在Python Datafram中选择列时出错

Question

我有一个python数据帧（称为df），在控制台中打印时如下所示：

 date                      2019-09-03 00:00:00  ...  OverallAtt
    students                                       ...            
    5c48943cbe8e95292564e163                  0.0  ...   78.321678
    5c48943dbe8e95292564e165                100.0  ...   87.500000
    5c48943dbe8e95292564e166                100.0  ...   86.713287
    5c48943dbe8e95292564e167                100.0  ...   95.804196
    5c48943dbe8e95292564e169                100.0  ...  100.000000
    5c48943dbe8e95292564e16b                100.0  ...   98.601399
    5c48943dbe8e95292564e16d                100.0  ...   85.314685
    5c48943dbe8e95292564e173                100.0  ...   96.503497
    5c48943dbe8e95292564e175                100.0  ...   83.216783

但是，当我尝试选择students列并将其放入单独的变量中时，如下所示：

Names = df['students']

它会出现以下错误：

KeyError: 'students'

有人知道为什么不行吗

更新

这是固定的，但现在我得到另一个错误，当我试图打印预测值。这是我的密码：

dataset = df
X = dataset
X = X.drop(['OverallAtt'], axis=1)
X = pd.DataFrame(X).fillna(0)
y = dataset['OverallAtt'] #Total Attendance ThisYear

from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)

from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_pred = regressor.predict(X_test)

import pickle
filename='Regressor_model.sav'
pickle.dump(regressor, open(filename, 'wb'))

load_lr_model =pickle.load(open(filename, 'rb'))

#PREDICT FROM NEW DATA
dataset = df
X = dataset
X = X.drop(['OverallAtt'], axis=1)
X = pd.DataFrame(X).fillna(0)
ActualAttendance = dataset['OverallAtt']
Names = df.reset_index(drop=False)['students']

NewX_test = (X)
y_load_predit=load_lr_model.predict(NewX_test)
Newdf = pd.DataFrame({'Full Name': Names, 'Actual Attendance': ActualAttendance, 'Predicted Attendance': y_load_predit})
print(Newdf)

我得到这个错误：

ValueError: array length 77 does not match index length 459

actualtendance和Names都是382。Y\u load\u predit也是382的数组。所以不知道我为什么会犯这个错误

Answer 1

看起来students是您的索引名。为了获得它，您可以重置索引：

Names = df.reset_index(drop=False)['students']