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Added contributor different section,added week 29 2023 newsletter
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‎doc/conf.py

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source_suffix = ['.rst', '.md']
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extensions = ['myst_parser','sphinx.ext.autodoc', 'sphinx.ext.autosummary','sphinx_copybutton']
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extensions = ['myst_parser','sphinx.ext.autodoc', 'sphinx.ext.autosummary','sphinx_copybutton','sphinx_contributors']
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templates_path = ['_templates']
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exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']

‎doc/contributors.rst

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Contributors
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============
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We would like to express our sincere gratitude to the following
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contributors who have made valuable contributions
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.. contributors:: python-world/Newsletter
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:avatars:
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:contributions:
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:order: DESC
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Thank you for your dedication and for enriching the Python community
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with your valuable insights, code snippets, and contributions! Happy
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coding! 🐍✨

‎doc/index.rst

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.. toctree::
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:maxdepth: 2
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modules
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contributors
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modules
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‎doc/modules.rst

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:maxdepth: 4
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newsletters/index.2023
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newsletters/index.2022
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newsletters/index.2022
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.. contributors:: sphinx-doc/sphinx

‎doc/newsletters/2023/WEEK_29.md

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# Week 29 - Jully 2023
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## Introduction and Highlights
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Welcome to the latest edition of the Python-World Newsletter! We bring
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you the most exciting updates and insightful content from the Python
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community. In this week’s edition, we have some incredible code
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snippets, community spotlights, upcoming events, useful resources, and
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more. Let’s dive in!
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## Code Snippets
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### 1. 💼 Simplifying Python Classes with Dataclasses.
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Python dataclasses are a fantastic addition to the Python standard library that simplifies the creation of classes for storing data. 🏰💾 With just a few lines of code, you can define classes that come with built-in methods for easy attribute initialization, comparison, and more. 🎁💻
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**🌟 Key Features of Python Dataclasses:**
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✅ Automatic attribute initialization - Say goodbye to writing tedious `__init__` methods!
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✅ Concise and clean class definitions using decorators.
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✅ Built-in methods for equality comparison, representation, and more.
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✅ Customizable field defaults and ordering.
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✅ Full integration with Python's standard library.
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**📝 Simple Examples to Get Started:**
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```python
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from dataclasses import dataclass
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@dataclass
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class Point:
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x: int
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y: int
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# Creating instances
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p1 = Point(1, 2)
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p2 = Point(3, 4)
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# Accessing attributes
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print(f"Point 1: x={p1.x}, y={p1.y}")
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print(f"Point 2: x={p2.x}, y={p2.y}")
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```
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**🚀 Use Cases for Python Dataclasses:**
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1. **Configuration Objects**: Use dataclasses to create clean and straightforward configuration objects for your applications. 🛠️🔧
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2. **Data Containers**: Dataclasses are perfect for holding and manipulating structured data, such as CSV records or JSON data. 📂🗃️
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3. **API Responses**: Parse API responses into dataclass instances, making the data easy to work with and comprehend. 📡💼
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4. **Immutable Objects**: By default, dataclasses are immutable, making them suitable for representing read-only data. 🔒🔏
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5. **Testing**: Dataclasses can be incredibly useful when writing test cases with complex input data. 🧪🧾
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**💡 Pro Tip:**
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Don't forget to explore advanced dataclass features like default values, ordering, and post-init processing. Dataclasses are highly customizable to fit your specific needs! 🔍🔧
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### 2. 🔦 Exploring Python's Hidden Secrets: The inspect Module
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The `inspect` module is your backstage pass to Python's internals. 🎭🎫 It provides functions for introspecting live objects like modules, classes, functions, and more. With `inspect`, you can retrieve information about code objects, inspect callables' signatures, and even perform source code analysis. 🕵️‍♀️📝
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**🔍 Key Features of `inspect`:**
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✅ Retrieving information about objects and modules at runtime.
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✅ Accessing source code and analyzing the structure of Python functions and classes.
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✅ Inspecting and manipulating the call signature of functions.
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✅ Extracting and examining documentation strings.
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✅ Finding the source file and line number of code objects.
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**📝 Examples to Get Started:**
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```python
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import inspect
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# Get the source code of a function
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def greet(name,age):
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return f"Hello, {name}!"
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source_code = inspect.getsource(greet)
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print('Source code:\n',source_code)
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# Inspect a function's signature
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signature = inspect.signature(greet)
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print("Signature:",signature)
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'''
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Output
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Source code:
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def greet(name,age):
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return f"Hello, {name}!"
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Signature: (name, age)
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'''
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```
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**🚀 Use Cases for `inspect`:**
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1. **Debugging and Logging**: Use `inspect` to gather information about the calling frames for powerful debugging and logging capabilities. 🐞📝
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2. **Documentation Generation**: Automate the generation of documentation by extracting docstrings and function signatures with `inspect`. 📚📜
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3. **Dynamic Function Invocation**: Dynamically call functions with the correct arguments using `inspect` to inspect their signatures. 📞🔧
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4. **Code Analysis**: Perform static code analysis and gather insights about your codebase using `inspect` functions. 📊📋
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5. **Custom Decorators**: Create custom decorators that can automatically introspect and modify the behavior of decorated functions. 🎭🔮
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**💡 Pro Tip:**
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Experiment with `inspect` interactively in a Python REPL to get a better feel for its capabilities. 🔄🔬
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### 3. 🔧 The Python Package Manager: Unleashing the Power of pip
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`pip` is your go-to tool for installing, managing, and distributing Python packages. 🛠️📚 With a vast repository of open-source libraries available on the Python Package Index (PyPI), `pip` makes it a breeze to enhance your Python projects with external functionality. 🌟📦
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**🌟 Key Features of `pip`:**
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✅ Installing packages with a simple command - `pip install package-name`.
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✅ Managing package versions and dependencies effortlessly.
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✅ Listing installed packages - `pip list`.
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✅ Upgrading packages to the latest versions - `pip install --upgrade package-name`.
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✅ Creating virtual environments for isolated package installations.
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**📝 Examples to Get Started:**
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```python
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# Install a package
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# Example: Installing the popular requests library
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# pip install requests
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# Listing installed packages
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# pip list
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# Upgrading a package
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# Example: Upgrading requests to the latest version
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# pip install --upgrade requests
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```
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**🚀 Use Cases for `pip`:**
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1. **Library Installation**: Use `pip` to easily install third-party libraries like NumPy, pandas, Django, and more, saving you time and effort. 📚💡
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2. **Package Management**: Keep your project's dependencies organized and up to date with `pip`, ensuring smooth collaboration. 📦🔄
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3. **Virtual Environments**: Create virtual environments to isolate package installations and avoid version conflicts. 🌐🧪
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4. **Continuous Integration**: Utilize `pip` to install project dependencies in CI/CD pipelines for automated testing and deployment. 🚀🔧
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5. **Package Distribution**: Share your Python projects with others by distributing them as packages on PyPI using `pip`. 🚀🌟
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**💡 Pro Tip:**
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Explore `pip`'s extensive options like installing packages from version control repositories or installing specific package versions to suit your project's requirements. 🔄🎛️
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### 4. 🎁 Unpacking the Magic of Tuples
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Tuples are one of Python's versatile data structures, and unpacking allows you to extract individual elements from a tuple effortlessly. 🎩🌟 It's like unwrapping a present and discovering the treasures within! 🎁💎
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**🧳 Packing Tuples for Efficiency:**
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Packing, on the other hand, involves creating tuples by grouping elements together. 🎒🌐 It's like packing your essentials for a journey, ensuring everything stays organized and accessible. 🧳🗺️
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**📝 Examples to Get Started:**
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```python
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# Tuple Unpacking
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point = (10, 20)
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x, y = point
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print(f"x: {x}, y: {y}")
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# Extended Unpacking
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numbers = (1, 2, 3, 4, 5)
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first, *middle, last = numbers
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print(f"First: {first}, Middle: {middle}, Last: {last}")
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# Tuple Packing
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person = "John", 30, "Engineer"
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print(person)
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```
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**🚀 Use Cases for Tuple Unpacking and Packing:**
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1. **Multiple Return Values**: Unpack tuples to receive multiple return values from functions in one assignment. 📤🔁
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2. **Swapping Values**: Swap variable values without using a temporary variable using tuple unpacking. ↔️🔄
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3. **Iterating over Multiple Elements**: Unpack tuples while iterating over lists or other iterable data structures. 🔄📑
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4. **Function Arguments**: Use tuple packing to pass multiple arguments to a function in a single parameter. 📋🎛️
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5. **Namedtuples**: Unpack namedtuples for concise access to individual attributes. 🏷️🗃️
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**💡 Pro Tip:**
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Remember that both tuple unpacking and packing support extended syntax, allowing you to handle multiple elements at once! 🎯🎯
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### 5. ➗ Divmod - Dividing and Modulo Together
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`divmod` is a Python built-in function that takes two numbers and returns a pair of values - the result of integer division and the remainder (modulo). 🧮🔢 It's like getting two for the price of one! 💲🎁
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**📝 Examples to Get Started:**
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```python
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# Using divmod
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result = divmod(20, 3)
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print(f"Result of divmod(20, 3): {result}")
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# Using divmod in a loop
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quotients = []
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remainders = []
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for num in range(1, 6):
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q, r = divmod(20, num)
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quotients.append(q)
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remainders.append(r)
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print(f"Quotients: {quotients}")
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print(f"Remainders: {remainders}")
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```
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**🚀 Use Cases for `divmod`:**
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1. **Time Conversion**: Convert seconds into hours, minutes, and remaining seconds using `divmod`. 🕐🕒
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2. **Loop Iterations**: Use `divmod` in loops to efficiently calculate quotients and remainders for a range of numbers. 🔃🔢
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3. **Formatting**: Combine `divmod` results to display complex numerical data elegantly. 🎨🔠
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4. **Resource Allocation**: Distribute resources evenly among a given number of entities using `divmod`. 💻📊
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5. **Currency Handling**: Calculate the number of denominations needed when making change with `divmod`. 💲💸
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**💡 Pro Tip:**
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Remember that `divmod` can be a powerful tool to optimize certain mathematical operations and simplify your code. 🧮🚀
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## Upcoming Events
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| Event Name | Date | Location | URL |
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|------------------|---------|-----------|---------------------------|
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| North Bay Python 2023 | July 29th & 30th | Petaluma, California | [Website](https://dev.events/conferences/north-bay-python-petaluma-6-2023) |
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| PyDelhi Conference 2023 | Aug 19th & 20th | Delhi | [Website](https://conference.pydelhi.org/) |
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| PyCon 2023 | Sept 29 To Oct 2 | HYDRABAD | [Website](https://in.pycon.org/2023/) |
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Stay updated with the latest events and conferences in the Python
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community. Mark your calendars and don’t miss out on these exciting
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opportunities to learn, network, and engage with fellow Python
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enthusiasts!
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Contact
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-------
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Sure! Here's the text formatted in proper Markdown format:
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If you have any questions or need further assistance, feel free to reach out to us at [[email protected]](mailto:[email protected]) or join the discussion on our [GitHub Discussions](https://github.com/Python-World/newsletter/discussions) board.
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## Contributors
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We would like to express our sincere gratitude to the following
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contributors who have made valuable contributions to this edition of the
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Python-World Newsletter:
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- [Ravishankar Chavare](https://github.com/chavarera/)
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- [Aahnik Daw](https://github.com/aahnik/)
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Thank you for your dedication and for enriching the Python community
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with your valuable insights, code snippets, and contributions! Happy
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coding! 🐍✨

‎doc/newsletters/index.2023.rst

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==========
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.. toctree::
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:maxdepth: 4
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2023 Jully - Week 29 <2023/WEEK_29.md>
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2023 Jully - Week 28 <2023/WEEK_28.md>
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2023 Jully - Week 27 <2023/WEEK_27.md>
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2023 June - Week 26 <2023/WEEK_26.rst>

‎requirements.txt

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ghp-import
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furo
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sphinx
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myst-parser
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ghp-import
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myst-parser
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sphinx_copybutton
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sphinx-contributors

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