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[scipy.md] Update np.random → Generator API #547

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mmcky merged 2 commits into from
May 8, 2026
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[scipy.md] Update np.random → Generator API #547

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bda4c57
Update scipy to new API
Chihiro2000GitHub May 6, 2026
eb67e74
Reuse main-text rng in Scipy lecture
Chihiro2000GitHub May 8, 2026
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13 changes: 8 additions & 5 deletions lectures/scipy.md
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Original file line number Diff line number Diff line change
Expand Up @@ -104,10 +104,11 @@ The `scipy.stats` subpackage supplies

### Random Variables and Distributions

Recall that `numpy.random` provides functions for generating random variables
Recall that `numpy.random` provides tools for generating random variables

```{code-cell} python3
np.random.beta(5, 5, size=3)
rng = np.random.default_rng()
rng.beta(5, 5, size=3)
```

This generates a draw from the distribution with the density function below when `a, b = 5, 5`
Expand Down Expand Up @@ -188,8 +189,9 @@ For example, `scipy.stats.linregress` implements simple linear regression
```{code-cell} python3
from scipy.stats import linregress

x = np.random.randn(200)
y = 2 * x + 0.1 * np.random.randn(200)
rng = np.random.default_rng()
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@HumphreyYang HumphreyYang May 8, 2026

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I remember the convention was to use a single rng for the main text and separate ones in solution blocks (see this PR discussion). According to that, I think we can reuse the rng on line 110 here.

x = rng.standard_normal(200)
y = 2 * x + 0.1 * rng.standard_normal(200)
gradient, intercept, r_value, p_value, std_err = linregress(x, y)
gradient, intercept
```
Expand Down Expand Up @@ -572,8 +574,9 @@ Set `M = 10_000_000`
Here is one solution:

```{code-cell} ipython3
rng = np.random.default_rng()
M = 10_000_000
S = np.exp(μ + σ * np.random.randn(M))
S = np.exp(μ + σ * rng.standard_normal(M))
return_draws = np.maximum(S - K, 0)
P = β**n * np.mean(return_draws)
print(f"The Monte Carlo option price is {P:3f}")
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