seaborn 第二章：不同形式的散点图

目录

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

​

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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')

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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')

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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')

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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')

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​

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')

​

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example 6

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

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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)

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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')

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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'
)

​

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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'
)

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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)

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example 12

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

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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)

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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'
)

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posted @ 2022-09-20 15:50  只会加减乘除  阅读(655)  评论(0)  编辑  收藏  举报