（二）seaborn教程——用分类数据绘图

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

一、分类散点图

tips = sns.load_dataset("tips")
tips.head()

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	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

sns.catplot(x="day", y="total_bill", data=tips);

jitter参数控制抖动的大小或完全禁用它

sns.catplot(x="day", y="total_bill", jitter=False, data=tips);

激活设置kind=”swarm”

sns.catplot(x="day", y="total_bill", kind="swarm", data=tips);

使用order参数在特定图表的基础上控制排序

sns.catplot(x="smoker", y="tip", order=["No", "Yes"], data=tips);

将变量赋值交换到轴

sns.catplot(x="total_bill", y="day", hue="time", kind="swarm", data=tips);

二、类别内观察的分布

1、箱线图

sns.catplot(x="day", y="total_bill", kind="box", data=tips);

添加hue语义时，语义变量的每个级别的框都沿着分类轴移动,不会重叠

sns.catplot(x="day", y="total_bill", hue="smoker", kind="box", data=tips);

tips.head(2)

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	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

#添加一列数据"weekend"
tips["weekend"] = tips["day"].isin(["Sat", "Sun"])

tips.head(2)

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	total_bill	tip	sex	smoker	day	time	size	weekend
0	16.99	1.01	Female	No	Sun	Dinner	2	True
1	10.34	1.66	Male	No	Sun	Dinner	3	True

sns.catplot(x="day", y="total_bill", hue="weekend",
            kind="box", dodge=False, data=tips);

函数boxenplot()(kind=”boxen”)绘制的图表类似于箱形图，但已经过优化，可显示有关分布形状的更多信息。它最适合更大的数据集：

diamonds = sns.load_dataset("diamonds")
diamonds.head()

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	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

sns.catplot(x="color", y="price", kind="boxen",
            data=diamonds.sort_values("color"));

三、类别内的统计估计

条形图

titanic = sns.load_dataset("titanic")
titanic.head(2)

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	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False

sns.catplot(x="sex", y="survived", hue="class", kind="bar", data=titanic);

点图:
pointplot()功能提供了一种可视化相同信息的替代方式(将条形图表示为变化的斜率)

sns.catplot(x="sex", y="survived", hue="class", kind="point", data=titanic);

加入线条样式与色调一起表示，使得观察更清晰

sns.catplot(x="class", y="survived", hue="sex",
            palette={"male": "g", "female": "m"},
            markers=["^", "o"], linestyles=["-", "--"],
            kind="point", data=titanic);

四、绘制“宽格式”数据

iris = sns.load_dataset("iris")
iris.head(3)

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	sepal_length	sepal_width	petal_length	petal_width	species
0	5.1	3.5	1.4	0.2	setosa
1	4.9	3.0	1.4	0.2	setosa
2	4.7	3.2	1.3	0.2	setosa

sns.catplot(data=iris, orient="h", kind="box");

五、显示与facet的多种关系

sns.catplot(x="day", y="total_bill", hue="smoker",
            col="time", aspect=.6,
            kind="swarm", data=tips);

自定义绘图，可以使用FacetGrid它返回的对象上的方法

titanic.head(3)

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	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True

g = sns.catplot(x="fare", y="survived", row="class",
                kind="box", orient="h", height=1.5, aspect=4,
                data=titanic.query("fare > 0"))
g.set(xscale="log");