update sources from tomyst(b50a6e0)

Author: mmcky

source/rst/about_py.md

@@ -57,7 +57,7 @@ Python has experienced rapid adoption in the last decade and is now one of the m
 
 ### Common Uses
 
-Python is a general-purpose language used in almost all application domains such as
+{index}`Python <single: Python; common uses>` is a general-purpose language used in almost all application domains such as
 
 * communications
 * web development

source/rst/getting_started.md

@@ -90,7 +90,7 @@ As a practice run, please execute the following
 For more information on conda,  type conda help in a terminal.
 
 (ipython_notebook)=
-## Jupyter Notebooks
+## {index}`Jupyter Notebooks <single: Jupyter Notebooks>`
 
 ```{index} single: Python; IPython
 ```
@@ -422,7 +422,7 @@ following questions:
 1. how should you execute it?
 1. How should you modify or edit it?
 
-#### Option 1: JupyterLab
+#### Option 1: {index}`JupyterLab <single: JupyterLab>`
 
 ```{index} single: JupyterLab
 ```

source/rst/matplotlib.md

@@ -18,7 +18,7 @@ kernelspec:
 </div>
 ```
 
-# Matplotlib
+# {index}`Matplotlib <single: Matplotlib>`
 
 ```{index} single: Python; Matplotlib
 ```

source/rst/need_for_speed.md

@@ -257,7 +257,7 @@ In fact, it's generally true that memory traffic is a major culprit when it come
 
 Let's look at some ways around these problems.
 
-## Vectorization
+## {index}`Vectorization <single: Vectorization>`
 
 ```{index} single: Python; Vectorization
 ```

source/rst/numba.md

@@ -77,7 +77,7 @@ Numba will be a key part of our lectures --- especially those lectures involving
 This lecture introduces the main ideas.
 
 (numba_link)=
-## Compiling Functions
+## {index}`Compiling Functions <single: Compiling Functions>`
 
 ```{index} single: Python; Numba
 ```

source/rst/numpy.md

@@ -18,7 +18,7 @@ kernelspec:
 </div>
 ```
 
-# NumPy
+# {index}`NumPy <single: NumPy>`
 
 ```{index} single: Python; NumPy
 ```

source/rst/pandas.md

@@ -18,7 +18,7 @@ kernelspec:
 </div>
 ```
 
-# Pandas
+# {index}`Pandas <single: Pandas>`
 
 ```{index} single: Python; Pandas
 ```
@@ -305,7 +305,7 @@ This can be done with a variety of methods.
 
 We start with a relatively low-level method and then return to pandas.
 
-### Accessing Data with requests
+### Accessing Data with {index}`requests <single: requests>`
 
 ```{index} single: Python; requests
 ```
@@ -384,7 +384,7 @@ Note that pandas offers many other file type alternatives.
 
 Pandas has [a wide variety](https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html) of top-level methods that we can use to read, excel, json, parquet or plug straight into a database server.
 
-### Using pandas_datareader to Access Data
+### Using {index}`pandas_datareader <single: pandas_datareader>` to Access Data
 
 ```{index} single: Python; pandas-datareader
 ```

source/rst/python_advanced_features.md

@@ -707,7 +707,7 @@ Next `g` is called via `y = g(10)`, leading to the following sequence of actions
 Note that the global `a` was not affected by the local `a`.
 
 (mutable_vs_immutable)=
-### Mutable Versus Immutable Parameters
+### {index}`Mutable <single: Mutable>` Versus {index}`Immutable <single: Immutable>` Parameters
 
 This is a good time to say a little more about mutable vs immutable objects.
 

source/rst/python_oop.md

@@ -18,7 +18,7 @@ kernelspec:
 </div>
 ```
 
-# OOP II: Building Classes
+# {index}`OOP II: Building Classes <single: OOP II: Building Classes>`
 
 ```{index} single: Python; Object-Oriented Programming
 ```

source/rst/scipy.md

@@ -18,7 +18,7 @@ kernelspec:
 </div>
 ```
 
-# SciPy
+# {index}`SciPy <single: SciPy>`
 
 ```{index} single: Python; SciPy
 ```
@@ -49,7 +49,7 @@ A more common approach is to get some idea of what's in the library and then loo
 
 In this lecture, we aim only to highlight some useful parts of the package.
 
-## SciPy versus NumPy
+## {index}`SciPy <single: SciPy>` versus {index}`NumPy <single: NumPy>`
 
 SciPy is a package that contains various tools that are built on top of NumPy, using its array data type and related functionality.
 
@@ -214,7 +214,7 @@ The unique root is approximately 0.408.
 
 Let's consider some numerical techniques for finding roots.
 
-### Bisection
+### {index}`Bisection <single: Bisection>`
 
 ```{index} single: SciPy; Bisection
 ```
@@ -271,7 +271,7 @@ from scipy.optimize import bisect
 bisect(f, 0, 1)
 ```
 
-### The Newton-Raphson Method
+### The {index}`Newton-Raphson Method <single: Newton-Raphson Method>`
 
 ```{index} single: SciPy; Newton-Raphson Method
 ```
@@ -358,7 +358,7 @@ fixed_point(lambda x: x**2, 10.0)  # 10.0 is an initial guess
 If you don't get good results, you can always switch back to the `brentq` root finder, since
 the fixed point of a function $f$ is the root of $g(x) := x - f(x)$.
 
-## Optimization
+## {index}`Optimization <single: Optimization>`
 
 ```{index} single: SciPy; Optimization
 ```
@@ -393,7 +393,7 @@ Constrained multivariate local optimizers include `fmin_l_bfgs_b`, `fmin_tnc`, `
 
 See the [documentation](http://docs.scipy.org/doc/scipy/reference/optimize.html) for details.
 
-## Integration
+## {index}`Integration <single: Integration>`
 
 ```{index} single: SciPy; Integration
 ```
@@ -423,7 +423,7 @@ There are also functions for multivariate integration.
 
 See the [documentation](http://docs.scipy.org/doc/scipy/reference/integrate.html) for more details.
 
-## Linear Algebra
+## {index}`Linear Algebra <single: Linear Algebra>`
 
 ```{index} single: SciPy; Linear Algebra
 ```