Layouts and multiple charts

Matplotlib Basics

2 min read

Published Oct 5 2025

ChartsGraphsMatplotlibNumPyPandasPythonVisualisation

Matplotlib gives you several flexible ways to arrange multiple charts (axes) in a single figure.

Key Concepts:

Figure = The overall canvas or page where all plots live
Axes = A single plot area (with x/y axes, labels, etc.)
Subplot = A single Axes positioned within a grid inside the Figure
Layout = How those Axes are arranged (rows × columns)

A single figure can contain multiple axes in a grid layout or even precisely positioned with gridspec.

The Three Main Approaches:

Method	When to use	Description
plt.subplot()	Simple, small grids	Quick creation (old style)
plt.subplots()	Modern and flexible	Preferred for most uses
GridSpec	Complex / unequal layouts	Fine control of positioning

Quick & simple — `plt.subplot()`

plt.subplot(nrows, ncols, index) Creates one subplot in a grid:

import matplotlib.pyplot as plt

plt.figure(figsize=(8, 4))

# 1st plot

plt.subplot(1, 2, 1)

plt.plot([1, 2, 3], [1, 4, 9])

plt.title("Left Plot")

# 2nd plot

plt.subplot(1, 2, 2)

plt.plot([1, 2, 3], [9, 4, 1])

plt.title("Right Plot")

plt.tight_layout()

plt.show()

Quick but limited — you have to call it before each plot.

Modern & preferred — `plt.subplots()`

This returns both a figure and axes objects, letting you manage everything cleanly:

import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 2, figsize=(8, 6))

axes[0, 0].plot([1, 2, 3], [2, 4, 6])

axes[0, 0].set_title("Top Left")

axes[0, 1].bar([1, 2, 3], [3, 5, 7])

axes[0, 1].set_title("Top Right")

axes[1, 0].scatter([1, 2, 3], [5, 3, 8])

axes[1, 0].set_title("Bottom Left")

axes[1, 1].hist([1, 2, 2, 3, 3, 3])

axes[1, 1].set_title("Bottom Right")

plt.tight_layout()

plt.show()

axes is a NumPy array — index as [row, column].

1D layouts — row or column only

If you only have one row or column, axes becomes 1D:

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 3, figsize=(9, 3))

for i, ax in enumerate(axes):

ax.plot([1, 2, 3], [j * (i+1) for j in [1, 2, 3]])

ax.set_title(f"Plot {i+1}")

plt.tight_layout()

plt.show()

Access as axes[0], axes[1], etc.

Shared axes

You can make subplots share an axis scale:

import matplotlib.pyplot as plt

fig, axes = plt.subplots(2, 1, sharex=True, figsize=(6, 5))

axes[0].plot([1, 2, 3], [1, 4, 9])

axes[1].plot([1, 2, 3], [9, 4, 1])

axes[0].set_title("Shared X-axis Example")

plt.show()

sharex=True or sharey=True

Adjusting spacing between Subplots

plt.subplots_adjust(left=0.1, right=0.95, top=0.9,

bottom=0.1, wspace=0.3, hspace=0.4)

or simply:

plt.tight_layout(pad=2.0)

tight_layout() automatically fits titles and labels neatly.

Unequal layouts — `GridSpec`

Use matplotlib.gridspec for non-uniform layouts:

import matplotlib.pyplot as plt

import matplotlib.gridspec as gridspec

fig = plt.figure(figsize=(8, 6))

gs = gridspec.GridSpec(3, 3)

# top spans all columns

ax1 = fig.add_subplot(gs[0, :])

# bottom-left

ax2 = fig.add_subplot(gs[1:, 0])

# bottom-right 2×2

ax3 = fig.add_subplot(gs[1:, 1:])

ax1.plot([1, 2, 3])

ax2.bar([1, 2, 3], [3, 2, 5])

ax3.hist([1, 2, 3, 2, 1])

plt.tight_layout()

plt.show()

Very flexible — choose cell ranges like [row_start:row_end, col_start:col_end].

Mixed plot types

You can mix bar, line, scatter, pie, etc., in one figure:

import matplotlib.pyplot as plt

fig, axs = plt.subplots(1, 3, figsize=(12, 4))

axs[0].bar([1,2,3], [3,4,5])

axs[0].set_title("Bar Chart")

axs[1].plot([1,2,3], [1,4,9])

axs[1].set_title("Line Chart")

axs[2].pie([10,20,30], labels=['A','B','C'])

axs[2].set_title("Pie Chart")

plt.tight_layout()

plt.show()

Each Axes object acts independently.

Compact multi-chart using `plt.subplot_mosaic`

A more readable layout system:

import matplotlib.pyplot as plt

fig, ax = plt.subplot_mosaic(

[['top', 'top'],

['bottom_left', 'bottom_right']],

figsize=(8, 6)

)

ax['top'].plot([1,2,3])

ax['bottom_left'].bar([1,2,3], [3,5,7])

ax['bottom_right'].scatter([1,2,3], [7,3,5])

plt.tight_layout()

plt.show()

To tell Matplotlib that "top" spans both columns, repeat the key name in the list.
You can reference axes by name, not index — great for complex layouts.

Dashboard style layout

import matplotlib.pyplot as plt

fig = plt.figure(constrained_layout=True, figsize=(10, 6))

gs = fig.add_gridspec(2, 3)

ax1 = fig.add_subplot(gs[0, 0])

ax2 = fig.add_subplot(gs[0, 1])

ax3 = fig.add_subplot(gs[0, 2])

ax4 = fig.add_subplot(gs[1, :])

ax1.bar([1,2,3], [3,4,5])

ax2.plot([1,2,3], [5,3,6])

ax3.pie([30,40,30], labels=['A','B','C'])

ax4.hist([1,1,2,2,3,3,3])

plt.suptitle("Multi-Chart Dashboard", fontsize=16)

plt.show()

constrained_layout=True automatically optimizes spacing between subplots.

Layouts and multiple charts

Matplotlib Basics

2 min read