Ticks in Matplotlib

Customising Ticks

The matplotlib library provides multiple options for customising the ticks to enhance data visualisation. The following are some standard methods for customising ticks.

Setting Tick Location and Colour

Tick location refers to the position of tick marks along the x and y-axis of the plot. We can explicitly give the tick locations and change the ticks' colour and font size.

We can set tick locations in matplotlib using the xticks and yticks functions. These functions take an array of tick values as input and set the tick positions on the x-axis and y-axis, respectively. 

Here's an example of how to set tick locations in matplotlib:

Code

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [2, 6, 4, 10, 8]

plt.plot(x, y)

# set x-axis tick locations at specific values and customising them
plt.xticks([1, 3, 5], color='red', fontsize='20')

# set y-axis tick locations at specific values
plt.yticks([2, 6, 10])

plt.show()

You can also try this code with Online Python Compiler

Run Code

Output

Explanation

In this example, we set the tick positions on the x-axis and y-axis by passing an array of tick values. We also changed the font size and colour of the x-axis ticks.

Setting Tick Format

Tick format defines how the tick labels are displayed, such as decimal places, units, scientific notation, date/time format, currency format, etc.

We can set the tick format using the ticklabel_format function of the Axes class. This function takes two arguments: axis ('x' or 'y') and style ('scientific', 'plain', etc.). Here's an example of how to set the tick format in matplotlib:

Code

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [20000, 60000, 40000, 100000, 80000]

plt.plot(x, y)

# Set y-axis tick format to scientific format
plt.ticklabel_format(axis='y', style='sci', scilimits=(0, 0))

plt.show()

You can also try this code with Online Python Compiler

Run Code

Output

Explanation

In this example, we set the tick format on the y-axis to scientific notation with a limit of zero decimal places.

Setting Tick Frequency

Tick frequency refers to the spacing between tick marks on an axis. By default, matplotlib automatically determines the tick frequency. Instead of specifying the tick locations explicitly, the tick frequency determines them implicitly.

We can set the tick frequency in matplotlib using the Locator class from matplotlib.ticker module and the set_major_locator function. Here's an example of how to set tick frequency in matplotlib:

Code

import matplotlib.pyplot as plt
from matplotlib.ticker import MultipleLocator

x = [1, 2, 3, 4, 5]
y = [2, 6, 4, 10, 8]

fig, ax = plt.subplots()
ax.plot(x, y)

# Set x-axis tick frequency to 0.75
ax.xaxis.set_major_locator(MultipleLocator(0.75))

plt.show()

You can also try this code with Online Python Compiler

Run Code

Output

Explanation

In this example, we set the tick frequency on the x-axis to every 0.75 units.

Setting Tick Rotation

Sometimes, the tick labels overlap, making them difficult to read. In these cases, we can rotate the tick labels to avoid overlapping and improve the readability of the plots. The xticks and yticks functions provide the rotation parameter, which we can use to set the rotation angle of the tick labels. Here's an example of how to set tick rotation in matplotlib:

Code

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 15, 25, 30]

plt.plot(x, y)

plt.xticks(rotation=45)

plt.show()

You can also try this code with Online Python Compiler

Run Code

Output

Explanation

In this example, we have rotated the x-axis tick labels by 45 degrees. 

Setting Custom Tick Labels

Sometimes, we may want to use custom labels for the tick values instead of the default numerical values. We can use the xticklabels and yticklabels functions to set custom labels for the tick values. Here's an example of how to set custom tick tables in matplotlib:

Code

import matplotlib.pyplot as plt

x = [1, 2, 3, 4, 5]
y = [10, 20, 15, 25, 30]

plt.plot(x, y)

labels = ['A', 'B', 'C', 'D', 'E']
plt.xticks(ticks=x, labels=labels)

plt.show()

You can also try this code with Online Python Compiler

Run Code

Output

Explanation

In this example, we set custom labels for the x-axis ticks to a pre-defined labels list.

Frequently Asked Questions

How to customise the appearance of ticks in Matplotlib, such as changing their colour or size?

The appearance of ticks in Matplotlib can be customised using the tick_params method. This method lets us modify the tick colour, size, width, and style.

What are the different types of plots that can be created using Matplotlib?

Matplotlib supports various plot types, including line plots, scatter plots, bar charts, pie charts, histograms, and more.

What file formats are supported by Matplotlib for saving plots?

Matplotlib supports various file formats for saving plots, including PNG, PDF and SVG.

What are minor tick marks?

Minor ticks are the smaller tick marks that appear between the major tick marks on a plot. They provide a more detailed view of the data and are often used to show smaller intervals or subdivisions of the data. In Matplotlib, minor ticks can be customised using the set_minor_locator method.

How can I remove tick labels in Matplotlib?

You can remove tick labels by setting the tick labels to an empty list using the set_ticklabels method.

Conclusion

This article discussed ticks in matplotlib, along with ways to customise them. By using these customisation options, we can improve the readability and accuracy of the plots.

To learn more about Python, we recommend reading the following articles:

• Introduction to Matplotlib and Seaborn
• Python keywords and identifiers
• Functions in Python
• Interview Questions for Python.
   

If you liked our article, do upvote our article and help other ninjas grow. You can refer to our Guided Path on Coding Ninjas Studio to upskill yourself in Data Structures and Algorithms, Competitive Programming, System Design, and many more!