seaborn

library0.13.2pypypi✓ verified 25d ago

Seaborn is a high-level Python library for creating statistical graphics, building on Matplotlib and integrating closely with Pandas data structures. It provides a dataset-oriented API to draw attractive and informative statistical plots with ease. The library is actively maintained with regular minor and major releases, currently at version 0.13.2, ensuring compatibility with evolving data science ecosystems.

data ai-ml

pip install seaborn

github ↗package ↗docs ↗

INSTALL

IMPORT

SIG · SEABORN

seaborn

datapythonv0.13.2

Install

11.8s avg

Import

3793ms

Disk

258MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.13.2 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 3.948s · 254.3MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 11.8s · import 3.638s · 243MB

258MB installed

● package 258MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

seaborn

✓ import seaborn as sns

The standard and widely adopted alias for Seaborn.

seaborn.objects

✓ import seaborn.objects as so

Recommended alias for the experimental declarative 'objects' interface introduced in v0.12.0.

This quickstart demonstrates loading a built-in dataset, applying a theme, and creating a relational scatter plot using Seaborn's high-level API. It also includes basic Matplotlib calls for customization and displaying the plot.

import seaborn as sns import matplotlib.pyplot as plt sns.set_theme(style="darkgrid") # Load an example dataset tips = sns.load_dataset("tips") # Create a scatter plot sns.scatterplot( data=tips, x="total_bill", y="tip", hue="smoker", style="time", size="size" ) plt.title("Total Bill vs. Tip by Smoker and Time") plt.xlabel("Total Bill ($)") plt.ylabel("Tip ($)") plt.show()

Debug

Known issues

breakingThe `seaborn.objects` interface was introduced in v0.12.0, offering a new declarative API. While powerful, it's considered experimental and may have breaking changes or rough edges in early minor releases.

fix

Consult the official `seaborn.objects` documentation for specific usage and known limitations. For simpler plots or existing code, continue using the traditional axes-level and figure-level functions.

affects: >=0.12.0

breakingSeaborn's categorical plotting functions (e.g., `boxplot`, `barplot`, `catplot`) underwent a major overhaul in v0.13.0. This includes changes to color defaults (now requiring explicit `hue` for multiple colors) and the introduction of the `native_scale` parameter.

fix

Review code using categorical plots. To reproduce previous color behavior, explicitly assign a redundant `hue` variable (e.g., `boxplot(data, x='x', y='y', hue='x')`). If mixing categorical and numerical data on an axis, consider `native_scale=True`.

affects: >=0.13.0

deprecatedPython 3.7 support was dropped in Seaborn v0.12.2.

fix

Upgrade to Python 3.8 or newer to ensure compatibility and receive future updates.

affects: >=0.12.2

gotchaPositional arguments for most plotting functions were deprecated in v0.11.0 and enforced to be keyword-only in v0.12.0. This applies to arguments like `x`, `y`, `hue`, etc.

fix

Always pass arguments like `x`, `y`, `data` using explicit keywords (e.g., `sns.scatterplot(data=df, x='col1', y='col2')` instead of `sns.scatterplot(df, 'col1', 'col2')`).

affects: >=0.12.0

gotchaA regression in v0.13.0 caused exceptions when working with non-NumPy data types (e.g., Pandas nullable dtypes), which was fixed in v0.13.1. Pandas dtypes can still sometimes cause issues as Matplotlib (and by extension, Seaborn) often expects NumPy dtypes.

fix

Update to Seaborn v0.13.1 or later. Ensure that data passed to Seaborn plotting functions are of appropriate (usually numerical) NumPy types, converting Pandas nullable dtypes explicitly if necessary (e.g., `.astype(float)`).

affects: 0.13.0

gotchaWhen combining categorical plots with other plot types (e.g., a bar plot and a line plot on the same axes), misalignment can occur because categorical plots internally map string/category values to integer indices.

fix

For Seaborn v0.13.0+, use `native_scale=True` in categorical plots if the categorical axis truly represents numeric or datetime data. For older versions or string categories, carefully manage axis limits and tick labels using Matplotlib to ensure alignment.

affects: all

gotchaThe test environment generated warnings or notices from `pip` related to running as the root user or an available update for `pip`. These are typically environment-specific messages from the package manager, not direct failures of the tested library.

fix

It is generally recommended to run `pip` in a virtual environment to avoid permission issues and system conflicts. To update `pip`, run `pip install --upgrade pip`. If running as root is intentional and understood, use `pip --root-user-action=ignore` to suppress the warning.

affects: all

Errors

Common errors & fixes

AttributeError: 'AxesSubplot' object has no attribute 'distplot'

The `distplot` function (or its replacements like `histplot` and `kdeplot`) is a top-level function in the `seaborn` module, not a method directly callable on a Matplotlib Axes object.

fix

Call `sns.distplot()` directly, passing the `ax` parameter if you want to draw on a specific axes, or use the recommended `sns.histplot` or `sns.kdeplot` which support the `ax` parameter directly.

ValueError: cannot convert float NaN to integer

This error commonly occurs in `seaborn.heatmap` when `annot=True` is used with a format string for integers (e.g., `fmt='d'`) while the underlying data contains `NaN` (Not a Number) values, which are floats and cannot be directly converted to integers.

fix

Handle `NaN` values by either filling them (e.g., `df.fillna(0)`) or using a float format string (e.g., `fmt='.1f'` or `fmt='g'`) with `annot=True`, or set `annot=False` if annotations are not strictly needed.

TypeError: kdeplot got an unexpected keyword argument 'shade'

The `shade` parameter in `seaborn.kdeplot` was deprecated and subsequently removed in Seaborn versions 0.11.0 and later, replaced by the `fill` parameter.

fix

Replace `shade=True` with `fill=True` when calling `sns.kdeplot`.

AttributeError: 'FacetGrid' object has no attribute 'set_title'

Figure-level functions in Seaborn (like `sns.relplot`, `sns.catplot`, `sns.displot`, `sns.FacetGrid`) return a `FacetGrid` object, which is not a Matplotlib Axes object and thus does not have a `set_title` method; titles need to be set differently for figure-level plots.

fix

Use the `g.fig.suptitle()` method for an overall figure title, or `g.set_titles()` to set titles for individual subplots within the `FacetGrid`.

Upgrade

Version history

0.13.2latest on PyPI · released Jan 25, 2024

Audit

Dependencies

numpyrequiredMandatory dependency for numerical operations.

pandasrequiredMandatory dependency, closely integrated for data structures (DataFrames).

matplotlibrequiredMandatory dependency, Seaborn builds on top of Matplotlib for plotting.

scipyoptionalRequired for some advanced statistical functionality.

statsmodelsoptionalRequired for some advanced statistical functionality, particularly regression plots.

Agent activity

18 hits · last 30 days

node

OpenAI (training)

Resources

githubhttps://github.com/mwaskom/seaborn ↗

packagehttps://pypi.org/project/seaborn/ ↗

docshttp://seaborn.pydata.org ↗