關鍵錯誤：[Int64Index...] dtype='int64] 均不在列中

Question

我正在嘗試使用 np.random.shuffle() 方法洗牌我的索引，但我不斷收到一個我不明白的錯誤。 如果有人能幫我解決這個問題，我將不勝感激。 謝謝！

當我在開始時創建 raw_csv_data 變量時，我嘗試使用 delimiter=',' 和 delim_whitespace=0 ，因為我認為這是另一個問題的解決方案，但它一直拋出相同的錯誤

    import pandas as pd 
    import numpy as np 
    from sklearn.preprocessing import StandardScaler

    #%%
    raw_csv_data= pd.read_csv('Absenteeism-data.csv')
    print(raw_csv_data)
    #%%
    df= raw_csv_data.copy()
    print(display(df))
    #%%
    pd.options.display.max_columns=None
    pd.options.display.max_rows=None
    print(display(df))
    #%%
    print(df.info())
    #%%
    df=df.drop(['ID'], axis=1)

    #%%
    print(display(df.head()))

    #%%
    #Our goal is to see who is more likely to be absent. Let's define
    #our targets from our dependent variable, Absenteeism Time in Hours
    print(df['Absenteeism Time in Hours'])
    print(df['Absenteeism Time in Hours'].median())
    #%%
    targets= np.where(df['Absenteeism Time in Hours']>df['Absenteeism Time 
    in Hours'].median(),1,0)
    #%%
    print(targets)
    #%%
    df['Excessive Absenteeism']= targets
    #%%
    print(df.head())

    #%%
    #Let's Separate the Day and Month Values to see if there is 
    correlation
    #between Day of week/month with absence
    print(type(df['Date'][0]))
    #%%
    df['Date']= pd.to_datetime(df['Date'], format='%d/%m/%Y')
    #%%
    print(df['Date'])
    print(type(df['Date'][0]))
    #%%
    #Extracting the Month Value
    print(df['Date'][0].month)
    #%%
    list_months=[]
    print(list_months)
    #%%
    print(df.shape)
    #%%
    for i in range(df.shape[0]):
        list_months.append(df['Date'][i].month)
    #%%
    print(list_months)
    #%%
    print(len(list_months))
    #%%
    #Let's Create a Month Value Column for df
    df['Month Value']= list_months
    #%%
    print(df.head())
    #%%
    #Now let's extract the day of the week from date
    df['Date'][699].weekday()
    #%%
    def date_to_weekday(date_value):
        return date_value.weekday()
    #%%
    df['Day of the Week']= df['Date'].apply(date_to_weekday)
    #%%
    print(df.head())
    #%%
    df= df.drop(['Date'], axis=1)
    #%%
    print(df.columns.values)
    #%%
    reordered_columns= ['Reason for Absence', 'Month Value','Day of the 
    Week','Transportation Expense', 'Distance to Work', 'Age',
     'Daily Work Load Average', 'Body Mass Index', 'Education', 
    'Children', 
    'Pets',
     'Absenteeism Time in Hours', 'Excessive Absenteeism']
    #%%
    df=df[reordered_columns]
    print(df.head())
    #%%
    #First Checkpoint
    df_date_mod= df.copy()
    #%%
    print(df_date_mod)

    #%%
    #Let's Standardize our inputs, ignoring the Reasons and Education 
    Columns
    #Because they are labelled by a separate categorical criteria, not 
    numerically
    print(df_date_mod.columns.values)
    #%%
    unscaled_inputs= df_date_mod.loc[:, ['Month Value','Day of the 
    Week','Transportation Expense','Distance to Work','Age','Daily Work 
    Load 
    Average','Body Mass Index','Children','Pets','Absenteeism Time in 
    Hours']]
    #%%
    print(display(unscaled_inputs))
    #%%
    absenteeism_scaler= StandardScaler()
    #%%
    absenteeism_scaler.fit(unscaled_inputs)
    #%%
    scaled_inputs= absenteeism_scaler.transform(unscaled_inputs)
    #%%
    print(display(scaled_inputs))
    #%%
    print(scaled_inputs.shape)
    #%%
    scaled_inputs= pd.DataFrame(scaled_inputs, columns=['Month Value','Day 
    of the Week','Transportation Expense','Distance to Work','Age','Daily 
    Work Load Average','Body Mass Index','Children','Pets','Absenteeism 
    Time 
    in Hours'])
    print(display(scaled_inputs))
    #%%
    df_date_mod= df_date_mod.drop(['Month Value','Day of the 
    Week','Transportation Expense','Distance to Work','Age','Daily Work 
    Load Average','Body Mass Index','Children','Pets','Absenteeism Time in 
    Hours'], axis=1)
    print(display(df_date_mod))
    #%%
    df_date_mod=pd.concat([df_date_mod,scaled_inputs], axis=1)
    print(display(df_date_mod))
    #%%
    df_date_mod= df_date_mod[reordered_columns]
    print(display(df_date_mod.head()))
    #%%
    #Checkpoint
    df_date_scale_mod= df_date_mod.copy()
    print(display(df_date_scale_mod.head()))
    #%%
    #Let's Analyze the Reason for Absence Category
    print(df_date_scale_mod['Reason for Absence'])
    #%%
    print(df_date_scale_mod['Reason for Absence'].min())
    print(df_date_scale_mod['Reason for Absence'].max())
    #%%
    print(df_date_scale_mod['Reason for Absence'].unique())
    #%%
    print(len(df_date_scale_mod['Reason for Absence'].unique()))
    #%%
    print(sorted(df['Reason for Absence'].unique()))
    #%%
    reason_columns= pd.get_dummies(df['Reason for Absence'])
    print(reason_columns)
    #%%
    reason_columns['check']= reason_columns.sum(axis=1)
    print(reason_columns)
    #%%
    print(reason_columns['check'].sum(axis=0))
    #%%
    print(reason_columns['check'].unique())
    #%%
    reason_columns=reason_columns.drop(['check'], axis=1)
    print(reason_columns)
    #%%
    reason_columns=pd.get_dummies(df_date_scale_mod['Reason for Absence'], 
    drop_first=True)
    print(reason_columns)
    #%%
    print(df_date_scale_mod.columns.values)
    #%%
    print(reason_columns.columns.values)
    #%%
    df_date_scale_mod= df_date_scale_mod.drop(['Reason for Absence'], 
    axis=1)
    print(df_date_scale_mod)
    #%%
    reason_type_1= reason_columns.loc[:, 1:14].max(axis=1)
    reason_type_2= reason_columns.loc[:, 15:17].max(axis=1)
    reason_type_3= reason_columns.loc[:, 18:21].max(axis=1)
    reason_type_4= reason_columns.loc[:, 22:].max(axis=1)
    #%%
    print(reason_type_1)
    print(reason_type_2)
    print(reason_type_3)
    print(reason_type_4)
    #%%
    print(df_date_scale_mod.head())
    #%%
    df_date_scale_mod= pd.concat([df_date_scale_mod, 
    reason_type_1,reason_type_2, reason_type_3, reason_type_4], axis=1)
    print(df_date_scale_mod.head())
    #%%
    print(df_date_scale_mod.columns.values)
    #%%
    column_names= ['Month Value','Day of the Week','Transportation 
    Expense',
     'Distance to Work','Age','Daily Work Load Average','Body Mass Index',
     'Education','Children','Pets','Absenteeism Time in Hours',
     'Excessive Absenteeism', 'Reason_1', 'Reason_2', 'Reason_3', 
     'Reason_4']

    df_date_scale_mod.columns= column_names
    print(df_date_scale_mod.head())
    #%%
    column_names_reordered= ['Reason_1', 'Reason_2', 'Reason_3', 
    'Reason_4','Month Value','Day of the Week','Transportation Expense',
     'Distance to Work','Age','Daily Work Load Average','Body Mass Index',
     'Education','Children','Pets','Absenteeism Time in Hours',
     'Excessive Absenteeism']

    df_date_scale_mod=df_date_scale_mod[column_names_reordered]
    print(display(df_date_scale_mod.head()))
    #%%
    #Checkpoint
    df_date_scale_mod_reas= df_date_scale_mod.copy()
    print(df_date_scale_mod_reas.head())
    #%%
    #Let's Look at the Education column now
    print(df_date_scale_mod_reas['Education'].unique())
    #This shows us that education is rated from 1-4 based on level
    #of completion
    #%%
    print(df_date_scale_mod_reas['Education'].value_counts())
    #The overwhelming majority of workers are highschool educated, while 
    the 
    #rest have higher degrees
    #%%
    #We'll create our dummy variables as highschool and higher education
    df_date_scale_mod_reas['Education']= 
    df_date_scale_mod_reas['Education'].map({1:0, 2:1, 3:1, 4:1})
    #%%
    print(df_date_scale_mod_reas['Education'].unique())
    #%%
    print(df_date_scale_mod_reas['Education'].value_counts())
    #%%
    #Checkpoint
    df_preprocessed= df_date_scale_mod_reas.copy()
    print(display(df_preprocessed.head()))
    #%%
    #%%
    #Split Inputs from targets
    scaled_inputs_all= df_preprocessed.loc[:,'Reason_1':'Absenteeism Time 
    in 
    Hours']
    print(display(scaled_inputs_all.head()))
    print(scaled_inputs_all.shape)
    #%%
    targets_all= df_preprocessed.loc[:,'Excessive Absenteeism']
    print(display(targets_all.head()))
    print(targets_all.shape)
    #%%
    #Shuffle Inputs and targets
    shuffled_indices= np.arange(scaled_inputs_all.shape[0])
    np.random.shuffle(shuffled_indices)
    shuffled_inputs= scaled_inputs_all[shuffled_indices]
    shuffled_targets= targets_all[shuffled_indices]

這是我嘗試洗牌時不斷遇到的錯誤：

 KeyError Traceback (most recent call last) in 1 shuffled_indices= np.arange(scaled_inputs_all.shape[0]) 2 np.random.shuffle(shuffled_indices) ----> 3 shuffled_inputs= scaled_inputs_all[shuffled_indices] 4 shuffled_targets= targets_all[shuffled_indices]

~\Anaconda3\lib\site-packages\pandas\core\frame.py in getitem (self, key) 2932 key = list(key) 2933 indexer = self.loc._convert_to_indexer(key, axis=1, -> 2934 raise_missing =True) 2935 2936 # take() 不接受布爾索引器

~\Anaconda3\lib\site-packages\pandas\core\indexing.py in _convert_to_indexer(self, obj, axis, is_setter, raise_missing) 1352 kwargs = {'raise_missing': True if is_setter else 1353
raise_missing} -> 1354 return self._get_listlike_indexer(obj, axis, **kwargs)[1] 1355 else: 1356 try:

~\Anaconda3\lib\site-packages\pandas\core\indexing.py in _get_listlike_indexer(self, key, axis, raise_missing) 1159 self._validate_read_indexer(keyarr, indexer, 1160
o._get_axis_number(axis), -> 1161 raise_missing=raise_missing) 1162 return keyarr, indexer
1163

~\Anaconda3\lib\site-packages\pandas\core\indexing.py 在 _validate_read_indexer（self，key，indexer，axis，raise_missing）1244 raise KeyError（1245
u"None of [{key}] are in the [{axis}]".format( -> 1246 key=key, axis=self.obj._get_axis_name(axis))) 1247 1248 # 我們（暫時）允許一些缺少帶有 .loc 的鍵，但在

KeyError：“沒有 [Int64Index([560, 320, 405, 141, 154, 370, 656, 26, 444, 307,\n ...\n 429, 542, 676, 588, 315, 284, 293, 607, 197, 250],\n dtype='int64', length=700)] 在 [columns]"

Answer 1

您使用loc函數創建了scaled_inputs_all DataFrame，因此它很可能不包含連續索引。

另一方面，您將shuffled_indices創建為僅從一系列連續數字中進行的隨機播放。

請記住scaled_inputs_all[shuffled_indices]獲取scaled_inputs_all的行，其索引值等於shuffled_indices的元素。

也許你應該寫：

scaled_inputs_all.iloc[shuffled_indices]

請注意， iloc提供基於整數位置的索引，而不管索引值如何，即正是您需要的。

Answer 2

可能有人在機器學習中使用 KFOLD 時也會遇到同樣的錯誤。

解決方案如下：

點擊這里觀看解決方案

您需要使用 iloc：

 X_train, X_test = X.iloc[train_index], X.iloc[test_index]

 y_train, y_test = y.iloc[train_index], y.iloc[test_index]

Answer 3

我也有這個問題。 我通過將數據框和系列更改為數組來解決它。

嘗試以下代碼行：

scaled_inputs_all.iloc[shuffled_indices].values 

Answer 4

如果您在從數據框中刪除行后重置索引，這應該會停止關鍵錯誤。

您可以在運行df.drop后運行它來做到這一點：

df = df.reset_index(drop=True)

或者，等效地：

df.reset_index(drop=True, inplace=True)

Answer 5

根據列值條件刪除具有索引的行時發生以下錯誤：

return self._engine.get_loc(key) 文件“pandas/_libs/index.pyx”，第 107 行，在 pandas._libs.index.IndexEngine.get_loc 文件“pandas/_libs/index.pyx”，第 131 行，在 pandas。 _libs.index.IndexEngine.get_loc 文件“pandas/_libs/hashtable_class_helper.pxi”，第 992 行，在 pandas._libs.hashtable.Int64HashTable.get_item 文件“pandas/_libs/hashtable_class_helper.pxi”，第 998 行，在 pandas._libs。 hashtable.Int64HashTable.get_item KeyError：226

在處理上述異常的過程中，又出現了一個異常：

回溯（最近一次通話最后）：

要解決此問題，請創建一個索引列表並立即刪除行，如下所示：

df.drop(index=list1,labels=None, axis=0, inplace=True,columns=None, level=None, errors='raise')

Answer 6

對於 Kfold 拆分，您需要 iloc ......我用過這個。

for train_i, test_i in kf.split(X):
    print("TRAIN:", train_i, "TEST:", test_i)
    X_train, X_test = X.iloc[train_i], X.iloc[test_i]
    y_train, y_test = y.iloc[train_i], y.iloc[test_i]

Answer 7

得到同樣的錯誤：

KeyError: "None of [Int64Index([26], dtype='int64')] are in the [index]"

通過將數據框保存到本地文件並打開它來解決，

如下所示：

df.to_csv('Step1.csv',index=False)
df = pd.read_csv('Step1.csv')