def find_median(data_points):
    # Data will be mapped category: population
    # For example:
    """
    {
        20: 2324, # $20 income, 2324 people in category
        60: 4108, # $60 income, 4108 people in category
    }
    """
    # List of all categories
    keys = []
    total_population = 0
    for key in data_points.keys():
        keys.append(key)
        total_population += data_points[key]
    # Sort in ascending order (needed to find median)
    keys = sorted(keys)
    # An even total population means we must calculate the median ourselves
    if total_population % 2 == 0:
        # Median will be midpoint between these two numbers
        # In most cases they'll probably be equal
        left_median = (total_population // 2) - 1
        right_median = (total_population // 2) + 1
        population_seen = 0
        median = [None, None]
        for key in keys:
            if population_seen <= left_median <= population_seen + data_points[key]:
                median[0] = key
            if population_seen <= right_median <= population_seen + data_points[key]:
                median[1] = key
            # If we've found both medians, no more work to do
            if median[0] is not None and median[1] is not None:
                break
            population_seen += data_points[key]
        # Make sure we actually found the relevant points
        assert median[0] is not None
        assert median[1] is not None
        # The mean between two middle points is the median
        return round(sum(median) / 2)
    else:
        median_person = (total_population + 1) // 2
        population_seen = 0
        for key in keys:
            if population_seen <= median_person <= population_seen + data_points[key]:
                return key
            population_seen += data_points[key]

census = Census2001().median_incomes()
for key in census:
    print(f"{key},{census[key]}")
print(find_median(census))