pandas 代码

 def get_train_data():
     df = pd.read_csv('data/train.csv', encoding='utf_8')

 #    df1 = pd.read_csv('data/test.csv', encoding='utf_8')
 #    df2 = pd.read_csv('data/count.csv', encoding='utf_8')
 #    df1['casual'] = df2['casual']
 #    df1['registered'] = df2['registered']
 #    df1['count'] = df2['count']
 #    df = pd.concat([df, df1])

     cur_time = pd.to_datetime(df['datetime'])

     df['year'] = cur_time.map(lambda x:x.year)
     df['month'] = cur_time.map(lambda x:x.month)
     df['day'] = cur_time.map(lambda x:x.day)
     df['hour'] = cur_time.map(lambda x:x.hour)
     df['dayOfWeek'] = cur_time.map(lambda x:x.isoweekday())
     df['segOfDay'] = df['hour'].apply(classfy)  

     year = pd.get_dummies(data=df.iloc[:,])

 #    df['spring'] = df['season'].map({1:1})
 #    df['summer'] = df['season'].map({2:1})
 #    df['autumn'] = df['season'].map({3:1})
 #    df['winter'] = df['season'].map({4:1})
 #    df = df.fillna(value={'spring':0,'summer':0,'autumn':0,'winter':0})
 #
 #    df['weather1'] = df['weather'].map({1:1})
 #    df['weather2'] = df['weather'].map({2:1})
 #    df['weather3'] = df['weather'].map({3:1})
 #    df['weather4'] = df['weather'].map({4:1})
 #    df = df.fillna(value={'weather1':0,'weather2':0,'weather3':0,'weather4':0})
 #
     df = df.replace({'windspeed':0}, 12.799)

     casual = df['casual']
     registered  = df['registered']
     df = df.drop(['datetime', 'season', 'weather','casual', 'registered', 'count'], axis=1)

     log_cas = casual.map(lambda x: math.log(x+1))
     log_reg = registered.map(lambda x: math.log(x+1))

     train_casual = log_cas.values
     train_registered = log_reg.values
     train_data = df.values

     return train_casual, train_registered, train_data