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Statistical data visualization using matplotlib
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MaozGelbart MNT: Remove unused code paths (#1965)
* TST: pandas==1.0 compat

* Remove old matplotlib paths

* Remove old scipy paths

* Remove _set_spine_position

The behavior that this function addressed has been resolved in matplotlib 1.4:
matplotlib/matplotlib#3104

* Clean more old compat code paths

Removed a few try/except clauses aimed to cover old matplotlib/pandas
versions that are no longer supported.

* Remove __future__ imports

* Improve code style
Latest commit 5498cdb Feb 27, 2020

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README.md

seaborn: statistical data visualization


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Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.

Documentation

Online documentation is available at seaborn.pydata.org.

The docs include a tutorial, example gallery, API reference, and other useful information.

Dependencies

Seaborn supports Python 3.6+ and no longer supports Python 2.

Installation requires numpy, scipy, pandas, and matplotlib. Some functions will optionally use statsmodels if it is installed.

Installation

The latest stable release (and older versions) can be installed from PyPI:

pip install seaborn

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

pip install git+https://github.com/mwaskom/seaborn.git#egg=seaborn

Testing

To test the code, run make test in the source directory. This will exercise both the unit tests and docstring examples (using pytest).

The doctests require a network connection (unless all example datasets are cached), but the unit tests can be run offline with make unittests. Run make coverage to generate a test coverage report and make lint to check code style consistency.

Development

Seaborn development takes place on Github: https://github.com/mwaskom/seaborn

Please submit bugs that you encounter to the issue tracker with a reproducible example demonstrating the problem. Questions about usage are more at home on StackOverflow, where there is a seaborn tag.

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