Может ли кто-нибудь помочь мне посеять это: ключевая ошибка: ни один из [int64index...] dtype='int64] не находится в Столбцах

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I'm trying to shuffle my indices using the np.random.shuffle() method, but I keep getting an error that I don't understand. I'd really appreciate it if someone could help me puzzle this out. Thank you!

Вот мой код:

#Goal: Preprocess the Data to Predict Excessive Employee absence

#Import Libraries
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]

Вот в чем ошибка:

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() does not accept boolean indexers

~\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 in _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             # We (temporarily) allow for some missing keys with .loc, except in

KeyError: "None of [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)] are in the [columns]"

Что я уже пробовал:

Я пытался использовать разделитель=',' и delim_whitespace=0 (два решения, которые я все равно не понял), когда я сделал свою переменную raw_csv_data в начале, так как я видел, что это решение другой проблемы, но она продолжала выдавать ту же ошибку

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