I don't understand why numeric filters are included. The library is written in python, so shouldn't a lambda function based filter be roughly as fast but much easier/clearer to write.
replies(2):
It does look like Python's comprehensions would be a better choice if you're writing them by hand anyway.
Here's the usage example from the README:
from leopards import Q
l = [{"name":"John","age":"16"}, {"name":"Mike","age":"19"},{"name":"Sarah","age":"21"}]
filtered= Q(l,{'name__contains':"k", "age__lt":20})
print(list(filtered))
[x for x in l if ('k' in x['name'] and int(x['age']) < 20)]
[{'name': 'Mike', 'age': '19'}]
> Even though, age was str in the dict, as the value of in the query dict was int, Leopards converted the value in dict automatically to match the query data type. This behaviour can be stopped by passing False to convert_types parameter.
filtered = Q(l,{'name__contains':"k", "age__lt":20})
filtered = [x for x in l if ('k' in x['name'] and int(x['age']) < 20)] {"name" (includes? "k"), "age" (< 20)}
{"name" #(includes? % "k"), "age" #(< % 20)}
{"name" (fn [name] (includes? name "k")), "age" (fn [age] (< age 20))}
You could serialize the patterns using EDN as a substitute for JSON.
Fun stuff.
I wrote something [similar][1] in javascript. With that it would be:
const is_k_kid = tisch.compileFunction((etc) => ({
'name': name => name.includes('k'),
'age': age => age < 20,
...etc
}));
const result = input.filter(is_k_kid);