Ash*_* E. 5 python numpy pandas scikit-learn
我正在尝试使用np.random.shuffle（）方法调整索引，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！
我一开始就将raw_csv_data变量设为变量时，曾尝试使用delimiter ='，'和delim_whitespace = 0，因为我将其视为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：
Run Code Online (Sandbox Code Playgroud)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，-> 2934raise_missing = True）2935 2936＃take（）不接受布尔索引器
_convert_to_indexer中的〜\ Anaconda3 \ lib \ site-packages \ pandas \ core \ indexing.py（自身，obj，轴，is_setter，raise_missing）1352 kwargs = {'raise_missing'：如果is_setter则为true，否则为1353
raise_missing}-> 1354返回self ._get_listlike_indexer（obj，axis，** kwargs）[1] 1355其他：1356尝试：〜\ 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 = 1162）返回关键点，索引器
1163_validate_read_indexer（自身，键，索引器，轴，raise_missing）中的〜\ Anaconda3 \ lib \ site-packages \ pandas \ core \ indexing.py 1244提高KeyError（1245
u“ [{key}]都不在[{axis }]“。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，但在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）]位于[列]“
您scaled_inputs_all使用loc
函数创建了DataFrame ，因此它很可能不包含连续索引。
另一方面，您shuffled_indices只是从一系列连续数字中创建了一个shuffle 。
请记住，scaled_inputs_all[shuffled_indices]得到的行scaled_inputs_all具有索引值等于元素shuffled_indices。
也许你应该写：
scaled_inputs_all.iloc[shuffled_indices]
请注意，它iloc提供基于整数位置的索引，而与索引值无关，也就是您所需要的。
小智 5
可能有人在机器学习中使用 KFOLD 时也会遇到同样的错误。
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]
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