Django ORM Simplifies Database Interactions with Python

🐍📰 Django Migrations: A Primer In this tutorial, you’ll get comfortable with Django migrations and learn how to create database tables without writing any SQL, how to automatically modify your database after you changed your models, and more. #python

Learn how to work with Databases in Python for your trading and investment projects. #applicationdevelopment #database #algotrading #python #hedgefunds #nseindia #nyse

---- Slow Requests in Python (Django): What’s Really the Problem? Slow APIs are not a Python issue , they’re an engineering failure. Most slow requests come from poor decisions in data access, logic, and system design, not the language itself. * Too many database queries (N+1 problem): This happens when the app queries the database inside loops instead of fetching related data efficiently. What should be one query turns into dozens or hundreds, killing performance and scalability. * Inefficient ORM usage: Fetching full objects when only a few fields are needed adds unnecessary weight to your queries. This increases memory usage and slows down both processing and serialization. * Blocking synchronous code: Long operations like external API calls, file handling, or heavy computations block the request cycle. Since Django runs synchronously by default, everything waits and your response time explodes. * No caching strategy: If your app keeps recalculating the same results or hitting the database for identical requests, you’re wasting resources. Caching avoids redundant work and drastically improves speed. * Heavy serialization: Returning large or deeply nested data structures makes responses slower. Overloaded serializers and missing pagination can turn even simple endpoints into performance bottlenecks. -> In conclusion : When you are in development phase, always think as if you are already in production. Your users are not 1 or 2 they can be thousands or even millions.

🐍📈 Become a Python Web Developer This learning path will provide you with the foundational skills you need to become a Python web developer. You’ll cover the most popular Python web development frameworks & working with databases. #python #learnpython

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

Stop writing Python like Java/C++! Building scalable applications in Python means embracing its unique strengths, not fighting them. A truly "clean" API in Python isn't just about naming conventions; it's about thinking in terms of Python's object model, its dynamic nature, and its emphasis on readability. Let's look at how we handle optional parameters. Okay: class Service: def process(self, data, config=None): if config is None: config = {} # Boilerplate to handle None # ... process with data and config Best (Pythonic): class Service: def process(self, data, config=None): config = config or {} # Concise and idiomatic # ... process with data and config The "Best" version uses Python's truthiness. None evaluates to False, so config or {} will assign an empty dictionary if config is None, otherwise it uses the provided config. It's shorter, clearer, and less prone to errors. Takeaway: Design APIs that leverage Python's expressiveness for clarity and conciseness. #Python #CodingTips

My Python loop processed 5 reports in 2.5 seconds. After adding one decorator: 0.54 seconds. I changed zero call sites. Here's how it works. A function that's slow because it does real work — database queries, aggregations — gets decorated with @app.direct_task. That's it. The caller doesn't change. Exception handling doesn't change. The return type doesn't change. In development: set one environment variable and the decorator is invisible - tests pass, the function runs inline as it always did. In production: start a worker. The same call now routes to a distributed runner. The caller still blocks and gets the value back directly. No .result. No futures. No refactoring. For parallelism without touching the call site at all: add parallel_func to the decorator with a small helper that describes how to split the input. Pynenc dispatches one task per chunk, collects results, and returns the same type the caller expected. Full write-up + runnable demo (no Docker, no Redis, runs in ~30 seconds): https://lnkd.in/eySD7h_H What's the slowest loop in your codebase right now? #python #backend #distributedsystems #opensource

🔥 Mastering JSON Parsing in Python! 🐍 Ever wondered how to work with JSON in Python? JSON (JavaScript Object Notation) is a popular format for data exchange due to its simplicity and readability. For developers, understanding JSON parsing is essential as it allows you to interact with APIs, handle configuration files, and exchange data between different systems effortlessly. Here are the steps to parse JSON in Python: 1️⃣ Load the JSON data using the `json.loads()` function. 2️⃣ Access the values using keys just like a Python dictionary. 3️⃣ Iterate through JSON arrays to extract multiple values. 👉 Pro Tip: Use `json.dumps()` to convert Python objects back to JSON. ❌ Common Mistake: Forgetting to handle exceptions when parsing JSON can lead to runtime errors. 🚀 Ready to level up your Python skills? Try parsing JSON with this code snippet: ``` import json # Sample JSON data json_data = '{"name": "John", "age": 30, "city": "New York"}' # Parse JSON data = json.loads(json_data) # Access values name = data['name'] age = data['age'] city = data['city'] print(name, age, city) ``` 🤔 What's your favorite way to work with JSON data in Python? 🌐 View my full portfolio and more dev resources at tharindunipun.lk #JSON #Python #APIs #DataExchange #CodingTips #JSONParsing #Programming #DeveloperCommunity #TechSkills

Postgres 18 makes generated columns virtual by default. Paolo Melchiorre, Python developer & Django contributor, explores what this means using concrete examples at POSETTE: An Event for Postgres 2026 Paolo walks through examples showing how generated columns evolved across PostgreSQL versions & what problems they solve well. The talk uses Django as a case study for how generated columns are exposed & used in production through a widely adopted Python framework: 🔹 How generated columns behave differently across PostgreSQL versions 🔹 New trade-offs around performance, storage & query behavior 🔹 How ORM abstractions influence the adoption of database features in production 🔹 Recent Django 6.0 improvements that better align with PostgreSQL's generated column behavior Mark your calendar for POSETTE Livestream 2 on Wed 17 Jun https://lnkd.in/gZzv6j7C #PosetteConf

As a long-time Java engineer, I continue to be impressed by how much Python has evolved. What once felt like a simple scripting language has grown into a remarkably capable ecosystem: C-backed libraries like NumPy, performance-oriented tooling in Rust, native coroutine support with async and await, and multiple concurrency models for very different workloads. One thing I find especially interesting is Python’s concurrency toolbox. Choosing the right model usually comes down to one question: What is your code actually waiting on? If your program is mostly waiting on the network, a database, or disk, you are likely dealing with an I/O-bound problem. In that case, asyncio can be a strong fit when the surrounding stack is async-native. If your program spends most of its time computing, parsing, or transforming data, you are likely dealing with a CPU-bound problem. In standard CPython, threads usually do not speed up pure Python CPU work because of the GIL. For that, multiprocessing is often the better fit. A few practical rules I keep in mind: • asyncio for high-concurrency I/O with async-native libraries • threads for blocking libraries or simpler concurrency • multiprocessing for CPU-heavy pure Python workloads • threads with native libraries when heavy work runs in C or Rust A good example is PyArrow and PyIceberg. PyArrow’s Parquet reader supports multi-threaded reads. That means you can get parallelism without rewriting everything around asyncio, because the heavy work happens in native code rather than Python bytecode. PyIceberg builds on this ecosystem. From the Python caller’s point of view, the workflow is still synchronous, while file access and data processing can benefit from native parallelism underneath. The key lesson for me: Not every high-performance I/O workflow in Python needs asyncio. If the underlying engine is native and already parallelizes efficiently, threads can be the right tool. If the stack is async-native, asyncio becomes much more compelling. A simple mental model: Async-native I/O → asyncio Native libraries parallelizing outside Python → threads CPU-heavy pure Python → multiprocessing Not sure → profile first That mindset is often more useful than memorizing any specific framework. #Python #Java #Concurrency #AsyncIO #Threading #Multiprocessing #Performance #SoftwareEngineering #DataEngineering