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RealPython
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Python Project: Build a Word Count Command-Line App
- Practice exercise to build a simplified version of the Unix wc command using Python.
- Tasks include reading content from files/stdin, counting lines, words, and bytes, handling Unicode, and implementing command-line arguments.
- Utilizes Python standard library modules like pathlib and argparse.
- Provides detailed instructions, examples, automated feedback, and support for learners.
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RealPython
3w
43
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How to Find an Absolute Value in Python
- Learn how to work with absolute values in Python using the built-in abs() function for numbers, arrays, and custom objects.
- Implement the absolute value function from scratch, use abs() with numbers, and customize its behavior for data types like NumPy arrays and pandas Series.
- Python's abs() function efficiently handles integers, floating-point numbers, complex numbers, and more.
- NumPy and pandas extend the abs() function to work directly with arrays and Series.
- Customize the behavior of abs() for your own data types by implementing the .__abs__() method.
- The abs() function can process fractions and decimals from Python's standard library.
- Absolute value helps determine the magnitude of an object, like a number or vector, regardless of direction.
- Absolute value function in Python can be implemented using conditional statements or mathematical operations.
- Visualizing absolute value as the distance from the origin on a number line or in multi-dimensional space like vectors.
- Understand how to calculate the length or magnitude of vectors using Euclidean norm.
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Dev
3w
170
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Dumping Credentials with Python: Automating LSASS Access and Credential Extraction Post-Exploitation
- Accessing LSASS in post-exploitation scenarios provides valuable data such as passwords, hashes, and credentials, making it a prime target for red teamers and attackers.
- Extracting data from LSASS with Python offers a powerful tool for red teamers, giving insight into detection and defense measures that might obstruct such attempts.
- LSASS plays a crucial role in storing authentication artifacts, enabling attackers with the right access to perform pass-the-hash or pass-the-ticket attacks.
- Microsoft has hardened LSASS against unauthorized access through measures like running it as a Protected Process Light and utilizing Virtual Secure Mode.
- Python-based approaches for LSASS dumping include direct memory access using ctypes and Windows APIs, minidump creation with MiniDumpWriteDump, and parsing dumps with pypykatz.
- Evasion techniques to stay stealthy during credential dumping involve using legitimate processes, forking LSASS, and avoiding disk I/O to prevent detection.
- Python's flexibility allows red teamers to automate LSASS credential extraction quietly and efficiently, navigating the evolving landscape of detection by defenders.
- Understanding the mechanisms of LSASS credential extraction and employing evasive workflows are essential for red teamers to maintain their capabilities effectively.
- Automating LSASS credential dumping using Python provides red teamers with a potent capability when executed with precision and stealth.
- LSASS access and credential extraction offer red teamers key insights into Windows systems, enabling them to navigate networks effectively and escalate privileges.
- For further information and updates, follow DevUnionX at https://x.com/DevUnionX.
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Medium
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375
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Quaternions: Creating a Class Using Dunder Methods in Python
- Quaternions are an extension of complex numbers and are useful in computer graphics for transformations.
- Creating a custom Quaternion class in Python involves implementing dunder methods for various operations.
- The __init__ method initializes Quaternion variables, checking for valid input types like ints and floats.
- The __str__ method converts Quaternions into readable strings using list concatenation.
- __repr__ method represents Quaternions for easily replicating objects with eval function.
- Arithmetic operations like addition, subtraction, multiplication, and division are implemented using dunder methods.
- Handling different data types like complex numbers, ints, and floats in arithmetic operations requires type checks.
- Additional methods for right hand operations, in-place operators, and comparison operators are implemented.
- Class methods are utilized for static functions like converting to/from ints and floats and creating identity Quaternions.
- Dunder methods simplify development by leveraging built-in operators, reducing additional methods needed for custom classes.
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Medium
3w
323
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Why Writing Python Feels Like Writing Poetry
- Writing Python feels like writing poetry due to its focus on readability, clarity, and simplicity.
- Python is designed to prioritize clarity and simplicity, a philosophy established by Guido van Rossum.
- The Python community upholds the value of readability and simplicity, evident in the Zen of Python guiding aphorisms.
- Python's language design emphasizes clarity, where form emerges from meaning and every word has significance.
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Medium
3w
65
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An Easy End-To-End Machine Learning Project In Python (Perfect For Beginners!) (1/2)
- Beginners in machine learning can approach the process by breaking it into manageable steps.
- This article guides you through a machine learning project in Python using real-world data.
- The project involves predicting housing prices in California using census data.
- It is a supervised multiple regression problem with batch learning approach.
- Performance measure like RMSE is used to evaluate the model.
- Exploratory data analysis includes understanding dataset characteristics and distributions.
- Stratified sampling is recommended for creating a reliable test set.
- Correlation analysis helps in identifying key features like median income for prediction.
- Creating new attributes like bedrooms_per_room can enhance model performance.
- Iterative exploration and model refinement are essential steps in a machine learning project.
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RealPython
3w
218
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Execute Your Python Scripts With a Shebang
- Shebang line (#!) in shell scripts specifies the path to the interpreter to execute the file.
- It allows running Python scripts directly without explicitly calling the Python interpreter.
- Include shebang for scripts requiring direct execution but not for import-only modules.
- Best practices involve using /usr/bin/env for portability and ensuring script executability.
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Medium
3w
1.6k
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What is Python? Easy Guide for Beginners
- Python is a popular programming language used to tell a computer what to do.
- Python is flexible and used by big companies like Google, Instagram, and Netflix.
- You can start learning Python for free and it's a great way to start your programming journey.
- If you can write a sentence in English, you can write a program in Python.
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11 Likes
Medium
3w
385
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Using Python with APIs to Automate Data Collection and App Integration
- Python is commonly used to work with APIs and automate data collection.
- APIs are a set of rules that describe how one system can request information from another using HTTP as the transport layer and often dealing in JSON data.
- When making an API call, you send a request, the server validates it, and sends back a response which can be data like current weather, stock prices, or user lists.
- APIs often require authentication through methods like API keys, bearer tokens, or client credentials to access data securely.
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Dev
3w
70
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Download YouTube Videos in 8K with Python: A Beginner-Friendly Guide Using yt-dlp
- This guide explains how to download YouTube videos in ultra-HD (up to 8K) quality using Python and yt-dlp library.
- The script utilizes yt-dlp, a powerful fork of youtube-dl, and ensures proper integration of FFmpeg for merging high-quality audio and video.
- Steps include setting FFmpeg path, configuring yt-dlp options for best video and audio quality, specifying output format, and running the script with the desired video URL.
- By following the steps and running the script, users can download and merge the best available video and audio, including 8K quality if the source supports it.
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Dev
3w
368
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🎁 Beginner-Friendly Guide to Solving "Maximum Candies You Can Get from Boxes" | LeetCode 1298(C++ | JavaScript | Python)
- LeetCode 1298 – Maximum Candies You Can Get from Boxes is a graph traversal problem to maximize collected candies by strategically opening boxes and using keys.
- Key observations suggest using BFS/Queue for exploring boxes level by level and revisiting locked boxes once the right key is found.
- The strategy involves using a queue to explore openable boxes, tracking opened boxes, collecting candies, and managing keys.
- Code implementations provided in JavaScript (ES6+), Python, and C++ offer solutions to the problem for learners at any level.
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Dev
3w
118
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Pure Python HTTP Server with Sockets – A Deep Dive into Web Server Internals
- The article discusses the integration of TCP connection pooling based on WSGI in Python to enhance the performance of web applications interacting with external services.
- WSGI serves as a standard interface between Python web applications and servers, facilitating compatibility among various web servers and frameworks.
- TCP connection pooling optimizes network communication by pre-establishing connections, reducing latency and resource consumption.
- The implementation involves initializing the pool, handling requests, managing connections, and ensuring efficient operation.
- The provided Python code includes classes for TCPConnectionPool and TCPConnectionPoolMiddleware to manage connections and middleware logic.
- The TCPConnectionPool class handles connection initialization, retrieval, release, and closure to maintain a pool of connections.
- The TCPConnectionPoolMiddleware class integrates the connection pool with WSGI application logic for request processing.
- Code analysis suggests improvements such as implementing connection health checks, dynamic pool size adjustment, and enhanced exception handling.
- Optimization directions include adding connection health checks to ensure connection validity and dynamically adjusting pool size based on load.
- In conclusion, the article emphasizes the importance of TCP connection pools for building high-performance Python web applications interacting with diverse network services.
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Feedspot
3w
223
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Java vs Python? Which Programming Language You Should Learn?
- The debate between Java and Python is similar to 'tabs vs spaces' or 'light mode vs dark mode' in the programming world.
- The choice between Java and Python depends on what you want to build, development speed, and career goals.
- Java, created in 1995, is used for enterprise applications, backend systems, and Android development with core values of reliability, portability, and security.
- Python, introduced in 1991, focuses on making programming accessible and enjoyable with a clean syntax and ease of learning.
- Java is a compiled language with the 'Write Once, Run Anywhere' feature, while Python is an interpreted language known for its simplicity and readability.
- Compiled languages like Java prioritize speed and efficiency, while interpreted languages like Python offer convenience and quick iteration.
- Java follows a two-step execution process converting code into bytecode running on the JVM, while Python executes code line by line.
- Java is faster due to JIT compilation, ideal for high-performance applications, while Python is slower but excels in AI, data science, and web development.
- Java requires static typing for variables, enhancing reliability, while Python's dynamic typing speeds up development.
- Java's memory management via JVM and Python's reliance on reference counting differ in efficiency, affecting large-scale applications.
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RealPython
3w
228
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How Can You Structure Your Python Script?
- Transitioning from interactive environments to structured scripts in Python is crucial for promoting readability and robust development practices.
- This tutorial helps transform messy scripts into well-organized, shareable code, incorporating standard Python practices and tools.
- Key learnings include organizing scripts logically, managing script state efficiently, enhancing interactivity with command-line arguments, and creating self-contained scripts with inline dependencies.
- The tutorial guides through steps like setting up the initial script, integrating external libraries, handling command-line arguments, structuring internal data, and enhancing feedback and robustness.
- A concrete script interacting with a web server to work with the Iris dataset is used to demonstrate these structuring concepts.
- Starting with the importance of the Shebang line for Unix-like systems, the tutorial covers organizing import statements following PEP 8 recommendations.
- The Shebang line, like #!/usr/bin/env python3, makes Python scripts directly executable from the command line without typing 'python'.
- Organizing import statements into standard library, third-party, and local imports helps in clarifying dependencies and improving code readability.
- Minimizing dependencies for easy sharing and structuring scripts for better collaboration are essential scripting practices emphasized in the tutorial.
- The tutorial offers a comprehensive guide on structuring Python scripts efficiently to enhance readability, collaboration, and development practices.
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Dev
3w
1.9k
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What is a coroutine object in python
- A coroutine is a special kind of function that can pause and resume its execution, allowing asynchronous programming.
- Coroutine objects represent tasks that haven’t run yet and must be awaited inside an async function to actually execute the code and get the result.
- When you call an async function, it returns a coroutine object, which needs to be awaited to run the code and obtain the result.
- In a FastAPI app, async functions return coroutine objects that need to be awaited so that FastAPI can send the actual data in the response.
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