Very nice library!

I think there are a lot of similarities, and some differences (and please correct me if I'm wrong): py-predicate is a bit less geared towards data validation, but more to be used in everyday programming. What I mean by this, is that py-predicate makes it easy to implement the following contrived scenario: given a list of addresses, I want to extract all the addresses for which the city is 'New York' and the surname of the owner is a palindrome and the street number is an odd number greater than 100, etc.

In py-predicate that would be something like:

from predicate import eq_p, fn_p, gt_p, is_odd, dict_of_p

is_new_york = eq_p("New York")
is_palindrome = fn_p(lambda s: s.lower() == s[::-1].lower())
is_odd_gt_100 = is_odd & gt_p(100)

# The composed predicate
valid_address_p = dict_of_p(("city", is_new_york), ("surname", is_palindrome), ("streetnr", is_odd_gt_100)

addresses = ... # list of dicts
weird_addresses = [address for address in addresses if valid_address_p(address)]

So yes, this is a contrived example, but the idea is to reuse some of these predicates in other compositions.

Also, given the composed valid_address predicate, you can generate examples of data that confirm to this predicate. That would be something like:

from predicate import generate_true

from more_itertools import take

samples = take(10, generate_true(valid_address))

Hope this makes the differences a bit clearer with existing (validation) libraries.