## Google
def normalize_linear_scale(examples_dataframe):
  """Returns a version of the input `DataFrame` that has all its features normalized linearly."""
  processed_features = pd.DataFrame()
  processed_features["latitude"] = linear_scale(examples_dataframe["latitude"])
  processed_features["longitude"] = linear_scale(examples_dataframe["longitude"])
  processed_features["housing_median_age"] = linear_scale(examples_dataframe["housing_median_age"])
  processed_features["total_rooms"] = linear_scale(examples_dataframe["total_rooms"])
  processed_features["total_bedrooms"] = linear_scale(examples_dataframe["total_bedrooms"])
  processed_features["population"] = linear_scale(examples_dataframe["population"])
  processed_features["households"] = linear_scale(examples_dataframe["households"])
  processed_features["median_income"] = linear_scale(examples_dataframe["median_income"])
  processed_features["rooms_per_person"] = linear_scale(examples_dataframe["rooms_per_person"])
  return processed_features


## Sergio
def normalize_linear_scale(examples_dataframe):
  """Returns a version of the input `DataFrame` that has all its features normalized linearly."""
  normalized_dataframe = pd.DataFrame()
  index = 0;
  for feature_name, series in examples_dataframe.items():
        normalized_series = linear_scale(series)
        normalized_dataframe.insert(index, feature_name, normalized_series)
        index += 1
  return normalized_dataframe