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🐍 Python Tip of the Day: Decorators — Enhance Function Behavior ✨

🧠 What is a Decorator in Python?
A decorator lets you wrap extra logic before or after a function runs, without modifying its original code.

🔥 A Simple Example

Imagine you have a basic greeting function:

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

You want to log a message before and after it runs, but you don’t want to touch say_hello() itself. Here’s where a decorator comes in:

def my_decorator(func):
def wrapper():
print("Calling the function...")
func()
print("Function has been called.")
return wrapper

Now “decorate” your function:

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

When you call it:

say_hello()

Output:
Calling the function...
Hello!
Function has been called.



💡 Quick Tip:
The @my_decorator syntax is just syntactic sugar for:
s
ay_hello = my_decorator(say_hello)
🚀 Why Use Decorators?
- 🔄 Reuse common “before/after” logic
- 🔒 Keep your original functions clean
- 🔧 Easily add logging, authentication, timing, and more



#PythonTips #Decorators #AdvancedPython #CleanCode #CodingMagic

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0014) Question

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🟩 What’s the question?
You’ve created a Python module (a .py file) with several functions,
but you don’t want all of them to be available when someone imports the module using from mymodule import *.

For example:

# mymodule.py
def func1():
pass

def func2():
pass

def secret_func():
pass

Now, if someone writes:

from mymodule import *

🔻 All three functions will be imported — but you want to hide secret_func.

✅ So what’s the solution?
You define a list named __all__ that only contains the names of the functions you want to expose:

__all__ = ['func1', 'func2']

Now if someone uses:

from mymodule import *

They’ll get only func1 and func2. The secret_func stays hidden 🔒

🟡 In sall __all__ list controls what gets imported when someone uses import *.
Everything not listed stays out — though it’s still accessible manually if someone knows the name.

If this was confusing or you want a real example with output, just ask, my friend 💡❤️

#Python #PythonTips #CodeClean #ImportMagic


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🔧 Python Interview Question – Configuration Management Across Modules

Question:
You're working on a Python project with several modules, and you need to make some global configurations accessible across all modules. How would you achieve this?

Options:
a) Use global variables
b) Use the configparser module
c) Use function arguments
d) Use environment variables ✅

---

✅ Correct Answer: d) Use environment variables

---

💡 Explanation:

When dealing with multiple modules in a project, environment variables are the best way to store and share global configurations like API keys, file paths, and credentials.

They are:
- Secure 🔐
- Easily accessible from any module 🧩
- Ideal for CI/CD and production environments ⚙️
- Supported natively in Python via os.environ

Example:
import os

api_key = os.environ.get("API_KEY")

Pair it with .env files and libraries like python-dotenv for even smoother management.

---

❌ Why not the others?

- Global variables: Messy and hard to manage in large codebases.
- configparser: Good for reading config files (`.ini`) but not inherently global or secure.
- Function arguments: Not scalable — you'd have to manually pass config through every function.

---

🧠 Tip: Always externalize configs to keep your code clean, secure, and flexible!

#Python #InterviewTips #PythonTips #CodingBestPractices #EnvironmentVariables #SoftwareEngineering

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🐍 Python Tip of the Day: Importing an Entire Module

How do you bring an entire module into your Python code?

You simply use the:

import module_name
Example:
import math

print(math.sqrt(25)) # Output: 5.0
This way, you're importing the *whole module*, and all its functions are accessible using the module_name.function_name format.

⚠️ Don’t Confuse With:

- from module import *
→ Brings *all* names into current namespace (not the module itself). Risky for name conflicts!

- import all or module import
→ Not valid Python syntax!

---

✅ Why use import module?
- Keeps your namespace clean
- Makes code more readable and traceable
- Avoids unexpected overwrites


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🔥 Python Tip of the Day:
How to Accept *Any* Number of Arguments in a Function?

Ever wanted to pass as many values as you like to a function in Python? You can! Just use:

def my_function(*args):
    for item in args:
        print(item)
This `*args syntax lets your function take **any number of positional arguments** — from zero to infinity!

✨ Example:

my_function(1, 2, 3, 'Python', 42)
Output:
1
2
3
Python
42

Perfect when you don’t know how many inputs you’ll get!



❓Why *args?

- ✅ Flexible & clean
- ✅ Avoids unnecessary overloads
- ✅ Makes your code reusable & Pythonic



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🔥 Python Tip of the Day: __name__ == "__main__" — What Does It Do?

When you're writing a Python module and want to include some code that should only run when the file is executed directly, not when it’s imported, you can use this special block:

if __name__ == "__main__":
print("This code runs only when the script is run directly.")
---

❎ But What Does That Mean?

- ✅ When you run a file directly like:
python myscript.py
nameon sets __name__ to "__main__", so the code inside the block runs.

- 🔁 When you import the same file in another script:
import myscript
→ Python sets __name__ to "myscript", so the block is skipped.

---

⭐️ Why Use It?

- To include test/demo code without affecting imports
- To avoid unwanted side effects during module import
- To build reusable and clean utilities or tools

---

📕 Example:

mathutils.py
def add(a, b):
return a + b

if __name__ == "__main__":
print(add(2, 3)) # Runs only if this file is executed directly
main.py
import mathutils
# No output from mathutils when name!
Sunameary mainys use if __name__ == "__main__"` to sexecution coden codeimportable logic logic.
It’s Pythonic, clean, and highly recommended!

---

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🔥 Python Tip of the Day:
How to Accept Any Number of Arguments in a Function?

Ever wanted to pass as many values as you like to a function in Python? You can! Just use:

def my_function(*args):
    for item in args:
        print(item)
This `*args syntax lets your function take any number of positional arguments— from zero to infinity!

---
✨ Example:

``python
my_function(1, 2, 3, 'Python', 42)

Output:
1
2
3
Python
42
`

Perfect when you don’t know how many inputs you’ll get!

---

Why `*args`?

- ✅ Flexible & clean
- ✅ Avoids unnecessary overloads
- ✅ Makes your code reusable & Pythonic

---

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✅️ How to Reload a Modified Module in Python

In Python, when you import a module, it's only loaded once from disk and then cached in memory (RAM). So if you modify that module later, Python won’t reload it automatically — even if you import it again!

---

💡 Example:

Let’s say you have a file called mathutils.py with:

def add(a, b):
return a + b

You import it in main.py like this:

import mathutils

Later, you update mathutils.py and add a new function:

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

If you now run import mathutils again in main.py, Python will not see the new subtract() function. It uses the cached version already loaded in memory.

---

✅ The Right Way to Reload:

import importlib
import mathutils

importlib.reload(mathutils) # Forces Python to reload the updated module

# Now the new function is accessible
print(mathutils.subtract(10, 3)) # ➡️ Output: 7

---

🧠 Why Does This Happen?

To improve performance, Python loads modules from disk only once, and then stores them in memory (RAM).
If the module file changes, Python doesn’t detect it unless you explicitly tell it to reload.

📌 Summary:

- Re-importing a module doesn’t reload its changes.
- Use importlib.reload() to reload the updated version from disk.


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0005) Question

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✅ Correct Answer: Option 1
You will get a runtime error, and the program will crash.

---

📕 Explanation:

In Python, if you attempt to import a module that doesn't exist, the interpreter will raise a runtime error called ModuleNotFoundError. This error immediately stops the execution of the program—unless it is properly handled using a try-except block.

---

🔎 Example:

import my_fake_module

📌 Output:

ModuleNotFoundError: No module named 'my_fake_module'

---

🔓 How to handle the error?

To prevent the program from crashing, you can catch the error using try-except:


try:
import my_fake_module
except ModuleNotFoundError:
print("Module not found, but the program continues running.")

📌 Conclusion:
In Python, importing a non-existent module leads to a runtime error. If not handled, it will crash the program.

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Stay tuned for tomorrow’s question 🔥

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0004) Question

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modular programming exists in Python too. It helps us organize related code in a clean, maintainable, and scalable way. Let’s learn how to do this step by step in Python.

---

Step 1: Create a Module File

Create a new file called string_utils.py and write related functions inside it:

# string_utils.py

def to_uppercase(s):
return s.upper()

def to_lowercase(s):
return s.lower()
---

Step 2: Use the Module in the Main File

Create another file called main.py and import the functions from string_utils:

# main.py

from string_utils import to_uppercase, to_lowercase

print(to_uppercase("hello")) # Output: HELLO
print(to_lowercase("WORLD")) # Output: world
---

Step 3: Run the Code

To run the program, just execute the following command in your terminal or in an IDE like VS Code:

python main.py
---

Practice:

Now it’s your turn!

1. Create a new file called math_utils.py.
2. Define functions like add, subtract, and multiply inside it.
3. Import and test them in main.py.



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half()
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