update sources from PR #51 sphinxcontrib-tomyst

Author: mmcky

source/rst/functions.md

@@ -192,8 +192,8 @@ We will break this program into two parts:
 
 1. A user-defined function that generates a list of random variables.
 1. The main part of the program that
-    1. calls this function to get dat
-    1. plots the dat
+    1. calls this function to get data
+    1. plots the data
 
 This is accomplished in the next program
 

source/rst/getting_started.md

@@ -20,7 +20,7 @@ single: Python
 
 In this lecture, you will learn how to
 
-1. get a Python environment up and runnin
+1. get a Python environment up and running
 1. execute simple Python commands
 1. run a sample program
 1. install the code libraries that underpin these lectures
@@ -43,7 +43,7 @@ The best such distribution is [Anaconda](https://www.anaconda.com/what-is-anacon
 
 Anaconda is
 
-* very popul
+* very popular
 * cross-platform
 * comprehensive
 * completely unrelated to the Nicki Minaj song of the same name
@@ -126,7 +126,7 @@ Once you have installed Anaconda, you can start the Jupyter notebook.
 
 Either
 
-* search for Jupyter in your applications menu, o
+* search for Jupyter in your applications menu, or
 * open up a terminal and type `jupyter notebook`
     * Windows users should substitute "Anaconda command prompt" for "terminal" in the previous line.
 
@@ -139,7 +139,7 @@ If you use the second option, you will see something like this
 The output tells us the notebook is running at `http://localhost:8888/`
 
 * `localhost` is the name of the local machine
-* `8888` refers to [port number](https://en.wikipedia.org/wiki/Port_%28computer_networking%29) 8888 on your compute
+* `8888` refers to [port number](https://en.wikipedia.org/wiki/Port_%28computer_networking%29) 8888 on your computer
 
 Thus, the Jupyter kernel is listening for Python commands on port 8888 of our local machine.
 
@@ -196,10 +196,10 @@ This means that the effect of typing at the keyboard **depends on which mode you
 The two modes are
 
 1. Edit mode
-    * Indicated by a green border around one cell, plus a blinking curso
+    * Indicated by a green border around one cell, plus a blinking cursor
     * Whatever you type appears as is in that cell
 1. Command mode
-    * The green border is replaced by a grey (or grey and blue) borde
+    * The green border is replaced by a grey (or grey and blue) border
     * Keystrokes are interpreted as commands --- for example, typing b adds a new cell below  the current one
 
 To switch to

source/rst/need_for_speed.md

@@ -79,7 +79,7 @@ libraries are
 For us, there's another (relatively new) library that will also be essential for
 numerical computing:
 
-* Numb
+* Numba
 
 Over the next few lectures we'll see how to use these libraries.
 

source/rst/numpy.md

@@ -74,8 +74,8 @@ NumPy arrays are somewhat like native Python lists, except that
 
 The most important of these dtypes are:
 
-* float64: 64 bit floating-point numbe
-* int64: 64 bit intege
+* float64: 64 bit floating-point number
+* int64: 64 bit integer
 * bool:  8 bit True or False
 
 There are also dtypes to represent complex numbers, unsigned integers, etc.

source/rst/oop_intro.md

@@ -97,7 +97,7 @@ Consider the following expression
 
 Here we are mixing types, and it's unclear to Python whether the user wants to
 
-* convert `'300'` to an integer and then add it to `400`, o
+* convert `'300'` to an integer and then add it to `400`, or
 * convert `400` to string and then concatenate it with `'300'`
 
 Some languages might try to guess but Python is *strongly typed*
@@ -239,7 +239,7 @@ This includes not just lists, strings, etc., but also less obvious things, such
 
 * functions (once they have been read into memory)
 * modules  (ditto)
-* files opened for reading or writin
+* files opened for reading or writing
 * integers, etc.
 
 Consider, for example, functions.

source/rst/pandas.md

@@ -39,7 +39,7 @@ Just as [NumPy](http://www.numpy.org/) provides the basic array data type plus c
 
 1. defines fundamental structures for working with data and
 1. endows them with methods that facilitate operations such as
-    * reading in dat
+    * reading in data
     * adjusting indices
     * working with dates and time series
     * sorting, grouping, re-ordering and general data munging 

source/rst/python_advanced_features.md

@@ -155,7 +155,7 @@ for line in f:
 The interpreter just keeps
 
 1. calling `f.__next__()` and binding `line` to the result
-1. executing the body of the loo
+1. executing the body of the loop
 
 This continues until a `StopIteration` error occurs.
 
@@ -800,7 +800,7 @@ var([1])
 The advantage is that we can
 
 * fail early, as soon as we know there will be a problem
-* supply specific information on why a program is failin
+* supply specific information on why a program is failing
 
 ### Handling Errors During Runtime
 

source/rst/python_oop.md

@@ -22,7 +22,7 @@ In an earlier lecture, we learned some foundations of object-oriented programmin
 
 The objectives of this lecture are
 
-* cover OOP in more dept
+* cover OOP in more depth
 * learn how to build our own objects, specialized to our needs
 
 For example, you already know how to
@@ -32,14 +32,14 @@ For example, you already know how to
 
 So imagine now you want to write a program with consumers, who can
 
-* hold and spend cas
+* hold and spend cash
 * consume goods
-* work and earn cas
+* work and earn cash
 
 A natural solution in Python would be to create consumers as objects with
 
 * data, such as cash on hand
-* methods, such as `buy` or `work` that affect this dat
+* methods, such as `buy` or `work` that affect this data
 
 Python makes it easy to do this, by providing you with **class definitions**.
 
@@ -91,7 +91,7 @@ A *class definition* is a blueprint for a particular class of objects (e.g., lis
 It describes
 
 * What kind of data the class stores
-* What methods it has for acting on these dat
+* What methods it has for acting on these data
 
 An  *object* or *instance* is a realization of the class, created from the blueprint
 
@@ -363,7 +363,7 @@ k_{t+1} = \frac{s z k_t^{\alpha} + (1 - \delta) k_t}{1 + n}
 Here
 
 * $s$ is an exogenously given saving rate
-* $z$ is a productivity paramete
+* $z$ is a productivity parameter
 * $\alpha$ is capital's share of income
 * $n$ is the population growth rate
 * $\delta$ is the depreciation rate
@@ -564,7 +564,7 @@ plt.show()
 
 The next program provides a function that
 
-* takes an instance of `Market` as a paramete
+* takes an instance of `Market` as a parameter
 * computes dead weight loss from the imposition of the tax
 
 ```{code-block} python3

source/rst/troubleshooting.md

@@ -34,7 +34,7 @@ You also need to keep the external code libraries, such as [QuantEcon.py](https:
 
 For this task you can either
 
-* use conda upgrade quantecon on the command line, o
+* use conda upgrade quantecon on the command line, or
 * execute !conda upgrade quantecon within a Jupyter notebook.
 
 If your local environment is still not working you can do two things.