from sklearn.impute import SimpleImputer
import numpy as np

#使用数据训练imp模型，按照原数据的内容对新数据的确实值进行补充
X = np.array([[1, 2, 3],
             [4, 5, 6],
             [7, 8, 9]])
X1 = np.array([[1, 2, np.nan],
               [4, np.nan, 6],
               [np.nan, 8, 9]])
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
imp.fit(X)
print(imp.transform(X1))

#根据缺失值的列剩余值的特点对缺失值进行补充，这里采用的是平均值
X1 = np.array([[1, 2, np.nan],
               [4, np.nan, 6],
               [np.nan, 8, 9]])
imp = SimpleImputer(missing_values=np.nan, strategy='mean')
print(imp.fit_transform(X1))

#将数据中某个值修改为指定值
X = np.array([[1, 2, 3],
             [4, 5, 6],
             [7, 8, 9]])

imp = SimpleImputer(missing_values=1, strategy='constant', fill_value=199)
print(imp.fit_transform(X))