DataFrame column(컬럼)간 상관관계 계산하기

학습목표

  1. corr 함수 이용하기
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline 

# data 출처: https://www.kaggle.com/hesh97/titanicdataset-traincsv/data
train_data = pd.read_csv('./train.csv')
train_data.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

변수(column) 사이의 상관계수(correlation)

  • corr함수를 통해 상관계수 연산 (-1, 1 사이의 결과)
    • 연속성(숫자형)데이터에 대해서만 연산
    • 인과관계를 의미하진 않음
train_data.corr() #1에 가까울 수록 인과관계가 크다는 의미
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.matshow(train_data.corr()) # 색깔이 밝을 수록 관계가 깊다

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output_5_1