Matplotlib’s settings for axis are numerous and confusing. You are probably confused about ticks, scales, and limit settings.

This article details how to customize axis in Matplotlib. Specific steps are presented to set up tick marks, change the scale, and control the range of the axis.

After reading the article, you will be able to freely set the axes of your graphs using Matplotlib. This will allow you to visualize your data even more effectively.

Please refer to the following article for a beginner’s tutorial

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Components of a Figure

The components of a figure in Matplotlib is shown in the figure below. There are four major components, Figure, Axes, Axis, and Artist, but they are a little complicated because they contain each other.

In this article, we will focus on Axes and Axis in particular.

Axis label (Axes.set_xlabel, set_ylabel)

Axis labels can be set with Axes.set_xlabel and Axes.set_ylabel

ax.set_xlabel('X Label'), ax.set_ylabel('Y Label')

The following tabs explain with codes and flowcharts.

• Python code
• Flowchart

# step0 Import libraries
import matplotlib.pyplot as plt
import numpy as np

# step1 Create data
x = np.linspace(0, 10, 100)
y1 = 4 + 2 * np.sin(2 * x)
y2 = 4 + 2 * np.cos(2 * x)

# step2 Create graph frames
fig, ax = plt.subplots(figsize=(6, 4))

# step3 Plot graphs
ax.plot(x, y1, 'o--')
ax.plot(x, y2, 'D--')

# step4 Set Axis
# Set axis labels
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')

ax.set_title('Label')

# step5 Call the Figure
plt.show()

STEP

Create data

x is an array of 100 equal parts in the range 0~10
y is an array of sine and cosine waves, respectively

x and y should be arrays with the same number of elements

numpy.linspace (start, stop, num) : ndarray
An array of equally spaced ndarrays is created with the specified interval as the last array with the specified number of elements

STEP

Create graph frames

To draw a graph in Matplotlib, you need to create a graph frame

Create the main fig and the ax that actually draws the graph in pyplot.subplots

pyplot.subplots (nrows, ncols, sharex, sharey, subplot_kw, constrained_layout) : fig, ax
Create a figure and a set of subplots.

STEP

Plot line graphs with Axes.plot

The graph is displayed with the x and y data created in step1.

The third argument is a format string named fmt, which allows specifying line type, line color, and marker at once
o-- : A dashed line with a circle marker

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STEP

Set axis labels

Add each setting to the Axes object

x label: ax.set_xlabel('str')
y label: ax.set_ylabel('str')

STEP

Call the Figure

The diagram is not displayed until plt.show() is called.

Not required in Jupyter Notebook

Space from axis label (labelpad)

Spacing in points from the Axes bounding box including ticks and tick labels.

If None, the previous value is left as is.

Specified by float value, default is 4.

# step4 Set Axis
# Set axis labels
ax.set_xlabel('X Label', labelpad=-10)
ax.set_ylabel('Y Labelpad=20', labelpad=20)

Labe position (loc)

Axis label positions are set to loc in Axes.set_xlabel and Axes.set_ylabel

X label : loc=(‘left’, ‘center’, ‘right’)

Y label : loc=(‘bottom’, ‘center’, ‘top’)

# step4 Set Axis
# Set axis labels
ax.set_xlabel('X Label', loc='left')
ax.set_ylabel('Y Label', loc='top')

Appearance of axis label (Text)

Textproperties control the appearance of the label.

X label : Times New Roman, italic

Y label : Bold, size=16, red

# step4 Set AXis
# Set axis labels
ax.set_xlabel('X Label', fontfamily='Times New Roman', fontstyle='italic')
ax.set_ylabel('Y Label', fontweight='bold', fontsize=16, color='red')  

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Axis Limit (Axes.set_xlim, set_ylim)

The upper and lower axis limits are set by Axes.set_xlim and Axes.set_ylim.

ax.set_xlim(0, 8), ax.set_ylim(0, 8)

The following tabs explain with codes and flowcharts

• Python code
• Flowchart

# step0 Import libraries
import matplotlib.pyplot as plt
import numpy as np

# step1 Create data
x = np.linspace(0, 10, 100)
y1 = 4 + 2 * np.sin(2 * x)
y2 = 4 + 2 * np.cos(2 * x)

# step2 Create graphframes
fig, ax = plt.subplots(figsize=(6, 4))

# step3 Plot graph
ax.plot(x, y1, 'o--')
ax.plot(x, y2, 'D--')

# step4 Set axis
# Axis Label
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')     

# Axis Limit
ax.set_xlim(0, 8)
ax.set_ylim(0, 8)

# Title
ax.set_title('Axis Limit')

# step5 Call the Figure
plt.show()

STEP

Create data

x is an array of 100 equal parts in the range 0~10
y is an array of sine and cosine waves, respectively

x and y should be arrays with the same number of elements

numpy.linspace (start, stop, num) : ndarray
An array of equally spaced ndarrays is created with the specified interval as the last array with the specified number of elements

STEP

Create graph frames

To draw a graph in Matplotlib, you need to create a graph frame

Create the main fig and the ax that actually draws the graph in pyplot.subplots

pyplot.subplots (nrows, ncols, sharex, sharey, subplot_kw, constrained_layout) : fig, ax
Create a figure and a set of subplots.

STEP

Plot line graphs with Axes.plot

The graph is displayed with the x and y data created in step1.

The third argument is a format string named fmt, which allows specifying line type, line color, and marker at once
o-- : A dashed line with a circle marker

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STEP

Set axis labels and limits

Add each setting to the Axes object

x label: ax.set_xlabel('str')
x limit: ax.set_xlim(0, 8)

STEP

Call the Figure

The diagram is not displayed until plt.show() is called.

Not required in Jupyter Notebook

Only one side of the upper or lower limit

Axes.set_xlim specifies left, Axes.set_ylim specifies top only

ax.set_xlim(0), ax.set_ylim(None, 8)

# step4 Set Axis
# Axis Limit
ax.set_xlim(0)
ax.set_ylim(None, 8)

Reversing axis, Decreasing axis

A decreasing axis can be used by reversing the order of the axis limits.

ax.set_xlim(8, 0)

# step4 Set axis
# Axis Limit
ax.set_xlim(8, 0)
ax.set_ylim(0, 8)

Axis Scale (Axes.xscale, yscale)

The axis scale can be set with Axes.set_xscale and Axes.set_yscale

ax.set_xscale('linear'), ax.set_yscale('log')

The following tabs explain with codes and flowcharts

• Python Code
• Flowchart

# step0 Import libraries
import matplotlib.pyplot as plt
import numpy as np

# step1 Create data
x = np.linspace(0, 10, 100)
y1 = np.exp(1.2*x) + np.sin(2*x)
y2 = np.exp(x) + 2*np.cos(x)

# step2 Create graphframes
fig, ax = plt.subplots(figsize=(6, 4), constrained_layout=True)

# step3 Plot graph
ax.plot(x, y1)
ax.plot(x, y2)

# step4 Set axis labes and a title
ax.set_xlabel('linear')
ax.set_ylabel('linear')
ax.set_title('Axis Scale')

# step5 Set axis scale
# linear scale
ax.set_xscale('linear')
ax.set_yscale('linear')

# step6 Call the Figure
plt.show()

STEP

Create data

x is an array of 100 equal parts in the range 0~10
y is an array of sine and cosine waves, respectively

x and y should be arrays with the same number of elements

numpy.linspace (start, stop, num) : ndarray
An array of equally spaced ndarrays is created with the specified interval as the last array with the specified number of elements

STEP

Create graph frames

To draw a graph in Matplotlib, you need to create a graph frame

Create the main fig and the ax that actually draws the graph in pyplot.subplots

pyplot.subplots (nrows, ncols, sharex, sharey, subplot_kw, constrained_layout) : fig, ax
Create a figure and a set of subplots.

STEP

Plot line graphs with Axes.plot

The graph is displayed with the x and y data created in step1.

The third argument is a format string named fmt, which allows specifying line type, line color, and marker at once
o-- : A dashed line with a circle marker

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STEP

Set axis labels and a title

Add each setting to the Axes object

x label: ax.set_xlabel('str')
title: ax.set_title('str')

STEP

Set axis scale

Add settings using the same method as in STEP 4

linear scale : ax.set_yscale('linea')
logarithmic scale : ax.set_yscale('log')

STEP

Call the Figure

The diagram is not displayed until plt.show() is called.

Not required in Jupyter Notebook

# step5 Set axis scale
ax.set_xscale('log')
ax.set_yscale('linear')

# step5 Set axis scale
ax.set_xscale('linear')
ax.set_yscale('log')

# step5 Set axis scale
ax.set_xscale('log')
ax.set_yscale('log')

Axis Tick (Axes.set_xticks, Axes.set_yticks)

Scale position and string display are set with Axes.set_xticks, Axes.set_yticks

ax.set_xticks([0, 1, 2, 3, 4, 5, 6, 7, 8]), ax.set_yticks([0, 4, 8])

The following tabs explain with codes and flowcharts

• Python code
• Flowchart

# step0 Import libraries
import matplotlib.pyplot as plt
import numpy as np

# step1 Create data
x = np.linspace(0, 10, 100)
y1 = 4 + 2 * np.sin(2 * x)
y2 = 4 + 2 * np.cos(2 * x)

# step2 Create graphframes
fig, ax = plt.subplots(figsize=(6, 4))

# step3 Plot graph
ax.plot(x, y1, 'o--')
ax.plot(x, y2, 'D--')

# step4 Set axis
# Axis Labels
ax.set_xlabel('X Label')
ax.set_ylabel('Y Label')
# Axis Limits
ax.set_xlim(0, 8)
ax.set_ylim(0, 8)

# Axis ticks
ax.set_xticks([0, 1, 2, 3, 4, 5, 6, 7, 8])
ax.set_yticks([0, 4, 8])
ax.set_title('Axis Ticks')

# step5 Call the Figure
plt.show()

STEP

Create data

x is an array of 100 equal parts in the range 0~10
y is an array of sine and cosine waves, respectively

x and y should be arrays with the same number of elements

numpy.linspace (start, stop, num) : ndarray
An array of equally spaced ndarrays is created with the specified interval as the last array with the specified number of elements

STEP

Create graph frames

To draw a graph in Matplotlib, you need to create a graph frame

Create the main fig and the ax that actually draws the graph in pyplot.subplots

pyplot.subplots (nrows, ncols, sharex, sharey, subplot_kw, constrained_layout) : fig, ax
Create a figure and a set of subplots.

STEP

Plot line graphs with Axes.plot

The graph is displayed with the x and y data created in step1.

The third argument is a format string named fmt, which allows specifying line type, line color, and marker at once
o-- : A dashed line with a circle marker

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STEP

Set Axis labels, limits and ticks

Add each setting to the Axes object

Label: ax.set_xlabel('str')
Limit: ax.set_xlim(0, 8)
Tick: ax.set_xticks([0, 1, 2, 3, 4, 5, 6, 7, 8])

STEP

Call the Figure

The diagram is not displayed until plt.show() is called.

Not required in Jupyter Notebook

String ticks (labels)

labels turns the ticks into strings

The position of the string is determined by the ticks used earlier

The number of elements in ticks and labels should be the same

# step4 Set axis
# Axis ticks
ax.set_xticks(
    [0, 1, 2, 3, 4, 5, 6, 7, 8], 
    labels=['a', 'b', 'c', 'e', 'f', 'g','h', 'i', 'j']
)
ax.set_yticks([0, 4, 8])

Minor scales (minor)

Axes.set_xticks specifies major ticks by default, and minor ticks can be specified with minor=True

Comparison was made between major and minor ticks when the following values were specified

ax.set_xticks([2.5, 3.5, 4.5, 5.5, 6.5])

# step4 Set axis
# Axis ticks
ax.set_xticks([2.5, 3.5, 4.5, 5.5, 6.5])

Major scalesminor=False

Minor scalesminor=True

Custom Scales (Axes.tick_params)

Axes.tick_params changes the appearance of ticks, tick labels, and grid lines.

Only a few customizations are presented in this article

x-axis : color and direction of scale, width
y-axis : label color and size

# step4 Set axis
# x-axis : color and direction of scale, width
ax.tick_params('x', color='red', direction='in', width=5)
# y-axis : label color and size
ax.tick_params('y', labelcolor='red', labelsize=16)

Add or Delete axis

How to add and remove axes is explained.

The Python code for step 1 data creation and step 2 graph frame creation is the same as in the previous chapters.

Add Axis (Axes.twinx)

To increase the number of axes in Matplotlib, use the Axes.twinx function (or twiny)

twin1 = ax.twinx()

# step3 Create y-axis
twin1 = ax.twinx()

# step4 Plot graphs
ax.plot(x, y1, color='C0')
twin1.plot(x, y2, color='C1')

# step4 Set axis
# Axis Label
ax.set_xlabel('X label')
ax.set_ylabel('Y1')
twin1.set_ylabel('Y2')
# Axis Color
ax.tick_params(axis='y', colors='C0')
twin1.tick_params(axis='y', colors='C1')

ax.set_title('Twin Axis Ticks')

# step5 Call the Figure
plt.show()

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Delete frame

The frame of the graph uses spines

Specify the box to hide from top, right, left, bottom
ax.spines[['top', 'right']].set_visible(False)

# step3 Plot graphs
ax.plot(x, y1, 'o--')
ax.plot(x, y2, 'D--')

# step4 Set axis
# Delete frame
ax.spines[['top', 'right']].set_visible(False)

Delete Scale

Axis class is used for graph scales

ax.yaxis.set_visible(False)

# step3 Plot graphs
ax.plot(x, y1, 'o--')
ax.plot(x, y2, 'D--')

# step4 Set axis
# Delete y-axis scale
ax.yaxis.set_visible(False)

Delete y-axis scale and frame

Delete the y-axis completely by combining the Delete frame and the Delete scale

Delete frame : ax.spines[['top', 'right', 'left']].set_visible(False)
Delete scale : ax.yaxis.set_visible(False)

# step3 Plot graphs
ax.plot(x, y1, 'o--')
ax.plot(x, y2, 'D--')

# step4 Set axis
# Delete scale
ax.yaxis.set_visible(False)
# Delete frame
ax.spines[['top', 'right', 'left']].set_visible(False)

Delete all scales and frames at once

Axes.set_axis_off() removes everything related to the x and y axes.

# step3 Plot graphs
ax.plot(x, y1, 'o--')
ax.plot(x, y2, 'D--')

# step4 Set axis
# Delete all axis
ax.set_axis_off()

References

Axis Label Position

Plots with different scales

Invert Axes