5 Best Ways to Recover a Shuffled Queue of People in Python

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πŸ’‘ Problem Formulation: Imagine a queue of people for a movie premiere that has been shuffled, and you are given the task to restore the original order based on a list of key indices representing each person’s original position. This article explores computational strategies to reorder a shuffled queue in Python, demonstrating how to translate positions into the correct sequence. For example, given a shuffled queue ["Alice", "Bob", "Charlie"] and their corresponding original positions [2, 0, 1], we aim to return the queue as ["Bob", "Charlie", "Alice"].

Method 1: Using a Custom Sort Function

Custom sort functions in Python allow you to establish sorting criteria beyond the standard ascending or descending order. In the context of recovering a shuffled queue, a custom sort function can be used to sort queue members according to their original positions as defined by a separate list of indices.

Here’s an example:

def recover_queue(names, positions):
    return [name for pos, name in sorted(zip(positions, names))]

names = ["Alice", "Bob", "Charlie"]
positions = [2, 0, 1]
restored_queue = recover_queue(names, positions)
print(restored_queue)

Output:

["Bob", "Charlie", "Alice"]

In this example, the recover_queue function sorts the zipped list of positions and names, then creates a new list in the correct order. The sorted() function is used with zip() to pair each position with the appropriate name. This is a concise and efficient method for lists that are not extensively large.

Method 2: Using Dictionary Mapping

Dictionary mapping in Python provides an efficient and intuitive method for pairing keys with values, offering a way to handle reordering based on associated values. In our scenario, we can map original positions to their corresponding names and then build a sorted list based on those positions.

Here’s an example:

def recover_queue_dict(names, positions):
    position_map = {pos: name for pos, name in zip(positions, names)}
    return [position_map[i] for i in range(len(names))]

names = ["Alice", "Bob", "Charlie"]
positions = [2, 0, 1]
restored_queue = recover_queue_dict(names, positions)
print(restored_queue)

Output:

["Bob", "Charlie", "Alice"]

The recover_queue_dict function creates a dictionary with positions as keys and names as values. This dictionary is then iterated over in the range of the list size to build a new list with names in their original order. Dictionaries in Python are especially suitable for fast lookups, making this method efficient for large lists.

Method 3: In-place List Element Swap

Sometimes, to optimize space, it might be preferable to reorder the list without creating a new data structure. This method implements in-place swapping of elements until the original order is achieved, using the positions list as the guide for swaps.

Here’s an example:

def recover_queue_inplace(names, positions):
    for i in range(len(names)):
        while positions[i] != i:
            idx = positions[i]
            names[i], names[idx] = names[idx], names[i]
            positions[i], positions[idx] = positions[idx], positions[i]
    return names

names = ["Alice", "Bob", "Charlie"]
positions = [2, 0, 1]
restored_queue = recover_queue_inplace(names, positions)
print(restored_queue)

Output:

["Bob", "Charlie", "Alice"]

This code iterates through each element, swapping names and their positions with the destination index until all elements are in their correct position. It’s an in-place operation, so it saves memory by not using additional data structures, although it might not be as clear or quick as other methods for large datasets with complex swaps.

Method 4: Using Enumerate and List Comprehension

List comprehensions provide a compact and readable way to create new lists. When combined with enumerate, we can achieve a one-liner solution that maps positions to the corresponding names efficiently.

Here’s an example:

names = ["Alice", "Bob", "Charlie"]
positions = [2, 0, 1]
restored_queue = [name for i, name in sorted(enumerate(names), key=lambda x: positions[x[0]])]
print(restored_queue)

Output:

["Bob", "Charlie", "Alice"]

This snippet employs enumerate to create pairs of indices and values and then sorts them according to the original position found in the positions list. The sorted result forms the reconstructed queue. This method provides brevity and Pythonic elegance, although the lambda function may obscure readability for novice programmers.

Bonus One-Liner Method 5: Using Itemgetter with Sort

The itemgetter function from the operator module allows precise control when sorting lists of tuples. It’s particularly useful when we have complex sorting criteria and need fast execution.

Here’s an example:

from operator import itemgetter

names = ["Alice", "Bob", "Charlie"]
positions = [2, 0, 1]
names.sort(key=itemgetter(*positions))
print(names)

Output:

["Bob", "Charlie", "Alice"]

This line utilizes itemgetter to create a sort key function that fetches elements based on the given positions. It serves as a concise and performant method for reordering, however, since itemgetter expects indices, ensuring the correctness of the input is crucial, and the method might be confusing for those unfamiliar with itemgetter.

Summary/Discussion

  • Method 1: Custom Sort Function. Simple and Pythonic. May not scale well with very large lists due to the creation of additional lists.
  • Method 2: Dictionary Mapping. Efficient and intuitive for large datasets due to constant time lookups. Slightly more code than some other methods.
  • Method 3: In-place List Element Swap. Memory efficient, as it avoids creating new data structures. May be harder to follow and inefficient for large lists with complex swaps.
  • Method 4: Using Enumerate and List Comprehension. Compact and elegant. The lambda function may decrease readability for those less familiar with Python.
  • Method 5: Bonus One-Liner with Itemgetter. Fast and concise for reordering based on indices. Relies on the operator module and can be obscure for beginners.