# # Customizing Figure Layouts Using GridSpec and Other Functions

How to create grid-shaped combinations of axes.

subplots() (opens new window)

GridSpec (opens new window)

SubplotSpec (opens new window)

subplot2grid() (opens new window)

import matplotlib
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec


## # Basic Quickstart Guide

These first two examples show how to create a basic 2-by-2 grid using both subplots() (opens new window) and gridspec (opens new window).

Using subplots() (opens new window) is quite simple. It returns a Figure (opens new window) instance and an array of Axes (opens new window) objects.

fig1, f1_axes = plt.subplots(ncols=2, nrows=2, constrained_layout=True)


For a simple use case such as this, gridspec (opens new window) is perhaps overly verbose. You have to create the figure and GridSpec (opens new window) instance separately, then pass elements of gridspec instance to the add_subplot() (opens new window) method to create the axes objects. The elements of the gridspec are accessed in generally the same manner as numpy arrays.

fig2 = plt.figure(constrained_layout=True)
spec2 = gridspec.GridSpec(ncols=2, nrows=2, figure=fig2)


The power of gridspec comes in being able to create subplots that span rows and columns. Note the Numpy slice (opens new window) syntax for selecting the part of the gridspec each subplot will occupy.

Note that we have also used the convenience method Figure.add_gridspec (opens new window) instead of gridspec.GridSpec (opens new window), potentially saving the user an import, and keeping the namespace cleaner.

fig3 = plt.figure(constrained_layout=True)
f3_ax1.set_title('gs[0, :]')
f3_ax2.set_title('gs[1, :-1]')
f3_ax3.set_title('gs[1:, -1]')
f3_ax4.set_title('gs[-1, 0]')
f3_ax5.set_title('gs[-1, -2]')


gridspec (opens new window) is also indispensable for creating subplots of different widths via a couple of methods.

The method shown here is similar to the one above and initializes a uniform grid specification, and then uses numpy indexing and slices to allocate multiple "cells" for a given subplot.

fig4 = plt.figure(constrained_layout=True)
anno_opts = dict(xy=(0.5, 0.5), xycoords='axes fraction',
va='center', ha='center')

f4_ax1.annotate('GridSpec[0, 0]', **anno_opts)


Another option is to use the width_ratios and height_ratios parameters. These keyword arguments are lists of numbers. Note that absolute values are meaningless, only their relative ratios matter. That means that width_ratios=[2, 4, 8] is equivalent to width_ratios=[1, 2, 4] within equally wide figures. For the sake of demonstration, we'll blindly create the axes within for loops since we won't need them later.

fig5 = plt.figure(constrained_layout=True)
widths = [2, 3, 1.5]
heights = [1, 3, 2]
height_ratios=heights)
for row in range(3):
for col in range(3):
label = 'Width: {}\nHeight: {}'.format(widths[col], heights[row])
ax.annotate(label, (0.1, 0.5), xycoords='axes fraction', va='center')


Learning to use width_ratios and height_ratios is particularly useful since the top-level function subplots() (opens new window) accepts them within the gridspec_kw parameter. For that matter, any parameter accepted by GridSpec (opens new window) can be passed to subplots() (opens new window) via the gridspec_kw parameter. This example recreates the previous figure without directly using a gridspec instance.

gs_kw = dict(width_ratios=widths, height_ratios=heights)
fig6, f6_axes = plt.subplots(ncols=3, nrows=3, constrained_layout=True,
gridspec_kw=gs_kw)
for r, row in enumerate(f6_axes):
for c, ax in enumerate(row):
label = 'Width: {}\nHeight: {}'.format(widths[c], heights[r])
ax.annotate(label, (0.1, 0.5), xycoords='axes fraction', va='center')


The subplots and gridspec methods can be combined since it is sometimes more convenient to make most of the subplots using subplots and then remove some and combine them. Here we create a layout with the bottom two axes in the last column combined.

fig7, f7_axs = plt.subplots(ncols=3, nrows=3)
gs = f7_axs[1, 2].get_gridspec()
# remove the underlying axes
for ax in f7_axs[1:, -1]:
ax.remove()
axbig.annotate('Big Axes \nGridSpec[1:, -1]', (0.1, 0.5),
xycoords='axes fraction', va='center')

fig7.tight_layout()


## # Fine Adjustments to a Gridspec Layout

When a GridSpec is explicitly used, you can adjust the layout parameters of subplots that are created from the GridSpec. Note this option is not compatible with constrained_layout or Figure.tight_layout (opens new window) which both adjust subplot sizes to fill the figure.

fig8 = plt.figure(constrained_layout=False)
gs1 = fig8.add_gridspec(nrows=3, ncols=3, left=0.05, right=0.48, wspace=0.05)


This is similar to subplots_adjust() (opens new window), but it only affects the subplots that are created from the given GridSpec.

For example, compare the left and right sides of this figure:

fig9 = plt.figure(constrained_layout=False)
gs1 = fig9.add_gridspec(nrows=3, ncols=3, left=0.05, right=0.48,
wspace=0.05)

gs2 = fig9.add_gridspec(nrows=3, ncols=3, left=0.55, right=0.98,
hspace=0.05)


## # GridSpec using SubplotSpec

You can create GridSpec from the SubplotSpec (opens new window), in which case its layout parameters are set to that of the location of the given SubplotSpec.

Note this is also available from the more verbose gridspec.GridSpecFromSubplotSpec (opens new window).

fig10 = plt.figure(constrained_layout=True)

gs00 = gs0[0].subgridspec(2, 3)
gs01 = gs0[1].subgridspec(3, 2)

for a in range(2):
for b in range(3):


## # A Complex Nested GridSpec using SubplotSpec

Here's a more sophisticated example of nested GridSpec where we put a box around each cell of the outer 4x4 grid, by hiding appropriate spines in each of the inner 3x3 grids.

import numpy as np
from itertools import product

def squiggle_xy(a, b, c, d, i=np.arange(0.0, 2*np.pi, 0.05)):
return np.sin(i*a)*np.cos(i*b), np.sin(i*c)*np.cos(i*d)

fig11 = plt.figure(figsize=(8, 8), constrained_layout=False)

# gridspec inside gridspec
outer_grid = fig11.add_gridspec(4, 4, wspace=0.0, hspace=0.0)

for i in range(16):
inner_grid = outer_grid[i].subgridspec(3, 3, wspace=0.0, hspace=0.0)
a, b = int(i/4)+1, i % 4+1
for j, (c, d) in enumerate(product(range(1, 4), repeat=2)):
ax.plot(*squiggle_xy(a, b, c, d))
ax.set_xticks([])
ax.set_yticks([])

all_axes = fig11.get_axes()

# show only the outside spines
for ax in all_axes:
for sp in ax.spines.values():
sp.set_visible(False)
if ax.is_first_row():
ax.spines['top'].set_visible(True)
if ax.is_last_row():
ax.spines['bottom'].set_visible(True)
if ax.is_first_col():
ax.spines['left'].set_visible(True)
if ax.is_last_col():
ax.spines['right'].set_visible(True)

plt.show()


### # References

The usage of the following functions and methods is shown in this example:

matplotlib.pyplot.subplots