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Using the dir() Function Introduction The dir() function in Python is a built-in function that provides a way to inspect the attributes and methods of objects, modules, or classes. It is particularly useful for exploring what is available within a module or an object, and for debugging and interactive exploration. Purpose of dir() Discover Attributes: To list the attributes and methods of an object, class, or module. Inspect Modules: To see what functions, classes, and variables are available within a module. Interactive Exploration: To interactively explore objects in a Python interpreter or notebook. Syntax  dir([object]) object (optional): The object whose attributes you want to list. If no object is passed, dir() returns the list of names in the current local scope. How dir() Works Without Arguments: When called without arguments, dir() returns a list of names in the current local scope. # Without arguments print(dir()) With an Object Argument: When called with an object, dir() returns a list of names (attributes and methods) of that object. # With an object argument import math print(dir(math)) Examples Using dir() with a Module You can use dir() to explore the contents of a module. For example, to see the functions and constants available in the math module:  import math # List all attributes and methods of the math module print(dir(math)) #Output ”’ [‘__doc__’, ‘__file__’, ‘__getattr__’, ‘__getattribute__’, ‘__gtraceback__’, ‘__import__’, ‘__initializing__’, ‘__loader__’, ‘__name__’, ‘__package__’, ‘__spec__’, ‘__spec__’, ‘acos’, ‘acosh’, ‘asin’, ‘asinh’, ‘atan’, ‘atan2’, ‘atanh’, ‘ceil’, ‘comb’, ‘copysign’, ‘cos’, ‘cosh’, ‘dist’, ‘e’, ‘erf’, ‘erfc’, ‘exp’, ‘expm1’, ‘fabs’, ‘factorial’, ‘floor’, ‘fmod’, ‘frexp’, ‘fsum’, ‘gamma’, ‘gcd’, ‘hypot’, ‘isclose’, ‘isfinite’, ‘isinf’, ‘isnan’, ‘ldexp’, ‘lgamma’, ‘log’, ‘log10’, ‘log1p’, ‘log2’, ‘modf’, ‘nextafter’, ‘num2words’, ‘pi’, ‘pow’, ‘prod’, ‘rad’, ‘remainder’, ‘sin’, ‘sinh’, ‘sqrt’, ‘tan’, ‘tanh’, ‘tau’, ‘trunc’] ”’ Using dir() with a Custom Object You can also use dir() to explore the attributes and methods of your own classes or objects.  class MyClass:     def __init__(self):         self.attribute1 = “value1″     def method1(self):         pass # Create an instance of MyClass obj = MyClass() # List attributes and methods of obj print(dir(obj)) # Output ”’ [‘__class__’, ‘__delattr__’, ‘__dict__’, ‘__dir__’, ‘__doc__’, ‘__eq__’, ‘__format__’, ‘__ge__’, ‘__getattribute__’, ‘__gt__’, ‘__hash__’, ‘__init__’, ‘__init_subclass__’, ‘__le__’, ‘__lt__’, ‘__ne__’, ‘__new__’, ‘__reduce__’, ‘__reduce_ex__’, ‘__repr__’, ‘__setattr__’, ‘__sizeof__’, ‘__str__’, ‘__subclasshook__’, ‘__weakref__’, ‘attribute1’, ‘method1′] ”’ Using dir() with a Class When using dir() with a class, it lists the class attributes, methods, and inherited attributes.  class Parent:     def parent_method(self):         pass class Child(Parent):     def child_method(self):         pass # List attributes and methods of the Child class print(dir(Child)) #Output: ”’ [‘__class__’, ‘__delattr__’, ‘__dict__’, ‘__dir__’, ‘__doc__’, ‘__eq__’, ‘__format__’, ‘__ge__’, ‘__getattribute__’, ‘__gt__’, ‘__hash__’, ‘__init__’, ‘__init_subclass__’, ‘__le__’, ‘__lt__’, ‘__ne__’, ‘__new__’, ‘__reduce__’, ‘__reduce_ex__’, ‘__repr__’, ‘__setattr__’, ‘__sizeof__’, ‘__str__’, ‘__subclasshook__’, ‘__weakref__’, ‘child_method’, ‘parent_method’] ”’ Best Practices Use dir() for Exploration: Use dir() to explore and understand the capabilities of modules, classes, and objects, especially during interactive development or debugging. Combine with help(): Use dir() in conjunction with the help() function for detailed information about methods and attributes. import math # Get detailed information about a specific function help(math.sqrt) Check for Special Methods: Note that dir() also lists special methods (those with double underscores, like __init__). These methods are part of Python’s data model and might not be relevant to your regular usage. Filter Results: If you only need certain attributes or methods, you can filter the results from dir() using list comprehensions or other methods. # List only methods of an object methods = [attr for attr in dir(obj) if callable(getattr(obj, attr))] print(methods) Understand Scope: When using dir() without arguments, remember that it lists names in the current local scope, which includes variables, functions, and imports defined in the current module or interactive session. In summary, the dir() function is a powerful tool for exploring the attributes and methods of Python objects, modules, and classes. It is useful for interactive development, debugging, and understanding the capabilities of different components in Python.

Built-in Modules Introduction Built-in modules are Python modules that come included with the standard installation of Python. They provide commonly used functionalities and are available without needing to install additional packages. These modules cover a wide range of features, from file manipulation to date and time management, and advanced mathematical operations. Using Built-in Modules To use a built-in module, you need to import it into your code using the import statement. Here are some common built-in modules and how to use them. math The math module provides mathematical functions such as trigonometric functions, logarithms, and mathematical constants like π (pi). Example  import math # Using the constant pi print(math.pi)  # Prints the value of π # Using the sqrt function for square root print(math.sqrt(16))  # Prints 4.0 datetime The datetime module is used for manipulating dates and times. It allows you to create, manipulate, and format date and time objects. Example  from datetime import datetime # Get the current date and time now = datetime.now() print(now)  # Prints the current date and time # Format a date formatted_date = now.strftime(“%Y-%m-%d %H:%M:%S”) print(formatted_date)  # Prints the date and time in the specified format os The os module provides an interface for interacting with the operating system. It allows you to manipulate files and directories, execute system commands, and more. Example  import os # Get the current working directory current_directory = os.getcwd() print(current_directory)  # Prints the current working directory # List files in the current directory files = os.listdir(current_directory) print(files)  # Prints the list of files in the current directory   sys The sys module provides functions and variables used to manipulate the Python runtime environment. It is often used to access command-line arguments or manipulate the module search path. Example  import sys # Print command-line arguments print(sys.argv)  # Prints a list of arguments passed to the script # Print Python version print(sys.version)  # Prints the version of Python being used json The json module allows you to work with JSON data. It provides functions to encode Python objects to JSON strings and decode JSON strings back to Python objects. Example  import json # Convert a dictionary to a JSON string data = {“name”: “John”, “age”: 30} json_string = json.dumps(data) print(json_string)  # Prints the JSON string # Convert a JSON string to a dictionary parsed_data = json.loads(json_string) print(parsed_data)  # Prints the Python dictionary List of Built-in Modules Python includes many built-in modules. Here are some of the most commonly used: math: Mathematical functions datetime: Date and time manipulation os: Interaction with the operating system sys: System-specific parameters and functions json: JSON data handling re: Regular expressions random: Random number generation itertools: Functions for working with iterators collections: Specialized data types like ordered lists and counters csv: Reading and writing CSV files Best Practices for Working with Built-in Modules Consult the Documentation: Each built-in module has official documentation detailing its functions, classes, and methods. Refer to it to understand how to use the modules correctly. Use Exploration Tools: Utilize the dir() and help() functions to explore the attributes and methods available in a module. Example  import math # List the attributes and methods of the math module print(dir(math)) # Get help on a specific function help(math.sqrt) Be Aware of Limitations: Built-in modules are designed to provide basic functionality. For more advanced or specialized needs, you might need third-party modules available via package managers like pip. Check Version Compatibility: Features in built-in modules can vary between Python versions. Make sure to check compatibility with the version of Python you are using. Avoid Redefinition: Avoid redefining or naming your own modules with the same names as built-in modules to prevent conflicts and confusion. In summary, built-in modules in Python offer a wide range of useful functionalities without the need for additional installations. By using them effectively, you can accomplish many common programming tasks while leveraging the tools provided by the standard Python environment.

Renaming a Module Introduction Renaming a module in Python may be necessary for various reasons, such as clarifying its purpose, resolving name conflicts, or improving project structure. However, it’s important to proceed carefully to avoid errors and inconsistencies in your code. Steps for Renaming a Module Change the File Name The first step is to rename the module file itself. Ensure that the new name follows Python naming conventions, such as using lowercase letters and underscores to separate words if necessary. Example If you have a module named old_name.py and want to rename it to new_name.py, you can do so using your file system or an IDE. Before: old_name.py After: new_name.py Update Imports After renaming the module file, you need to update all import statements in your code that use the old module name. Search for all occurrences of the old name in your source code and replace them with the new name. Example If you had imports like this:  # Before import old_name from old_name import some_function  You need to update them to:  # After import new_name from new_name import some_function  Update Internal References If the renamed module is used in other modules or scripts, make sure to update all internal references in these files as well. This includes function calls, class references, and other usages of the module. Example If old_name.py contains:  # old_name.py def greet():     return “Hello”  And another_module.py contains:  # another_module.py import old_name print(old_name.greet()) You need to update another_module.py to:  # another_module.py import new_name print(new_name.greet())  Check Tests If you have automated tests for your code, ensure that the tests are also updated to use the new module name. Run the tests to verify that everything works correctly after the name change. Example If you have a test file test_old_name.py, make sure to rename it to test_new_name.py and update the imports in this file. # Before import old_name def test_greet():    assert old_name.greet() == “Hello” # After import new_name def test_greet():     assert new_name.greet() == “Hello” Update Documentation Make sure that any documentation associated with your project is updated to reflect the new module name. This includes comments in the code, README files, and any other related documentation. Example If your documentation mentions old_name, update the occurrences to reflect new_name:  ## Module Usage ### old_name To use the `old_name` module, import it as follows:  import old_name  After Renaming To use the new_name module, import it as follows:  import new_name Best Practices Communicate Changes: Inform your team members about the module name change so they can update their own code accordingly. Use Find and Replace Tools: Utilize find and replace tools in your code editor to help update imports and references efficiently. Run Comprehensive Tests: Ensure all tests pass after renaming the module to check for regressions or issues. Check Dependencies: If your module is used by other projects or modules, ensure that dependencies are updated to reflect the new name. Manage Versions: If you are managing versions for your project, update version numbers and configuration files as needed to indicate that the module has been renamed. In summary, renaming a module in Python involves changing the file name, updating all import statements and references, checking tests, and updating documentation. By following these steps carefully, you can avoid errors and ensure that your code remains functional and consistent after the name change.

Naming a Module Introduction The name of a module in Python plays a significant role in how it is identified and used in code. A well-chosen module name can make your code more readable and maintainable. Python modules are simply files with a .py extension containing Python code. Naming Conventions Use Meaningful Names Choose a name that clearly describes the functionality or purpose of the module. This helps other developers (and your future self) understand what the module does at a glance. Example If you have a module that handles database connections, naming it database.py or db_connection.py would be more descriptive than a generic name like utils.py. Use Lowercase and Underscores According to Python’s PEP 8 style guide, module names should be written in lowercase letters with words separated by underscores if necessary. This makes the module names more readable and consistent with the naming conventions for functions and variables. Example Good Names: file_utils.py, data_processing.py, math_operations.py Bad Names: FileUtils.py, DataProcessing.py, MathOperations.py       . Avoid Using Reserved Words Do not use Python reserved words or built-in function names as module names. Using reserved words can cause confusion and errors in your code. Reserved Words Example Avoid: def.py, class.py, list.py, import.py Avoid Conflicts with Standard Library Modules Try to avoid naming your modules with names that are the same as or similar to Python’s standard library modules. This helps prevent conflicts and confusion when importing modules. Example Avoid: Naming your module math.py if it is not related to mathematical operations, as it may conflict with Python’s built-in math module. Practical Examples Simple Module Name If you have a module that provides utility functions for string manipulation, you might name it string_utils.py.  # string_utils.py def reverse_string(s):     return s[::-1] def is_palindrome(s):     return s == s[::-1]  Descriptive Module Name If the module handles configurations for an application, you could name it config.py.  # config.py DATABASE_URL = “localhost” API_KEY = “your-api-key-here”  Avoiding Name Clashes If you create a module for logging but don’t want to clash with the standard library logging module, consider naming it app_logging.py.  # app_logging.py import logging def setup_logging():     logging.basicConfig(level=logging.INFO) File System Considerations File Extensions Make sure your module files use the .py extension. This is necessary for Python to recognize the files as modules. Correct: utils.py, my_module.py Incorrect: utils.txt, my_module.doc Directory Structure For complex projects, organize modules into packages (directories with an __init__.py file) to maintain a clear and manageable directory structure. Example      __init__.py     database/         __init__.py         connection.py         queries.py     utils/         __init__.py         helpers.py     main.py  Best Practices Consistency: Follow consistent naming conventions throughout your project. This improves readability and helps maintain a cohesive structure. Descriptive Naming: Choose names that convey the module’s purpose. Avoid ambiguous or overly generic names. Avoid Special Characters: Stick to letters, numbers, and underscores in module names. Avoid using hyphens, spaces, or other special characters. Reflect Functionality: Ensure that the module name reflects its functionality or content. This makes it easier for others to understand the module’s role in your project. Check for Conflicts: Before finalizing a module name, check if it conflicts with standard library modules or third-party packages. In summary, naming a module in Python involves choosing a clear, descriptive name that follows conventions such as lowercase letters and underscores. Avoid using reserved words, conflicting names with standard libraries, and ensure that your module names reflect their functionality. This practice helps in creating a more readable, maintainable, and conflict-free codebase.

Variables in a Module Introduction Variables in a Python module are essential for storing values that can be used throughout the module or accessed from scripts that import the module. These variables can represent constants, configurations, or shared data. Types of Variables in a Module Global Variables: Defined at the module level, they are accessible throughout the module and can be used in functions and classes defined within that module. Module-Level Variables: Similar to global variables but are often used to store values that need to be accessible to other parts of the code once the module is imported. Defining and Using Variables in a Module Defining Global Variables You can define global variables in a module just like you would define variables in a regular Python script. Example Create a module named settings.py:  # settings.py # Global variables for configuration APP_NAME = “MyApp” VERSION = “1.0.0” MAX_USERS = 100  In another script, you can import and use these variables:  # main.py import settings print(f”Application Name: {settings.APP_NAME}”) print(f”Version: {settings.VERSION}”) print(f”Max Users: {settings.MAX_USERS}”)  State-Dependent Variables Variables can also be defined and modified based on state or conditions within the module. Example Create a module named counter.py:  # counter.py counter = 0 def increment():     global counter     counter += 1     return counter def reset():     global counter     counter = 0  Use these variables and functions in another script:  # main.py import counter print(counter.increment())  # Output: 1 print(counter.increment())  # Output: 2 counter.reset() print(counter.increment())  # Output: 1  Constants Variables defined in uppercase are often used to represent constants in a module. While Python doesn’t have formal constant support, using uppercase is a convention to indicate that these values should not be modified. Example Create a module named constants.py:  # constants.py PI = 3.14159 E = 2.71828  Use these constants in another script:  # main.py import constants print(f”Value of PI: {constants.PI}”) print(f”Value of E: {constants.E}”)  Visibility and Access to Variables Public Variables By default, variables defined in a module are public and accessible from outside the module after import. Example In public_vars.py:  # public_vars.py public_var = “I am public”  In main.py:  # main.py import public_vars print(public_vars.public_var)  Private Variables To indicate that a variable is private and should not be accessed outside the module, use a name starting with an underscore (_). This does not actually prevent access but serves as a convention to signal that the variable is intended for internal use. Example In private_vars.py:  # private_vars.py _private_var = “I am private” In main.py:  # main.py import private_vars # Access possible but not recommended print(private_vars._private_var)  Managing Module Variables Imported Variables You can import specific variables from a module if you do not want to import the entire module. Example In data.py:  # data.py VALUE = 42  In main.py:  # main.py from data import VALUE print(f”Value: {VALUE}”)  Modifying Variables Module variables can be modified from other modules if imported. However, it is important to manage these modifications carefully to avoid unintended side effects. Example In config.py:  # config.py setting = “Default”  In main.py:  # main.py import config print(f”Original setting: {config.setting}”) # Modify the variable config.setting = “Updated” print(f”Updated setting: {config.setting}”)  Best Practices Use Descriptive Names: Choose clear and descriptive names for your variables to improve code readability. Follow Conventions: Use uppercase for constants and underscores for private variables. Limit Modifications: Avoid modifying module variables from multiple places to maintain good encapsulation and avoid unexpected side effects. In summary, variables in a module are defined similarly to those in a standard Python script but provide additional benefits when used to share information between different files. By defining global variables, constants, and using conventions for visibility, you can effectively organize and manage data within your modules.

Using a Module Introduction Once you’ve created a module, you can use it in other Python files by importing it. Importing a module allows you to reuse the functions, classes, and variables defined within it, which helps in organizing and reusing code efficiently. How to Import a Module There are several ways to import a module or its specific elements. Here are common methods: Import the Entire Module This method imports the entire module. You access the functions, classes, and variables in the module using dot notation (i.e., module.element). Example Suppose you have a module named math_utils.py:  # math_utils.py def add(a, b):     return a + b def subtract(a, b):     return a – b  You can import this module into another file as follows:  # main.py import math_utils result = math_utils.add(5, 3) print(f”The result of addition is: {result}”) result = math_utils.subtract(10, 4) print(f”The result of subtraction is: {result}”)  Import Specific Elements This method imports only the specific elements you need from the module, which can make the code more readable and prevent unnecessary imports. Example To import only the add function from math_utils:  # main.py from math_utils import add result = add(5, 3) print(f”The result of addition is: {result}”)  Import with an Alias You can use an alias for the module when importing, which can make the code shorter and easier to read, especially if the module name is long. Example To import the math_utils module with an alias mu:  # main.py import math_utils as mu result = mu.add(5, 3) print(f”The result of addition is: {result}”) result = mu.subtract(10, 4) print(f”The result of subtraction is: {result}”)  Using Imported Elements Once you have imported a module or its elements, you can use them just like any other Python code. Here are some important points: Accessing Functions When importing the entire module, use dot notation to call functions:  import math_utils result = math_utils.add(5, 3)  If you import specific functions, you can call them directly:  from math_utils import add result = add(5, 3)  Accessing Classes Classes defined in a module can be instantiated and used in the same way as classes defined in the same file: Example Suppose you have a module named shapes.py:  # shapes.py class Circle:     def __init__(self, radius):         self.radius = radius     def area(self):         import math         return math.pi * (self.radius ** 2)  You can use the Circle class in another file like this:  # main.py from shapes import Circle my_circle = Circle(5) print(f”The area of the circle is: {my_circle.area()}”)  Accessing Variables Variables defined in a module can be used directly after importing: Example In a module named config.py:  # config.py PI = 3.14159  In another file:  # main.py import config print(f”The value of PI is: {config.PI}”)  Managing Imports Absolute and Relative Imports Absolute Import: Use the full name of the module to import it. This is what we’ve used in the examples above (import math_utils). Relative Import: Used within packages to import modules within the same package. For example, if you have a package mypackage with a sub-module mymodule.py, you can import it like this from another sub-module in the same package:  # mypackage/submodule.py from . import mymodule  This means “import the module mymodule from the same package”. Import Errors ModuleNotFoundError: Occurs when Python cannot find the specified module. Ensure the module file is in the correct directory or that the path is correct. ImportError: Occurs when trying to import elements that do not exist in the module. Verify that you are importing the correct names. Best Practices Import Only What You Need: Import only the necessary functions, classes, or variables to avoid unnecessary imports and name conflicts. Use Aliases: When module or function names are long, use aliases to make the code more readable. Organize Modules into Packages: For complex projects, organize your modules into packages for better management and modularity. In summary, using a module in Python involves importing the module or its specific elements into your script. You can import an entire module, specific elements, or use aliases to simplify your code. Once imported, you can access and use the functions, classes, and variables defined in the module, making it easier to organize and reuse code effectively.

Creating a Module What is a Module? A module in Python is essentially a file with a .py extension that contains Python code. Modules are used to organize and reuse code across different projects. Steps to Create a Module Create a Python File: Create a new file with a .py extension. This file will serve as your module. Define Functions, Classes, and Variables: Inside the file, you can define functions, classes, and variables. These definitions will be available to any script that imports this module. Add Executable Code (Optional): You can include code that executes when the module is run directly. This is usually done using the if __name__ == “__main__”: construct. Example: Creating a Simple Module Step 1: Create a File Create a file named calculator.py. Step 2: Add Code to the File Here’s an example of what you might put in calculator.py:  # calculator.py # Define a variable PI = 3.14159 # Define a function to add two numbers def add(x, y):     return x + y # Define a function to subtract two numbers def subtract(x, y):     return x – y # Define a function to multiply two numbers def multiply(x, y):     return x * y # Define a function to divide two numbers def divide(x, y):     if y == 0:         return “Cannot divide by zero”     return x / y # Define a class for a basic calculator class BasicCalculator:     def __init__(self):         self.result = 0     def add(self, x, y):         self.result = x + y         return self.result     def subtract(self, x, y):         self.result = x – y         return self.result     def multiply(self, x, y):         self.result = x * y         return self.result     def divide(self, x, y):         if y == 0:             return “Cannot divide by zero”         self.result = x / y         return self.result # Code to execute if module is run directly if __name__ == “__main__”:     print(“Calculator module”)     print(“PI =”, PI)     print(“Add 5 + 3 =”, add(5, 3))     print(“Subtract 10 – 2 =”, subtract(10, 2))     print(“Multiply 4 * 7 =”, multiply(4, 7))     print(“Divide 20 / 4 =”, divide(20, 4))  Explanation of the Code Variables: PI is a constant variable defined in the module. Functions: add, subtract, multiply, and divide are functions that perform basic arithmetic operations. Classes: BasicCalculator is a class with methods to perform arithmetic operations. It has an attribute result to store the result of calculations. Executable Code: The code block under if __name__ == “__main__”: will execute only when calculator.py is run directly. This allows for simple testing or example usage. Using the Module To use the module, you need to import it into another Python script or module. Example: Importing the Whole Module Create another file named test_calculator.py:  # test_calculator.py import calculator print(calculator.PI) print(calculator.add(10, 5)) print(calculator.subtract(10, 5)) print(calculator.multiply(10, 5)) print(calculator.divide(10, 5)) calc = calculator.BasicCalculator() print(calc.add(10, 5)) print(calc.subtract(10, 5)) print(calc.multiply(10, 5)) print(calc.divide(10, 5))  Example: Importing Specific Elements Alternatively, you can import specific functions or classes from the module:  # test_calculator.py from calculator import add, BasicCalculator print(add(15, 25)) calc = BasicCalculator() print(calc.add(15, 25))  Module File Naming Conventions Naming: The module file name should be descriptive and use lowercase letters. Use underscores to separate words if needed (e.g., my_module.py). Avoid Conflicts: Ensure that the module name does not conflict with standard library module names to avoid import errors. Adding Documentation It’s a good practice to include docstrings in your module, functions, and classes to document their purpose and usage. Example:  # calculator.py “”” This module provides basic arithmetic functions and a calculator class. “”” def add(x, y):     “””     Add two numbers.     :param x: First number.     :param y: Second number.     :return: Sum of x and y.     “””     return x + y Testing the Module You can test the functionality of your module directly by running it. The code inside the if __name__ == “__main__”: block will execute, allowing you to test the functions and classes. Best Practices Modularity: Keep modules focused on a single responsibility or related functionality. Naming: Choose meaningful names for your modules, functions, and classes. Documentation: Document your code using docstrings for better readability and maintenance. In summary, creating a module in Python involves defining your code in a .py file, organizing it into functions, classes, and variables, and optionally adding executable code for direct testing. This modular approach helps in organizing, reusing, and managing code effectively.

What is a Module? Definition In Python, a module is simply a file containing Python code. This code can define functions, classes, variables, and runnable code. Modules help to organize code by grouping related functionality together in separate files. This enhances code reusability and maintainability. Why Use Modules? Code Organization: Modules allow you to break down code into manageable, logically grouped units. Reusability: You can reuse the same modules across different projects without rewriting code. Encapsulation: Modules help encapsulate implementation details and expose only necessary functionalities. Maintainability: With modular code, it’s easier to locate and fix issues. Contents of a Module A module can include: Functions: Reusable blocks of code. Classes: Custom types that define objects. Variables: Constants or configuration settings. Executable Code: Code that runs when the module is imported. Structure of a Module A module is simply a Python file with a .py extension. Here’s a typical structure of a module:  # module_example.py # Variable Declaration VERSION = “1.0” # Function Declaration def greet(name):     return f”Hello, {name}!” # Class Declaration class Person:     def __init__(self, name):         self.name = name     def get_name(self):         return self.name # Executable Code if __name__ == “__main__”:     print(greet(“World”))     person = Person(“Alice”)     print(person.get_name())  Details of the Code Variables: VERSION is a variable defined in the module that can store constant information. Functions: greet is a function that takes an argument and returns a formatted string. Classes: Person is a class defined in the module with attributes and methods. Executable Code: The if __name__ == “__main__”: block runs code only when the module is executed directly, not when imported. Importing a Module To use a module, you need to import it. For example, if the module is named module_example.py, you can import and use its elements as follows:  # script.py import module_example print(module_example.greet(“Alice”)) print(module_example.VERSION) person = module_example.Person(“Bob”) print(person.get_name())  Selective Import You can also import specific elements from a module:  # script.py from module_example import greet, VERSION print(greet(“Alice”)) print(VERSION)  Modules and Packages Modules can be grouped into packages. A package is a directory containing multiple modules, and it must include an __init__.py file to be recognized as a package by Python. Example of a Package  __init__.py module1.py module2.py You can import modules from a package like this:  from my_package import module1 from my_package.module2 import specific_function  Documenting Modules You can also add docstrings to modules, functions, and classes to document their usage. Here’s an example:  # module_example.py “”” This module provides functions and classes for demonstrations. “”” def greet(name):     “””     Returns a greeting message.     :param name: The name of the person to greet.     :return: A string containing the greeting message.     “””     return f”Hello, {name}!”  Docstrings can be accessed using the help() function or the .__doc__ attribute of an object.  import module_example print(help(module_example.greet)) In summary, a module in Python is a file containing Python code that defines functions, classes, variables, and executable code, which helps in organizing, reusing, and managing code effectively.