diff --git a/examples/pylab_examples/histogram_demo_extended.py b/examples/pylab_examples/histogram_demo_extended.py
deleted file mode 100644
index bd262007edde..000000000000
--- a/examples/pylab_examples/histogram_demo_extended.py
+++ /dev/null
@@ -1,122 +0,0 @@
-#!/usr/bin/env python
-import numpy as np
-import pylab as P
-
-#
-# The hist() function now has a lot more options
-#
-
-#
-# first create a single histogram
-#
-mu, sigma = 200, 25
-x = mu + sigma*P.randn(10000)
-
-# the histogram of the data with histtype='step'
-n, bins, patches = P.hist(x, 50, normed=1, histtype='stepfilled')
-P.setp(patches, 'facecolor', 'g', 'alpha', 0.75)
-
-# add a line showing the expected distribution
-y = P.normpdf( bins, mu, sigma)
-l = P.plot(bins, y, 'k--', linewidth=1.5)
-
-
-#
-# create a histogram by providing the bin edges (unequally spaced)
-#
-P.figure()
-
-bins = [100,125,150,160,170,180,190,200,210,220,230,240,250,275,300]
-# the histogram of the data with histtype='step'
-n, bins, patches = P.hist(x, bins, normed=1, histtype='bar', rwidth=0.8)
-
-#
-# now we create a cumulative histogram of the data
-#
-P.figure()
-
-n, bins, patches = P.hist(x, 50, normed=1, histtype='step', cumulative=True)
-
-# add a line showing the expected distribution
-y = P.normpdf( bins, mu, sigma).cumsum()
-y /= y[-1]
-l = P.plot(bins, y, 'k--', linewidth=1.5)
-
-# create a second data-set with a smaller standard deviation
-sigma2 = 15.
-x = mu + sigma2*P.randn(10000)
-
-n, bins, patches = P.hist(x, bins=bins, normed=1, histtype='step', cumulative=True)
-
-# add a line showing the expected distribution
-y = P.normpdf( bins, mu, sigma2).cumsum()
-y /= y[-1]
-l = P.plot(bins, y, 'r--', linewidth=1.5)
-
-# finally overplot a reverted cumulative histogram
-n, bins, patches = P.hist(x, bins=bins, normed=1,
-    histtype='step', cumulative=-1)
-
-
-P.grid(True)
-P.ylim(0, 1.05)
-
-
-#
-# histogram has the ability to plot multiple data in parallel ...
-# Note the new color kwarg, used to override the default, which
-# uses the line color cycle.
-#
-P.figure()
-
-# create a new data-set
-x = mu + sigma*P.randn(1000,3)
-
-n, bins, patches = P.hist(x, 10, normed=1, histtype='bar',
-                            color=['crimson', 'burlywood', 'chartreuse'],
-                            label=['Crimson', 'Burlywood', 'Chartreuse'])
-P.legend()
-
-#
-# ... or we can stack the data
-#
-P.figure()
-
-n, bins, patches = P.hist(x, 10, normed=1, histtype='bar', stacked=True)
-
-P.show()
-
-#
-# we can also stack using the step histtype
-#
-
-P.figure()
-
-n, bins, patches = P.hist(x, 10, histtype='step', stacked=True, fill=True)
-
-P.show()
-
-#
-# finally: make a multiple-histogram of data-sets with different length
-#
-x0 = mu + sigma*P.randn(10000)
-x1 = mu + sigma*P.randn(7000)
-x2 = mu + sigma*P.randn(3000)
-
-# and exercise the weights option by arbitrarily giving the first half
-# of each series only half the weight of the others:
-
-w0 = np.ones_like(x0)
-w0[:len(x0)/2] = 0.5
-w1 = np.ones_like(x1)
-w1[:len(x1)/2] = 0.5
-w2 = np.ones_like(x2)
-w2[:len(x2)/2] = 0.5
-
-
-
-P.figure()
-
-n, bins, patches = P.hist( [x0,x1,x2], 10, weights=[w0, w1, w2], histtype='bar')
-
-P.show()
diff --git a/examples/statistics/histogram_demo_cumulative.py b/examples/statistics/histogram_demo_cumulative.py
new file mode 100644
index 000000000000..b555f0060eca
--- /dev/null
+++ b/examples/statistics/histogram_demo_cumulative.py
@@ -0,0 +1,30 @@
+"""
+Demo of the histogram (hist) function used to plot a cumulative distribution.
+
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+from matplotlib import mlab
+
+
+mu = 200
+sigma = 25
+n_bins = 50
+x = mu + sigma*np.random.randn(10000)
+
+n, bins, patches = plt.hist(x, n_bins, normed=1,
+                            histtype='step', cumulative=True)
+
+# Add a line showing the expected distribution.
+y = mlab.normpdf(bins, mu, sigma).cumsum()
+y /= y[-1]
+plt.plot(bins, y, 'k--', linewidth=1.5)
+
+# Overlay a reversed cumulative histogram.
+plt.hist(x, bins=bins, normed=1, histtype='step', cumulative=-1)
+
+plt.grid(True)
+plt.ylim(0, 1.05)
+plt.title('cumulative step')
+
+plt.show()
diff --git a/examples/statistics/histogram_demo_histtypes.py b/examples/statistics/histogram_demo_histtypes.py
new file mode 100644
index 000000000000..898b90cae17f
--- /dev/null
+++ b/examples/statistics/histogram_demo_histtypes.py
@@ -0,0 +1,27 @@
+"""
+Demo of the histogram (hist) function with different ``histtype`` settings.
+
+* Histogram with step curve that has a color fill.
+* Histogram with with unequal bin widths.
+
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+mu = 200
+sigma = 25
+x = mu + sigma*np.random.randn(10000)
+
+fig, (ax0, ax1) = plt.subplots(ncols=2, figsize=(8, 4))
+
+ax0.hist(x, 20, normed=1, histtype='stepfilled', facecolor='g', alpha=0.75)
+ax0.set_title('stepfilled')
+
+# Create a histogram by providing the bin edges (unequally spaced).
+bins = [100, 150, 180, 195, 205, 220, 250, 300]
+ax1.hist(x, bins, normed=1, histtype='bar', rwidth=0.8)
+ax1.set_title('unequal bins')
+
+plt.tight_layout()
+plt.show()
diff --git a/examples/statistics/histogram_demo_multihist.py b/examples/statistics/histogram_demo_multihist.py
new file mode 100644
index 000000000000..57dd2ac00691
--- /dev/null
+++ b/examples/statistics/histogram_demo_multihist.py
@@ -0,0 +1,38 @@
+"""
+Demo of the histogram (hist) function with multiple data sets.
+
+Plot histogram with multiple sample sets and demonstrate:
+
+    * Use of legend with multiple sample sets
+    * Stacked bars
+    * Step curve with a color fill
+    * Data sets of different sample sizes
+"""
+import numpy as np
+import matplotlib.pyplot as plt
+
+
+n_bins = 10
+x = np.random.randn(1000, 3)
+
+fig, axes = plt.subplots(nrows=2, ncols=2)
+ax0, ax1, ax2, ax3 = axes.flat
+
+colors = ['red', 'tan', 'lime']
+ax0.hist(x, n_bins, normed=1, histtype='bar', color=colors, label=colors)
+ax0.legend(prop={'size': 10})
+ax0.set_title('bars with legend')
+
+ax1.hist(x, n_bins, normed=1, histtype='bar', stacked=True)
+ax1.set_title('stacked bar')
+
+ax2.hist(x, n_bins, histtype='step', stacked=True, fill=True)
+ax2.set_title('stepfilled')
+
+# Make a multiple-histogram of data-sets with different length.
+x_multi = [np.random.randn(n) for n in [10000, 5000, 2000]]
+ax3.hist(x_multi, n_bins, histtype='bar')
+ax3.set_title('different sample sizes')
+
+plt.tight_layout()
+plt.show()
diff --git a/examples/tests/backend_driver.py b/examples/tests/backend_driver.py
index 1c7a4587538f..0edf4332e586 100755
--- a/examples/tests/backend_driver.py
+++ b/examples/tests/backend_driver.py
@@ -74,7 +74,10 @@
 files['statistics'] = [
     'errorbar_demo.py',
     'errorbar_demo_features.py',
+    'histogram_demo_cumulative.py',
     'histogram_demo_features.py',
+    'histogram_demo_histtypes.py',
+    'histogram_demo_multihist.py',
     ]
 
 files['pie'] = [
@@ -172,7 +175,6 @@
     'hexbin_demo.py',
     'hexbin_demo2.py',
     'hist_colormapped.py',
-    'histogram_demo_extended.py',
     'vline_hline_demo.py',
 
     'image_clip_path.py',