count-combinations

name	count-combinations
description	For probability and counting: permutations, combinations, sample spaces, Monte Carlo simulation, brute-force enumeration, card/dice problems.

count-combinations

When to Use

• Probability calculations
• Counting permutations or combinations
• Enumerating all possibilities (brute force)
• Monte Carlo simulation
• Card game probabilities
• Dice roll distributions
• Urn/ball problems

When NOT to Use

• When closed-form formula exists and is simpler
• Astronomically large sample spaces (use simulation)
• When approximation is acceptable (use sampling)

The Pattern

Define sample space explicitly, then count favorable outcomes.

from fractions import Fraction
from itertools import combinations, permutations, product

def P(event, space):
    """Probability = favorable outcomes / total outcomes."""
    favorable = event & space if isinstance(event, set) else {x for x in space if event(x)}
    return Fraction(len(favorable), len(space))

# Sample spaces
die = {1, 2, 3, 4, 5, 6}
two_dice = {(a, b) for a in die for b in die}
deck = [r + s for r in 'A23456789TJQK' for s in 'SHDC']
hands = set(combinations(deck, 5))

# Events as sets or predicates
even = {2, 4, 6}
is_flush = lambda hand: len(set(c[1] for c in hand)) == 1

Example (from pytudes Probability.ipynb)

from fractions import Fraction
from itertools import combinations

def P(event, space):
    """The probability of an event, given a sample space."""
    favorable = {x for x in space if x in event} if isinstance(event, set) \
                else {x for x in space if event(x)}
    return Fraction(len(favorable), len(space))

# Urn problem: 6 blue, 9 red, 8 white balls; draw 6
def balls(color, n):
    return [f'{color}{i}' for i in range(1, n + 1)]

urn = balls('B', 6) + balls('R', 9) + balls('W', 8)
U6 = set(combinations(urn, 6))

def select(color, n, space=U6):
    """Event: exactly n balls of given color."""
    return {s for s in space if sum(  b  s  b[] == color) == n}


P(select(, ) & select(, ) & select(, ), U6)

Key Principles

1. Enumerate explicitly: When feasible, list all outcomes
2. Use Fraction: Exact arithmetic, no floating point errors
3. Events as sets: Use set operations (union, intersection)
4. Events as predicates: Use functions for complex conditions
5. itertools for generation: combinations, permutations, product