💡 Problem Formulation: The task is determining if a given string can be transformed into a target string by changing its characters, with the restriction that you can only make a maximum of ‘k’ character changes. For example, given the initial string “abc” and the target string “def”, we wish to know if it’s possible to turn “abc” into “def” using at most three moves (one per character).
Method 1: Iterative Comparison
This method uses a straightforward iterative approach for comparison. It looks at each character position and decides if a change is needed. If a change is required, it decrements ‘k’. If ‘k’ reaches zero and the string is not yet equal to the target, the method returns False; otherwise, True.
Here’s an example:
def can_convert(s1, s2, k):
if len(s1) != len(s2):
return False
for i in range(len(s1)):
if s1[i] != s2[i]:
k -= 1
if k < 0:
return False
return True
# Example usage
print(can_convert("abc", "def", 3))Output: True
This code snippet defines a function can_convert() that compares two strings of equal length and checks if we can make the second string from the first using at most ‘k’ moves. It’s a direct and intuitive way to solve the problem.
Method 2: Using zip and sum Functions
The zip and sum functions provide a Pythonic way to tally the positions in two strings where the corresponding characters differ. This method relies on creating pairs of characters from both strings and then counting the ones that are unequal.
Here’s an example:
def can_convert(s1, s2, k):
if len(s1) != len(s2):
return False
return sum(1 for a, b in zip(s1, s2) if a != b) <= k
# Example usage
print(can_convert("abc", "adc", 1))Output: True
In this snippet, can_convert() utilizes the zip() function to pair characters from both strings and the sum() function to count differing characters, making it a concise solution.
Method 3: Using Comprehension and all Function
Utilizing list comprehension along with the all() function creates a compact approach. This method checks if the number of differing characters is within the permissible ‘k’ moves using boolean operations.
Here’s an example:
def can_convert(s1, s2, k):
return all(s1[i] == s2[i] or k > sum(s1[j] != s2[j] for j in range(i)) for i in range(len(s1)))
# Example usage
print(can_convert("abc", "axc", 2))Output: True
The can_convert() function defined in the snippet employs list comprehension and the all() function to validate each character position and ensure the number of changes does not exceed ‘k’. This approach is elegant but can be less intuitive and efficient due to repeat counting.
Method 4: Early Termination
This method stops checking as soon as the total number of required changes exceed ‘k’. It’s an optimized version of the iterative comparison that prevents unnecessary comparisons once the answer is evident.
Here’s an example:
def can_convert(s1, s2, k):
changes_needed = 0
for a, b in zip(s1, s2):
if a != b:
changes_needed += 1
if changes_needed > k:
return False
return True
# Example usage
print(can_convert("abc", "axc", 1))Output: False
The can_convert() function efficiently iterates through character pairs and tracks the number of changes needed, aborting the process if ‘k’ is surpassed, thus saving processing time on unattainable cases.
Bonus One-Liner Method 5: Lambda with Map and Filter
For enthusiasts of one-liner solutions, a lambda function combined with map and filter can accomplish the task. Though such solutions are compact, they can be challenging to read and understand for some.
Here’s an example:
can_convert = lambda s1, s2, k: False if len(s1) != len(s2) else k >= len(list(filter(lambda x: x[0] != x[1], zip(s1, s2))))
# Example usage
print(can_convert("abc", "abd", 1))Output: True
This one-liner assigns a lambda to can_convert that filters non-matching character pairs and compares the length of the resulting list to ‘k’. It’s a condensed representation of the solution.
Summary/Discussion
- Method 1: Iterative Comparison. It’s simple and straightforward. However, it can be inefficient for long strings with early mismatches.
- Method 2: Using zip and sum Functions. This method is Pythonic and has good performance. Could be less readable for beginners.
- Method 3: Using Comprehension and all Function. Elegant, but less efficient for large strings due to repeat work.
- Method 4: Early Termination. The most optimized iterative approach, stops early on failures, superb for longer strings.
- Bonus Method 5: Lambda with Map and Filter. Extremely compact and appealing for code golf, but potentially the least readable and least efficient due to list creation.