# Seaborn

🔗 [GitHub](https://github.com/seaborn/seaborn) | [Website](https://seaborn.pydata.org/)

Seaborn is a library for making statistical graphics in Python. It builds on top of matplotlib and integrates closely with pandas data structures.

Seaborn helps you explore and understand your data. Its plotting functions operate on dataframes and arrays containing whole datasets and internally perform the necessary semantic mapping and statistical aggregation to produce informative plots. Its dataset-oriented, declarative API lets you focus on what the different elements of your plots mean, rather than on the details of how to draw them.

## Documentation

Online documentation is available at [seaborn.pydata.org](https://seaborn.pydata.org).

The docs include a [tutorial](https://seaborn.pydata.org/tutorial.html), [example gallery](https://seaborn.pydata.org/examples/index.html), [API reference](https://seaborn.pydata.org/api.html), and other useful information.

## Dependencies

Seaborn supports Python 3.7+ and no longer supports Python 2.

Installation requires [numpy](https://numpy.org/), [pandas](https://pandas.pydata.org/), and [matplotlib](https://matplotlib.org/). Some functions will optionally use [scipy](https://www.scipy.org/) and/or [statsmodels](https://www.statsmodels.org/) if they are available.

## Installation

The latest stable release (and required dependencies) can be installed from PyPI:

```
pip install seaborn
```

It is also possible to include the optional dependencies:

```
pip install seaborn[all]
```

You may instead want to use the development version from Github:

```
pip install git+https://github.com/mwaskom/seaborn.git
```

Seaborn is also available from Anaconda and can be installed with conda:

```
conda install seaborn
```

[![Visitors](https://api.visitorbadge.io/api/visitors?path=https%3A%2F%2Fgithub.com%2Fdrshahizan\&labelColor=%23697689\&countColor=%23555555\&style=plastic)](https://visitorbadge.io/status?path=https%3A%2F%2Fgithub.com%2Fdrshahizan)


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://drshahizan.gitbook.io/eda1/tools-and-software/python/seaborn.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.