A NumPy array stores generated numerical values in a form that pandas Series can consume. Unlike Python’s random.randint, a NumPy generator returns an array rather than one integer. section 1.3
Generate reproducible integer arrays by:
- Creating a generator with np.random.default_rng. section 1.3
- Calling integers with a minimum, an exclusive upper bound, and an output length. section 1.3
- Passing the same seed when comparing solutions or repeating an experiment. section 1.3
Create Series data
Generate ten values from 70 through 100, then pass the resulting array to Series: g = np.random.default_rng(0); s = Series(g.integers(70, 101, 10)). section 1.3
g.integers(0, 100, 10)
Inspect the compact generator call that produces a NumPy array of random integers. section 1.3
