两个变量之间的相关性检验
1、皮尔森相关系数：它们之间的协方差cov(X,Y)除以它们各自标准差的乘积(σX, σY)。

• 要判断两个变量之间是否存在相关性：可以采用画散点图的方式，进行直观性的分析。
• 使用python进行person检验

import pandas as pd
import numpy as np
import missingno

df = pd.read_csv('./titanic/train.csv')
df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S

df.columns

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp','Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],dtype='object')

import seaborn as sns 
from matplotlib import pyplot as plt

df = df[['PassengerId','Survived','Pclass','Sex','Age','SibSp','Parch']]
sns.pairplot(df,size = 2.5,diag_kind= 'kde',kind = 'reg')

c:\python39\lib\site-packages\seaborn\axisgrid.py:1969: UserWarning: The `size` parameter has been renamed to `height`; please update your code.warnings.warn(msg, UserWarning)<seaborn.axisgrid.PairGrid at 0x1e8bb9a7370>

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df.corr()

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
PassengerId	1.000000	-0.005007	-0.035144	0.036847	-0.057527	-0.001652	0.012658
Survived	-0.005007	1.000000	-0.338481	-0.077221	-0.035322	0.081629	0.257307
Pclass	-0.035144	-0.338481	1.000000	-0.369226	0.083081	0.018443	-0.549500
Age	0.036847	-0.077221	-0.369226	1.000000	-0.308247	-0.189119	0.096067
SibSp	-0.057527	-0.035322	0.083081	-0.308247	1.000000	0.414838	0.159651
Parch	-0.001652	0.081629	0.018443	-0.189119	0.414838	1.000000	0.216225
Fare	0.012658	0.257307	-0.549500	0.096067	0.159651	0.216225	1.000000

plt.figure(figsize = (12,8))
sns.heatmap(df.corr());

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