In object-oriented API, first, we create a canvas on which we have to plot the graph and then we plot the graph. Many people prefer object-oriented API because it is easy to use as compared to functional API.
Let’s try to understand this with some examples.
Example #1:
# importing matplotlib library import matplotlib.pyplot as plt # x axis values x =[0, 5, 3, 6, 8, 4, 5, 7] # y axis values y =[5, 3, 6, 3, 7, 5, 6, 8] # creating the canvas fig = plt.figure() # setting the size of canvas axes = fig.add_axes([0, 0, 1, 1]) # plotting the graph axes.plot(x, y, 'mo--') # displaying the graph plt.show() |
Output:
Everything is pretty much clear in the first example but there is a thing that needs to be focused on, “Setting size of the canvas”, what this basically means is to set the size of the figure on which you want to plot the graph, the syntax is like this.
add_axes([left, bottom, width, height])
The values of left, bottom, height and width lies between 0 to 1. Another example will make you more clear about this concept.
Example #2:
# importing matplotlib library import matplotlib.pyplot as plt # x-axis values x =[0, 1, 2, 3, 4, 5, 6] # y-axis values y =[0, 1, 3, 6, 9, 12, 17] # creating the canvas fig = plt.figure() # setting size of first canvas axes1 = fig.add_axes([0, 0, 0.7, 1]) # plotting graph of first canvas axes1.plot(x, y, 'mo--') # setting size of second canvas axes2 = fig.add_axes([0.1, 0.5, 0.3, 0.3]) # plotting graph of second canvas axes2.plot(x, y, 'go--') # displaying both graphs plt.show() |
Output:
