FIX: lecture cleanup batch from the 2026-08 tracker audit (#595)

* FIX: lecture cleanup batch from the 2026-08 tracker audit

- polars: guard Exercise 1 first/last with drop_nulls (#589)
- polars, pandas: migrate the remaining legacy np.random.* call sites
  to the Generator API (#552)
- pandas_panel: use direct raw.githubusercontent.com data URLs (#580)
- python_by_example: include the rng line in the white-noise snippet
  and correct the line count (#552)
- functions, scipy: define rng in the cells that use it (#552)
- numpy: document DiscreteRV's seed parameter (#552) and reference
  qe.Timer() instead of the retired tic/toc wording (#594)
- about_py: standardize on PyTorch (#552)
- autodiff: split the PRNG key once into three single-use keys (#529)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

* FIX: drop redundant rng re-creation in functions.md

The three added `rng = np.random.default_rng()` lines re-bound a generator
that is already created at the top of the "Random Draws" section and carried
forward through notebook state, so they changed nothing at execution time
while adding boilerplate to cells whose subject is function structure.

Reverts functions.md to match main.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

* FIX: tidy remaining rng bindings in scipy.md and polars.md

scipy.md: drop the redundant rng re-creation before the linregress example,
which re-bound a generator already created earlier in the lecture. Reverts
scipy.md to match main.

polars.md: rename the seeded benchmark generator to bench_rng so it no longer
shadows the unseeded rng created in the Series section with one carrying
different semantics.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

* Revert the bench_rng rename in polars.md

Reusing the rng name for the seeded benchmark generator is fine: the rename
was a readability preference with no effect on output, and nothing downstream
of the benchmark cell reads rng. The seed is unchanged, so the benchmark data
stays reproducible.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>

---------

Co-authored-by: Claude Fable 5 <noreply@anthropic.com>

Author: mmcky

lectures/about_py.md

@@ -139,7 +139,7 @@ popularity of a single Python deep learning library
 
 ```{figure} /_static/lecture_specific/about_py/pytorch_vs_matlab.png
 ```
-Pytorch is just one of several Python libraries for deep learning and AI.
+PyTorch is just one of several Python libraries for deep learning and AI.
 
 
 
@@ -349,7 +349,7 @@ We will discuss the details later in the lecture series, where we cover NumPy in
 While NumPy is still the king of array processing in Python, there are now
 important competitors.
 
-Libraries such as [JAX](https://github.com/jax-ml/jax), [Pytorch](https://pytorch.org/), and [CuPy](https://cupy.dev/) also have
+Libraries such as [JAX](https://github.com/jax-ml/jax), [PyTorch](https://pytorch.org/), and [CuPy](https://cupy.dev/) also have
 built in array types and array operations that can be very fast and efficient.
 
 In fact these libraries are better at exploiting parallelization and fast hardware, as

lectures/autodiff.md

@@ -350,11 +350,11 @@ Let's generate some simulated data:
 ```{code-cell} ipython3
 n = 100
 key = jax.random.key(1234)
-x = jax.random.uniform(key, (n,))
+key, x_key, ϵ_key = jax.random.split(key, 3)
+x = jax.random.uniform(x_key, (n,))
 
 α, β, σ = 0.5, 1.0, 0.1  # Set the true intercept and slope.
-key, subkey = jax.random.split(key)
-ϵ = jax.random.normal(subkey, (n,))
+ϵ = jax.random.normal(ϵ_key, (n,))
 
 y = α * x + β + σ * ϵ
 ```

lectures/numpy.md

@@ -1252,7 +1252,11 @@ class DiscreteRV:
     def __init__(self, q, seed=None):
         """
         The argument q is a NumPy array, or array like, nonnegative and sums
-        to 1
+        to 1.
+
+        The argument seed sets the seed for the underlying random number
+        generator; with the default seed=None, draws are not reproducible
+        across runs.
         """
         self.q = q
         self.Q = cumsum(q)
@@ -1431,7 +1435,7 @@ print(A)
 
 **Part 2**: Move on to replicate the result of the following broadcasting operation. Meanwhile, compare the speeds of broadcasting and the `for` loop you implement.
 
-For this part of the exercise you can use the `tic`/`toc` functions from the `quantecon` library to time the execution. 
+For this part of the exercise you can use the `qe.Timer()` context manager from the `quantecon` library to time the execution. 
 
 Let's make sure this library is installed.
 

lectures/pandas.md

@@ -89,7 +89,8 @@ Let's start with Series.
 We begin by creating a series of four random observations
 
 ```{code-cell} ipython3
-s = pd.Series(np.random.randn(4), name='daily returns')
+rng = np.random.default_rng()
+s = pd.Series(rng.standard_normal(4), name='daily returns')
 s
 ```
 

lectures/pandas_panel.md

@@ -77,7 +77,7 @@ countries and assign it to `realwage`.
 The dataset can be accessed with the following link:
 
 ```{code-cell} ipython3
-url1 = 'https://github.com/QuantEcon/data-lectures/raw/main/lectures/realwage.csv'
+url1 = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/realwage.csv'
 ```
 
 ```{code-cell} ipython3
@@ -197,7 +197,7 @@ function.
 The dataset can be accessed with the following link:
 
 ```{code-cell} ipython3
-url2 = 'https://github.com/QuantEcon/data-lectures/raw/main/lectures/countries.csv'
+url2 = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/countries.csv'
 ```
 
 ```{code-cell} ipython3
@@ -506,7 +506,7 @@ in Europe by age and sex from [Eurostat](https://ec.europa.eu/eurostat/data/data
 The dataset can be accessed with the following link:
 
 ```{code-cell} ipython3
-url3 = 'https://github.com/QuantEcon/data-lectures/raw/main/lectures/employ.csv'
+url3 = 'https://raw.githubusercontent.com/QuantEcon/data-lectures/main/lectures/employ.csv'
 ```
 
 Reading in the CSV file returns a panel dataset in long format. Use `.pivot_table()` to construct

lectures/polars.md

@@ -78,7 +78,8 @@ Let's start with Series.
 We begin by creating a series of four random observations
 
 ```{code-cell} ipython3
-s = pl.Series(name='daily returns', values=np.random.randn(4))
+rng = np.random.default_rng()
+s = pl.Series(name='daily returns', values=rng.standard_normal(4))
 s
 ```
 
@@ -114,7 +115,7 @@ For example, to associate ticker symbols with returns:
 ```{code-cell} ipython3
 df = pl.DataFrame({
     'company': ['AMZN', 'AAPL', 'MSFT', 'GOOG'],
-    'daily returns': np.random.randn(4)
+    'daily returns': rng.standard_normal(4)
 })
 df
 ```
@@ -463,13 +464,13 @@ a grouped weighted average.
 
 ```{code-cell} ipython3
 n = 5_000_000
-np.random.seed(42)
+rng = np.random.default_rng(42)
 
-groups = np.random.choice(['A', 'B', 'C', 'D'], n)
-values = np.random.randn(n)
-weights = np.random.rand(n)
-extra1 = np.random.randn(n)
-extra2 = np.random.randn(n)
+groups = rng.choice(['A', 'B', 'C', 'D'], n)
+values = rng.standard_normal(n)
+weights = rng.random(n)
+extra1 = rng.standard_normal(n)
+extra2 = rng.standard_normal(n)
 
 big_pd = pd.DataFrame({
     'group': groups, 'value': values,
@@ -685,7 +686,7 @@ Calculate percentage changes using Polars expressions:
 
 ```{code-cell} ipython3
 price_change = ticker.select([
-    ((pl.col(tick).last() / pl.col(tick).first() - 1) * 100)
+    ((pl.col(tick).drop_nulls().last() / pl.col(tick).drop_nulls().first() - 1) * 100)
     .alias(tick)
     for tick in ticker_list.keys()
 ]).transpose(

lectures/python_by_example.md

@@ -173,19 +173,22 @@ Then it's harder for readers to know where `sqrt` came from, should they wish to
 
 ### Random Draws
 
-Returning to our program that plots white noise, the remaining three lines
+Returning to our program that plots white noise, the remaining four lines
 after the import statements are
 
 ```{code-cell} ipython
+rng = np.random.default_rng()
 ϵ_values = rng.standard_normal(100)
 plt.plot(ϵ_values)
 plt.show()
 ```
 
-The first line generates 100 (quasi) independent standard normals and stores
+The first line creates a random number generator `rng`.
+
+The second line generates 100 (quasi) independent standard normals and stores
 them in `ϵ_values`.
 
-The next two lines genererate the plot.
+The last two lines generate the plot.
 
 We can and will look at various ways to configure and improve this plot below.