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How to create multiple plots in Matplotlib

Onyejiaku Theophilus Chidalu

Overview

Plots in matplot library (matplotlib) are used to give a visual representation of a given data. With the help of the pyplot module in the matplotlib, we could make a plot representation of given data.

A multiple plot is a situation in which there are more plots than one on the same figure. This is usually done when a programmer wants to compare or differentiate between different data values.

How to add multiple plots

With the pyplot.subplot() function in matplotlib, we can create or draw multiple plots in one figure. The pyplot.subplot() function takes three parameters that help describe the layout of the figure. They include:

  1. row: to specify the number of plots you want on a row.
  2. column: to specify the number of plots you want on a column.
  3. index: to specify the index or position of the current plot (e.g., first (1), second (2), third (3) etc.) in a figure.

How to call the subplot() function

Using the parameters mentioned above, calling the subplot() function will look like:

Example 1

pyplot.subplot(2, 2, 1)

In this example, the obtained figure will contain 2 rows, 2 columns, and it is the first plot of the figure (index = 1).

Example 2

pyplot.subplot(2, 3, 2)

In this example, the obtained figure will contain 2 rows, 3 columns, and it is the second plot of the figure (index = 2).

Important to note: The number of rows and columns specified must remain the same in a single code because it tells us how many rows and columns the figure contains. The index parameter will keep changing as it represents the position (1st, 2nd, 3rd, etc.) of a given plot in a figure.

Example

Now let’s create six plots in a single figure:

import matplotlib.pyplot as plt
import numpy as np

x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])

# the first plot
plt.subplot(2, 3, 1)
plt.plot(x,y)

x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])

# the second plot
plt.subplot(2, 3, 2)
plt.plot(x,y)

x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])

# the third plot
plt.subplot(2, 3, 3)
plt.plot(x,y)

x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])

# the fourth plot
plt.subplot(2, 3, 4)
plt.plot(x,y)

x = np.array([0, 1, 2, 3])
y = np.array([3, 8, 1, 10])


# the fifth plot
plt.subplot(2, 3, 5)
plt.plot(x,y)

x = np.array([0, 1, 2, 3])
y = np.array([10, 20, 30, 40])

# the sixth plot
plt.subplot(2, 3, 6)
plt.plot(x,y)

plt.show()

Output of the code

Explanation

Here is a line-by-line explanation of the code above.

  • Lines 1-2: We import the needed modules and libraries required for the arrays and plot.

  • Lines 4-5: We create our data values x and y for the first plot.

  • Lines 8-9: Using the subplot() and plot()functions, we are able to plot y against x.

  • Lines 11-45: In these lines, we are repeating the same process for all other five plots that we did for the first plot in lines 4-5 and 8-9.

  • Line 47: We export all six plots to the figure using the show() function.

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Onyejiaku Theophilus Chidalu
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