In Python, How Does One Catch Warnings as If They Were Exceptions

In Python, how does one catch warnings as if they were exceptions?

To handle warnings as errors simply use this:

import warnings
warnings.filterwarnings("error")

After this you will be able to catch warnings same as errors, e.g. this will work:

try:
    some_heavy_calculations()
except RuntimeWarning:
    breakpoint()

You can also reset the behaviour of warnings by running:

warnings.resetwarnings()

P.S. Added this answer because the best answer in comments contains misspelling: filterwarnigns instead of filterwarnings.

Is it possible to capture all warnings raised in a try block in python?

Were you expecting something like the following:

import warnings
warnings.filterwarnings('error')

def warning_func():
    print('hello')
    warnings.warn(Warning('Warn1'))
    print('hi')
    warnings.warn(Warning('Warn2'))

with warnings.catch_warnings(record=True) as w:
    warnings.simplefilter("always")
    warning_func()
    print(w)

How do I catch a numpy warning like it's an exception (not just for testing)?

It seems that your configuration is using the print option for numpy.seterr:

>>> import numpy as np
>>> np.array([1])/0   #'warn' mode
__main__:1: RuntimeWarning: divide by zero encountered in divide
array([0])
>>> np.seterr(all='print')
{'over': 'warn', 'divide': 'warn', 'invalid': 'warn', 'under': 'ignore'}
>>> np.array([1])/0   #'print' mode
Warning: divide by zero encountered in divide
array([0])

This means that the warning you see is not a real warning, but it's just some characters printed to stdout(see the documentation for seterr). If you want to catch it you can:

1. Use numpy.seterr(all='raise') which will directly raise the exception. This however changes the behaviour of all the operations, so it's a pretty big change in behaviour.
2. Use numpy.seterr(all='warn'), which will transform the printed warning in a real warning and you'll be able to use the above solution to localize this change in behaviour.

Once you actually have a warning, you can use the warnings module to control how the warnings should be treated:

>>> import warnings
>>> 
>>> warnings.filterwarnings('error')
>>> 
>>> try:
...     warnings.warn(Warning())
... except Warning:
...     print 'Warning was raised as an exception!'
... 
Warning was raised as an exception!

Read carefully the documentation for filterwarnings since it allows you to filter only the warning you want and has other options. I'd also consider looking at catch_warnings which is a context manager which automatically resets the original filterwarnings function:

>>> import warnings
>>> with warnings.catch_warnings():
...     warnings.filterwarnings('error')
...     try:
...         warnings.warn(Warning())
...     except Warning: print 'Raised!'
... 
Raised!
>>> try:
...     warnings.warn(Warning())
... except Warning: print 'Not raised!'
... 
__main__:2: Warning: 

Catching numpy runtimewarning as exception and suppressing them

Thanks to Jürg Merlin Spaak for his comment, I found a better and simpler solution. It's obviously better to catch the exception outside the function which I reverted back to the original version:

def ExtractValues(d):
    for v in d.values():
        if isinstance(v, dict):
            yield from ExtractValues(v)
        else:
            if isinstance(v,list):
                v = np.mean(v)  
            yield v

I've set everything on warn in the main piece of the of the code:

np.seterr(all='warn')

Then catch them:

with warnings.catch_warnings():
                    warnings.filterwarnings('error')
                    try:
                        raw_features = list(ExtractValues(data)) 
                    except Warning as e:
                        print('Houston, we have a warning:', e)
                        print('The bad guy is: ' + current_file)
                        print('This sample will not be considered.')
                        pass
                    else:
                        #Whatever

Worth noting for whoever comes here for the same exception. I succeeded to catch both warnings, but print(e) will tell you only "mean of empty slice". I can guess why, but I'm too tired to further investigate.

Raise warning in Python without interrupting program

You shouldn't raise the warning, you should be using warnings module. By raising it you're generating error, rather than warning.

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