Efficiently Grouping Strings by K Length Using Python

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💡 Problem Formulation: You are given a list of strings and your task is to write a Python program to group them by a specified substring length (k). For instance, if our input is ["banana", "apple", "orange"] and k is set to 3, we need to output a dictionary where each key is a 3-letter suffix and the corresponding value is a list of strings sharing that suffix. The desired output would be something like: {"ana": ["banana"], "ple": ["apple"], "nge": ["orange"]}.

Method 1: Using Defaultdict from Collections

This method utilizes the defaultdict class from Python’s collections module to automatically handle the initialization of list structures for each suffix when grouping the strings. defaultdict simplifies the creation of dictionaries where each key is expected to be associated with a default value.

Here’s an example:

from collections import defaultdict

def group_strings(suffix_length, strings):
    grouped = defaultdict(list)
    for string in strings:
        suffix = string[-suffix_length:]
        grouped[suffix].append(string)
    return grouped

print(group_strings(3, ["banana", "apple", "orange"]))

Output: {"ana": ["banana"], "ple": ["apple"], "nge": ["orange"]}

This code defines a function group_strings() that receives a suffix length and a list of strings. It iterates over each string and slices the last k characters to get the suffix. This suffix is used as a key in the defaultdict to group all strings that share the same suffix.

Method 2: Using Dictionary Comprehension

Python’s dictionary comprehension can be used to construct a dictionary where the keys are distinct suffixes of length k, and the values are lists of the corresponding strings. This approach is elegant and leverages Python’s expressive syntax.

Here’s an example:

def group_strings(suffix_length, strings):
    return {string[-suffix_length:]: [s for s in strings if s.endswith(string[-suffix_length:])]
            for string in strings}

print(group_strings(3, ["apple", "pickle", "simple", "example"]))

Output: {"ple": ["apple", "simple", "example"], "kle": ["pickle"]}

Each iteration in the comprehension takes the last k characters of a string and checks all other strings to see if they share the same suffix, creating a key-value entry in the resulting dictionary.

Method 3: Using GroupBy from itertools

The itertools.groupby() function is ideal for grouping elements of a list, especially when the list is sorted by the desired key. This method does require the input list to be sorted in advance based on the suffix.

Here’s an example:

from itertools import groupby

def group_strings(suffix_length, strings):
    sorted_strings = sorted(strings, key=lambda x: x[-suffix_length:])
    grouped = {suffix: list(group) for suffix, group in groupby(sorted_strings, key=lambda x: x[-suffix_length:])}
    return grouped

print(group_strings(3, ["apple", "pickle", "simple", "example"]))

Output: {"ple": ["apple", "simple", "example"], "kle": ["pickle"]}

The code begins by sorting the input strings by their suffixes. It then groups them using the groupby function, which requires that the list be sorted by the same key function used for grouping.

Method 4: Using a Loop and Set

If you prefer a more traditional approach, you can achieve the same result by iterating through the list and using a set to keep track of seen suffixes. This method is straightforward but may not be as concise as other methods.

Here’s an example:

def group_strings(suffix_length, strings):
    grouped = {}
    seen_suffixes = set()
    for string in strings:
        suffix = string[-suffix_length:]
        if suffix not in seen_suffixes:
            seen_suffixes.add(suffix)
            grouped[suffix] = [string]
        else:
            grouped[suffix].append(string)
    return grouped

print(group_strings(3, ["apple", "pickle", "simple", "example"]))

Output: {"ple": ["apple", "simple", "example"], "kle": ["pickle"]}

This code snippet manually manages a dictionary to group strings by their suffixes. It uses a set to record unique suffixes and then a dictionary to group the strings accordingly.

Bonus One-Liner Method 5: Using a Lambda and Reduce

This one-liner is for the functional programming enthusiasts. It uses functools.reduce() to apply a lambda function that aggregates strings into a dictionary, grouped by their suffixes.

Here’s an example:

from functools import reduce

group_strings = lambda k, ss: reduce(lambda a, s: (a[s[-k:]].append(s), a)[-1], ss, {s[-k:]: [] for s in ss})
print(group_strings(3, ["apple", "pickle", "simple", "example"]))

Output: {"ple": ["apple", "simple", "example"], "kle": ["pickle"]}

This compact one-liner first initializes a dictionary comprehension to create an empty list for each unique suffix. Then it uses reduce() to fill these lists with the appropriate strings.

Summary/Discussion

  • Method 1: Using Defaultdict from Collections. Excels in simplicity and automatic handling of new keys. Performance is optimal due to reduced key checking overhead. However, it requires importing an additional module.
  • Method 2: Using Dictionary Comprehension. Offers a clear and concise syntax typical of Python’s expressive capabilities. However, it might be less efficient for large lists of strings due to repeated looping for each string.
  • Method 3: Using GroupBy from itertools. Provides a neat and idiomatic way to group items. It’s most efficient when working with sorted lists but requires the overhead of sorting beforehand.
  • Method 4: Using a Loop and Set. This method is transparent and easily understandable but does not provide the conciseness of other collection-centric methods.
  • Bonus One-Liner Method 5: Using a Lambda and Reduce. A fun and clever one-liner for those who enjoy functional programming styles. However, it can be cryptic and less readable for many programmers, especially those less familiar with functional programming concepts.