如何从PCA 2D -biplot中删除目标变量

Question

我想使用我的数据集(信用卡流失数据集)绘制二维双线图。但是我的图表也包含了我的目标变量作为一个特性。怎样才能删除它？

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我已经附上了我用过的代码

import pandas as pd
import numpy as np
from sklearn.preprocessing import StandardScaler
from bioinfokit.analys import get_data
from bioinfokit.visuz import cluster
from sklearn.decomposition import PCA
# load iris dataset

df=pd.read_csv(r'G:\\Edu\\My academics\\MSc in CS\\3rd sem\\Research\\Python files\\PCA.csv')
df.head(2)
df.loc[df['Attrition_Flag'] == 'Existing Customer', 'Attrition_Flag'] = 0
df.loc[df['Attrition_Flag'] == 'Attrited Customer', 'Attrition_Flag'] = 1
df.Attrition_Flag = df.Attrition_Flag.astype(int)

X = df.iloc[:,0:4]
target = df['Attrition_Flag'].to_numpy()
X.head(2)


X_st =  StandardScaler().fit_transform(X)
pca_out = PCA().fit(X_st)

# component loadings
loadings = pca_out.components_
print(loadings)


# get eigenvalues (variance explained by each PC)  
print(pca_out.explained_variance_)


# get biplot
pca_scores = PCA().fit_transform(X_st)
cluster.biplot(cscore=pca_scores, loadings=loadings, labels=X.columns.values, var1=round(pca_out.explained_variance_ratio_[0]*100, 2),
    var2=round(pca_out.explained_variance_ratio_[1]*100, 2), colorlist=target)

Answer 1

在target = df['Attrition_Flag'].to_numpy()之后删除目标列如何？

即df.drop(columns=['Attrition_Flag'], inplace=True)