marcusps
diff --git a/‎doc/users/next_whats_new/custom_cap_widths.rst
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diff --git a/‎examples/statistics/boxplot_demo.py
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diff --git a/‎examples/user_interfaces/embedding_in_tk_sgskip.py
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diff --git a/‎tutorials/introductory/pyplot.py
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diff --git a/‎tutorials/introductory/quick_start.py
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@@ -12,5 +12,5 @@ widths of the caps in box and whisker plots.
     x = np.linspace(-7, 7, 140)
     x = np.hstack([-25, x, 25])
     fig, ax = plt.subplots()
-    ax.boxplot([x, x], notch=1, capwidths=[0.01, 0.2])
+    ax.boxplot([x, x], notch=True, capwidths=[0.01, 0.2])
     plt.show()
@@ -105,7 +105,7 @@
 fig.canvas.manager.set_window_title('A Boxplot Example')
 fig.subplots_adjust(left=0.075, right=0.95, top=0.9, bottom=0.25)
 
-bp = ax1.boxplot(data, notch=0, sym='+', vert=1, whis=1.5)
+bp = ax1.boxplot(data, notch=False, sym='+', vert=True, whis=1.5)
 plt.setp(bp['boxes'], color='black')
 plt.setp(bp['whiskers'], color='black')
 plt.setp(bp['fliers'], color='red', marker='+')
@@ -221,7 +221,7 @@ def fake_bootstrapper(n):
 fig, ax = plt.subplots()
 pos = np.arange(len(treatments)) + 1
 bp = ax.boxplot(treatments, sym='k+', positions=pos,
-                notch=1, bootstrap=5000,
+                notch=True, bootstrap=5000,
                 usermedians=medians,
                 conf_intervals=conf_intervals)
 
@@ -239,7 +239,7 @@ def fake_bootstrapper(n):
 x = np.hstack([-25, x, 25])
 fig, ax = plt.subplots()
 
-ax.boxplot([x, x], notch=1, capwidths=[0.01, 0.2])
+ax.boxplot([x, x], notch=True, capwidths=[0.01, 0.2])
 
 plt.show()
 
 
@@ -62,6 +62,6 @@ def update_frequency(new_val):
 button_quit.pack(side=tkinter.BOTTOM)
 slider_update.pack(side=tkinter.BOTTOM)
 toolbar.pack(side=tkinter.BOTTOM, fill=tkinter.X)
-canvas.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=1)
+canvas.get_tk_widget().pack(side=tkinter.TOP, fill=tkinter.BOTH, expand=True)
 
 tkinter.mainloop()
@@ -327,7 +327,7 @@ def f(t):
 x = mu + sigma * np.random.randn(10000)
 
 # the histogram of the data
-n, bins, patches = plt.hist(x, 50, density=1, facecolor='g', alpha=0.75)
+n, bins, patches = plt.hist(x, 50, density=True, facecolor='g', alpha=0.75)
 
 
 plt.xlabel('Smarts')
 
@@ -288,7 +288,7 @@ def my_plotter(ax, data1, data2, param_dict):
 x = mu + sigma * np.random.randn(10000)
 fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
 # the histogram of the data
-n, bins, patches = ax.hist(x, 50, density=1, facecolor='C0', alpha=0.75)
+n, bins, patches = ax.hist(x, 50, density=True, facecolor='C0', alpha=0.75)
 
 ax.set_xlabel('Length [cm]')
 ax.set_ylabel('Probability')