data = {
const tempResult = await py`housing_clean = ${housing}.dropna(subset=['total_bedrooms'])
housing_cat = housing_clean[['ocean_proximity']]
ordinal_encoder = ${p.sk_pre.OrdinalEncoder}()
housing_cat_encoded = ordinal_encoder.fit_transform(housing_cat)
housing_clean['housing_cat_encoded'] = housing_cat_encoded
housing_clean = housing_clean.drop(['ocean_proximity'], axis=1)
train, test = ${p.sk_ms.train_test_split}(housing_clean, test_size=0.2, random_state=42)
train_X = train.drop(['median_house_value'], axis=1)
train_y = ${p.pd.DataFrame}(train['median_house_value'])
test_X = test.drop(['median_house_value'], axis=1)
test_y = ${p.pd.DataFrame}(test['median_house_value'])
{'train_X':train_X, 'train_y':train_y, 'test_X':test_X, 'test_y':test_y}
`
let temp = {};
const arr = Array.from(tempResult.keys());
for (const k of arr) {
temp[k] = tempResult.get(k);
}
return temp;
}