{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 数据可视化-Seaborn简易入门"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Matplotlib试着让简单的事情更加简单，困难的事情变得可能，而Seaborn就是让困难的东西更加简单。\n",
"\n",
"seaborn是针对统计绘图的，一般来说，seaborn能满足数据分析90%的绘图需求。\n",
"\n",
"Seaborn其实是在matplotlib的基础上进行了更高级的API封装，从而使得作图更加容易，在大多数情况下使用seaborn就能做出很具有吸引力的图，应该把Seaborn视为matplotlib的补充，而不是替代物。\n",
"\n",
"用matplotlib最大的困难是其默认的各种参数，而Seaborn则完全避免了这一问题。\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### seaborn一共有5个大类21种图，分别是：\n",
"\n",
"* Relational plots 关系类图表\n",
" 1. relplot() 关系类图表的接口，其实是下面两种图的集成，通过指定kind参数可以画出下面的两种图\n",
" 2. scatterplot() 散点图\n",
" 3. lineplot() 折线图\n",
"\n",
"* Categorical plots 分类图表\n",
" 1. catplot() 分类图表的接口，其实是下面八种图表的集成，，通过指定kind参数可以画出下面的八种图\n",
" 2. stripplot() 分类散点图\n",
" 3. swarmplot() 能够显示分布密度的分类散点图\n",
" 4. boxplot() 箱图\n",
" 5. violinplot() 小提琴图\n",
" 6. boxenplot() 增强箱图\n",
" 7. pointplot() 点图\n",
" 8. barplot() 条形图\n",
" 9. countplot() 计数图\n",
"\n",
"* Distribution plot 分布图\n",
" 1. jointplot() 双变量关系图\n",
" 2. pairplot() 变量关系组图\n",
" 3. distplot() 直方图，质量估计图\n",
" 4. kdeplot() 核函数密度估计图\n",
" 5. rugplot() 将数组中的数据点绘制为轴上的数据\n",
"\n",
"* Regression plots 回归图\n",
" 1. lmplot() 回归模型图\n",
" 2. regplot() 线性回归图\n",
" 3. residplot() 线性回归残差图\n",
"\n",
"* Matrix plots 矩阵图\n",
" 1. heatmap() 热力图\n",
" 2. clustermap() 聚集图\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"# 如果不添加这句，是无法直接在jupyter里看到图的\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"有一套的参数可以控制绘图元素的比例。\n",
"\n",
"首先，让我们通过`set()`重置默认的参数：\n",
"\n",
"有五种seaborn的风格，它们分别是：`darkgrid`, `whitegrid`, `dark`, `white`, `ticks`。它们各自适合不同的应用和个人喜好。默认的主题是`darkgrid`。\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"sns.set(style=\"ticks\")"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
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