0011) Question

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0012) Question

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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

🔍By: https://t.iss.one/DataScienceQ

0013) Question

https://t.iss.one/DataScienceQ

Follow me on linkedin (important for you)

https://www.linkedin.com/in/hussein-sheikho-4a8187246

🟩 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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0014) Question

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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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0015) Question

https://t.iss.one/DataScienceQ

🧠 What is a Generator in Python?
A generator is a special type of iterator that produces values lazily—one at a time, and only when needed—without storing them all in memory.

---

❓ How do you create a generator?
✅ Correct answer:
Option 1: Use the yield keyword inside a function.

🔥 Simple example:

def countdown(n):
    while n > 0:
        yield n
        n -= 1

When you call this function:

gen = countdown(3)
print(next(gen))  # 3
print(next(gen))  # 2
print(next(gen))  # 1

Each time you call next(), the function resumes from where it left off, runs until it hits yield, returns a value, and pauses again.

---

⛔ Why are the other options incorrect?

- Option 2 (class with __iter__ and __next__):
It works, but it’s more complex. Using yield is simpler and more Pythonic.

- Options 3 & 4 (for or while loops):
Loops are not generators themselves. They just iterate over iterables.

---

💡 Pro Tip:
Generators are perfect when working with large or infinite datasets. They’re memory-efficient, fast, and clean to write.

---

📌 #Python #Generator #yield #AdvancedPython #PythonTips #Coding


🔍By: https://t.iss.one/DataScienceQ