matplotlib multiple Y-axis pandas plot

Question

I would like to do multiple Y axis plots.

This is some made up data below, how could I put Temperature its own Y axis, Pressure on its own Y axis, and then have both Value1 and Value2 on the same Y axis. I am trying to go for the same look and feel of this SO post answer.

I don't understand ax3 = ax.twinx() process; do I need to define an ax.twinx() for each separate Y axis plot I need?

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt



rows,cols = 8760,4
data = np.random.rand(rows,cols) 
tidx = pd.date_range('2019-01-01', periods=rows, freq='H') 
df = pd.DataFrame(data, columns=['Temperature','Value1','Pressure','Value2'], index=tidx)


# using subplots() function
fig, ax = plt.subplots(figsize=(25,8))
plt.title('Multy Y Plot')
 
ax2 = ax.twinx()    
ax3 = ax.twinx()    
ax4 = ax.twinx()

plot1, = ax.plot(df.index, df.Temperature)
plot2, = ax2.plot(df.index, df.Value1, color = 'r')
plot3, = ax3.plot(df.index, df.Pressure, color = 'g')
plot4, = ax4.plot(df.index, df.Value2, color = 'b')

ax.set_xlabel('Date')
ax.set_ylabel('Temperature')
ax2.set_ylabel('Value1')
ax3.set_ylabel('Pressure')
ax4.set_ylabel('Value2')


plt.legend([plot1,plot2,plot3,plot4],list(df.columns))

# defining display layout
plt.tight_layout()

# show plot
plt.show()

This will output everything jumbled up on the same side without separate Y axis for Pressure, Value1, and Value2.

tzinie · Accepted Answer · 2021-11-08 21:00:17Z

2

You are adding 4 different plots in one, which is not helpful. I would recommend breaking it into 2 plots w/ shared x-axis "Date":

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt

rows,cols = 8760,4
data = np.random.rand(rows,cols) 
tidx = pd.date_range('2019-01-01', periods=rows, freq='H') 
df = pd.DataFrame(data, columns=['Temperature','Value1','Pressure','Value2'], index=tidx)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(25,8))
plt.title('Multy Y Plot')
 
ax1b = ax1.twinx()    
plot1a, = ax1.plot(df.index, df.Temperature)
plot1b, = ax1b.plot(df.index, df.Pressure, color='r')

ax1.set_ylabel('Temperature')
ax1b.set_ylabel('Pressure')

ax2b = ax2.twinx() 
plot2a, = ax2.plot(df.index, df.Value1, color='k')
plot2b, = ax2b.plot(df.index, df.Value2, color='g')

ax2.set_xlabel('Date')
ax2.set_ylabel('Value1')
ax2b.set_ylabel('Value2')


plt.legend([plot1a, plot1b, plot2a, plot2b], df.columns)

# defining display layout
plt.tight_layout()

# show plot
plt.show()

Here I have added in the first plot (on the top) Temperature and Pressure and on the second plot (on the bottom) Value 1 and Value 2. Normally, we add in the same plot things that make sense to compare on the same x-axis. Pressure and Temperature is a valid combination that is why I combined those two together. But you can do as you wish.

answered Nov 8, 2021 at 21:00

tzinie

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4 Comments

bbartling Over a year ago

Any chance you could help me one step further? On the bottom plot of value 1 value 2 is it possible to get those on the same Y axis? (not split) In my real world scenario these have the same units

bbartling Over a year ago

Actually I think I figured it out. Ill post an answer for how to do this as well. Let me know if that there is a better approach! And thanks again for the help!!!

bbartling Over a year ago

What does plt.tight_layout() do?

tzinie Over a year ago

@HenryHub Adjust the padding between and around subplots.

bbartling · Accepted Answer · 2021-11-09 12:38:18Z

This answer below uses mpatches is how to make the subplot of Value1 and Value2 on the same axis. The solution for this post has subplot for Value1 and Value2 on different axis. Thanks for the help @tzinie!

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches


rows,cols = 8760,4
data = np.random.rand(rows,cols) 
tidx = pd.date_range('2019-01-01', periods=rows, freq='H') 
df = pd.DataFrame(data, columns=['Temperature','Value1','Pressure','Value2'], index=tidx)
fig, (ax1, ax2) = plt.subplots(2, 1, figsize=(25,8))
plt.title('Multy Y Plot')
 
ax1b = ax1.twinx()    
plot1a, = ax1.plot(df.index, df.Temperature, color='r') # red
plot1b, = ax1b.plot(df.index, df.Pressure, color='b') # blue

ax1.set_ylabel('Temperature')
ax1b.set_ylabel('Pressure')


ax2.plot(df.index, df.Value1, color='k') # black
ax2.plot(df.index, df.Value2, color='g') # green
ax2.set_xlabel('Date')
ax2.set_ylabel('Value1 & Value2')


red_patch = mpatches.Patch(color='red', label='Temperature')
blue_patch = mpatches.Patch(color='blue', label='Pressure')
green_patch = mpatches.Patch(color='green', label='Value2')
black_patch = mpatches.Patch(color='black', label='Value1')
plt.legend(handles=[red_patch,blue_patch,green_patch,black_patch])

# defining display layout
#plt.tight_layout()

# show plot
plt.show()

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