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.
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.
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.
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.
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.
It’s not an explicit design goal, but it explains a lot of the Zen of Python and other pushback on PIPs, so to me it’s always been an unwritten design goal (be as close to pseudocode as practical, but no closer). It’s also how I generally write code (start with Python “pseudocode,” then decide what to use in production).
For example, from the Zen of Python:
There should be one-- and preferably only one --obvious way to do it.
Being clever in Python is a bad thing, just as it is in pseudocode. Python will never win awards for performance, so if you need that, you drop in something non-Python to do the expensive operations to keep the rest of the code clean and obvious.
If you think of Python as pseudocode, everything else makes a ton more sense.
You can test for values being 0 before dividing, or catching an exception when it is.
Ideally, you just test for input variables outside of the function and do neither. Something like:
defcalc(x, y):
assert x > 0assert y != 0
...
This throw exceptions if the preconditions fail, but those can (and should) be removed for production since their primary purpose is to inform the developer of the preconditions and catch mistakes in development. In production, you’d rely on some kind of schema validation to ensure the asserts never trigger (I’m partial to Pydantic).
So ideally you’d never expect a divide by zero or clutter your code with zero checks outside of those asserts (which shouldn’t be relied on) because you’ve already prevented those cases from happening.
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.
deleted by creator
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.
deleted by creator
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.
deleted by creator
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.
deleted by creator
deleted by creator
It’s not an explicit design goal, but it explains a lot of the Zen of Python and other pushback on PIPs, so to me it’s always been an unwritten design goal (be as close to pseudocode as practical, but no closer). It’s also how I generally write code (start with Python “pseudocode,” then decide what to use in production).
For example, from the Zen of Python:
Being clever in Python is a bad thing, just as it is in pseudocode. Python will never win awards for performance, so if you need that, you drop in something non-Python to do the expensive operations to keep the rest of the code clean and obvious.
If you think of Python as pseudocode, everything else makes a ton more sense.
Ideally, you just test for input variables outside of the function and do neither. Something like:
def calc(x, y): assert x > 0 assert y != 0 ...This throw exceptions if the preconditions fail, but those can (and should) be removed for production since their primary purpose is to inform the developer of the preconditions and catch mistakes in development. In production, you’d rely on some kind of schema validation to ensure the asserts never trigger (I’m partial to Pydantic).
So ideally you’d never expect a divide by zero or clutter your code with zero checks outside of those asserts (which shouldn’t be relied on) because you’ve already prevented those cases from happening.