As you have seen, there are many data transformation steps that need to be executed in the right order. Fortunately, Scikit-Learn provides the Pipeline class to help with such sequences of transformations.
Copy paste the code below as is. Here we are using a pipeline to process the data by first imputing it using SimpleImputer, then using the custom transformer created earlier to merge the columns, and finally, use the StandardScaler class to scale the entire training data
col_names = "total_rooms", "total_bedrooms", "population", "households"
rooms_ix, bedrooms_ix, population_ix, households_ix = [
housing.columns.get_loc(c) for c in col_names]
housing_extra_attribs = pd.DataFrame(
housing_extra_attribs,
columns=list(housing.columns)+["rooms_per_household", "population_per_household"],
index=housing.index)
housing_extra_attribs.head()
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
num_pipeline = Pipeline([
('imputer', SimpleImputer(strategy="median")),
('attribs_adder', CombinedAttributesAdder()),
('std_scaler', StandardScaler()),
])
housing_num_tr = num_pipeline.fit_transform(housing_num)
from sklearn.compose import ColumnTransformer
num_attribs = list(housing_num)
cat_attribs = ["ocean_proximity"]
full_pipeline = ColumnTransformer([
("num", num_pipeline, num_attribs),
("cat", OneHotEncoder(), cat_attribs),
])
Finally, we will fit_transform the entire training data
housing_prepared = full_pipeline.<<your code goes here>>(housing)
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