def calculate_forecast_errors(df, prediction_size):
    """Calculate MAPE and MAE of the forecast.

       Args:
           df: joined dataset with 'y' and 'yhat' columns.
           prediction_size: number of days at the end to predict.
    """

    # Make a copy
    df = df.copy()

    # Now we calculate the values of e_i and p_i according to the formulas given in the article above.
    df['e'] = df['y'] - df['yhat']
    df['p'] = 100 * df['e'] / df['y']

    # Recall that we held out the values of the last `prediction_size` days
    # in order to predict them and measure the quality of the model.

    # Now cut out the part of the data which we made our prediction for.
    predicted_part = df[-prediction_size:]

    # Define the function that averages absolute error values over the predicted part.
    error_mean = lambda error_name: np.mean(np.abs(predicted_part[error_name]))

    # Now we can calculate MAPE and MAE and return the resulting dictionary of errors.
    return {'MAPE': error_mean('p'), 'MAE': error_mean('e')}