Rotate and customize axis and axis tick labels in seaborn and matplotlib

We've all worked through hours of analysis, building models, cleaning data, and then the deadline to share results comes up. Now it's about how to present data, findings, early exploratory data analysis, to share the compelling data story and logic behind each step of the process. But sometimes plots start out messy, like the one below.

In this post, we'll use matplotlib and seaborn together to create customized, beautiful axis labels, axis tick labels, and titles for your plots so that your data can speak for itself. Click through our table of contents on the left to see each example, or open and fork the canvas below for the full code.

For context, the dataset comes from Kaggle, and contains information about museums around the United States.

Import and setup

Note: even though we're using seaborn to create the base visualizations, we need matplotlib to customize the various axis and axis tick labels.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_theme()

df["Museum Type"].value_counts()

Output:

Out[5]: 
HISTORY MUSEUM                                   1093
ART MUSEUM                                        380
ARBORETUM, BOTANICAL GARDEN, OR NATURE CENTER     292
SCIENCE & TECHNOLOGY MUSEUM OR PLANETARIUM        132
Name: Museum Type, dtype: Int64

From the output, we can see that the subset of the data has only 4 museum types. Now let's create a count plot, using seaborn.

Example 1: rotate axis tick labels

# Customize x-axis tick labels version 1
plot = sns.countplot(data = df, x = "Museum Type")

# Get x-axis tick locations and labels
xtick_loc = plot.get_xticks()
xtick_labels = plot.get_xticklabels()

# Set the x-axis ticks
plot.set_xticks(ticks = xtick_loc, labels = xtick_labels, rotation = 45, ha = 'right', fontsize = 'x-small')

print("x-axis tick locations: " + str(xtick_loc))
print("x-axis tick labels: " + str(plot.get_xticklabels()))

Output:

x-axis tick locations: [0 1 2 3]
x-axis tick labels: [Text(0, 0, 'ART MUSEUM'), Text(1, 0, 'HISTORY MUSEUM'), Text(2, 0, 'ARBORETUM, BOTANICAL GARDEN, OR NATURE CENTER'), Text(3, 0, 'SCIENCE & TECHNOLOGY MUSEUM OR PLANETARIUM')]

From the above code, we can see that we can get the location of the x-axis ticks using plot.get_xticks() and we can get the labels that the plot will use via plot.get_xtickslabels(). Then we feed the following arguments into the function plot.set_xticks(ticks, labels, rotation, ha, fontsize):

• ticks: location of the tick labels
• labels: list of label names
• rotation: amount of rotation in degrees
• ha or horizontalalignment: how the labels should be aligned, options include "center", "left", "right"
• fontsize: size of font, can be a string or a number (as we'll see below)

BONUS: use generative AI to customize charts

As important as it is to be able to recognize the code and have a working knowledge of how to customize charts and graphs effectively, generative AI can greatly speed up the process and save you tons of time. In Einblick, we've implemented an AI agent, Einblick Prompt, into each of our AI-native data notebooks. Prompt can create data workflows from as little as one sentence. Check out how we created a comparable graph just using natural language prompts:

Using Generative AI in Einblick

1. Open an Einblick canvas (you can fork any embedded canvas!)
2. Connect to your data > Create a Table
3. Select the Table > Click the Prompt icon
4. Type in: "Create a countplot of art museums, history museums, arboretums, and science museums."
5. Select the Python cell > Shift + Enter to run code
6. Select the Python cell > Click the Prompt icon
7. Type in: "Duplicate the plot but make sure the axis tick labels don't overlap"

Let us know if you try out Prompt, and share any results you get!

Example 2: more axis tick label customization – alignment, size, font family, etc.

# Create list of labels that wrap the longer labels
xtick_labels = ["ART MUSEUM", "HISTORY MUSEUM", "ABORETUM, BOTANICAL GARDEN, \n OR NATURE CENTER", 
                "SCIENCE & TECHNOLOGY MUSEUM \n OR PLANETARIUM"]

# Plot data
plot = sns.countplot(data = df, x = "Museum Type")

# Customize x-axis tick labels
plot.set_xticks(ticks = xtick_loc, labels = xtick_labels, rotation = 45, ha = 'right', fontsize = 8)

# Specfiy axis labels and title
plot.set(xlabel = 'Musem Type', ylabel = 'Count', title = 'Number of Museums by Type')
plt.show()

Output:

In this example, we used a number (fontsize = 8) for font size. Additionally, we created a list of labels called xtick_labels (labels = xtick_labels) to help with the formatting. In addition, we used the plot.set() function to add in and customize the x-axis and y-axis labels, as well as the title. Note that this function only lets you edit the text, NOT the appearance of the text. We'll do that in the next example.

Example 3: customize axis labels and title

# Create plot
plot = sns.countplot(data = df, x = "Museum Type")

# Customize x-axis tick labels
plot.set_xticks(ticks = xtick_loc, labels = xtick_labels, rotation = 45, ha = 'right', fontsize = 8)

# Specfiy axis labels and title
plot.set_xlabel("Customized X-axis label", fontsize = 18, fontfamily = "DejaVu Sans Mono")
plot.set_ylabel("Customized Y-axis label", fontsize = 10, fontweight = "bold")
plot.set_title("Customized Title", fontsize = 24, fontfamily = "serif", horizontalalignment = "center")
plt.show()

Output:

In this final example, we use a few plot.set_XXX() functions to customize the appearance of the x-axis label (set_xlabel), y-axis label (set_ylabel), and the title (set_title). We customized them using the following arguments:

• xlabel, ylabel, label: string, actual label
• fontsize: size of the font, can be number or string
• fontfamily: name of the font or a generic font family name like "serif" or "monospace" (to see the fonts available, you can use matplotlib's font_manager)
• fontweight: how the label should be weighted, such as "bold" or "semi-bold"
• horizontalalignment: alignment of the label

About

Einblick is an AI-native data science platform that provides data teams with an agile workflow to swiftly explore data, build predictive models, and deploy data apps. Founded in 2020, Einblick was developed based on six years of research at MIT and Brown University. Einblick is funded by Amplify Partners, Flybridge, Samsung Next, Dell Technologies Capital, and Intel Capital. For more information, please visit www.einblick.ai and follow us on LinkedIn and Twitter.