用sklearn库快速实现数据处理

数据预处理

（1）缺失值处理

from sklearn.preprocessing import Imputer

1）用平均数填充

2）用中位数填充

3）用最频繁值（众数）填充

import pandas as pd
import numpy as np
from sklearn.preprocessing import Imputer

//均值填充
data1 = data
imp = Imputer(missing_values='NaN',strategy='mean')
imp.fit(data1)
data1 = imp.transform(data1)

//中位数填充
data2 = data
imp = Imputer(missing_values='NaN',strategy='median')
imp.fit(data2)
data2 = imp.transform(data2)

//众数填充
data3 = data
imp = Imputer(missing_values='NaN',strategy='most_frequent')
imp.fit(data3)
data3 = imp.transform(data3)

（2）数据标准化

from sklearn.preprocessing import StandardScaler

from sklearn.preprocessing import StandardScaler
data = pd.read_excel("data_path")
data1 = data
scaler = StandardScaler()
scaler.fit(data1)
data1 = scaler.transform(data1)