5 Best Ways to Program to Find Airports in Correct Order in Python

Rate this post

πŸ’‘ Problem Formulation: Python programmers often need to arrange data in a specific order based on a given criterion. For instance, suppose you have a list of airports along with their associated city names and the sequence of a multi-stop flight; you would want to order these airports as per the flight itinerary. Given an input of unsorted airport codes, such as ['JFK', 'LAX', 'ORD'], the desired output would be a sorted list that reflects the correct order of airports visited, like ['ORD', 'JFK', 'LAX'].

Method 1: Using Custom Sorting Function

Creating a custom sorting function in Python allows you to define the exact criteria by which the list should be sorted. This is helpful when dealing with complex data that requires a non-standard sorting algorithm. The custom function can be used as the key argument in the list’s sort method or with the sorted function to order the elements as desired.

Here’s an example:

airports = [('ORD', 2), ('JFK', 1), ('LAX', 3)]
airports.sort(key=lambda x: x[1])
sorted_airports = [code for code, order in airports]

Output: ['JFK', 'ORD', 'LAX']

This code snippet creates a list of tuples where each tuple contains an airport code and its respective order in the itinerary. The custom sorting function, defined by a lambda expression, sorts the tuples based on the second element, which is the order. After sorting, the list comprehension is used to extract the sorted airport codes.

Method 2: Utilizing a Dictionary for Ordering

Dictionaries in Python are ideal for maintaining mappings between sets of data. You can use a dictionary to associate each airport code with its order in the flight path. By doing this, you can sort the airports effectively by looking up their respective order using the dictionary.

Here’s an example:

airport_order = {'ORD': 2, 'JFK': 1, 'LAX': 3}
codes = list(airport_order.keys())

Output: ['JFK', 'ORD', 'LAX']

In this snippet, we have a dictionary where keys are airport codes and values are the order in which these airports are visited. By calling the sort() method with the get method of the dictionary as the key argument, we ensure that the list is sorted according to the order defined in the dictionary.

Method 3: Using the pandas Library

The pandas library is equipped with data manipulation tools and is especially useful when handling tabular data. A DataFrame can hold the airport codes along with their orders, and by using the sort_values method, the DataFrame can be sorted according to the desired criterion.

Here’s an example:

import pandas as pd

df = pd.DataFrame({
    'Airport': ['ORD', 'JFK', 'LAX'],
    'Order': [2, 1, 3]
df_sorted = df.sort_values(by='Order')
sorted_codes = df_sorted['Airport'].tolist()

Output: ['JFK', 'ORD', 'LAX']

The code creates a pandas DataFrame with an “Airport” column for airport codes and an “Order” column indicating the position in the itinerary. We then sort the DataFrame in-place based on the “Order” column, and finally, we convert the sorted “Airport” column back into a list of airport codes.

Method 4: Sorting With a Graph-Based Approach

If the airports represent a sequence of a trip, you could model the itinerary as a directed graph where each airport is a node and the flight from one to the next is a directed edge. Sorting the airports can then be thought of as finding a path through the graph.

Here’s an example:

import networkx as nx

graph = nx.DiGraph()
edges = [('ORD', 'JFK'), ('JFK', 'LAX')]
sorted_airports = list(nx.topological_sort(graph))

Output: ['ORD', 'JFK', 'LAX']

This snippet constructs a directed graph using the NetworkX library. Each airport is added as a node with directed edges representing the sequence of travel from one airport to the next. The topological_sort() function returns a list of nodes (airports) in topological order, which corresponds to the order of the flight itinerary.

Bonus One-Liner Method 5: Using Sorted with a Pre-Defined List

If you have a pre-defined list that already specifies the correct order of airports, you can quickly sort any list of airport codes with a one-liner using the sorted function and a lambda function.

Here’s an example:

correct_order = ['ORD', 'JFK', 'LAX']
unordered_airports = ['JFK', 'LAX', 'ORD']
sorted_airports = sorted(unordered_airports, key=correct_order.index)

Output: ['ORD', 'JFK', 'LAX']

This line sorts the unordered_airports list by applying the index() method of the correct_order list as a key function within the sorted() method. This ranks the airports based on their index in the pre-defined list, effectively reordering them.


  • Method 1: Custom Sorting Function. Offers flexibility for complex criteria. Requires a bit of extra code to set up.
  • Method 2: Dictionary for Ordering. Provides clear key-value association. Can be less efficient for very large datasets.
  • Method 3: pandas Library. Ideal for larger and more complex datasets. The overhead of using a library might be unnecessary for small or simple tasks.
  • Method 4: Graph-Based Approach. Suits ordered sequences and itineraries. May be overkill for simple sort operations.
  • Method 5: Pre-Defined List and Sorted. Simplest for a known sequence. Not dynamic and requires prior knowledge of the order.