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    <p>First off, (though this won't change the performance at all) consider cleaning up your code, similar to this:</p> <pre><code>import matplotlib.pyplot as plt import numpy as np import time x = np.arange(0, 2*np.pi, 0.01) y = np.sin(x) fig, axes = plt.subplots(nrows=6) styles = ['r-', 'g-', 'y-', 'm-', 'k-', 'c-'] lines = [ax.plot(x, y, style)[0] for ax, style in zip(axes, styles)] fig.show() tstart = time.time() for i in xrange(1, 20): for j, line in enumerate(lines, start=1): line.set_ydata(np.sin(j*x + i/10.0)) fig.canvas.draw() print 'FPS:' , 20/(time.time()-tstart) </code></pre> <p>With the above example, I get around 10fps. </p> <p>Just a quick note, depending on your exact use case, matplotlib may not be a great choice. It's oriented towards publication-quality figures, not real-time display.</p> <p>However, there are a lot of things you can do to speed this example up.</p> <p>There are two main reasons why this is as slow as it is. </p> <p>1) Calling <code>fig.canvas.draw()</code> redraws <em>everything</em>. It's your bottleneck. In your case, you don't need to re-draw things like the axes boundaries, tick labels, etc. </p> <p>2) In your case, there are a lot of subplots with a lot of tick labels. These take a long time to draw. </p> <p>Both these can be fixed by using blitting.</p> <p>To do blitting efficiently, you'll have to use backend-specific code. In practice, if you're really worried about smooth animations, you're usually embedding matplotlib plots in some sort of gui toolkit, anyway, so this isn't much of an issue.</p> <p>However, without knowing a bit more about what you're doing, I can't help you there.</p> <p>Nonetheless, there is a gui-neutral way of doing it that is still reasonably fast.</p> <pre><code>import matplotlib.pyplot as plt import numpy as np import time x = np.arange(0, 2*np.pi, 0.1) y = np.sin(x) fig, axes = plt.subplots(nrows=6) fig.show() # We need to draw the canvas before we start animating... fig.canvas.draw() styles = ['r-', 'g-', 'y-', 'm-', 'k-', 'c-'] def plot(ax, style): return ax.plot(x, y, style, animated=True)[0] lines = [plot(ax, style) for ax, style in zip(axes, styles)] # Let's capture the background of the figure backgrounds = [fig.canvas.copy_from_bbox(ax.bbox) for ax in axes] tstart = time.time() for i in xrange(1, 2000): items = enumerate(zip(lines, axes, backgrounds), start=1) for j, (line, ax, background) in items: fig.canvas.restore_region(background) line.set_ydata(np.sin(j*x + i/10.0)) ax.draw_artist(line) fig.canvas.blit(ax.bbox) print 'FPS:' , 2000/(time.time()-tstart) </code></pre> <p>This gives me ~200fps.</p> <p>To make this a bit more convenient, there's an <code>animations</code> module in recent versions of matplotlib.</p> <p>As an example:</p> <pre><code>import matplotlib.pyplot as plt import matplotlib.animation as animation import numpy as np x = np.arange(0, 2*np.pi, 0.1) y = np.sin(x) fig, axes = plt.subplots(nrows=6) styles = ['r-', 'g-', 'y-', 'm-', 'k-', 'c-'] def plot(ax, style): return ax.plot(x, y, style, animated=True)[0] lines = [plot(ax, style) for ax, style in zip(axes, styles)] def animate(i): for j, line in enumerate(lines, start=1): line.set_ydata(np.sin(j*x + i/10.0)) return lines # We'd normally specify a reasonable "interval" here... ani = animation.FuncAnimation(fig, animate, xrange(1, 200), interval=0, blit=True) plt.show() </code></pre>
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    1. POwhy is plotting with Matplotlib so slow?
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    1. COyour code is very fast indeed, however I end up with 2000 lines per axis! somehow "line.set_ydata" creates a new line instead of updating it - or is the background just not being cleared? Plus, why is your version so much faster? just because you dropped "draw()" and replaced it with "ax.draw_artist"?
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    2. COIn which example? (I tested them, but it's possible copy-pasted the wrong version into the answer.) Also, which version of matplotlib are you using?
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    3. COI mean the second example. I use matplotlib version 1.0.1.
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