Key Error: None of [Int64Index…] dtype='int64] are in the columns
Question:
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 appreciate it if someone could help me puzzle this out. Thank you!
I’ve tried to use the delimiter=’,’ and delim_whitespace=0 when I made my raw_csv_data variable at the beginning, as I saw that as the solution of another problem, but it kept throwing the same error
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]
This is the error I keep getting when I try to shuffle my indices:
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]
~Anaconda3libsite-packagespandascoreframe.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
~Anaconda3libsite-packagespandascoreindexing.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:
~Anaconda3libsite-packagespandascoreindexing.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
~Anaconda3libsite-packagespandascoreindexing.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]”
Answers:
You created your scaled_inputs_all DataFrame using loc
function, so it most likely contains no consecutive indices.
On the other hand, you created shuffled_indices as a shuffle
from just a range of consecutive numbers.
Remember that scaled_inputs_all[shuffled_indices] gets rows
of scaled_inputs_all which have index values equal to
elements of shuffled_indices.
Maybe you should write:
scaled_inputs_all.iloc[shuffled_indices]
Note that iloc provides integer-location based indexing, regardless of
index values, i.e. just what you need.
I had this problem too. I solved it by changing the data frame and series to array.
try the follwing codeline:
scaled_inputs_all.iloc[shuffled_indices].values
Got the same error:
KeyError: "None of [Int64Index([26], dtype='int64')] are in the [index]"
Solved by Saving the dataframe to a local file and open it,
like below:
df.to_csv('Step1.csv',index=False)
df = pd.read_csv('Step1.csv')
This below error occured while deleting the row with index based on the column value condition:
return self._engine.get_loc(key) File “pandas/_libs/index.pyx”, line
107, in pandas._libs.index.IndexEngine.get_loc File
“pandas/_libs/index.pyx”, line 131, in
pandas._libs.index.IndexEngine.get_loc File
“pandas/_libs/hashtable_class_helper.pxi”, line 992, in
pandas._libs.hashtable.Int64HashTable.get_item File
“pandas/_libs/hashtable_class_helper.pxi”, line 998, in
pandas._libs.hashtable.Int64HashTable.get_item KeyError: 226
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
To resolve this make a list of index and delete the rows at once as below:
df.drop(index=list1,labels=None, axis=0, inplace=True,columns=None, level=None, errors='raise')
might have someone also get the same error in working with KFOLD in machine learning.
And the solution for this is as below:
You need to use 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]
If you reset your index after dropping rows from your dataframe, this should stop the key error.
You can do this by running this after you run df.drop:
df = df.reset_index(drop=True)
Or, equivalently:
df.reset_index(drop=True, inplace=True)
For Kfold splitting you need iloc… I used this.
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]
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 appreciate it if someone could help me puzzle this out. Thank you!
I’ve tried to use the delimiter=’,’ and delim_whitespace=0 when I made my raw_csv_data variable at the beginning, as I saw that as the solution of another problem, but it kept throwing the same error
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]
This is the error I keep getting when I try to shuffle my indices:
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]~Anaconda3libsite-packagespandascoreframe.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~Anaconda3libsite-packagespandascoreindexing.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:~Anaconda3libsite-packagespandascoreindexing.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~Anaconda3libsite-packagespandascoreindexing.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 inKeyError: “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]”
You created your scaled_inputs_all DataFrame using loc
function, so it most likely contains no consecutive indices.
On the other hand, you created shuffled_indices as a shuffle
from just a range of consecutive numbers.
Remember that scaled_inputs_all[shuffled_indices] gets rows
of scaled_inputs_all which have index values equal to
elements of shuffled_indices.
Maybe you should write:
scaled_inputs_all.iloc[shuffled_indices]
Note that iloc provides integer-location based indexing, regardless of
index values, i.e. just what you need.
I had this problem too. I solved it by changing the data frame and series to array.
try the follwing codeline:
scaled_inputs_all.iloc[shuffled_indices].values
Got the same error:
KeyError: "None of [Int64Index([26], dtype='int64')] are in the [index]"
Solved by Saving the dataframe to a local file and open it,
like below:
df.to_csv('Step1.csv',index=False)
df = pd.read_csv('Step1.csv')
This below error occured while deleting the row with index based on the column value condition:
return self._engine.get_loc(key) File “pandas/_libs/index.pyx”, line
107, in pandas._libs.index.IndexEngine.get_loc File
“pandas/_libs/index.pyx”, line 131, in
pandas._libs.index.IndexEngine.get_loc File
“pandas/_libs/hashtable_class_helper.pxi”, line 992, in
pandas._libs.hashtable.Int64HashTable.get_item File
“pandas/_libs/hashtable_class_helper.pxi”, line 998, in
pandas._libs.hashtable.Int64HashTable.get_item KeyError: 226During handling of the above exception, another exception occurred:
Traceback (most recent call last):
To resolve this make a list of index and delete the rows at once as below:
df.drop(index=list1,labels=None, axis=0, inplace=True,columns=None, level=None, errors='raise')
might have someone also get the same error in working with KFOLD in machine learning.
And the solution for this is as below:
You need to use 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]
If you reset your index after dropping rows from your dataframe, this should stop the key error.
You can do this by running this after you run df.drop:
df = df.reset_index(drop=True)
Or, equivalently:
df.reset_index(drop=True, inplace=True)
For Kfold splitting you need iloc… I used this.
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]