我正在尝试使用np.random.shuffle（）方法对索引进行混洗，但是我不断收到我不理解的错误。如果有人可以帮助我解决这个问题，我将不胜感激。谢谢！
一开始我将raw_csv_data变量设为变量时，我曾尝试使用delimiter ='，'和delim_whitespace = 0，因为我认为这是另一个问题的解决方案，但它始终抛出相同的错误
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]
这是我尝试改组索引时不断遇到的错误：
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在 getitem （（自身，键））2932键=列表（键）2933索引器= self.loc._convert_to_indexer（键，轴= 1， -> 2934 raise_missing = True）2935 2936＃take（）不接受 布尔索引器
〜\ Anaconda3 \ lib \ site-packages \ pandas \ core \ indexing.py在 _convert_to_indexer（自身，obj，轴，is_setter，raise_missing）1352 kwargs = {'raise_missing'：如果is_setter则为true 1353
引发} -> 1354返回self._get_listlike_indexer（obj，axis，** kwargs）[1] 1355其他：1356试试：〜\ Anaconda3 \ lib \ site-packages \ pandas \ core \ indexing.py在 _get_listlike_indexer（自身，键，轴，提升缺失）1159自我._validate_read_indexer（keyarr，indexer，1160
o._get_axis_number（axis）， -> 1161raise_missing = raise_missing）1162返回关键字，索引器
1163〜\ Anaconda3 \ lib \ site-packages \ pandas \ core \ indexing.py在 _validate_read_indexer（自身，键，索引器，轴，raise_missing）1244提高KeyError（1245
中除外
u“ [{key}]都不在[{axis}]中” .format（ -> 1246 key = key，axis = self.obj._get_axis_name（axis））1247 1248＃我们 （暂时）允许使用.loc丢失一些键，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'，长度= 700）]是 在[列]”中
答案 0 :(得分：2)
您使用scaled_inputs_all创建了loc数据框
函数，因此很可能不包含连续索引。
另一方面，您将shuffled_indices创建为随机播放
来自一系列连续数字。
请记住，scaled_inputs_all[shuffled_indices]获取行
的scaled_inputs_all中索引值等于
shuffled_indices的元素。
也许您应该写：
scaled_inputs_all.iloc[shuffled_indices]
请注意，iloc提供基于整数位置的索引，无论
索引值，也就是您所需要的。
答案 1 :(得分：1)
遇到相同的错误：
org.springframework.kafka.listener.adapter.RecordFilterStrategy通过将数据框保存到本地文件并打开它来解决，
如下所示：
KeyError: "None of [Int64Index([26], dtype='int64')] are in the [index]"
答案 2 :(得分：1)
根据列值条件删除具有索引的行时，发生以下错误：
返回self._engine.get_loc（key）文件“ pandas / _libs / index.pyx”，行 107，在pandas._libs.index.IndexEngine.get_loc文件中 “ pandas / _libs / index.pyx”，第131行 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
在处理上述异常期间，发生了另一个异常：
回溯（最近通话最近一次）：
要解决此问题，请列出索引并立即删除行，如下所示：
df.drop(index=list1,labels=None, axis=0, inplace=True,columns=None, level=None, errors='raise')
答案 3 :(得分：1)
在使用KFOLD进行机器学习时，可能还会有人遇到相同的错误。
解决方案如下：
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]
答案 4 :(得分：0)
我也有这个问题。我通过将数据框和序列更改为数组来解决它。
尝试以下代码行：
scaled_inputs_all.iloc[shuffled_indices].values
答案 5 :(得分：0)
如果在从数据框中删除行后重置索引，这应该会停止关键错误。
您可以通过在运行 df.drop 后运行它来实现：
df = df.reset_index(drop=True)
或者，等效地：
df.reset_index(drop=True, inplace=True)