5 Best Ways to Find Length of Contiguous Strictly Increasing Sublist in Python

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πŸ’‘ Problem Formulation: Given a list of integers, the task is to find the length of the longest contiguous, strictly increasing sublist. Given input such as [1, 3, 5, 4, 7], the expected output would be 3, corresponding to the length of the sublist [1, 3, 5].

Method 1: Using a Simple Iterative Approach

The first method involves creating a loop that iterates through the list, keeping track of the current sublist’s length and updating a maximum length variable as necessary. This method is straightforward and uses no additional libraries.

Here’s an example:

def find_longest_increasing_sublist(lst):
    max_length = 1
    current_length = 1
    for i in range(1, len(lst)):
        if lst[i] > lst[i - 1]:
            current_length += 1
            max_length = max(max_length, current_length)
        else:
            current_length = 1
    return max_length

# Example usage:
print(find_longest_increasing_sublist([1, 3, 5, 4, 7]))

Output:

3

This function find_longest_increasing_sublist iterates through the input list, compares each element with the preceding one, and counts the length of increasing sequences. When a non-increasing element is encountered, the count is reset, ensuring only strictly increasing sublists are considered.

Method 2: Using the itertools.groupby Function

Python’s itertools.groupby function can be used to group items according to a key function. Here, it groups contiguous sequences of increasing numbers. This method is useful if you wish to leverage Python’s standard library for more concise code.

Here’s an example:

from itertools import groupby
from operator import lt

def find_longest_increasing_sublist(lst):
    return max(len(list(group)) for key, group in groupby(lst, lambda x, _cache=[None]: lt(_cache[0], x) and _cache.__setitem__(0, x) or _cache.__setitem__(0, x)))

# Example usage:
print(find_longest_increasing_sublist([1, 3, 5, 4, 7]))

Output:

3

In this example, groupby is used with a key function that compares each element with its predecessor. This function uses a cache list to store the last processed item and determine whether the current item forms an increasing sequence. The lengths of these sequences are then determined and the maximum length is returned.

Method 3: Using Dynamic Programming

Method 3 employs a dynamic programming approach to this problem by defining a sublist length array that keeps track of the longest increasing sublist at each index. This method is efficient and suits larger datasets.

Here’s an example:

def find_longest_increasing_sublist(lst):
    if not lst:
        return 0
    lengths = [1] * len(lst)
    for i in range(1, len(lst)):
        if lst[i] > lst[i - 1]:
            lengths[i] = lengths[i - 1] + 1
    return max(lengths)

# Example usage:
print(find_longest_increasing_sublist([1, 3, 5, 4, 7]))

Output:

3

This function initializes a list lengths to keep track of the increasing sublist lengths. It iterates over the main list and updates the lengths list based on the comparison of consecutive elements. This array builds up to provide the max length of increasing sublists.

Method 4: Using Enumerate and Max with a Custom Function

This technique uses Python’s built-in enumerate function to iterate through elements and their indices and applies a custom comparison to determine the lengths of increasing sublists. This approach offers readability and good use of Python’s intuitive syntax.

Here’s an example:

def find_longest_increasing_sublist(lst):
    max_length = current_length = (lst[0], 1)
    for idx, num in enumerate(lst[1:], 1):
        current_length = (num, current_length[1] + 1 if num > lst[idx-1] else 1)
        max_length = max(max_length, current_length, key=lambda x: x[1])
    return max_length[1]

# Example usage:
print(find_longest_increasing_sublist([1, 3, 5, 4, 7]))

Output:

3

The function utilizes enumerate to get both the index and value of each element in the list, allowing comparisons between adjacent values. A tuple is used to track the number and the current sublist length. The max function determines the maximum length at each step.

Bonus One-Liner Method 5: Using List Comprehensions

This one-liner is a condensed form using list comprehensions and zip function for those who prefer a more Pythonic, compact code. It is recommended for experienced Python developers familiar with list comprehensions.

Here’s an example:

find_longest_increasing_sublist = lambda lst: max(sum(1 for _ in g) for k, g in groupby(zip(lst, lst[1:]), lambda x: x[0] < x[1]) if k)

# Example usage:
print(find_longest_increasing_sublist([1, 3, 5, 4, 7]))

Output:

3

This one-liner combines zip to pair each element with its successor and groupby to create groups where consecutive numbers are increasing. It then sums the lengths of these groups to find the maximum.

Summary/Discussion

  • Method 1: Simple Iterative Approach. This method is straightforward and easy to understand. It works well with small to moderate lists but may not be the most elegant or fastest solution for large data sets.
  • Method 2: itertools.groupby Function. This provides a more Pythonic approach and is concise. However, it may not be immediately clear to those unfamiliar with itertools or lambda functions.
  • Method 3: Dynamic Programming. This approach is more efficient for large lists and is a classic technique in algorithm design for optimizing computations over sequences. It requires a bit more code but often has better performance.
  • Method 4: Enumerate and Max with Custom Function. While this method is readable and elegantly uses Python’s syntax, it may not be as straightforward for beginners to grasp, and the performance is similar to the iterative approach.
  • Method 5: One-Liner List Comprehensions. This method is compact and demonstrates the power of Python’s list comprehensions. It’s not the most readable and is recommended for more experienced Pythonistas.