Seaborn包 画出好看的分布图（Python）

参考：http://www.open-open.com/lib/view/open1434182977754.html

           http://blog.youkuaiyun.com/pipisorry/article/details/49515745

Seaborn介绍

官方文档： https://pypi.python.org/pypi/seaborn/

Matplotlib是Python主要的绘图库。但是不建议你直接使用它，原因与不推荐你使用NumPy是一样的。虽然Matplotlib很强大，它本身就很复杂，你的图经过大量的调整才能变精致。因此，作为替代推荐一开始使用Seaborn。

Seaborn本质上使用Matplotlib作为核心库（就像Pandas对NumPy一样）。

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的优点：

1. 默认情况下就能创建赏心悦目的图表。（只有一点，默认不是jet colormap）
2. 创建具有统计意义的图
3. 能理解pandas的DataFrame类型，所以它们一起可以很好地工作。

安装pip install seaborn

就算你不用seaborn绘图，只要在matplotlib绘图中加上seaborn的import语句，就会以seaborn的图形方式展示图片，具有seaborn的效果。如：

import seaborn
import matplotlib.pyplot as plt

注意要显示出图形，需要引入matplotlib并plt.show()出来。

Seaborn使用

Style functions: API | Tutorial

Color palettes: API | Tutorial

Distribution plots: API | Tutorial

Regression plots: API | Tutorial

Categorical plots: API | Tutorial

Axis grid objects: API | Tutorial

分布图绘制Distribution plots

jointplot(x, y[, data, kind, stat_func, ...])	Draw a plot of two variables with bivariate and univariate graphs.
pairplot(data[, hue, hue_order, palette, ...])	Plot pairwise relationships in a dataset.
distplot(a[, bins, hist, kde, rug, fit, ...])	Flexibly plot a univariate distribution of observations.
kdeplot(data[, data2, shade, vertical, ...])	Fit and plot a univariate or bivariate kernel density estimate.
rugplot(a[, height, axis, ax])	Plot datapoints in an array as sticks on an axis.

单变量绘制distplot