Frequency Counter Pattern Using Hash Map in DSA Go - Time & Space Complexity

Time Complexity: Frequency Counter Pattern Using Hash Map

O(n)

Understanding Time Complexity

We want to understand how fast counting item frequencies using a hash map works.

How does the time needed grow as the list gets bigger?

Scenario Under Consideration

Analyze the time complexity of the following code snippet.

func frequencyCounter(arr []int) map[int]int {
    freq := make(map[int]int)
    for _, val := range arr {
        freq[val]++
    }
    return freq
}

This code counts how many times each number appears in the list.

Identify Repeating Operations

Identify the loops, recursion, array traversals that repeat.

• Primary operation: Looping through the array once.
• How many times: Exactly once for each item in the array.

How Execution Grows With Input

As the list gets bigger, the code does more counting steps, one per item.

Input Size (n)	Approx. Operations
10	About 10 steps
100	About 100 steps
1000	About 1000 steps

Pattern observation: The number of steps grows directly with the number of items.

Final Time Complexity

Time Complexity: O(n)

This means the time needed grows in a straight line with the size of the input list.

Common Mistake

[X] Wrong: "Counting frequencies takes more than one pass or nested loops."

[OK] Correct: The hash map lets us count in just one pass, so no extra loops are needed.

Interview Connect

Knowing how to count frequencies quickly is a key skill that helps solve many problems efficiently.

Self-Check

"What if we used a sorted array instead of a hash map? How would the time complexity change?"

Practice

1. What is the main purpose of using a hash map in the Frequency Counter Pattern?

easy

A. To count how many times each item appears in a collection

B. To sort the items in ascending order

C. To remove duplicate items from the collection

D. To reverse the order of items in the collection

Solution

1. Step 1: Understand the role of a hash map

   A hash map stores key-value pairs, allowing quick lookup and update of counts.

2. Step 2: Apply to Frequency Counter Pattern

   We use the hash map to keep track of how many times each item appears by incrementing counts.

3. Final Answer:

   To count how many times each item appears in a collection -> Option A

4. Quick Check:

   Frequency counting = counting items [OK]

Hint: Hash map keys track items, values track counts [OK]

Common Mistakes:

• Thinking hash map sorts items
• Confusing frequency counting with removing duplicates
• Assuming hash map reverses order

2. Which of the following Go code snippets correctly initializes an empty hash map to count frequencies of strings?

easy

A. freq := map[int]string{}

B. freq := make(map[string]int)

C. freq := []string{}

D. freq := make([]int, 0)

Solution

1. Step 1: Identify correct map initialization syntax

   In Go, make(map[string]int) creates a map with string keys and int values.

2. Step 2: Check other options

   freq := map[int]string{} reverses key-value types, C and D create slices, not maps.

3. Final Answer:

   freq := make(map[string]int) -> Option B

4. Quick Check:

   Correct map syntax = make(map[string]int) [OK]

Hint: Use make(map[keyType]valueType) for maps [OK]

Common Mistakes:

• Using slices instead of maps
• Swapping key and value types
• Forgetting to use make() for map initialization

3. What will be the output of the following Go code?

package main
import "fmt"
func main() {
  words := []string{"apple", "banana", "apple", "orange", "banana", "apple"}
  freq := make(map[string]int)
  for _, w := range words {
    freq[w]++
  }
  fmt.Println(freq["apple"])
}

medium

A. 1

B. 2

C. 3

D. 0

Solution

1. Step 1: Count occurrences of "apple" in the slice

   "apple" appears at indices 0, 2, and 5, total 3 times.

2. Step 2: Check frequency map update and print

   freq[w]++ increments count for each word; freq["apple"] is 3.

3. Final Answer:

   3 -> Option C

4. Quick Check:

   Count of "apple" = 3 [OK]

Hint: Count each occurrence, then print frequency [OK]

Common Mistakes:

• Counting unique words instead of occurrences
• Off-by-one errors in counting
• Printing wrong key frequency

4. Identify the error in the following Go code that tries to count frequencies of integers:

package main
import "fmt"
func main() {
  nums := []int{1, 2, 2, 3, 1}
  freq := make(map[int]int)
  for i := 0; i <= len(nums); i++ {
    freq[nums[i]]++
  }
  fmt.Println(freq)
}

medium

A. Loop condition should be i < len(nums), not i <= len(nums)

B. Map initialization is incorrect

C. Frequency increment syntax is wrong

D. Slice nums is empty

Solution

1. Step 1: Check loop boundary conditions

   Loop uses i <= len(nums), which causes index out of range error because last index is len(nums)-1.

2. Step 2: Correct loop condition

   Change to i < len(nums) to avoid accessing invalid index.

3. Final Answer:

   Loop condition should be i < len(nums), not i <= len(nums) -> Option A

4. Quick Check:

   Index out of range fixed by i < len(nums) [OK]

Hint: Use i < len(slice) to avoid index errors [OK]

Common Mistakes:

• Using <= instead of < in loops
• Misinitializing the map
• Incorrect frequency increment syntax

5. Given two slices of integers, a and b, which Go code snippet correctly checks if b contains the same frequency of elements as a using the Frequency Counter Pattern?

hard

A. freqA := make(map[int]int)
freqB := make(map[int]int)
for _, v := range a {
freqA[v]++
}
for _, v := range b {
freqB[v]++
}
for k, v := range freqA {
if freqB[k] != v {
fmt.Println(false)
return
}
}
fmt.Println(true)

B. freq := make(map[int]int)
for _, v := range a {
freq[v] = 1
}
for _, v := range b {
if freq[v] == 0 {
fmt.Println(false)
return
}
}
fmt.Println(true)

C. freq := make(map[int]int)
for i := 0; i < len(a); i++ {
freq[a[i]]++
freq[b[i]]++
}
for _, count := range freq {
if count%2 != 0 {
fmt.Println(false)
return
}
}
fmt.Println(true)

D. freq := make(map[int]int)
for _, v := range a {
freq[v]++
}
for _, v := range b {
freq[v]--
}
for _, count := range freq {
if count != 0 {
fmt.Println(false)
return
}
}
fmt.Println(true)

Solution

1. Step 1: Build frequency map for a

   Increment counts for each element in a.

2. Step 2: Decrement frequency map for b

   Decrement counts for each element in b.

3. Step 3: Check if all counts are zero

   If any count is not zero, frequencies differ; otherwise, they match.

4. Final Answer:

   freq := make(map[int]int) for _, v := range a { freq[v]++ } for _, v := range b { freq[v]-- } for _, count := range freq { if count != 0 { fmt.Println(false) return } } fmt.Println(true) -> Option D

5. Quick Check:

   Increment a, decrement b, check zero counts [OK]

Hint: Increment for a, decrement for b, then check zeros [OK]

Common Mistakes:

• Only checking presence, not frequency
• Using separate maps without comparing properly
• Assuming counts add up without balancing increments and decrements