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How to impute multiple categorical variable with their column name
How do I pass a variable by reference?How to access environment variable values?Delete column from pandas DataFrame by column namePython Error IndexError: only integersReading multiple images in kerashow to import DBSCAN labels-Pythoni think an error appears to have occurred during “If”Curve_fit error - IndexError: only integers, slices (`:`), ellipsis (`…`), numpy.newaxis (`None`) and integer or boolean arrays are valid indicesPython only integers, slices … and integer or boolean arrays are valid indicesreading a pickle, IndexError: only integers, slices (`:`), ellipsis (`…`), numpy.newaxis (`None`) and integer or blean arrays are valid indices
X = train.iloc[:, :-1].values
y = train.iloc[:, -1].values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)
# Appliquer la methode fit sur X
imputer = imputer.fit(X[['GarageCond', 'GarageType', 'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']])
# Tranformer imputer et affecter la valeur a X
X[['GarageCond', 'GarageType', 'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']] = imputer.transform(X[:, ['GarageCond', 'GarageType', 'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']])
IndexError Traceback (most recent call last)
in ()
2 imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)
3 # Appliquer la methode fit sur X
----> 4 imputer =
imputer.fit(X[['GarageCond', 'GarageType',
'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']])
5 6 # Tranformer imputer et affecter la valeur a X IndexError: only
integers, slices (:), ellipsis (...), numpy.newaxis (None) and
integer or boolean arrays are valid indices`**
How to impute multiple categorical data by their column name not by index number.
python scikit-learn
edited Mar 9 at 12:26
AI_Learning
3,86021034
asked Mar 7 at 21:52
JAFFAR JAWEDJAFFAR JAWED
12
add a comment |
X = train.iloc[:, :-1].values
y = train.iloc[:, -1].values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)
# Appliquer la methode fit sur X
imputer = imputer.fit(X[['GarageCond', 'GarageType', 'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']])
# Tranformer imputer et affecter la valeur a X
X[['GarageCond', 'GarageType', 'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']] = imputer.transform(X[:, ['GarageCond', 'GarageType', 'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']])
IndexError Traceback (most recent call last)
in ()
2 imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)
3 # Appliquer la methode fit sur X
----> 4 imputer =
imputer.fit(X[['GarageCond', 'GarageType',
'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']])
5 6 # Tranformer imputer et affecter la valeur a X IndexError: only
integers, slices (:), ellipsis (...), numpy.newaxis (None) and
integer or boolean arrays are valid indices`**
How to impute multiple categorical data by their column name not by index number.
python scikit-learn
edited Mar 9 at 12:26
AI_Learning
3,86021034
asked Mar 7 at 21:52
JAFFAR JAWEDJAFFAR JAWED
12
What this has to do with impute? You want to replace the nans with column_names?
– mad_
Mar 7 at 22:00
add a comment |
X = train.iloc[:, :-1].values
y = train.iloc[:, -1].values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)
# Appliquer la methode fit sur X
imputer = imputer.fit(X[['GarageCond', 'GarageType', 'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']])
# Tranformer imputer et affecter la valeur a X
X[['GarageCond', 'GarageType', 'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']] = imputer.transform(X[:, ['GarageCond', 'GarageType', 'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']])
IndexError Traceback (most recent call last)
in ()
2 imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)
3 # Appliquer la methode fit sur X
----> 4 imputer =
imputer.fit(X[['GarageCond', 'GarageType',
'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']])
5 6 # Tranformer imputer et affecter la valeur a X IndexError: only
integers, slices (:), ellipsis (...), numpy.newaxis (None) and
integer or boolean arrays are valid indices`**
How to impute multiple categorical data by their column name not by index number.
python scikit-learn
edited Mar 9 at 12:26
AI_Learning
3,86021034
asked Mar 7 at 21:52
JAFFAR JAWEDJAFFAR JAWED
12
X = train.iloc[:, :-1].values
y = train.iloc[:, -1].values
from sklearn.preprocessing import Imputer
imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)
# Appliquer la methode fit sur X
imputer = imputer.fit(X[['GarageCond', 'GarageType', 'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']])
# Tranformer imputer et affecter la valeur a X
X[['GarageCond', 'GarageType', 'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']] = imputer.transform(X[:, ['GarageCond', 'GarageType', 'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']])
IndexError Traceback (most recent call last)
in ()
2 imputer = Imputer(missing_values = 'NaN', strategy = 'most_frequent', axis = 0)
3 # Appliquer la methode fit sur X
----> 4 imputer =
imputer.fit(X[['GarageCond', 'GarageType',
'GarageFinish','GarageQual','BsmtExposure','BsmtFinType2','BsmtCond','BsmtQual','BsmtFinType1','MasVnrType','MasVnrArea','Electrical']])
5 6 # Tranformer imputer et affecter la valeur a X IndexError: only
integers, slices (:), ellipsis (...), numpy.newaxis (None) and
integer or boolean arrays are valid indices`**
How to impute multiple categorical data by their column name not by index number.
python scikit-learn
python scikit-learn
edited Mar 9 at 12:26
AI_Learning
3,86021034
asked Mar 7 at 21:52
JAFFAR JAWEDJAFFAR JAWED
12
edited Mar 9 at 12:26
AI_Learning
3,86021034
asked Mar 7 at 21:52
JAFFAR JAWEDJAFFAR JAWED
12
edited Mar 9 at 12:26
AI_Learning
3,86021034
edited Mar 9 at 12:26
AI_Learning
3,86021034
edited Mar 9 at 12:26
AI_Learning
3,86021034
3,86021034
asked Mar 7 at 21:52
JAFFAR JAWEDJAFFAR JAWED
12
asked Mar 7 at 21:52
JAFFAR JAWEDJAFFAR JAWED
12
asked Mar 7 at 21:52
JAFFAR JAWEDJAFFAR JAWED
12
12
What this has to do with impute? You want to replace the nans with column_names?
– mad_
Mar 7 at 22:00
add a comment |
What this has to do with impute? You want to replace the nans with column_names?
– mad_
Mar 7 at 22:00
What this has to do with impute? You want to replace the nans with column_names?
– mad_
Mar 7 at 22:00
What this has to do with impute? You want to replace the nans with column_names?
– mad_
Mar 7 at 22:00
add a comment |
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What this has to do with impute? You want to replace the nans with column_names?
– mad_
Mar 7 at 22:00