Integrating GraphQL with Python using Strawberry

🚀 Ever wondered how to efficiently organize your code using modules in Python? Let's break it down! 🐍 Modules are simply Python files that consist of functions and variables for specific tasks. They help keep your code organized, manageable, and reusable. 👨💻 Why does this matter for developers? By using modules, you can effectively break down your code into smaller, logical components, making it easier to collaborate with others, maintain and scale your projects. 🔍 Here's the step-by-step breakdown: 1️⃣ Create a Python file for your module, e.g., "my_module.py". 2️⃣ Define functions and variables within the module. 3️⃣ Import the module in your main Python script. 4️⃣ Access functions and variables using dot notation. 🧩 Full code example: ``` # my_module.py def greet(name): return "Hello, " + name ``` ``` # main.py import my_module print(my_module.greet("Alice")) ``` 💡 Pro tip: Keep your module names meaningful and descriptive to enhance code readability and maintainability. ❌ Common mistake to avoid: Forgetting to add an empty "__init__.py" file in the module folder, which is required for Python to recognize it as a package. 🤔 What creative ways have you used modules in your Python projects? Share in the comments below! 👨💼💬 🌐 View my full portfolio and more dev resources at tharindunipun.lk 🚀 #PythonModules #CodeOrganization #DeveloperTips #PythonCoding #CodingLife #CodeReuse #TechSkills #SoftwareDevelopment #LearnToCode

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

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

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

Who knew code written in Python could be faster than Rust? SQLGlot's compiled mode (pip install "sqlglot[c]") now matches or beats Rust-based SQL parsers across most benchmarks. Thanks to the amazing mypyc project, SQLGlot can compile down into efficient C, the only thing we needed to do was add type annotations! The speed comes from compiling the core parsing and expression modules with mypyc the same tool mypy uses internally. No Rust, no C, no FFI... just Python that gets compiled to efficient C extensions. The pure Python version (zero dependencies, works everywhere) is already faster than most alternatives. The compiled version closes the gap with native code entirely. Some highlights from the benchmarks (ratio vs pure Python sqlglot): - sqlglot[c]: 0.17x–0.33x (3–6x faster than Python runtime) - sqloxide (Rust): 0.15x–0.58x - polyglot-sql (Rust): 0.17x–0.44x - sqlparse (Python): 3.6x–19x slower - sqlfluff (Python): 40x–177x slower Full benchmark suite with 16 query types is in the repo. Numbers are on Python 3.14. What I find most interesting is that this disproves the common assumption that you need to rewrite in Rust to get fast performance in Python. A well structured Python codebase compiled with mypyc gets you incredibly far.

🐍 Python PIP – Package Manager Explained PIP (Preferred Installer Program) is a package manager used to install and manage Python packages (modules). It allows developers to easily add external libraries to their projects. If you are using Python 3.4 or later, PIP is included by default. 🔹 What is a Package? A package contains all the files required for a module. These modules are reusable Python code libraries that can be imported into your project to extend functionality. 🔹 Check if PIP is Installed You can verify PIP installation using: pip --version As shown on page 2, this command displays the installed PIP version and confirms that it is available on your system. 🔹 Install a Package Downloading and installing packages is simple with PIP: pip install camelcase This command installs the camelcase package, as demonstrated in the example on page 2. 🔹 Using Installed Packages Once installed, packages can be imported and used in Python programs: import camelcase c = camelcase.CamelCase() txt = "hello world" print(c.hump(txt)) As shown on page 3, this converts text into camel case format. 🔹 Remove a Package To uninstall a package: pip uninstall camelcase PIP will ask for confirmation before removing the package. 🔹 List Installed Packages You can view all installed packages using: pip list The table shown on page 4 displays installed packages along with their versions (e.g., pip, pymongo, setuptools). 💡 PIP is an essential tool for Python developers, making it easy to manage libraries, dependencies, and project environments efficiently. #Python #PIP #PythonPackages #Programming #BackendDevelopment #DataScience #SoftwareDevelopment #AshokIT

The best Python setup is not the one with the most extensions. It’s the one with the fewest surprises. I shared a practical Medium post on the best VS Code extensions for Python development and how to avoid editor overload. Read here: https://lnkd.in/dVgDwEHG

🚀 Python Basics Every Beginner Should Know Starting your journey in Python? 🐍 Here are some must-know basic commands that every beginner should master 👇 🔹 1. Print Output print("Hello World") 🔹 2. Take Input name = input("Enter your name: ") 🔹 3. Variables x = 10 name = "Python" 🔹 4. Data Types int, float, str, bool, list, tuple, dict 🔹 5. Conditional Statements if x > 5: print("Greater") else: print("Smaller") 🔹 6. Loops for i in range(5): print(i) 🔹 7. Functions def greet(): print("Hello!") 🔹 8. Lists fruits = ["apple", "banana", "mango"] 🔹 9. Dictionaries data = {"name": "John", "age": 25} 🔹 10. Import Libraries import math 💡 Mastering these basics is the first step towards becoming a Python Developer or Automation Tester. ✨ Consistency > Perfection 💬 What was the first Python command you learned? #Python #Programming #CodingForBeginners #AutomationTesting #QA #TechLearning #100DaysOfCode #Developers #LearnPython

Mini Python Automation Project – Bulk File Renamer Today, I worked on a simple but very useful Python automation task. Using Python, I created a script that automatically renames all files inside a folder in a proper sequence. 1.What this script does: • Reads all files from a selected folder • Renames each file one by one • Adds a custom prefix and numbering • Helps save time and reduce manual work 2.Why this is useful: This kind of automation can be very helpful for: • Organizing project files • Managing reports/documents • Cleaning up downloaded files • Reducing repetitive manual tasks 3.What I learned: • How to use the **os module** • How to access files inside a folder • How to rename files using Python • How automation can simplify daily tasks 4.Sample Python Code: python import os folder_path = "your_folder_path_here" files = os.listdir(folder_path) for index, file_name in enumerate(files, start=1): old_path = os.path.join(folder_path, file_name) if os.path.isfile(old_path): file_extension = os.path.splitext(file_name) new_file_name = f"file_{index}{file_extension}" new_path = os.path.join(folder_path, new_file_name) os.rename(old_path, new_path) print("All files renamed successfully!") This is a small project, but it helped me understand the practical use of Python scripting and automation. #Python #Automation #PythonProjects #Scripting #Coding #LearningJourney #TechSkills #Programming #BeginnerProjects #ITSupport