How do you generate reproducible random data with NumPy?

import numpy as np

# --- Legacy API (still common in older code) ---
np.random.seed(42)
np.random.rand(3)       # [0.374, 0.951, 0.732] — same every time

# --- Modern API: Generator (preferred since NumPy 1.17) ---
rng = np.random.default_rng(seed=42)
# Using a Generator is thread-safe and has better statistical properties

rng.random(5)                # uniform [0, 1)
rng.standard_normal(5)       # N(0, 1)
rng.normal(loc=170, scale=10, size=1000)  # N(mean, std)
rng.integers(0, 100, size=10)  # random ints in [0, 100)
rng.choice(['a','b','c'], size=5, replace=True)  # random sampling
rng.shuffle(arr)              # in-place shuffle
rng.permutation(arr)          # shuffled copy

# --- Distributions used in ML simulations ---
rng.binomial(n=10, p=0.3, size=100)      # number of successes in n trials
rng.poisson(lam=5, size=100)             # events per interval
rng.exponential(scale=2, size=100)       # time between Poisson events
rng.uniform(low=0, high=10, size=100)    # uniform distribution