multiple axis in matplotlib with different scales [duplicate]

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

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")

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.

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:

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