Python绘图总结(seaborn篇)之数据分类

学习https://seaborn.pydata.org 记录，描述不一定准确，具体请参考官网

%matplotlib inline
import numpy as np
import pandas as pd
from scipy import stats, integrate
import seaborn as sns
import matplotlib.pyplot as plt

# seaborn中文乱码解决方案
from matplotlib.font_manager import FontProperties
myfont=FontProperties(fname=r'C:\Windows\Fonts\simhei.ttf',size=14)
sns.set(font=myfont.get_name(), color_codes=True)

# 加载数据  https://github.com/mwaskom/seaborn-data 数据地址
np.random.seed(sum(map(ord, "categorical")))
titanic = sns.load_dataset("titanic")
tips = sns.load_dataset("tips")
iris = sns.load_dataset("iris")

# tips数据
tips[::25].head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
25	17.81	2.34	Male	No	Sat	Dinner	4
50	12.54	2.50	Male	No	Sun	Dinner	2
75	10.51	1.25	Male	No	Sat	Dinner	2
100	11.35	2.50	Female	Yes	Fri	Dinner	2

1、stripplot() 条形散点－重叠

# jitter=True 表示沿轴随机分布，相对避免重叠
fig, axes = plt.subplots(1,2,figsize=(12, 5),sharex=True, sharey=True)
sns.stripplot(x="day", y="total_bill", data=tips, ax=axes[0])
sns.stripplot(x="day", y="total_bill", data=tips, jitter=True, ax=axes[1])

2、swarmplot() 条形散点－不重叠

fig, axes = plt.subplots(1,2,figsize=(12, 5)) 
sns.swarmplot(x="day", y="total_bill",  hue="sex", data=tips, ax=axes[0])
sns.swarmplot(x="total_bill", y="size", data=tips, orient='h', ax=axes[1])

3、boxplot() 箱形图

fig, axes = plt.subplots(1,2,figsize=(12, 5)) 
sns.boxplot(x="day", y="total_bill", hue="time", data=tips, ax=axes[0])
tips["weekend"] = tips["day"].isin(["Sat", "Sun"])
sns.boxplot(x="day", y="total_bill", hue="weekend", data=tips, dodge=False, ax=axes[1])

4、violinplot() 琴形图

fig, axes = plt.subplots(2,2,figsize=(12, 10)) 
sns.violinplot(x="total_bill", y="day", hue="time", data=tips,ax=axes[0,0])
sns.violinplot(x="total_bill", y="day", hue="time", data=tips,bw=.1, scale="count", scale_hue=False,ax=axes[0,1])
sns.violinplot(x="day", y="total_bill", hue="sex", data=tips,split=True, inner="stick", palette="Set3",ax=axes[1,0])
sns.violinplot(x="day", y="total_bill", data=tips, inner=None,ax=axes[1,1])
sns.swarmplot(x="day", y="total_bill", data=tips, color="w", alpha=.5,ax=axes[1,1])

# titanic 数据
titanic[::-20][::10]

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
890	0	3	male	32.0	0	0	7.7500	Q	Third	man	True	NaN	Queenstown	no	True
690	1	1	male	31.0	1	0	57.0000	S	First	man	True	B	Southampton	yes	False
490	0	3	male	NaN	1	0	19.9667	S	Third	man	True	NaN	Southampton	no	False
290	1	1	female	26.0	0	0	78.8500	S	First	woman	False	NaN	Southampton	yes	True
90	0	3	male	29.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

5、barplot() 柱状图

fig, axes = plt.subplots(1,2,figsize=(12, 5)) 
sns.barplot(x="sex", y="survived", hue="class", data=titanic,ax=axes[0])
sns.barplot(x="sex", y="survived", hue="class", data=titanic, dodge=False, errwidth=0, ax=axes[1])

6、countplot() 柱状图－统计

fig, axes = plt.subplots(1,2,figsize=(12, 5)) 
sns.countplot(x="deck", data=titanic, palette="Reds_d", ax=axes[0])
sns.countplot(y="deck", hue="class", data=titanic, palette="Blues_d", ax=axes[1])

7、pointplot() 两点关系

fig, axes = plt.subplots(1,2,figsize=(12, 5)) 
sns.pointplot(x="sex", y="survived", hue="class", data=titanic, ax=axes[0])
sns.pointplot(x="class", y="survived", hue="sex", data=titanic, ax=axes[1],
              palette={"male": "g", "female": "m"},
              markers=["^", "o"], linestyles=["-", "--"]);

8、多变量分类绘图 factorplot()

fig, axes = plt.subplots(1,2,figsize=(12, 5)) 
sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips, ax=axes[0])
sns.factorplot(x="day", y="total_bill", hue="smoker", data=tips, kind='bar', ax=axes[1])

sns.factorplot(x="day", y="total_bill", hue="smoker",col="time", data=tips, kind="swarm")

sns.factorplot(x="time", y="total_bill", hue="smoker",
               col="day", data=tips, kind="box", size=4, aspect=.5)

8、多变量分类绘图 PairGrid()

g = sns.PairGrid(tips,
                 x_vars=["smoker", "time", "sex"],
                 y_vars=["total_bill", "tip"],
                 aspect=.75, size=3.5)
g.map(sns.violinplot, palette="pastel")