diff --git a/examples/event_handling/pipong.py b/examples/event_handling/pipong.py
@@ -126,18 +126,18 @@ def __init__(self, ax):
         self.ax = ax
         ax.set_ylim([-1, 1])
         ax.set_xlim([0, 7])
-        padAx = 0
-        padBx = .50
-        padAy = padBy = .30
-        padBx += 6.3
+        pad_a_x = 0
+        pad_b_x = .50
+        pad_a_y = pad_b_y = .30
+        pad_b_x += 6.3
 
         # pads
-        pA, = self.ax.barh(padAy, .2,
+        pA, = self.ax.barh(pad_a_y, .2,
                            height=.3, color='k', alpha=.5, edgecolor='b',
                            lw=2, label="Player B",
                            animated=True)
-        pB, = self.ax.barh(padBy, .2,
-                           height=.3, left=padBx, color='k', alpha=.5,
+        pB, = self.ax.barh(pad_b_y, .2,
+                           height=.3, left=pad_b_x, color='k', alpha=.5,
                            edgecolor='r', lw=2, label="Player A",
                            animated=True)
 
@@ -169,8 +169,8 @@ def __init__(self, ax):
         self.on = False
         self.inst = True    # show instructions from the beginning
         self.background = None
-        self.pads = [Pad(pA, padAx, padAy),
-                     Pad(pB, padBx, padBy, 'r')]
+        self.pads = [Pad(pA, pad_a_x, pad_a_y),
+                     Pad(pB, pad_b_x, pad_b_y, 'r')]
         self.pucks = []
         self.i = self.ax.annotate(instructions, (.5, 0.5),
                                   name='monospace',

diff --git a/examples/statistics/boxplot_demo.py b/examples/statistics/boxplot_demo.py
@@ -8,8 +8,8 @@
 The following examples show off how to visualize boxplots with
 Matplotlib. There are many options to control their appearance and
 the statistics that they use to summarize the data.
-
 """
+
 import matplotlib.pyplot as plt
 import numpy as np
 from matplotlib.patches import Polygon
@@ -130,23 +130,23 @@
 medians = np.empty(num_boxes)
 for i in range(num_boxes):
     box = bp['boxes'][i]
-    boxX = []
-    boxY = []
+    box_x = []
+    box_y = []
     for j in range(5):
-        boxX.append(box.get_xdata()[j])
-        boxY.append(box.get_ydata()[j])
-    box_coords = np.column_stack([boxX, boxY])
+        box_x.append(box.get_xdata()[j])
+        box_y.append(box.get_ydata()[j])
+    box_coords = np.column_stack([box_x, box_y])
     # Alternate between Dark Khaki and Royal Blue
     ax1.add_patch(Polygon(box_coords, facecolor=box_colors[i % 2]))
     # Now draw the median lines back over what we just filled in
     med = bp['medians'][i]
-    medianX = []
-    medianY = []
+    median_x = []
+    median_y = []
     for j in range(2):
-        medianX.append(med.get_xdata()[j])
-        medianY.append(med.get_ydata()[j])
-        ax1.plot(medianX, medianY, 'k')
-    medians[i] = medianY[0]
+        median_x.append(med.get_xdata()[j])
+        median_y.append(med.get_ydata()[j])
+        ax1.plot(median_x, median_y, 'k')
+    medians[i] = median_y[0]
     # Finally, overplot the sample averages, with horizontal alignment
     # in the center of each box
     ax1.plot(np.average(med.get_xdata()), np.average(data[i]),
@@ -193,22 +193,20 @@
 # We can then use the boxplot along with this function to show these intervals.
 
 
-def fakeBootStrapper(n):
+def fake_bootstrapper(n):
     """
     This is just a placeholder for the user's method of
     bootstrapping the median and its confidence intervals.
 
-    Returns an arbitrary median and confidence intervals
-    packed into a tuple
+    Returns an arbitrary median and confidence interval packed into a tuple.
     """
     if n == 1:
         med = 0.1
-        CI = (-0.25, 0.25)
+        ci = (-0.25, 0.25)
     else:
         med = 0.2
-        CI = (-0.35, 0.50)
-
-    return med, CI
+        ci = (-0.35, 0.50)
+    return med, ci
 
 inc = 0.1
 e1 = np.random.normal(0, 1, size=500)
@@ -217,10 +215,10 @@ def fakeBootStrapper(n):
 e4 = np.random.normal(0, 1 + 2*inc, size=500)
 
 treatments = [e1, e2, e3, e4]
-med1, CI1 = fakeBootStrapper(1)
-med2, CI2 = fakeBootStrapper(2)
+med1, ci1 = fake_bootstrapper(1)
+med2, ci2 = fake_bootstrapper(2)
 medians = [None, None, med1, med2]
-conf_intervals = [None, None, CI1, CI2]
+conf_intervals = [None, None, ci1, ci2]
 
 fig, ax = plt.subplots()
 pos = np.array(range(len(treatments))) + 1

diff --git a/examples/statistics/customized_violin.py b/examples/statistics/customized_violin.py
@@ -60,12 +60,12 @@ def set_axis_style(ax, labels):
 whiskers = np.array([
     adjacent_values(sorted_array, q1, q3)
     for sorted_array, q1, q3 in zip(data, quartile1, quartile3)])
-whiskersMin, whiskersMax = whiskers[:, 0], whiskers[:, 1]
+whiskers_min, whiskers_max = whiskers[:, 0], whiskers[:, 1]
 
 inds = np.arange(1, len(medians) + 1)
 ax2.scatter(inds, medians, marker='o', color='white', s=30, zorder=3)
 ax2.vlines(inds, quartile1, quartile3, color='k', linestyle='-', lw=5)
-ax2.vlines(inds, whiskersMin, whiskersMax, color='k', linestyle='-', lw=1)
+ax2.vlines(inds, whiskers_min, whiskers_max, color='k', linestyle='-', lw=1)
 
 # set style for the axes
 labels = ['A', 'B', 'C', 'D']

diff --git a/examples/units/basic_units.py b/examples/units/basic_units.py
@@ -122,12 +122,8 @@ def __new__(cls, value, unit):
         try:
             subcls = type(f'TaggedValue_of_{value_class.__name__}',
                           (cls, value_class), {})
-            if subcls not in units.registry:
-                units.registry[subcls] = basicConverter
             return object.__new__(subcls)
         except TypeError:
-            if cls not in units.registry:
-                units.registry[cls] = basicConverter
             return object.__new__(cls)
 
     def __init__(self, value, unit):
@@ -378,6 +374,4 @@ def cos(x):
         return math.cos(x.convert_to(radians).get_value())
 
 
-basicConverter = BasicUnitConverter()
-units.registry[BasicUnit] = basicConverter
-units.registry[TaggedValue] = basicConverter
+units.registry[BasicUnit] = units.registry[TaggedValue] = BasicUnitConverter()