filtered = Series([True, False, True], index=df.index)
condition_loc = df.loc[df. LoanAmount.head() < 500]
boolean_i = df.iloc[[True , False , True ]]
boolean = df.loc[['True' , 'False' , 'True' ]].values
generates error
IndexError: Boolean index has wrong length: 3 instead of 614
KeyError: "None of [Index(['True', 'False', 'True'], dtype='object', name='Loan_ID')] are in the [index]"
IndexingError(
pandas.core.indexing.IndexingError: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).
raise ValueError(
ValueError: Length of values (3) does not match length of index (614)
Snapshot of data
Loan_ID Gender Married Dependents Education Self_Employed ApplicantIncome CoapplicantIncome LoanAmount Loan_Amount_Term Credit_History Property_Area Loan_Status
0 LP001002 Male No 0 Graduate No 5849 0 100 360 1 Urban Y
1 LP001003 Male Yes 1 Graduate No 4583 1508 128 360 1 Rural N
2 LP001005 Male Yes 0 Graduate Yes 3000 0 66 360 1 Urban Y
3 LP001006 Male Yes 0 Not Graduate No 2583 2358 120 360 1 Urban Y
Data is [614 rows x 12 columns] Intention is to generate given a list of boolean values select rows where value is true Have tried every available link that gets generated by any and every error mentioned above. Seems like no one has failed to generate values using above syntax. Please direct me to link where this can be resolved. Have tried to explain as much as possible. New to pandas. Thanks for your time!
Edit:
filtered = Series([True, False, True] )
removing index solved the first issue.
Edit 2:
df.loc[Series([True, False, True])]
gives
raise IndexingError(
pandas.core.indexing.IndexingError: Unalignable boolean Series provided as indexer (index of the boolean Series and of the indexed object do not match).
Suggested link only talks about series and not how to use it in conjunction with loc or iloc.
Edit 3:
import pandas as pd
mydict = [
{"a": 1, "b": 2, "c": 3, "d": 4},
{"a": 100, "b": 200, "c": 300, "d": 400},
{"a": 1000, "b": 2000, "c": 3000, "d": 4000},
]
df = pd.DataFrame(mydict)
print(df)
print(df.iloc[[True, False, True]])
gives
a b c d
0 1 2 3 4
1 100 200 300 400
2 1000 2000 3000 4000
a b c d
0 1 2 3 4
2 1000 2000 3000 4000
Works on above code where rows are equal to boolean but generates error when
print(df.iloc[[True, True]])
Edit 4:
condition_loc = list(filter(lambda x:x.head()>500,df.loc))
gives
KeyError: 0
The above exception was the direct cause of the following exception:
raise KeyError(key) from errKeyError: 0
Edit 5:
boolean = list(compress(loan_df, list1))
print(boolean )
prints column names!
Edit 6:
list1 = [True , False , True ]
boolean = list(compress(df, list1))
for i in boolean :
print(df.loc[boolean])
gives
raise KeyError(f"None of [{key}] are in the [{axis_name}]")
KeyError: "None of [Index(['Gender', 'Dependents'], dtype='object', name='Loan_ID')] are in the [index]"
Edit 7: iloc issue resolved
all_rows_df = list(range(0, len(df))) # gives integer values
boolean = list(compress(all_rows_df, list1)) # selects values by comparison
print(boolean)
for i in boolean :
print(i)
print(df.iloc[i]) # Index position of rows in integer or list of integer
gives
[0, 2]
Gender Male
Married No
Dependents 0
Education Graduate
Self_Employed No
ApplicantIncome 5849
CoapplicantIncome 0.0
LoanAmount NaN
Loan_Amount_Term 360.0
Credit_History 1.0
Property_Area Urban
Loan_Status Y
Name: LP001002, dtype: object
Gender Male
Married Yes
Dependents 0
Education Graduate
Self_Employed Yes
ApplicantIncome 3000
CoapplicantIncome 0.0
LoanAmount 66.0
Loan_Amount_Term 360.0
Credit_History 1.0
Property_Area Urban
Loan_Status Y
Name: LP001005, dtype: object
But the above method gives error on loc
[0, 2]
0
KeyError: 0
The above exception was the direct cause of the following exception:
return self._getitem_axis(maybe_callable, axis=axis)
return self._get_label(key, axis=axis)
return self.obj.xs(label, axis=axis)
loc = index.get_loc(key)
raise KeyError(key) from errKeyError: 0
Currently I am stuck on this
