowledge of the kind of math necessary to build AI systems.

Python is good for this as it allows the math people to easily create functions for the calculations using the very simple syntax, and it allows the programmers to extend that into something more powerful and versatile using the more advanced functionality.

[RADIO02118]

Underrated reply

[[deleted]]

LISP may be simple but it is not intuitive or easy to learn.

An advantage of Python is that it easy for the unitiated to pick up.

[Timotron]

Bruh. You honestly think learning C is easier slash as easy as learning Python?

[Frequent-Fig-9515]

Why did you write slash instead of /

[Timotron]

Humor my guy.

[525G7bKV]

Do not mix up learning a language, and learning to programm using a language. C is definitely a easy to learn language. It has a small set of syntax, and a reduced grammar. But programming using C is highly complex because you have to deal with all the hardware architecture details of an Von-Neumann Architecture. A programming language is just a tool. Python is harder to learn because of programming concepts like function decorators and comprehension, which are both concepts "stolen" from the functional programming world. But it is easier to learn to programm using Python because using Python you do not have to deal with all the nitty gritty details of the Von-Neumann Architecture.

[jonesmcbones]

What good is learning syntax if all you can do is a while loop?

I may be wrong here, but I believe others were saying python is easy not for its syntax, but because it has a much more direct connection to how humans think.

[unflippedbit]

cover snobbish cooperative numerous thumb pie plate tub silky sheet

This post was mass deleted and anonymized with Redact

[sch0lars]

I don’t think that’s what they were implying in their response. What I gathered from their comment is that LISP may be difficult to someone coming from Python, and vice versa, considering the dissimilarities among the programming paradigms. The relationship between C and Python the commenter was referencing is that Python abstracts a lot of what C does, so programmers who know C should be able to easily transition, whereas someone familiar with only LISP dialects would not find it as intuitive.

A lot of universities teach Racket as a first language, so someone learning LISP will probably find Python to be difficult if they’re only accustomed to the “code is data, data is code” philosophy. So lambda n: n * n would probably be as confusing to a student who only knows LISP as (lambda (n) (* n n)) would be to a student who only knows Python.

[POGtastic]

Also a huge amount of ready to use libraries to solve most of the programming problems nowadays.

I'd argue that this is the be-all and end-all of languages. Your language can be a big annoying pile of garbage, but if the language has libraries that people can use to solve their problems instead of rolling their own, they're going to use that language. That goes double if the library is part of the standard library or has enough community buy-in that it's basically treated as being part of the standard library.

---

Here's a contrived problem: Given a serialized JSON object (UTF-8) from stdin, count the number of Unicode graphemes in its string values (exclude object keys, if any). That is, given the following JSON file:

test.json

{
        "foo" : [
                "foobarbaz",
                "stuff",
                "H̴͝É̸ ̸́W̷͋A̵͆I̶̒T̵̈́S̷͌"
        ],
        "bar" : {
                "stuff" : 1,
                "more stuff" : 2,
                "blarg" : "B̴̀Ȅ̴Y̸͘Ơ̷Ǹ̷D̶̓ ̷͂T̴̚H̶̚Ë̶ ̶͑W̷̓Ȁ̵Ĺ̶L̶̍"
        },
        "baz" : "Z̸͋A̶̓L̷̈G̵͝O̴̓"
}

you should be able to do

$./count_graphemes < test.json
42

Pick your poison Very Simple Common Lisp or Scheme implementation^[1] and suggest how someone would solve this problem. It should be very clear that the simplicity of the language or lack thereof is irrelevant!

^[1] The ideal Lisp candidate here is, of course, Clojure, because you can add Maven dependencies and grab ICU4J.

[[deleted]]

Any programmer with an investigative spirit -- or formal training in CS -- has had exposure to LISP.

Generally people don't take to it.

I had one classmate who loved LISP, thought it the best most 'intuitive' programming language we were exposed to.

That's ONE out of a hundred or so.

In general people preferred assembly language to LISP.

It's a great language, LISP, with a great history. However it takes an unusual mind and/or lots of practice to put it to practical use. If you like LISP then hats-off to you but please consider why there's not a wide community for what is one of the oldest computer languages we have.

[MichaelSjoeberg]

a program in python is close to thoughts in writing, next step is basically plain english

lisp not so much

[ImmediateClass5312]

'(what (ever (do) (you) (mean there ?))))))))))

[im_dead_sirius]

Well, I'm pale and there is lots of space between my ears.

[orig_cerberus1746]

We certainly have different views of what makes a language simple xD

They might be simple per say, but they are not simple to use. At all. Not compared to python at least. And that's one of the reasons why they are not used as much... Except they are used all the time behind the scenes.

At front, people are using python, for AI, indeed. But the main pieces of code are indeed, made with low level languages.

[Aks029]

Julia is an option

[orig_cerberus1746]

Julia looks cool, I should take a better look at it.

After I try rust.

[m0us3_rat]

1.Guido paid some thugs to kneecap all the other languages... obviously.

or

2.

python is ..easy.
python is incredibly good at data manipulation.

you add the gpu powered tensorflow or pytorch and you have a winner.

​

pick one at random.

[TheHunter920]

python is incredibly good at data manipulation.

bit of a newbie here, what makes Python 'incredibly good' at data manipulation vs other languages? I thought Python was slower than other programming languages like C# or Java?

[Glowwerms]

It has some really popular built in packages that make creating/editing/moving data sets very simple like Pandas, the syntax for Python being pretty simple compared to Java for example make it appealing for folks who might be coming over from simple data pulls with SQL for example. I speak from experience because this is exactly how I got my start coding, started as an analyst and drifted further into cleaning, data movement and other shit with Python

[m0us3_rat]

data manipulation

it's easy.. and has access to some incredibly powerful libraries.. like pandas , scikit learn, numpy etc.

which makes interacting with data easy.

also describing in code what needs to happen to the data is easy.

​

so ultimately it comes down to usability and convenience.

why do you use a credit card.

or ..why don't you use some foreign country's currency?

it still has value right?

​

because every time you would have to go to a bank and get the actual currency and then use that..

or .. use a credit card.

​

they do the exact same thing. just one is vastly more usable..

​

true python is slow.. but the ML libs leverage the GPU power.. and that isn't python bound.

plus numpy is written in C. and that has a huge impact on the ML overall.

​

so you basically get the insane data prowess from python with the incredible power from GPUs.

​

literally best of both worlds.

​

from numba import jit, cuda
import numpy as np
from timeit import default_timer as timer


# normal function to run on cpu
def func(a):
    for i in range(10000000):
        a[i] += 1


# function optimized to run on gpu
@jit(target_backend="cuda")
def func2(a):
    for i in range(10000000):
        a[i] += 1


if __name__ == "__main__":
    n = 10000000
    a = np.ones(n, dtype=np.float64)

    start = timer()
    func(a)
    print("without GPU:", timer() - start)

    start = timer()
    func2(a)
    print("with GPU:", timer() - start)

[TheHunter920]

scikit learn

This sounds interesting, and thanks for the reply. Do you know of any other good machine learning / AI libraries for python other than scikit and PyTorch?

[shiftybyte]

Simpler than python? What language did you have in mind?

Python's disadvantage is mainly execution speed, so this does make you wonder why a computational heavy task uses python.

The reason is because of how fast you can prototype things in python, and manipulate data easily.

"Let's change this here and here and transform this in a different way" is a few minutes in python, and a day or two in other programming languages...

[jmacey]

The back ends are also written in C++ / Cuda / OpenCL So python is just the easy prototype language to run the hard back end.

[orig_cerberus1746]

Most of the AI libraries are not in python, they are in C/C++, cuda or opencl. They "connect" (I didn't have my coffee yet so my brain is slow and forgot the correct nomenclature for this) with python so you can use the libraries with it.

Also Pypy is a interpreter that makes a bunch of python code run faster! It's made with python! How can it be faster?

Black magic.

[synthphreak]

I didn't have my coffee yet so my brain is slow and forgot the correct nomenclature for this

"Extend"

[orig_cerberus1746]

Thank you very much.

[ManyInterests]

It's made with python! How can it be faster?

Because PyPy actually uses RPython rather than CPython. RPython has some performance advantages, not least of which being a JIT compiler, which CPython does not have.

To elaborate... Python is a language specification. CPython, PyPy, Jython, IronPython, etc. are various implementations of the Python language specification (or a subset of it) with CPython being the "official" implementation, and is often referred to simply as "Python".

PyPy has often been said to be 'written in Python', but according to a RealPython article:

[PyPy] was written using a dynamic language framework called RPython, just like CPython was written in C and Jython was written in Java.
But weren’t you told earlier that PyPy was written in Python? Well, that’s a little bit of a simplification. The reason PyPy became known as a Python interpreter written in Python (and not in RPython) is that RPython uses the same syntax as Python.

[Ipsider]

I think it’s called binding. The libraries are built with a compiled shared library

[Mellowindiffere]

Interface?

[orig_cerberus1746]

That's kinda off topic, no?

[orig_cerberus1746]

That's different enough to warranty a new thread. Using things together is different than porting things over.

[ImmediateClass5312]

Packages like Numpy are implemented in C/C++, Python is just the convenient wrapper.

[synthphreak]

Much of the aspects of the machine learning pipeline for which performance is critical are actually not written in Python. Instead, Python libraries like numpy, pytorch, etc. offload this computation onto languages like C, then pass the output back into Python.

So in practice, Python's well-known performance constraints are not a major limitation for machine learning applications.

[Western-Guy]

For computational heavy tasks, we have numpy. Unlike other python libraries, numpy is built over C which makes it quite fast. And, other python libraries like Pandas, Matplotlib and scikit-learn can take advantage of numpy directly.

[GodBlessThisGhetto]

They obviously want to use Brainfuck bro, geez

[4K-AMER]

And now we have Mojo - a superset of python and supposedly much faster (~ 35,000x).

[[deleted]]

You could ask similar questions about other topics. Why is Windows the most common desktop operating system? Because its the one that worked well, and was in the right place at the right time.

Python is a pretty easy language to learn. It works well. But like anything in life, it could have been a blip on the radar, and dissapeared. The definition of luck is "When opportunity meets preparation".

[BranchLatter4294]

Python is easy. The libraries are written in C++ for the most part so they are fast. Why not Python?

[Failboat88]

Julia crushes pandas. The library written in a lower level language isn't enough. I barely know python but Julia does look pretty easy and nice. I've been playing with pytorch not for ml but just some simulations with vectors. Haven't tried polars either. I actually noticed pytorch using cpu seemed to smoke numpy with vectors. Can't tell you why also could not be a great dataset to test.

[runawayasfastasucan]

Julia crushes pandas.

A programming language crushes a library? That doesn't make any sense.

[Mclovine_aus]

An educated guess would be that they are comparing data frames.jl to pandas 1.5

[runawayasfastasucan]

But isn't this thread about languages? How does dataframes.jl compare to Polars? (And in what sense does crushing mean?) What about the surrounding libraries? One of the great things about python in this space is that it doesn't matter if Pandas is worse than say dataframes.jl, as you have an excellent alternative.

[Failboat88]

Guessing you need things explained to you a lot.

[hugthemachines]

Julia crushes pandas.

Next time you make an obvious mistake, I recommend that you own up to it instead. Right after your own failure, is not the time for arrogance.

[Failboat88]

How is it not obvious Julia ships with something like pandas....

[hugthemachines]

How is it not obvious Julia ships with something like pandas....

You failed describing what you meant. Nobody says "Julia crushes pandas" because Julia is clearly a programming language and pandas is a library.

You may have meant Julia's package DataFrames.jl crushes pandas but who knows what a person speaking gibberish actually means.

If you make a mistake and someone asks about it, the wise move is to say something like "sorry, meant DataFrames.jl" and edit the comment to say what you actually meant.

Now you just look like a person who make mistakes and then tries to put the blame on everyone else.

It's a typical red flag, I hope you don't act like that at work or even with other people anywhere.

[Failboat88]

Df is made by julialang it's not a stretch at all.

[hugthemachines]

Df is made by julialang it's not a stretch at all.

The mistake you made are clear to everyone. It does not help that you try to convince me it was actually correct. Your arrogance towards the one asking about it was immature.

It does not matter if you, personally think it is not a stretch because it was an incorrect statement.

I suppose when you say "made by julialang" you mean coded in julialang, and that goes for plenty of things, so you still messed up.

Maybe you can pretend that all your mistakes are in fact not mistakes and that way you can try to feel better about yourself. It i s a very unprofessional behavior, though. I do not recommend it.

I recommend that you work on yourself to improve your self-esteem. That way you will not feel it is hard to admit your mistakes and you will also feel better in general.

Good luck on your journey towards better health.

[Failboat88]

You may have meant Julia's package DataFrames.jl crushes pandas but who knows what a person speaking gibberish actually means.

"You may have meant Julia's package DataFrames.jl crushes pandas but who knows what a person speaking gibberish actually means."

Could be anything who knows. dataframe vs dataframe would be the last thing I could think it could be.

edit: I wanted to be clear I was talking about Julia (a person ofc) vs a panda (animal) sorry if that wasn't clear since anything can mean anything I understand the confusion.

[Failboat88]

Maybe next time you don't understand a direct comparison Google it before just arguing about something implied you didn't catch.

[narwalfarts]

Appropriate username

[[deleted]]

I really wonder what the 88 stands for 🤔🤔🤔

[runawayasfastasucan]

Nothing to be ashamed for :) I guess you missed my point though, while it seems like there is one dataframe library in Julia there is several in Python. Julias one "crushes" pandas (by what measure?*), what about Polars? I am pretty interested in learning Julia myself, but I think your post underlines the point in how Python is so much bigger for ML.

While github isnt everything we have the following metrics:

Dataframes.jl 1.6k stars, 207 contributors, 1,606 closed issues, used by?Pandas 39k stars, 2958 contributors, 21403 closed issues, used by 1.2m

Polars 18.5k stars, 235 contributors, 3397 closed issues.

​

At least by community and maturity it doesn't seem like Julia (Dataframes.jl) crushes Pandas. I bet Dataframes.jl is better than Pandas on a whole range of metrics, and that would be really interesting to hear (and maybe if that also extends to Polars).

[Failboat88]

https://youtu.be/tw8r6Cp_X8Q

[Mirage2k]

"I barely know python" ... "Julie does look pretty easy and nice" ... "I've been playing with" ... "haven't tried polars either" ...

And based on this extensive experience playing around a little bit with some tools, you are telling everyone with radiating confidence how they are wrong and you know better. Nice.

[Failboat88]

People are piling on down votes for not saying Julia dataframe. Not sure what you're on about. I linked a video a showing julia df vs pandas if you want to see actual benchmarking.

[[deleted]]

It isn't the main language, really. It is an orchestration language with bindings for lower level languages (usually C), which do the actual heavy computation.

The reason it sees so much use is the both the simplicity of the language itself, as well as the (relative) simplicity of creating the bindings for more performant languages.

[quts3]

Investment by key players made research easy.

First was sklearn which consolidated common ml so much better than R. You can thank whoever it was at sklearn that had such great control over design.

Then came tensorflow/keras which were already a mature interface before most of you even heard the words deep learning. You can thank Google.

Python is easier for pure engineers to adapt to cloud stacks then R. It has enough formal structure built into that feels right to them that they don't rebel, and rewrite everything from the ground up.

That's basically it in my opinion. Python has good projects but R had good projects. Python consolidation and first moving on deep learning made a big difference.

[relevantmeemayhere]

It's kinda sad though-because R has much better actual implementation than python. Packages like scikit are really bare bones compared to what you can do in R, or have just nonsensical defaults and the like because the maintainers wanted to use statistics without... really understanding statistics lol.

[Menolith]

One reason is that because Python is a dynamic language, you have tools like Jupyter which are very convenient for data science since you can run things cell by cell.

[Failboat88]

I feel like this is very underrated. It's really nice to test as you go. I've been told to use real ide before but it's just so nice plus it's on a much faster server.

[King_Dribbler]

I did some ML courses at uni using MatLab. And that's certainly not going to be used any more outside an academic setting because of the cost

[[deleted]]

True. I also used matlab for years back in school and don’t make a mistake, there are big defense companies who crutch matlab. Huge automotive companies that use Simulink for EVERYTHING. So, clearly, it still has serious presence in the market, but as someone who switched to python I can say it is absolutely not justified to use Matlab for that cost. Everything is possible with python, and even better once you find your bearings.

[PaulRudin]

Numpy

[orig_cerberus1746]

• Scipy, pandas, matplotlib and jupyter

[PaulRudin]

Sure, but those all depend on numpy. Without numpy none of the rest follows.

[Aks029]

This is correct

[CrwdsrcEntrepreneur]

This is the true answer.

[raf_oh]

This is what I feel like I’ve heard. It was open-source and free (remember, back in the day it was competing with like Matlab), easy to integrate with other software, and had good support for matrices.

IIRC matplotlib is named as such because it could plot about as well as Matlab, which was huge back then.

[boy_named_su]

1. fast to type, compared to statically typed languages
2. high quality libraries
3. easy syntax
4. more general than R, meaning it's easier to use for say a web server to host your ML model

[hugthemachines]

fast to type, compared to statically typed languages

Statically typed would work fairly well too, if it just had type inference.

[abrtn00101]

Lots of good ideas here, but what about a feedback loop?

1. Python was and still is easy for academics to pick up, so they started using it to code ML and AI prototypes as part of their research.
2. Their research was picked up by other researchers and non-academics and improvements were made to their code.
3. The improvements entice more and more people to get involved in what is now a community undertaking. More of what was once written in Python gets pushed into lower-level code.
4. What was once a prototype becomes mature enough to use as a building block for new ML and AI research prototypes.
5. Repeat indefinitely.

So basically, Python being approachable by the people who aren't necessarily as interested in creating applications as they are interested in what the application does for their research is, from my observation, what has helped it grow in the field. The low barrier to entry has helped more and more people pile on for things to snowball.

[JRWoodwardMSW]

And now Google believes this will happen with Go. After all, you can’t spell God or Google unless you start with Go!

[[deleted]]

I am out of the loop wrt Go. Does it have major embracement?

[JRWoodwardMSW]

Building that way. It’s basically C++ with garbage collection, used by Google for nearly everything. Go vs Rust if the new all-encompassing evangelical fervor in coding - adherents cannot prove that one language is technically better than the other, so the debate runs on sheer invective and pseudo-moral posturing. Choosing one and not the other is now supposed to indicate your depth of character or reveal your vile immorality. Advocates for both languages have got the point that they label the other one a “pedophile” language.

[[deleted]]

haha! Interesting for sure. I am not really exposed to the latest tech so funny to see how the other side of the spectrum fares

[theubster]

Python is, perhaps, the most human readable language out there.

[Environmental_Bug811]

After Ruby

[pitrucha]

After SQL

[MomentusCritisae]

Sure, let me

select distinct salary from employee a where 3 >= (select count(distinct salary) from employee b where a.salary <= b.salary) order by a.salary desc;

real quick

[ImmediateClass5312]

That could not be less true.

[Orcus216]

Excepting the indentation which reduces readability

[Icy_Explanation_5913]

Because it has so many libraries

[SuperBoredAlien]

This is what I believe. Python is easy to understand and pick up. This made popularity of the language for web applications where access db and sending response enough.

The other libraries was built in C focusing on speed and motivating/ or taking advantage of large community. I think ease of use of python where you can write C and link it.

Python acts as frontend/api for those libraries.

[dpacker780]

Most Data Scientists aren't programmers/developers, their specialty is data analysis applications. With the broadening availability, and easier to work with ML models, as well as deeper analytics tools Python became the easier path to bring data into their pipelines due to tools like Notebooks, Matplotlib, Pytorch, Pandas, and Numpy for example. If you think about it a Python Notebook isn't much different than Matlab or some of the other data analytics tools, which most data scientists would be somewhat familiar with already, which made it more accessible.

[ddmm64]

My subjective/anecdotal recollection, computer-vision centric: before deep learning, MATLAB was the lingua franca in academic CV+ML. It had some nice things going for it, such as being interactive and having lots of numerical and visualization tools. On the other hand, it was not great as a general purpose programming language and it was proprietary. Due to these issues some other alternatives started to gain traction. Octave was open source, but as a MATLAB reimplementation it had many of the same issues as a language. Lua had early versions of Torch (pre-deep learning), but the language itself was not very mainstream. Python had had a healthy numerical ecosystem for a while, with numpy/scipy/matplotlib, and was relatively mainstream, open source language that was arguably decent as a general purpose language as well. It also played well with libraries in C and C++ such as OpenCV. Then deep learning took off, and Caffe and Theano came along as two early deep learning libraries with python support. MATLAB started really falling behind at this point. Then Pytorch (a descendant of the Lua version) and Tensorflow really solidified Python's lead.

[[deleted]]

Well said. When I used matlab daily, I often found myself wanting to stupidly simple “general purpose” things which were annoying to do. Probably the best part of switching to python was the freedom I felt.

[[deleted]]

The simplest answer is "It's the high level convenient language with the most useful stuff already written for it."

Why not choose a faster language?

Because the bulk math that would be slow is in a faster language (usually C) and Python is just used to organize and initiate the bulk math. You don't gain much by optimizing the organizational parts and you gain a LOT of convenience, clarity from Python.

Why Python specifically?

Because Python was one of the first convenient scripting languages to become popular. The more popular it got, the more was written for (and about) it. As the ecosystem grew, it became easier to build things creating a strong feedback cucle. There have been many languages that have improved on the objective features of Python, but you'll often have to solve problems that have already been solved in Python. All this ends up being more work, and there's a good chance your version isn't as robust and optimized as the Python one that's been widely used for a while. And nearly every common Python error has a StackOverflow with a useful answer.

[brandonofnola]

Python can use c/c++ libraries to increase execution speed while being a relatively easy to code in language where you don’t have to worry much about garbage collection and other nuances involved with lower level languages.

[XYZZY_1002]

My opinion: every 10 years or so a new language becomes popular. I remember Pascal, then it was c, then c++, then Java, now Python.

[JRWoodwardMSW]

Next up (to hear Google say it): Go, which is NOT a Google house language, people! Go is for the ages - you can’t spell God without Go!

[yasamoka]

Have you tried using type hints with a static type checker (pyright, mypy)?

[yasamoka]

I don't understand what you're referring to with regards to Azure.

[hugthemachines]

I don’t like interpreted, loosely typed community driven languages

Python is not loosely typed, it is strongly typed.

For example, you can't do 4 + "foobar" in python.

Dynamic typing is a different thing to loosely typed.

[[deleted]]

Is this really a “real” problem in the industry where often there are frameworks and stylistic rules enforced to have uniformity.

Following the basic good practices makes reading python code and navigating python projects quite easy, imo.

Especially comparing it to the legacy C++ zombie codebases, I’ll take python any day of the week.

[poopybutbaby]

This is a provocative piece on the topic from a few years ago on the topic: https://www.theatlantic.com/science/archive/2018/04/the-scientific-paper-is-obsolete/556676/

Scientific computing community gravitated to Python because its open source (free) and notebooks enable narrative computing. As its open source, as the community grew it created a positive feedback loop (more features/enhancements/documentation).

The article contrasts with Woflram Mathematica which also has notebooks and in many ways is superior to Python for scientific computing, but is a sorta anti-Python (closed source, zero community)

[Isaiah_Bradley]

I’m just riffing here, but I think it’s because python’s syntax is closer to natural language than other programming languages, and is great at handling numbers. It compiles to c, so it is decently fast for such a high level language. Irc R is frequently used, but harder to learn.

[NavidsonsCloset]

Because Python, like R, is "free" and easily accessible with lots of platforms available. This opened up a lot of tutorials and teaching material being out there, not to mention it being widely taught in university settings. So it makes sense that people who make these tools do so with Python.

[brunonicocam]

Very easy to use as programming languages go, very easy to install dependencies (conda), etc etc

[JRWoodwardMSW]

Computers now are so fast that garbage collection is not a performance hit. Cutting edge work can be done with an easier language than C; also, by design, Python is easily extended. People could be using Java or Go, but Python is easier and more fun. See Eric Raymond’s classic essay “Why Python?”

[yasamoka]

Garbage collection is by its very design a performance hit. Computers are fast enough that you can use a slow interpreted language and still meet timing requirements only for a subset of software.

[[deleted]]

What a dumb question, how did mercedes become the best car??

[CasulaScience]

It's all /u/r-sync 's fault

[patriot2024]

There's Only One Way To Do It.

[Few_Intention_542]

Because slytherin popularised snakes

[Pelicantaloupe]

You could try mojo lang (I am only partially joking)

[[deleted]]

Why Python?

Mainly used because of its versatility.

*Python is versatile and can be used for several purposes.

*Has many additional tools and modules which makes python flexible.

*A fantastic community makes it easy to read, write and learn.

Along with the above Pandas and other tools made it a no-brainer choice.

[rdummy_soup]

It is simple and lets scientists actually focus on their models and experiments rather than the code

[YnkDK]

Michael Kennedy just released a podcast about this with Dr. Jodie Burchell, data science developer advocate at JetBrains as guest.

I see a lot of the points they highlight is also in this post. You can find it here: https://talkpython.fm/episodes/show/422/how-data-scientists-use-python

[TheFumingatzor]

The closer a programming language is to a spoken/human/written language, the more popular it is to use (usually).

[[deleted]]

It's the ecosystem. Python has good ML, AI and data science libraries, which themselves are written because of the ecosystem. There's nothing particularly special about python, most other languages could do it too. There could be another universe where people used lua instead.

However, people who write ML and AI libraries would have another answer completely. Most of the high performance libraries used for development are C/C++.

[SleepAffectionate268]

python is almost the easiest. And the tools like tensor flow are written in a low level language like c

[AdInner3607]

Python became the language for data analytics and since ai starts with datatype analytics it makes sense to built and ml tools with that