5 Best Ways to Find Maximum Length Sub-List in a Nested List in Python

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πŸ’‘ Problem Formulation: When working with nested lists in Python, a common task is to identify the longest sub-list within the structure. A nested list, sometimes known as a “list of lists,” may contain sub-lists of varying lengths. The goal here is to determine which sub-list has the maximum length. For instance, given the nested list [[1, 2, 3], [4, 5], [6], [7, 8, 9, 10]], the desired output would be [7, 8, 9, 10] due to its length of 4.

Method 1: Using a Simple Loop

This method employs a straightforward for-loop to iterate over each sub-list in the nested list, keeping track of the longest one encountered. By comparing the lengths of the sub-lists to the maximum found so far, this method efficiently identifies the sub-list with the maximum length.

Here’s an example:

nested_list = [[1, 2, 3], [4, 5], [6], [7, 8, 9, 10]]

max_list = []
for sublist in nested_list:
    if len(sublist) > len(max_list):
        max_list = sublist

print(max_list)

[7, 8, 9, 10]

The code snippet initiates an empty list called max_list. As it iterates through nested_list, it compares the length of each sub-list to the current max_list. If a longer sub-list is found, max_list is updated. The result is printed out, showing the longest sub-list.

Method 2: Using the max() Function with key=len

Python’s built-in max() function is used here with the key parameter set to len. This tells the max() function to return the sub-list whose length is the greatest. This method is more succinct and idiomatic.

Here’s an example:

nested_list = [[1, 2, 3], [4, 5], [6], [7, 8, 9, 10]]

max_list = max(nested_list, key=len)

print(max_list)

[7, 8, 9, 10]

In this snippet, the max() function is directly applied on nested_list. The key parameter specifies that the sub-lists should be compared based on their lengths. The function returns the sub-list with the greatest length, which is then printed.

Method 3: Using List Comprehension and max()

This method exploits list comprehension to create a new list containing the lengths of each sub-list. Then, the max() function is used to find the maximum length, and the sub-list corresponding to this length is retrieved.

Here’s an example:

nested_list = [[1, 2, 3], [4, 5], [6], [7, 8, 9, 10]]

lengths = [len(sublist) for sublist in nested_list]
max_length = max(lengths)
max_list = nested_list[lengths.index(max_length)]

print(max_list)

[7, 8, 9, 10]

Here, we create a list lengths that contains the length of each sub-list using list comprehension. After finding the max_length, we then get the index of this maximum length in the lengths list and use it to fetch the corresponding sub-list from nested_list.

Method 4: Using sort() and a Custom Key Function

The sort() method is applied to the nested list with a custom key function that sorts the sub-lists by length. The last element in the sorted list is then the longest sub-list.

Here’s an example:

nested_list = [[1, 2, 3], [4, 5], [6], [7, 8, 9, 10]]
nested_list.sort(key=len)  # Sort in-place by the length of sublists

max_list = nested_list[-1]  # The last element is the longest sublist
print(max_list)

[7, 8, 9, 10]

The nested list is sorted in-place, with the sub-lists ordered by length due to the key=len parameter. The longest sub-list is now at the end of the nested_list, which we access with nested_list[-1] and print.

Bonus One-Liner Method 5: Combining max() and List Comprehension

A pythonic one-liner that again utilizes the max() function, this time combined with list comprehension, to find and return the longest sub-list directly.

Here’s an example:

max_list = max([[1, 2, 3], [4, 5], [6], [7, 8, 9, 10]], key=len)
print(max_list)

[7, 8, 9, 10]

This concise code snippet uses the max() function and specifies len as the comparison key. It outputs the sub-list with the maximum length in a straightforward manner.

Summary/Discussion

  • Method 1: Using a Simple Loop. Strengths: Clear and straightforward. Weaknesses: Verbosity could be considered a drawback.
  • Method 2: Using the max() Function with key=len. Strengths: Idiomatic use of Python’s built-in functions. Weaknesses: Assumes knowledge of how the key argument works.
  • Method 3: Using List Comprehension and max(). Strengths: Shows the utility of list comprehension and is versatile. Weaknesses: Involves multiple steps and might not be as straightforward.
  • Method 4: Using sort() with a Custom Key Function. Strengths: In-place sorting can be efficient for very large lists. Weaknesses: Alters the original list, which may not be desirable.
  • Method 5: Bonus One-Liner Method. Strengths: Concise and elegant. Weaknesses: Less readability for beginners.