multiple axis in matplotlib with different scales

Since Steve Tjoa's answer always pops up first and mostly lonely when I search for multiple y-axes at Google, I decided to add a slightly modified version of his answer. This is the approach from this matplotlib example.

Reasons:

  • His modules sometimes fail for me in unknown circumstances and cryptic intern errors.
  • I don't like to load exotic modules I don't know (mpl_toolkits.axisartist, mpl_toolkits.axes_grid1).
  • The code below contains more explicit commands of problems people often stumble over (like single legend for multiple axes, using viridis, ...) rather than implicit behavior.

Plot

import matplotlib.pyplot as plt 

# Create figure and subplot manually
# fig = plt.figure()
# host = fig.add_subplot(111)

# More versatile wrapper
fig, host = plt.subplots(figsize=(8,5)) # (width, height) in inches
# (see https://matplotlib.org/3.3.3/api/_as_gen/matplotlib.pyplot.subplots.html)
    
par1 = host.twinx()
par2 = host.twinx()
    
host.set_xlim(0, 2)
host.set_ylim(0, 2)
par1.set_ylim(0, 4)
par2.set_ylim(1, 65)
    
host.set_xlabel("Distance")
host.set_ylabel("Density")
par1.set_ylabel("Temperature")
par2.set_ylabel("Velocity")

color1 = plt.cm.viridis(0)
color2 = plt.cm.viridis(0.5)
color3 = plt.cm.viridis(.9)

p1, = host.plot([0, 1, 2], [0, 1, 2],    color=color1, label="Density")
p2, = par1.plot([0, 1, 2], [0, 3, 2],    color=color2, label="Temperature")
p3, = par2.plot([0, 1, 2], [50, 30, 15], color=color3, label="Velocity")

lns = [p1, p2, p3]
host.legend(handles=lns, loc='best')

# right, left, top, bottom
par2.spines['right'].set_position(('outward', 60))

# no x-ticks                 
par2.xaxis.set_ticks([])

# Sometimes handy, same for xaxis
#par2.yaxis.set_ticks_position('right')

# Move "Velocity"-axis to the left
# par2.spines['left'].set_position(('outward', 60))
# par2.spines['left'].set_visible(True)
# par2.yaxis.set_label_position('left')
# par2.yaxis.set_ticks_position('left')

host.yaxis.label.set_color(p1.get_color())
par1.yaxis.label.set_color(p2.get_color())
par2.yaxis.label.set_color(p3.get_color())

# Adjust spacings w.r.t. figsize
fig.tight_layout()
# Alternatively: bbox_inches='tight' within the plt.savefig function 
#                (overwrites figsize)

# Best for professional typesetting, e.g. LaTeX
plt.savefig("pyplot_multiple_y-axis.pdf")
# For raster graphics use the dpi argument. E.g. '[...].png", dpi=200)'

if you want to do very quick plots with secondary Y-Axis then there is much easier way using Pandas wrapper function and just 2 lines of code. Just plot your first column then plot the second but with parameter secondary_y=True, like this:

df.A.plot(label="Points", legend=True)
df.B.plot(secondary_y=True, label="Comments", legend=True)

This would look something like below:

enter image description here

You can do few more things as well. Take a look at Pandas plotting doc.


If I understand the question, you may interested in this example in the Matplotlib gallery.

enter image description here

Yann's comment above provides a similar example.


Edit - Link above fixed. Corresponding code copied from the Matplotlib gallery:

from mpl_toolkits.axes_grid1 import host_subplot
import mpl_toolkits.axisartist as AA
import matplotlib.pyplot as plt

host = host_subplot(111, axes_class=AA.Axes)
plt.subplots_adjust(right=0.75)

par1 = host.twinx()
par2 = host.twinx()

offset = 60
new_fixed_axis = par2.get_grid_helper().new_fixed_axis
par2.axis["right"] = new_fixed_axis(loc="right", axes=par2,
                                        offset=(offset, 0))

par2.axis["right"].toggle(all=True)

host.set_xlim(0, 2)
host.set_ylim(0, 2)

host.set_xlabel("Distance")
host.set_ylabel("Density")
par1.set_ylabel("Temperature")
par2.set_ylabel("Velocity")

p1, = host.plot([0, 1, 2], [0, 1, 2], label="Density")
p2, = par1.plot([0, 1, 2], [0, 3, 2], label="Temperature")
p3, = par2.plot([0, 1, 2], [50, 30, 15], label="Velocity")

par1.set_ylim(0, 4)
par2.set_ylim(1, 65)

host.legend()

host.axis["left"].label.set_color(p1.get_color())
par1.axis["right"].label.set_color(p2.get_color())
par2.axis["right"].label.set_color(p3.get_color())

plt.draw()
plt.show()

#plt.savefig("Test")