💡 Problem Formulation: You have a matrix where each element is a string. Your task is to sort the rows of the matrix based on the length of the longest string in each row. The goal is to have rows ordered from the one with the shortest maximum string length to the one with the longest. For example, given a matrix [["a", "abcd"], ["xyz", "ab"]], the desired output would be [["xyz", "ab"], ["a", "abcd"]] as the second row has the longest string (“abcd”).
Method 1: Using a Custom Sort Function
This method involves defining a custom function that calculates the maximum string length in a row, and using that function as the key in the matrix’s sort method. Sort efficiency depends on the max function and the sort implementation in Python, which is usually TimSort.
Here’s an example:
matrix = [["a", "abcd"], ["xyz", "ab"]] matrix.sort(key=lambda x: max(len(s) for s in x)) print(matrix)
Output:
[['xyz', 'ab'], ['a', 'abcd']]
In the code example, lambda creates an anonymous function that is passed to key argument of sort(). The function computes the maximum string length for each row, which sort() then uses to order the rows in the matrix.
Method 2: Using the Sorted Function with a Custom Key
This method uses the built-in sorted() function. Similar to the custom sort function, it applies a key function to determine the maximum length of strings within the rows, but returns a new sorted list, leaving the original matrix unchanged.
Here’s an example:
matrix = [["a", "abcd"], ["xyz", "ab"]] sorted_matrix = sorted(matrix, key=lambda x: max(len(s) for s in x)) print(sorted_matrix)
Output:
[['xyz', 'ab'], ['a', 'abcd']]
This code uses sorted() instead of sort(), making it useful when you do not want to modify the original matrix. The custom key function works the same way as in Method 1.
Method 3: Sorting Using itemgetter and max with map
Instead of a lambda function, this method leverages the itemgetter from the operator module in combination with max() and map() functions to achieve the sorting based on maximum string length in each row.
Here’s an example:
from operator import itemgetter matrix = [["a", "abcd"], ["xyz", "ab"]] matrix.sort(key=itemgetter(0), reverse=True) matrix.sort(key=lambda x: max(map(len, x))) print(matrix)
Output:
[['xyz', 'ab'], ['a', 'abcd']]
This example first sorts the matrix by the first column in reverse to ensure stable sorting when applied later. Then it sorts again, this time based on the maximum string length in each row, calculated using a combination of map() to apply len() to each element and max() to find the maximum value.
Method 4: Using List Comprehension for a More Pythonic Approach
This method involves Python list comprehension to sort the matrix based on the maximum string length in a row. It shows Python’s capability to handle such operations in a very succinct and readable way.
Here’s an example:
matrix = [["a", "abcd"], ["xyz", "ab"]] matrix = [x for x in sorted(matrix, key=lambda y: max(len(i) for i in y))] print(matrix)
Output:
[['xyz', 'ab'], ['a', 'abcd']]
This code uses a list comprehension to generate a new sorted list. The sorted() function is used with a lambda function that determines the maximum string length of each row, similar to Method 2.
Bonus One-Liner Method 5: Using the Sort Function with a Generous Use of Lambda
For those who prefer concise code, this one-liner uses a lambda function within the sort method to immediately calculate each row’s maximum string length without breaking the process into multiple steps.
Here’s an example:
matrix = [["a", "abcd"], ["xyz", "ab"]] matrix.sort(key=lambda r: -max(map(len, r))) print(matrix)
Output:
[['xyz', 'ab'], ['a', 'abcd']]
This code sorts the matrix in place by a descending length of the longest string in each row. It combines lambda functions with the map function in a one-liner for brevity and directness.
Summary/Discussion
- Method 1: Custom Sort Function. Straightforward implementation using sort with a lambda. Affects original list.
- Method 2: Sorted Function with Custom Key. Non-destructive as it creates a new sorted list. Slight performance overhead due to creating a new list.
- Method 3: Using itemgetter and max with map. Optimizes the key function calculation with a combination of itemgetter and map. Requires a preliminary sorting step for stable results.
- Method 4: List Comprehension for a Pythonic Approach. Readable and concise. Offers a similar non-destructive new list creation like Method 2 with the added readability of list comprehensions.
- Method 5: Bonus One-Liner. Maximum conciseness achieved with a one-liner. Suitable for quick, in-place sort operations with negative in lambda for reverse sorting.