Perhaps you should google "Logic Programming in Python". Pyke looks promising:

Pyke introduces a form of Logic Programming (inspired by Prolog) to the Python community by providing a knowledge-based inference engine (expert system) written in 100% Python.

Unlike Prolog, Pyke integrates with Python allowing you to invoke Pyke from Python and intermingle Python statements and expressions within your expert system rules.

You may want to use pyDatalog, a logic programming library that I developed for Python implementing Datalog. It also works with SQLAlchemy to query relational databases using logic clauses.

As we are heading toward 2019, I recommend PySWIP over others recommended here. It is actively maintained and has an easy interface, unlike Pyke (9 years ago) or PyLog (6 years ago).

LogPy is an implementation of miniKanren, a relational programming language, in Python. It follows in th tradition of core.logic, the preeminent logic programming solution in Clojure. LogPy was designed for interoperability with pre-existing codebases.

Another option is Yield Prolog

You could also look at Dee, which adds relations to Python: http://www.quicksort.co.uk

A recent Prolog implementation in Python (or rather RPython) in Pyrolog. It is still rather experimental.

You should also check PyLog:

http://cdsoft.fr/pylog/

It has a very clean and simple syntax and implementation.

You can have a look at pytholog. It is written in python totally with no interfaces with prolog and it mimics prolog s syntax, approach and backtracking. Simply initiate a KnowledgeBase and feed it with facts and rules then run queries.

import pytholog as pl
food_kb = pl.KnowledgeBase("food")
food_kb(["food_type(gouda, cheese)",
        "food_type(ritz, cracker)",
        "food_type(steak, meat)",
        "food_type(sausage, meat)",
        "food_type(limonade, juice)",
        "food_type(cookie, dessert)",
        "flavor(sweet, dessert)",
        "flavor(savory, meat)",
        "flavor(savory, cheese)",
        "flavor(sweet, juice)",
        "food_flavor(X, Y) :- food_type(X, Z), flavor(Y, Z)"])

print(food_kb.query(pl.Expr("food_flavor(What, sweet)")))
# [{ What :  limonade }, { What :  cookie }]
print(food_kb.query(pl.Expr("flavor(sweet, dessert)")))
# [ Yes ]

It also supports calculations and probabilities

battery_kb = pl.KnowledgeBase("battery")
battery_kb([
    "battery(dead, P) :- voltmeter(battery_terminals, abnormal, P2), P is P2 + 0.5",
    "battery(dead, P) :- electrical_problem(P), P >= 0.8",
    "battery(dead, P) :- electrical_problem(P2), age(battery, old, P3), P is P2 * P3 * 0.9",
    "electrical_problem(0.7)",
    "age(battery, old, 0.8)",
    "voltmeter(battery_terminals, abnormal, 0.3)"])

battery_kb.query(pl.Expr("battery(dead, Probability)"))
# [{ Probability : 0.8}, { Probability :  No }, { Probability : 0.504}]

It can also be used to find a path between nodes in graphs.

graph = pl.KnowledgeBase("graph")
graph([
    "edge(a, b, 6)", "edge(a, c, 1)", "edge(b, e, 4)",
    "edge(b, f, 3)", "edge(c, d, 3)", "edge(d, e, 8)",
    "edge(e, f, 2)",
    "path(X, Y, W) :- edge(X , Y, W)",
    "path(X, Y, W) :- edge(X, Z, W1), path(Z, Y, W2), W is W1 + W2"])

answer, path = graph.query(pl.Expr("path(a, f, W)"), show_path = True)
print(answer)
# [{ W : 9}, { W : 12}, { W : 14}]

print([x for x in path if str(x) > "Z"])
# [ d ,  b ,  e ,  c ]

answer, path = graph.query(pl.Expr("path(a, e, W)"), show_path = True, cut = True)
print(answer)
# [{ W : 10}]

print([x for x in path if str(x) > "Z"])
# [ b ]

Another option is to use in-memory relational databases. After all, SQL is the most popular relational language, and it has a lot of similarity with Prolog.

• pandas (comparison with SQL)

• SQLite3