Added more explanation of pyplot/OOP

jeromefv · jeromefv · commit 2ccbdeaccf50 · 2020-11-06T12:04:57.000-08:00
diff --git a/tutorials/introductory/getting_started.py b/tutorials/introductory/getting_started.py
@@ -22,8 +22,8 @@
 #
 # There are two main ways of producing plots 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.
+# Oriented Programming (OOP), are the foundation for creating and manipulating
+# data into visualizations.
 #
 # The stateful `pyplot` approach to generate plots is simple. It is helpful
 # for basic plots and for interactive environments, such as Jupyter Notebooks.
@@ -119,12 +119,12 @@
 # data structures.
 #
 
-"""Sample Data"""
+# Sample Data
 
 months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun',
           'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']
-income = [950, 950, 950, 950, 950, 950, 950, 950,
-          950, 950, 950, 950]
+income = [950, 950, 950, 950, 950, 950,
+          950, 950, 950, 950, 950, 950]
 chk_acct_09 = [1250, 1325, 1200, 1220, 1100, 1055,
                1255, 1090, 1190, 1205, 1205, 1180]
 svg_acct_09 = [1000, 1050, 1100, 1150, 1200, 1250,
@@ -138,7 +138,7 @@
 #
 # .. 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`
 # ------------------
@@ -149,7 +149,7 @@
 # Additional parts of the figure are also available through the module
 # methods.
 
-"""Stateful programming with pyplot"""
+# Stateful programming with pyplot
 
 x = months
 y1 = income
@@ -166,15 +166,22 @@
 
 ##############################################################################
 #
+# In the example above, the `pyplot` module contains its own methods of
+# actionable tasks for the data. The `plt.plot` plots data as a line graph
+# with various keyword arguments as additional customizations. The module also
+# includes other methods for generating parts of the visualization. These parts
+# use different methods from the OOP approach.
+#
 # Stateless: Object Oriented Programming (OOP)
 # --------------------------------------------
 #
-# To use stateless programming for Matplotlib involves using a single
-# instance of the `pyplot` module to unpack the explicit figure(s) and
-# ax(es). Each axes has its own methods to plot data. Also, each axes
-# also uses separate methods to create and manage parts of a figure.
+# To use stateless programming for Matplotlib involves using a single instance
+# 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.
 
-"""Stateless programming with OOP"""
+# Stateless programming with OOP
 
 y4 = chk_acct_10
 y5 = svg_acct_10
@@ -191,10 +198,32 @@
 
 ##############################################################################
 #
-# Customizations
-# ==============
+# 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
+# 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
+#
+# .. 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.
+
+##############################################################################
 #
+# Customizations
+# ==============
 #
+# There are four main parts to building a visualization with Matplotlib, the
+# figure, the axes, the axis, and the artist(s).
 #
+# Components of Matplotlib Figure
+# -------------------------------
 #
+# This
