NumPy supplies arrays for numerical work, including the random values used to build a pandas Series. Its random generator returns an array rather than one integer. section 1.3
Generate reproducible values
Create a generator with np.random.default_rng(0), then call g.integers(70, 101, 10) to produce ten reproducible integers from 70 through 100. Reusing the same seed reproduces the same sequence.
Keep these distinctions in mind:
- np.random.default_rng creates a generator; its integers method accepts a minimum, an exclusive upper bound, and an array length. section 1.3
- A seed makes exercises and comparisons predictable, while modern generator objects are preferred over np.random.seed. section 1.3
