Calculate the average distance between cities.

We can solve this problem in the following steps:

1. Normalizing City Pairs: We create a new column city_pair where we combine the origin and destination cities in alphabetical order. This ensures that pairs like CityA and CityB are always represented as CityA-CityB, regardless of the order in the original data.
2. Grouping and Calculating Average: We group the DataFrame by the city_pair column and calculate the mean of the distance column for each group. This step gives us the average distance for each pair of cities.
3. Rounding and Sorting: We round the average distances to two decimal places for precision. Finally, we sort the results by average_distance in ascending order to meet the requirements of the problem statement.

Python
def find_average_distance(gps_data: pd.DataFrame) -> pd.DataFrame:
    # Create a new column with city pairs in alphabetical order
    gps_data['city_pair'] = gps_data.apply(
        lambda row: '-'.join(sorted([row['origin'], row['destination']])), axis=1
    )

    # Group by the city pair and calculate the average distance
    result = gps_data.groupby('city_pair')['distance'].mean().reset_index()

    # Round the average distances to two decimal places
    result['distance'] = result['distance'].round(2)

    # Rename columns to match the expected output
    result.columns = ['city_pair', 'average_distance']

    # Sort by average_distance in ascending order
    result = result.sort_values(by='average_distance').reset_index(drop=True)

    return result