PYTHON - SEABORN - IMPLEMENTATION

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seaborn: statistical data visualization

Seaborn is a Python data visualization library based on [matplotlib](https://matplotlib.org/). It provides a high-level interface for drawing attractive and informative statistical graphics.

For a brief introduction to the ideas behind the library, you can read the [introductory notes](https://seaborn.pydata.org/tutorial/introduction.html) or the [paper](https://joss.theoj.org/papers/10.21105/joss.03021). Visit the [installation page](https://seaborn.pydata.org/installing.html) to see how you can download the package and get started with it. You can browse the [example gallery](https://seaborn.pydata.org/examples/index.html) to see some of the things that you can do with seaborn, and then check out the [tutorials](https://seaborn.pydata.org/tutorial.html) or [API reference](https://seaborn.pydata.org/api.html) to find out how.

To see the code or report a bug, please visit the [GitHub repository](https://github.com/mwaskom/seaborn). General support questions are most at home on [stackoverflow](https://stackoverflow.com/questions/tagged/seaborn/), which has a dedicated channel for seaborn.

Contents
[Installing](https://seaborn.pydata.org/installing.html)
[Gallery](https://seaborn.pydata.org/examples/index.html)
[Tutorial](https://seaborn.pydata.org/tutorial.html)
[API](https://seaborn.pydata.org/api.html)
[Releases](https://seaborn.pydata.org/whatsnew/index.html)
[Citing](https://seaborn.pydata.org/citing.html)
[FAQ](https://seaborn.pydata.org/faq.html)
Features
New Objects: [API](https://seaborn.pydata.org/api.html#objects-api) | [Tutorial](https://seaborn.pydata.org/tutorial/objects_interface.html)

Relational plots: [API](https://seaborn.pydata.org/api.html#relational-api) | [Tutorial](https://seaborn.pydata.org/tutorial/relational.html)

Distribution plots: [API](https://seaborn.pydata.org/api.html#distribution-api) | [Tutorial](https://seaborn.pydata.org/tutorial/distributions.html)

Categorical plots: [API](https://seaborn.pydata.org/api.html#categorical-api) | [Tutorial](https://seaborn.pydata.org/tutorial/categorical.html)

Regression plots: [API](https://seaborn.pydata.org/api.html#regression-api) | [Tutorial](https://seaborn.pydata.org/tutorial/regression.html)

Multi-plot grids: [API](https://seaborn.pydata.org/api.html#grid-api) | [Tutorial](https://seaborn.pydata.org/tutorial/axis_grids.html)

Figure theming: [API](https://seaborn.pydata.org/api.html#style-api) | [Tutorial](https://seaborn.pydata.org/tutorial/aesthetics.html)

Color palettes: [API](https://seaborn.pydata.org/api.html#palette-api) | [Tutorial](https://seaborn.pydata.org/tutorial/color_palettes.html)

The issue contains a general Seaborn overview but names no repository files, tests, entry points, requested behavior, or acceptance criteria. Clarify what visualization should be implemented in the warehouse management application, then identify the relevant entry point and define how completion will be verified.

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