Day 27 – Generators in Python
🔹 Definition:
A generator is a special type of function that returns values one at a time using yield instead of returning all values at once.


⸻


🔹 Example:

def my_gen():
    yield 1
    yield 2
    yield 3

g = my_gen()

for i in g:
    print(i)


Output:
1
2
3


⸻


🔹 How it Works:
👉 yield pauses the function and remembers its state
👉 Next value is generated only when needed
👉 Saves memory compared to lists


⸻


🔹 Generator vs List
# List
nums = [1, 2, 3]

# Generator
nums = (x for x in range(3))
👉 List → stores all values in memory
👉 Generator → produces values one by one


⸻


🔹 Generator Expression

gen = (x*x for x in range(5))

for i in gen:
    print(i)


Output:
0
1
4
9
16


⸻


🔹 Real-Time Example

def even_numbers(n):
    for i in range(n):
        if i % 2 == 0:
            yield i

for num in even_numbers(10):
    print(num)


Output:
0
2
4
6
8


⸻


❌ Common Mistake:

def test():
    yield 1

print(test())


Output:
<generator object test at 0x...>
❌ Because generator must be iterated to get values


⸻


✅ Summary:
✔ Uses yield instead of return
✔ Generates values one by one
✔ Memory efficient
✔ Useful for large data


⸻


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Day 28 – Decorators in Python
🔹 Definition: A decorator is a function that modifies the behavior of another function without changing its code.

⸻

🔹 Basic Example:

def my_decorator(func):
    def wrapper():
        print("Before function")
        func()
        print("After function")
    return wrapper

@my_decorator
def say_hello():
    print("Hello!")

say_hello()

Output:
Before function
Hello!
After function

⸻

🔹 How it Works: 👉 @decorator_name is used above a function 👉 It wraps another function 👉 Adds extra functionality

⸻

🔹 Without Using @ Syntax

def greet():
    print("Hello")

greet = my_decorator(greet)
greet()

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🔹 Decorator with Arguments

def my_decorator(func):
    def wrapper(name):
        print("Welcome")
        func(name)
    return wrapper

@my_decorator
def greet(name):
    print(name)

greet("Mani")

Output:
Welcome
Mani

⸻

🔹 Real Use Case: 👉 Logging 👉 Authentication 👉 Performance tracking

⸻

❌ Common Mistake:

def deco(func):
    def wrapper():
        func()
    return wrapper

❌ Missing return value handling if function returns something

⸻

✅ Summary: ✔ Used to extend function behavior ✔ Uses @ syntax ✔ Keeps code clean & reusable ✔ Very useful in real-world apps 🚀

⸻

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Day 29 – Generators in Python
🔹 Definition: A generator is a function that returns values one at a time using yield, instead of returning all values at once.

⸻

🔹 Basic Example:

def my_generator():
    yield 1
    yield 2
    yield 3

gen = my_generator()

for i in gen:
    print(i)

Output:
1
2
3

⸻

🔹 Key Difference (return vs yield): 👉 return → ends function & returns single value 👉 yield → pauses function & resumes later

⸻

🔹 How it Works: ✔ Function execution pauses at yield ✔ Remembers last state ✔ Continues from same point

⸻

🔹 Generator with Loop:

def count(n):
    for i in range(n):
        yield i

for num in count(5):
    print(num)

Output:
0
1
2
3
4

⸻

🔹 Why Use Generators? 👉 Memory efficient (no full list stored) 👉 Faster for large data 👉 Useful in streaming data

⸻

🔹 Real Use Case: ✔ Reading large files ✔ Handling API data ✔ Infinite sequences

⸻

❌ Common Mistake:
gen = my_generator()
print(gen)
❌ This prints generator object, not values

⸻

✅ Summary: ✔ Uses yield keyword ✔ Generates values one by one ✔ Saves memory ✔ Ideal for large datasets 🚀

⸻

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Day 30 – Lambda Functions in Python
🔹 Definition: A lambda function is a small anonymous function written in a single line without using def.

⸻

🔹 Basic Syntax:
lambda arguments: expression

⸻

🔹 Basic Example:
add = lambda a, b: a + b
print(add(2, 3))
Output:
5

⸻

🔹 Key Points: ✔ No function name ✔ Single expression only ✔ Returns value automatically

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🔹 With map():
nums = [1, 2, 3, 4]
squares = list(map(lambda x: x*x, nums))
print(squares)
Output:
[1, 4, 9, 16]

⸻

🔹 With filter():
nums = [1, 2, 3, 4, 5]
even = list(filter(lambda x: x % 2 == 0, nums))
print(even)
Output:
[2, 4]

⸻

🔹 With sorted():
data = [(1, 'b'), (3, 'a'), (2, 'c')]
result = sorted(data, key=lambda x: x[1])
print(result)
Output:
[(3, 'a'), (1, 'b'), (2, 'c')]

⸻

🔹 When to Use: 👉 Short, simple functions 👉 One-time usage 👉 Functional programming (map, filter, sort)

⸻

❌ Common Mistake: Using lambda for complex logic ❌ 👉 Makes code hard to read

⸻

✅ Summary: ✔ One-line anonymous function ✔ Uses lambda keyword ✔ Best for small tasks ✔ Improves code conciseness 🚀

⸻

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Day 31 – Modules & Packages in Python
🔹 Definition: A module is a file containing Python code (functions, variables). A package is a collection of multiple modules organized in folders.

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🔹 Example (Module):
👉 Create a file math_utils.py
def add(a, b):
return a + b
👉 Use in another file:
import math_utils

print(math_utils.add(2, 3))
Output:
5

⸻

🔹 Import Methods:
import math
print(math.sqrt(16))
from math import sqrt
print(sqrt(25))
from math import *
print(pow(2, 3))

⸻

🔹 Creating Package Structure:
my_package/
├── __init__.py
├── module1.py
└── module2.py
👉Definitionpy makes folder a package

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🔹 Using Package:
from my_package import module1

⸻

🔹 Why Use Modules & Packages? ✔ Organize large code ✔ Improve readability ✔ Reuse code easily ✔ Avoid duplication

⸻

🔹 Built-in Modules Examples: 👉 math 👉 random 👉 datetime

⸻

❌ Common Mistake:
from math import *
❌ Imports everything → can cause conflicts

⸻

✅ Summary: ✔ Module = single Python file ✔ Package = collection of modules ✔ Use import to access ✔ Keeps code clean & scalable 🚀

⸻

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Day 32 – Virtual Environment in Python
🔹 Definition: A virtual environment is an isolated space where you can install Python packages separately for each project.

⸻

🔹 Why Use Virtual Environment? 👉 Avoid package conflicts 👉 Manage project dependencies 👉 Keep projects clean and independent

⸻

🔹 Create Virtual Environment:
python -m venv myenv

⸻

🔹 Activate Virtual Environment:
👉 Windows:
myenv\Scripts\activate
👉 Mac/Linux:
source myenv/bin/activate

⸻

🔹 Install Packages:
pip install requests

⸻

🔹 Deactivate Environment:
deactivate

⸻

🔹 Check Installed Packages:
pip list

⸻

🔹 Freeze Requirements:
pip freeze > requirements.txt
👉 Helps to share project dependencies

⸻

🔹 Install from Requirements File:
pip install -r requirements.txt

⸻

❌ Common Mistake: 👉 Installing packages globally instead of using virtual environment ❌

⸻

✅ Summary: ✔ Isolated Python environment ✔ Avoids dependency conflicts ✔ Essential for real-world projects ✔ Use venv module 🚀

⸻

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Day 33 – File Handling in Python
🔹 Definition: File handling allows you to create, read, write, and manage files using Python.

⸻

🔹 Open a File:
file = open("data.txt", "r")
👉 Modes:
"r" → Read
"w" → Write (overwrites)
"a" → Append
"x" → Create

⸻

🔹 Read File:
file = open("data.txt", "r")
print(file.read())
file.close()

⸻

🔹 Read Line by Line:
file = open("data.txt", "r")
for line in file:
print(line)
file.close()

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🔹 Write to File:
file = open("data.txt", "w")
file.write("Hello World")
file.close()

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🔹 Append Data:
file = open("data.txt", "a")
file.write("\nNew Line")
file.close()

⸻

🔹 Best Practice (with statement):
with open("data.txt", "r") as file:
print(file.read())
👉 Automatically closes file ✅

⸻

🔹 Check if File Exists:
import os

print(os.path.exists("data.txt"))

⸻

❌ Common Mistakes: 👉 Forgetting to close file ❌ 👉 Using wrong mode (w deletes data) ❌

⸻

✅ Summary: ✔ Open, read, write files easily ✔ Use correct mode ✔ Prefer with for safety ✔ Important for real-world apps 🚀

⸻

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Day 34 – Working with JSON in Python
🔹 Definition: JSON (JavaScript Object Notation) is a format used to store and exchange data. Python provides a built-in json module to work with it.

⸻

🔹 Convert Python → JSON:
import json

data = {"name": "John", "age": 25}
json_data = json.dumps(data)

print(json_data)
👉 Converts dictionary into JSON string

⸻

🔹 Convert JSON → Python:
import json

json_data = '{"name": "John", "age": 25}'
data = json.loads(json_data)

print(data["name"])
👉 Converts JSON string into dictionary

⸻

🔹 Write JSON to File:
import json

data = {"name": "Alice", "age": 22}

with open("data.json", "w") as file:
json.dump(data, file)

⸻

🔹 Read JSON from File:
import json

with open("data.json", "r") as file:
data = json.load(file)

print(data)

⸻

🔹 Pretty Print JSON:
import json

data = {"name": "Sam", "age": 30}
print(json.dumps(data, indent=4))

⸻

🔹 Common Use Cases: ✔ APIs (sending & receiving data) ✔ Configuration files ✔ Data storage

⸻

❌ Common Mistakes: 👉 Using single quotes in JSON ❌ 👉 Confusing dump vs dumps ❌

⸻

✅ Summary: ✔ JSON = data exchange format ✔ dumps / loads → string ✔ dump / load → file ✔ Widely used in real-world apps 🚀

⸻

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Day 35 – Working with APIs in Python
🔹 Definition: API (Application Programming Interface) allows applications to communicate with each other and exchange data.

⸻

🔹 Why Use APIs? 👉 Get real-time data (weather, users, payments) 👉 Connect frontend ↔ backend 👉 Integrate third-party services

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🔹 Install Requests Library:
pip install requests

⸻

🔹 Make GET Request:
import requests

response = requests.get("https://api.github.com")

print(response.status_code)
print(response.text)

⸻

🔹 Get JSON Data:
import requests

response = requests.get("https://api.github.com")
data = response.json()

print(data)

⸻

🔹 POST Request Example:
import requests

data = {"name": "John"}

response = requests.post("https://httpbin.org/post", json=data)

print(response.json())

⸻

🔹 Status Codes: 👉 200 → Success ✅ 👉 404 → Not Found ❌ 👉 500 → Server Error ❌

⸻

🔹 Headers Example:
headers = {"Authorization": "Bearer token"}
requests.get("https://api.example.com", headers=headers)

⸻

❌ Common Mistakes: 👉 Not checking status code ❌ 👉 Forgetting .json() for JSON response ❌

⸻

✅ Summary: ✔ APIs connect applications ✔ Use requests module ✔ GET → fetch data ✔ POST → send data ✔ Used in real-world apps 🚀

⸻

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Day 36 – Introduction to Databases (SQLite in Python)
🔹 Definition: A database is used to store, manage, and retrieve structured data efficiently. SQLite is a lightweight, built-in database in Python.

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🔹 Why Use Database? 👉 Store large data 👉 Retrieve data quickly 👉 Avoid data loss 👉 Used in real-world applications

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🔹 Connect to Database:
import sqlite3

conn = sqlite3.connect("mydb.db")
cursor = conn.cursor()

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🔹 Create Table:
cursor.execute("""
CREATE TABLE users (
id INTEGER PRIMARY KEY,
name TEXT,
age INTEGER
)
""")

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🔹 Insert Data:
cursor.execute("INSERT INTO users (name, age) VALUES (?, ?)", ("John", 25))
conn.commit()

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🔹 Fetch Data:
cursor.execute("SELECT * FROM users")

rows = cursor.fetchall()
for row in rows:
print(row)

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🔹 Update Data:
cursor.execute("UPDATE users SET age = ? WHERE name = ?", (30, "John"))
conn.commit()

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🔹 Delete Data:
cursor.execute("DELETE FROM users WHERE name = ?", ("John",))
conn.commit()

⸻

🔹 Close Connection:
conn.close()

⸻

❌ Common Mistakes: 👉 Forgetting commit() ❌ 👉 Not closing connection ❌

⸻

✅ Summary: ✔ SQLite = built-in database ✔ Store & manage structured data ✔ Use SQL queries in Python ✔ Essential for backend development 🚀

⸻

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Day 37 – Command Line Arguments in Python
🔹 Definition: Command line arguments allow you to pass input values to a Python script when running it from the terminal.

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🔹 Why Use It? 👉 Pass dynamic input without changing code 👉 Useful for scripts & automation 👉 Common in real-world tools

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🔹 Using sys Module:
import sys

print(sys.argv)
👉 sys.argv stores all command line inputs as a list

⸻

🔹 Example:
python script.py hello 123
import sys

print(sys.argv[0]) # script name
print(sys.argv[1]) # hello
print(sys.argv[2]) # 123

⸻

🔹 Convert Input Type:
import sys

num = int(sys.argv[1])
print(num * 2)

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🔹 Using argparse (Better Way):
import argparse

parser = argparse.ArgumentParser()

parser.add_argument("name")
args = parser.parse_args()

print("Hello", args.name)

⸻

🔹 Run Script:
python script.py John
Output:
Hello John

⸻

❌ Common Mistakes: 👉 Forgetting index starts from 0 ❌ 👉 Not converting string to int ❌

⸻

✅ Summary: ✔ Use sys.argv for basic input ✔ Use argparse for advanced usage ✔ Helpful for automation scripts ✔ Widely used in real-world tools 🚀

⸻

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Day 38 – Scope & LEGB Rule in Python
🔹 Definition: Scope determines where a variable can be accessed in a Python program.
Python mainly follows four levels of scope:
👉 L – Local 👉 E – Enclosing 👉 G – Global 👉 B – Built-in
Together, these are called the LEGB rule.

⸻

🔹 1️⃣ Local Scope
👉 A variable created inside a function is called a local variable. 👉 It can normally be accessed only inside that function.
Example:
def greet():
message = "Hello"
print(message)

greet()
Output:
Hello
❌ This will cause an error:
def greet():
message = "Hello"

greet()
print(message)
👉 message exists only inside greet().

⸻

🔹 2️⃣ Global Scope
👉 A variable created outside a function is called a global variable. 👉 It can be accessed from different parts of the program.
Example:
name = "Python"

def show():
print(name)

show()
Output:
Python

⸻

🔹 3️⃣ Enclosing Scope
👉 This occurs when one function is defined inside another function. 👉 A variable from the outer function can be accessed by the inner function.
Example:
def outer():
message = "Hello"

def inner():
print(message)

inner()

outer()
Output:
Hello

⸻

🔹 4️⃣ Built-in Scope
👉 Python provides many built-in names that can be used directly.
Examples:
print(len("Python"))
print(max(10, 20))
Output:
6
20
👉 print(), len(), max(), sum() etc. are built-in functions.

⸻

🔹 LEGB Rule
When Python looks for a variable, it searches in this order:
👉 L → Local 👉 E → Enclosing 👉 G → Global 👉 B → Built-in
Example:
x = "Global"

def outer():
x = "Enclosing"

def inner():
x = "Local"
print(x)

inner()

outer()
Output:
Local
👉 Python finds the nearest x first.

⸻

🔹 global Keyword
👉 The global keyword allows a function to modify a global variable.
Example:
count = 10

def update():
global count
count = 20

update()

print(count)
Output:
20

⸻

🔹 nonlocal Keyword
👉 The nonlocal keyword allows an inner function to modify a variable from its enclosing function.
Example:
def outer():
count = 10

def inner():
nonlocal count
count = 20

inner()
print(count)

outer()
Output:
20

⸻

❌ Common Mistakes:
🚫 Confusing local and global variables 🚫 Trying to access a local variable outside its function 🚫 Using global unnecessarily

⸻

✅ Summary:
✔ Scope → where a variable can be accessed ✔ Local → inside current function ✔ Enclosing → outer function ✔ Global → outside functions ✔ Built-in → Python’s predefined names ✔ LEGB → order Python uses to find variables 🚀

⸻

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Day 39 – map(), filter() & reduce() in Python
🔹 Definition: These are functions used to process collections such as lists and perform operations on multiple values efficiently.

⸻

🔹 1️⃣ map()
👉 map() applies a function to every element of an iterable and returns the results.
Example:
numbers = [1, 2, 3, 4]

result = list(map(lambda x: x * 2, numbers))

print(result)
Output:
[2, 4, 6, 8]
👉 Every number is multiplied by 2.

⸻

🔹 2️⃣ filter()
👉 filter() selects only the elements that satisfy a condition.
Example:
numbers = [1, 2, 3, 4, 5, 6]

result = list(filter(lambda x: x % 2 == 0, numbers))

print(result)
Output:
[2, 4, 6]
👉 Only even numbers are selected.

⸻

🔹 3️⃣ reduce()
👉 reduce() repeatedly combines elements and produces one final value.
👉 It is available in the functools module.
Example:
from functools import reduce

numbers = [1, 2, 3, 4]

result = reduce(lambda a, b: a + b, numbers)

print(result)
Output:
10
👉 Calculation:
1 + 2 + 3 + 4 = 10

⸻

🔹 map() Example Without Lambda
def square(x):
return x * x

numbers = [1, 2, 3, 4]

result = list(map(square, numbers))

print(result)
Output:
[1, 4, 9, 16]

⸻

🔹 filter() Example Without Lambda
def is_positive(x):
return x > 0

numbers = [-2, -1, 0, 1, 2]

result = list(filter(is_positive, numbers))

print(result)
Output:
[1, 2]

⸻

🔹 Important Difference
👉 map() → Transforms every element
👉 filter() → Selects elements
👉 reduce() → Combines elements into one result

⸻

🔹 Real-World Example
Suppose we have marks:
marks = [35, 80, 45, 90, 20]

passed = list(filter(lambda x: x >= 40, marks))

print(passed)
Output:
[80, 45, 90]
👉 filter() keeps only students who scored 40 or above.

⸻

❌ Common Mistakes:
🚫 Forgetting list() when you want to display the results directly
🚫 Using filter() when you actually need to transform values
🚫 Forgetting to import reduce

⸻

✅ Summary:
✔ map() → transform values ✔ filter() → select values ✔ reduce() → combine values ✔ Often used with lambda functions ✔ Very useful for processing collections 🚀

⸻

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Day 40 – Iterators in Python
🔹 Definition: An iterator is an object that allows you to access elements one at a time, without accessing all elements at once.
👉 Python uses iter() to create an iterator and next() to get the next value.

⸻

🔹 Basic Example:
numbers = [10, 20, 30]

iterator = iter(numbers)

print(next(iterator))
print(next(iterator))
print(next(iterator))
Output:
10
20
30
👉 Each next() call returns the next element.

⸻

🔹 How iter() Works
👉 iter() converts an iterable such as a list into an iterator.
Example:
numbers = [1, 2, 3]

iterator = iter(numbers)

print(iterator)
👉 The iterator keeps track of where it is in the sequence.

⸻

🔹 How next() Works
👉 next() retrieves the next available value from an iterator.
Example:
numbers = [10, 20, 30]

iterator = iter(numbers)

print(next(iterator))
print(next(iterator))
Output:
10
20

⸻

🔹 What Happens When Values Are Finished?
If there are no more values, Python raises StopIteration.
Example:
numbers = [1, 2]

iterator = iter(numbers)

print(next(iterator))
print(next(iterator))
print(next(iterator))
Output:
1
2
Traceback (most recent call last):
...
StopIteration
👉 StopIteration tells Python that there are no more elements.

⸻

🔹 Iterator with for Loop
You normally don’t need to call next() manually.
Example:
numbers = [10, 20, 30]

for num in numbers:
print(num)
Output:
10
20
30
👉 The for loop internally uses the iterator mechanism.

⸻

🔹 Iterable vs Iterator
👉 Iterable: An object whose elements can be accessed one by one.
Examples:
numbers = [1, 2, 3]
name = "Python"
👉 Iterator: An object that remembers its current position while producing values.
Example:
numbers = [1, 2, 3]

iterator = iter(numbers)

⸻

🔹 Creating Your Own Iterator
A class can be made into an iterator using __iter__() and __next__().
Example:
class Count:
def __init__(self):
self.num = 1

def __iter__(self):
return self

def __next__(self):
if self.num <= 3:
value = self.num
self.num += 1
return value
raise StopIteration

counter = Count()

for num in counter:
print(num)
Output:
1
2
3

⸻

🔹 Iterator vs Generator
👉 Iterator → object that implements __iter__() and __next__()
👉 Generator → simpler way to create an iterator using yield
Example:
def numbers():
yield 1
yield 2
yield 3

for num in numbers():
print(num)
Output:
1
2
3

⸻

❌ Common Mistakes:
🚫 Calling next() after all elements are consumed 🚫 Confusing an iterable with an iterator 🚫 Forgetting that an iterator keeps its current position

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✅ Summary:
✔ Iterator → accesses values one at a time ✔ iter() → creates an iterator ✔ next() → gets the next value ✔ StopIteration → indicates no more values ✔ for loop uses iteration internally ✔ Generators are an easy way to create iterators 🚀

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Day 41 – zip() & enumerate() in Python
🔹 Definition: zip() and enumerate() are built-in Python functions that make it easier to work with lists and other iterables.

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🔹 1️⃣ zip()
👉 zip() combines elements from two or more iterables position by position.
Example:
names = ["Mani", "Rahul", "Priya"]
ages = [22, 24, 21]

result = zip(names, ages)

print(list(result))
Output:
[('Mani', 22), ('Rahul', 24), ('Priya', 21)]
👉 First name is combined with the first age, second with second, and so on.

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🔹 Using zip() with a for Loop
names = ["Mani", "Rahul", "Priya"]
marks = [85, 90, 78]

for name, mark in zip(names, marks):
print(name, mark)
Output:
Mani 85
Rahul 90
Priya 78

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🔹 2️⃣ enumerate()
👉 enumerate() adds a counter/index while looping through an iterable.
Example:
names = ["Mani", "Rahul", "Priya"]

for index, name in enumerate(names):
print(index, name)
Output:
0 Mani
1 Rahul
2 Priya
👉 By default, counting starts from 0.

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🔹 Start enumerate() from 1
names = ["Mani", "Rahul", "Priya"]

for index, name in enumerate(names, start=1):
print(index, name)
Output:
1 Mani
2 Rahul
3 Priya

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🔹 Why Use enumerate()?
❌ Without enumerate():
names = ["Mani", "Rahul", "Priya"]

for i in range(len(names)):
print(i, names[i])
✅ With enumerate():
names = ["Mani", "Rahul", "Priya"]

for i, name in enumerate(names):
print(i, name)
👉 enumerate() makes the code cleaner and easier to read.

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🔹 Combining zip() + enumerate()
names = ["Mani", "Rahul", "Priya"]
marks = [85, 90, 78]

for index, (name, mark) in enumerate(zip(names, marks), start=1):
print(index, name, mark)
Output:
1 Mani 85
2 Rahul 90
3 Priya 78

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🔹 Important Point About zip()
👉 If the iterables have different lengths, zip() stops when the shortest iterable ends.
Example:
names = ["Mani", "Rahul", "Priya"]
ages = [22, 24]

print(list(zip(names, ages)))
Output:
[('Mani', 22), ('Rahul', 24)]
👉 Priya has no matching age, so she is not included.

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❌ Common Mistakes:
🚫 Forgetting that indexes start from 0 🚫 Assuming zip() fills missing values 🚫 Forgetting to convert zip() to list() when you want to display all pairs directly

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✅ Summary:
✔ zip() → combines values position by position ✔ enumerate() → adds index while looping ✔ enumerate(..., start=1) → starts counting from 1 ✔ zip() stops at the shortest iterable ✔ Both make loops cleaner and easier to understand 🚀

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Day 42 – any(), all(), min(), max() & sum() in Python
🔹 Definition: Python provides several built-in functions that make it easier to check conditions and perform calculations on collections of values.

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🔹 1️⃣ any()
👉 any() returns True if at least one value in an iterable is True.
Example:
numbers = [1, 3, 5, 8]

result = any(x % 2 == 0 for x in numbers)

print(result)
Output:
True
👉 8 is even, so at least one condition is True.

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🔹 2️⃣ all()
👉 all() returns True only when every value in an iterable is True.
Example:
numbers = [2, 4, 6, 8]

result = all(x % 2 == 0 for x in numbers)

print(result)
Output:
True
👉 Every number is even.

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🔹 any() vs all()
👉 any() → At least one must be True
👉 all() → Every condition must be True
Example:
numbers = [2, 4, 7, 8]

print(any(x % 2 == 0 for x in numbers))
print(all(x % 2 == 0 for x in numbers))
Output:
True
False
👉 At least one number is even → True
👉 Not all numbers are even → False

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🔹 3️⃣ min()
👉 min() returns the smallest value from a collection.
Example:
numbers = [10, 5, 20, 3]

print(min(numbers))
Output:
3

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🔹 4️⃣ max()
👉 max() returns the largest value from a collection.
Example:
numbers = [10, 5, 20, 3]

print(max(numbers))
Output:
20

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🔹 5️⃣ sum()
👉 sum() calculates the total of numeric values.
Example:
numbers = [10, 20, 30, 40]

print(sum(numbers))
Output:
100

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🔹 Real-World Example
Suppose we have student marks:
marks = [75, 82, 68, 91, 88]

print("Highest:", max(marks))
print("Lowest:", min(marks))
print("Total:", sum(marks))
print("All passed:", all(mark >= 40 for mark in marks))
Output:
Highest: 91
Lowest: 68
Total: 404
All passed: True

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🔹 Checking if Any Student Failed
marks = [75, 82, 35, 91, 88]

failed = any(mark < 40 for mark in marks)

print(failed)
Output:
True
👉 At least one student scored below 40.

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❌ Common Mistakes:
🚫 Confusing any() with all()
🚫 Using sum() with non-numeric values
🚫 Forgetting that min() and max() work based on comparison

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✅ Summary:
✔ any() → at least one condition is True ✔ all() → every condition is True ✔ min() → smallest value ✔ max() → largest value ✔ sum() → total value
🚀 These built-in functions are extremely useful when working with lists and other collections.

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