Distant Barcodes - Solution & Explanation

This approach leverages a priority queue to ensure that the most frequent barcodes are spaced apart. By sorting the barcodes by frequency, we can fill in the positions, alternating between the most available barcodes to handle the adjacency constraint efficiently.

This Python implementation uses the collections.Counter to count the frequencies of each barcode type. A max heap (priority queue) is utilized to always extract the barcode with the highest frequency first, and we keep track of the last processed barcode to avoid immediate reuse.

In this approach, the barcodes are first sorted by frequency, then they are placed alternately in even and odd indices within the result array. This method efficiently arranges the barcodes such that no two adjacent barcodes are equal.

This C++ implementation first counts the occurrences of each barcode type. It sorts the barcodes based on frequency and then arranges them by filling even indices first, followed by odd indices. This ensures an alternating pattern that prevents adjacent duplicates.