good suggestions, manual edit

Author: mmcky

lectures/scipy.md

@@ -23,6 +23,14 @@ kernelspec:
 ```{index} single: Python; SciPy
 ```
 
+In addition to what’s in Anaconda, this lecture will need the following libraries:
+
+```{code-cell} ipython3
+:tags: [hide-output]
+
+!pip install --upgrade quantecon
+```
+
 ## Overview
 
 [SciPy](https://scipy.org/) builds on top of NumPy to provide common tools for scientific programming such as
@@ -49,20 +57,27 @@ In this lecture, we aim only to highlight some useful parts of the package.
 
 SciPy is a package that contains various tools that are built on top of NumPy, using its array data type and related functionality.
 
-In older versions of SciPy, importing SciPy would also import NumPy symbols into the global namespace. 
+````{note} 
+In older versions of SciPy (`scipy < 0.15.1`), importing the package would also import NumPy symbols into the global namespace, as can be seen from this excerpt the SciPy initialization file:
 
-However, modern versions of SciPy (1.15+) have a cleaner approach and no longer automatically import NumPy symbols. This is better practice as it avoids namespace pollution.
-
-Instead, you should import NumPy explicitly:
+```python
+from numpy import *
+from numpy.random import rand, randn
+from numpy.fft import fft, ifft
+from numpy.lib.scimath import *
+```
 
+However, it is better practice to use NumPy functionality explicitly.
 
-```{code-cell} python3
+```python
 import numpy as np
-import quantecon as qe
 
 a = np.identity(3)
 ```
 
+More recent versions of SciPy (1.15+) no longer automatically import NumPy symbols.
+```
+
 What is useful in SciPy is the functionality in its sub-packages
 
 * `scipy.optimize`, `scipy.integrate`, `scipy.stats`, etc.