Python Developer Toolkit - 5 Essential Scripts

DotNetPy is the only .NET–Python interop library with Native AOT support — call Python from C# in 4 lines, manage Python versions and dependencies directly from C# via uv, and get compile-time injection warnings from a built-in Roslyn analyzer. { author: 남정현 } https://lnkd.in/eP3y8R5k

MarkItDown is a lightweight Python utility for converting various files to Markdown for use with LLMs and related text analysis pipelines.

What if you could call Python from C# in 4 lines — with Native AOT support, no project file, and compile-time security checks? That's DotNetPy, and v0.5.0 is now live on NuGet. Unlike existing options (pythonnet, CSnakes), DotNetPy is built for where .NET is heading: file-based apps, AOT compilation, and declarative dependency management via uv. No Source Generators, no heavy runtime — just inline Python execution with clean data marshaling. This has been a passion project born from real-world needs at the intersection of .NET and AI/data science workflows. I'd love your feedback. 🔗 https://lnkd.in/g23PEys8 #dotnet #python #opensource #nativeaot #csharp

Low code using pysimplegui #machinelearning #datascience #lowcode #pysimplegui PySimpleGUI was designed to be a uniquely Python solution to creating user interfaces that makes the developer the focal point. Your code is not required to have an object oriented architecture which makes the package usable by a larger audience. While the architecture is simple to understand, it does not necessarily limit you to only simple problems. Some programs are not well-suited for PySimpleGUI however. By definition, PySimpleGUI implements a subset of the underlying GUI frameworks' capabilities. It's difficult to define exactly which programs are well suited for PySimpleGUI and which are not. It depends on the details of your program. Duplicating Excel in every detail is an example of something not well suited for PySimpleGUI. https://lnkd.in/gfkQrcSs

🧠 Python OOP – The 4 Concepts Every Automation Engineer Should Know When building automation frameworks, writing scripts is only half the job. The real challenge is writing clean, reusable, and maintainable code. That’s where Object-Oriented Programming (OOP) becomes extremely useful. Here’s a quick cheat sheet I often refer to while working with Python automation frameworks 👇 🔹 Encapsulation Bundle data and methods together inside a class. Helps protect internal data and keeps test utilities organized. 🔹 Inheritance Reuse code from a base class instead of rewriting it. Very useful for creating Base Test classes in automation frameworks. 🔹 Polymorphism Same method name, different behavior depending on the class. Makes frameworks flexible when handling different test scenarios. 🔹 Abstraction Hide complex implementation details and expose only what’s necessary. Keeps test cases simple and readable. 💡 In real automation frameworks, these concepts help in: • Reducing duplicate code • Building scalable test frameworks • Improving maintainability of automation suites Sometimes the difference between a script collection and a real automation framework is simply how well these concepts are applied. Curious to know 👇 Which OOP concept do you use the most in your automation frameworks? #Python #AutomationTesting #OOP #TestAutomation #RobotFramework #SoftwareTesting

My Second Open-Source Project: DepshieldX - A Security Shield for Python Packages! 🔐🐍 Introducing DepshieldX – a Python package wrapper that ensures your dependencies are safe before you install them! It resolves all dependencies, checks for vulnerabilities across 4 sources, and validates package provenance. Key Features: • Full dependency resolution before install • Provenance checks & vulnerability scanning • Optional Docker + Trivy validation • Signed JSON receipts for audits 🛠️ Install now: python -m pip install depshieldx Or python3.11 -m pip install depshieldx Project links: PyPI: https://lnkd.in/geKMJSfm Docs: https://lnkd.in/g_ZkXNyP Source: https://lnkd.in/gZ7AJiJN It’s exciting to share my second open-source project with the community! Check it out and help make Python more secure! #opensource #python #security #devtools #devsecops #softwaredevelopment

Converting Python Dictionaries to JSON: A Simple Guide Working with JSON in Python is crucial, especially when integrating with web APIs or handling configuration files. JSON (JavaScript Object Notation) is a lightweight data interchange format that is both human-readable and machine-readable. In the example, we start by importing the JSON module, which is part of Python’s standard library. Next, we define a simple Python dictionary. This data structure is versatile and easy to manipulate, making it a perfect candidate for JSON conversion. The function `json.dumps()` is then used to convert the dictionary into a JSON string. This serialization process transforms the dictionary into a format that can be easily transmitted or stored. When printed, the JSON string appears as expected, structured to facilitate data exchange. To convert the JSON string back into a Python dictionary, we use `json.loads()`, which deserializes the JSON, allowing us to manipulate the data in its original form. JSON's simplicity and flexibility make it an essential tool for data exchange in web development, where compatibility and efficiency are critical. Understanding its serialization and deserialization processes opens doors to various applications, from handling API responses to saving configurations. Quick challenge: How would you modify the code to handle a nested dictionary for JSON conversion? #WhatImReadingToday #Python #PythonProgramming #JSON #WebDevelopment #Programming

Python TIP : filter() vs List Comprehension After working with Python in production systems for years, one thing I’ve noticed is how often we need to filter data efficiently.... especially in backend services and data pipelines. A simple example: filter(lambda amount: amount > 800, transactions) What this does: • Iterates through each item • Applies the condition (amount > 800) • Returns only the matching values Example output: [900, 1300, 2200] My take after using this in real projects: • filter() is concise and works well in functional-style pipelines • It’s useful when chaining transformations (especially with map()) • That said, in many production codebases, I still prefer list comprehensions for readability Equivalent using list comprehension: [amount for amount in transactions if amount > 800] Why this matters: • Readability often beats cleverness in team environments • Consistency across the codebase is more important than personal preference • Choosing the right approach depends on context, not just syntax One quick reminder: filter() returns an iterator, so wrap it with list() if needed. After years of writing and reviewing code, I lean toward clarity first, but it’s always good to know both approaches. #Python #Programming #SoftwareDevelopment #Coding #Developer #PythonTips

📌 A Simple Python Project Setup Cheat Sheet for Beginners If you’re starting with Python projects (or even if you’ve been doing it for a while), one thing that often creates confusion is: 👉 Environment setup 👉 Python version management 👉 Dependencies breaking across systems So I decided to simplify this into a clean, repeatable cheat sheet. Sharing it here so others can benefit too 👇 💡 Step 1: Setup the foundation (UV + VS Code) Install VS Code, then install UV: powershell -ExecutionPolicy ByPass -c "irm https://lnkd.in/dJ2sstef | iex" If that doesn’t work: winget install --id astral-sh.uv -e Verify: uv If not recognized: setx VARIABLE_NAME "variable_value" 🐍 Step 2: Install & manage Python versions uv python install 3.12 uv python install 3.14 Pin a version: uv python pin 3.12 📁 Step 3: Initialize your project uv init Creates: main.py .gitignore .python-version pyproject.toml README.md 💡 pyproject.toml = your project’s backbone 📦 Step 4: Create virtual environment uv sync 📚 Step 5: Manage dependencies Add: uv add numpy 👉 Automatically updates pyproject.toml 🔄 Step 6: Handle Python versions New project → pin new version OR Clone → delete .venv → re-pin → sync ▶️ Step 7: Activate & run .venv\Scripts\activate ⚡ Why this helps ✔ No “works on my machine” issues ✔ Easy onboarding for new team members ✔ Clean dependency tracking ✔ Consistent project structure #Python #DataScience #MLOps #SoftwareEngineering #Beginners #DeveloperTools #BestPractices

What if one tool could manage both your Python packages and compiled system libraries? uv installs Python packages from PyPI, but it doesn't support compiled C/C++ libraries. The typical workaround is to install system libraries separately using an OS package manager, then manually align versions with your Python dependencies. Since these system dependencies aren't captured in project files, reproducing the environment across machines can be unreliable. pixi solves this by managing both Python packages from PyPI and compiled system libraries from conda-forge in a single tool. Quick comparison: • uv: fast, reliable lockfiles, Python-only • conda: system libraries supported, but slower and no lockfiles • pixi: fast, unified, with system libraries, lockfiles, and a built-in task runner In this article, I compare uv and pixi on a real ML project so you can see how they perform in practice. 🚀 Link: https://bit.ly/4t16m34 #Python #PackageManagement #DataScience