00_Getting Started

Template

coding_interview_steps.jpeg

Β 

https://github.com/mdmzfzl/NeetCode-Solutions

def solve_problem(inputs):
    # Step 1: Understand the Problem
    # - Parse inputs and outputs.
    # - Clarify constraints (e.g., time, space).
    # - Identify edge cases.

    # Step 2: Plan and Design
    # - Think about the brute-force approach.
    # - Optimize: Can you use dynamic programming, divide & conquer, etc.?
    # - Choose the appropriate data structures (e.g., arrays, hashmaps, heaps).
    
    # Step 3: Implement the Solution
    # - Use helper functions for modularity.
    # - Write clear, well-commented code.
    
    def helper_function(args):
        # Optional: For recursion, BFS, DFS, etc.
        pass

    # Main logic
    result = None  # Initialize result or output variable.
    
    # Example logic
    # for num in inputs:
    #    result += num  # Or other computations.

    return result

# Driver code (for testing locally)
if __name__ == "__main__":
    inputs = []  # Replace with example test cases.
    print(solve_problem(inputs))

Understanding BIG O Calculations

Big O notation is a way to describe how the algorithm grow as the input size increases. Two things it considers:Β 

1. Basic Idea

2. Common Time Complexities

big_o_graph_annotated.png

In order of least dominating term O(1) to most dominating term (the one that grows the fastest)

3. Understanding Through Examples

Constant Time – O(1)

def get_first_element(lst):
    return lst[0]

Regardless of list size, only one operation is performed.

Linear Time – O(n)

def find_max(lst):
    max_val = lst[0]
    for num in lst:
        if num > max_val:
            max_val = num
    return max_val

In theΒ find_max function, every element is inspected once, so the time grows linearly with the list size.

Quadratic Time – O(nΒ²)

def print_all_pairs(lst):
    for i in range(len(lst)):
        for j in range(len(lst)):
            print(lst[i], lst[j])

Here, for each element, you’re iterating over the entire list, leading to a quadratic number of comparisons.

4. Why Big O Matters

5. Space Complexity

6. Visualizing Growth Rates

Imagine a graph where the x-axis is the input size and the y-axis is the time taken:

7. Combining big-o

When you have different parts of an algorithm with their own complexities, combining them depends on whether they run sequentially or are nested within each other.

1. Sequential Operations

If you perform one operation after the other, you add their complexities. For example, if one part of your code is O(n) and another is O(nΒ²), then the total time is:

This is because, as n grows, the O(nΒ²) term dominates and the lower-order O(n) becomes negligible.

def sequential_example(nums):
    # First part: O(n)
    for num in nums:
        print(num)

    # Second part: O(nΒ²)
    for i in range(len(nums)):
        for j in range(len(nums)):
            print(nums[i], nums[j])

Here, the overall time complexity is O(n) + O(nΒ²), which simplifies to O(nΒ²).

2. Nested Operations

If one operation is inside another (nested loops), you multiply their complexities. For example, if you have an outer loop that runs O(n) times and an inner loop that runs O(n) times for each iteration, then the total time is:

def nested_example(nums):
    for i in range(len(nums)):          # O(n)
        for j in range(len(nums)):      # O(n) for each i
            print(nums[i], nums[j])
This nested structure gives you O(nΒ²) overall.

3. Combining Different Parts

In real problems, your code might have a mix of sequential and nested parts.

The key idea is:

  1. Add the complexities for sequential parts.

  2. Multiply the complexities for nested parts.

  3. Drop the lower-order terms and constant factors.

When adding complexities, the dominating term (the one that grows the fastest) is the one that determines the overall Big O notation.

Examples:

def combined_example(arr):
    # Sequential part: Loop that processes each element.
    # This loop runs O(n) times.
    for x in arr:
        print(x)  # O(1) operation per element.

    # Nested part: For each element in arr, loop over arr again.
    # The inner loop is nested within the outer loop, giving O(n) * O(n) = O(nΒ²) operations.
    for x in arr:
        for y in arr:
            print(x, y)  # O(1) operation per inner loop iteration.

# If we call combined_example, the overall complexity is:
# O(n) [from the first loop] + O(nΒ²) [from the nested loops].
# For large n, O(nΒ²) dominates O(n), so we drop the lower-order term and constant factors,
# and the overall time complexity is O(nΒ²).

if __name__ == "__main__":
    sample_list = [1, 2, 3, 4, 5]
    combined_example(sample_list)
# Binary search on a sorted list
def binary_search(arr, target):
    low, high = 0, len(arr) - 1
    while low <= high:
        mid = (low + high) // 2
        if arr[mid] == target:
            return mid
        elif arr[mid] < target:
            low = mid + 1
        else:
            high = mid - 1
    return -1

# Outer loop processes each query, each using binary search
def search_all(sorted_arr, queries):
    results = []
    for query in queries:         # O(n) if queries contains n items
        index = binary_search(sorted_arr, query)  # O(log n)
        results.append(index)
    return results

if __name__ == '__main__':
    # Assume this list is already sorted
    sorted_arr = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
    # List of queries (if its size is roughly n, overall complexity is O(n log n))
    queries = [3, 7, 10]
    print("Search results:", search_all(sorted_arr, queries))

This example demonstrates how nested operations (a loop with an inner binary search) yield an O(n log n) algorithm.

Special Note:Β If using python sort method, it uses TimSort which worse case is O(n log n)

Loops

loops-to-use.png

🟒 Standard

Use when you want to loop directly over the items (values only).

Input Example Output
["apple", "banana"] for fruit in ["apple", "banana"]: print(fruit) apple banana
"hi" for char in "hi": print(char) h i
{ "a": 1, "b": 2 } for key in {"a": 1, "b": 2}: print(key) a b
dict.items() for k, v in {"a": 1}.items(): print(k, v) a 1

πŸ”· RANGE LOOPS

Use when you need to count, index, or control the steps of iteration.

Input Example Output
range(3) for i in range(3): print(i) 0 1 2
range(2, 5) for i in range(2, 5): print(i) 2 3 4
range(0, 6, 2) for i in range(0, 6, 2): print(i) 0 2 4
range(3, 0, -1) for i in range(3, 0, -1): print(i) 3 2 1
range(len(list)) for i in range(len(fruits)): print(fruits[i]) values in fruits

🟣 ENUMERATE LOOPS

Use when you want both index and value from a list or iterable.

Input Example Output
["x", "y"] for i, v in enumerate(["x", "y"]): print(i, v) 0 x 1 y
["a", "b", "c"] for i, letter in enumerate(["a", "b", "c"]): ... 0 a 1 b 2 c

🧱 NESTED LOOPS 

Loop Type Input Example Output
πŸ”· Range range(2), range(2) for i in range(2): for j in range(2): print(i, j) 0 0, 0 1, 1 0, 1 1
πŸ”· Range 2D array (matrix) by index for i in range(len(matrix)):for j in range(len(matrix[0])): Access each matrix[i][j]
πŸ”· Range All pairs in list for i in range(len(nums)):for j in range(i+1, len(nums)): Pairs (i, j)
🟣 Enumerate List of lists (2D grid) for i, row in enumerate(grid):for j, val in enumerate(row): (i, j, val) per cell
🟒 Normal List of lists (values only) for row in matrix:for val in row: print(val) Each cell value
🟒 Normal Two arrays (cross product) for a in ["a", "b"]:for n in ["1", "2"]: print(a, n) a 1, a 2, b 1, b 2

1. βœ… Standard for loop

Use when you want to iterate over items in a list, set, or string.

colors = ['red', 'green', 'blue']
for color in colors:
    print(color)

2. βœ… enumerate() loop

Use when you need both the index and the value.

for i, color in enumerate(colors):
    print(i, color)

3. βœ… range() loop

Use when you want to:


4. βœ… while loop

Use when:

x = 0
while x < 5:
    print(x)
    x += 1

πŸ“Œ Warning: Always make sure the condition will eventually become False or you'll get an infinite loop!

🧠 When to Use Which?

Loop Type When to Use
for item in list Clean, readable, item-based loops
enumerate() Index + value
range() Loop by number or index, brute-force logic
while Loop while a condition is true (uncertain end)

Optional Daily Practice Prompts

Want to write one of each daily?

Want me to add this to your daily fundamentals checklist or export it as a mini Anki flashcard set or markdown file you can keep open?

OOP for Python

Absolutely! Here's a practical set of Python OOP code examples for each item in your 🧱 Object-Oriented Programming (OOP) checklist β€” perfect for review or muscle-memory practice.

🧱 Object-Oriented Programming (OOP) in Python β€” With Code Examples

βœ… 1. Create a class with __init__()

class Dog:
    def __init__(self, name):
        self.name = name

d = Dog("Fido")
print(d.name)  # Fido

βœ… 2. Add a method to the class

class Dog:
    def __init__(self, name):
        self.name = name

    def bark(self):
        return f"{self.name} says woof!"

d = Dog("Rex")
print(d.bark())  # Rex says woof!

βœ… 3. Use @property for a read-only attribute

class Circle:
    def __init__(self, radius):
        self._radius = radius

    @property
    def area(self):
        return 3.14 * self._radius ** 2

c = Circle(3)
print(c.area)  # 28.26
# c.area = 50  # ❌ Error: can't set attribute

βœ… 4. Inherit from another class

class Animal:
    def speak(self):
        return "Some sound"

class Dog(Animal):
    def speak(self):
        return "Bark"

d = Dog()
print(d.speak())  # Bark

βœ… 5. Override a method

class Animal:
    def speak(self):
        return "Some sound"

class Cat(Animal):
    def speak(self):
        return "Meow"

c = Cat()
print(c.speak())  # Meow

βœ… 6. Use super() to call base class method

class Animal:
    def speak(self):
        return "Some sound"

class Dog(Animal):
    def speak(self):
        base = super().speak()
        return base + " + Bark"

d = Dog()
print(d.speak())  # Some sound + Bark

βœ… 7. Use __repr__() or __str__()

class Dog:
    def __init__(self, name):
        self.name = name

    def __repr__(self):
        return f"Dog('{self.name}')"

    def __str__(self):
        return f"My dog's name is {self.name}"

d = Dog("Buddy")
print(repr(d))  # Dog('Buddy')
print(str(d))   # My dog's name is Buddy

Absolutely! Here's the full ChatGPT response on polymorphism, polished and formatted so you can save it, review it, or turn it into a markdown or flashcard:

🧱 What is Polymorphism in Python?

πŸ” Definition:

Polymorphism means "many forms" β€” the same function or method behaves differently depending on the object it’s acting on.

πŸ’‘ Real-World Analogy:

πŸ§ͺ Python Polymorphism Example 1: Method Overriding

This is classic runtime polymorphism through inheritance:

class Animal:
    def speak(self):
        return "Some sound"

class Dog(Animal):
    def speak(self):
        return "Bark"

class Cat(Animal):
    def speak(self):
        return "Meow"

animals = [Dog(), Cat()]

for animal in animals:
    print(animal.speak())  # Bark, Meow

βœ… You call the same method (speak()), but each class handles it differently.

πŸ¦† Python Polymorphism Example 2: Duck Typing

Python doesn’t require a shared parent class β€” it just cares if the object has the right method.

class Bird:
    def fly(self):
        print("Flapping wings")

class Plane:
    def fly(self):
        print("Engines on")

def lift_off(flier):
    flier.fly()

lift_off(Bird())   # Flapping wings
lift_off(Plane())  # Engines on

βœ… As long as the object has a .fly() method, it can be passed in β€” β€œif it quacks like a duck...”

🧠 TL;DR:

Concept Meaning
Polymorphism Same method name, different behavior
Method override Subclasses implement their own version
Duck typing Type doesn't matter, behavior does
Benefit Cleaner, extensible, decoupled code

Would you like me to export this explanation as a .md, .txt, or add it to a reference doc for your interview prep?

Data Types

Absolutely β€” knowing your core data types inside and out is essential for LeetCode and coding interviews. These are the types you’ll see constantly and will use to build efficient solutions.

Here’s a curated list of the must-know data types for LeetCode β€” including how to use them, when to choose them, and a one-liner to remember what they’re best at.

🧠 LeetCode Data Types You Should Know (Python)

βœ… 1. int – Whole numbers

Used for counting, math, binary, etc.

n = 42

πŸ§ͺ Use cases:

βœ… 2. float – Decimal numbers

Used rarely, but important for precision-sensitive problems.

pi = 3.14

πŸ§ͺ Watch out for rounding errors.

βœ… 3. str – Strings

πŸ”₯ High-frequency on LeetCode

s = "leetcode"

πŸ§ͺ Know:

βœ… 4. list – Ordered, mutable collection

πŸ”₯ Most used structure in LeetCode

arr = [1, 2, 3]

πŸ§ͺ Know:

βœ… 5. tuple – Ordered, immutable collection

Used for keys in sets/dicts or returning multiple values.

t = (1, 2)

πŸ§ͺ Know:

βœ… 6. set – Unordered, unique items

πŸ”‘ Great for fast lookup and duplicates

s = set([1, 2, 3])

πŸ§ͺ Know:

βœ… 7. dict – Key-value pairs

πŸ”₯ Critical for hash maps / fast lookups

d = {"a": 1, "b": 2}

πŸ§ͺ Know:

βœ… 8. collections.deque – Double-ended queue

⚑ Fast append and popleft() β†’ great for BFS

from collections import deque
q = deque([1, 2])
q.append(3)
q.popleft()

βœ… 9. heapq / Min Heap (via list)

πŸ’‘ Use for priority queue, top K elements

import heapq
h = [5, 3, 8]
heapq.heapify(h)
heapq.heappop(h)

πŸ§ͺ Python only has min-heaps β€” invert values for max-heap.

βœ… 10. collections.Counter – Fast frequency counting

from collections import Counter
c = Counter("aabbcc")

πŸ§ͺ Useful for:

🧠 TL;DR: Memorize These for LeetCode

Type What it's good for
int Counting, math
float Precision, rarely needed
str Manipulation, parsing, search
list Arrays, stacks, sliding window
tuple Hashable keys, return multiple items
set Uniqueness, fast lookup
dict Key-value, counting, hash maps
deque BFS, queue (O(1) pops)
heapq Priority queue, top K problems
Counter Fast frequency counting

Would you like: