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Improved description of seaborn (pandas-dev#18495)
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doc/source/ecosystem.rst

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@@ -53,6 +53,18 @@ the latest web technologies. Its goal is to provide elegant, concise constructio
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graphics in the style of Protovis/D3, while delivering high-performance interactivity over
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large data to thin clients.
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`seaborn <https://seaborn.pydata.org>`__
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Seaborn is a Python visualization library based on `matplotlib
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<http://matplotlib.org>`__. It provides a high-level, dataset-oriented
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interface for creating attractive statistical graphics. The plotting functions
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in seaborn understand pandas objects and leverage pandas grouping operations
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internally to support concise specification of complex visualizations. Seaborn
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also goes beyond matplotlib and pandas with the option to perform statistical
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estimation while plotting, aggregating across observations and visualizing the
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fit of statistical models to emphasize patterns in a dataset.
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`yhat/ggplot <https://github.com/yhat/ggplot>`__
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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@@ -64,15 +76,6 @@ but a faithful implementation for python users has long been missing. Although s
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(as of Jan-2014), the `yhat/ggplot <https://github.com/yhat/ggplot>`__ project has been
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progressing quickly in that direction.
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`Seaborn <https://github.com/mwaskom/seaborn>`__
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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Although pandas has quite a bit of "just plot it" functionality built-in, visualization and
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in particular statistical graphics is a vast field with a long tradition and lots of ground
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to cover. The `Seaborn <https://github.com/mwaskom/seaborn>`__ project builds on top of pandas
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and `matplotlib <http://matplotlib.org>`__ to provide easy plotting of data which extends to
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more advanced types of plots then those offered by pandas.
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`Vincent <https://github.com/wrobstory/vincent>`__
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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