diff --git a/examples/statistics/hist.py b/examples/statistics/hist.py
index 98e1f95d43eb..a7e6473dd17c 100644
--- a/examples/statistics/hist.py
+++ b/examples/statistics/hist.py
@@ -63,7 +63,7 @@
     thispatch.set_facecolor(color)
 
 # We can also normalize our inputs by the total number of counts
-axs[1].hist(x, bins=n_bins, normed=True)
+axs[1].hist(x, bins=n_bins, density=True)
 
 # Now we format the y-axis to display percentage
 axs[1].yaxis.set_major_formatter(PercentFormatter(xmax=1))
diff --git a/examples/statistics/histogram_features.py b/examples/statistics/histogram_features.py
index 8c056c49d17c..598b1ca6864e 100644
--- a/examples/statistics/histogram_features.py
+++ b/examples/statistics/histogram_features.py
@@ -35,7 +35,7 @@
 fig, ax = plt.subplots()
 
 # the histogram of the data
-n, bins, patches = ax.hist(x, num_bins, normed=1)
+n, bins, patches = ax.hist(x, num_bins, density=True)
 
 # add a 'best fit' line
 y = mlab.normpdf(bins, mu, sigma)
diff --git a/lib/matplotlib/tests/test_axes.py b/lib/matplotlib/tests/test_axes.py
index 34cba9d75c51..9378046d90f7 100644
--- a/lib/matplotlib/tests/test_axes.py
+++ b/lib/matplotlib/tests/test_axes.py
@@ -2932,7 +2932,7 @@ def test_hist_stacked_normed():
     d2 = np.linspace(0, 10, 50)
     fig = plt.figure()
     ax = fig.add_subplot(111)
-    ax.hist((d1, d2), stacked=True, normed=True)
+    ax.hist((d1, d2), stacked=True, density=True)
 
 
 @image_comparison(baseline_images=['hist_stacked_normed'], extensions=['png'])
diff --git a/tutorials/introductory/pyplot.py b/tutorials/introductory/pyplot.py
index f2bc82e7beb4..d51d8338f457 100644
--- a/tutorials/introductory/pyplot.py
+++ b/tutorials/introductory/pyplot.py
@@ -333,8 +333,7 @@ def f(t):
 x = mu + sigma * np.random.randn(10000)
 
 # the histogram of the data
-n, bins, patches = plt.hist(x, 50, normed=1, facecolor='g', alpha=0.75)
-
+n, bins, patches = plt.hist(x, 50, density=True, facecolor='g', alpha=0.75)
 
 plt.xlabel('Smarts')
 plt.ylabel('Probability')