Python is awful. There are so many one offs in libraries, none agree on a style, it’s slow, and it’s way too easy to do the wrong thing. I often work with data scientists and have to productionize their jupyter notebooks which is pure suboptimal hell. I guess it must be a good easy learning curve for research/scratchpad
> I often work with data scientists and have to productionize their jupyter notebooks
I'm not a huge Python fan, despite working with it fulltime, but this feels like mixing correlation and causation. Data scientists would not be writing good, optimized code in any language.
Misery is trying to retrofit "bool", True/False, and nil/null to a language. C had to do that. Python had to do that. Getting those wrong is one of the classic language design mistakes. It seems like treating "True" as a value that equates to 1 will work, but then the special cases get you. Like being able to perform arithmetic on True.
Common language design boners:
- Not building in strings. That's now in the past. Everybody has strings. (Well, C...)
- Not building in multidimensional arrays of the numeric types. Everything that number-crunches needs them, and having multiple definitions is Not Fun and may lead to expensive re-copying between different libraries.
This is an enormous blind spot in language design. It's one of the reasons FORTRAN, which has good multidimensional numeric arrays, is still often used for number-crunching.
- Not standardizing the small vectors (vec2, vec3, vec4) and their matrix friends. Graphics code depends on these, and it's really annoying if there are multiple slightly incompatible implementations. Especially since GPUs have hardware for those types, and you want CPU and GPU to use the same representations.
- Not having arrays of bits. Pascal had PACKED ARRAY[0..N] of BOOLEAN but that was lost in later languages. It's useful to have that as a language construct, because most modern CPUs have good hardware for dealing with bit strings, and you'd like the compiler to use it.
Most useful languages acquire these features, but, when they come in late, there are multiple similar implementations, and libraries made incompatible by depending on different implementations.
(Amusingly, when Second Life switched from Linden Scripting Language to Luau, they initially had True, TRUE, and true all in use, as different types with different semantics. I was able to persuade the devs to unify the boolean types.)
> Not having arrays of bits. Pascal had PACKED ARRAY[0..N] of BOOLEAN but that was lost in later languages. It's useful to have that as a language construct, because most modern CPUs have good hardware for dealing with bit strings, and you'd like the compiler to use it.
I'm not sure exactly which features are responsible (I'm inclined to blame templates), but C++'s std::vector<bool> is a rough edge. For those unfamiliar, the standard specifies this vector template in a way that's not compatible with other vectors.
The __debug__ constant is really weird - any block of code guarded with `if __debug__:` will be entirely omitted from the bytecode under PYTHONOPTIMIZE=1. This and `assert` are the only two examples of real “conditional compilation” in Python. This is also the reason why you cannot assign to __debug__: doing so would make it possible to invalidate the compiler’s assumption about `if __debug__:` statements.
I honestly have never even heard of this constant and I feel like I've been using python for a pretty long time. Although maybe my memory for some things just gets garbage collected if I don't use it enough. Does it actually get used that often in real world code? Seems like it might be kind of risky.
I feel like it's the kind of thing you might wind up caring about if you're micro-optimizing your python, but in my experience that's a losing game and you're better served rewriting it in another language than bothering with trying to speed up the execution of the raw python code (it's not that you can't optimize python code, but only in broader strokes. If you are looking at the bytecode you're in too deep and every time I've seen it tried the code has been ported shortly afterwards).
I see asserts used in production code as part of flow control way too frequently, so I assume the majority of python users aren't aware of the -O flag, much less this behavior- which I too haven't ever heard of.
Of recently, I've noticed claude is a big fan of asserts too.
Don't mind the web designers calling themselves engineers.
There's a lot of annoying issues with Python, but compared to the billions of dollars and thousands of man hours that has been spent trying to fix Javascript and how horrible it still is, it's a perfectly cromulent language.
I used to like Python in the 2010s when it felt like a breath of fresh air relative to PHP and Perl.
Now it feels like a weird PHP itself that is slow, brittle, and dangerous to write code at scale in.
The loose typing, potluck standard library, and horrible package manager (insofar as the community does not know how to package code) all feel so dated.
Python certainly has some baggage, especially the typing system (which is still not finished, if you're looking at static typing and so is implemented differently by type checkers) and pip's safety, or lack thereof. But comparing it to PHP or Perl is rhetoric leading you one step too far.
Comparing it with PHP is unfair… to PHP. The amount of hard work that the PHP community has done to advance and keep their language relevant is impressive and admirable, and Python is perhaps the most extreme counterexample there is.
The Python community has spent the last 15 years refusing to improve in any meaningful way, or to learn anything from their peers. As someone who used to choose only jobs that would let me work with Python, I’ve gone through every phase of grief, and now just try to forget that it exists.
Lol, Python has had incredible improvements over the last decade plus, while uv fixed packaging. It's the best/comprehensive glue language ever made, even with a few remaining warts.
Most of the time downvoters don't explain their downvote, but I'll explain mine. I voted this comment down because it's just plain incorrect.I worked with PHP for nearly ten years (and I never want to go back). Maybe PHP has improved since I worked with it (PHP 7.4 was the most recent version when I last worked with it, I have never used PHP 8), but I doubt it.
But to describe the Python community as "spen[ding] the last 15 years refusing to improve in any meaningful way" is just laughably wrong. I can't give details as I haven't been doing much Python work, but even so I know of multiple changes, such as the typing system, or packaging improvements, which have significantly improved the language AFAICT. If there's a reason why you would not consider those to be "improv[ing] in any meaningful way", please enlighten me.
PHP has evolved a lot, but it also still has a lot of cruft from its earlier days. And it has made breaking changes on a scale python probably couldn't get away with.
I keep on hearing people be excited about PHP. Having first attempted to use PHP in early 00s, I simply cannot bring myself to attempt it again. I once had to rewrite large chunks of a site because it simply couldn't deal with the fact that a string had an apostrophe in it.
I felt the same way about moving to Python versus PHP and Perl.
I still really enjoy using python though. It's not really a fair comparison because I hadn't used PHP and Perl for as long but I just don't hit some mystifying issue every single session like I did with those languages when I'm using python. I honestly have never even read about that __debug__ constant. It's fun to hear about it but it's just not something that's comes up much.
I don't understand how people talk about how Python is "easy to learn for beginners" or "easy to understand." To me it's so hard to remember and follow all the weirdness. Racket / Scheme / I dare say even Haskell would just be so much simpler for learners.
I'm with Conal Elliot when he said on Type Theory for All that it is sooo much harder to understand a program in Python.
> Racket / Scheme / I dare say even Haskell would just be so much simpler for learners.
I have used all three languages; and you clearly have no idea of the notion of usability of a language. So many things contradict this, let me list them off the top of my head
- Getting a running toolchain working: Prexisting (most OSes bundle a Python interpreter) or a package install away for Python. Scheme / Racket is some odd mix of custom IDEs with Dr. in the name, or someone's 20 page essay on how SLIME is the best thing ever. Haskell gets into odd stuff with ghci, cabal, and stack, and all of them are extremely slow.
- Tutorials: Python has a ton of them, they all get you printing to stdout and calculating things in about 10 minutes. Scheme / Racket typically spends multiple chapters navel-gazing about lists, cons, and such. Haskell is actually better in terms of the Hello World stuff, but ghci v/s ghc bites you again; and no one has a clear idea of which one to use.
- Advanced concepts: Python has mainstream but halfhearted OOP; and things like decorators and metaprogramming. Quickly intelligible if you learned something else like Java or C++. Or if you learned shell scripts you can get quite a bit done with just imperative. Racket/Scheme: 3 chapters in and you're still trying to figure out tail recursion. Haskell: Instead of just doing fun things with take and foldl you're being hit with trivia about typeclasses.
I feel the same way. It was, back then “the second best language for everything, the first best at nothing”
Can’t take credit for the quote, read it somewhere.
The whole language changed when they kicked what’s-his-name out, and it’s a tool I almost never reach for anymore, whereas 15 years ago it was my Swiss Army knife.
Who cares tho. The agents deal with all of that, if you’re still looking at the code or caring about anything other than the loops and orbs you’re at the wrong level of abstraction. The important thing is the models have tons of python in their training data.
Python is awful. There are so many one offs in libraries, none agree on a style, it’s slow, and it’s way too easy to do the wrong thing. I often work with data scientists and have to productionize their jupyter notebooks which is pure suboptimal hell. I guess it must be a good easy learning curve for research/scratchpad
> Python is awful.
> I often work with data scientists and have to productionize their jupyter notebooks
I'm not a huge Python fan, despite working with it fulltime, but this feels like mixing correlation and causation. Data scientists would not be writing good, optimized code in any language.
Seems like an excellent user for LLMs
Past: https://news.ycombinator.com/item?id=49284392 (with my comment), https://news.ycombinator.com/item?id=49250370 .
Nice to see it get attention this time.
I remember reading that in early versions of Python there was no built in True and False. Each user would implement this themselves as
True = 1
False = 0
then later these got added to the language. In Python 2 you could still reassign and swap them so that 'if False' was actually true!
True, False = False, True
Python 3 you could no longer reassign them.
Misery is trying to retrofit "bool", True/False, and nil/null to a language. C had to do that. Python had to do that. Getting those wrong is one of the classic language design mistakes. It seems like treating "True" as a value that equates to 1 will work, but then the special cases get you. Like being able to perform arithmetic on True.
Common language design boners:
- Not building in strings. That's now in the past. Everybody has strings. (Well, C...)
- Not building in multidimensional arrays of the numeric types. Everything that number-crunches needs them, and having multiple definitions is Not Fun and may lead to expensive re-copying between different libraries. This is an enormous blind spot in language design. It's one of the reasons FORTRAN, which has good multidimensional numeric arrays, is still often used for number-crunching.
- Not standardizing the small vectors (vec2, vec3, vec4) and their matrix friends. Graphics code depends on these, and it's really annoying if there are multiple slightly incompatible implementations. Especially since GPUs have hardware for those types, and you want CPU and GPU to use the same representations.
- Not having arrays of bits. Pascal had PACKED ARRAY[0..N] of BOOLEAN but that was lost in later languages. It's useful to have that as a language construct, because most modern CPUs have good hardware for dealing with bit strings, and you'd like the compiler to use it.
Most useful languages acquire these features, but, when they come in late, there are multiple similar implementations, and libraries made incompatible by depending on different implementations.
(Amusingly, when Second Life switched from Linden Scripting Language to Luau, they initially had True, TRUE, and true all in use, as different types with different semantics. I was able to persuade the devs to unify the boolean types.)
> Not having arrays of bits. Pascal had PACKED ARRAY[0..N] of BOOLEAN but that was lost in later languages. It's useful to have that as a language construct, because most modern CPUs have good hardware for dealing with bit strings, and you'd like the compiler to use it.
I'm not sure exactly which features are responsible (I'm inclined to blame templates), but C++'s std::vector<bool> is a rough edge. For those unfamiliar, the standard specifies this vector template in a way that's not compatible with other vectors.
It is certainly the case that isinstance(True, int) returns True, even today.
I got a bug for not remembering it, a couple of years ago: https://jpscaletti.com/p/8/true-false-one-and-zero
The __debug__ constant is really weird - any block of code guarded with `if __debug__:` will be entirely omitted from the bytecode under PYTHONOPTIMIZE=1. This and `assert` are the only two examples of real “conditional compilation” in Python. This is also the reason why you cannot assign to __debug__: doing so would make it possible to invalidate the compiler’s assumption about `if __debug__:` statements.
I honestly have never even heard of this constant and I feel like I've been using python for a pretty long time. Although maybe my memory for some things just gets garbage collected if I don't use it enough. Does it actually get used that often in real world code? Seems like it might be kind of risky.
I feel like it's the kind of thing you might wind up caring about if you're micro-optimizing your python, but in my experience that's a losing game and you're better served rewriting it in another language than bothering with trying to speed up the execution of the raw python code (it's not that you can't optimize python code, but only in broader strokes. If you are looking at the bytecode you're in too deep and every time I've seen it tried the code has been ported shortly afterwards).
I see asserts used in production code as part of flow control way too frequently, so I assume the majority of python users aren't aware of the -O flag, much less this behavior- which I too haven't ever heard of. Of recently, I've noticed claude is a big fan of asserts too.
Isn't "..." then also behaving like True, False and None, i.e. being a lexical token that rewolves to a hardwired value during parsing?
rewolves? EDIT: ah, "resolves" typo. was v curious about python's mysterious "wolfing" aspects
It is, but Ellipsis is just an ordinary pre-defined constant (with the same value).
Python is three scripting languages in a trench-coat.
Wow, I wish to understand the internal details of Python implementation that makes it behave in such a way :)
I made a constant library for python which I liked some years ago. I wonder if any of my ideas made it in:
https://github.com/nucypher/constantSorrow/blob/master/tests...
just my 2c but py is honestly one of the worst languages and ecosystems i’ve used in my life.
for all the hate js used to get, py is at least a few magnitudes worse.
my opinion ofc. don’t get mad xD
JavaScript has its share of wtfs, so I wonder how much of it is which one someone experienced during some formative window in their learning.
Did you encounter JS first?
I'm not mad, but curious - I use Python for years and only dabbled with JS. Could you elaborate what makes Python magnitide worse than JavaScript?
Don't mind the web designers calling themselves engineers.
There's a lot of annoying issues with Python, but compared to the billions of dollars and thousands of man hours that has been spent trying to fix Javascript and how horrible it still is, it's a perfectly cromulent language.
Not OP, but dependency management, for one.
I used to like Python in the 2010s when it felt like a breath of fresh air relative to PHP and Perl.
Now it feels like a weird PHP itself that is slow, brittle, and dangerous to write code at scale in.
The loose typing, potluck standard library, and horrible package manager (insofar as the community does not know how to package code) all feel so dated.
Python certainly has some baggage, especially the typing system (which is still not finished, if you're looking at static typing and so is implemented differently by type checkers) and pip's safety, or lack thereof. But comparing it to PHP or Perl is rhetoric leading you one step too far.
Comparing it with PHP is unfair… to PHP. The amount of hard work that the PHP community has done to advance and keep their language relevant is impressive and admirable, and Python is perhaps the most extreme counterexample there is.
The Python community has spent the last 15 years refusing to improve in any meaningful way, or to learn anything from their peers. As someone who used to choose only jobs that would let me work with Python, I’ve gone through every phase of grief, and now just try to forget that it exists.
Lol, Python has had incredible improvements over the last decade plus, while uv fixed packaging. It's the best/comprehensive glue language ever made, even with a few remaining warts.
Most of the time downvoters don't explain their downvote, but I'll explain mine. I voted this comment down because it's just plain incorrect.I worked with PHP for nearly ten years (and I never want to go back). Maybe PHP has improved since I worked with it (PHP 7.4 was the most recent version when I last worked with it, I have never used PHP 8), but I doubt it.
But to describe the Python community as "spen[ding] the last 15 years refusing to improve in any meaningful way" is just laughably wrong. I can't give details as I haven't been doing much Python work, but even so I know of multiple changes, such as the typing system, or packaging improvements, which have significantly improved the language AFAICT. If there's a reason why you would not consider those to be "improv[ing] in any meaningful way", please enlighten me.
It is 30+ years old with all the baggage you would expect. It’s very much a product of its time.
It’s not like that cannot be changed. Look at PHP, which managed to evolve brilliantly over the last decade and gets tons of things right now.
PHP has evolved a lot, but it also still has a lot of cruft from its earlier days. And it has made breaking changes on a scale python probably couldn't get away with.
I keep on hearing people be excited about PHP. Having first attempted to use PHP in early 00s, I simply cannot bring myself to attempt it again. I once had to rewrite large chunks of a site because it simply couldn't deal with the fact that a string had an apostrophe in it.
Python 3(000) was an opportunity to fix all the things, so in a sense, the modern Python is less than 20 years old.
I felt the same way about moving to Python versus PHP and Perl.
I still really enjoy using python though. It's not really a fair comparison because I hadn't used PHP and Perl for as long but I just don't hit some mystifying issue every single session like I did with those languages when I'm using python. I honestly have never even read about that __debug__ constant. It's fun to hear about it but it's just not something that's comes up much.
I don't understand how people talk about how Python is "easy to learn for beginners" or "easy to understand." To me it's so hard to remember and follow all the weirdness. Racket / Scheme / I dare say even Haskell would just be so much simpler for learners.
I'm with Conal Elliot when he said on Type Theory for All that it is sooo much harder to understand a program in Python.
> Racket / Scheme / I dare say even Haskell would just be so much simpler for learners.
I have used all three languages; and you clearly have no idea of the notion of usability of a language. So many things contradict this, let me list them off the top of my head
- Getting a running toolchain working: Prexisting (most OSes bundle a Python interpreter) or a package install away for Python. Scheme / Racket is some odd mix of custom IDEs with Dr. in the name, or someone's 20 page essay on how SLIME is the best thing ever. Haskell gets into odd stuff with ghci, cabal, and stack, and all of them are extremely slow.
- Tutorials: Python has a ton of them, they all get you printing to stdout and calculating things in about 10 minutes. Scheme / Racket typically spends multiple chapters navel-gazing about lists, cons, and such. Haskell is actually better in terms of the Hello World stuff, but ghci v/s ghc bites you again; and no one has a clear idea of which one to use.
- Advanced concepts: Python has mainstream but halfhearted OOP; and things like decorators and metaprogramming. Quickly intelligible if you learned something else like Java or C++. Or if you learned shell scripts you can get quite a bit done with just imperative. Racket/Scheme: 3 chapters in and you're still trying to figure out tail recursion. Haskell: Instead of just doing fun things with take and foldl you're being hit with trivia about typeclasses.
I feel the same way. It was, back then “the second best language for everything, the first best at nothing”
Can’t take credit for the quote, read it somewhere.
The whole language changed when they kicked what’s-his-name out, and it’s a tool I almost never reach for anymore, whereas 15 years ago it was my Swiss Army knife.
Who cares tho. The agents deal with all of that, if you’re still looking at the code or caring about anything other than the loops and orbs you’re at the wrong level of abstraction. The important thing is the models have tons of python in their training data.
I've heard of orbs but what is it actually?