diff --git a/tutorials/introductory/quick_start.py b/tutorials/introductory/quick_start.py
index 42edbc03b10f..882d1a23737a 100644
--- a/tutorials/introductory/quick_start.py
+++ b/tutorials/introductory/quick_start.py
@@ -28,7 +28,7 @@
# `.Axes.plot` to draw some data on the Axes:
fig, ax = plt.subplots() # Create a figure containing a single axes.
-ax.plot([1, 2, 3, 4], [1, 4, 2, 3]); # Plot some data on the axes.
+ax.plot([1, 2, 3, 4], [1, 4, 2, 3]) # Plot some data on the axes.
###############################################################################
# .. _figure_parts:
@@ -126,7 +126,7 @@
fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
ax.scatter('a', 'b', c='c', s='d', data=data)
ax.set_xlabel('entry a')
-ax.set_ylabel('entry b');
+ax.set_ylabel('entry b')
##############################################################################
# .. _coding_styles:
@@ -159,7 +159,7 @@
ax.set_xlabel('x label') # Add an x-label to the axes.
ax.set_ylabel('y label') # Add a y-label to the axes.
ax.set_title("Simple Plot") # Add a title to the axes.
-ax.legend(); # Add a legend.
+ax.legend() # Add a legend.
###############################################################################
# or the pyplot-style:
@@ -173,7 +173,7 @@
plt.xlabel('x label')
plt.ylabel('y label')
plt.title("Simple Plot")
-plt.legend();
+plt.legend()
###############################################################################
# (In addition, there is a third approach, for the case when embedding
@@ -213,7 +213,7 @@ def my_plotter(ax, data1, data2, param_dict):
data1, data2, data3, data4 = np.random.randn(4, 100) # make 4 random data sets
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(5, 2.7))
my_plotter(ax1, data1, data2, {'marker': 'x'})
-my_plotter(ax2, data3, data4, {'marker': 'o'});
+my_plotter(ax2, data3, data4, {'marker': 'o'})
###############################################################################
# Note that if you want to install these as a python package, or any other
@@ -235,7 +235,7 @@ def my_plotter(ax, data1, data2, param_dict):
x = np.arange(len(data1))
ax.plot(x, np.cumsum(data1), color='blue', linewidth=3, linestyle='--')
l, = ax.plot(x, np.cumsum(data2), color='orange', linewidth=2)
-l.set_linestyle(':');
+l.set_linestyle(':')
###############################################################################
# Colors
@@ -248,7 +248,7 @@ def my_plotter(ax, data1, data2, param_dict):
# from the interior:
fig, ax = plt.subplots(figsize=(5, 2.7))
-ax.scatter(data1, data2, s=50, facecolor='C0', edgecolor='k');
+ax.scatter(data1, data2, s=50, facecolor='C0', edgecolor='k')
###############################################################################
# Linewidths, linestyles, and markersizes
@@ -272,7 +272,7 @@ def my_plotter(ax, data1, data2, param_dict):
ax.plot(data2, 'd', label='data2')
ax.plot(data3, 'v', label='data3')
ax.plot(data4, 's', label='data4')
-ax.legend();
+ax.legend()
###############################################################################
#
@@ -298,7 +298,7 @@ def my_plotter(ax, data1, data2, param_dict):
ax.set_title('Aardvark lengths\n (not really)')
ax.text(75, .025, r'$\mu=115,\ \sigma=15$')
ax.axis([55, 175, 0, 0.03])
-ax.grid(True);
+ax.grid(True)
###############################################################################
# All of the `~.Axes.text` functions return a `matplotlib.text.Text`
@@ -342,7 +342,7 @@ def my_plotter(ax, data1, data2, param_dict):
ax.annotate('local max', xy=(2, 1), xytext=(3, 1.5),
arrowprops=dict(facecolor='black', shrink=0.05))
-ax.set_ylim(-2, 2);
+ax.set_ylim(-2, 2)
###############################################################################
# In this basic example, both *xy* and *xytext* are in data coordinates.
@@ -360,7 +360,7 @@ def my_plotter(ax, data1, data2, param_dict):
ax.plot(np.arange(len(data1)), data1, label='data1')
ax.plot(np.arange(len(data2)), data2, label='data2')
ax.plot(np.arange(len(data3)), data3, 'd', label='data3')
-ax.legend();
+ax.legend()
##############################################################################
# Legends in Matplotlib are quite flexible in layout, placement, and what
@@ -391,7 +391,7 @@ def my_plotter(ax, data1, data2, param_dict):
axs[0].plot(xdata, data)
axs[1].set_yscale('log')
-axs[1].plot(xdata, data);
+axs[1].plot(xdata, data)
##############################################################################
# The scale sets the mapping from data values to spacing along the Axis. This
@@ -413,7 +413,7 @@ def my_plotter(ax, data1, data2, param_dict):
axs[1].plot(xdata, data1)
axs[1].set_xticks(np.arange(0, 100, 30), ['zero', '30', 'sixty', '90'])
axs[1].set_yticks([-1.5, 0, 1.5]) # note that we don't need to specify labels
-axs[1].set_title('Manual ticks');
+axs[1].set_title('Manual ticks')
##############################################################################
# Different scales can have different locators and formatters; for instance
@@ -435,7 +435,7 @@ def my_plotter(ax, data1, data2, param_dict):
data = np.cumsum(np.random.randn(len(dates)))
ax.plot(dates, data)
cdf = mpl.dates.ConciseDateFormatter(ax.xaxis.get_major_locator())
-ax.xaxis.set_major_formatter(cdf);
+ax.xaxis.set_major_formatter(cdf)
##############################################################################
# For more information see the date examples
@@ -447,7 +447,7 @@ def my_plotter(ax, data1, data2, param_dict):
fig, ax = plt.subplots(figsize=(5, 2.7), layout='constrained')
categories = ['turnips', 'rutabaga', 'cucumber', 'pumpkins']
-ax.bar(categories, np.random.rand(len(categories)));
+ax.bar(categories, np.random.rand(len(categories)))
##############################################################################
# One caveat about categorical plotting is that some methods of parsing
@@ -561,7 +561,7 @@ def my_plotter(ax, data1, data2, param_dict):
['lowleft', 'right']], layout='constrained')
axd['upleft'].set_title('upleft')
axd['lowleft'].set_title('lowleft')
-axd['right'].set_title('right');
+axd['right'].set_title('right')
###############################################################################
# Matplotlib has quite sophisticated tools for arranging Axes: See
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