Merge pull request #16 from QuantEcon/update-84a11e7

[UPDATE] rebuild with sphinxcontrib-tomyst(84a11e7)

source/rst/about_py.md

@@ -302,14 +302,10 @@ the last few years.
 
 #### Pandas
 
-Duplicate implicit target name: "pandas".
-
 ```{index}
 single: Pandas
 ```
 
-Duplicate explicit target name: "pandas".
-
 One of the most popular libraries for working with data is [pandas](http://pandas.pydata.org/).
 
 Pandas is fast, efficient, flexible and well designed.

source/rst/debugging.md

@@ -8,8 +8,6 @@
 
 # Debugging
 
-Duplicate implicit target name: "debugging".
-
 ```{index}
 single: Debugging
 ```
@@ -48,8 +46,6 @@ import matplotlib.pyplot as plt
 
 ## Debugging
 
-Duplicate implicit target name: "debugging".
-
 ```{index}
 single: Debugging
 ```

source/rst/functions.md

@@ -8,8 +8,6 @@
 
 # Functions
 
-Duplicate implicit target name: "functions".
-
 ```{index}
 single: Python; User-defined functions
 ```
@@ -339,8 +337,6 @@ Use no import besides `from numpy.random import uniform`.
 
 ### Exercise 1
 
-Duplicate implicit target name: "exercise 1".
-
 Here's one solution.
 
 ```{code-block} python3
@@ -355,8 +351,6 @@ factorial(4)
 
 ### Exercise 2
 
-Duplicate implicit target name: "exercise 2".
-
 ```{code-block} python3
 from numpy.random import uniform
 
@@ -373,8 +367,6 @@ binomial_rv(10, 0.5)
 
 ### Exercise 3
 
-Duplicate implicit target name: "exercise 3".
-
 Here's a function for the first random device.
 
 ```{code-block} python3

source/rst/getting_started.md

@@ -335,8 +335,6 @@ Save it somewhere, navigate to it from the Jupyter dashboard and then run as dis
 
 ### QuantEcon Notes
 
-Duplicate implicit target name: "quantecon notes".
-
 QuantEcon has its own site for sharing Jupyter notebooks related
 to economics -- [QuantEcon Notes](http://notes.quantecon.org/).
 
@@ -509,9 +507,7 @@ For example, if you've installed the command line version, open up a terminal an
 
 As the 2nd task,
 
-1. Duplicate explicit target name: "github".
-
-   Sign up to [GitHub](https://github.com/).
+1. Sign up to [GitHub](https://github.com/).
 1. Look into 'forking' GitHub repositories (forking means making your own copy of a GitHub repository, stored on GitHub).
 1. Fork [QuantEcon.py](https://github.com/QuantEcon/QuantEcon.py).
 1. Clone your fork to some local directory, make edits, commit them, and push them back up to your forked GitHub repo.
@@ -520,9 +516,7 @@ As the 2nd task,
 For reading on these and other topics, try
 
 * [The official Git documentation](http://git-scm.com/doc).
-* Duplicate explicit target name: "github".
-
-  Reading through the docs on [GitHub](https://github.com/).
+* Reading through the docs on [GitHub](https://github.com/).
 * [Pro Git Book](http://git-scm.com/book) by Scott Chacon and Ben Straub.
 * One of the thousands of Git tutorials on the Net.
 

source/rst/matplotlib.md

@@ -8,8 +8,6 @@
 
 # Matplotlib
 
-Duplicate implicit target name: "matplotlib".
-
 ```{index}
 single: Python; Matplotlib
 ```
@@ -294,8 +292,6 @@ The output should look like this
 
 ### Exercise 1
 
-Duplicate implicit target name: "exercise 1".
-
 Here's one solution
 
 ```{code-block} ipython3

source/rst/numba.md

@@ -509,8 +509,6 @@ Hints:
 
 ### Exercise 1
 
-Duplicate implicit target name: "exercise 1".
-
 Here is one solution:
 
 ```{code-block} python3
@@ -547,8 +545,6 @@ characters.
 
 ### Exercise 2
 
-Duplicate implicit target name: "exercise 2".
-
 We let
 
 - 0 represent "low"

source/rst/numpy.md

@@ -22,8 +22,6 @@ single: Python; NumPy
 
 ## Overview
 
-Duplicate implicit target name: "numpy".
-
 [NumPy](https://en.wikipedia.org/wiki/NumPy) is a first-rate library for numerical programming
 
 * Widely used in academia, finance and industry.
@@ -789,8 +787,6 @@ import matplotlib.pyplot as plt
 
 ### Exercise 1
 
-Duplicate implicit target name: "exercise 1".
-
 This code does the job
 
 ```{code-block} python3
@@ -815,8 +811,6 @@ print(q(x))
 
 ### Exercise 2
 
-Duplicate implicit target name: "exercise 2".
-
 Here's our first pass at a solution:
 
 ```{code-block} python3
@@ -876,8 +870,6 @@ using descriptors that behaves as we desire can be found
 
 ### Exercise 3
 
-Duplicate implicit target name: "exercise 3".
-
 An example solution is given below.
 
 In essence, we've just taken [this

source/rst/oop_intro.md

@@ -57,8 +57,6 @@ These concepts are defined and discussed sequentially below.
 
 ### Type
 
-Duplicate implicit target name: "type".
-
 ```{index}
 single: Python; Type
 ```
@@ -117,8 +115,6 @@ int('300') + 400   # To add as numbers, change the string to an integer
 
 ### Identity
 
-Duplicate implicit target name: "identity".
-
 ```{index}
 single: Python; Identity
 ```
@@ -179,8 +175,6 @@ These attributes are important, so let's discuss them in-depth.
 
 ### Methods
 
-Duplicate implicit target name: "methods".
-
 ```{index}
 single: Python; Methods
 ```

source/rst/pandas.md

@@ -24,8 +24,6 @@ In addition to what’s in Anaconda, this lecture will need the following librar
 
 ## Overview
 
-Duplicate implicit target name: "pandas".
-
 [Pandas](http://pandas.pydata.org/) is a package of fast, efficient data analysis tools for Python.
 
 Its popularity has surged in recent years, coincident with the rise
@@ -483,8 +481,6 @@ Complete the program to show summary statistics and plot the result as a time se
 
 ### Exercise 1
 
-Duplicate implicit target name: "exercise 1".
-
 There are a few ways to approach this problem using Pandas to calculate
 the percentage change.
 
@@ -520,8 +516,6 @@ plt.show()
 
 ### Exercise 2
 
-Duplicate implicit target name: "exercise 2".
-
 Following the work you did in Exercise 1, you can query the data using `read_data` by updating the start and end dates accordingly.
 
 ```{code-block} python3

source/rst/parallelization.md

@@ -429,8 +429,6 @@ For the size of the Monte Carlo simulation, use something substantial, such as
 
 ### Exercise 1
 
-Duplicate implicit target name: "exercise 1".
-
 Here is one solution:
 
 ```{code-block} python3

source/rst/python_advanced_features.md

@@ -1136,8 +1136,6 @@ In the opinion of many people, this makes the decorator syntax a significant imp
 
 ### Descriptors
 
-Duplicate implicit target name: "descriptors".
-
 ```{index}
 single: Python; Descriptors
 ```
@@ -1664,8 +1662,6 @@ Using `try` -- `except`, write a program to read in the contents of the file and
 
 ### Exercise 1
 
-Duplicate implicit target name: "exercise 1".
-
 Here's the standard solution
 
 ```{code-block} python3
@@ -1686,8 +1682,6 @@ print([x(i) for i in range(10)])
 
 ### Exercise 2
 
-Duplicate implicit target name: "exercise 2".
-
 One solution is as follows
 
 ```{code-block} python3
@@ -1715,8 +1709,6 @@ for date in dates:
 
 ### Exercise 3
 
-Duplicate implicit target name: "exercise 3".
-
 Let's save the data first
 
 ```{code-block} python3

source/rst/python_by_example.md

@@ -536,8 +536,6 @@ Your hints are as follows:
 
 ### Exercise 1
 
-Duplicate implicit target name: "exercise 1".
-
 Here's one solution.
 
 ```{code-block} python3
@@ -555,8 +553,6 @@ plt.show()
 
 ### Exercise 2
 
-Duplicate implicit target name: "exercise 2".
-
 ```{code-block} python3
 α_values = [0.0, 0.8, 0.98]
 T = 200
@@ -574,8 +570,6 @@ plt.show()
 
 ### Exercise 3
 
-Duplicate implicit target name: "exercise 3".
-
 Here's one solution:
 
 ```{code-block} python3
@@ -593,8 +587,6 @@ plt.show()
 
 ### Exercise 4
 
-Duplicate implicit target name: "exercise 4".
-
 Here's one way:
 
 ```{code-block} python3
@@ -632,8 +624,6 @@ plt.show()
 
 ### Exercise 5
 
-Duplicate implicit target name: "exercise 5".
-
 Consider the circle of diameter 1 embedded in the unit square.
 
 Let $A$ be its area and let $r=1/2$ be its radius.

source/rst/python_essentials.md

@@ -432,8 +432,6 @@ single: Python; List comprehension
 
 We can also simplify the code for generating the list of random draws considerably by using something called a *list comprehension*.
 
-Duplicate implicit target name: "list comprehensions".
-
 [List comprehensions](https://en.wikipedia.org/wiki/List_comprehension) are an elegant Python tool for creating lists.
 
 Consider the following example, where the list comprehension is on the
@@ -797,9 +795,7 @@ returns `True` if every element in `seq_a` is also an element of `seq_b`, else
 `False`.
 
 * By "sequence" we mean a list, a tuple or a string.
-* Duplicate explicit target name: "sets".
-
-  Do the exercise without using [sets](https://docs.python.org/3/tutorial/datastructures.html#sets) and set methods.
+* Do the exercise without using [sets](https://docs.python.org/3/tutorial/datastructures.html#sets) and set methods.
 
 ### Exercise 5
 
@@ -838,8 +834,6 @@ for i in range(n):
 
 ### Exercise 1
 
-Duplicate implicit target name: "exercise 1".
-
 #### Part 1 Solution:
 
 Here's one possible solution
@@ -894,8 +888,6 @@ sum([x % 2 == 0 and y % 2 == 0 for x, y in pairs])
 
 ### Exercise 2
 
-Duplicate implicit target name: "exercise 2".
-
 ```{code-block} python3
 def p(x, coeff):
     return sum(a * x**i for i, a in enumerate(coeff))
@@ -907,8 +899,6 @@ p(1, (2, 4))
 
 ### Exercise 3
 
-Duplicate implicit target name: "exercise 3".
-
 Here's one solution:
 
 ```{code-block} python3
@@ -933,8 +923,6 @@ count_uppercase_chars('The Rain in Spain')
 
 ### Exercise 4
 
-Duplicate implicit target name: "exercise 4".
-
 Here's a solution:
 
 ```{code-block} python3
@@ -960,8 +948,6 @@ def f(seq_a, seq_b):
 
 ### Exercise 5
 
-Duplicate implicit target name: "exercise 5".
-
 ```{code-block} python3
 def linapprox(f, a, b, n, x):
     """
@@ -1001,8 +987,6 @@ def linapprox(f, a, b, n, x):
 
 ### Exercise 6
 
-Duplicate implicit target name: "exercise 6".
-
 Here's one solution.
 
 ```{code-block} python3