Day 18 – Exception Handling in Python
🔹 Definition: Exception handling is used to handle runtime errors so that the program does not crash and runs smoothly.

⸻

🔹 What is an Exception?
👉 An error that occurs during program execution
Example:
print(10 / 0)
❌ Output: ZeroDivisionError

⸻

🔹 try & except
👉 Used to handle errors

try:
    print(10 / 0)
except:
    print("Error occurred")


✅ Output: Error occurred

⸻

🔹 Handling Specific Exception

try:
    num = int("abc")
except ValueError:
    print("Invalid input")



⸻

🔹 Multiple Exceptions

try:
    a = int(input("Enter number: "))
    print(10 / a)
except ValueError:
    print("Enter valid number")
except ZeroDivisionError:
    print("Cannot divide by zero")



⸻

🔹 else Block
👉 Runs if no error occurs

try:
    print(10 / 2)
except:
    print("Error")
else:
    print("Success")



⸻

🔹 finally Block
👉 Always runs (error or not)

try:
    print(10 / 2)
except:
    print("Error")
finally:
    print("Execution completed")



⸻

🔹 Raising Exception
👉 Manually create error

age = -1

if age < 0:
    raise ValueError("Age cannot be negative")



⸻

🔹 Custom Exception

class MyError(Exception):
    pass

raise MyError("Custom error occurred")

⸻

❌ Common Mistakes
🚫 Using only except: (not specific) 🚫 Ignoring errors instead of handling 🚫 Not using finally for cleanup

⸻

✅ Summary
✔ try → test code ✔ except → handle error ✔ else → runs if no error ✔ finally → always runs ✔ raise → create error

Day 19 – Modules & Packages in Python
🔹 What is a Module?
👉 A module is a file that contains Python code (functions, variables, classes) which can be reused.
👉 Example:
If you create a file math_operations.py, it becomes a module.


⸻


🔹 Creating a Module
📄 math_operations.py
def add(a, b):
return a + b

def sub(a, b):
return a - b


⸻


🔹 Importing a Module
import math_operations

print(math_operations.add(5, 3))


⸻


🔹 Import Specific Functions
from math_operations import add

print(add(10, 5))


⸻


🔹 Import with Alias
import math_operations as mo

print(mo.sub(10, 3))


⸻


🔹 Built-in Modules
👉 Python already provides many modules
Examples:
math
random
datetime
os
import math
print(math.sqrt(16))


⸻


🔹 What is a Package?
👉 A package is a collection of multiple modules organized in folders.
📁 Example Structure:
my_package/
__init__.py
module1.py
module2.py


⸻


🔹 Import from Package
from my_package import module1

module1.function_name()


⸻


🔹
init.py File
👉 Marks a folder as a package
👉 Can be empty or contain initialization code


⸻


🔹 dir() Function
👉 Shows all functions/variables in a module
import math
print(dir(math))


⸻


❌ Common Mistakes
🚫 Wrong file path
🚫 Module name conflict (same as built-in module)
🚫 Forgetting init.py in package


⸻


✅ Summary
✔ Module → single Python file
✔ Package → collection of modules
✔ import → use module
✔ from → import specific items
✔ alias → rename module
✔ built-in modules → ready to use

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 🚀


⸻


📢 Follow 👉 https://t.me/futurestack45
🔁 Share with friends to grow together 🚀

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 🚀

⸻

📢 Follow 👉 https://t.me/futurestack45 🔁 Share with friends to grow together 🚀

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

⸻

📢 Follow 👉 https://t.me/futurestack45 🔁 Share with friends to grow together 🚀

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 🚀

⸻

📢 Follow 👉 https://t.me/futurestack45 🔁 Share with friends to grow together 🚀

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 🚀

⸻

📢 Follow 👉 https://t.me/futurestack45 🔁 Share with friends to grow together 🚀

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 🚀


⸻


📢 Follow 👉 https://t.me/futurestack45
🔁 Share with friends to grow together 🚀

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


⸻


📢 Follow 👉 https://t.me/futurestack45
🔁 Share with friends to grow together 🚀

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 🚀

⸻

📢 Follow 👉 https://t.me/futurestack45 🔁 Share with friends to grow together 🚀

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 🚀

⸻

📢 Follow 👉 https://t.me/futurestack45 🔁 Share with friends to grow together 🚀

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 🚀

⸻

📢 Follow 👉 https://t.me/futurestack45 🔁 Share with friends to grow together 🚀

Google Free Certificate Courses — No Fees, No Experience Needed

Google offers genuinely free certificate courses across three platforms: Digital Garage, Skillshop, and Cloud Skills Boost.

Who can apply:

• Anyone with a Gmail account, no fixed eligibility criteria
• No prior experience or technical background required
• Open globally, including India

What you get:

• Google Digital Garage: Free courses on digital marketing, career development, and data skills, with certificates on completion
• Google Skillshop: 26+ official certifications in Google Ads, Analytics, and Search Ads 360, most requiring recertification every 12 months
• Google Cloud Skills Boost: 1,300+ free courses, learning pathways, and hands on labs, with completion and skill badges
• Note: Google Career Certificates on Coursera (Data Analytics, IT Support, UX Design, etc.) are not fully free, they require a paid Coursera subscription unless you qualify for financial aid or a scholarship

Documents needed: None, just a Google account

How to apply:

1. Choose your platform based on your interest: Digital Garage for marketing/career skills, Skillshop for Ads/Analytics certifications, or Cloud Skills Boost for cloud/data skills
2. Sign in with your Gmail account
3. Enroll in your chosen course
4. Complete the video lessons, quizzes, and assignments
5. Download your certificate or badge upon completion

Deadline: None, self-paced and available anytime

Apply here:
Digital Garage: https://grow.google/digitalgarage
Skillshop: https://skillshop.withgoogle.com
Cloud Skills Boost: https://cloudskillsboost.google

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 🚀

⸻

📢 Follow 👉 https://t.me/futurestack45 🔁 Share with friends to grow together 🚀

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 🚀

⸻

📢 Follow 👉 https://t.me/futurestack45 🔁 Share with friends to grow together 🚀

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 🚀

⸻

📢 Follow 👉 https://t.me/futurestack45 🔁 Share with friends to grow together 🚀

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 🚀

⸻

📢 Follow 👉 https://t.me/futurestack45 🔁 Share with friends to grow together 🚀

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

⸻

🔹 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 🚀

⸻

📢 Follow 👉 https://t.me/futurestack45 🔁 Share with friends to grow together 🚀