Day 20 – Lambda, Map, Filter & Reduce in Python
These are very important for interviews + real-world coding.

⸻

🔹 1. Lambda Functions
👉 Anonymous (no-name) functions 👉 Used for short, one-line operations

square = lambda x: x * x
print(square(5))


✅ Output:
25
👉 Multiple arguments:

add = lambda a, b: a + b
print(add(3, 4))



⸻

🔹 2. map() Function
👉 Applies a function to all elements in a list

nums = [1, 2, 3, 4]

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


✅ Output:
[2, 4, 6, 8]

⸻

🔹 3. filter() Function
👉 Filters elements based on condition

nums = [1, 2, 3, 4, 5]

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


✅ Output:
[2, 4]

⸻

🔹 4. reduce() Function
👉 Reduces list to single value

from functools import reduce

nums = [1, 2, 3, 4]

result = reduce(lambda x, y: x + y, nums)
print(result)


✅ Output:
10

⸻

🔹 Difference Between Them
Function
lambda: Create small function
map: Transform data
filter: Select data
reduce: Combine data

⸻

🔹 Real-Time Example

nums = [10, 15, 20, 25]

# Step 1: filter even
evens = list(filter(lambda x: x % 2 == 0, nums))

# Step 2: square them
squares = list(map(lambda x: x * x, evens))

print(squares)


✅ Output:
[100, 400]

⸻

❌ Common Mistakes
🚫 Forgetting list() around map/filter 🚫 Not importing reduce 🚫 Using lambda for complex logic (avoid)

⸻

✅ Summary
✔ lambda → one-line function ✔ map → apply function ✔ filter → condition check ✔ reduce → single output

Day 21 – List Comprehension & *args / **kwargs
🔹 Definition:
List comprehension is a short and powerful way to create lists in a single line.


⸻


🔹 Basic Example:

nums = [1, 2, 3, 4]

squares = [x*x for x in nums]
print(squares)


Output:
[1, 4, 9, 16]


⸻


🔹 With Condition:

nums = [1, 2, 3, 4, 5, 6]

evens = [x for x in nums if x % 2 == 0]
print(evens)


Output:
[2, 4, 6]


⸻


🔹 Normal Loop vs List Comprehension
❌ Normal Way:

nums = [1, 2, 3]
result = []

for x in nums:
    result.append(x*x)

print(result)


✅ Short Way:

result = [x*x for x in [1,2,3]]
print(result)




⸻


🔹 *args (Multiple Arguments)
👉 Allows function to accept multiple values
Example:

def add(*nums):
    return sum(nums)

print(add(1, 2, 3, 4))


Output:
10


⸻


🔹 **kwargs (Keyword Arguments)
👉 Accepts data in key-value format
Example:

def info(**data):
    print(data)

info(name="Ram", age=20)


Output:
{'name': 'Ram', 'age': 20}


⸻


🔹 Combining args and kwargs

def show(*args, **kwargs):
    print(args)
    print(kwargs)

show(1, 2, 3, name="Mani", age=25)


Output:
(1, 2, 3)
{'name': 'Mani', 'age': 25}


⸻


❌ Common Mistakes:
🚫 Forgetting brackets in list comprehension
🚫 Confusing *args with list
🚫 Using kwargs without key=value format


⸻


✅ Summary:
✔ List comprehension → short & clean list creation
✔ *args → multiple values
✔ **kwargs → key-value inputs
✔ Improves code readability 🚀


⸻


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Day 22 – Decorators & Generators in Python
🔹 Definition (Decorator): A decorator is used to modify or extend the behavior of a function without changing its code.

⸻

🔹 Basic Example (Decorator):

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

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

greet()


Output:
Before function
Hello!
After function

⸻

🔹 Without @ Syntax (Understanding)

def greet():
    print("Hello!")

greet = my_decorator(greet)
greet()



⸻

🔹 Why Use Decorators?
👉 Add extra functionality 👉 Code reuse 👉 Used in frameworks (Django, Flask)

⸻

🔹 Definition (Generator):
A generator is a function that returns values one by one using yield instead of returning all at once.

⸻

🔹 Basic Example (Generator):

def count():
    for i in range(3):
        yield i

for num in count():
    print(num)


Output:
0
1
2

⸻

🔹 Difference: return vs yield
return
Ends function
Uses more memory
Returns one value
yield
Pauses function
Memory efficient
Returns multiple values
⸻

🔹 Generator Example (Real Use)

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

print(list(even_numbers(10)))


Output:
[0, 2, 4, 6, 8]

⸻

❌ Common Mistakes:
🚫 Forgetting yield in generator 🚫 Confusing decorator with normal function 🚫 Not using @ syntax properly

⸻

✅ Summary:
✔ Decorator → modifies function behavior ✔ @ → used to apply decorator ✔ Generator → produces values one by one ✔ yield → key for generators ✔ Saves memory 🚀

⸻

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Day 23 – JSON, Regex & DateTime in Python
🔹 These are very important for real-world projects (APIs, data handling, validation)

⸻

🔹 1. JSON in Python
🔹 Definition: JSON (JavaScript Object Notation) is used to store and exchange data.

⸻

🔹 Convert JSON → Python
import json



data = '{"name": "Mani", "age": 22}'
result = json.loads(data)

print(result)


Output:
{'name': 'Mani', 'age': 22}

⸻

🔹 Convert Python → JSON

import json

data = {"name": "Mani", "age": 22}
result = json.dumps(data)

print(result)


Output:
{"name": "Mani", "age": 22}

⸻

🔹 2. Regular Expressions (Regex)
🔹 Definition: Regex is used to search and match patterns in text.

⸻

🔹 Basic Example

import re

text = "My number is 9876543210"
result = re.findall(r'\d+', text)

print(result)


Output:
['9876543210']

⸻

🔹 Check Email Pattern

import re

email = "test@gmail.com"

if re.match(r'^\S+@\S+\.\S+$', email):
    print("Valid Email")
else:
    print("Invalid Email")



⸻

🔹 3. Date & Time
🔹 Definition: Used to work with date and time in Python.

⸻

🔹 Current Date & Time

from datetime import datetime

now = datetime.now()
print(now)



⸻

🔹 Format Date

from datetime import datetime

now = datetime.now()
print(now.strftime("%d-%m-%Y"))


Output:
10-07-2026

⸻

❌ Common Mistakes:
🚫 Forgetting to import modules 🚫 Wrong regex patterns 🚫 Confusing loads() and dumps()

⸻

✅ Summary:
✔ JSON → data exchange ✔ loads() → JSON to Python ✔ dumps() → Python to JSON ✔ Regex → pattern matching ✔ datetime → date & time handling

⸻

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Day 24 – Mini Project (Putting It All Together)
🔹 Definition: A mini project helps you apply all the concepts you learned (functions, loops, JSON, etc.) in a real-world scenario.

⸻

🔹 Project: Student Record Manager
👉 Features: ✔ Add student ✔ View students ✔ Save data (JSON)

⸻

🔹 Step 1: Import Module

import json



⸻

🔹 Step 2: Create Functions

def add_student(data):
    name = input("Enter name: ")
    age = input("Enter age: ")
    
    data.append({"name": name, "age": age})
    return data



⸻

🔹 Step 3: View Data

def view_students(data):
    for student in data:
        print(student)



⸻

🔹 Step 4: Save Data to File

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



⸻

🔹 Step 5: Load Data

def load_data():
    try:
        with open("students.json", "r") as file:
            return json.load(file)
    except:
        return []



⸻

🔹 Step 6: Main Program

data = load_data()

while True:
    print("\n1. Add Student")
    print("2. View Students")
    print("3. Exit")
    
    choice = input("Enter choice: ")
    
    if choice == "1":
        data = add_student(data)
    elif choice == "2":
        view_students(data)
    elif choice == "3":
        save_data(data)
        break
    else:
        print("Invalid choice")



⸻

👉 Output Example:

1. Add Student
2. View Students
3. Exit
Enter choice: 1
Enter name: Mani
Enter age: 22



⸻

❌ Common Mistakes:
🚫 Not saving data before exit 🚫 File not found error 🚫 Wrong JSON format

⸻

✅ Summary:
✔ Uses functions ✔ Uses loops ✔ Uses JSON ✔ Real-world logic building ✔ Great for beginners 🚀

⸻

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Day 25 – String Methods in Python
🔹 Definition: Strings are sequences of characters, and Python provides built-in methods to manipulate them.

⸻

🔹 Basic Example

text = "hello world"
print(text.upper())


Output:
HELLO WORLD

⸻

🔹 Common String Methods
🔹 1. upper() – Convert to uppercase

text = "python"
print(text.upper())


Output:
PYTHON

⸻

🔹 2. lower() – Convert to lowercase
text = "PYTHON"
print(text.lower())
Output:
python

⸻

🔹 3. title() – First letter capital

text = "hello world"
print(text.title())


Output:
Hello World

⸻

🔹 4. strip() – Remove spaces

text = "  hello  "
print(text.strip())


Output:
hello

⸻

🔹 5. replace() – Replace text

text = "I like Java"
print(text.replace("Java", "Python"))


Output:
I like Python

⸻

🔹 6. split() – Convert string to list

text = "apple,banana,grapes"
print(text.split(","))


Output:
['apple', 'banana', 'grapes']

⸻

🔹 7. find() – Find position

text = "hello"
print(text.find("e"))


Output:
1

⸻

🔹 8. count() – Count occurrences

text = "banana"
print(text.count("a"))


Output:
3

⸻

🔹 9. startswith() / endswith()

text = "python.py"
print(text.startswith("python"))
print(text.endswith(".py"))


Output:
True
True

⸻

❌ Common Mistakes:
🚫 Strings are immutable (cannot change directly) 🚫 Forgetting case sensitivity 🚫 Using wrong method names

⸻

✅ Summary:
✔ Strings → text data ✔ Many built-in methods ✔ Immutable (cannot modify directly) ✔ Used in almost every program 🚀

⸻

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Day 26 – Lists in Python
🔹 Definition:
A list is a collection of multiple items stored in a single variable.


⸻


🔹 Creating a List

numbers = [1, 2, 3, 4]
print(numbers)


Output:
[1, 2, 3, 4]


⸻


🔹 Accessing Elements

nums = [10, 20, 30]

print(nums[0])   # first element
print(nums[-1])  # last element


Output:
10
30


⸻


🔹 Adding Elements

nums = [1, 2]

nums.append(3)
print(nums)


Output:
[1, 2, 3]


⸻


🔹 Insert at Position

nums = [1, 3]

nums.insert(1, 2)
print(nums)


Output:
[1, 2, 3]


⸻


🔹 Remove Elements

nums = [1, 2, 3]

nums.remove(2)
print(nums)


Output:
[1, 3]


⸻


🔹 Loop Through List

nums = [1, 2, 3]

for x in nums:
    print(x)




⸻


🔹 List Length

nums = [1, 2, 3, 4]

print(len(nums))


Output:
4


⸻


🔹 List Slicing

nums = [1, 2, 3, 4, 5]

print(nums[1:4])


Output:
[2, 3, 4]


⸻


🔹 List with Different Data Types

data = [1, "Python", True]
print(data)




⸻


❌ Common Mistakes:
🚫 Index out of range
🚫 Confusing remove() and pop()
🚫 Modifying list while looping


⸻


✅ Summary:
✔ List → collection of items
✔ Ordered & changeable
✔ Supports different data types
✔ Very important for logic building 🚀


⸻


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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()

⸻

🔹 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

⸻

🔹 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.

⸻

🔹 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

⸻

🔹 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()

⸻

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

⸻

🔹 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

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🔹 Write JSON to File:
import json

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

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

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🔹 Read JSON from File:
import json

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

print(data)

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🔹 Pretty Print JSON:
import json

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

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

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

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✅ 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.

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🔹 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

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🔹 Make GET Request:
import requests

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

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

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🔹 Get JSON Data:
import requests

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

print(data)

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🔹 POST Request Example:
import requests

data = {"name": "John"}

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

print(response.json())

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

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

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

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✅ 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()

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🔹 Close Connection:
conn.close()

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

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✅ 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

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🔹 Example:
python script.py hello 123
import sys

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

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🔹 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)

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

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

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✅ 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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