数据可视化之Seaborn绘图

知识结构

Seaborn简介

话不多说，我们首先来欣赏几张 Seaborn 绘制的图形，一睹为快。

是不是很酷炫？学习完Seaborn的相关内容，你也可以轻松地画出这样的图。

Seaborn 是 Python 中基于 matplotlib 的统计绘图模块。如果说 matplotlib 是“tries to make easy things easy and hard things possible”，那么 Seaborn 则是让“hard things”也变简单。

Seaborn 在 matplotlib 的基础上进行了更高级的封装，这样做图会更加方便快捷。我们可以仅仅通过一些极简的代码，就能做出很具有分析价值而且又十分美观的图形。

导入Seaborn我们使用：

import seaborn as sns

通常情况下我们习惯使用 sns 作为 Seaborn 的别名。

成功导入Seaborn之后我们就可以开启绘图之旅了。在今天的学习中，我们主要来为大家介绍连续数值型数据绘图，我们将从连续型单变量数据分布图、连续型双变量数据分布图和连续型多变量数据分布图三个角度来一起学习。

单变量分布图

单变量其实就是我们通常接触到的数据集中的一列数据。在这里我们研究的是连续数值型数据的分布，那么什么样的数据是连续数值型数据呢？

连续变量的数值是连接不断的，相邻两值之间可作无限分割，例如，身高、体重、年龄等都是连续变量。

np.random.normal(loc=0,sigma=1,size)

normal会产生服从正态分布的size个数值，其中，loc表示均值，默认值为0，sigma是标准差，默认值为1，size表示产生的随机数的个数，即normal默认会产生服从标准正太分布的size个随机数，我们可以通过设置loc和sigma产生服从其他分布的随机数。

import numpy as np
#random是Numpy的一个‘随机’模块，在random模块中的normal方法表示从标准正态（高斯）分布中随机产生size个数值
data = np.random.normal(size=1000)
#查看数据类型
type(data)
#查看数据，查看数据的表现形式
data

import numpy as np
#random是Numpy的一个‘随机’模块，在random模块中的normal方法表示从标准正态（高斯）分布中随机产生size个数值
data = np.random.normal(size=1000)

type(data)

numpy.ndarray

data

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