我目前正在处理 Kaggle 的泰坦尼克号比赛，并试图弄清楚该Survived专栏与其他专栏之间的相关性。我numpy.corrcoef()用来矩阵列之间的相关性，这就是我所拥有的：

The correlation between pClass & Survived is: [[ 1.         -0.33848104]
 [-0.33848104  1.        ]]

The correlation between Sex & Survived is: [[ 1.         -0.54335138]
 [-0.54335138  1.        ]]

The correlation between Age & Survived is:[[ 1.         -0.07065723]
 [-0.07065723  1.        ]]

The correlation between Fare & Survived is: [[1.         0.25730652]
 [0.25730652 1.        ]]

The correlation between Parent-Children & Survived is: [[1.         0.08162941]
 [0.08162941 1.        ]]

The correlation between Sibling-Spouse & Survived is: [[ 1.        -0.0353225]
 [-0.0353225  1.       ]]

The correlation between Embarked & Survived is: [[ 1.         -0.16767531]
 [-0.16767531  1.        ]]

Survived和 [ pClass, sex, ]之间应该有更高的相关性Sibling-Spouse，但值非常低。我是新手，所以我知道一个简单的方法不是找到相关性的最佳方法，但目前，这并没有加起来。

这是我的完整代码（没有printf()调用）：

import pandas as pd
import numpy as np

train = pd.read_csv("https://raw.githubusercontent.com/oo92/Titanic-Kaggle/master/train.csv")
test = pd.read_csv("https://raw.githubusercontent.com/oo92/Titanic-Kaggle/master/test.csv")

survived = train['Survived']
pClass = train['Pclass']
sex = train['Sex'].replace(['female', 'male'], [0, 1])
age = train['Age'].fillna(round(float(np.mean(train['Age'].dropna()))))
fare = train['Fare']
parch = train['Parch']
sibSp = train['SibSp']
embarked = train['Embarked'].replace(['C', 'Q', 'S'], [1, 2, 3])