diff --git a/examples/showcase/bachelors_degrees_by_gender.py b/examples/showcase/bachelors_degrees_by_gender.py
index fb5132cc4224..d891fbe52153 100644
--- a/examples/showcase/bachelors_degrees_by_gender.py
+++ b/examples/showcase/bachelors_degrees_by_gender.py
@@ -1,3 +1,11 @@
+"""
+A graph of multiple time series which demonstrates extensive custom
+styling of plot frame, tick lines and labels, and line graph properties.
+
+Also demonstrates the custom placement of text labels along the right edge
+as an alternative to a conventional legend.
+"""
+
 import matplotlib.pyplot as plt
 from matplotlib.mlab import csv2rec
 from matplotlib.cbook import get_sample_data
diff --git a/examples/statistics/boxplot_color_demo.py b/examples/statistics/boxplot_color_demo.py
index e16bf3822ea2..474f427d76eb 100644
--- a/examples/statistics/boxplot_color_demo.py
+++ b/examples/statistics/boxplot_color_demo.py
@@ -1,24 +1,36 @@
-# Box plots with custom fill colors
+"""Box plots with custom fill colors.
+
+This plot illustrates how to create two types of box plots
+(rectangular and notched), and how to fill them with custom
+colors by accessing the properties of the artists of the
+box plots. Additionally, the ``labels`` parameter is used to
+provide x-tick labels for each sample.
+"""
 
 import matplotlib.pyplot as plt
 import numpy as np
 
 # Random test data
 np.random.seed(123)
-all_data = [np.random.normal(0, std, 100) for std in range(1, 4)]
+all_data = [np.random.normal(0, std, size=100) for std in range(1, 4)]
+labels = ['x1', 'x2', 'x3']
 
 fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(12, 5))
 
 # rectangular box plot
 bplot1 = axes[0].boxplot(all_data,
-                         vert=True,   # vertical box aligmnent
-                         patch_artist=True)   # fill with color
+                         vert=True,  # vertical box alignment
+                         patch_artist=True,  # fill with color
+                         labels=labels)  # will be used to label x-ticks
+axes[0].set_title('Rectangular box plot')
 
 # notch shape box plot
 bplot2 = axes[1].boxplot(all_data,
                          notch=True,  # notch shape
-                         vert=True,   # vertical box aligmnent
-                         patch_artist=True)   # fill with color
+                         vert=True,  # vertical box alignment
+                         patch_artist=True,  # fill with color
+                         labels=labels)  # will be used to label x-ticks
+axes[1].set_title('Notched box plot')
 
 # fill with colors
 colors = ['pink', 'lightblue', 'lightgreen']
@@ -29,12 +41,7 @@
 # adding horizontal grid lines
 for ax in axes:
     ax.yaxis.grid(True)
-    ax.set_xticks([y+1 for y in range(len(all_data))], )
-    ax.set_xlabel('xlabel')
-    ax.set_ylabel('ylabel')
-
-# add x-tick labels
-plt.setp(axes, xticks=[y+1 for y in range(len(all_data))],
-         xticklabels=['x1', 'x2', 'x3', 'x4'])
+    ax.set_xlabel('Three separate samples')
+    ax.set_ylabel('Observed values')
 
 plt.show()
diff --git a/examples/statistics/boxplot_vs_violin_demo.py b/examples/statistics/boxplot_vs_violin_demo.py
index acec4f00ceb5..c4150e5db7bb 100644
--- a/examples/statistics/boxplot_vs_violin_demo.py
+++ b/examples/statistics/boxplot_vs_violin_demo.py
@@ -1,13 +1,15 @@
-# Box plot - violin plot comparison
-#
-# Note that although violin plots are closely related to Tukey's (1977) box plots,
-# they add useful information such as the distribution of the sample data (density trace).
-#
-# By default, box plots show data points outside 1.5 x the inter-quartile range as outliers
-# above or below the whiskers wheras violin plots show the whole range of the data.
-#
-# Violin plots require matplotlib >= 1.4.
+"""Box plot - violin plot comparison.
 
+Note that although violin plots are closely related to Tukey's (1977) box
+plots, they add useful information such as the distribution of the sample
+data (density trace).
+
+By default, box plots show data points outside 1.5 x the inter-quartile range
+as outliers above or below the whiskers whereas violin plots show the whole
+range of the data.
+
+Violin plots require matplotlib >= 1.4.
+"""
 import matplotlib.pyplot as plt
 import numpy as np
 
@@ -20,20 +22,20 @@
 axes[0].violinplot(all_data,
                    showmeans=False,
                    showmedians=True)
-axes[0].set_title('violin plot')
+axes[0].set_title('Violin plot')
 
 # plot box plot
 axes[1].boxplot(all_data)
-axes[1].set_title('box plot')
+axes[1].set_title('Box plot')
 
 # adding horizontal grid lines
 for ax in axes:
     ax.yaxis.grid(True)
-    ax.set_xticks([y+1 for y in range(len(all_data))])
-    ax.set_xlabel('xlabel')
-    ax.set_ylabel('ylabel')
+    ax.set_xticks([y + 1 for y in range(len(all_data))])
+    ax.set_xlabel('Four separate samples')
+    ax.set_ylabel('Observed values')
 
 # add x-tick labels
-plt.setp(axes, xticks=[y+1 for y in range(len(all_data))],
+plt.setp(axes, xticks=[y + 1 for y in range(len(all_data))],
          xticklabels=['x1', 'x2', 'x3', 'x4'])
 plt.show()