GridSpec for complex layouts in Matplotlib - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

What is the output of this GridSpec subplot arrangement?

Consider the following code using matplotlib.gridspec.GridSpec. What will be the number of subplots created and their arrangement?

Matplotlib

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

fig = plt.figure()
gs = GridSpec(3, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, :])
ax2 = fig.add_subplot(gs[1, :-1])
ax3 = fig.add_subplot(gs[1:, -1])
ax4 = fig.add_subplot(gs[-1, 0])
plt.close(fig)
print(len(fig.axes))

A 5

B 4

C 3

D 6

💡 Hint

Count each add_subplot call and check if any overlap or replacement occurs.

The code creates 4 subplots using different GridSpec slices. Each add_subplot adds a new axis, so total is 4.

❓ data_output

intermediate

What is the shape of the GridSpec layout?

Given this GridSpec code, what is the shape of the grid and which cells does gs[1:3, 0:2] cover?

Matplotlib

from matplotlib.gridspec import GridSpec

gs = GridSpec(4, 4)
covered_cells = [(r, c) for r in range(1, 3) for c in range(0, 2)]
print((gs.nrows, gs.ncols), covered_cells)

A (4, 4) and cells [(1, 0), (1, 1), (2, 0), (2, 1)]

B (3, 3) and cells [(1, 0), (1, 1), (2, 0), (2, 1)]

C (4, 4) and cells [(0, 1), (0, 2), (1, 1), (1, 2)]

D (3, 3) and cells [(0, 1), (0, 2), (1, 1), (1, 2)]

💡 Hint

GridSpec(4,4) creates a 4x4 grid. The slice selects rows 1 and 2, columns 0 and 1.

The GridSpec is 4 rows by 4 columns. The slice 1:3 means rows 1 and 2, and 0:2 means columns 0 and 1, so the covered cells are those four coordinates.

❓ visualization

advanced

Identify the correct GridSpec layout visualization

Which option shows the correct layout of subplots created by this code?

Matplotlib

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

fig = plt.figure(figsize=(6, 4))
gs = GridSpec(2, 3, figure=fig)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[0, 1:])
ax3 = fig.add_subplot(gs[1, :2])
ax4 = fig.add_subplot(gs[1, 2])
plt.close(fig)
# The question is about the layout shape, not the plot content.

A One small subplot on top left, one spanning top right two columns, one spanning bottom left two columns, one small bottom right

B Four equally sized subplots in a 2x2 grid

C Three subplots stacked vertically

D Two subplots side by side on top, two stacked vertically on bottom left

💡 Hint

Look at the slices: gs[0, 0], gs[0, 1:], gs[1, :2], gs[1, 2].

The first subplot is top-left cell. The second spans top row columns 1 and 2. The third spans bottom row columns 0 and 1. The last is bottom row column 2 alone.

🔧 Debug

advanced

What error does this GridSpec code raise?

What error will this code raise when executed?

Matplotlib

import matplotlib.pyplot as plt
from matplotlib.gridspec import GridSpec

fig = plt.figure()
gs = GridSpec(2, 2, figure=fig)
ax1 = fig.add_subplot(gs[2, 0])
plt.close(fig)

A TypeError: 'GridSpec' object is not subscriptable

B ValueError: Invalid subplot specification

C IndexError: index 2 is out of bounds for axis 0 with size 2

D No error, subplot created successfully

💡 Hint

Check the grid size and the index used in gs[2, 0].

The GridSpec has 2 rows (indices 0 and 1). Index 2 is out of range, so an IndexError is raised.

🚀 Application

expert

How to create a complex layout with nested GridSpec?

You want to create a figure with two main columns. The left column has two rows of equal height. The right column is a single subplot spanning full height. Which code snippet correctly creates this layout using nested GridSpec?

A

fig = plt.figure()
gs = GridSpec(2, 2, figure=fig)
ax1 = fig.add_subplot(gs[0, 0])
ax2 = fig.add_subplot(gs[1, 0])
ax3 = fig.add_subplot(gs[:, 1])

B

fig = plt.figure()
gs = GridSpec(2, 1, figure=fig)
gs_left = GridSpec(1, 2, subplot_spec=gs[0, 0])
ax1 = fig.add_subplot(gs_left[0, 0])
ax2 = fig.add_subplot(gs_left[0, 1])
ax3 = fig.add_subplot(gs[1, 0])

C

fig = plt.figure()
gs = GridSpec(1, 2, figure=fig)
gs_left = GridSpec(1, 2, subplot_spec=gs[0, 0])
ax1 = fig.add_subplot(gs_left[0, 0])
ax2 = fig.add_subplot(gs_left[0, 1])
ax3 = fig.add_subplot(gs[0, 1])

D

fig = plt.figure()
gs = GridSpec(1, 2, figure=fig)
gs_left = GridSpec(2, 1, subplot_spec=gs[0, 0])
ax1 = fig.add_subplot(gs_left[0, 0])
ax2 = fig.add_subplot(gs_left[1, 0])
ax3 = fig.add_subplot(gs[0, 1])

💡 Hint

Think about how to split the figure into two columns, then split the left column into two rows.

Option D uses a nested GridSpec: outer GridSpec(1, 2) splits into two columns. The left column (gs[0, 0]) is subdivided with nested GridSpec(2, 1) into two equal-height rows. The right column (gs[0, 1]) is a single subplot spanning the full height.