Python方差特征过滤的实例分析

def variance_demo():
 """
 过滤低方差特征
 :return:
 """
 # 1. 获取数据
 data = pd.read_csv('factor_returns.csv')
 data = data.iloc[:, 1:-2]
 print('data:\n', data)
 
 # 2. 实例化一个转换器类
 transfer = VarianceThreshold(threshold=10)
 
 # 3. 调用fit_transform()
 data_new = transfer.fit_transform(data)
 print('data_new:\n', data_new, data_new.shape)
 

 return None

import pandas as pd
import numpy as np
np.random.seed(1) #设置随机种子，实现每次生成的随机数矩阵都一样
a= np.random.randint(0, 200,10)
b= np.random.randint(0, 200,10)
c= np.random.randint(0, 200,10)
d= [9,9,9,9,9,9,9,9,9,9]
data=pd.DataFrame({"A" : a,"B" : b,"C" : c,"D" : d})
data
from sklearn.feature_selection import VarianceThreshold
sel_model = VarianceThreshold(threshold = 0)
#删除不合格特征之后的新矩阵
sel_model.fit_transform(data)