Continued work on prose

jeromefv · jeromefv · commit ef07beb6de0e · 2020-11-07T19:46:00.000-08:00
diff --git a/tutorials/introductory/getting_started.py b/tutorials/introductory/getting_started.py
@@ -20,7 +20,7 @@
 # visualizations involve taking data and transforming it through functions and
 # methods.
 #
-# There are two main ways of producing plots with Matplotlib, stateful and
+# There are two main ways of producing graphs with Matplotlib, stateful and
 # stateless. Stateful code, using `pyplot`, or stateless code, using Object
 # Oriented Programming (OOP), are the foundation for creating and manipulating
 # data into visualizations.
@@ -34,7 +34,7 @@
 # Stateless programming, OOP, helps users generalize code and is useful for
 # repeated uses or larger projects. This is also a more robust way of
 # controlling customizations for visualizations. Users looking to have control
-# over every part of the plot can call methods on each item.
+# over every part of the graph can call methods on each item.
 #
 #
 #
@@ -45,33 +45,38 @@
 # Requirements
 # ============
 #
-# Matplotlib is a Python library. Depending on your operating system, Python
-# may already be installed on your machine.
+# Matplotlib is a Python library and an installed version of Python 3.6 or
+# higher is required. Depending on your operating system, Python may already
+# be installed on your machine.
 #
 # Installing Maptlotlib is required in order to generate plots with the
 # library. You can install Matplotlib for your own development environment(s)
 # or use a third-party package distribution.
 #
 # Third-party package distributions, such as
-# `Anaconda <https://www.anaconda.com/>`_
-# ,  `ActiveState <https://www.activestate.com/activepython/downloads>`_
-# , or `WinPython <https://winpython.github.io/>`_,
-# already provide Matplotlib and its dependencies. These packages work as is
-# and do not require additional installations.
+# `Anaconda <https://www.anaconda.com/>`_,
+# `ActiveState <https://www.activestate.com/activepython/downloads>`_,
+# or `WinPython <https://winpython.github.io/>`_,
+# already provide Matplotlib and its dependencies. They have the added benefit
+# of including other scientific Python libraries as well. These packages work
+# as is and do not require additional installations.
 #
 # Installation from source
 # ------------------------
 #
-# The following command line executions use Python to install Matplotlib.
+# In order to install Matplotlib from the source directory, you can run the
+# following command line executions using Python and installer program `pip`
+# for the latest version of Matplotlib and its dependencies. This will compile
+# the library from the current directory on your machine.
 #
-# `python -m pip install -U pip`
-# `python -m pip install -U matplotlib`
+# `python -m pip install matplotlib`
 #
 # Interactive environments
 # ------------------------
 #
-# The Matplotlib community suggests using `IPython <https://ipython.org/>`_ as
-# the primary interactive environment.
+# The Matplotlib community suggests using `IPython <https://ipython.org/>`_
+# through `Jupyter <https://jupyter.org/index.html>`_ as the primary
+# interactive environment.
 
 ##############################################################################
 #
@@ -91,14 +96,15 @@
 # area.
 #
 # These are the two common strategies for creating plots with Matplotlib.
-# - Stateful: The programming is designed to remember preceding events or
-# interactions. Matplotlib automatically manages figures and axes.
-#   - `pyplot`, most similar to MATLAB and convenient for interactive
-# environments.
-# - Stateless: Code has explicit references to objects. Users create objects
-# for specific figures and axes and call on methods for manipulating data.
-#   - Object-oriented programming (OOP), robust control and useful for
-# generalized code.
+#
+# * Stateful: The programming is designed to remember preceding events or
+#   interactions. Matplotlib automatically manages figures and axes.
+#     * `pyplot`, most similar to MATLAB and convenient for interactive
+#       environments.
+# * Stateless: Code has explicit references to objects. Users create objects
+#   for specific figures and axes and call on methods for manipulating data.
+#     * Object-oriented programming (OOP), robust control and useful for
+#       generalized code.
 #
 # .. note::
 #
@@ -138,7 +144,8 @@
 #
 # .. note::
 #
-# Other containers, such as `pandas` data objects, may not work as intended.
+#    Other containers, such as `pandas` data objects, may not work as
+#    intended.
 #
 # Stateful: `pyplot`
 # ------------------
@@ -179,7 +186,7 @@
 # of the `pyplot` module to unpack a set or sets of explicit figure and axes.
 # Each axes has its own methods to plot data. In addition, each axes also
 # uses separate methods to create and manage parts of a figure. These methods
-# are different from those using stateful programming.
+# are different from those of the stateful programming approach.
 
 # Stateless programming with OOP
 
@@ -200,20 +207,21 @@
 #
 # For the OOP example, the figure and axes are unpacked from the module using
 # a single instance of `pyplot`. This convention uses `plt.subplots()` and
-# defaults to one figure,`fig`, and one axes, `ax`. The section below on
+# defaults to one figure, `fig`, and one axes, `ax`. The section below on
 # customizations contains additional information about multiple visulizations
 # and other modifications.
 #
 # Using the OOP approach allows for `fig` and `ax` to use separate methods to
-# manipulate the visualization. Instead of using the module `pyplot` for each
-# instance of managing the objects, the specfic axes refers to OOP usage and
-# manages data
+# manipulate the visualization. Instead of using the module `pyplot` for all
+# instances of managing objects, the specfic axes refers to OOP usage and
+# manages the respective data.
 #
 # .. note::
 #
-# The names and spelling for methods may be similar for both `pyplot` and OOP
-# approaches. Errors may occur when using the wrong corresponding method.
-# Confirm with documentation API for specific method names.
+#    The names and spelling for methods may be similar for both `pyplot` and
+#    OOP approaches. Errors may occur when using the wrong corresponding
+#    method. Confirm with the documentation API for specific method names
+#    according to your programming.
 
 ##############################################################################
 #
@@ -226,4 +234,9 @@
 # Components of Matplotlib Figure
 # -------------------------------
 #
-# This
+# The image below depicts each visible element of a Matplotlib graph.
+#
+# * Figure
+# * Axes
+# * Axis
+# * Artist(s)
