A string s
can be partitioned into groups of size k
using the following procedure:
k
characters of the string, the second group consists of the next k
characters of the string, and so on. Each character can be a part of exactly one group.k
characters remaining, a character fill
is used to complete the group.Note that the partition is done so that after removing the fill
character from the last group (if it exists) and concatenating all the groups in order, the resultant string should be s
.
Given the string s
, the size of each group k
and the character fill
, return a string array denoting the composition of every group s
has been divided into, using the above procedure.
Example 1:
Input: s = "abcdefghi", k = 3, fill = "x" Output: ["abc","def","ghi"] Explanation: The first 3 characters "abc" form the first group. The next 3 characters "def" form the second group. The last 3 characters "ghi" form the third group. Since all groups can be completely filled by characters from the string, we do not need to use fill. Thus, the groups formed are "abc", "def", and "ghi".
Example 2:
Input: s = "abcdefghij", k = 3, fill = "x" Output: ["abc","def","ghi","jxx"] Explanation: Similar to the previous example, we are forming the first three groups "abc", "def", and "ghi". For the last group, we can only use the character 'j' from the string. To complete this group, we add 'x' twice. Thus, the 4 groups formed are "abc", "def", "ghi", and "jxx".
Constraints:
1 <= s.length <= 100
s
consists of lowercase English letters only.1 <= k <= 100
fill
is a lowercase English letter.This problem involves partitioning a string s
into groups of size k
. If the string's length isn't a multiple of k
, we pad the last group with a specified character fill
to make it length k
.
The most efficient way to solve this is through direct simulation. We iterate through the string in steps of k
, extracting substrings of length k
. For the last group, if its length is less than k
, we pad it with the fill
character until it reaches length k
.
Time Complexity: O(n), where n is the length of the string s
. This is because we iterate through the string once to create the groups. The padding operation in the last group also takes linear time in the worst case (when the last group is almost empty).
Space Complexity: O(n) in the worst case. The space used is primarily for storing the resulting array of strings. In the worst-case scenario where k=1
, the output array will have n
strings, each of length 1.
The following code snippets demonstrate the solution in various programming languages. The core logic remains the same across all implementations: iterative substring extraction with padding for the last group.
class Solution:
def divideString(self, s: str, k: int, fill: str) -> List[str]:
result = []
for i in range(0, len(s), k):
group = s[i:i+k]
group = group.ljust(k, fill) # Pad with fill character if needed
result.append(group)
return result
class Solution {
public String[] divideString(String s, int k, char fill) {
List<String> result = new ArrayList<>();
for (int i = 0; i < s.length(); i += k) {
String group = s.substring(i, Math.min(i + k, s.length()));
group = String.format("%-" + k + "s", group).replace(' ', fill); //Pad with fill character
result.add(group);
}
return result.toArray(new String[0]);
}
}
#include <string>
#include <vector>
#include <algorithm>
class Solution {
public:
std::vector<std::string> divideString(std::string s, int k, char fill) {
std::vector<std::string> result;
for (int i = 0; i < s.length(); i += k) {
std::string group = s.substr(i, std::min((int)s.length() - i, k));
group.resize(k, fill); //Resize and fill with fill character
result.push_back(group);
}
return result;
}
};
/**
* @param {string} s
* @param {number} k
* @param {string} fill
* @return {string[]}
*/
var divideString = function(s, k, fill) {
let result = [];
for (let i = 0; i < s.length; i += k) {
let group = s.substring(i, Math.min(i + k, s.length));
group = group.padEnd(k, fill); //Pad with fill character
result.push(group);
}
return result;
};
These implementations all follow the same core algorithm, making them efficient and easy to understand. The choice of language depends on the specific project requirements and programmer preference. The time and space complexities remain consistent across all implementations.