Debugging Code With Decorators

Thanks for the awesome tutorial on Decorators. If I had to study decorators for an interview this would be my one stop shop for decorators starting right from the Beginner

Thanks for the awesome tutorial on Decorators. If I had to study decorators for an interview this would be my one stop shop as it goes right from BASICS to a bit ADVANCED on one topic. Unlike a few other tutorials like OOPS which finished at a BEGINNER stage. Would have appreciated it even more had it cover some more advanced topics like multiple inheritance and usage of super. As I mentioned earlier many of us would like these video courses to be a one stop shop for any topic that is newly introduced. Thanks for all the effort in putting this together. One small feedback , if we could select a default video resolution for an entire topic or series instead of choosing manually for each video/lecture

Hello,

Thank you for the tutorial. Quick question.

Do you need always the boiling script to run the others?

Hi AugustoVal! The ‘boilerplate’ part of the decorators module isn’t required for running or applying the decorators to your functions. It is in the tutorial and used to give you a template for copying and modifying to your needs. It is not called or referenced in the other code.

@Anonymous:

One small feedback , if we could select a default video resolution for an entire topic or series instead of choosing manually for each video/lecture

I just added that as a feature—you can set the default video quality in your account settings :)

hey i just wanted to find out why, doing this ” sqrt = debug(sqrt) gave and error but “math.sqrt = debut(math.sqrt)” did not. the error message was SyntaxError: invalid syntax

Hi Tumise, It’s hard to tell what your SyntaxError could have been from without a bit more context. If I were to guess why it worked properly in your second example, instead of the first. You imported the math module by typing in ‘import math’. In that case you would need to prefix all the ‘math’ functions/methods with ‘math. ’ to access them, such as ‘math.factorial’ or ‘math.sqrt’. If you were to import ‘sqrt’ by itself using ‘from math import sqrt’ you could then access it by typing only ‘sqrt’.

@3:21 in the video, line 46 uses dunder !r while line 43 has no !r. Can you explain why this is so?

My understanding is that !r adds quotes around your string.

@Ian Christy That would be the case. More specifically, however, adding the !r modifier enforces the repr() function to be called on an object to convert it to string before formatting.

This lesson was great for me. It connects multiple loose ends and gives the experience of closure. I will be playing around with this some more in the coming weeks. I always want slow speekers: Christopher speaks way too fast for me, but it is just about perfect at speed 0.75.

Thanks a lot for this one. Do not throw it away ever. It might me an older video, but I think it has great value.

Hi Chris,

thanks for a great tutorial. I was wondering what the best way is to use decorators as a “conditional checking function” which enables us to separate what a function does and when it does that. For example (sorry for the silly example), say I have a function multiply(a,b): def multiply(a,b): return a*b

Now I want this function to return ab only if a>0 and b>0, if they’re both negative i want it to return “--=+” and if one of the two is negative I want it to return “watch out, one of your inputs is negative!”.

I could simply add these conditionals to the “multiply” function and be done, but lets say I want to use decorators for this. How could I do it?

Do you have some other tips on using decorators for conditionals?

@Ivanhoe,

here is an example doing something similar to what you describe:

import functools
import itertools


def all_nonnegative(func):
    @functools.wraps(func)
    def _all_nonnegative(*args, **kwargs):
        if any((negative := v) < 0 for v in itertools.chain(args, kwargs.values())):
            raise ValueError(f"Expected only non-negative arguments, got {negative}")
        else:
            return func(*args, **kwargs)

    return _all_nonnegative


@all_nonnegative
def multiply(a, b):
    return a * b

Here’s an example of using it:

>>> multiply(3, 41)
123

>>> multiply(-3, 41)
Traceback (most recent call last):
    ...
ValueError: Expected only non-negative arguments, got -3

As you note, this may not be the most useful example but it shows one way to use decorators for validation.

In practice, you’re probably better off using things like Pydantic validators to ensure your data are as you expect them: pydantic-docs.helpmanual.io/usage/validators/