PEP 484 introduced type hints, at this time documenting exceptions was left to docstrings. I seek to suggest a reason this feature might be desirable along with how it might be used. Error handling in python does an excellent job of keeping the error-path out of the way when writing the normal flow of logic, however for larger code bases it is not always clear what exceptions may be caused by calling existing code. Since these cases are easily missed they may reach a higher level than intended ...
This is a discussion on Python’s forums about adding something akin to a throws keyword in python.
@sugar_in_your_tea If you’re expecting exceptions, make custom ones. That’s the best way to distinguish between those you expect and those you don’t. Using custom exceptions improves readability too.
My point is that I don’t like using exceptions for communicating regular errors, only unrecoverable faults. So adding features to document exceptions better just doesn’t feel like the right direction.
Maybe that’s un-Pythonic of me, idk. From the zen of Python:
Errors should never pass silently.
Unless explicitly silenced.
Using monads could let programmers silently pass errors.
I just really don’t like the exception model after years of using other languages (mostly Rust and Go), I much prefer to be forced to contend with errors as they happen instead of just bubbling them up by default.
@sugar_in_your_tea The idea of exceptions is that you can choose when to deal with them. So if you want to deal with them immediately,
nothing is stopping you.
If you think handling errors with every function call explicitly is easier, I guess you’re using very few functions. For the project I’m working on, your proposal would probably double the number of lines. Thanks, but no thanks.
Handling can mean a lot of things. You can use a sigil to quickly return early from the function without cluttering up your code. For example, in Rust (code somewhat invalid because I couldn’t post the generic arg to Result because lemmy formatting rules):
fn my_func() -> Result {
let val = some_func_that_can_error()?;
return Some(val.operation_that_can_error());
}
let val = match my_func() {
Err(err) => {
println!("Your error: {err}");
return;
}
Some(val) => val,
};
// use val here
That question mark inside my_func shows the programmer that there’s a potential error, but that the caller will handle it.
I’m suggesting something similar for Python, where you can easily show that there’s a potential error in the code, without having to do much to deal with it when it happens if the only thing you want to do is bubble it up.
If we use exceptions, it isn’t obvious where the errors could occur, and it’s easy to defer handling it much too late unless you want to clutter your code.
@sugar_in_your_tea I’m by far not qualified to discuss this in depth. But it seems to me that almost every function call ever can fail. Therefore, do you need to do this with every single function call?
That seems terribly inefficient and bloated. How is that readable for anyone?
That’s where the difference between exceptional cases comes in. Rust and Go both have the concept of a panic, which is an error that can only be caught with a special mechanism (not a try/except).
So that’ll cover unexpected errors like divide by zero, out of memory, etc, and you’d handle other errors as data (e.g. record not found, validation error, etc).
I don’t think Python should necessarily go as far as Go or Rust, just that handling errors like data should be an option instead of being forced to use try/except, which I find to be gross. In general, I want to use try/except if I want a stack trace, and error values when I don’t.
@sugar_in_your_tea But isn’t all that possible in Python? Don’t monads cover exactly what you want? Why does it need to be implemented some different way?
Also, divide by zero should be data just as well. Failing to program around having nothing to divide by is not a reason to have a program panic.
Also, having two systems for largely the same behavior doesn’t seem to improve usability and clarity, in my opinion.
I disagree. You should be checking your input data so the divide by zero is impossible. An invalid input error is data and it can probably be recovered from, whereas a divide by zero is something your program should never do.
If having the error is expected behavior (e.g. records/files can not exist, user data can be invalid, external service is down, etc), it’s data. If it’s a surprise, it’s an exception and should crash.
doesn’t seem to improve usability
I’m proposing that the programmer chooses. The whole design ethos around Python is that it should look like pseudocode. Pseudocode generally ignores errors, but if it doesn’t, it’s reasonable to express it as either an exception or data.
Documenting functions with “throws” isn’t something I’d do in pseudocode because enumerating the ways something can fail generally isn’t interesting. However, knowing that a function call can fail is interesting, so I think error passing in the Rust way is an interesting, subtle way of doing that.
I’m not saying we should absolutely go with monadic error returns, I’m saying that if we change error handling, I’d prefer to go that route than Java’s throws, because I think documenting exceptions encourages bad use of exceptions. The code I work on already has way too many try/except blocks, I’m concerned this would cement that practice.
@sugar_in_your_tea Since when is Python supposed to equal pseudo code? It should be easily readable, but that doesn’t mean it should *equal* pseudo code.
You can either test for values being 0 before dividing, or catching an exception when it is. Especially when dividing multiple times in one function, I would go for the latter option.
@sugar_in_your_tea If you’re expecting exceptions, make custom ones. That’s the best way to distinguish between those you expect and those you don’t. Using custom exceptions improves readability too.
My point is that I don’t like using exceptions for communicating regular errors, only unrecoverable faults. So adding features to document exceptions better just doesn’t feel like the right direction.
Maybe that’s un-Pythonic of me, idk. From the zen of Python:
Using monads could let programmers silently pass errors.
I just really don’t like the exception model after years of using other languages (mostly Rust and Go), I much prefer to be forced to contend with errors as they happen instead of just bubbling them up by default.
@sugar_in_your_tea The idea of exceptions is that you can choose when to deal with them. So if you want to deal with them immediately,
nothing is stopping you.
If you think handling errors with every function call explicitly is easier, I guess you’re using very few functions. For the project I’m working on, your proposal would probably double the number of lines. Thanks, but no thanks.
Handling can mean a lot of things. You can use a sigil to quickly return early from the function without cluttering up your code. For example, in Rust (code somewhat invalid because I couldn’t post the generic arg to Result because lemmy formatting rules):
fn my_func() -> Result { let val = some_func_that_can_error()?; return Some(val.operation_that_can_error()); } let val = match my_func() { Err(err) => { println!("Your error: {err}"); return; } Some(val) => val, }; // use val hereThat question mark inside
my_funcshows the programmer that there’s a potential error, but that the caller will handle it.I’m suggesting something similar for Python, where you can easily show that there’s a potential error in the code, without having to do much to deal with it when it happens if the only thing you want to do is bubble it up.
If we use exceptions, it isn’t obvious where the errors could occur, and it’s easy to defer handling it much too late unless you want to clutter your code.
@sugar_in_your_tea I’m by far not qualified to discuss this in depth. But it seems to me that almost every function call ever can fail. Therefore, do you need to do this with every single function call?
That seems terribly inefficient and bloated. How is that readable for anyone?
That’s where the difference between exceptional cases comes in. Rust and Go both have the concept of a panic, which is an error that can only be caught with a special mechanism (not a try/except).
So that’ll cover unexpected errors like divide by zero, out of memory, etc, and you’d handle other errors as data (e.g. record not found, validation error, etc).
I don’t think Python should necessarily go as far as Go or Rust, just that handling errors like data should be an option instead of being forced to use try/except, which I find to be gross. In general, I want to use try/except if I want a stack trace, and error values when I don’t.
@sugar_in_your_tea But isn’t all that possible in Python? Don’t monads cover exactly what you want? Why does it need to be implemented some different way?
Also, divide by zero should be data just as well. Failing to program around having nothing to divide by is not a reason to have a program panic.
Also, having two systems for largely the same behavior doesn’t seem to improve usability and clarity, in my opinion.
I disagree. You should be checking your input data so the divide by zero is impossible. An invalid input error is data and it can probably be recovered from, whereas a divide by zero is something your program should never do.
If having the error is expected behavior (e.g. records/files can not exist, user data can be invalid, external service is down, etc), it’s data. If it’s a surprise, it’s an exception and should crash.
I’m proposing that the programmer chooses. The whole design ethos around Python is that it should look like pseudocode. Pseudocode generally ignores errors, but if it doesn’t, it’s reasonable to express it as either an exception or data.
Documenting functions with “throws” isn’t something I’d do in pseudocode because enumerating the ways something can fail generally isn’t interesting. However, knowing that a function call can fail is interesting, so I think error passing in the Rust way is an interesting, subtle way of doing that.
I’m not saying we should absolutely go with monadic error returns, I’m saying that if we change error handling, I’d prefer to go that route than Java’s throws, because I think documenting exceptions encourages bad use of exceptions. The code I work on already has way too many try/except blocks, I’m concerned this would cement that practice.
@sugar_in_your_tea Since when is Python supposed to equal pseudo code? It should be easily readable, but that doesn’t mean it should *equal* pseudo code.
You can either test for values being 0 before dividing, or catching an exception when it is. Especially when dividing multiple times in one function, I would go for the latter option.