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キーエラー：[Int64Index ...] dtype = 'int64]は列にありません

Np.random.shuffle（）メソッドを使用してインデックスをシャッフルしようとしていますが、理解できないエラーが発生し続けます。誰かが私を困惑させてくれると助かります。ありがとうございました！

Raw_csv_data変数を最初に作成したとき、別の問題の解決策としてそれを見たので、delimiter = '、'およびdelim_whitespace = 0を使用しようとしましたが、同じエラーがスローされ続けました

    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]

これは、インデックスをシャッフルしようとしたときに発生するエラーです。

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（）はブールインデクサーを受け入れません

〜\ Anaconda3\lib\site-packages\pandas\core\indexing.py in _convert_to_indexer（self、obj、axis、is_setter、raise_missing）1352 kwargs = {'raise_missing'：is_setter else true 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 in _validate_read_indexer（self、key、indexer、axis、raise_missing）1244 raise KeyError（1245
u "[{key}]のどれも[{axis}]"にありません。format（-> 1246 key = key、axis = self.obj._get_axis_name（axis）））1247 1248＃We（一時的に）.locでいくつかの欠落したキーを許可します

KeyError：「[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）]は[列]にあります "

pythonpandasnumpyscikit-learn
6
Ashley E.

この以下のエラーは、列の値の条件に基づいてインデックスを持つ行を削除しているときに発生しました：

return self._engine.get_loc（key）ファイル「pandas/_libs/index.pyx」、行107、pandas._libs.index.IndexEngine.get_locファイル「pandas/_libs/index.pyx」、行131、pandas _libs.index.IndexEngine.get_locファイル "pandas/_libs/hashtable_class_helper.pxi"、行992、pandas._libs.hashtable.Int64HashTable.get_itemファイル "pandas/_libs/hashtable_class_helper.pxi"、行998、pandas._libs。 hashtable.Int64HashTable.get_item KeyError：226

上記の例外の処理中に、別の例外が発生しました：

トレースバック（最新の呼び出しが最後）：

これを解決するには、インデックスのリストを作成し、以下のように行を一度に削除します。

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

同じエラーが発生しました：

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

データフレームをローカルファイルに保存して開くことで解決します。

以下のように：

df.to_csv('Step1.csv',index=False)
df = pd.read_csv('Step1.csv')
0
Malasani

私もこの問題を抱えていました。データフレームとシリーズを配列に変更することで解決しました。

次のコードラインを試してください：

scaled_inputs_all.iloc[shuffled_indices].values
0
Jabbar