Matplotlib Tutorial
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Matplotlib - Multiplots



The Matplotlib.pyplot.subplot() function adds an Axes to the current figure or retrieve an existing Axes. This is a wrapper of Figure.add_subplot() which provides additional behavior when working with the implicit API. The syntax for using this function is given below:

Syntax

matplotlib.pyplot.subplot(nrows, ncols, index, **kwargs)

Parameters

nrows, ncols, index Specify the position of the subplot described by:
  • three integers (nrows, ncols, index). The subplot will take the index position on a grid with nrows rows and ncols columns. index starts at 1 in the upper left corner and increases to the right. index can also be a two-tuple specifying the (first, last) indices. e.g., subplot(3, 1, (1, 2)) makes a subplot that spans the upper 2/3 of the figure.
  • A 3-digit integer. The digits are interpreted as if given separately as three single-digit integers, i.e. subplot(235) is the same as subplot(2, 3, 5). Note that this can only be used if there are no more than 9 subplots.

Example:

In the example below, a multiplot with four plot is created using nrows=2 and cols=2. In this 2x2 grid, each plot is displaying a different graph.

import matplotlib.pyplot as plt
import numpy as np

#creating a array of values between
#0.1 to 10 with a difference of 0.1
x = np.arange(0.1, 10, 0.1)

#first plot
plt.subplot(2,2,1)
plt.plot(x, np.sin(x))
plt.title("Sine")

#second plot
plt.subplot(2,2,2)
plt.plot(x, np.tan(x))
plt.title("Tan")

#third plot
plt.subplot(2,2,3)
plt.plot(x, np.exp(x))
plt.title("Exp")

#fourth plot
plt.subplot(2,2,4)
plt.plot(x, np.log(x))
plt.title("Log")

#displaying the figure
plt.tight_layout()
plt.show()

The output of the above code will be:

Multiplots

Example: two-tuple index

The index can be a two-tuple specifying the (first, last) indices. Like subplot(2, 2, (1, 2)) used here, makes the subplot spanning the upper half of the figure.

import matplotlib.pyplot as plt
import numpy as np

#creating a array of values between
#0.1 to 10 with a difference of 0.1
x = np.arange(0.1, 10, 0.1)

#first plot
plt.subplot(2,2,(1,2))
plt.plot(x, np.sin(x))
plt.title("Sine")

#second plot
plt.subplot(2,2,3)
plt.plot(x, np.exp(x))
plt.title("Exp")

#third plot
plt.subplot(224)
plt.plot(x, np.log(x))
plt.title("Log")

#displaying the figure
plt.tight_layout()
plt.show()

The output of the above code will be:

Multiplots

Super Title

The suptitle() function can be used to add title to the entire figure.

Example:

Consider the example below, where a super title is added to the above plot.

import matplotlib.pyplot as plt
import numpy as np

#creating a array of values between
#0.1 to 10 with a difference of 0.1
x = np.arange(0.1, 10, 0.1)

#first plot
plt.subplot(2,2,(1,2))
plt.plot(x, np.sin(x))
plt.title("Sine")

#second plot
plt.subplot(2,2,3)
plt.plot(x, np.exp(x))
plt.title("Exp")

#third plot
plt.subplot(224)
plt.plot(x, np.log(x))
plt.title("Log")

#setting super title
plt.suptitle("Mathematical Functions")

#displaying the figure
plt.tight_layout()
plt.show()

The output of the above code will be:

Multiplots

Overlapping plots

The add_subplot() function of the figure class can be used to create a overlapping plot. The function do not overwrite the existing plot.

Example:

Consider the example below, where add_subplot() function is used to create a overlapping plot.

import matplotlib.pyplot as plt
import numpy as np

#creating a array of values between
#0.1 to 10 with a difference of 0.1
x = np.arange(0.1, 10, 0.1)

#bigger plot
fig = plt.figure()
plt.plot(x, np.log(x))
plt.title("Bigger Plot")

#smaller plot
fig.add_subplot(2,2,4, facecolor='y')
plt.plot(x, np.exp(x))
plt.title("Smaller plot")

#displaying the figure
plt.tight_layout()
plt.show()

The output of the above code will be:

Multiplots

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