Minor changes for flake8

jeromefv · jeromefv · commit 494b1aa5a454 · 2020-11-06T08:10:53.000-08:00
diff --git a/tutorials/introductory/getting_started.py b/tutorials/introductory/getting_started.py
@@ -70,7 +70,7 @@
 # Interactive environments
 # ------------------------
 #
-# The Matplotlib community suggests using `IPython<https://ipython.org/>`_ as
+# The Matplotlib community suggests using `IPython <https://ipython.org/>`_ as
 # the primary interactive environment.
 
 ##############################################################################
@@ -80,25 +80,26 @@
 #
 # The common conventions for preparing to plot data involve importing the
 # necessary libraries with abbreviations for convenience. Both stateful and
-# stateless programming require the
-# following.
+# stateless programming require the following.
 
 import matplotlib.pyplot as plt
 
+##############################################################################
+#
 # The `pyplot` module in Matplotlib is a collection of functions. The module's
 # functions create, manage, and manipulate the current figure and the plotting
-# area. NumPy is another scientific library for Python.
-
+# 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 ax(es).
+# 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 ax(es) and call on methods for manipulating data.
-#   - Object-oriented programming, robust control and useful for generalized
-# code.
-
+# for specific figures and axes and call on methods for manipulating data.
+#   - Object-oriented programming (OOP), robust control and useful for
+# generalized code.
+#
 # .. note::
 #
 #     The Matplotlib community does not recommend interchanging stateful and
@@ -108,7 +109,10 @@
 #
 # For other techniques of creating plots with Matplotlib, refer to
 # """insert reference""".
-
+#
+# Data
+# ----
+#
 # The Matplotlib library manages data in the form of iterables and/or
 # sequenced items. These can also take the form of NumPy arrays like
 # `numpy.array` or `numpy.ma.masked_array`. All plotting functions take these
@@ -130,6 +134,8 @@
 svg_acct_10 = [1550, 1600, 1650, 1700, 1750, 1800,
                1850, 1900, 1950, 2000, 2050, 2100]
 
+##############################################################################
+#
 # .. note::
 #
 #  Other containers, such as `pandas` data objects, may not work as intended.
@@ -158,20 +164,22 @@
 plt.title("Personal Financial Tracking from 2009")
 plt.legend()
 
-# Stateless: Object Oriented Programming
-# --------------------------------------
+##############################################################################
+#
+# 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.
 
-"""Stateless programming with Object Oriented Programming"""
+"""Stateless programming with OOP"""
 
 y4 = chk_acct_10
 y5 = svg_acct_10
 
-fig, ax = plt.subplots()  # Figure & axes unpacked separately w/ module.
+fig, ax = plt.subplots()  # Figure & axes unpacked separately with module.
 
 ax.plot(x, y1, label='Checking Account')
 ax.plot(x, y4, label='Savings Account')
