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Dev
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How to generate dynamic invoices for SaaS applications in Python 💸
- Invoice generation in SaaS applications can be simplified using reportgen.io, offering automation, scalability, and security.
- Integrating reportgen.io involves defining dynamic templates with conditional logic, like VAT calculations and subtotals.
- Creating Python functions to dynamically calculate invoice data and generate PDFs synchronously or asynchronously is key.
- Functions include generating invoice data based on line items and VAT percentage, synchronous PDF generation, and asynchronous PDF queuing.
- Usage involves providing dynamic data inputs and calling the Python functions to generate invoices in PDF format.
- reportgen.io stands out for supporting complex document generation, scalability, developer-friendly features, security, and competitive advantages.
- Future steps include integrating with services for automated invoice emailing, payment links attachment, and CRM updates for workflow enhancement.
- Utilize reportgen.io to enhance automation in SaaS applications and explore additional integrations for improved functionality.
- Access the source code on GitHub and try reportgen.io for free with 150 reports monthly without requiring a credit card.
- Share feedback or code improvements in the comments section for further collaboration and refinement of the process.
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Medium
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275
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Why Python Even Has Complex Numbers (And What You Can Actually Do With Them)
- Python supports complex numbers out of the box, allowing for easy manipulation of real and imaginary values in a single variable.
- Complex numbers are useful for describing 2D direction, rotation, signal waves, and orbital paths efficiently in Python.
- The .real and .imag attributes in Python provide control to analyze, transform, or visualize each axis of movement, simplifying complex manipulations.
- Python's built-in support for complex numbers simplifies tasks like signal processing, graphics, and simulations, allowing developers to focus on building applications rather than intricate math operations.
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Dev
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261
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How to Accurately Extract Data from Subimages in Python?
- Data extraction from images containing multiple subimages can be challenging, especially with tools like Gemini yielding incorrect results.
- To accurately extract data from individual subimages using Python, you can follow these steps:
- Install required libraries like Pillow and pytesseract.
- Load the image, split it into subimages, extract text from each subimage using OCR, and clean up the extracted data for accuracy.
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Dev
1M
67
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How to Set Album Art for MP3 Files using Python?
- Many people want to enhance their MP3 collections by adding visual elements like album art, as it improves the listening experience by providing context to the music.
- Mutagen is a Python module that offers a simple way to handle audio metadata, supporting various formats including MP3, FLAC, and others.
- To set album art for an MP3 file using Mutagen, you need to install it using pip and then follow a step-by-step process, including importing necessary libraries, loading the MP3 file, setting the album art by reading and encoding an image file, and checking if the art was added successfully.
- If Mutagen doesn't meet your needs, alternatives like eyed3 and tinytag can be considered for working with ID3 tags in MP3 files or reading metadata respectively.
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Medium
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203
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Go Just Outshined Python for This One Reason
- In the world of programming, developers compare languages to find the best tool for their projects.
- Python is known for its simplicity and versatility, while Go (Golang) is gaining traction for performance and concurrency.
- Go outshines Python in concurrency and scalability for high-performance systems due to its design and concurrency model.
- The article emphasizes Go's strengths in concurrency, making it a compelling choice for certain applications, while acknowledging Python's advantages.
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Dev
1M
388
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🧱 I Built My Python Web App Like a City (Not an App)
- The article discusses treating a Flask web app like a city, with modules represented as districts, endpoints as buildings, routes as roads, and middleware as traffic control.
- Different aspects of a city, such as neighborhoods, roads, buildings, utilities, decay, and sprawl, are metaphorically linked to components of the web app.
- The author outlined the approach taken in different phases: City Blueprint (zoning the app into districts), Traffic & Infrastructure (adding middlewares for traffic control), and Sanitation & Decay (cleaning up old or unused components).
- This city-planning approach prompted considerations on scalability, user flow, resource usage, and legacy within the app structure.
- The author suggests expanding the metaphor to include concepts like pollution tracking, public transport, crime prevention, disaster response, and using city-planning tools for app visualization.
- The shift in mindset encourages developers to prioritize logical grouping over individual functions, enhance flow, build sustainable communities of logic, and ensure long-term livability in the codebase.
- By adopting the city-building mindset, developers can improve code organization, prioritize usability, and plan for long-term maintenance and growth.
- The article also offers free GitHub bundles containing various resources for developers to build faster and smarter, with options ranging from open-source projects to developer libraries and system projects.
- The author concludes by encouraging readers to approach app development like city planning, considering aspects like city center, sprawl, traffic congestion, and safety to create more efficient and user-friendly applications.
- Overall, the metaphor of building apps like cities promotes a more holistic and thoughtful approach to software development, emphasizing usability, scalability, and long-term sustainability.
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Dev
1M
424
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🧑🏫 How to Teach GPT to Code Python your way
- The article discusses how AI can be trained to assist developers in coding, likening the AI to an intern with potential but lacking judgment.
- It emphasizes the importance of mentoring and guiding AI like an apprentice to improve its coding skills.
- The concept of 'AI Pair Programming' is introduced as a way to work with AI in a structured manner to solve coding problems.
- Guidelines are provided on setting up an AI coding flow, outlining problems, coding basic versions, and documenting the process.
- The article suggests training AI to learn developer preferences and habits to produce cleaner code with fewer bugs.
- Building feedback loops for auto-evaluation is recommended to help AI improve itself based on set standards and unit tests.
- Resources like Typer Docs, Rich, Python Type Hints, and pytest are suggested to aid in smart prompting and debugging while training AI.
- The article concludes by emphasizing that developers are not being replaced by AI but rather elevated through the mentorship and training of AI tools.
- It encourages developers to start training AI using scripts, guiding them through each project and prompt, with a reminder to come back for support.
- Additionally, ten recommended courses related to computing, tech history, and programming weirdness are highlighted for further learning.
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Dev
1M
388
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⚡ Real-Time Data Dashboards with Python: Build a Lightning-Fast Web App for Live Metrics
- Businesses now require instant visibility into key metrics, achievable through real-time dashboards built with Python and modern web tools.
- The guide demonstrates constructing a real-time web dashboard using FastAPI, WebSocket integration with Starlette, Redis for pub/sub, Chart.js + HTMX for front-end updates, PostgreSQL for historical storage, and Tailwind CSS for UI.
- An example scenario involves monitoring smart sensors in warehouses for temperature/humidity with live charts, historical data, alerts, and responsive design.
- The tech stack includes FastAPI for the backend, Tailwind + HTMX for the frontend, WebSockets + Redis for real-time updates, PostgreSQL for storage, and Redis for caching.
- Project structure includes backend with FastAPI, models, WebSocket, and DB setup, frontend with templates and static files, and a script for simulating sensor data.
- Additional features cover storing data in PostgreSQL, triggering alerts with thresholds, and deployment tips using Uvicorn, Gunicorn, NGINX, and platforms like Render or DigitalOcean.
- The tutorial encompasses Async WebSockets, Redis Pub/Sub, FastAPI Routing + BackgroundTasks, HTMX + Chart.js, PostgreSQL integration, and real-world hardware modeling.
- Real applications for such dashboards include factory sensors, crypto tickers, fleet management, fitness visualizers, and server monitoring tools.
- Python's modern ecosystem enables developers to create and deploy real-time dashboards without the complexities of React or Firebase, offering vast business potential.
- Done-for-you article kits are available for quick content creation, including topics like DIY computing, hacker techniques, bot invasions, data manipulation, and forgotten tech frontiers.
- One can use these articles to boost authority, attract traffic, monetize, and customize with branding, affiliate links, and quick publishing for blogs, newsletters, social posts, and more.
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PlanetPython
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280
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Talk Python to Me: #505: t-strings in Python (PEP 750)
- Python has introduced a new string formatting feature called t-strings for advanced use-cases such as SQL, HTML, and DSLs.
- t-strings are seen as the next evolution of Python string formatting after f-strings, aiming to address security concerns and runtime code manifestations.
- PEP 750 introduces Template Strings and is designed to enable writing domain-specific languages.
- Guests on the show include Paul Everitt, David Peck, and Jim Baker discussing the new t-strings feature in Python.
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16 Likes
Dev
1M
58
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The Role of Automation in Email Marketing Using Python
- Email marketing remains a highly effective channel for businesses, but manual efforts are not feasible in today's digital world, leading to the rise of automation.
- Python plays a crucial role in email marketing automation due to its flexibility, control, and integration capabilities with various systems.
- Automation benefits include saving time, increasing engagement, improving accuracy, and enabling scalability for reaching a larger audience.
- Using Python for email automation offers beginner-friendly features, rich libraries for handling emails, scheduling, data processing, and easy integration with external APIs and databases.
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Medium
1M
13
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Displaying LaTeX-Style Formulas Using Python
- Python libraries can be used to format equations like LaTeX for clean math formatting.
- Matplotlib supports LaTeX-style math using a simplified math expression parser.
- SymPy is Python's symbolic mathematics library that allows formatting and pretty-printing formulas using ASCII, Unicode, or LaTeX.
- Manim is a math animation engine for creating professional and presentation-ready math animations.
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Medium
1M
4.1k
*Let's now understand the A–Z of Python programming concept in more detail:*
- Python programming concepts from A to Z:
- - Positional, keyword, default, and variable-length are common argument types.
- - Built-in functions simplify tasks like printing, finding length, etc.
- - Comprehensions provide a concise way to create lists, sets, and dictionaries.
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Arxiv
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293
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Dialz: A Python Toolkit for Steering Vectors
- Dialz is introduced as a Python toolkit for enhancing research on steering vectors for open-source LLMs.
- It allows users to adjust activations at inference time to magnify or lessen specific concepts, offering a more effective option than prompting or fine-tuning.
- Dialz supports various tasks like generating contrastive pair datasets, computing and applying steering vectors, and visualizations, focusing on modularity and usability.
- The toolkit aids in reducing harmful outputs and understanding model behavior, promoting safe and controllable language generation, faster research progress, and improved AI system transparency.
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Medium
1M
330
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Optimizing Bank Loan Approvals with Genetic Algorithms in Python
- Genetic algorithms are being used to optimize loan approvals in banks by a data scientist.
- Genetic algorithms offer a more efficient solution compared to traditional grid search and brute-force approaches.
- GA is inspired by natural selection and evolves solutions over generations to find optimal outcomes.
- This article explains the intuition behind genetic algorithms and their application in optimizing loan approval thresholds.
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Towards Data Science
1M
213
Running Python Programs in Your Browser
- WebAssembly (WASM) technology enables running Python code directly in the browser, expanding web capabilities beyond HTML, CSS, and JavaScript.
- Pyodide library leverages WebAssembly, benefiting Python developers by allowing execution of Python code in the browser.
- Benefits of using Pyodide include access to popular Python libraries like NumPy, Pandas, Scikit-learn, and Matplotlib for data science and machine learning tasks.
- Pyodide facilitates building interactive dashboards and tools by combining Python's processing power with web technologies like HTML, CSS, and JavaScript.
- WebAssembly offers portability, high performance, security, and allows developers to write code in languages like C, C++, Rust, and Python, providing near-native execution speed.
- Common use cases of WebAssembly include high-performance web apps, porting legacy code, multimedia processing, scientific computing, and running multiple languages like Python in the browser.
- Pyodide's integration with WebAssembly enables running Python in the browser, offering benefits such as extensive library ecosystem usage, enhanced responsiveness, and simplified deployment.
- The Pyodide project involves porting the CPython interpreter to WebAssembly, allowing for a functional Python interpreter optimized for web environments.
- Code examples demonstrate running Python code in the browser using Pyodide, including simple print statements, mathematical calculations, calling Python functions from JavaScript, and utilizing libraries like NumPy and Matplotlib.
- Further examples showcase running Pyodide in a Web Worker for heavy computations, creating data dashboards directly in the browser, and visualizing data using Numpy and Matplotlib.
- The combination of Python, Pyodide, and WebAssembly offers a powerful toolset for developers to run Python programs within browsers, opening up possibilities for interactive web applications.
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