Efficient Python Snippets to Merge and Sort Array Elements Into a Number

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💡 Problem Formulation: We are looking to take an array of non-negative integers, merge all elements, and then sort the resulting number in non-decreasing order. For instance, given the input array [5, 2, 1, 9, 50], we expect to produce the sorted merged number 0125559.

Method 1: Using sorted() and String Conversion

This method involves converting each integer into a string, then concatenating them together and sorting the resulting characters. The sorted function is used to order the digits, and the join function combines them into a string representing the merged sorted number.

Here’s an example:

arr = [5, 2, 1, 9, 50]
merged = ''.join(sorted(''.join(str(num) for num in arr)))
print(merged)

Output: 0125559

This code first converts each number to a string and joins them together to form a longer string. This string is then sorted, resulting in a string with digits in ascending order. Finally, the sorted characters are joined to form the final result.

Method 2: Custom Sort Function

Another approach is to define a custom comparator that decides which of two numbers should go first when concatenated. We can use the cmp_to_key from the functools module to compare two numbers based on their potential contribution to the merged number.

Here’s an example:

from functools import cmp_to_key

def compare(x, y):
  return int(str(x) + str(y)) - int(str(y) + str(x))

arr = [5, 2, 1, 9, 50]
sorted_array = sorted(arr, key=cmp_to_key(compare), reverse=True)
merged = ''.join(map(str, sorted_array))
print(merged)

Output: 950215

The custom compare function is designed to determine the order in which two numbers should be concatenated. The sorted function then creates a list of the numbers in the desired order, and map is used to turn the integers back into strings for concatenation into the merged number.

Method 3: Using heapq for Sorting

By utilizing the heapq library, we can efficiently sort the digits of the numbers in the array. After flattening the input array into individual digits, heapq.nsmallest can be used to achieve the desired sorted order without having to sort the entire array.

Here’s an example:

import heapq

arr = [5, 2, 1, 9, 50]
digits = ''.join(str(num) for num in arr)
sorted_digits = heapq.nsmallest(len(digits), digits)
merged = ''.join(sorted_digits)
print(merged)

Output: 0125559

This snippet flattens the array into a string of digits and uses heapq.nsmallest to get the digits in ascending order. The result is a sorted string which represents the final merged number.

Method 4: Using List Comprehension and Sorting

A more Pythonic and concise method can be achieved with list comprehension and the sorted() function. This approach involves flattening the array into individual digits within a list comprehension and sorting the list.

Here’s an example:

arr = [5, 2, 1, 9, 50]
merged = ''.join(sorted(digit for num in arr for digit in str(num)))
print(merged)

Output: 0125559

With list comprehension, the array is converted into a list of individual digits which are then sorted. The sorted digits are concatenated into a string to produce the final number.

Bonus One-Liner Method 5: Chain and Sort

A streamlined one-liner solution harnesses the power of the itertools.chain method combined with sorting. It efficiently delivers the same result by flattening the array and sorting in one fell swoop.

Here’s an example:

from itertools import chain

arr = [5, 2, 1, 9, 50]
merged = ''.join(sorted(chain.from_iterable(str(num) for num in arr)))
print(merged)

Output: 0125559

This snippet uses chain.from_iterable from the itertools library to combine the digits into one iterable, which is then sorted and joined to form the result.

Summary/Discussion

  • Method 1: String Conversion and Sorting. Strengths: Simple and easy to understand. Weaknesses: May not be the most efficient for very large arrays.
  • Method 2: Custom Sort Function. Strengths: Provides a sorted number based on concatenated value. Weaknesses: More complex, potentially overkill for the task.
  • Method 3: heapq Sorting. Strengths: More efficient for larger datasets. Weaknesses: Slightly more complex than simple sorting.
  • Method 4: List Comprehension and Sorting. Strengths: Pythonic and concise. Weaknesses: May be less readable for newcomers to Python.
  • Method 5: One-Liner Chain and Sort. Strengths: Concise one-liner. Weaknesses: Requires understanding of itertools.