seaborn 第二章：不同形式的散点图，

目录

• 二、散点图
  • sns.scatterplot() 其他案例
    • example 1
    • example 2
    • example 3
    • example 4
    • example 5
    • example 6
    • example 7
    • example 8
    • example 9
    • example 10
    • example 11
    • example 12
    • example 13
    • example 14

二、散点图

import seaborn as sns
import matplotlib.pyplot as plt
sns.set_theme(style = 'whitegrid')

# 加载 diamonds 数据集
diamonds = sns.load_dataset('diamonds')

diamonds.head()

	carat	cut	color	clarity	depth	table	price	x	y	z
0	0.23	Ideal	E	SI2	61.5	55.0	326	3.95	3.98	2.43
1	0.21	Premium	E	SI1	59.8	61.0	326	3.89	3.84	2.31
2	0.23	Good	E	VS1	56.9	65.0	327	4.05	4.07	2.31
3	0.29	Premium	I	VS2	62.4	58.0	334	4.20	4.23	2.63
4	0.31	Good	J	SI2	63.3	58.0	335	4.34	4.35	2.75

# 画出 点的大小 和 颜色 不同的高维散点图
f, ax = plt.subplots(figsize = (6.5, 6.5))
sns.despine(f, left = True, bottom = True)
clarity_ranking = ['I1', 'SI2', 'SI1', 'VS2', 'VS1', 'VVS2', 'VVS1', 'IF']
sns.scatterplot(
    x = 'carat', y = 'price',
    hue = 'clarity', size = 'depth',
    palette = 'ch:r=-.2,d=.3_r',
    hue_order = clarity_ranking,
    sizes = (1, 8), linewidth = 0,
    data = diamonds, ax = ax
) # hue: 色调，size: 大小

​

​

sns.scatterplot() 其他案例

来自：https://seaborn.pydata.org/generated/seaborn.scatterplot.html#seaborn.scatterplot

注：seaborn 数据集下载地址 https://github.com/mwaskom/seaborn-data

## 导入数据集
import pandas as pd
tips = pd.read_csv("../../seaborn-data-master/tips.csv")
tips.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

example 1

## 以 total_bill 为 x 轴，tip 为 y 轴，绘制散点图
sns.scatterplot(data = tips, x = 'total_bill', y = 'tip')

​

​

example 2

## 以 total_bill 为 x 轴，tip 为 y 轴，time 为分类因子，画出不同 time 类的散点图（用不同颜色区分）
sns.scatterplot(data = tips, x = 'total_bill', y = 'tip', hue = 'time')

​

​

example 3

## 在 example 的基础上，增加散点形状区分的条件
sns.scatterplot(data = tips, x = 'total_bill', y = 'tip', hue = 'time', style = 'time')

​

​

example 4

## 散点的形状和颜色的分类依据可以不同
sns.scatterplot(data = tips, x = 'total_bill', y = 'tip', hue = 'day', style = 'time')

​

​

example 5

# 若分配给色版的变量是数字，会使用不同的默认调色板
sns.scatterplot(data = tips, x = 'total_bill', y = 'tip', hue = 'size')

​

​

example 6

## 可以使用 palette 更改色调
sns.scatterplot(data = tips, x = 'total_bill', y = 'tip', hue = 'size', palette = 'deep')

​

​

example 7

## 如果有大量的唯一数值，图例将显示一个具有代表性的等间距集合
tip_rate = tips.eval('tip / total_bill').rename('tip_rate')
sns.scatterplot(data = tips, x = 'total_bill', y = 'tip', hue = tip_rate)

​

​

example 8

## 在 example 5 的基础上还能增加点的大小
sns.scatterplot(data = tips, x = 'total_bill', y = 'tip', hue = 'size', size = 'size')

​

​

example 9

## 可以更改散点的大小
sns.scatterplot(
    data = tips, x = 'total_bill', y = 'tip', hue = 'size', size = 'size',
    sizes = (20, 200), legend = 'full'
)

​

​

example 10

## 传入一个元组或matplotlib.colors.Normalize以控制色调
sns.scatterplot(
    data = tips, x = 'total_bill', y = 'tip', hue = 'size', size = 'size',
    sizes = (20, 200), hue_norm = (0,7), legend = 'full'
)

​

example 11

## 传入一个字典控制散点形状
markers = {'Lunch' : 's', 'Dinner' : 'X'}
sns.scatterplot(data = tips, x = 'total_bill', y = 'tip', style = 'time', markers = markers)

​

​

example 12

sns.scatterplot(data = tips, x = 'total_bill', y = 'tip', s = 100, color = '.2', marker = "+")

​

example 13

## 可以绘制不同 类型 数据 的 时间序列图像
import numpy as np
import pandas as pd
index = pd.date_range("1 1 2000", periods=100, freq='m', name='date')
data = np.random.randn(100, 4).cumsum(axis=0)
wide_df = pd.DataFrame(data, index, ['a','b','c','d'])
sns.scatterplot(data=wide_df)

​

​

wide_df.head()

	a	b	c	d
date
2000-01-31	3.381766	-0.360579	-0.080106	1.578611
2000-02-29	2.724598	0.351141	-0.914548	1.825725
2000-03-31	2.276614	0.855341	-0.227480	0.075641
2000-04-30	1.385905	0.793799	-0.392478	-0.053513
2000-05-31	-0.011497	0.985883	-0.829674	1.539929

example 14

## 按类别拆分数据，画到不同的子图上
sns.relplot(
    data = tips, x = 'total_bill', y = 'tip',
    col = 'time', hue = 'day', style = 'day',
    kind = 'scatter'
)

​