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python数据分析与机器学习实战-16.多变量分析绘图

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import pandas as pd
import numpy as np
import matplotlib as mpl
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style = "whitegrid",color_codes=True)
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tips = sns.load_dataset("tips")
iris = sns.load_dataset("iris")
titanic = sns.load_dataset("titanic")
sns.stripplot(x="day",y="total_bill",data=tips)
<matplotlib.axes._subplots.AxesSubplot at 0x10de58518>

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重叠很正常，但是影响观察数据量。

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sns.stripplot(x="day",y="total_bill",data=tips,jitter=True)
<matplotlib.axes._subplots.AxesSubplot at 0x108208b00>

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sns.swarmplot(x="day",y="total_bill",data=tips)
<matplotlib.axes._subplots.AxesSubplot at 0x1a1ac1a7b8>

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sns.swarmplot(x="day",y="total_bill",hue="sex",data=tips)
<matplotlib.axes._subplots.AxesSubplot at 0x1a1ac68d30>

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sns.violinplot(x="total_bill",y="day",hue="time",data=tips)
<matplotlib.axes._subplots.AxesSubplot at 0x1a1ad26390>

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sns.violinplot(x="total_bill",y="day",hue="sex",data=tips,split=True)
<matplotlib.axes._subplots.AxesSubplot at 0x1a1af157b8>

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sns.violinplot(x="day",y="total_bill",data=tips,inner=None)
sns.swarmplot(x="day",y="total_bill",data=tips,color='w',alpha=0.5)
<matplotlib.axes._subplots.AxesSubplot at 0x1a1b4a4f60>

png

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titanic
sns.barplot(x="sex",y="survived",data=titanic,hue="class")
<matplotlib.axes._subplots.AxesSubplot at 0x1a1b7ba0b8>

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点图可以很好的描绘数据的差异

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sns.pointplot(x="sex",y="survived",hue = "class",data=titanic)
<matplotlib.axes._subplots.AxesSubplot at 0x1a1b9f2860>

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#多层面板分类图
sns.factorplot(x="day",y="total_bill",hue="smoker",data=tips)
<seaborn.axisgrid.FacetGrid at 0x1a1b884d30>

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sns.factorplot(x="day",y="total_bill",hue="smoker",data=tips,kind="bar")
<seaborn.axisgrid.FacetGrid at 0x1a1bb5b710>

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sns.factorplot(x="day",y="total_bill",hue="smoker",data=tips,kind="swarm",col="time")
<seaborn.axisgrid.FacetGrid at 0x1a1be4dac8>

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sns.factorplot(x="day",y="total_bill",hue="smoker",data=tips,kind="box",col="day",size=4,aspect=0.5)
<seaborn.axisgrid.FacetGrid at 0x1a1c0a5588>

png

# seaborn