Displaying multiple plots using subplots()
Matplotlib subplots() function let us plot multiple plots using the same data or the axis. Let us see a few examples for a better understanding:
Stacking Subplots in One Direction
In this example, we will plot two plots that share the y-axis. The nrows and ncols parameters are set to 1 and 2 respectively, which means the plot will have 1 row and 2 columns or 2 subplots. We can access these subplots using the index [0] and [1].
Python3
# Implementation of matplotlib functionimport numpy as npimport matplotlib.pyplot as plt# First create some toy data:x = np.linspace(0, 2 * np.pi, 400)y1 = np.sin(x)y2 = np.sin(x**2)# create 2 subplotsfig, ax = plt.subplots(nrows=1, ncols=2)ax[0].plot(x, y1)ax[1].plot(x, y2)# plot 2 subplotsax[0].set_title('Simple plot with sin(x)')ax[1].set_title('Simple plot with sin(x**2)')fig.suptitle('Stacked subplots in one direction')plt.show() |
Output:
Stacking subplots in one direction
Stacking Subplots in Two Directions
This example is similar to the previous one. The only difference is that we provided the values of nrows and ncols to 2. This means that the plot is divided into 2 rows and 2 columns which gives us a total of 4 subplots matplotlib. We can access these plots using the index.
Python3
# Implementation of matplotlib functionimport numpy as npimport matplotlib.pyplot as plt# First create some toy data:x = np.linspace(0, 2 * np.pi, 400)y1 = np.sin(x)y2 = np.sin(x**2)y3 = y1**2y4 = y2**2fig, ax = plt.subplots(nrows=2, ncols=2)ax[0, 0].plot(x, y1, c='red')ax[0, 1].plot(x, y2, c='red')ax[1, 0].plot(x, y3, c='blue')ax[1, 1].plot(x, y3, c='blue')ax[0, 0].set_title('Simple plot with sin(x)')ax[0, 1].set_title('Simple plot with sin(x**2)')ax[1, 0].set_title('Simple plot with sin(x)**2')ax[1, 1].set_title('Simple plot with sin(x**2)**2')fig.suptitle('Stacked subplots in two direction')plt.show() |
Output:
Stacking subplots in two directions
Sharing Axis
In this example, we will plot the graphs that share the same axis. We will create plots that will share the y-axis and the label but will have their own x-axis and label. This can be done by passing a value to the ‘num’ parameter of the subplot() function. The ‘sharex’ parameter is set to True, which means the plots created will share X-axis among them.
Python3
# Implementation of matplotlib functionimport numpy as npimport matplotlib.pyplot as plt# First create some toy data:x = np.linspace(0, 2 * np.pi, 400)y1 = np.sin(x)y2 = np.sin(x**2)fig, (ax1, ax2) = plt.subplots(2, sharex=True)ax1.plot(x, y1, c='red')ax2.plot(x, y2, c='red')ax1.set_ylabel('Simple plot with sin(x)')ax2.set_ylabel('Simple plot with sin(x**2)')fig.suptitle('Subplots with shared axis')plt.show() |
Output:
Subplots with shared axis
Polar Axis
In this example, we will plot the graphs using the polar coordinates. The subplot_kw parameter of the subplot() function is given a dictionary value of projection set to ‘polar’ which tells the subplot() function to create a polar graph.
Python3
# Implementation of matplotlib functionimport numpy as npimport matplotlib.pyplot as plt# First create some toy data:x = np.linspace(0, 1.5 * np.pi, 100)y = np.sin(x**2)+np.cos(x**2)fig, axs = plt.subplots(nrows=2, ncols=2, subplot_kw = dict(polar = True))axs[0, 0].plot(x, y)axs[1, 1].scatter(x, y)fig.suptitle('matplotlib.pyplot.subplots() Example')plt.show() |
Output:
matplotlib.pyplot.subplots() function example
Matplotlib.pyplot.subplots() in Python
Matplotlib is a library in Python and it is a numerical-mathematical extension for the NumPy library. Pyplot is a state-based interface to a Matplotlib module which provides a MATLAB-like interface. subplots() function in Python simplifies the creation of multiple subplots Matplotlib within a single figure, allowing for organized and simultaneous visualization of various datasets or plots.
Example:
Here is an example of a simple Python code to plot a graph using the Matplotlib library.
Python3
# sample codeimport matplotlib.pyplot as plt plt.plot([1, 2, 3, 4], [16, 4, 1, 8]) plt.show() |
Output:
Plot using Python matplotlib