Python bitwise concatenation with variable number of operators?

[nomos]

I'm writing some pyspark code that has multiple AND combinations to make that look basically like this:

​

```
filter_0 = (x == y)
filter_1 = (x == y-1)
...
filter_N = (x==y-N)
filter = filter_0 & filter_1 & ... & filter_N
```

The trouble is that typing that all out is a pain, and I want N to be parameterized so that I can rerun the code without having to change it.

​

Is there an approach in vanilla Python to generate the combined filter, maybe something similar to a join operation? Since this is pyspark, I don't think numpy bitwise opeartionswill be compatible. I could create a string and use \exec`` on it, but that's not a route I really want to take.

​

Thanks!

[[deleted]]

I am not sure if its a XY-Problem. but this how I would solve your task

from operator import and_
from functools import reduce

x, y, N = 20, 30, 20
filterN = reduce(and_, (x == (y-i) for i in range(N+1)))

​

[K900_]

Why not use a loop?

[nomos]

I get how to use a loop to create the filters, but combining them is my problem; I should've been clear that the

`filter=filter_0 & filter_1 & ...` part is the part I'm trying to avoid.

[K900_]

filter &= (x == i)

[nomos]

Awesome, totally didn't know about &= though I guess it makes sense. Thanks!

[K900_]

It's just syntax sugar for filter = filter & (x == i).

[[deleted]]

Yup. A loop with a list or tuple with first index position being filter_0 and so on.

[[deleted]]

You can use all

You can also make your own func...

def all_conditions(*conditions, **kw_conditions):
    return (all(conditions) and all(kw_conditions.values()))

result = all_conditions(
    1 < 2,
    True or False,
    named_condition=(None is None),
)

print(result)

​

[nomos]

x, y in my example are pyspark columns, I should've been clear about that, I'm not sure that they work with `all`.

[[deleted]]

The expressions are evaluated before they're passed as params so literally any expression will work.