guards 1.0.4

An alternative to try/except/else statements

GUARDS: Handle Exceptions Like Never Before

# Before
def main():
    key_str: str = input("Insert key -> ")
    try:
        key: int = int(key_str)
    except ValueError:
        print("Key is not an integer")
        return
    try:
        value = my_dict[key]
    except KeyError:
        value = "not found"
    print(f"Value at key {key} is {value}")

# After
from guards import *
from operator import getitem
def main():
    safe_int = guard(int, ValueError)
    key_maybe: Outcome[int, ValueError] = safe_int(input("Insert key -> "))
    if iserror(key_maybe):
        print("Key is not an integer")
        return
    key: int = key_maybe.ok
    value = guard(getitem, KeyError)(my_dict, key)
    print(f"Value at key {key} is {value.ok if isok(value) else 'not found'}")

# More compact
from guards import *
from operator import getitem
def main():
    safe_int = guard(int, ValueError)
    key_maybe = safe_int(input("Insert key -> "))
    if let_not_ok(key := key_maybe.let):
        print("Key is not an integer")
        return
    value = guard(getitem, KeyError)(my_dict, key).or_else("not found")
    print(f"Value at key {key} is {value}")

guards is a Python library implementing an alternative to the classic try/except/else statements. You still throw errors with raise, but can catch them in a sweeter and more functional approach. Requires Python 3.10+.

Guards let you handle errors in expressions, not just statements.

safe_int = guard(int, ValueError)
# Impossible with try/except (needs indentation and blocks)
result = [safe_int(x).or_else(0) for x in user_inputs]

This means:

• Chain operations without nested try blocks

from operator import getitem
l = [6, 2, 5]
safe_get = guard(getitem, IndexError)
text = outcome_do(
    x1 + x2 + x3
    for x1 in safe_get(l, 0)
    for x2 in safe_get(l, 1)
    for x3 in safe_get(l, 2)
).or_else(0)

• Pass error-handling logic as values

def assert_raises(func, exception, *args, **kwargs):
    match guard(func, exception)(*args, **kwargs):
        Ok(value): raise AssertionError(f"Expected a raised {exception}, but got value {value}")
        Error(exc): return

• Type-check your error handling

def f(x: str) -> int | None:
    outcome = guard("Hello World".index, ValueError)(x)
    if isok(outcome):
        reveal_type(outcome) # Ok[int]
        return outcome.ok
    reveal_type(outcome) # Error[ValueError]
    #return outcome.ok # Would raise an issue by the type checker

• And more!

my_list = ["4.2", "2.7", "pizza"]
numbers, errors = outcome_partition(guard(float, ValueError)(x) for x in my_list)

Installation

Install this library with pip like usual:

pip install guards

Summary

The guard() function blocks another function from raising a set of errors. It takes a function f to guard and one or more BaseException types to guard against, and returns a new function.

open_safe = guard(open, FileNotFoundError, PermissionError)
age_outcome = guard(int, ValueError)(input("Insert age -> "))

The returned function calls the original function f with the same arguments passed to it. The difference comes after the function was called:

• If f returned a value, return an Ok(value) object.
• If f raised an exception exc it is guarded against, return an Error(exc) object.
• If f raised an exception it is not guarded against, the exception is propagated.

safe_float = guard(float, ValueError)
print(safe_float("25"))
print(safe_float("ten"))
print(safe_float([4, 2]))

Outputs:

Ok(25.0)
Error(ValueError("could not convert string to float: 'ten'"))
Traceback (most recent call last):
  File ".../script.py", line 5, in <module>
    print(safe_float([4, 2]))
          ^^^^^^^^^^^^^^^^^^
  File ".../guards.py", line 368, in inner_func
    ok = f(*args, **kwargs)
         ^^^^^^^^^^^^^^^^^^
TypeError: float() argument must be a string or a real number, not 'list'

The union of the return types of a guarded function Ok | Error is called Outcome, which is similar to "result" types in other programming languages.

The simplest way to handle exceptions is to use isok() and iserror() functions to check the type of an outcome, then access their internal values with .ok and .error properties.

file_outcome = guard(open, FileNotFoundError, PermissionError)(PATH)
if isok(file_outcome):
    with file_outcome.ok as file:
        print("File contents:")
        print(file.read())
else: # elif iserror(file_outcome):
    os_error = file_outcome.error
    if isinstance(os_error, PermissionError):
        print("Cannot read the file.")
    else: # elif isinstance(os_error, FileNotFoundError):
        print("The file doesn't exist!")

This is just the basic of error-handling. See the documentation or examples.md for more.

Try/Except VS Guards

Try/Except	Guards
❌Needs blocks and indentation	✅Can be used inside expressions
❌Strictly procedural	✅Multi-paradigm
❌Encourages coarse-grained error handling	✅Encourages fine-grained error handling (coarse-grained is still possible)
❌Terrible when used often	✅Multiple uses are not a problem
❌Hard to pass around	✅Returned outcome can be passed around
❌Bare except antipattern	✅Specifying no types to guard against is a runtime warning
❌Risk of unbound variables	✅No need to worry about variables
❌Try/Except is all you get	✅Methods and functions for the most common use-cases

Exceptions and Guards VS Pure Result Values

Exceptions and Guards	Pure Result Values
Handling errors is the default	Propagating errors is the default
✅Raised exceptions are all you need	❌Needs panics to work well
✅Only the exceptions you care about are handled	❌Needs unwrapping the error even if you only care about a specific one
✅Can be learned whenever you have to handle errors	❌Must be learned when writing or using a fallible function
✅Great compatibility with Python	❌Hard to integrate in a language like Python
✅Exceptions automatically create stack traces	❌You often need libraries for backtraces
❌Raised errors cannot be typed (out of this module's scope)	✅Functions' errors are typed
❌Rarely clear whether a function can error	✅Often clear whether a function can error
❌Needs understanding of functions as values	✅Easier to understand
❌Uses a different control flow	✅Control flow is clearer
❌Error handling is easy to ignore	✅Errors must always be dealt with

Contributing

If you found a problem or want to contribute to this package, you can open a new Issue or Pull Request.