🔰  4 Unique Steps to Become a Python Expert in 2026

1️⃣ Understand Python Internals:

Learn how Python handles memory (GIL), garbage collection, and optimize code performance.

✨ Example: Debugging a slow noscript by identifying memory leaks.

2️⃣ Leverage Async Programming:

Master async/await to build scalable and faster applications.

✨ Example: Using async to handle thousands of API requests without crashing.

3️⃣ Create & Publish Python Packages:

Build reusable libraries, document them, and share on PyPI.

✨ Example: Publishing your own data-cleaning toolkit for others to use.

4️⃣ Master Python for Emerging Tech:

Dive into areas like quantum computing (Qiskit) or AI (Hugging Face).

✨ Example: Building an AI chatbot with Hugging Face APIs.

🚀 Python Mastery Roadmap

1️⃣ Basics – Syntax, loops, functions, exceptions

2️⃣ DS & Algorithms – Arrays, stacks, recursion, sorting

3️⃣ Modules & Advanced – Lambdas, decorators, regex

4️⃣ OOP – Classes, inheritance, dunder methods

5️⃣ Packages & Comprehensions – pip, conda, list comps

6️⃣ Frameworks & Concurrency – Flask, FastAPI, threading

7️⃣ Pro Skills – Typing, Testing, Docs, Docker, CI/CD

🎯 Stay consistent, build projects, master Python.

9 Hard Truths About Learning Python

10 Python Built-in Functions You Should Know

What's the output of the code snippet?

What's the output of the code snippet?

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Pass by Object Reference in Python

Is Python pass-by-value or pass-by-reference? 

➡️ Actually, Python uses Pass by Object Reference.

That means:
The function receives a reference to the same object.

📌 Example: Mutable Object

def modify(lst):
    lst.append(4)

nums = [1, 2, 3]
modify(nums)

print(nums)

Output:
[1, 2, 3, 4]

👉 The original list changed 
Because the function received a reference 
to the SAME object in memory.


📌 Example: Immutable Object

def modify(x):
    x = x + 1
    print("Inside:", x)

num = 10
modify(num)

print("Outside:", num)

Output:
Inside: 11
Outside: 10

👉 Integer did not change 
Because integers are immutable 
Reassignment creates a new object.


🎯 Core Idea

• Functions receive object references 
• Mutable objects can be modified 
• Immutable objects create new objects 
• Python is NOT pure pass-by-value 
• Python is NOT pure pass-by-reference 

✅ It is Pass by Object Reference.

🧠 Garbage Collection & Reference Counting in Python

Python automatically manages memory. 
Objects are deleted when they are no longer used.

📌 Reference Counting

Every object tracks how many variables point to it. 
When the count becomes 0, it is removed.

a = [1, 2, 3]
b = a

del a
del b

Reference count:
a → 1
a, b → 2
after del → 0
Object deleted

📌 Circular Reference Problem

Two objects pointing to each other 
will never reach reference count 0.

class Node:
    def __init__(self):
        self.ref = None

a = Node()
b = Node()

a.ref = b
b.ref = a

del a
del b

Both objects still reference each other
Reference count ≠ 0

📌 Garbage Collector (GC)

Python’s GC:
• Detects unreachable cycles 
• Breaks circular references 
• Frees memory safely 

🎯 Core Idea

Reference Counting → Main mechanism 
GC → Handles circular references

What's the output of the code snippet?

🔥 is vs == in Python

Many developers confuse these two operators. 
But they check completely different things.

📌 == (Equality Operator)

== checks whether values are equal.

a = [1, 2, 3]
b = [1, 2, 3]

print(a == b)

```text
Output:
True

👉 Values are the same  
👉 Even though they are different objects


📌 is (Identity Operator)

is checks whether two variables point to the same object in memory.

python
a = [1, 2, 3]
b = [1, 2, 3]

print(a is b)

text
Output:
False

👉 They are stored at different memory locations  
👉 So identity is different


📌 When is returns True

python
x = [1, 2, 3]
y = x

print(x is y)

text
Output:
True`


👉 Both reference the SAME object

🎯 Quick Difference

• == → Compares values 
• is → Compares memory identity 
• Use == for value comparison 
• Use is mainly for None checks

🚀 Shallow Copy vs Deep Copy in Python

When copying objects in Python, behavior changes for nested data structures.


📌 Shallow Copy

A shallow copy creates a new outer object 
but inner objects remain shared references 🔗 

import copy

original = [[1, 2], [3, 4]]
shallow = copy.copy(original)

shallow[0].append(99)

print(original)
print(shallow)

Output:
[[1, 2, 99], [3, 4]]
[[1, 2, 99], [3, 4]]

👉 Nested objects are shared.


📌 Deep Copy

A deep copy creates a completely independent copy 
including all nested objects 🧠 

import copy

original = [[1, 2], [3, 4]]
deep = copy.deepcopy(original)

deep[0].append(99)

print(original)
print(deep)

Output:
[[1, 2], [3, 4]]
[[1, 2, 99], [3, 4]]

👉 No shared references.


🎯 Quick Difference

• Shallow → Copies outer layer only 
• Deep → Copies entire structure 
• Use deep copy for nested data