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Python | Matplotlib Sub plotting using object oriented API
  • Last Updated : 25 Jul, 2019

Plotting using Object Oriented(OO) API in matplotlib is an easy approach to plot graphs and other data visualization methods.

The simple syntax to create the class and object for sub-plotting is –

class_name, object_name = matplotlib.pyplot.subplots(‘no_of_rows’, ‘no_of_columns’)

Let’s take some examples to make it more clear.

Example #1:



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# importing the matplotlib library
import matplotlib.pyplot as plt
  
# defining the values of X
x =[0, 1, 2, 3, 4, 5, 6]
  
# defining the value of Y
y =[0, 1, 3, 6, 9, 12, 17]
  
# creating the canvas with class 'fig'
# and it's object 'axes' with '1' row 
# and '2' columns
fig, axes = plt.subplots(1, 2)
  
# plotting graph for 1st column
axes[0].plot(x, y, 'g--o')
  
# plotting graph for second column
axes[1].plot(y, x, 'm--o')
  
# Gives a clean look to the graphs
fig.tight_layout()

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Output :

In the above example, we used ‘axes'(the object of the class ‘fig’) as an array at the time of plotting graph, it is because when we define the number of rows and columns then array of the objects is created with ‘n’ number of elements where ‘n’ is the product of rows and columns, so if we have 2 columns and two rows then there will be array of 4 elements.

 
Example #2:

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# importing the matplotlib library
import matplotlib.pyplot as plt
  
# defining the values of X
x =[0, 1, 2, 3, 4, 5, 6]
  
# defining the value of Y
y =[0, 1, 3, 6, 9, 12, 17]
  
# creating the canvas with class 'fig'
# and it's object 'axes' with '1' row 
# and '2' columns
fig, axes = plt.subplots(2, 2)
  
# plotting graph for 1st element
axes[0, 0].plot(x, y, 'g--o')
  
# plotting graph for 2nd element
axes[0, 1].plot(y, x, 'm--o')
  
# plotting graph for 3rd element
axes[1, 0].plot(x, y, 'b--o')
  
# plotting graph for 4th element
axes[1, 1].plot(y, x, 'r--o')
  
# Gives a clean look to the graphs
fig.tight_layout()

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Output :

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