sklearn tutorial impute missing values with mean of training data code example

Example 1: sciket learn imputer code

from sklearn.preprocessing import Imputerimputer = Imputer(missing_values = 'NaN', strategy = 'mean', axis = 0)imputer = imputer.fit(X[:, 1:3])X[:, 1:3] = imputer.transform(X[:, 1:3])

Example 2: Marking imputed values

# Marking imputed values

from sklearn.impute import MissingIndicator
X = np.array([[-1, -1, 1, 3]),
              [4, -1, 0, -1],
              [8, -1, 1, 0]])
indicator = MissingIndicator(missing_values=-1)
mask_missing_values_only = indicator.fit_transform(X)
mask_missing_values_only
# array([[ True,  True, False],
#        [False,  True,  True],
#        [False,  True, False]])

indicator.features_
# array([0, 1, 3])

indicator = MissingIndicator(missing_values=-1, features="all")
mask_all = indicator.fit_transform(X)
mask_all
# array([[ True,  True, False, False],
#        [False,  True, False,  True],
#        [False,  True, False, False]])
indicator.features_
# array([0, 1, 2, 3])

from sklearn.datasets import load_iris
from sklearn.impute import SimpleImputer, MissingIndicator
from sklearn.model_selection import train_test_split
from sklearn.pipeline import FeatureUnion, make_pipeline
from sklearn.tree import DecisionTreeClassifier
X, y = load_iris(return_X_y=True)
mask = np.random.randint(0, 2, size=X.shape).astype(np.bool)
X[mask] = np.nan
  X_train, X_test, y_train, _ = train_test_split(X, y, test_size=100,
                                                 random_state=0)
  
transformer = FeatureUnion(
  transformer_list=[
    ('features', SimpleImputer(strategy='mean')),
    ('indicators', MissingIndicator())])
transformer = transformer.fit(X_train, y_train)
results = transformer.transform(X_test)
results.shape
# (100, 8)

clf = make_pipeline(transformer, DecisionTreeClassifier())
clf = clf.fit(X_train, y_train)
results = clf.predict(X_test)
results.shape
# (100,)