diff --git a/examples/statistics/boxplot_demo.py b/examples/statistics/boxplot_demo.py
index 83a566e0a2fa..570e9a2428f7 100644
--- a/examples/statistics/boxplot_demo.py
+++ b/examples/statistics/boxplot_demo.py
@@ -82,9 +82,8 @@
 # properties of the original sample, and a boxplot is one visual tool
 # to make this assessment
 
-numDists = 5
-randomDists = ['Normal(1,1)', ' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)',
-               'Triangular(2,9,11)']
+random_dists = ['Normal(1,1)', ' Lognormal(1,1)', 'Exp(1)', 'Gumbel(6,4)',
+                'Triangular(2,9,11)']
 N = 500
 
 norm = np.random.normal(1, 1, N)
@@ -95,15 +94,14 @@
 
 # Generate some random indices that we'll use to resample the original data
 # arrays. For code brevity, just use the same random indices for each array
-bootstrapIndices = np.random.random_integers(0, N - 1, N)
-normBoot = norm[bootstrapIndices]
-expoBoot = expo[bootstrapIndices]
-gumbBoot = gumb[bootstrapIndices]
-lognBoot = logn[bootstrapIndices]
-triaBoot = tria[bootstrapIndices]
-
-data = [norm, normBoot, logn, lognBoot, expo, expoBoot, gumb, gumbBoot,
-        tria, triaBoot]
+bootstrap_indices = np.random.randint(0, N, N)
+data = [
+    norm, norm[bootstrap_indices],
+    logn, logn[bootstrap_indices],
+    expo, expo[bootstrap_indices],
+    gumb, gumb[bootstrap_indices],
+    tria, tria[bootstrap_indices],
+]
 
 fig, ax1 = plt.subplots(figsize=(10, 6))
 fig.canvas.set_window_title('A Boxplot Example')
@@ -126,21 +124,19 @@
 ax1.set_ylabel('Value')
 
 # Now fill the boxes with desired colors
-boxColors = ['darkkhaki', 'royalblue']
-numBoxes = numDists*2
-medians = list(range(numBoxes))
-for i in range(numBoxes):
+box_colors = ['darkkhaki', 'royalblue']
+num_boxes = len(data)
+medians = np.empty(num_boxes)
+for i in range(num_boxes):
     box = bp['boxes'][i]
     boxX = []
     boxY = []
     for j in range(5):
         boxX.append(box.get_xdata()[j])
         boxY.append(box.get_ydata()[j])
-    boxCoords = np.column_stack([boxX, boxY])
+    box_coords = np.column_stack([boxX, boxY])
     # Alternate between Dark Khaki and Royal Blue
-    k = i % 2
-    boxPolygon = Polygon(boxCoords, facecolor=boxColors[k])
-    ax1.add_patch(boxPolygon)
+    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 = []
@@ -149,39 +145,40 @@
         medianX.append(med.get_xdata()[j])
         medianY.append(med.get_ydata()[j])
         ax1.plot(medianX, medianY, 'k')
-        medians[i] = medianY[0]
+    medians[i] = medianY[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])],
+    ax1.plot(np.average(med.get_xdata()), np.average(data[i]),
              color='w', marker='*', markeredgecolor='k')
 
 # Set the axes ranges and axes labels
-ax1.set_xlim(0.5, numBoxes + 0.5)
+ax1.set_xlim(0.5, num_boxes + 0.5)
 top = 40
 bottom = -5
 ax1.set_ylim(bottom, top)
-ax1.set_xticklabels(np.repeat(randomDists, 2),
+ax1.set_xticklabels(np.repeat(random_dists, 2),
                     rotation=45, fontsize=8)
 
 # Due to the Y-axis scale being different across samples, it can be
 # hard to compare differences in medians across the samples. Add upper
 # X-axis tick labels with the sample medians to aid in comparison
 # (just use two decimal places of precision)
-pos = np.arange(numBoxes) + 1
-upperLabels = [str(np.round(s, 2)) for s in medians]
+pos = np.arange(num_boxes) + 1
+upper_labels = [str(np.round(s, 2)) for s in medians]
 weights = ['bold', 'semibold']
-for tick, label in zip(range(numBoxes), ax1.get_xticklabels()):
+for tick, label in zip(range(num_boxes), ax1.get_xticklabels()):
     k = tick % 2
-    ax1.text(pos[tick], top - (top*0.05), upperLabels[tick],
-             horizontalalignment='center', size='x-small', weight=weights[k],
-             color=boxColors[k])
+    ax1.text(pos[tick], .95, upper_labels[tick],
+             transform=ax1.get_xaxis_transform(),
+             horizontalalignment='center', size='x-small',
+             weight=weights[k], color=box_colors[k])
 
 # Finally, add a basic legend
-fig.text(0.80, 0.08, str(N) + ' Random Numbers',
-         backgroundcolor=boxColors[0], color='black', weight='roman',
+fig.text(0.80, 0.08, f'{N} Random Numbers',
+         backgroundcolor=box_colors[0], color='black', weight='roman',
          size='x-small')
 fig.text(0.80, 0.045, 'IID Bootstrap Resample',
-         backgroundcolor=boxColors[1],
+         backgroundcolor=box_colors[1],
          color='white', weight='roman', size='x-small')
 fig.text(0.80, 0.015, '*', color='white', backgroundcolor='silver',
          weight='roman', size='medium')
@@ -213,10 +210,10 @@ def fakeBootStrapper(n):
     return med, CI
 
 inc = 0.1
-e1 = np.random.normal(0, 1, size=(500,))
-e2 = np.random.normal(0, 1, size=(500,))
-e3 = np.random.normal(0, 1 + inc, size=(500,))
-e4 = np.random.normal(0, 1 + 2*inc, size=(500,))
+e1 = np.random.normal(0, 1, size=500)
+e2 = np.random.normal(0, 1, size=500)
+e3 = np.random.normal(0, 1 + inc, size=500)
+e4 = np.random.normal(0, 1 + 2*inc, size=500)
 
 treatments = [e1, e2, e3, e4]
 med1, CI1 = fakeBootStrapper(1)