python-scikit-learnSimple imputer example
import numpy as np
from sklearn import impute
im = impute.SimpleImputer(missing_values=np.nan, strategy='mean')
X = [[7, 2, 3], [4, np.nan, 6], [10, 5, 9]]
Xi = im.fit_transform(X)ctrl + cfrom sklearn importimport module from scikit-learn | import numpyimport Numpy module |
.SimpleImputer(univariate imputer for completing missing values with simple strategies. | missing_valueswhich values to treat as missing |
strategyselect strategy for filling missing values | .fit_transform(trains and transforms given data with missing values filled |
Usage example
import numpy as np
from sklearn import impute
im = impute.SimpleImputer(missing_values=np.nan, strategy='mean')
X = [[7, 2, 3], [4, np.nan, 6], [10, 5, 9]]
Xi = im.fit_transform(X)
print(Xi)output
[[ 7. 2. 3. ]
[ 4. 3.5 6. ]
[10. 5. 9. ]]
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