Added multiple graphs and additional text.

jeromefv · jeromefv · commit 3933158412df · 2020-11-09T10:34:23.000-08:00
diff --git a/tutorials/introductory/getting_started.py b/tutorials/introductory/getting_started.py
@@ -101,7 +101,7 @@
 #   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
+# * Stateless: Code has explicit references to objects. Users manage objects
 #   for specific figures and axes and call on methods for manipulating data.
 #     * Object-oriented programming (OOP), robust control and useful for
 #       generalized code.
@@ -175,7 +175,7 @@
 #
 # 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
+# with various keyword arguments as customizable options. The module also
 # includes other methods for generating parts of the visualization. These parts
 # use different methods from the OOP approach.
 #
@@ -184,7 +184,7 @@
 #
 # 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
+# Each axes has its own methods to graph data. In addition, each axes also
 # uses separate methods to create and manage parts of a figure. These methods
 # are different from those of the stateful programming approach.
 
@@ -228,15 +228,85 @@
 # Customizations
 # ==============
 #
-# There are four main parts to building a visualization with Matplotlib, the
-# figure, the axes, the axis, and the artist(s).
+# There are two main parts to building a visualization with Matplotlib, the
+# figure and the axes.
 #
 # Components of Matplotlib Figure
 # -------------------------------
 #
 # The image below depicts each visible element of a Matplotlib graph.
 #
 # * Figure
+#    * The figure is the working space for the programming. All visible
+#      objects on a graph are located within the figure.
 # * Axes
-# * Axis
-# * Artist(s)
+#    * Axes are subplots within the figure. They contain figure elements and
+#      are responsible for plotting and configuring additional details.
+#       * Note: Axes and Axis are not synonymous. Axis refers to
+#         """insert reference""".
+#
+# Multiple Graphs within a Figure
+# -------------------------------
+#
+# For multiple graphs using a single figure, stateful and stateless approaches
+# use a similar convention for mapping out multiple axes. Matplotlib manages
+# more than one axes in a two-dimensional matrix. They are arranged by row
+# amount and then by column amount. The third argument represents the specific
+# axes involved.
+#
+# When looking for more complex solutions to multiple graphs within a figure,
+# use the GridSpec module to organize the layout.
+
+# Stateful with pyplot
+
+plt.subplot(1, 2, 1)
+plt.plot(x, y1, label='Income')
+plt.plot(x, y2, label='Checking')
+plt.plot(x, y1, color='green', label='Savings')
+plt.xticks(x, months, rotation=270)
+plt.title('2009', fontsize='small')
+
+plt.subplot(1, 2, 2)
+plt.plot(x, y1, label='Income')
+plt.plot(x, y4, label='Checking')
+plt.plot(x, y5, color='green', label='Savings')
+plt.xticks(x, months, rotation=270)
+plt.title('2009', fontsize='small')
+plt.legend(bbox_to_anchor=(1, 1), loc='upper left')
+
+plt.suptitle('Personal Financial Tracking', weight='black')
+plt.tight_layout()
+
+##############################################################################
+#
+# The `pyplot` example above uses two axes to graph data. In each instance,
+# Matplotlib auotmatically manages the specific axes so that each action of
+# plotting data does not interfere with the previous instance.
+
+# Stateless with OOP
+
+fig, (ax1, ax2) = plt.subplots(2, 1, sharey='row',
+                               figsize=[8, 4], constrained_layout=True)
+# Note the different method name for OOP.
+
+fig.suptitle('Personal Financial Tarcking \'09 - \'10',
+             fontsize=16, weight='black')
+
+ax1.plot(x, y1, label='Income')
+ax1.plot(x, y2, label='Checking')
+ax1.plot(x, y3, color='green', label='Savings')
+ax1.set_xticklabels(months, rotation=270)
+ax1.set_title('2009', fontsize='small')
+ax1.legend(loc='upper left')
+
+ax2.plot(x, y1, label='Income')
+ax2.plot(x, y2, label='Checking')
+ax2.plot(x, y3, color='green', label='Savings')
+ax2.set_xticklabels(months, rotation=270)
+ax2.set_title('2010', fontsize='small')
+
+##############################################################################
+#
+# The OOP example above also uses two axes to graph the data. However, the OOP
+# approach must refer to an explicitly generated axes after creating both the
+# figure and axes.
