def exit(
self,
exc_type,
exc_val,
exc_tb
):

print(
"Connection Closed Safely"
)


with DatabaseConnection() as db:

print(
"Running queries..."
)

---

## Why Context Managers Matter

Without context managers:

python
Open resource
Do work
Manually close resource

Easy to forget.

---

With context managers:

python
Open resource
Do work
Automatically close resource

Safer and cleaner.

---

Common Uses

- Files
- Databases
- Network connections
- Cloud resources

---

# The AI Architect Mindset

Advanced Python is not only about writing code.

It is about designing systems that are:

- Scalable
- Maintainable
- Efficient
- Professional

Use:

text
OOP → Structure
Decorators → Flexibility
Generators → Scale
Asyncio → Speed
Context Managers → Safety

These concepts form the foundation of professional software engineering and AI architecture. 5

---

# Practice Exercises

### 1. OOP Challenge

Create a class called:

python
Robot

Add:

python
name
version
speak()

---

### 2. Decorator Challenge

Create a decorator that prints:

text
Function Started
Function Finished

before and after execution.

---

### 3. Generator Challenge

Create a generator that yields:

python
1 to 100

one number at a time.

---

### 4. Async Challenge

Create three asynchronous functions and execute them using:

python
asyncio.gather()

---

### 5. Context Manager Challenge

Create a custom context manager that prints:

text
Resource Opened

when entering and:

text
Resource Closed
`

when exiting.

━━━━━━━━━━━━━━━━━━━━━━
🎓 COURSE COMPLETED
━━━━━━━━━━━━━━━━━━━━━━

Congratulations!

You have successfully completed the Python Programming Masterclass.

From Python fundamentals to Advanced Python Architecture, you have gained the knowledge and skills needed to continue your journey in software development, data science, and AI.

However, learning does not stop here.

In the coming days, this channel will continue to provide:

✓ Practice Questions
✓ Quizzes and Tests
✓ Programming Challenges
✓ Projects
✓ Advanced Tips
✓ Learning Resources
✓ Course Updates
✓ AI & Technology Content

Make sure to stay active, participate in discussions, and complete the upcoming exercises to strengthen your understanding.

The course may be complete, but the journey of mastery has only begun.

Keep learning.
Keep building.
Keep improving.

━━━━━━━━━━━━━━━━━━━━━━
Course Status: COMPLETED ✓
Next Phase: Practice & Mastery
━━━━━━━━━━━━━━━━━━━━━━

By Yehya Oumer
Mizan Institute of Technology
2026

📢 New Chapter Started

The lessons are finished.

Now we focus on what matters most:

Building.

Expect daily Python content:
✓ Projects
✓ Useful Scripts
✓ Coding Challenges
✓ AI Tools
✓ Python Tips
✓ Career Advice
✓ Advanced Concepts

Don't become someone who completed a course.

Become someone who uses what they learned.

Stay tuned.

🐍 Python Tip #1: List Comprehensions

List comprehensions allow you to create lists in a shorter and cleaner way.

Instead of:

numbers = []

for i in range(1, 6):
    numbers.append(i)

print(numbers)

You can write:

numbers = [i for i in range(1, 6)]

print(numbers)

Output:

[1, 2, 3, 4, 5]

---

You can also perform operations while creating the list.

Example:

squares = [x**2 for x in range(1, 6)]

print(squares)

Output:

[1, 4, 9, 16, 25]

---

Using conditions:

even_numbers = [
    x for x in range(1, 11)
    if x % 2 == 0
]

print(even_numbers)

Output:

[2, 4, 6, 8, 10]

---

Why use list comprehensions?

✓ Cleaner code
✓ Fewer lines
✓ Faster execution
✓ Widely used in Data Science and AI

Challenge:

Create a list comprehension that generates:

[10, 20, 30, 40, 50]

without manually typing the numbers.

💡 Python Tip #2: The enumerate() Function

When looping through a list, you often need both the item's index and its value.

Instead of writing:

fruits = ["Apple", "Banana", "Orange"]

for i in range(len(fruits)):
    print(i, fruits[i])

You can use enumerate():

fruits = ["Apple", "Banana", "Orange"]

for index, fruit in enumerate(fruits):
    print(index, fruit)

Output:

0 Apple
1 Banana
2 Orange

---

Starting the Index from a Different Number

By default, enumerate() starts counting from 0.

You can change the starting number:

fruits = ["Apple", "Banana", "Orange"]

for index, fruit in enumerate(fruits, start=1):
    print(index, fruit)

Output:

1 Apple
2 Banana
3 Orange

---

Why use enumerate()?

✓ Cleaner and more readable code
✓ No need to manually manage indexes
✓ Reduces common indexing mistakes
✓ Frequently used in real-world Python projects

---

Challenge:

Without using range(len()), print the following output using enumerate():

Student 1: Ahmed
Student 2: Sara
Student 3: John

using this list:

students = ["Ahmed", "Sara", "John"]

🔥 Python Trick #3: Multiple Variable Assignment

Python allows you to assign multiple variables in a single line.

Instead of:

name = "Yehya"
age = 16
country = "Ethiopia"

You can write:

name, age, country = "Yehya", 16, "Ethiopia"

---

You can also assign the same value to multiple variables.

Example:

x = 0
y = 0
z = 0

Simplified:

x = y = z = 0

---

Unpacking a Collection

Python can unpack a list or tuple directly into variables.

Example:

languages = ["Python", "Java", "C++"]

first, second, third = languages

print(first)
print(second)
print(third)

Output:

Python
Java
C++

---

Ignoring Values

If you only need some values, use _ for the ones you don't care about.

Example:

numbers = (10, 20, 30)

first, _, third = numbers

print(first)
print(third)

Output:

10
30

---

Why use multiple assignment?

✓ Cleaner code
✓ Less typing
✓ Easier to read
✓ Commonly used in professional Python code

---

Challenge:

Without creating temporary variables, write Python code that swaps the values of:

a = 15
b = 40

so the final output becomes:

a = 40
b = 15

🐍 Python Tip #4: The zip() Function

The zip() function combines two or more iterables element by element.

Example:

names = ["Ahmed", "Sara", "John"]
scores = [95, 88, 91]

for name, score in zip(names, scores):
    print(name, score)

Output:

Ahmed 95
Sara 88
John 91

---

Using zip() with Three Lists

You can combine more than two lists.

Example:

names = ["Ahmed", "Sara", "John"]
ages = [18, 19, 20]
countries = ["Egypt", "Ethiopia", "Canada"]

for name, age, country in zip(names, ages, countries):
    print(name, age, country)

Output:

Ahmed 18 Egypt
Sara 19 Ethiopia
John 20 Canada

---

Converting zip() to a List

The zip() function returns a zip object.

To see all paired values, convert it into a list.

Example:

letters = ["A", "B", "C"]
numbers = [1, 2, 3]

result = list(zip(letters, numbers))

print(result)

Output:

[('A', 1), ('B', 2), ('C', 3)]

---

Why use zip()?

✓ Iterate over multiple lists at the same time
✓ Write cleaner and shorter code
✓ Avoid manual indexing with range()
✓ Frequently used in data processing and automation

---

Challenge:

Using zip(), print the following output:

Ali scored 90
Sara scored 85
John scored 95

using these lists:

students = ["Ali", "Sara", "John"]
marks = [90, 85, 95]

⚡ Python Tip #5: The any() and all() Functions

Python provides two useful built-in functions for checking multiple conditions: any() and all().

---

Using any()

The any() function returns True if at least one value is True.

Example:

values = [False, False, True]

print(any(values))

Output:

True

---

Using all()

The all() function returns True only if every value is True.

Example:

values = [True, True, True]

print(all(values))

Output:

True

---

Another Example

numbers = [10, 20, 30, 40]

print(all(num > 5 for num in numbers))

Output:

True

Since every number is greater than 5, all() returns True.

---

Example with any()

numbers = [10, 20, 30, 40]

print(any(num > 35 for num in numbers))

Output:

True

Only one number (40) is greater than 35, so any() returns True.

---

Why use any() and all()?

✓ Makes code shorter and cleaner
✓ Eliminates unnecessary loops
✓ Easy way to evaluate multiple conditions
✓ Commonly used in data validation and filtering

---

Challenge:

Given the list:

numbers = [12, 18, 25, 30]

1. Check if all numbers are even.
2. Check if any number is greater than 20.

Try solving it before running the code!

🚀 Python Tip #6: The *args and **kwargs Parameters

Sometimes you don't know how many arguments a function will receive.

Python provides *args and **kwargs to handle this flexibly.

---

Using *args

The *args parameter allows a function to accept any number of positional arguments.

Example:

def add_numbers(*args):
    print(sum(args))

add_numbers(5, 10)
add_numbers(1, 2, 3, 4, 5)

Output:

15
15

---

Using **kwargs

The **kwargs parameter allows a function to accept any number of keyword arguments.

Example:

def student_info(**kwargs):
    for key, value in kwargs.items():
        print(f"{key}: {value}")

student_info(
    name="Ahmed",
    age=18,
    country="Ethiopia"
)

Output:

name: Ahmed
age: 18
country: Ethiopia

---

Using Both Together

A function can use both *args and **kwargs.

Example:

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

display(
    10,
    20,
    name="Sara",
    age=19
)

Output:

(10, 20)
{'name': 'Sara', 'age': 19}

---

Why use *args and **kwargs?

✓ Accept a flexible number of arguments
✓ Build reusable functions
✓ Useful for libraries and frameworks
✓ Commonly used in Flask, Django, and AI libraries

---

Challenge:

Create a function called multiply() that accepts any number of numbers using *args and returns their product.

Example:

multiply(2, 3, 4)

Expected Output:

24

🐍 Python Tip #7: The enumerate() Function

When looping through a list, you sometimes need both the index and the value.

Instead of manually tracking the index, Python provides the enumerate() function.

---

Without enumerate()

fruits = ["Apple", "Banana", "Orange"]

index = 0

for fruit in fruits:
    print(index, fruit)
    index += 1

Output:

0 Apple
1 Banana
2 Orange

---

With enumerate()

fruits = ["Apple", "Banana", "Orange"]

for index, fruit in enumerate(fruits):
    print(index, fruit)

Output:

0 Apple
1 Banana
2 Orange

---

Starting from a Different Number

You can choose where the counting begins.

Example:

fruits = ["Apple", "Banana", "Orange"]

for index, fruit in enumerate(fruits, start=1):
    print(index, fruit)

Output:

1 Apple
2 Banana
3 Orange

---

Why use enumerate()?

✓ Cleaner code
✓ No need for a manual counter
✓ Easier to read and maintain
✓ Commonly used in Python projects

---

Challenge:

Using enumerate(), print the following output:

Question 1: What is Python?
Question 2: What is a List?
Question 3: What is a Dictionary?

using this list:

questions = [
    "What is Python?",
    "What is a List?",
    "What is a Dictionary?"
]

⚡ Python Tip #8: The sorted() Function

The sorted() function returns a new sorted list without modifying the original one.

---

Basic Example

numbers = [5, 2, 8, 1, 9]

result = sorted(numbers)

print(result)

Output:

[1, 2, 5, 8, 9]

The original list remains unchanged.

---

Sorting in Descending Order

Use the reverse=True parameter.

numbers = [5, 2, 8, 1, 9]

result = sorted(numbers, reverse=True)

print(result)

Output:

[9, 8, 5, 2, 1]

---

Sorting Strings

fruits = ["Orange", "Apple", "Banana", "Mango"]

print(sorted(fruits))

Output:

['Apple', 'Banana', 'Mango', 'Orange']

---

Difference Between sorted() and sort()

sorted()

numbers = [3, 1, 2]

new_numbers = sorted(numbers)

print(numbers)
print(new_numbers)

Output:

[3, 1, 2]
[1, 2, 3]

---

`list.sort()

``python
numbers = [3, 1, 2]

numbers.sort()

print(numbers)

Output:

python
[1, 2, 3]

Unlike `sorted()`, the `sort()` method modifies the original list.

---

Why use `sorted()`?

✓ Keeps the original data unchanged  
✓ Easy to sort numbers and strings  
✓ Supports ascending and descending order  
✓ Commonly used in data processing and reporting

---

Challenge:

Given the list:

python
scores = [88, 95, 72, 100, 67]
`

1. Print the scores in ascending order.
2. Print the scores in descending order.
3. Verify that the original list remains unchanged after using `sorted()`.

💡 Python Tip #9: Lambda Functions

A lambda function is a small anonymous function that can have any number of arguments but only one expression.

Lambda functions are useful when you need a simple function for a short period of time.

---

Basic Syntax

lambda arguments: expression

---

Example

Instead of writing:

def square(x):
    return x ** 2

print(square(5))

You can write:

square = lambda x: x ** 2

print(square(5))

Output:

25

---

Multiple Arguments

Lambda functions can accept multiple arguments.

Example:

multiply = lambda a, b: a * b

print(multiply(6, 4))

Output:

24

---

Using lambda with sorted()

You can sort complex data using a custom key.

Example:

students = [
    ("Ali", 85),
    ("Sara", 95),
    ("John", 78)
]

result = sorted(
    students,
    key=lambda student: student[1]
)

print(result)

Output:

[('John', 78), ('Ali', 85), ('Sara', 95)]

---

Why use Lambda Functions?

✓ Write short functions quickly
✓ Useful with sorted(), map(), and filter()
✓ Makes code more concise
✓ Commonly used in data analysis and automation

---

Challenge:

Using a lambda function, create a function that returns the cube of a number.

Example:

cube = lambda x: x ** 3

print(cube(4))

Expected Output:

64

🚀 Python Tip #10: The collections.Counter Class

Counting how many times items appear in a list is a common task.

Instead of writing loops manually, Python provides the Counter class from the collections module.

---

Importing Counter

from collections import Counter

---

Basic Example

from collections import Counter

fruits = [
    "apple",
    "banana",
    "apple",
    "orange",
    "banana",
    "apple"
]

count = Counter(fruits)

print(count)

Output:

Counter({
    'apple': 3,
    'banana': 2,
    'orange': 1
})

---

Counting Characters

from collections import Counter

text = "programming"

count = Counter(text)

print(count)

Output:

Counter({
    'g': 2,
    'r': 2,
    'm': 2,
    'p': 1,
    'o': 1,
    'a': 1,
    'i': 1,
    'n': 1
})

---

Finding the Most Common Items

Use the most_common() method.

Example:

from collections import Counter

votes = [
    "Python",
    "Java",
    "Python",
    "C++",
    "Python",
    "Java"
]

count = Counter(votes)

print(count.most_common(1))

Output:

[('Python', 3)]

---

Why use Counter?

✓ Count items with a single line of code
✓ Find the most common elements quickly
✓ Useful for text analysis and data processing
✓ Frequently used in Data Science and Machine Learning

---

Challenge:

Given the following list:

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

1. Count how many times each number appears.
2. Print the most common number.
3. Which number appears exactly once?

🔥 Python Tip #11: The setdefault() Dictionary Method

When working with dictionaries, you may need to access a key that doesn't exist.

Instead of checking if the key exists first, Python provides the setdefault() method.

---

Basic Example

student = {
    "name": "Ahmed",
    "age": 18
}

print(student.setdefault("age", 20))

Output:

18

Since `"age" already exists, its value remains unchanged.

---

Adding a New Key

If the key does not exist, setdefault() creates it using the default value.

Example:

``python
student = {
"name": "Ahmed"
}

student.setdefault("country", "Ethiopia")

print(student)

Output:

python
{
'name': 'Ahmed',
'country': 'Ethiopia'
}

---

Without `setdefault()`

python
if "country" not in student:
student["country"] = "Ethiopia"

---

With `setdefault()`

python
student.setdefault("country", "Ethiopia")

Both produce the same result, but `setdefault()` is shorter and cleaner.

---

Practical Example

Count words without worrying about missing keys.

python
words = ["apple", "banana", "apple"]

counts = {}

for word in words:
counts.setdefault(word, 0)
counts[word] += 1

print(counts)

Output:

python
{
'apple': 2,
'banana': 1
}

---

Why use `setdefault()`?

✓ Avoids `KeyError`
✓ Reduces repetitive code
✓ Automatically creates missing keys
✓ Useful when counting, grouping, or organizing data

---

Challenge:

Given the list:

python
cities = [
"Addis Ababa",
"Adama",
"Addis Ababa",
"Hawassa",
"Adama"
]
`

Use `setdefault()` to count how many times each city appears.

🐍 Python Tip #12: The pathlib Module

Working with files and folders is a common task in Python.

Instead of using traditional string paths, Python provides the pathlib module, which offers a cleaner and more object-oriented approach.

---

Importing Path

from pathlib import Path

---

Creating a Path

from pathlib import Path

file = Path("notes.txt")

print(file)

Output:

notes.txt

---

Checking if a File Exists

from pathlib import Path

file = Path("notes.txt")

print(file.exists())

Output:

True

or

```python
False

depending on whether the file exists.

---

Getting File Information

python
from pathlib import Path

file = Path("report.pdf")

print(file.name)
print(file.stem)
print(file.suffix)

Output:

python
report.pdf
report
.pdf

---

Creating a Folder

python
from pathlib import Path

folder = Path("Projects")

folder.mkdir(exist_ok=True)

The `exist_ok=True` argument prevents an error if the folder already exists.

---

Joining Paths

Instead of writing:

python
path = "Projects/" + "Python/" + "main.py"

Use:

python
from pathlib import Path

path = Path("Projects") / "Python" / "main.py"

print(path)

Output:

python
Projects/Python/main.py

---

Why use `pathlib`?

✓ Cleaner and easier to read  
✓ Works across Windows, Linux, and macOS  
✓ Object-oriented design  
✓ Recommended for modern Python projects

---

Challenge:

Create a program that:

1. Creates a folder named:

python
Python_Practice

2. Checks whether a file named:

python
notes.txt

exists inside that folder.

3. Prints:

text
File found

or

text
File not found
`

based on the result.

🧪 Python Tip #13: Using MagicMock for Testing

When writing unit tests, you don't always want to call real databases, APIs, or external services.

Instead, you can use MagicMock from Python's unittest.mock module to simulate their behavior.

---

Importing MagicMock

from unittest.mock import MagicMock

---

Why Use MagicMock?

Imagine you have a function that connects to a database.

In a real application, this function might:

- Take several seconds to execute
- Require an internet connection
- Access a real database
- Return different results each time

During testing, this is slow and unreliable.

---

Without MagicMock

def get_user_from_db(user_id):
    # Slow database query
    pass

user = get_user_from_db(1)

print(user)

This would execute the real database logic.

---

With MagicMock

from unittest.mock import MagicMock

def get_user_from_db(user_id):
    # Slow database query
    pass

get_user_from_db = MagicMock(
    return_value={
        "id": 1,
        "name": "John Doe"
    }
)

user = get_user_from_db(1)

print(user)

Output:

{'id': 1, 'name': 'John Doe'}

---

How It Works

MagicMock(return_value=...)

Creates a fake version of the function.

Whenever the function is called, it immediately returns the specified value instead of executing the original code.

---

Why use MagicMock?

✓ Makes tests run much faster
✓ No need for a real database or API
✓ Produces predictable results
✓ Essential for professional unit testing

---

Challenge:

Create a MagicMock that simulates a weather API and returns:

{
    "city": "Addis Ababa",
    "temperature": 22,
    "condition": "Sunny"
}

Then print the returned dictionary.

If there is any questions lemme know in the discussion group

⚡ Python Tip #14: The Walrus Operator (:=)

Introduced in Python 3.8, the walrus operator allows you to assign a value to a variable as part of an expression.

This can make your code shorter and avoid repeating calculations.

---

Without the Walrus Operator

name = input("Enter your name: ")

while name != "exit":
    print(f"Hello, {name}!")
    name = input("Enter your name: ")

---

With the Walrus Operator

while (name := input("Enter your name: ")) != "exit":
    print(f"Hello, {name}!")

The program behaves exactly the same but requires fewer lines of code.

---

Another Example

Without the walrus operator:

numbers = [5, 8, 12, 20]

length = len(numbers)

if length > 3:
    print(f"The list contains {length} items.")

---

With the walrus operator:

numbers = [5, 8, 12, 20]

if (length := len(numbers)) > 3:
    print(f"The list contains {length} items.")

---

When Should You Use It?

The walrus operator is useful when:

- A value is needed more than once
- You want to avoid repeating a function call
- You want cleaner and more efficient code

However, don't overuse it. Sometimes a normal assignment is easier to read.

---

Why use the Walrus Operator?

✓ Reduces repeated calculations
✓ Makes some loops cleaner
✓ Can improve performance in certain situations
✓ Useful in data processing and file handling

---

Challenge:

Using the walrus operator, write a program that repeatedly asks the user to enter a number until they type:

quit

Print each number entered before the program exits.

💡 Python Tip #15: The is vs == Operators

Many beginners confuse is and ==, but they have different purposes.

---

Using ==

The == operator checks whether two values are equal.

Example:

a = [1, 2, 3]
b = [1, 2, 3]

print(a == b)

Output:

True

Both lists contain the same values.

---

Using is

The is operator checks whether two variables refer to the exact same object in memory.

Example:

a = [1, 2, 3]
b = [1, 2, 3]

print(a is b)

Output:

False

Although the values are equal, they are stored as different objects.

---

Same Object Example

a = [1, 2, 3]
b = a

print(a is b)
print(a == b)

Output:

True
True

Both variables point to the same object.

---

Checking for None

The recommended way to check for None is with is.

Correct:

name = None

if name is None:
    print("No name provided")

Avoid:

if name == None:
    print("No name provided")

Using `is None is the Pythonic approach.

---

Why does this matter?

✓ Prevents subtle bugs
✓ Improves code readability
✓ Follows Python best practices
✓ Commonly used in professional Python projects

---

Challenge:

What will this code print?

``python
x = [10, 20]
y = x
z = [10, 20]

print(x == z)
print(x is z)
print(x is y)
`

Try to predict the output before running the code.

🔥 Python Tip #16: The with Statement

When working with files, it's important to close them after you're done.

Python's with statement automatically handles this for you.

---

Without with

file = open("notes.txt", "r")

content = file.read()

print(content)

file.close()

If an error occurs before file.close(), the file may remain open.

---

With with

with open("notes.txt", "r") as file:
    content = file.read()
    print(content)

Once the block ends, Python automatically closes the file.

---

Writing to a File

with open("hello.txt", "w") as file:
    file.write("Hello, Python!")

This creates the file if it doesn't exist, or overwrites it if it does.

---

Appending to a File

Use "a" mode to add new content without deleting the existing content.

with open("hello.txt", "a") as file:
    file.write("\nWelcome back!")

---

Why use the with statement?

✓ Automatically closes files
✓ Prevents resource leaks
✓ Cleaner and more readable code
✓ Recommended for all file operations

---

Challenge:

Create a program that:

1. Creates a file named:

tasks.txt

2. Writes three tasks into the file.

3. Opens the same file and prints all its contents using the with statement.

🎯 Python Tip #17: The match-case Statement

Introduced in Python 3.10, the match-case statement provides a cleaner alternative to long chains of if...elif...else.

It is similar to the "switch" statement found in many other programming languages.

---

Without match-case

day = 3

if day == 1:
    print("Monday")
elif day == 2:
    print("Tuesday")
elif day == 3:
    print("Wednesday")
else:
    print("Invalid day")

---

With match-case

day = 3

match day:
    case 1:
        print("Monday")
    case 2:
        print("Tuesday")
    case 3:
        print("Wednesday")
    case _:
        print("Invalid day")

Output:

Wednesday

---

Matching Strings

command = "start"

match command:
    case "start":
        print("Program started")
    case "stop":
        print("Program stopped")
    case "restart":
        print("Program restarted")
    case _:
        print("Unknown command")

---

The Wildcard _

The underscore (_) acts as the default case.

Example:

case _:
    print("No match found")

It is equivalent to the else statement.

---

Why use match-case?

✓ Cleaner than multiple if...elif statements
✓ Easier to read and maintain
✓ Perfect for menus and command handling
✓ Great for pattern matching in modern Python

> Note: match-case is available only in Python 3.10 and later.

---

Challenge:

Write a program that asks the user to enter one of these commands:

add
remove
update
list

Use `match-case to print the corresponding action. If the command is not recognized, print:

``text
Invalid command
`