Learn and practice the Top K Elements and Heaps patterns to solve coding interview problems.

Speedrun the Apple Coding Interview: Top Problems in JavaScript

This module will teach you the underlying concepts and equip you with the techniques needed to use heaps to efficiently solve a diverse range of problems. In many problems, we need to find the k most/least frequent or k largest/smallest elements in a given set of elements. Such problems, common in data analysis, natural language processing, online algorithms, as well as in recommendation systems, may be solved using the Top K Elements pattern. The fast insertion and deletion operations possible with heaps make them ideal to implement efficient algorithms for such problems. Further, we can use two heaps when we need to simultaneously keep track of the k largest elements in a set, as well as the k smallest elements in the same set. Extending this idea, each heap may be based on a separate dataset.

Heaps

# Statement
Given a string, `str`, rearrange it so that any two adjacent characters are not the same. If such a reorganization of the characters is possible, output any possible valid arrangement. Otherwise, return an empty string.

**Constraints:**
* $1\leq$ `str.length` $\leq500$
* Input string consists of lowercase English letters.

# Examples



# Understand the problem

Let’s take a moment to make sure you’ve correctly understood the problem. The quiz below helps you check that if you’re solving the correct problem:


(Select all that apply.) What is the output if `str = "programming"`?

(Select all that apply.) What is the output if <code>str = &quot;programming&quot;</code>?

(Select all that apply.) What is the output if `str = "bbbbaa"`?

(Select all that apply.) What is the output if <code>str = &quot;bbbbaa&quot;</code>?

(Select all that apply.) What is the output if `str = "hello"`?

(Select all that apply.) What is the output if <code>str = &quot;hello&quot;</code>?

Reorganize String 

Reorganize String

# Figure it out!

We have a game for you to play. Rearrange the logical building blocks to develop a clearer understanding of how to solve this problem.


Implement your solution in the following coding playground:





// Complete, updated Heap core with MinHeap/MaxHeap wrappers.
// Comparator returns true if `a` has higher priority than `b`.

class Heap {
 constructor(compare) {
 this.data = [];
 this.compare = compare;
 }

 // ----- Public API -----
 size() { return this.data.length; }
 peek() { return this.size() ? this.data[0] : undefined; }

 push(val) {
 this.data.push(val);
 this.heapifyUp(this.size() - 1);
 }

 pop() {
 const n = this.size();
 if (n === 0) return undefined;
 if (n === 1) return this.data.pop();
 const top = this.data[0];
 this.data[0] = this.data.pop();
 this.heapifyDown(0);
 return top;
 }

 // O(n) heap construction (Floyd’s algorithm)
 build(arr) {
 this.data = [...arr];
 for (let i = Math.floor(this.size() / 2) - 1; i >= 0; i--) {
 this.heapifyDown(i);
 }
 }

 // Replace root and re-heapify in one step
 replace(value) {
 if (this.size() === 0) return undefined;
 const root = this.data[0];
 this.data[0] = value;
 this.heapifyDown(0);
 return root;
 }

 // Iterate in priority order (non-destructive: works on a copy)
 *[Symbol.iterator]() {
 const copy = new Heap(this.compare);
 copy.build(this.data);
 while (copy.size()) yield copy.pop();
 }

 // ----- Internals -----
 _parent(i) { return Math.floor((i - 1) / 2); }
 _left(i) { return 2 * i + 1; }
 _right(i) { return 2 * i + 2; }
 _swap(i, j) { [this.data[i], this.data[j]] = [this.data[j], this.data[i]]; }

 heapifyUp(i) {
 while (i > 0) {
 const p = this._parent(i);
 if (!this.compare(this.data[i], this.data[p])) break;
 this._swap(i, p);
 i = p;
 }
 }

 heapifyDown(i) {
 const n = this.size();
 while (this._left(i) < n) {
 let best = this._left(i);
 const r = this._right(i);
 if (r < n && this.compare(this.data[r], this.data[best])) best = r;
 if (this.compare(this.data[i], this.data[best])) break;
 this._swap(i, best);
 i = best;
 }
 }
}

// Treat [x] the same as x; support lexicographic compare for tuples.
function head1(x) { return (Array.isArray(x) && x.length === 1) ? x[0] : x; }
function less(a, b) {
 a = head1(a);
 b = head1(b);
 if (Array.isArray(a) && Array.isArray(b)) {
 const n = Math.min(a.length, b.length);
 for (let i = 0; i < n; i++) {
 if (a[i] !== b[i]) return a[i] < b[i];
 }
 return a.length < b.length;
 }
 return a < b;
}

export class MinHeap extends Heap {
 constructor() { super((a, b) => less(a, b)); } // min-heap
}

export class MaxHeap extends Heap {
 constructor() { super((a, b) => less(b, a)); } // max-heap
}

# Quick Fix\nYou are Edward, a Senior Software Engineer who specializes in coding interview patterns, data structures and algorithms, and is great at debugging issues in code.\n\n- Edward, please keep in mind that you do not have to print the data of the placeholders. Also, do not mention anything about yourself.\n\nEdward, you are given the following data in the placeholders:\n- Function name: `reorganizeString`\n- Candidate's submitted solution code: USER_CODE\n- Error: ERROR_DESCRIPTION\n- Failed test case: FAILED_TEST_CASE\n- Coding language: `JavaScript`\n\n- Edward, your task is to understand the type of error that is occurring in the coding language provided.\n- Once you've understood the error as per the error’s description, and why the test case failed, you will suggest how the candidate can improve their solution code to fix the error.\n- Edward, you will not give the corrected code, just a hint on what the error tells, and how to fix it.\n- Edward, please limit your response strictly to 100 words. No more words than the limit.

# Review Code\nEdward, your current task is to review the `reorganizeString` function(s) and the other function(s) invoked in it/them.\n\n-Take your time and review the candidate's solution code.\n- Do not review any extra/helper files or functions not invoked in the solution function `reorganizeString`.\n- Do not review the code of data structure objects instantiated like a binary tree, linked list, trie, union find, etc., in the solution functions. Just review the solution/algorithm function of the problem. \n- Also, if the solution code doesn’t use the coding pattern stated above, then please encourage the candidate to reattempt the problem using that pattern corectly.

# Review time complexity\n- Please provide a brief time complexity analysis of the function(s) `reorganizeString`. \n- While calculating time complexity, include the time complexity of built-in JavaScript functions invoked by the code. \n- The answer should be to the point in approximately 60 words.

# Review space complexity\n- Please provide a brief space complexity analysis of the function(s) `reorganizeString`. \n- While calculating space complexity, include the space complexity of built-in JavaScript functions invoked by the code. \n- Do not include the space used for the input parameters and the space used for just returning an output (not used as a scratch space). \n- The answer should be to the point in approximately 60 words.

def evaluate_results(self) -> object:
        """
        self.__inputs is the list of inputs that are passed as input to function
        self.__actual_output is a list of outputs that is produced by the learner's code
        return : self.__edu__result object
        """
        from collections import Counter
        import heapq

        def verify_reorganization(s):
            new_s = ""
            prev = ""
            for c in s:
                if len(new_s) == 0:
                    new_s += c
                    prev = c
                if c == prev:
                    continue
                else:
                    new_s += c
                    prev = c
            return new_s

        def reorganize_string_test(input_string):
            # convert input_string to char_counter
            # store counts of each char along with their occurence
            char_counter = Counter(input_string)

            # initializing heap
            most_freq_chars = []

            # store char and its negative frequency in the array
            for char, count in char_counter.items():
                most_freq_chars.append([-count, char])

            # heapify the array
            heapq.heapify(most_freq_chars)

            # initializing variables
            previous = None
            result = ""

            while len(most_freq_chars) > 0 or previous:

                if previous and len(most_freq_chars) == 0:
                    return ""

                count, char = heapq.heappop(most_freq_chars)
                result = result + char
                count = count + 1

                # pushing the char back to heap
                if previous:
                    heapq.heappush(most_freq_chars, previous)
                    previous = None

                # setting previous to the most recent used char
                if count != 0:
                    previous = [count, char]

            return result + ""

        expected = reorganize_string_test(self.__inputs[0])
        actual = self.__actual_outputs[0]

        # multiple outputs will be added at each index respectively for a single test_case
        expected_outputs = [] 
        if actual == None or (len(expected) !=len(actual)):
            expected_outputs.append(expected)
            self.__edu_result.store_result(expected_outputs, TestCaseResult.FAIL)  
        elif expected == actual:
            expected_outputs.append(expected) 
            self.__edu_result.store_result(expected_outputs, TestCaseResult.PASS)
        elif expected !=actual and len(expected)==len(actual):
            temp = verify_reorganization(actual);
            if actual == temp:
                expected_outputs.append(actual) 
                self.__edu_result.store_result(expected_outputs, TestCaseResult.PASS)
            else:
                expected_outputs.append(expected) 
                self.__edu_result.store_result(expected_outputs, TestCaseResult.FAIL)
        else: 
            expected_outputs.append(expected)
            self.__edu_result.store_result(expected_outputs, TestCaseResult.FAIL)  

        return self.__edu_result

import { MaxHeap, MinHeap } from "./Heap.js";

/* The following definition is for MinHeap.

  You can use the same methods for MaxHeap.
  class MinHeap {

    size();          // return number of elements
    peek();          // return top element without removing
    push(val);       // insert element
    pop();           // remove and return top element
  }
*/

export function reorganizeString(str){

    // Replace this placeholder return statement with your code
    return ""
}

reorganize_string (1).yaml

testcases:
  - name: "testcase 1"
    inputs:
    - 1 : abb
    output: 
    - 1 : bab
  - name: "testcase 2"
    inputs:
    - 1: aaaaabbbbbbb
    output:
    - 1 : ""
  - name: "testcase 3"
    inputs:
    - 1: jjjjj
    output:
    - 1: ""
  - name: "testcase 4"
    inputs:
    - 1: aaabc
    output:
    - 1: abaca
  - name: "testcase 5"
    inputs:
    - 1: fofjjb
    output:
    - 1: fofjbj

def sum(input_1, input_2):
    return input_1+input_2

def validate_inputs(self) -> None:
 """
 self.__inputs is the list of inputs that are passed as input to function
 store the result using the self.store_result(error_msg, status)
 return : void
 """
 
 str = self.__inputs[0]

 if not 1 <= len(str) <= 500:
 self.store_result("'str' length should be in the range [1,500]", ValidationFunction.FAIL)
 elif not str.islower() or not str.isalpha():
 self.store_result ("'str' should consist of lowercase English alphabets", ValidationFunction.FAIL)
 else:
 self.store_result("", ValidationFunction.PASS)

Try to solve the Reorganize String problem.


Reorganize String

Try to solve the Reorganize String problem.

Top K Elements

Heaps

Conclusion

Reorganize String

Statement

Examples

Understand the problem

Figure it out!

Try it yourself