When I first started prepping for coding interviews, I treated Python like a language I “kind of knew.” Big mistake. The moment I got hit with a question about generators and decorators in a Meta interview, my brain went blank. I realized knowing syntax wasn’t enough I needed to think in Python.

If you’ve ever frozen mid-question trying to remember how list slicing works or when to use a dictionary over a set, you’re not alone. That’s precisely why I built Interview Coder’s AI Interview Assistant to help you practice Python interview questions the same way I did when landing internships at Amazon, Meta, and TikTok. It walks you through real questions, checks your code, and gives you feedback that actually makes sense, no fluff, just clear improvement each time you practice.

36 Python Basic Interview Questions (With My Straightforward Takes)

1. Python Overview: Why I Still Use It Daily

Let’s Start With The Obvious One

I learned Python because I was tired of staring at Java boilerplate, wondering if I actually liked coding. Python felt like cheating. But in a good way.

It’s clean.
It’s readable.

And it doesn’t make you fight the syntax before solving the problem.

Here’s what makes Python the first language I recommend to anyone starting out (or switching teams):

It reads like English. Fewer curly braces. More actual logic.
You don’t have to declare types (until you want to).
You can debug stuff easily since it runs line-by-line.
The library support is insane. Want to mess with data? Pandas. Build a backend? Django. Do math that makes your brain hurt? NumPy.
Works on every OS, no drama moving between Mac, Windows, and Linux.

If you're prepping for interviews, Python Programming will not slow you down, which is precisely why I used it at Amazon, Meta, and TikTok.

2. Lists vs. Tuples: Know When to Lock It or Leave It Open

I messed this up in my first coding interview. Knew what a list was. Blank stare when they asked why I didn’t use a tuple.

Here’s the breakdown, with zero fluff:

List

You can change it. Add stuff. Remove stuff. Shuffle it all around.
Uses more memory, but that’s the tradeoff for flexibility.
Slower than tuples, but better when you need to mutate the data.
Comes with a ton of built-in methods.

a_list = ["Data", "Camp", "Tutorial"]

a_list.append("Session")

print(a_list) # Output: ['Data', 'Camp', 'Tutorial', 'Session']

Tuple

Locked. Once it’s made, it stays that way.
Faster to loop through. Less memory hogging.
Doesn’t come with all the list toys.

a_tuple = ("Data", "Camp", "Tutorial")

print(a_tuple) # Output: ('Data,' 'Camp,' 'Tutorial')

If you’re not changing the values, use a tuple. Otherwise, lists are your friend.

Also, check out our Python Lists guide, it’ll save you from small but dumb mistakes.

3. What’s init() Really Doing?

Ah yes, the part of Python that makes it feel like real object-oriented programming.

Here’s The Real Talk Version

When you make a new object in Python, __init__() is the method that runs automatically. Think of it as the function that sets things up, like giving your object its starting gear.

Here's a small example, pulled straight from something I built during my internship prep:

class book_shop:

def __init__(self, title):

self.title = title

def book(self):

print('The title of the book is', self.title)

b = book_shop('Sandman')

b.book()

# Output: The title of the book is Sandman

What’s Happening

__init__() stores the title when the object is created.
book() is a normal method that just prints the title.
b = book_shop("Sandman") creates the object and runs the constructor automatically.

That’s it. Don’t overthink it.

It’s just your way of setting up the object’s internal state when it’s born.

4. Mutable vs Immutable: How Python Handles Change

When I was just getting started with Python, this tripped me up. I'd update one list, and somehow another variable changed too. I thought Python was haunted.

It turns out it’s just... mutability.

Let me break it down the way I wish someone had explained it to me.

Mutable Types

You can change them. They don’t mind.

Think: list, dict, set. They’re flexible, add stuff, remove stuff, edit in place.

# List

a_list = [1, 2, 3]

a_list.append(4)

print(a_list) # [1, 2, 3, 4]

# Dictionary

a_dict = {'a': 1, 'b': 2}

a_dict['c'] = 3

print(a_dict) # {'a': 1, 'b': 2, 'c': 3}

Immutable Types

Once you make them, that’s it. You want to change something? Python makes a brand new object.

Think: int, float, str, tuple.

# Integer

a = 10

a = 20 # New integer

print(a) # 20

# String

s = "hello"

s = "world" # New string

print(s) # world

# Tuple

t = (1, 2, 3)

# t[0] = 9 # Nope. TypeError.

print(t) # (1, 2, 3)

Mutable = “edit in place.”
Immutable = “copy, then change.”

5. Comprehensions & Generator Expressions: One-Liners That Actually Make Sense

Have you ever written a for loop that looks like five lines of boilerplate just to build a list?

Yeah, same.

Then I discovered comprehensions, and suddenly I felt like a wizard.

List Comprehension

Take any iterable and spin it into a new list in one line.

my_list = [i for i in range(1, 10)]

print(my_list)

# [1, 2, 3, 4, 5, 6, 7, 8, 9]

Cleaner. Faster. Readable.

Dict Comprehension

Same idea, but for dictionaries.

my_dict = {i: i**2 for i in range(1, 10)}

print(my_dict)

# {1: 1, 2: 4, 3: 9, ..., 9: 81}

Tuple “Comprehension”

Spoiler: it doesn’t exist.

If you wrap a comprehension in (), Python gives you a generator — not a tuple. If you want a tuple, you have to build one from a generator.

my_gen = (i for i in range(1, 10))

print(my_gen)

# <generator object>

my_tuple = tuple(i for i in range(1, 10))

print(my_tuple)

# (1, 2, 3, 4, 5, 6, 7, 8, 9)

Don't get tricked by the parentheses. They’re not what they seem.

6. GIL: Why Python Threads Don’t Really Thread

Let me save you the 6-hour debugging rabbit hole I went down.

In CPython, only one thread can run Python code at a time. Yep. One.

That’s the Global Interpreter Lock (GIL). It’s like a bouncer guarding the CPU only one thread gets in at a time to execute Python bytecode.

What this means

If you’re doing CPU-heavy work: multithreading won’t help.
If you’re doing I/O-bound stuff (network requests, file access): threads are fine.

# Threaded network calls = good

# Threaded matrix multiplication = waste of time

Starting with Python 3.13, there's a version without the GIL (PEP 703), and 3.14 is making it semi-official. But we’re still early. Lots of packages will need time to catch up.

Moral of the story: if your code is CPU-bound, go multi-process or async. Don't fight the GIL. It always wins.

7. Searching & Graph Traversal: Stuff You Actually Need to Know

Interviewers love this stuff. And honestly? It’s kinda fun once it clicks.

Here’s how I think about the main ones.

Binary Search

Only works on sorted stuff.Cuts the search space in half every step.

# 0(log n) — efficient and clean

AVL Tree

A tree that stays balanced. Always.

Whenever you insert/delete something, it does the math to keep itself from getting skewed.

BFS (Breadth-First Search)

Level by level. Good for finding the shortest path when everything costs the same.

Think: finding the shortest route in an unweighted maze.

DFS (Depth-First Search)

Goes deep down a branch before backtracking.

Good for:

Maze-solving
Tree recursion
Cycle detection

A* Algorithm

Intelligent pathfinding with a built-in sense of direction.

If you’ve played a game with an AI that knows how to chase you, it probably used A*.

8. KeyError: Why Python Yells at You Over Missing Keys

So you try to grab a value from a dictionary using a key that doesn’t exist. KeyError.

It’s Python’s way of saying “That key? Never heard of it.”

Here’s how to deal:

1. Use .get()

Returns None (or a fallback) if the key isn’t there.

my_dict = {'a': 1}

print(my_dict.get('b')) # None

print(my_dict.get('b', 0)) # 0

2. Use try / except

try:

value = my_dict['b']

except KeyError:

value = 0

3. Use "key" in dict check

if 'b' in my_dict:

value = my_dict['b']

I usually go with .get() unless I need to raise an error. Cleaner.

More in our post: Python KeyError Exceptions and How to Fix Them

9. How Python Handles Memory: The Garbage Collector’s Behind-the-Scenes Magic

Python doesn’t ask you to free memory manually. That’s nice.

Instead, it tracks how many references exist to an object. When that count hits zero? Time to delete it.

This is done via reference counting + a cyclic garbage collector for edge cases (like circular references).

import gc

gc.collect() # You can call this manually, but 99% of the time you don’t need to.

You can toggle it on/off with gc.enable() / gc.disable() too.

But unless you're writing memory-critical code or benchmarking stuff, you can pretty much ignore this.

10. Shallow Copy vs Deep Copy: Because Python’s Sneaky Like That

If you're copying a list of lists and changes in one copy magically show up in the other — congrats. You've just met shallow copy.

Here’s the breakdown:

Shallow Copy

Copies the outer object, but not the stuff inside.

import copy

original = [[1, 2], [3, 4]]

shallow = copy.copy(original)

shallow[0][0] = 99

print(original)

# [[99, 2], [3, 4]] — surprise!

Deep Copy

Makes a new version of Everything, even nested stuff.

deep = copy.deepcopy(original)

deep[0][0] = 42

print(original)

# [[99, 2], [3, 4]] — untouched

Use shallow when your structure is flat.Use deep when you want complete separation and sleep at night.

More examples python copy list what you should know

Not gonna lie I slept on collections way longer than I should’ve. You know those moments where you're writing like 10 lines of defensive dictionary code just to avoid a KeyError? Yeah. Python had a fix for that years ago.

When I finally got serious about FAANG prep, I started reaching for tools like Counter, defaultdict, and deque the same way I go for Ctrl+Z. They're just... better. Cleaner. Less try-except soup.

Here’s one of my go-tos:

from collections import Counter

data = ['a', 'b', 'c', 'a', 'b', 'a']

count = Counter(data)

print(count) # Output: Counter({'a': 3, 'b': 2, 'c': 1})

Could you do this without Counter? Sure. Do you want to? Nope. Interviewers notice when you skip boilerplate and just write clean, readable code. Use the tools.

12. Monkey Patching: When You Want To Change The Rules Mid-Game

This one's spicy. Monkey patching lets you overwrite behavior at runtime. Feels like cheating? Kind of. But sometimes it’s exactly what you need, especially in mocks, tests, or when the original code won’t cooperate.

Here’s a simple example:

class monkey:

def patch(self):

print("patch() is being called")

def monk_p(self):

print("monk_p() is being called")

monkey.patch = monk_p # boom, replaced it

obj = monkey()

obj.patch() # monk_p() is being called

Cool, right? But don’t go full chaos monkey. This is a use only when necessary kind of tool. Break glass when testing, mocking, or doing low-level black magic. Otherwise, it just makes your code weird for no reason.

13. With: Write Cleaner Code And Stop Forgetting .close()

Let me guess, you've written this at least once:

file = open('thing.txt', 'w')

try:

file.write("blah blah blah")

finally:

file.close()

Congrats, that’s five lines that should’ve been one. The with statement exists so you don’t have to babysit resources.

Here's the adult version:

with open('myfile.txt', 'w') as file:

file.write('This just works.')

No leaks. No forgetting to close things. Interviewers love this kind of thing not because it’s fancy, but because it shows you’ve actually written real code before.

14. Why Use Else In Try/Except Construct In Python? Header: Else In Try/Except: Run Code When No Exception Occurs

Let’s be real, most people slap a try and expect around sketchy code and call it a day. But Python quietly gives you an else, and if you’ve been ignoring it, same. I didn’t use it for years because I didn’t really get why it mattered. Until one night when my script threw zero errors… and still printed the wrong thing.

Here’s The Deal

try runs your code.
If it breaks, except catches it.
If it doesn’t break? That’s when else runs. It’s your clean exit.
Use it when you’ve got logic that should only run if everything inside the try didn’t blow up.

Example

Let’s look at this using a simple input case.

On the first try, I entered 2 as the numerator and d as the denominator. Bad idea. Python choked, and except kicked in with “Invalid input!”

On the second try, I used 2 and 1. Everything ran fine, so else took its turn and printed “Division is successful.”

Try

num1 = int(input('Enter Numerator: '))

num2 = int(input('Enter Denominator: '))

division = num1/num2

print(f'Result is: {division}')

except:

print('Invalid input!')

else:

print('Division is successful.')

## Try 1 ##

Enter Numerator: 2

Enter Denominator: d

Invalid input!

## Try 2 ##

Enter Numerator: 2

Enter Denominator: 1

Result is: 2.0

Division is successful.

I used to jam all the “success” messages inside the try block and then wonder why stuff printed even when things failed halfway. Don’t do that. Else is cleaner, safer, and way easier to debug when you're deep in a loop of 200 inputs.

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Take the Python Fundamentals skill track to gain the foundational skills you need to become a Python programmer.

15. What Are Decorators In Python?

Decorators: Wrap Functions To Extend Behavior

If you’ve ever looked at @something above a Python function and thought, “Is this magic or is my code just haunted?” congrats, you’ve met decorators.

I used to skip them entirely when I was prepping for interviews. Too weird. Too meta. But once I got serious about writing cleaner code (and reading other people’s code without crying), decorators clicked.

Here’s the simple idea: A decorator lets you wrap a function with another function without changing the original one.It’s like putting a jacket on your function that says, “Before you do your thing, do this first. And maybe this after.”

Real-life use? Logging, auth checks, retry logic the boring stuff you don’t want to copy-paste everywhere.

Example:

import functools

def my_decorator(func):

@functools.wraps(func) # keeps the original function's name & docstring

def wrapper(*args, **kwargs):

print("Something is happening before the function is called.")

result = func(*args, **kwargs)

print("Something is happening after the function is called.")

return result

return wrapper

@my_decorator

def say_hello():

print("Hello!")

say_hello()

Output

Something is happening before the function is called.

Hello!

Something is happening after the function is called.

Here’s how I remember it:

That @my_decorator line is shorthand for: say_hello = my_decorator(say_hello)
You’re wrapping say_hello in another function without touching say_hello itself.
Think of it like sticking an extra step before/after the function runs.

Once you get used to it, you’ll catch yourself using decorators like .strip() casually and everywhere.

Take the Python Fundamentals skill track and actually understand the stuff you’ve been skipping over for years.

16. What Are Context Managers In Python, And How Are They Implemented?

Context Managers: How Python Quietly Handles Cleanup So You Don’t Shoot Yourself In The Foot

I used to write scripts that opened files like it was no big deal until one day, I forgot to close a few during a take-home assignment. Everything ran fine until it didn’t. It took me hours to debug. It turns out, I was leaking file handles like crazy. That’s when I finally understood what I was actually doing.

In Python, context managers exist to prevent you from messing things up. They're Python’s way of helping you handle stuff like files, locks, or DB connections without leaving a mess behind. The most common place you’ll see this is in the with statement.

Example:

class FileManager:

def __init__(self, filename, mode):

self.filename = filename

self.mode = mode

def __enter__(self):

self.file = open(self.filename, self.mode)

return self.file

def __exit__(self, exc_type, exc_value, traceback):

self.file.close()

with FileManager('test.txt', 'w') as f:

f.write('Hello, world!')

Let me break it down:

__enter__() runs first and gives you the object you’ll work with.
__exit__() runs afterward — whether things went well or blew up.
You don’t need to remember to clean up. Python handles that for you.

This is one of those small things that keeps your code tight. If you’re writing anything that touches files, sockets, or external APIs, context managers are a no-brainer. And yes — they show up in interviews way more often than you’d think.

Want to sound like you know what you're doing? Use one. Want actually to see what you’re doing? Write a couple yourself.

17. Metaclasses:When Python Lets You Mess With How Classes Are Made

Classes make objects. Metaclasses make classes. Yeah, that sounds like something you’d hear from a guy who owns too many mechanical keyboards, but stick with me.

I ignored metaclasses for years because they sounded like dark magic. Then I hit a library that used one to rename every class that broke a rule automatically, and suddenly it clicked.

Here’s the quick mental model:

A class decides how your objects behave.
A metaclass chooses to how your classes behave.

Example time:

class Meta(type):

def __new__(cls, name, bases, dct):

print(f"Creating class {name}")

return super().__new__(cls, name, bases, dct)

class MyClass(metaclass=Meta):

pass

# Output: Creating class MyClass

You probably won’t need this on most projects, but if you’re building a library, a plugin system, or something that enforces structure across classes, metaclasses are your backstage pass.

If you’re aiming for senior-level interviews, understanding this shows you’ve gone past “copying from Stack Overflow” and actually get how Python builds itself. You don’t need to master every quirk, just know what it is and when someone might use it.

18. Numpy: Why Arrays Leave Lists In The Dust

During my TikTok internship, I thought lists could handle everything. Then I tried crunching a few million data points, and my laptop sounded like a jet engine.

Enter NumPy

Here’s why it changed everything for me:

Memory: Arrays sit neatly in memory, so your computer doesn’t have to chase pointers all day.
Speed: It runs on C under the hood. Translation: it’s stupid fast.
Math made easy: Add, multiply, divide — all in one go—no endless for loops.
Built-ins: You get half of linear algebra right out of the box.

Example:

import numpy as np

a = np.array([1, 2, 3])

b = np.array([4, 5, 6])

print(a + b) # [5 7 9]

If you’re serious about data or machine learning interviews, NumPy isn’t optional. It’s the difference between thinking like a programmer and thinking like a data engineer.

19. Merge, Join, Concat: The Pandas Triple Threat

I’ll be honest: this one confused the hell out of me early on. merge(), join(), concat() they sound the same, but they don’t play the same game.

Here’s my cheat sheet from years of trial and error:

merge() → Think SQL. Use it when you want to match rows by a key (on='id'). pd.merge(df1, df2, how='outer', on='Id')

join() → Works on indexes. Fast and clean when your data’s already aligned. df1.join(df2)

concat() → Just stick them together. Vertically or horizontally. Doesn’t care about matching anything. pd.concat([df1, df2], axis=0)

The rule:

If you’re combining by keys → use merge.
If it’s by index → join.
If you’re just stacking stuff → concat.

Mix them up in an interview, and you’ll sound like you learned pandas from a meme page. Don’t be that person.

20. Dealing With Missing Values: Without Nuking Your Dataset

Here’s a confession: the first time I saw NaN in my dataset, I panicked and deleted everything with a missing value. Lost half the data. Big mistake.

Here’s how to do it properly.

Finding Missing Values

df.isnull().sum()

Example

import pandas as pd

import numpy as np

data = {

'id': [1, 4, np.nan, 9],

'Age': [30, 45, np.nan, np.nan],

'Score': [np.nan, 140, 180, 198]

}

df = pd.DataFrame(data)

print(df.isnull().sum())

Fixing Missing Values

Options:

Drop rows/columns (only if you’ve got enough left to work with)df.dropna()

Fill with something logical (mean, median, 0, whatever makes sense) df.fillna(0)

Backfill/Forward fill

df.fillna(method='bfill')

Interpolate

df.interpolate(method='linear')

Deleting rows is easy but lazy. Before you drop you might be throwing away signal along with noise.

21. Visualization Libraries: What To Use And When

When I got into data science prep, I learned the hard way that nobody cares about your model’s accuracy if your chart looks like it came from Excel 2003.

Here’s what I use:

Matplotlib

The classic. Old-school but super flexible.

Seaborn

Cleaner visuals, less code. My go-to for fast plots.

Plotly

Interactive. Perfect for dashboards and interview demos.

Bokeh

Great for web apps with heavy data visuals.

Example:

import matplotlib.pyplot as plt

import seaborn as sns

sns.set(style='whitegrid')

tips = sns.load_dataset("tips")

sns.boxplot(x="day", y="total_bill", data=tips)

plt.show()

My quick rules:

Quick analysis? → Seaborn.
Need interaction or presentation-level charts? → Plotly.
Massive dashboard project? → Bokeh.
Enjoy pain? → Matplotlib.

Interview tip: learn two of these really well. You don’t need to be a data artist just know when each one makes your story clearer.

22. How Would You Normalize Or Standardize A Dataset In Python?

Scaling Features: Normalization And Standardization

This is one of those questions that feels basic — until it tanks your model. I learned this the hard way during a Meta ML round when I forgot to scale the inputs. The model just flatlined. No errors, just vibes. I walked out knowing I was done.

Here’s what I wish I’d known earlier:

Normalization scales your data to a fixed range, usually [0, 1].
Standardization centers it around 0 with a standard deviation of 1.

They’re not the same thing. Use the wrong one and your gradient descent will wander like it’s lost in IKEA.

Here’s how you actually do it in Python:

from sklearn.preprocessing import MinMaxScaler, StandardScaler

import numpy as np

data = np.array([[1, 2], [3, 4], [5, 6]])

# Normalize

normalizer = MinMaxScaler()

normalized = normalizer.fit_transform(data)

print(normalized)

# Standardize

scaler = StandardScaler()

standardized = scaler.fit_transform(data)

print(standardized)

That's it. No filler. No overthinking. Just prep it right and move on.

23. Replace Spaces In Strings: No Fluff, Just Code

This one's straight from my first week grinding LeetCode like it was my full-time job.

Back when I couldn’t even land a phone screen, I remember seeing a string problem like this and thinking, “Wait, this counts as an interview question?” Turns out, yeah. And if you can’t write a one-liner for it, you’re not ready for round 1.

Here’s the deal: you get a string like "l vey u" and a character like "o". Your job is to swap out the spaces with that character. So you go from "l vey u" to "loveyou".

Basic? Yes. But you'd be surprised how many folks try to brute-force this when Python gives you the tools.

def str_replace(text, ch):

return text.replace(" ", ch)

text = "D t C mpBl ckFrid yS le"

ch = "a"

str_replace(text, ch)

# 'DataCampBlackFridaySale'

If you’re not using str.replace(), I need you to close 12 Chrome tabs and focus for a sec.

24. Perfect Squares — Yes Or No?

Here’s how I used to screw this up: I’d use math.sqrt() and forget that floats are messy. Floats lie. You think you got a clean square, and suddenly you're debugging a rounding error at 2 AM.

This version? Bulletproof.

import math

def valid_square(num):

if num < 0:

return False

square = math.isqrt(num)

return square * square == num

valid_square(10)

# False

valid_square(36)

# True

No decimals. No guesswork. Just check the integer root, square it again, and you're done.

25. Trailing Zeros In Factorial: Don't Fall For The Trap

Here’s where beginners get baited. You’re told to count trailing zeroes in n! and you think, “Cool, I’ll just get the factorial and count some zeros.”

Nope.

The only zeros that matter here are the ones at the end of the number. Like, literally at the end. Not all zeros. Just the ones hanging off the back.

Example: 7! = 5040 → has two zeros, but only one at the end.

Here’s how I handle it when I’m feeling lazy and just want it to work:

def factorial_trailing_zeros(n):

fact = n

while n > 1:

fact *= n - 1

n -= 1

result = 0

for i in str(fact)[::-1]:

if i == "0":

result += 1

else:

break

return result

factorial_trailing_zeros(10)

# 2

factorial_trailing_zeros(18)

# 3

Not the most efficient method out there, but it’s readable and interview-friendly if they just want to know you get the concept. If you're gunning for top-tier algo questions, you’ll wanna rewrite this using factor counting (dividing by 5 repeatedly). But that's another post.

Want more stuff like this without scrolling through HackerRank forums? Try Interview Coder for free and stop wasting hours guessing what to study.

26. Word Break Problem: Can You Actually Split That String?

Ever stared at a long string and thought, “Yeah, I should know how to break this up into actual words... but my brain just said nope”? I’ve been there. During my second year of uni, I blanked on this exact question in a mock interview. Full-on “I know this, but I don’t” moment.

Let me save you from that mess.

Here’s the idea: You’ve got a string like "datacamp" and a dictionary of words like ["data", "camp", "cam," "lack"]. The goal? Figure out if you can break that long boy into valid words from the dictionary. No guessing, no brute force just solid logic with some help from memoization.

This is how I usually walk through it:

Loop through the string from left to right.
Chop it at every position and test the left part against the dictionary.
If the left part exists, check the correct part recursively.
If everything checks out, return True.
Throw in a memo dict, so you’re not repeating work like a clown.

Here’s how it looks in code:

def can_segment_str(s, dictionary, memo=None):

if memo is None:

memo = {}

if s in memo:

return memo[s]

if not s:

return True

for i in range(1, len(s) + 1):

first_str = s[0:i]

if first_str in dictionary:

second_str = s[i:]

if (

not second_str

or second_str in dictionary

or can_segment_str(second_str, dictionary, memo)

memo[s] = True

return True

memo[s] = False

return False

s = "datacamp"

dictionary = ["data", "camp", "cam", "lack"]

can_segment_str(s, dictionary)

# True

This is one of those questions where the solution isn’t hard once you’ve seen it. But in an interview? Your brain might betray you. Practice this one. Trust me.

27. Remove Duplicates In-Place: You Get One Array, So Make It Count

This one's sneaky. You’re handed a sorted list and told to remove duplicates in-place. No fresh list. No append magic. Just make that same list work, like it’s interview day and your brain’s on 3 hours of sleep and black coffee.

Let’s be clear: the goal isn’t to just return the cleaned-up list. You’re supposed to shift the unique values to the front of the original array and return the new length k. The elements after k? Doesn’t matter. They’re leftovers. Garbage.

Here’s what I tell people:

Start from the second element (index 1).
Keep a pointer called insertIndex which tracks where to drop the next unique value.
Every time you see a new value, move it to insertIndex, then bump the pointer.
Done? Return insertIndex. That’s your k.

Here’s the version I’d write in an actual interview:

def removeDuplicates(array):

size = len(array)

if size == 0:

return 0

insertIndex = 1

for i in range(1, size):

if array[i - 1] != array[i]:

array[insertIndex] = array[i]

insertIndex += 1

return insertIndex

array_1 = [1, 2, 2, 3, 3, 4]

k1 = removeDuplicates(array_1)

# 4; array_1[:k1] -> [1, 2, 3, 4]

array_2 = [1, 1, 3, 4, 5, 6, 6]

k2 = removeDuplicates(array_2)

# 5; array_2[:k2] -> [1, 3, 4, 5, 6]

Interviewers love this one because it looks simple but people panic, try to use .remove() or .append(), and then boom time’s gone. Practice in-place logic. It’s a filter for sloppy thinking.

28. Find the Missing Number: Just Use Math, Don’t Overthink

You’ve got a list that’s supposed to contain numbers from 1 to n, but one’s missing. Classic question. People either get it in 10 seconds or waste 10 minutes doing nonsense like looping twice or using set() like it’s a cheat code.

The correct answer is stupid simple.

Use math.

You know the formula for the sum of numbers from 1 to n. It’s n*(n+1)/2. Just compare that to the sum of the numbers you actually have. The difference? That’s your missing number.

Here’s what I usually write:

def find_missing(input_list):

sum_of_elements = sum(input_list)

n = len(input_list) + 1

actual_sum = (n * (n + 1)) / 2

return int(actual_sum - sum_of_elements)

list_1 = [1, 5, 6, 3, 4]

find_missing(list_1)

# 2

That's it.

You don’t need to sort, you don’t need to check each number, and you definitely don’t need to get fancy. Just do the math. It's one of those interview questions that rewards people who keep their heads clear.

29. Write A Python Function To Determine If A Given String Is A Palindrome.

Palindrome Check: Normalize Then Compare

Palindromes are one of those questions interviewers love tossing in, not because they’re hard, but because they want to catch you slipping on input handling. I remember bombing this exact one in an early Amazon phone screen because I forgot to strip spaces and punctuation—rookie mistake.

The fix? Strip the noise, lowercase it, and compare the cleaned-up string to its reverse. That’s it. No drama.

def is_palindrome(s):

s = ''.join(e for e in s if e.isalnum()).lower() # Remove non-alphanumeric and convert to lowercase

return s == s[::-1]

print(is_palindrome("A man, a plan, a canal: Panama")) # Output: True

print(is_palindrome("hello")) # Output: False

Normalize first. Then compare. Otherwise, "race a car" is going to ruin your day.

30. Can You Find The Maximum Single Sell Profit?

Single-Trade Max Profit: Buy Low, Sell High

I’ve seen this one wreck plenty of candidates, not because it’s conceptually complex, but because people overthink it. I once tried some wild two-pointer thing for this during a mock with a friend. Took me 10 minutes. He solved it in 5 lines.

Here’s the actual mindset:

Find the lowest dip.
Track the best profit after that.

If you're stuck in a downtrend, minimize how much you bleed.

def buy_sell_stock_prices(stock_prices):

current_buy = stock_prices[0]

global_sell = stock_prices[1]

global_profit = global_sell - current_buy

for i in range(1, len(stock_prices)):

current_profit = stock_prices[i] - current_buy

if current_profit > global_profit:

global_profit = current_profit

global_sell = stock_prices[i]

if current_buy > stock_prices[i]:

current_buy = stock_prices[i]

return global_sell - global_profit, global_sell

stock_prices_1 = [10, 9, 16, 17, 19, 23]

buy_sell_stock_prices(stock_prices_1)

# (9, 23)

stock_prices_2 = [8, 6, 5, 4, 3, 2, 1]

buy_sell_stock_prices(stock_prices_2)

# (6, 5)

Yes, it's one of those “trick you into thinking it needs recursion” problems. But it doesn’t. Clean logic wins.

31. Can You Find A Pythagorean Triplet In An Array?

Pythagorean Triplet: Detect a² + b² = c²

This one always felt like a math teacher’s revenge disguised as a coding problem.

But it's simple when broken down.

Square everything
Sort
Use a set to see if a² + b² = c² holds for any combo

If I can do it while barely passing my college math gen-eds, you can too.

def checkTriplet(array):

n = len(array)

for i in range(n):

array[i] = array[i] ** 2

array.sort()

for i in range(n - 1, 1, -1):

s = set()

for j in range(i - 1, -1, -1):

if (array[i] - array[j]) in s:

return True

s.add(array[j])

return False

arr = [3, 2, 4, 6, 5]

checkTriplet(arr)

# True

If you remember 3-4-5 is a valid triple, you're already halfway there.

32. How Many Ways Can You Make Change With Coins And A Total Amount?

Coin Change: Counting Combinations With Dynamic Programming

This problem almost made me rage-quit the first time. It took me a minute to realize it’s not about brute force, it’s about counting options, not computing values.

The trick? Dynamic programming. Let the code do the remembering.

def solve_coin_change(denominations, amount):

solution = [0] * (amount + 1)

solution[0] = 1

for den in denominations:

for i in range(den, amount + 1):

solution[i] += solution[i - den]

return solution[amount]

denominations = [1, 2, 5]

amount = 5

solve_coin_change(denominations, amount)

# 4

Think of it like Lego: build up from 0 to your target amount, adding pieces as you go. Not pretty, but it works every time.

33. Define A Lambda Function, An Iterator, And A Generator In Python

Lambda, Iterator, Generator: Compact Function And Lazy Iteration

Okay, this one’s less of a gotcha and more of a vibe-check. They want to see if you actually code Python regularly or if you just watched a Udemy crash course the night before.

Quick breakdown:

Lambda = mini function you don’t name
Iterator = object you can step through (lists, sets, etc.)
Generator = function that pauses (with yield) instead of finishing

# Lambda

double = lambda x: x * 2

print(double(4)) # 8

# Iterator

nums = [1, 2, 3]

it = iter(nums)

print(next(it)) # 1

# Generator

def countdown(n):

while n > 0:

yield n

n -= 1

for val in countdown(3):

print(val)

You don’t have to be fancy. You just need to know how they behave.

34. Given An Srray arr[], find the maximum j – I Such That arr[j] > arr[i]

Max Index Difference With arr[j] > arr[i] Brute Force Approach

Let me be honest this one looks easy and then eats your time. Don’t ask me how long I spent optimizing it before realizing brute force works for most test cases.

def max_index_diff(array):

n = len(array)

max_diff = -1

for i in range(0, n):

j = n - 1

while(j > i):

if array[j] > array[i] and max_diff < (j - i):

max_diff = j - i

j -= 1

return max_diff

array_1 = [20, 70, 40, 50, 12, 38, 98]

max_index_diff(array_1)

# 6

There are more innovative ways to do this (like prefix min + suffix max arrays), but this one still works and helps you think in loops.

35. How Would You Use The Ternary Operators In Python?

Ternary Conditional Expression Compact If-Else

I love ternary expressions. Cleaner, tighter, less scrolling. You shouldn’t abuse them but if your logic is simple? Go for it.

Here’s how I rewrite bulky if-else junk:

# Normal if-else

score = 75

if score < 70:

if score < 50:

print('Fail')

else:

print('Merit')

else:

print('Distinction')

# One-liner version

print('Fail' if score < 50 else 'Merit' if score < 70 else 'Distinction')

# Distinction

Feels good, looks better. Just don’t get cocky and cram nested logic where it doesn’t belong.

36. How Would You Implement an LRU Cache in Python?

Implementing Lru Cache: Built-In Decorator And Manual Approach

This is the “oh you know standard libs?” interview flex. Bonus points if you’ve built one manually. I did it once to prove a point then never again.

The lazy smart way?

from functools import lru_cache

@lru_cache(maxsize=3)

def add(a, b):

return a + b

print(add(1, 2)) # Cached

print(add(1, 2)) # Still cached

Want to roll your own version? Use OrderedDict or read the source code for functools.lru_cache like a real nerd.

18 More Python Coding Interview Questions (That Actually Came Up)

I didn’t learn Python in some fancy classroom. I learned it on the subway, cramming Leetcode on 2% battery, trying not to miss my stop.

This section? It’s what I wish I had back when I bombed my first Amazon interview because I blanked on __init__. Real questions. Real code. No filler.

Let’s get into it.

1. What init Actually Does

If you’re still thinking of __init__ as just “that thing every class has,” let’s clean that up.

This method runs every time you make a new object. It sets stuff up. Think of it like the move-in day checklist for your object’s attributes.

class Student:

def __init__(self, fname, lname, age, section):

self.firstname = fname

self.lastname = lname

self.age = age

self.section = section

stu1 = Student("Sara", "Ansh", 22, "A2")

Now stu1 has everything pre-packed and ready. Miss this step, and your object’s just wandering around with no ID.

2. Arrays vs Lists: What You Actually Need to Know

This one messed me up in an old-school screening. They asked why I’d use an array over a list. I gave a vague answer about “performance.” Yeah. Didn’t fly.

Here’s the real difference:

Arrays

Only same-type stuff allowed. Lower memory. Closer to C arrays.

Lists

Throw whatever you want in. Way more flexible, but costs you in memory.

import array

a = array.array('i', [1, 2, 3])

print(list(a)) # Output: [1, 2, 3]

b = [1, 2, "string"]

print(b) # Output: [1, 2, 'string']

Don’t pretend they’re the same. Know which tool fits.

3. Making a Python Script Executable on Unix

No magic. Just start your file with this:

#!/usr/bin/env python

That line tells Unix, “Yo, run this with Python.” Without it, you’re just typing in a .txt file pretending to be code.

4. Slicing: Not Just For Lists

Slicing is Python’s chillest power move. You’re telling Python what parts you want and how to skip.

numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

print(numbers[1::2]) # Output: [2, 4, 6, 8, 10]

Syntax

[start:stop:step]

Defaults? Start at 0, go to the end, step by 1. You can slice strings, lists, tuples pretty much anything with order.

5. Docstrings: If You’re Gonna Explain, Explain Right

You ever read your own code a week later and go, “What the hell did I do here?” Yeah. Same.

Use docstrings.

def add(a, b):

"""

Adds two numbers and returns the result.

"""

return a + b

Multiline. Clear. For humans, not just for interviewers.

6. Unit Tests: Save Future You From Debugging Purgatory

The first time I broke prod, it was because I changed something in one part of the code and didn’t realize I nuked everything downstream.

Unit tests are how you stop that.

They don’t check if the whole app works. They check if this tiny thing does what it’s supposed to. That way, when something breaks, you don’t have to play detective for three hours.

You’ll use Python’s unittest module, or sometimes pytest if you’re feeling fancy. But the idea’s the same: break your code into pieces and check each piece like it’s guilty until proven innocent.

7. break, continue, and pass: How I Actually Remembered What They Do

Let’s be real. The first time I saw break, continue, and pass in someone’s code, I stared at it like it was a cryptic message from a senior dev trying to haze me. I fumbled through it during my Amazon internship prep and kept mixing up continue and break like a clown. Here's how I finally got it straight:

break

Yeets you out of the loop immediately.

continue

Skips the rest of the loop and jumps to the next item.

pass

Basically, a shrug, it’s Python going “eh, I’ll deal with this later.”

Here’s a quick example I actually messed around with during prep. This one stuck:

pat = [1, 3, 2, 1, 2, 3, 1, 0, 1, 3]

for p in pat:

pass # placeholder because I wasn’t sure what to put here initially

if (p == 0):

current = p

break

elif (p % 2 == 0):

continue

print(p) # output => 1 3 1 3 1

print(current) # output => 0

Moral of the story Use pass when your code isn’t ready. Use continue when you're picky. Use break when you're done pretending you care.

8. self in Python: Yeah, It's Not a Keyword: But You’ll Use It Like One

I used to think self was a Python keyword. It’s not. But honestly, it might as well be because every time you're working with a class, it’s showing up uninvited.

Here’s how I think about it: self is just a way for the class to keep track of itself. Like:

“Hey, I’m this specific object, not one of those other ones.”

You’ll use it to access attributes and methods inside the class. It’s like giving your code a mirror and saying, “Yeah, that’s me.”

9. Global vs Protected vs Private: Or... How Much Should You Trust Other Devs?

Okay, this one took me forever to wrap my head around, mostly because Python is way too chill about access control.

Global

Just... don’t. Avoid unless you have a good reason.

Protected (_var)

Basically saying, “Please don’t touch this.” But Python won’t stop anyone.

Private (__var)

A little more strict. Try accessing it, and Python throws shade at you. But you can still access it using some weird syntax if you’re stubborn.

If you're building something serious, respect the double underscores. But in most interviews, just show that you know why you'd use them, not that you're memorizing rules like a robot.

10. Modules and Packages: Yes, You Should Split Your Code

You ever scroll through a 500-line Python file and wonder why your brain hurts?

That’s why modules exist.

Here’s the version I wish someone had told me earlier:

A module is just a .py file. That’s it.
A package is a folder with a bunch of those .py files.

You can import the whole thing or just grab what you need:

from my_utils import format_money

It keeps your code clean-ish and your brain from melting. Bonus: You’ll look organized during your interview screen share.

11. Pass Again? Yep: Because Python Throws A Fit Without It

Look, sometimes you’ve got a function or class or loop... and no idea what to put inside it yet.

Try this:

def to_be_written_later():

pass

That’s it. Without a pass, Python just starts yelling about indentation. With it? Silence. Glorious, quiet, judgment-free silence.

12. Built-in Data Types: Know These, Or Cry in Your Coding Interview

I didn’t take Python seriously until I bombed a warm-up round because I forgot how frozenset worked. Lesson learned.

Here’s the short version of what you actually need to care about:

None Type

None is Python’s version of “nothing to see here.”
Use it to initialize stuff or return when there’s no result.

Numeric Types

int, float, complex, bool
Reminder: True is just 1, and False is 0. Weird but useful.

Sequence Types

list: Your go-to. Mutable. Use it 90% of the time.
tuple: Same as a list, but locked down. Good for fixed data.
range: Lightweight number generator for loops.
str: Strings, obviously.

Strings are immutable. You change one letter, you're copying the whole thing.

Mapping Types

dict: Key-value pairs. You’ll use this everywhere. Probably way more than you should.

Set Types

set: Unordered, no duplicates, super fast lookup.
frozenset: Same but frozen. Interviewers love asking this for no reason.

Modules

Python files that can be imported. You already do this with stuff like math or random.

Callable Types

Anything you can slap () on and it runs. Functions, methods, and even some classes.

If you remember nothing else, at least get comfortable with list, dict, and str. Everything else is seasoning.

13. Lists vs. Tuples: The Mutability Thing No One Tells You

I remember messing this up during an internship coding challenge. Changed a tuple, thinking it was a list. Broke half the test cases. Classic.

Anyway, here’s the deal: lists can change, tuples can’t. That’s really the whole point. Python gives you two ways to store sequences, and they look similar at first glance, but they behave entirely differently.

A list is written like this: ['sara', 6, 0.19]
A tuple looks like this: ('ansh', 5, 0.97)

Now here’s where beginners get wrecked:

my_tuple = ('sara', 6, 5, 0.97)

my_list = ['sara', 6, 5, 0.97]

print(my_tuple[0]) # 'sara'

print(my_list[0]) # 'sara'

my_tuple[0] = 'ansh' # throws TypeError

my_list[0] = 'ansh' # no problem

print(my_tuple[0]) # still 'sara'

print(my_list[0]) # now 'ansh'

So when should you use which? Use a list when your data is gonna change, like user input, mutable configs, etc. Use a tuple when your data should stay locked, like fixed options, constants, or keys in a dictionary.

That’s it. No need to overthink it.

14. Scope in Python: Or Why Your Variables Disappear

Python’s got this invisible fence thing going on. Every variable you declare lives somewhere, but not everywhere. I’ve lost hours wondering why my variable wasn’t doing what I thought; turns out it was living in the wrong “scope.”

Here’s how I think about it:

Local Scope

lives inside the function

Global Scope

lives across the whole file

Module-Level Scope

global inside a specific module

Built-In Scope

Python’s own stuff (like len() or range())

And yeah, they all play nice… until they don’t. If you try to change a global variable from inside a function, Python just creates a new local one unless you explicitly say global. Ask me how I found that out.

15. PEP 8: The Stuff Everyone Ignores Until Their PR Gets Rejected

PEP stands for Python Enhancement Proposal. There are a bunch of them. PEP 8 is the one that tells you how your code should look, spacing, line length, variable names, etc.

If you're freelancing or contributing to open source, you will get called out for not following it. Trust me, it’s better to just get used to it early. Use a linter. Save yourself the humiliation of a “can you reformat this?” comment 15 seconds after you open your pull request.

16. Interpreted Language: You Write It, It Runs

Python doesn’t need to be compiled. It reads your code line-by-line and runs it on the fly. That’s great for debugging. You can write a little, run it, tweak it, run it again without waiting for a whole build process.

Other interpreted languages? JavaScript, PHP, Ruby.

The Main Takeaway

Python doesn’t do a “build step,” it just goes.

17. Dynamically Typed: Python Trusts You… Maybe Too Much

Python doesn’t ask you to declare variable types. It guesses.

x = 5 # cool, it’s an int

x = "five" # also cool, now it’s a string

This makes Python super flexible, but it also means bugs can sneak in like ninjas.

Example

"1" + 2 # throws a TypeError in Python

But JavaScript? That’ll just give you "12" and call it a day.

So yeah, Python is strongly typed (doesn’t like mixing types) but also dynamically typed (figures it out at runtime). Great for fast coding. Not so great for catching errors before things break.

18. What Python Actually Is (No Buzzwords)

Here’s how I explain Python to new people:

It’s a programming language.
You can build almost anything with it, such as web apps, scripts, data pipelines, whatever.
It’s readable as hell. That’s the main reason it’s so popular.

Python Has Batteries Included

It handles memory for you, supports object-oriented programming, and plays nicely with all kinds of third-party libraries.

The best part? The community is massive. Whether you're stuck on a bug or trying to build your first side project, you’ll find examples, Stack Overflow answers, or GitHub repos to crib from.

Nail Coding Interviews with our AI Interview Assistant − Get Your Dream Job Today

I remember bombing my first technical interview because I blanked on a fundamental sliding window problem. My mind just went silent. That “oh no” spiral? Brutal. Back then, I was grinding LeetCode in isolation and praying I'd memorized the right patterns—dumb move.

That's why I built Interview Coder not to be some shiny gadget, but the tool I actually needed when I was clawing my way into Amazon, Meta, and TikTok. It’s not here to hold your hand. It’s here to quietly sit next to you, see what you’re typing, and whisper the right suggestion at the right time without anyone knowing.

What It Actually Does During Your Interview

Picture this: you’re mid-coding on CoderPad. Interviewer’s silent. You’re sweating. You’re not sure if your two-pointer setup is off-by-one.

Interview Coder’s got your back quietly.

It watches your code in real-time (locally or via a secure link). Sees your logic and nudges you with suggestions:

“That while loop condition? Might be inverted.”
“Here’s a cleaner way using collections.defaultdict.”
“Your time complexity just went from O(n²) to O(n log n). Good call.”

Everything stays private. No screen-sharing shenanigans. It works with your IDE and interview tool. You look sharp, because you’re not guessing.

Stop Playing LeetCode Roulette

Most candidates are playing a game they don’t even understand—random question here, another random one there, hoping that something clicks.

I did that for months. It sucked. And worse? It didn’t work.

Interview Coder trains you on actual interview patterns. Not 5,000 problems. Just the ones that come up over and over again:

Sliding window
Two pointers
Hash tables
Recursion and backtracking

You don’t need more problems. You need better reps. The kind that helps you recognize the shape of the question before it even finishes loading on the screen.

Python Data Types Without the “Wait… Why Did That Happen?” Moments

Interviewers love to throw subtle bugs at you using Python’s mutability quirks. You think you’re copying a list. You’re not. You believe your dictionary merge is safe. It’s not.

Interview Coder helps you:

Trace variable references
Spot when you’re mutating an object, you shouldn’t
See the difference between list[:], copy.deepcopy(), and just passing the damn thing

It’s like having a Python sensei sitting next to you whispering, “Yeah, don’t do that.”

Loops, Conditions, and Other Sneaky Gotchas

Most people trip on the small stuff, like modifying a list while looping over it, and or writing an if statement that never triggers because they forgot how short-circuiting works.

Here’s where Interview Coder steps in:

Suggests when to use zip() instead of index juggling
Converts clunky nested loops into clean list comprehensions
Highlights where a generator expression could save memory in a tight spot

You’re still writing the code. But you’re writing it faster and cleaner like someone who’s done this before (even if you haven’t).

Functions, Lambdas, and That Annoying Default Argument Bug

I once failed an interview because I used a mutable default argument in a helper function. True story.

Interview Coder doesn’t just spot the bug. It shows you how to fix it and how never to make it again.

It helps with:

Pure function design
Generator patterns (like running averages or filtering on the fly)
Compact lambda usage for sorting, mapping, and all the interview tricks you should know but probably forgot

Want a generator that chunks a stream of data on-the-fly? Ask it. Need to trace a closure’s scope across three nested functions? It’ll walk you through it.

Object-Oriented Python: No, You Don’t Need to Overengineer It

When I first learned about classes in Python, I thought I had to build some “real world” Dog–Cat–Animal hierarchy to impress an interviewer. Nope. What actually matters is whether you can tell the difference between a class variable and an instance one, and why your code breaks when it doesn’t behave the way you thought it would. Interview Coder keeps it practical.

It walks through the parts that trip people up: constructors, inheritance, when you should just use composition instead, and those magic methods (__init__, __str__, __eq__) that seem to sneak into every interview just to mess with your head.

Errors, Tests, and Debugging: What Actually Gets You Hired

I’ve seen so many candidates write the perfect algorithm… and then choke when the interviewer says: “Cool, what happens if it fails here?” If you’re not testing edge cases or wrapping code in try/except, you’re winging it. I don’t recommend winging it. Interview Coder walks through real testing flows not just unittest boilerplate, but where to mock stuff, what exceptions actually make sense, and how to debug without going down a two-hour rabbit hole.

Algorithms, Big O, and That One Recursive Question You Always Miss

You’re not getting out of a decent interview without talking about time and space complexity. Period. Interview Coder doesn’t just throw Big O labels at you; it breaks down why it matters and how to tell when your recursion is about to blow the stack. You’ll get help reworking that brute-force recursion into something more innovative (or iterative, if your brain works better that way). Plus, walkthroughs on the usual suspects: binary search, merge sort, memoization, and dynamic programming problems that people love to overcomplicate.

Python Interview Problems: The Real Ones You’ll Get Asked

No one’s asking you to build a full-stack app in an interview. But they will ask you to reverse a string, flatten a list, or merge two sorted arrays. Interview Coder gives you multiple ways to solve those not just the clever one-liners, but the “okay, let me write this out so it’s readable” versions too. You’ll see where built-ins make sense (reversed, join, bisect) and when you should write something yourself. Because yes, they’ll ask why you picked one over the other.

Talking Through Code Without Sounding Like a Robot

You know what interviewers hate? Code that runs perfectly but is explained like a Wikipedia page. You need to walk through your logic like you’re teaching it to someone competent, not flexing on LeetCode. Interview Coder gives feedback on your explanations, like, Did you actually say why you chose that loop? Did you mention edge cases up front? That kind of stuff separates “yeah, they studied” from “yeah, I’d hire them.”

How I Used Interview Coder in My Own Prep

Here’s how I used it when prepping for Amazon and Meta: I’d block off 45 minutes, open up the tool, and just grind focused reps, hashing, recursion, binary trees. No distractions, no YouTube in the background. Then I’d switch to mock mode and get real-time coaching on how I explained stuff. That repetition, saying it out loud, catching where I hesitated, that’s what pushed me from “I think I got it” to “I’m ready.”

One Last Thing on Ethics

This tool isn’t about cheating. If that’s your plan, you’ll get exposed in the first real interview. The whole point is to get better, not fake it. Use Interview Coder to figure out what you actually understand and where you're still shaky. It helps you fix those blind spots before someone else points them out in a 45-minute panel interview.

Want to See It in Action?

Ask me for something specific. Want to walk through reversing a linked list with and without recursion? Or why using a deque instead of a list changes how your queue works? Or how to write a memoized Fibonacci that doesn’t look like spaghetti? Cool. Let’s pick one and break it down together.

36 Python Basic Interview Questions (With My Straightforward Takes)

1. Python Overview: Why I Still Use It Daily

Let’s Start With The Obvious One

2. Lists vs. Tuples: Know When to Lock It or Leave It Open

List

Tuple

3. What’s __init__() Really Doing?

Here’s The Real Talk Version

What’s Happening

4. Mutable vs Immutable: How Python Handles Change

Mutable Types

Immutable Types

5. Comprehensions & Generator Expressions: One-Liners That Actually Make Sense

List Comprehension

Dict Comprehension

Tuple “Comprehension”

6. GIL: Why Python Threads Don’t Really Thread

What this means

7. Searching & Graph Traversal: Stuff You Actually Need to Know

Binary Search

AVL Tree

BFS (Breadth-First Search)

DFS (Depth-First Search)

A* Algorithm

8. KeyError: Why Python Yells at You Over Missing Keys

1. Use .get()

2. Use try / except

3. Use "key" in dict check

9. How Python Handles Memory: The Garbage Collector’s Behind-the-Scenes Magic

10. Shallow Copy vs Deep Copy: Because Python’s Sneaky Like That

Shallow Copy

Deep Copy

12. Monkey Patching: When You Want To Change The Rules Mid-Game

13. With: Write Cleaner Code And Stop Forgetting .close()

14. Why Use Else In Try/Except Construct In Python? Header: Else In Try/Except: Run Code When No Exception Occurs

Here’s The Deal

Example

Try

## Try 1 ##

## Try 2 ##

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15. What Are Decorators In Python?

Decorators: Wrap Functions To Extend Behavior

16. What Are Context Managers In Python, And How Are They Implemented?

Context Managers: How Python Quietly Handles Cleanup So You Don’t Shoot Yourself In The Foot

17. Metaclasses:When Python Lets You Mess With How Classes Are Made

18. Numpy: Why Arrays Leave Lists In The Dust

Enter NumPy

19. Merge, Join, Concat: The Pandas Triple Threat

20. Dealing With Missing Values: Without Nuking Your Dataset

Finding Missing Values

Example

Fixing Missing Values

Backfill/Forward fill

Interpolate

21. Visualization Libraries: What To Use And When

Matplotlib

Seaborn

Plotly

Bokeh

22. How Would You Normalize Or Standardize A Dataset In Python?

Scaling Features: Normalization And Standardization

23. Replace Spaces In Strings: No Fluff, Just Code

24. Perfect Squares — Yes Or No?

25. Trailing Zeros In Factorial: Don't Fall For The Trap

26. Word Break Problem: Can You Actually Split That String?

27. Remove Duplicates In-Place: You Get One Array, So Make It Count

28. Find the Missing Number: Just Use Math, Don’t Overthink

29. Write A Python Function To Determine If A Given String Is A Palindrome.

Palindrome Check: Normalize Then Compare

30. Can You Find The Maximum Single Sell Profit?

Single-Trade Max Profit: Buy Low, Sell High

31. Can You Find A Pythagorean Triplet In An Array?

Pythagorean Triplet: Detect a² + b² = c²

32. How Many Ways Can You Make Change With Coins And A Total Amount?

Coin Change: Counting Combinations With Dynamic Programming

33. Define A Lambda Function, An Iterator, And A Generator In Python

Lambda, Iterator, Generator: Compact Function And Lazy Iteration

34. Given An Srray arr[], find the maximum j – I Such That arr[j] > arr[i]

Max Index Difference With arr[j] > arr[i] Brute Force Approach

3. What’s init() Really Doing?

1. What init Actually Does