💡 Problem Formulation: We need to determine if a list contains exclusively even numbers within a specified range. For instance, given the list [2, 4, 6, 8, 10] and the range from 1 to 10, our test would confirm that all elements are even and fall within the range. The desired output is a boolean value: True if all elements meet the criteria; otherwise, False.
Method 1: Using a for Loop and if Statement
This method iterates over each element in the list and checks whether it is both even and within the specified range. If any element fails the test, it returns False; otherwise, True.
Here’s an example:
def all_even_in_range(lst, start, end):
for num in lst:
if num % 2 != 0 or not(start <= num <= end):
return False
return True
# Test the function
print(all_even_in_range([2, 4, 6, 8, 10], 1, 10))
Output:
True
This code defines a function all_even_in_range() which takes a list and a range to test if all elements are even and fall within the stated range. The for loop is straightforward, but the function may not be efficient for large lists or complex conditions.
Method 2: Using the all() Function and List Comprehension
The all() function in conjunction with a list comprehension provides a concise way to test whether all elements in the list are even and within the given range.
Here’s an example:
def all_even_in_range(lst, start, end):
return all(num % 2 == 0 and start <= num <= end for num in lst)
# Test the function
print(all_even_in_range([2, 4, 6, 8, 10], 1, 10))
Output:
True
In this example, we define a function that leverages the all() function with a generator expression to check the condition for all elements in one line. It’s a clean and efficient solution for small to medium-sized lists.
Method 3: Using filter() and lambda Functions
The filter() function can be used with a lambda to sieve out elements that do not comply with both being even and within the defined range. This method works well for larger lists, as it returns an iterator rather than a list.
Here’s an example:
def all_even_in_range(lst, start, end):
return not bool(list(filter(lambda x: x % 2 != 0 or not (start <= x <= end), lst)))
# Test the function
print(all_even_in_range([2, 4, 6, 8, 10], 1, 10))
Output:
True
In this code snippet, the filter() function creates an iterator of elements that are not even or not within the range. If the list conversion of this iterator is empty (boolean false), we can conclude that all elements passed the test. It’s elegant but may be less intuitive due to the double negative check.
Method 4: Dispatching Functions with Python’s functools
We can utilize Python’s functools module to dispatch functions based on the type of the input elements, providing greater flexibility if the list can hold different types.
Here’s an example:
from functools import singledispatch
@singledispatch
def is_even_and_in_range(number, start, end):
raise NotImplementedError("Only integers are supported")
@is_even_and_in_range.register
def _(number: int, start: int, end: int):
return number % 2 == 0 and start <= number <= end
def all_even_in_range(lst, start, end):
return all(is_even_and_in_range(num, start, end) for num in lst)
# Test the function
print(all_even_in_range([2, 4, 6, 8, 10], 1, 10))
Output:
True
Using singledispatch allows us to have a generic function tailored to specific types. Here the test is applied only to integers. This method scales well for complex scenarios where a list might have multiple types. However, it’s an overhead for simple cases.
Bonus One-Liner Method 5: Using a Set for Range Verification
A set-based approach checks if the intersection of the set created from the range and list equals the set made from the list, provided that all list elements are even.
Here’s an example:
def all_even_in_range(lst, start, end):
return set(range(start, end+1, 2)).intersection(lst) == set(lst)
# Test the function
print(all_even_in_range([2, 4, 6, 8, 10], 1, 10))
Output:
True
This concise approach utilizes the properties of sets for mathematical set operations to determine if the elements fall within range and are all even. It’s a quick check but uses extra memory for the set conversion and may not be efficient for very large lists.
Summary/Discussion
- Method 1: For Loop and if Statement. Simple and straightforward. Performance may degrade with large lists.
- Method 2: all() Function and List Comprehension. One-liner and Pythonic. Best for small to medium lists.
- Method 3: filter() and lambda Functions. Efficient for large lists. Less readable due to double negative check.
- Method 4: functools and Dispatching. Provides great flexibility for different types within a list. Overkill for simple cases.
- Bonus Method 5: Set Intersection. Elegant one-liner. Potentially memory-intensive for large inputs.