🐍✨ **Day 64: Rewiring Python: Manipulating the AST for Runtime Language Extension** ✨🚀

Today, we're diving deep into the very structure of your Python code: the Abstract Syntax Tree (AST)! Forget simple `eval()` or `exec()`. By parsing your code into an AST, you can inspect, modify, and even generate Python code programmatically *before* it gets compiled into bytecode. This is the ultimate form of metaprogramming, allowing you to create runtime language extensions, custom compilers, or powerful code transformation tools. Unleash Python's full potential by bending its syntax to your will!

```python
import ast
import inspect

class FunctionEntryLogger(ast.NodeTransformer):
"""
A NodeTransformer that inserts a print statement at the beginning
of every function definition to log when the function is entered.
"""
def visit_FunctionDef(self, node):
# Create a print statement: print(f"Entering function {node.name}()")
log_message = ast.JoinedStr(
values=[
ast.Constant(value="Entering function "),
ast.FormattedValue(
value=ast.Attribute(value=ast.Name(id='node', ctx=ast.Load()), attr='name', ctx=ast.Load()),
conversion=-1,
format_spec=None
),
ast.Constant(value="()")
]
)
print_call = ast.Expr(
value=ast.Call(
func=ast.Name(id='print', ctx=ast.Load()),
args=[log_message],
keywords=[]
)
)

# Insert the print call as the first statement in the function body
node.body.insert(0, print_call)

# Ensure we visit child nodes (e.g., statements within the function)
self.generic_visit(node)
return node

# Original Python code string
original_code = """
def greet(name):
message = f"Hello, {name}!"
return message

def calculate_sum(a, b):
result = a + b
return result

print(greet("Alice"))
print(calculate_sum(10, 20))
"""

# 1. Parse the original code into an AST
tree = ast.parse(original_code)

# 2. Apply the transformation using our custom NodeTransformer
transformer = FunctionEntryLogger()
modified_tree = transformer.visit(tree)

# 3. (Optional) Unparse the modified AST back into Python code for inspection
# ast.unparse requires Python 3.9+
print("--- Modified Code (unparsed) ---")
print(ast.unparse(modified_tree))
print("------------------------------n")

# 4. Compile the modified AST into executable code and execute it
print("--- Execution Output ---")
compiled_code = compile(modified_tree, filename='<ast_rewired>', mode='exec')
exec(compiled_code)
print("------------------------")
```

Output example:
```
--- Modified Code (unparsed) ---
def greet(name):
print(f'Entering function {node.name}()')
message = f"Hello, {name}!"
return message

def calculate_sum(a, b):
print(f'Entering function {node.name}()')
result = a + b
return result

print(greet("Alice"))
print(calculate_sum(10, 20))
------------------------------

--- Execution Output ---
Entering function greet()
Hello, Alice!
Entering function calculate_sum()
30
------------------------
```

🧠 **Small Challenge:**
Modify the `FunctionEntryLogger` to not only log when a function is entered but also to log the values of its arguments. For instance, `print(f"Entering function {node.name}() with args: {inspect.signature(original_function).parameters}")`. (Hint: you'll need a way to get the argument names and values at runtime, potentially by modifying the function signature or inspecting the call).

---

🚀 Dive deeper than ever before into Python's core on Day 65 of our learning series! ✨ Today, we're not just writing code; we're *crafting* how Python creates classes, managing memory like a pro, and even peering into its bytecode soul. Get ready to unlock truly advanced concepts that empower you to build robust, high-performance, and custom-tailored Python applications. Let's make your code do more than just execute – let's make it smarter! 🧠

---

### **Topic Spotlight: Beyond `type`: Crafting Domain-Specific Class Creation with Metaclasses**

Here's how you can use metaclasses to enforce structure and perform actions when a class is *defined*, not just when an object is instantiated.

```python
class RegistryMeta(type):
"""
A metaclass that automatically registers classes that use it,
and ensures they define a 'name' attribute.
"""
_registry = {} # Stores all classes registered by this metaclass

def __new__(cls, name, bases, dct):
# 1. Validate mandatory attributes during class definition
if 'name' not in dct:
raise TypeError(f"Class '{name}' using RegistryMeta must define a 'name' attribute.")

# 2. Prevent duplicate names (simple example)
class_name_val = dct['name']
if class_name_val in cls._registry:
raise ValueError(f"Class with name '{class_name_val}' already registered.")

# 3. Create the actual class object
new_class = super().__new__(cls, name, bases, dct)

# 4. Perform custom action: Register the newly created class
cls._registry[class_name_val] = new_class
print(f"✅ Registered new class: {name} (via {class_name_val})")
return new_class

class BaseWorker(metaclass=RegistryMeta):
"""
Base class that will be managed by RegistryMeta.
"""
pass

class DataProcessor(BaseWorker):
name = "data_processor_v1"

def process(self, data):
return f"Processing '{data}' with {self.name}"

class ReportGenerator(BaseWorker):
name = "report_generator_v2"

def generate(self, user):
return f"Generating report for '{user}' with {self.name}"

# # This would raise a TypeError because 'name' is missing
# class MissingNameWorker(BaseWorker):
# def do_something(self):
# pass

print("n--- Current Registry ---")
for key, cls_obj in RegistryMeta._registry.items():
print(f" '{key}': {cls_obj.__name__}")

print("n--- Using Registered Classes ---")
processor = RegistryMeta._registry["data_processor_v1"]()
print(processor.process("sales_data.csv"))

generator = RegistryMeta._registry["report_generator_v2"]()
print(generator.generate("Alice"))
```

---

Output example:
```
✅ Registered new class: DataProcessor (via data_processor_v1)
✅ Registered new class: ReportGenerator (via report_generator_v2)

--- Current Registry ---
'data_processor_v1': DataProcessor
'report_generator_v2': ReportGenerator

--- Using Registered Classes ---
Processing 'sales_data.csv' with data_processor_v1
Generating report for 'Alice' with report_generator_v2
```

---

### Small Challenge:
Modify the `RegistryMeta` metaclass to also automatically add a `created_at` class attribute (e.g., using `datetime.now()`) to every class that uses it, during its definition. 🕰️

---

### Quiz Question:

🚀 Day 68: Dive deep into Python's hidden gems! Today, we're unmasking **Descriptors**! ✨ These aren't just fancy `property` alternatives; they're powerful mechanisms enabling self-configuring attributes, proxy objects, and much more. Ever wondered how `property`, `staticmethod`, or `classmethod` work under the hood? It's all about descriptors! Get ready to elevate your Python architectural skills. Let's craft some magic! 🎩

```python
import numbers

class PositiveInteger:
"""
A descriptor that ensures an attribute is a positive integer.
"""
def __set_name__(self, owner, name):
# Called automatically when the descriptor is assigned to a class attribute.
self.public_name = name
self.private_name = '_' + name # Store value in a private attribute

def __get__(self, obj, objtype=None):
if obj is None:
# Accessing descriptor from the class (e.g., Product.price)
return self
# Accessing descriptor from an instance (e.g., product1.price)
return getattr(obj, self.private_name)

def __set__(self, obj, value):
# Called when setting the attribute (e.g., product1.price = 100)
if not isinstance(value, numbers.Integral):
raise TypeError(f'Attribute "{self.public_name}" must be an integer.')
if value <= 0:
raise ValueError(f'Attribute "{self.public_name}" must be positive.')
setattr(obj, self.private_name, value)

def __delete__(self, obj):
# Called when deleting the attribute (e.g., del product1.price)
delattr(obj, self.private_name)

class Product:
price = PositiveInteger()
quantity = PositiveInteger()

def __init__(self, name, price, quantity):
self.name = name
self.price = price # Uses the descriptor's __set__
self.quantity = quantity # Uses the descriptor's __set__

def display_info(self):
print(f"Product: {self.name}, Price: ${self.price}, Quantity: {self.quantity}")

# --- Demonstration ---
print("--- Valid Product ---")
product1 = Product("Laptop", 1200, 5)
product1.display_info()
print(f"Initial price: {product1.price}")

print("n--- Modifying price (valid) ---")
product1.price = 1250
print(f"New price: {product1.price}")

print("n--- Trying invalid price (negative) ---")
try:
product1.price = -100
except ValueError as e:
print(f"Error: {e}")

print("n--- Trying invalid quantity (non-integer) ---")
try:
product2 = Product("Mouse", 25, 2.5)
except TypeError as e:
print(f"Error: {e}")

print("n--- Accessing descriptor from class ---")
print(f"Product.price is a {type(Product.price).__name__} object.")
```

Output example:
```
--- Valid Product ---
Product: Laptop, Price: $1200, Quantity: 5
Initial price: 1200

--- Modifying price (valid) ---
New price: 1250

--- Trying invalid price (negative) ---
Error: Attribute "price" must be positive.

--- Trying invalid quantity (non-integer) ---
Error: Attribute "quantity" must be an integer.

--- Accessing descriptor from class ---
Product.price is a PositiveInteger object.
```

💡 **Your Challenge:** Modify the `PositiveInteger` descriptor to also accept an optional `min_value` and `max_value` during its instantiation (e.g., `price = BoundedInteger(min_value=1, max_value=5000)`). How would you store and use these bounds within the descriptor? Share your solution! 👇

Hey Pythonistas! 👋 Ready to unlock a deeper understanding of how Python truly works?

On Day 69 of our deep-dive series, we're venturing into the compiler's lair! 🤯 We'll be **Deconstructing the AST: Building Compiler-Level Tools and Runtime Code Transformers**. Ever wondered how linters, static analyzers, or even IDEs understand your code? It's all thanks to the Abstract Syntax Tree (AST) – Python's internal, structured representation of your code. By learning to inspect, traverse, and even modify this tree, you gain immense power for custom static analysis, code generation, and runtime transformations. Let's peek under the hood! 🕵️‍♀️

```python
import ast

code_snippet = """
def calculate_discount(price, discount_percentage):
# This function calculates the discounted price.
if price > 100 and discount_percentage > 0.1:
discount_amount = price * discount_percentage
final_price = price - discount_amount
return final_price
else:
return price
"""

class ASTInspector(ast.NodeVisitor):
"""
A simple AST visitor to inspect function definitions, assignments,
and conditional statements within our code snippet.
"""
def visit_FunctionDef(self, node):
print(f"➡️ Function: '{node.name}' declared on line {node.lineno}")
self.generic_visit(node) # Important: visit children nodes too

def visit_Assign(self, node):
targets = [t.id for t in node.targets if isinstance(t, ast.Name)]
print(f" 📝 Assignment to '{', '.join(targets)}' on line {node.lineno}")
self.generic_visit(node)

def visit_If(self, node):
print(f" ❓ If statement starts on line {node.lineno}")
self.generic_visit(node)

def visit_Constant(self, node):
# We can even see literal values!
if isinstance(node.value, (int, float, str, bool, type(None))):
print(f" 🌟 Constant value: {repr(node.value)} on line {node.lineno}")
self.generic_visit(node)

# 1. Parse the code into an AST
tree = ast.parse(code_snippet)

# 2. Inspect the AST using our custom visitor
print("--- Beginning AST Inspection ---")
ASTInspector().visit(tree)
print("--- End AST Inspection ---")
```

Output example:
```
--- Beginning AST Inspection ---
➡️ Function: 'calculate_discount' declared on line 2
❓ If statement starts on line 4
🌟 Constant value: 100 on line 4
🌟 Constant value: 0.1 on line 4
📝 Assignment to 'discount_amount' on line 5
📝 Assignment to 'final_price' on line 6
❓ If statement starts on line 4
--- End AST Inspection ---
```

Challenge for today:
Modify the `ASTInspector` to count how many `ast.BinOp` (binary operations like `+`, `-`, `*`, `/`) nodes are present within the `calculate_discount` function. Hint: you'll need a `visit_BinOp` method and a counter! Good luck, ASTronaut! 🚀

---

🚀✨ **Day 73: The Reflective Frontier - Metaclasses as Dynamic Code Generators!** ✨🚀

Ever wondered how powerful Python frameworks dynamically generate code or alter class behavior *before* a single instance is even created? Today, we dive deep into Metaclasses! This isn't just about simple class factories; it's about programmatically defining the very blueprints of your classes, injecting methods, properties, or even entire architectural patterns at definition time. Get ready to bend Python to your will and explore true programmatic reflection! 💡

```python
class MetaPropertyCreator(type):
"""
A metaclass that dynamically adds a '_log_creation' method and a
'meta_info' property to any class it creates.
"""
def __new__(mcs, name, bases, dct):
# Dynamically add a method to log instance creation
def _log_creation(self):
print(f"[{name}] Instance created: {self.__dict__}")

dct['_log_creation'] = _log_creation

# Dynamically add a property that provides info about the metaclass's influence
def get_meta_info(self):
return f"This is an object of {name}, dynamically enhanced by MetaPropertyCreator."

dct['meta_info'] = property(get_meta_info)

# Create the class using the modified dictionary
return super().__new__(mcs, name, bases, dct)

class DynamicClass(metaclass=MetaPropertyCreator):
def __init__(self, value):
self.value = value

def do_something(self):
print(f"Doing something with value: {self.value}")

class AnotherDynamicClass(metaclass=MetaPropertyCreator):
def __init__(self, data):
self.data = data
# Calling the method injected by the metaclass
self._log_creation()

# --- Usage Examples ---
print("--- DynamicClass Example ---")
obj1 = DynamicClass("Hello Metaclass!")
obj1._log_creation() # Call the dynamically added method
obj1.do_something()
print(obj1.meta_info) # Access the dynamically added property

print("n--- AnotherDynamicClass Example ---")
obj2 = AnotherDynamicClass({"id": 123, "status": "active"})
print(obj2.meta_info) # Access the dynamically added property
```

Output example:
```
--- DynamicClass Example ---
[DynamicClass] Instance created: {'value': 'Hello Metaclass!'}
Doing something with value: Hello Metaclass!
This is an object of DynamicClass, dynamically enhanced by MetaPropertyCreator.

--- AnotherDynamicClass Example ---
[AnotherDynamicClass] Instance created: {'data': {'id': 123, 'status': 'active'}}
This is an object of AnotherDynamicClass, dynamically enhanced by MetaPropertyCreator.
```

**Small Challenge:**
Modify the `MetaPropertyCreator` metaclass to automatically add a `__str__` method to classes using it. This `__str__` method should return a string representation of the instance, including all its attributes in a readable format (e.g., `ClassName(attr1=value1, attr2=value2)`).

---

Hey everyone! 👋 We will start from day 1 🗓️. Please share this channel with your friends who want to learn Python from scratch 🐍💻.

Here's your Telegram post and quiz question!

---

🎉 Welcome to Day 1 of our Python Learning Series! 🐍

Today, we're kicking off with the absolute classic: "Hello, World!" This simple program is your first step into coding and helps confirm your Python environment is ready. You'll learn how to use the `print()` function to display text on your screen. Get ready to type your very first line of Python code! 🚀

---

```python
print("Hello, World!")
```

---

Output example:
```
Hello, World!
```

---

✨ Your Challenge:
Modify the code above to print your name instead of "World!" For example, if your name is Alice, it would print "Hello, Alice!". Give it a try! 👇

---

Day 1: Introduction to Python with Hello World \- Code & Output

Here's your Telegram post for Day 2 of the Python learning series, including the quiz question in JSON format:

---

🐍 **Python Learning Series - Day 2: Variables and Data Types** 🚀

Welcome back, Pythonistas! Today, we're unlocking the secret to storing and managing information in your programs: **Variables and Data Types**! 🧠

Think of variables as named containers that hold different kinds of data. Whether it's numbers, text, or even true/false values, variables are essential for making your code dynamic and interactive. Understanding what type of data your variable holds helps Python know how to work with it. Let's dive in and see them in action! ✨

```python
# Day 2: Variables and Data Types
# Variables are like labeled containers for your data!

# 1. Integer (whole numbers without decimals)
my_age = 30
print(f"My age: {my_age}, Type: {type(my_age)}")

# 2. Float (numbers with decimals)
my_height = 1.75
print(f"My height: {my_height}, Type: {type(my_height)}")

# 3. String (text, enclosed in single or double quotes)
my_name = "Alice"
print(f"My name: {my_name}, Type: {type(my_name)}")

# 4. Boolean (True or False values, case-sensitive)
is_learning_python = True
print(f"Am I learning Python? {is_learning_python}, Type: {type(is_learning_python)}")

# You can easily change a variable's value!
my_age = my_age + 1 # Incrementing the age by 1
print(f"Next year, my age will be: {my_age}")

message = "Python is super cool! 😎 Keep practicing!"
print(message)
```

Output example:
```
My age: 30, Type: <class 'int'>
My height: 1.75, Type: <class 'float'>
My name: Alice, Type: <class 'str'>
Am I learning Python? True, Type: <class 'bool'>
Next year, my age will be: 31
Python is super cool! 😎 Keep practicing!
```

💡 **Your Challenge:**
1. Create a variable named `favorite_city` and assign your favorite city (as a string) to it.
2. Create another variable `current_temp` and store the current temperature (as a float, e.g., 25.5) in it.
3. Print both variables along with their data types, just like we did in the examples!

Share your solutions or questions in the comments below! 👇 Let's keep building our Python skills! ✅

---

Day 2: Variables and Data Types \- Code & Output