NBA lottery monte carlo method

NBAlottery.py

import random

def trial():
  sequence = []
  # dist =  [0.140,0.140,0.140,0.125,0.105,0.090,0.075,0.060,0.045,0.030,0.020,0.015,0.010,0.005]
  dist = [114,113,112,111,99,89,79,69,59,49,39,29,19,9,6,4]
  pool = [idx+1 for idx,elem in enumerate(dist)]
  # for i in range(4):
  for i in range(5):
    selected = random.choices(population=pool, weights=dist)[0]
    sequence.append(selected)
    idx = pool.index(selected)
    del pool[idx]
    del dist[idx]
  sequence.extend(pool)
  return sequence

import numpy as np
np.set_printoptions(linewidth=150,precision=4,suppress=True)
from collections import Counter

def simulate(rounds=1000):
  trial_runs = []
  for i in range(rounds):
    trial_runs.append(trial())
  
  arrays = []
  # for j in range(14):
  for j in range(16):
    count = Counter([t[j] for t in trial_runs])
    count = {k:v/rounds for k,v in count.items()}
    # count = [round(count[i],3) if i in count else 0 for i in range(1,15)]
    count = [round(count[i],3) if i in count else 0 for i in range(1,17)]
    arrays.append(count)

  arr = np.array(arrays)
  return arr.transpose()
    
 simulate(rounds=5000000)