Seaborn 统计数据可视化图标库 Overview

统计数据可视化图标库 Overview"/>

Seaborn 统计数据可视化图标库 Overview

Seaborn是一个python统计数据可视化图标库（statistical data visualization）

An introduction to seaborn 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 built-in themes that improve on the default matplotlib
aesthetics Tools for choosing color palettes to make beautiful plots
that reveal patterns in your data Functions for visualizing univariate
and bivariate distributions or for comparing them between subsets of
data Tools that fit and visualize linear regression models for
different kinds of independent and dependent variables Functions that
visualize matrices of data and use clustering algorithms to discover
structure in those matrices A function to plot statistical timeseries
data with flexible estimation and representation of uncertainty around
the estimate High-level abstractions for structuring grids of plots
that let you easily build complex 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 example gallery, tutorial or
API reference.
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