Note
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Zorder DemoΒΆ
The drawing order of artists is determined by their zorder
attribute, which
is a floating point number. Artists with higher zorder
are drawn on top.
You can change the order for individual artists by setting their zorder
.
The default value depends on the type of the Artist:
Artist | Z-order |
---|---|
Images (AxesImage , FigureImage , BboxImage ) |
0 |
Patch , PatchCollection |
1 |
Line2D , LineCollection (including minor ticks, grid lines) |
2 |
Major ticks | 2.01 |
Text (including axes labels and titles) |
3 |
Legend |
5 |
Any call to a plotting method can set a value for the zorder of that particular item explicitly.
Note
set_axisbelow
and rcParams["axes.axisbelow"]
(default: 'line'
) can further modify the
zorder of ticks and grid lines.
import matplotlib.pyplot as plt
import numpy as np
r = np.linspace(0.3, 1, 30)
theta = np.linspace(0, 4*np.pi, 30)
x = r * np.sin(theta)
y = r * np.cos(theta)
The following example contains a Line2D
created by plot()
and the dots (a PatchCollection
) created by scatter()
.
Hence, by default the dots are below the line (first subplot).
In the second subplot, the zorder
is set explicitly to move the dots
on top of the line.
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(6, 3.2))
ax1.plot(x, y, 'C3', lw=3)
ax1.scatter(x, y, s=120)
ax1.set_title('Lines on top of dots')
ax2.plot(x, y, 'C3', lw=3)
ax2.scatter(x, y, s=120, zorder=2.5) # move dots on top of line
ax2.set_title('Dots on top of lines')
plt.tight_layout()
Many functions that create a visible object accepts a zorder
parameter.
Alternatively, you can call set_order()
on the created object later.
x = np.linspace(0, 7.5, 100)
plt.rcParams['lines.linewidth'] = 5
plt.figure()
plt.plot(x, np.sin(x), label='zorder=2', zorder=2) # bottom
plt.plot(x, np.sin(x+0.5), label='zorder=3', zorder=3)
plt.axhline(0, label='zorder=2.5', color='lightgrey', zorder=2.5)
plt.title('Custom order of elements')
l = plt.legend(loc='upper right')
l.set_zorder(2.5) # legend between blue and orange line
plt.show()
Total running time of the script: ( 0 minutes 1.529 seconds)
Keywords: matplotlib code example, codex, python plot, pyplot Gallery generated by Sphinx-Gallery