💡 Problem Formulation: In Python programming, the task is to extract elements containing a single, repeated digit from a given list. For instance, from the input list [121, 444, 56, 7777, 32], we aim to derive the output [444, 7777], as these elements consist exclusively of repeated instances of a single digit.
Method 1: Using List Comprehension and String Methods
This method involves utilizing a list comprehension to filter elements that consist of the same digit repeated. We convert each element to a string and check if all its characters (digits) are identical using the string method all().
Here’s an example:
input_list = [121, 444, 56, 7777, 32] mono_digits = [num for num in input_list if str(num) == str(num)[0] * len(str(num))] print(mono_digits)
Output:
[444, 7777]
This code snippet filters out mono digit numbers by comparing each number to a string composed of a repeated first digit. It’s concise and efficient for smaller lists but may be less optimal for very large lists due to the repeated string conversions.
Method 2: Using a Function with a Loop
Define a function that iterates over the list and, for each number, iterates through its digits to check consistency. If a digit differs, break the loop; otherwise, add the number to the results list.
Here’s an example:
def extract_mono_digits(numbers):
mono_digits_list = []
for num in numbers:
str_num = str(num)
if all(digit == str_num[0] for digit in str_num):
mono_digits_list.append(num)
return mono_digits_list
input_list = [121, 444, 56, 7777, 32]
print(extract_mono_digits(input_list))Output:
[444, 7777]
The provided function iterates over each element and checks for uniformity in its digits. While potentially slower than list comprehension for large datasets, the added clarity and encapsulation into a function might be beneficial when integrating this task into larger applications.
Method 3: Using Regular Expressions
This technique employs Python’s re module to match numbers comprised of a single repeated digit using a regular expression pattern. Numbers that match the pattern are included in the final listing.
Here’s an example:
import re input_list = [121, 444, 56, 7777, 32] pattern = r'^(\\d)\\1*$' mono_digits = [num for num in input_list if re.match(pattern, str(num))] print(mono_digits)
Output:
[444, 7777]
The regular expression pattern '^(\\d)\\1*$' matches any string that starts and ends with groupings of a single digit. Being a regex-based solution, it’s robust and very quick for larger datasets, although it can be less readable to those not familiar with regex syntax.
Method 4: Set Comparison
Utilizing set comparison after converting each number to a string is an efficient way to check if the number is a mono digit. If a string representation of the number, when converted to a set, has only one element, it’s a mono digit.
Here’s an example:
input_list = [121, 444, 56, 7777, 32] mono_digits = [num for num in input_list if len(set(str(num))) == 1] print(mono_digits)
Output:
[444, 7777]
This method leverages the nature of sets to eliminate duplicates, thereby offering a succinct and intuitive way to identify mono digit numbers. Its performance is typically strong, but creating sets for extremely large numbers might be less efficient.
Bonus One-Liner Method 5: Using Filter and Lambda
Utilize Python’s filter function alongside a lambda to achieve the same result in a clean, functional programming style one-liner. This is elegant and concise for users familiar with lambdas and higher-order functions.
Here’s an example:
input_list = [121, 444, 56, 7777, 32] mono_digits = list(filter(lambda x: len(set(str(x))) == 1, input_list)) print(mono_digits)
Output:
[444, 7777]
This code snippet utilizes a lambda function passed to the filter function to succinctly extract the mono digit numbers from the list. It’s an elegant solution but may be less readable for those not accustomed to the functional programming paradigm.
Summary/Discussion
Method 1: List Comprehension and String Methods. Quick and concise. May encounter performance issues with very large lists due to string conversions.
Method 2: Function with Loop. Clear logic and reusability. Potentially slower with a larger number of iterations compared to list comprehension.
Method 3: Regular Expressions. Highly efficient and robust. Can be less readable to those unfamiliar with regex patterns.
Method 4: Set Comparison. Utilizes a unique approach, great for performance. Might be less efficient for very large numbers.
Method 5: Filter and Lambda. Elegant one-liner. Potentially less readable due to the use of higher-order functions.