Seaborn is a library for making attractive and informative statistical graphics in Python. It is built on top of matplotlib and tightly integrated with the PyData stack, including support for numpy and pandas data structures and statistical routines from scipy and statsmodels.
Some of the features that seaborn offers are
- Several :ref:`built-in themes <aesthetics_tutorial>` that improve on the default matplotlib aesthetics
- Tools for choosing :ref:`color palettes <palette_tutorial>` to make beautiful plots that reveal patterns in your data
- Functions for visualizing :ref:`univariate <distplot_options>` and :ref:`bivariate <joint_kde>` distributions or for :ref:`comparing <grouped_violinplots>` them between subsets of data
- Tools that fit and visualize :ref:`linear regression <anscombes_quartet>` models for different kinds of :ref:`independent <pointplot_anova>` and :ref:`dependent <logistic_regression>` variables
- Functions that visualize :ref:`matrices of data <heatmap_annotation>` and use clustering algorithms to :ref:`discover structure <structured_heatmap>` in those matrices
- A function to plot :ref:`statistical timeseries <timeseries_from_dataframe>` data with flexible estimation and :ref:`representation <timeseries_bootstrapped>` of uncertainty around the estimate
- High-level abstractions for structuring :ref:`grids of plots <faceted_histogram>` that let you easily build :ref:`complex <many_facets>` visualizations
Seaborn aims to make visualization a central part of exploring and understanding data. The plotting functions operate on dataframes and arrays containing a whole dataset and internally perform the necessary aggregation and statistical model-fitting to produce informative plots. If matplotlib "tries to make easy things easy and hard things possible", seaborn tries to make a well-defined set of hard things easy too.
The plotting functions try to do something useful when called with a minimal set of arguments, and they expose a number of customizable options through additional parameters. Some of the functions plot directly into a matplotlib axes object, while others operate on an entire figure and produce plots with several panels. In the latter case, the plot is drawn using a Grid object that links the structure of the figure to the structure of the dataset in an abstract way.
Because seaborn uses matplotlib, the graphics can be further tweaked using matplotlib tools and rendered with any of the matplotlib backends to generate publication-quality figures. Seaborn can also be used to target web-based graphics through the mpld3 and Bokeh libraries.
Seaborn should be thought of as a complement to matplotlib, not a replacement
for it. When using seaborn, it is likely that you will often invoke matplotlib
functions directly to draw simpler plots already available through the
pyplot namespace. Further, while the seaborn functions aim to make plots
that are reasonably "production ready" (including extracting semantic
information from Pandas objects to add informative labels), full customization
of the figures will require a sophisticated understanding of matplotlib objects.
For more detailed information and copious examples of the syntax and resulting plots, you can check out the :ref:`example gallery <example_gallery>`, :ref:`tutorial <tutorial>` or :ref:`API reference <api_ref>`.