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max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))train_df['average_montly_hours']=train_df[['average_montly_hours']].apply(max_min_scaler)train_df

函数

def Data_normalization(train):    max_min_scaler = lambda x : (x-np.min(x))/(np.max(x)-np.min(x))    dest_feature=['TransactionDT','TransactionAmt', 'card1','card2','card3','card5']    for name in dest_feature:        train_df[name]=train_df[[name]].apply(max_min_scaler)    return train_dftrain_df=Data_normalization(train_df)train_df