Python Learning

🐍 Let’s Fix How You’re Learning Python If you feel slow in Python, it usually is not because Python is hard. It is because of the learning approach. Let me be direct. ⚠️ Avoid these habits - Jumping between many tutorials - Copying code without thinking - Ignoring error messages - Watching more than building 👉 Instead, build this discipline - Follow one main learning path - After every concept, write your own small code - When an error appears, read it slowly - Build small projects every week 📌 Python becomes easier the moment you start treating it like a tool, not a subject.

NumPy Cheatsheet (Basic to Advanced)

🚀 List vs Tuple in Python Both store collections of data.  But they differ in mutability and internal behavior. 1️⃣ List (Mutable) 📦 Can be modified after creation.

nums = [1, 2, 3]
nums.append(4)
print(nums)

Output: [1, 2, 3, 4] ➤ How: Stored as a dynamic array  ➤ Wins: Flexible, easy to modify  ➤ Risk: Slightly higher memory usage  2️⃣ Tuple (Immutable) 🔒 Cannot be modified after creation.

nums = (1, 2, 3)
nums.append(4)

Output: AttributeError: 'tuple' object has no attribute 'append' ➤ How: Fixed-size structure  ➤ Wins: Faster iteration, lower memory usage  ➤ Risk: No modification allowed  💡 Key Difference • List → Mutable & flexible  • Tuple → Immutable & lightweight  Use List when data changes.  Use Tuple when data should stay constant.

🧠 LEGB Rule in Python (Scope Resolution) When Python looks for a variable,  it follows a fixed order called LEGB. L → Local  E → Enclosing  G → Global  B → Built-in  Python searches in this order. 1️⃣ Local Scope (L) 📍  Variables defined inside a function.

x = 10

def func():
    x = 5
    print(x)

func()

Output: 5 ➤ Python finds x inside the function first. 2️⃣ Enclosing Scope (E) 🔄  Variables in outer function (nested functions).

def outer():
    x = 20
    def inner():
        print(x)
    inner()

outer()

Output: 20 ➤ Python finds x in the enclosing function. 3️⃣ Global Scope (G) 🌍  Variables defined outside all functions.

x = 30

def func():
    print(x)

func()

Output: 30 ➤ Python uses global x. 4️⃣ Built-in Scope (B) ⚙️  Predefined names like len, print, etc.

print(len([1, 2, 3]))

Output: 3 💡 Search Order Local → Enclosing → Global → Built-in  Python stops searching once it finds the name.

🚀 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

🔥 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

What's the output of the code snippet?

🧠 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

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.

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What's the output of the code snippet?

What's the output of the code snippet?

10 Python Built-in Functions You Should Know