Python: Merge subplot over two subplotcells in matplotlib or with other libaries [duplicate]

Question

Based on this article of multiple subplots on matplotlib with python, it is very easy to create subplots in form of an 2x2-matrix for example. With the code from example 4 I can get the supblots of following form: with code:

import matplotlib.pyplot as plt
import numpy as np

fig, axs = plt.subplots(2, 2)
axs[0, 0].plot(x, y)
axs[0, 0].set_title('Axis [0, 0]')
axs[0, 1].plot(x, y, 'tab:orange')
axs[0, 1].set_title('Axis [0, 1]')
axs[1, 0].plot(x, -y, 'tab:green')
axs[1, 0].set_title('Axis [1, 0]')
axs[1, 1].plot(x, -y, 'tab:red')
axs[1, 1].set_title('Axis [1, 1]')

for ax in axs.flat:
    ax.set(xlabel='x-label', ylabel='y-label')

# Hide x labels and tick labels for top plots and y ticks for right plots.
for ax in axs.flat:
    ax.label_outer()

My aim now are 3 supblots of following formm but I did not found a usable source in web to get this:

stackoverflow.com/a/2265506/12040795

andy
– andy

2021-11-17 12:20:00 +00:00
Commented Nov 17, 2021 at 12:20 — andy
– andy, Commented Nov 17, 2021 at 12:20

Jody Klymak · Accepted Answer · 2021-11-17 12:22:35Z

2

Simplest is probably to use subplots_mosaic: https://matplotlib.org/stable/tutorials/provisional/mosaic.html

import matplotlib.pyplot as plt
import numpy as np

# Some example data to display
x = np.linspace(0, 2 * np.pi, 400)
y = np.sin(x ** 2)

fig, axd = plt.subplot_mosaic([['left', 'right'],['bottom', 'bottom']],
                              constrained_layout=True)
axd['left'].plot(x, y, 'C0')
axd['right'].plot(x, y, 'C1')
axd['bottom'].plot(x, y, 'C2')
plt.show()

answered Nov 17, 2021 at 12:22

Jody Klymak

6,1572 gold badges20 silver badges37 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

Python: Merge subplot over two subplotcells in matplotlib or with other libaries [duplicate]

1 Answer 1

Comments

Linked

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Linked

Related