prepare_data_4.py

def encode_categorical(train_data, test_data, feature_name):
    # Get the unique elements from the training set.
    unique_elements = train_data[feature_name].dropna().unique()

    for data in [train_data, test_data]:
        element_indices = []
        for unique_element in unique_elements:
            # Collect all row indices the element occurs in the data.
            element_indices += [data[feature_name].index[
                data[feature_name].apply(lambda x: x == unique_element)]]

        # Encode the elements with a category index.
        for element_encoding, element_indices_i in enumerate(element_indices):
            data.loc[element_indices_i, feature_name] = element_encoding

        data[feature_name] = data[feature_name].astype(np.int)
        
# Encode categorical columns as categorical indices.
encode_categorical(train_data, test_data, 'Pclass')
encode_categorical(train_data, test_data, 'Embarked')
encode_categorical(train_data, test_data, 'Sex')