If you’re preparing for software engineering interviews, welcome to Day 1 of your coding journey.
Today, weβll focus on one of the most powerful and frequently tested data structures: HashMaps (called dictionaries in Python).
Understanding HashMaps can dramatically improve your problem-solving speed β especially in coding interviews where efficiency matters.
π§ What is a HashMap?
A HashMap is a data structure that stores data in keyβvalue pairs.
In Python, itβs implemented using a dictionary:
my_map = {
"name": "Alice",
"age": 25
}
Why are HashMaps so important?
Because they offer:
- β‘ O(1) average time complexity for:
- Insert
- Delete
- Lookup
That means extremely fast operations β exactly what interviewers want to see.
π When Should You Use a HashMap?
In interviews, you should immediately think about HashMaps when you see:
- Counting frequencies
- Checking duplicates
- Looking for complements (e.g., Two Sum)
- Grouping data
- Fast lookups
π οΈ Core Operations in Python
1. Insert / Update
my_map["city"] = "Santiago"
2. Access
print(my_map["name"]) # Alice
3. Safe Access
print(my_map.get("country", "Chile"))
4. Delete
del my_map["age"]
π‘ Interview Pattern #1 β Frequency Counter
One of the most common patterns.
Problem:
Count how many times each element appears.
def count_elements(arr):
freq = {}
for num in arr:
freq[num] = freq.get(num, 0) + 1
return freq
Example:
arr = [1, 2, 2, 3, 1, 1]
print(count_elements(arr))
Output:
{1: 3, 2: 2, 3: 1}
π‘ Interview Pattern #2 β Two Sum
This is a classic interview question.
Problem:
Find two numbers that add up to a target.
def two_sum(nums, target):
seen = {}
for i, num in enumerate(nums):
diff = target - num
if diff in seen:
return [seen[diff], i]
seen[num] = i
return []
Why this works:
- We store numbers we’ve already seen
- For each number, we check if its complement exists
- This reduces complexity from O(nΒ²) β O(n)
π‘ Interview Pattern #3 β Group Anagrams
This is one of the most popular HashMap problems in interviews.
Problem:
Given an array of strings, group the anagrams together.
Two words are anagrams if they contain the same letters in a different order.
Example:"eat"and"tea"
π§ Key Idea
If two strings are anagrams, their sorted version is the same.
"eat"β"aet""tea"β"aet"
So we can use the sorted string as a key in a HashMap.
π οΈ Solution in Python
def group_anagrams(strs):
anagrams = {}
for word in strs:
# Sort the word to create a key
key = "".join(sorted(word))
# Append the original word to the correct group
if key not in anagrams:
anagrams[key] = []
anagrams[key].append(word)
return list(anagrams.values())
β Example
words = ["eat", "tea", "tan", "ate", "nat", "bat"]
print(group_anagrams(words))
Output:
[['eat', 'tea', 'ate'], ['tan', 'nat'], ['bat']]
β‘ Complexity Analysis
- Time Complexity:
- Sorting each word β
O(k log k) - For
nwords β O(n * k log k)
- Sorting each word β
- Space Complexity:
- O(n * k) for storing results
π― Why This Problem Matters
This problem teaches you:
- How to transform data into keys
- How to group efficiently using HashMaps
- How to think beyond brute force
β οΈ Common Mistakes
Avoid these during interviews:
- β Forgetting
.get()and causing KeyErrors - β Using lists instead of HashMaps (slower lookups)
- β Not explaining time complexity
- β Overcomplicating simple problems
π§ͺ Practice Problems
Try these to reinforce todayβs concept:
- First non-repeating character
- Valid anagram
- Contains duplicate
- Longest substring without repeating characters
π― Key Takeaways
- HashMaps = fast lookups (O(1))
- Essential for almost every interview
- Learn to recognize patterns:
- Counting
- Matching
- Tracking seen values
π Your Challenge (Day 1)
Solve this problem:
Given a list of integers, return the first number that appears twice.
Try solving it using a HashMap (or set π).
π Final Thoughts
Mastering HashMaps is a must if you want to succeed in coding interviews.
This is just Day 1 β and it already unlocks a huge portion of problems youβll face.
Stay consistent, and tomorrow weβll level up. πͺ