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PlanetPython
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Python Software Foundation: Announcing Python Software Foundation Fellow Members for Q1 2025! ����
- Python Software Foundation has announced its first batch of PSF Fellows for Q1 2025.
- Several individuals have been recognized for their exceptional contributions to the Python community.
- The new PSF Fellows have been involved in various activities such as supporting Python ecosystem, maintaining libraries, organizing events, and more.
- Criteria for becoming a Fellow member is available on the PSF website, and nominations for Q2 2025 are open until May 20th, 2025.
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Dev
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Essential Python Libraries for Data Science, Machine Learning, and Web Development
- NumPy: Used for fast mathematical calculations, especially with arrays.
- Pandas: Used to work with tabular data like Excel spreadsheets for data manipulation and analysis.
- Matplotlib: Used for drawing graphs and charts like line, bar, and pie charts.
- SciPy: Used for scientific and technical calculations such as integration, optimization, and solving equations.
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Dev
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781
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Selenium with Python for Beginners: Your First Automation Script
- Web automation with Selenium and Python allows you to programmatically control a web browser to perform repetitive tasks efficiently.
- Selenium, a popular web automation tool, combined with Python provides a powerful framework for beginners to start automating tasks.
- Python's clear syntax and Selenium's bindings make it easy to connect Python code with a web browser for automation.
- To begin, ensure Python is installed, install the Selenium library using 'pip install selenium', and set up the browser driver.
- Your first automation script involves opening a website, finding elements, interacting with them, and closing the browser.
- Key steps include setting up the Chrome Driver, opening a website like Google, finding elements, typing text, and simulating key presses.
- Running the script will automate actions like searching on Google using the Selenium-driven Chrome browser.
- By understanding the basics of automation scripts, such as importing necessary tools, locating elements, and interacting with them, you can build more complex workflows.
- Exploring tutorials on Selenium with Python can help you grasp advanced concepts like handling dropdowns, waiting for page loads, and interacting with different elements.
- This introductory script serves as a foundation for automating tasks and learning more about Selenium with Python for efficient workflow automation.
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Dev
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Steps to Build Binary Executables for Python Code with GitHub Actions
- Step 1: Add PyInstaller to Your Requirements. Make sure to include PyInstaller in your requirements.txt file along with any other dependencies.
- Step 2: Create a GitHub Actions Workflow. Set up a GitHub Actions workflow by creating a YAML file with detailed steps for building and releasing executables for Linux, Windows, and macOS.
- Step 3: Trigger the Workflow. Go to the Actions tab in GitHub, select the Build and Release Executables workflow, run the workflow with a specified version, and trigger the process.
- Step 4: Output. Once the workflow is completed, executables for all platforms will be available as GitHub Actions artifacts, and a GitHub Release will be created with downloadable binaries for each operating system.
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Hackaday
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Hardware Built For Executing Python (Not Pythons)
- PyXL is a project aiming to run Python directly in hardware for maximum speed by compiling Python code into custom assembly and running it on a pipelined processor built from scratch.
- Currently, PyXL exists as logic running on a Zynq-7000 FPGA on an Arty-Z7-20 devboard, and it boasts significantly faster execution speed compared to MicroPython.
- In a GPIO latency test, PyXL running at 100 MHz achieved a round-trip latency of 480 nanoseconds, making it 30x faster than MicroPython and 50x faster when normalized for clock speed differences.
- This project aims to make it possible to write performance-critical code in Python from the beginning, despite Python not traditionally being a real-time language.
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Stackexchange
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bitcoin testnet transaction creation(in python)
- The code snippet provided demonstrates creating a Bitcoin testnet transaction in Python.
- It includes defining SECp256k1 curve parameters, helper functions like decoding Base58 addresses, ECC classes like Point, PrivateKey, Signature, Script, TxIn, TxOut, and Tx.
- The main script constructs, signs, and attempts to broadcast the transaction to the Bitcoin testnet.
- It uses a private key, decodes addresses, creates scriptPubKey and scriptSig, signs the transaction, and serializes the transaction.
- The code fails to broadcast the transaction due to an error in validating the referenced input transaction that has already been spent.
- The private key and WIF (Wallet Import Format) key are displayed for reference.
- Overall, the code provides a detailed example of working with Bitcoin transactions in a testnet environment.
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Hackers-Arise
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Mr Robot Hacks: Building a Deadman’s Switch in Python
- In the TV show Mr. Robot, the protagonist Elliot creates a digital deadman’s switch as a safety mechanism against his enemy Vera.
- A deadman’s switch triggers an action if no user action is detected, ensuring safety or executing a specified task.
- Elliot uses Python to build a digital deadman’s switch, a common concept in various industrial applications.
- The script uses threading in Python to allow concurrent operations within the same process.
- The requests module is used to send an SMS message through textbelt.com in case of trigger.
- The main function prompts the user to set a timeout period for the deadman’s switch to activate.
- If user action (pressing Enter) is not detected within the set time, the action function sends an SMS message.
- The script provides a customizable solution for executing critical tasks if the user fails to act within the specified time frame.
- This tutorial showcases using Python to implement a Deadman’s Switch for safety and automated actions.
- Python's flexibility and simplicity make it a suitable language for cybersecurity and AI applications.
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Dev
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How We Built a PDF Compression Tool with Python and Flask
- RevisePDF has developed a PDF compression tool using Python and Flask that effectively reduces file sizes while maintaining quality.
- Technical Stack: Python/Flask for web app, PyMuPDF for PDF analysis, Ghostscript for compression, Supabase for authentication.
- Compression Pipeline includes file upload and validation, PDF analysis, and compression processing with focus on text quality and image handling.
- Results show an average compression rate of 65-75% reduction in file size. Users can try the tool in beta phase at RevisePDF.com.
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Towards Data Science
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Website Feature Engineering at Scale: PySpark, Python & Snowflake
- The article discusses the challenge of identifying top candidates for a business proposal based on website quality scores, aiming to automate the process with machine learning pipelines.
- It covers technical implementations like fetching HTML content in Python using Snowflake dataset or CSV, and assigning a quality score using PySpark for feature extraction and processing.
- Legal and ethical considerations in web scraping are highlighted, emphasizing responsible practices, retention policies, and potential permissions from site owners.
- The article provides instructions on getting started with the project, including the folder structure, Snowflake data preparation, and usage of scripts for fetching website content.
- Advantages of using a comprehensive fetching script over a basic approach are outlined, showcasing benefits like asynchronous requests, rotating User-Agents, and efficient batching.
- Storing raw HTML in databases like Snowflake is recommended for scalability, as large-scale scraping and feature engineering are more reliable when done in a suitable data warehouse.
- The process of extracting features from HTML content using PySpark via Snowpark is detailed, including creating UDFs, applying feature extraction functions, and generating quality scores.
- Country-specific configurations are utilized for defining keywords and patterns that signal good merchant sites, making the feature extraction adaptable across different regions.
- The article concludes by emphasizing the website quality score as a key input for predictive models, showcasing its significance in ranking and recommending partners effectively for better business outcomes.
- A GitHub repository link is provided for the implementation details and a disclaimer clarifies that the data and scripts used are examples and not from real business scenarios.
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Fb
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Enhancing the Python ecosystem with type checking and free threading
- Meta and Quantsight have improved key libraries in the Python Ecosystem and invite community involvement.
- Efforts are focused on unlocking performance wins through free-threaded Python and improving type annotations.
- Type hints in Python enhance code understanding and IDE functionality.
- The inconsistency in specifying and maintaining type annotations in open source projects is being addressed.
- Improvements in type coverage have been made for pandas-stubs and numpy.
- Key introductions include new type aliases and improvements in core Dataframe operations.
- Free-threaded Python allows true parallelism and improved performance.
- The benefits include utilizing multi-core processors without the need for multiple processes.
- The ecosystem is being prepared for free-threaded Python compatibility by enhancing popular packages.
- Community involvement is crucial in further improving type annotations and free-threading in Python libraries.
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RealPython
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Sets in Python
- Python provides a built-in set data type which is an unordered collection of unique elements.
- A set in Python is defined as a well-defined collection of unique objects with no duplicate elements.
- Sets are useful for running set operations, removing duplicates, running efficient membership tests, among other things.
- Python sets are mutable, unordered, and useful for removing duplicates, with efficient membership operations.
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Medium
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402
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Comprehensions in python for dummies
- Comprehensions in Python are compact, elegant ways to create new collections from existing ones.
- Comprehensions work for lists, sets, and dictionaries making it easier to manipulate data.
- Comprehensions can be used for tasks like squaring numbers, filtering even or odd numbers, counting frequency, extracting prime numbers, separating vowels and consonants, and more.
- They provide a simpler and more readable alternative to traditional for loops.
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Medium
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Python for AI Engineer: Control Flow & Logic | 2 0f 5
- Conditional statements are essential for control flow in Python, guiding decision-making in scripts, models, pipelines, and logic-driven programs.
- Python evaluates certain values as False in conditionals, with everything else considered True.
- Use cases in AI and ML workflows include iterating over training epochs, looping through data batches, applying transformations to inputs, and evaluating models over multiple runs.
- Common control flow elements like while loops, break and continue statements, enumerate(), zip(), and generator expressions are important tools for AI engineers in managing iterations, conditions, and data processing.
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Dev
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441
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Creating ASCII Art with Python: A Fun Guide to Text Transformations
- Creating ASCII art with Python using the pyfiglet library is a fun way to practice coding.
- ASCII art can be used in command-line tools, banners in terminal apps, messages in CLI games, or just for fun.
- To get started, install the pyfiglet package using pip and you can easily convert text into ASCII art.
- You can customize the output by choosing different fonts, allowing user input for personalized ASCII art, and exploring further ideas for projects.
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PlanetPython
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Talk Python to Me: #504: Developer Trends in 2025
- In this episode of Talk Python to Me, a panel consisting of Gina Huge, Ines Montani, Richard Campbell, and Calvin Hendryx-Parker discuss the recent Stack Overflow Developer survey results and share insights on rising and falling trends in the Python and broader developer space.
- Key topics and technologies mentioned include Explosion, spaCy, OctoPrint, .NET Rocks, Six Feet Up, Stack Overflow, Python.org, GitHub Copilot, OpenAI ChatGPT, Claude, LM Studio, Hetzner, Docker, Aider Chat, Goose AI, IndyPy, OctoPrint Community Forum, spaCy GitHub, and Hugging Face.
- Listeners are encouraged to check out the Stack Overflow Survey Results and explore various resources and tools mentioned by the panelists.
- Stay connected with Talk Python on YouTube, Bluesky, Mastodon, as well as with Michael Kennedy on Bluesky and Mastodon for more updates and discussions.
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