How to set axes labels & limits in a Seaborn plot?

Seaborn automatically adjusts labels and axes limits to make plots more understandable, but sometimes you need custom control. Setting appropriate axes labels helps viewers understand what the plot represents, while adjusting limits lets you focus on specific data ranges. We can use matplotlib functions like xlabel(), ylabel(), xlim(), and ylim() to customize Seaborn plots.

Core Functions for Axes Customization

Here are the main functions used to set labels and limits:

• plt.xlabel() ? Sets the x-axis label text

• plt.ylabel() ? Sets the y-axis label text

• plt.xlim() ? Sets the x-axis range limits

• plt.ylim() ? Sets the y-axis range limits

Setting Labels and Limits in Scatter Plot

Here's how to customize a scatter plot with labels and limits:

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
x_values = [1, 2, 3, 4, 5]
y_values = [2, 4, 6, 8, 10]

# Create scatter plot
sns.scatterplot(x=x_values, y=y_values)

# Set custom labels and limits
plt.xlabel('Time (hours)')
plt.ylabel('Distance (km)')
plt.xlim(0, 6)
plt.ylim(0, 12)

plt.show()

Customizing Line Plot

Line plots work similarly with the same functions:

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
x_values = [1, 2, 3, 4, 5]
y_values = [3, 7, 4, 8, 5]

# Create line plot
sns.lineplot(x=x_values, y=y_values)

# Customize axes
plt.xlabel('Month')
plt.ylabel('Sales (thousands)')
plt.xlim(0, 6)
plt.ylim(0, 10)

plt.show()

Bar Plot with Custom Labels

For categorical data, you typically only need to set y-axis limits:

import seaborn as sns
import matplotlib.pyplot as plt

# Categorical data
categories = ['Product A', 'Product B', 'Product C', 'Product D']
values = [25, 40, 30, 45]

# Create bar plot
sns.barplot(x=categories, y=values)

# Set labels and y-limit
plt.xlabel('Products')
plt.ylabel('Revenue (thousands)')
plt.ylim(0, 50)

plt.show()

Histogram with Range Control

Histograms benefit from both axis customizations:

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
scores = [85, 90, 78, 92, 88, 76, 95, 89, 82, 91, 87, 84]

# Create histogram
sns.histplot(scores)

# Customize axes
plt.xlabel('Test Scores')
plt.ylabel('Frequency')
plt.xlim(70, 100)

plt.show()

Box Plot Customization

Box plots often need only label and y-limit adjustments:

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
temperatures = [22, 25, 28, 24, 26, 23, 27, 29, 25, 24, 26, 28]

# Create box plot
sns.boxplot(data=temperatures)

# Set labels and limits
plt.xlabel('Daily Temperature')
plt.ylabel('Temperature (°C)')
plt.ylim(20, 30)

plt.show()

Summary

Conclusion

Use plt.xlabel() and plt.ylabel() to add descriptive labels to your Seaborn plots. Control the visible data range with plt.xlim() and plt.ylim() to focus on important data regions and improve plot readability.