So far I have the following code:
colors = ('k','r','b') ax = [] for i in range(3): ax.append(plt.axes()) plt.plot(datamatrix[:,0],datamatrix[:,i],colors[i]+'o') ax[i].set(autoscale_on=True)
With the autoscale_on=True
option for each axis, I thought each plot should have its own y-axis limits, but it appears they all share the same value (even if they share different axes). How do I set them to scale to show the range of each datamatrix[:,i]
(just an explicit call to .set_ylim()
?) And also, how can I create an offset y-axis for the third variable (datamatrix[:,2]
) that might be required above? Thanks all.
The way to make a plot with two different y-axis is to use two different axes objects with the help of twinx() function. We first create figure and axis objects and make a first plot. In this example, we plot year vs lifeExp. And we also set the x and y-axis labels by updating the axis object.
You can use the plot(x,y) method to create a line chart.
It is considered useful to have dual x or y axes in a figure. Moreso, when plotting curves with different units together. Matplotlib supports this with the twinxand twiny functions. In the following example, the plot has dual y axes, one showing exp(x) and the other showing log(x) − import matplotlib.
It sounds like what you're wanting is subplots... What you're doing now doesn't make much sense (Or I'm very confused by your code snippet, at any rate...).
Try something more like this:
import matplotlib.pyplot as plt import numpy as np fig, axes = plt.subplots(nrows=3) colors = ('k', 'r', 'b') for ax, color in zip(axes, colors): data = np.random.random(1) * np.random.random(10) ax.plot(data, marker='o', linestyle='none', color=color) plt.show()
Edit:
If you don't want subplots, your code snippet makes a lot more sense.
You're trying to add three axes right on top of each other. Matplotlib is recognizing that there's already a subplot in that exactly size and location on the figure, and so it's returning the same axes object each time. In other words, if you look at your list ax
, you'll see that they're all the same object.
If you really want to do that, you'll need to reset fig._seen
to an empty dict each time you add an axes. You probably don't really want to do that, however.
Instead of putting three independent plots over each other, have a look at using twinx
instead.
E.g.
import matplotlib.pyplot as plt import numpy as np # To make things reproducible... np.random.seed(1977) fig, ax = plt.subplots() # Twin the x-axis twice to make independent y-axes. axes = [ax, ax.twinx(), ax.twinx()] # Make some space on the right side for the extra y-axis. fig.subplots_adjust(right=0.75) # Move the last y-axis spine over to the right by 20% of the width of the axes axes[-1].spines['right'].set_position(('axes', 1.2)) # To make the border of the right-most axis visible, we need to turn the frame # on. This hides the other plots, however, so we need to turn its fill off. axes[-1].set_frame_on(True) axes[-1].patch.set_visible(False) # And finally we get to plot things... colors = ('Green', 'Red', 'Blue') for ax, color in zip(axes, colors): data = np.random.random(1) * np.random.random(10) ax.plot(data, marker='o', linestyle='none', color=color) ax.set_ylabel('%s Thing' % color, color=color) ax.tick_params(axis='y', colors=color) axes[0].set_xlabel('X-axis') plt.show()
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