I Learned Something New About Boolean Checks In Python

I learned something new about boolean checks in Python. #python #code #shorts

[EdmondKachale]

For completeness, it's worth mentioning the any() method that verifies the existence of at least one True item in an iterable.

[Reubenh]

You can also do wife = input(“yes/no:? “)

[abdulsomadabiolajimoh]

I used it in a similar pattern in a project once, it's a streamlit app deployed for an ML solution

So I used the 'all' keyword to check whether every input box has been filled, otherwise it returns an error message (specified). I used it in a similar way, by making every input a variable and putting them all in an array, it wasn't a waste since I will use the array to take predictions from the ML model

glad to see it's a good practice

[jc_]

all() can be used for and checks
any() can be used for or checks
not all() can be used for none checks

[viatori]

Neat. Just coded up some test cases, and it works great.

[Kakashi]

Honestly since match causeway added to Python I've been acting a fool with that function.

[Moist_elbows]

I’m more impressed you found a way to have wife and money True, i can only have 1 true at a time 😂

[naturexmusic]

why don't you do some in java too

[DrDeuteron]

Warning! There is a major syntactic difference, as logical operators are short-circuited, while any and all NOT:
So:
>>>4 and 5 # short circuit
5
>>>all([4, 5]) # logical eval
True

Moreover, in IPython, all and any maybe shadowed by np.all and np.any (which has bugged me more than once), and then the output is flaky:

>>>5 and np # short-circuited
<module 'numpy' from

>>>np.all([5, np])
<module ...blah blah>

Thus the following (hideous) code works:
>>>np.all([5, np]).nanmean([5, 6, float('nan')])
5.5

works.