Typing Stubs for Seaborn

0.13.2.20260408 · active · verified Mon Apr 13

types-seaborn is a type stub package providing static type annotations for the popular Seaborn statistical data visualization library. It allows type checkers (like MyPy or Pyright) to verify code that uses Seaborn, improving code quality and catching potential errors during development. This package is part of the broader typeshed project, and its current version, 0.13.2.20260408, aims to provide accurate annotations for seaborn==0.13.2.

Warnings

gotcha types-seaborn is solely a type stub package. Installing it does not provide the Seaborn library's functionality. You must install the actual 'seaborn' package separately to use the library at runtime.
Fix: Ensure 'pip install seaborn' is run alongside 'pip install types-seaborn'.
breaking Type annotations provided by 'types-seaborn' are specific to a particular version of the 'seaborn' library. Mismatches between the installed 'types-seaborn' version and the 'seaborn' library version can lead to incorrect or missing type hints, causing type checkers to report errors or fail to detect issues.
Fix: Always check the 'types-seaborn' PyPI description for the targeted 'seaborn' version (e.g., 'types-seaborn==0.13.2.20260408' targets 'seaborn==0.13.2'). Try to keep the versions aligned.
gotcha Contributions and bug fixes for 'types-seaborn' should not be directed to a dedicated 'types-seaborn' repository. As part of the typeshed project, all improvements to these stubs must be contributed to the main typeshed GitHub repository.
Fix: Refer to the typeshed GitHub repository (github.com/python/typeshed) for reporting issues or contributing changes to the type stubs.

Install

pip install types-seaborn Install types-seaborn
pip install seaborn matplotlib pandas numpy Install Seaborn and its core dependencies

Imports

seaborn
```
import seaborn as sns
```
matplotlib.pyplot
```
import matplotlib.pyplot as plt
```
pandas
```
import pandas as pd
```

Quickstart

This quickstart demonstrates a typical use of Seaborn to create a statistical visualization. It imports Seaborn (aliased as `sns`), Matplotlib for displaying the plot, and Pandas for data handling. It then loads a sample dataset, applies a default Seaborn theme, creates a complex relational plot, and finally displays it.

import seaborn as sns
import matplotlib.pyplot as plt
import pandas as pd # Often used with seaborn

# Apply the default seaborn theme for aesthetics
sns.set_theme()

# Load an example dataset provided by seaborn
tips = sns.load_dataset("tips")

# Create a relational plot
sns.relplot(
    data=tips, 
    x="total_bill", 
    y="tip", 
    col="time", 
    hue="smoker", 
    style="smoker", 
    size="size"
)

plt.title("Tips by Total Bill, Time, and Smoker Status")
plt.show()

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