可以使用scikit-learn库中的MinMaxScaler函数实现数据归一化例子

Sure, MinMaxScaler is a function in the scikit-learn library that can be used for data normalization. Here is an example:

from sklearn.preprocessing import MinMaxScaler

data = [[10, 100], [20, 200], [30, 300]]

scaler = MinMaxScaler()

data_normalized = scaler.fit_transform(data)

print(data_normalized)

This will normalize the data such that each feature (column) ranges from 0 to 1.