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timhoffmtimhoffm
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Merge pull request #23796 from gustavi/gustavi-patch-1
Remove useless semicolons in "Introductory / Basic Usage" tutorial
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‎tutorials/introductory/quick_start.py

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

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