sklearn.preprocessing.MinMaxScaler

# class sklearn.preprocessing.MinMaxScaler(feature_range=(0,1))

X_std = (X - X.min(axis=0) / (X.max(axis=0 - X.min(axis=0)
X_scaled = X_std * (max-min) + min

# use
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
scaler.transform(data)

# fix(X,[y])
# fit_transform(X[,y])
# transform(X)