Boost Python Performance with Architecture Shifts

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

🐍 Python Tip: Stop copying lists the wrong way! Every Python developer hits this bug at some point: a = [1, 2, 3, 4] b = a b.append(5) print(a) # [1, 2, 3, 4, 5] 😱 When you write b = a, you're NOT copying the list. You're just creating another variable pointing to the SAME memory (call by reference). ✅ The 3 ways to properly copy a list: • b = a[:]    → Slice copy • b = a.copy()  → Built-in method • b = list(a)   → Constructor copy ⚡ Which one is fastest? a[:] wins — it's a direct C-level memory slice with zero Python overhead. Speed ranking: a[:] > a.copy() > list(a) ⚠️ BUT — all 3 are SHALLOW copies. If your list contains nested lists or objects, changes will still bleed through: a = [[1, 2], [3, 4]] b = a.copy() b[0].append(99) print(a) # [[1, 2, 99], [3, 4]] 😬 For nested structures, use deepcopy: import copy b = copy.deepcopy(a) Quick rule: 📦 Flat list → a[:] or a.copy() 📦 Nested list → copy.deepcopy(a) Small detail. Big bugs if you get it wrong. ♻️ Repost if this helped someone on your team! #Python #Programming #SoftwareDevelopment #CodingTips #PythonTips

You’re probably writing more Python code than you need to. Small tricks can make your code cleaner, faster, and easier to read which AI might also miss if not prompted well. I came across a collection of 100 Python tips that covers both basics and practical patterns 1] Cleaner syntax • List, set, and dictionary comprehensions for concise code   • Swapping variables and unpacking in a single line  Less code, same logic. 2] Built-in power • Modules like collections, itertools, datetime   • Functions like enumerate, zip, sorted  Python already gives you most of what you need. 3] Writing efficient code • Generators vs list comprehensions (memory vs speed tradeoff)   • Using built-ins instead of manual loops  Efficiency often comes from using the right abstraction. 4] Working with real tasks • File handling, PDFs, screenshots, web automation   • Data handling with pandas  5] Debugging and readability • Assertions for early error detection   • Zen of Python principles  Good code is easy to understand. Python is simple. But writing clean Python is a skill. #python #programming #developer #coding #softwareengineering

One Python feature I wish I started using earlier: **`dataclasses`**. When you’re building backend services, you often create “data-only” objects (DTOs, request/response models, internal payloads). Instead of writing repetitive boilerplate (`__init__`, `__repr__`, comparisons), you can do this: ```python from dataclasses import dataclass @dataclass(frozen=True, slots=True) class User: id: int name: str ``` Why I like it: - **Less boilerplate** → cleaner, more readable code - **`frozen=True`** → immutability (safer, fewer accidental changes) - **`slots=True`** → lower memory usage and often better performance Small change, big improvement—especially when your codebase grows. What’s one Python feature you wish you had used earlier? #Python #BackendDevelopment #SoftwareEngineering #CleanCode #Programming

Your Python scripts are slow because you're waiting for things unnecessarily. asyncio - Run multiple tasks simultaneously without threading complexity What it does: → Execute I/O operations concurrently → Handle API calls in parallel → Process data streams efficiently → Built into Python (no install needed) Cost: $0 The speed difference: Making 100 API calls sequentially: 5 minutes Making 100 API calls with asyncio: 8 seconds When to use it: API calls (waiting for responses) Database queries (I/O bound) File operations (reading/writing) Web scraping (multiple pages) Data pipeline processing When NOT to use it: CPU-heavy tasks (use multiprocessing instead) Simple scripts (overhead not worth it) This is day-1 knowledge for backend engineers. But most data scientists never learn it. Then wonder why their pipelines take hours. 🔗 https://lnkd.in/g9CQTpx3 #Python #Performance #AsyncIO

Two Python tools. One decision. TypedDict stays a plain dict at runtime. Dataclass becomes a real object with rules. Picking the wrong one early costs you later. Use TypedDict when: 🔹 The data crosses a JSON or API boundary 🔹 The consumer expects a plain dict 🔹 No validation, no defaults, no methods needed Use Dataclass when: 🔸 Fields need default values 🔸 Bad inputs should raise at construction time 🔸 The data carries behaviour — fingerprinting, serialisation helpers The card shows both patterns side by side with code you can copy directly into a production ML pipeline. Which one do you reach for first when designing a new ML component — and has that choice ever cost you? --- I write about the engineering layer between the model and production: typed contracts, domain objects, and the boundaries where ML systems fail.

The SFrame project from GraphLab/Dato/Turi I still consider to be one of my best pieces of work. It being C++ though, means it did not age particularly well. The C++ <-> Python bindings in particular, are very fragile. I have always wanted to reimplement it in Rust, and now with the assistance of Claude ... I now have SFrameRust https://lnkd.in/gpd_TgSp :-) Python bindings included. The entire repo is AI coded with a lot of human direction for design. - It appears binary compatible with the original SFrame; it can load the original SFrame format. (I gave it an original SFrame to validate against) - The completely insane parallel CSV parser (which can read JSONL) has been ported and seems to all work. - Performance is *pretty darn good*. (I have not validated every method yet). For methods which I have guided Claude to optimize, it is on par or faster. One of the key features of SFrame is that all its algorithms are designed to keep pretty tight within a memory budget, regardless of size of input. So with care over a few days, I directed Claude (Opus[1m]) to do a most excellent port of the original SFrame sorting algorithm. The current implementation sorts TPC-DS catalog_sales (sc-100 144M rows) in 1:42 min on my laptop (Mac M2 Max). DuckDB took 5 min. (memory limit 8GB) (Note: All benchmarks are unfair and this is no different :-) This is not intended to be a competition. The SFrame sort is *very specialized* for reading/writing SFrame formats and I don't think will work with Parquet though I have not thought too hard about it) Code quality is all considered pretty decent, but I had to continuously provide careful architectural direction. The initial port (done by a Ralph loop) took much too many shortcuts. The issue is that the plans Claude generates are not detailed enough, are non-hierarchical, and are immutable. If it can plan at different levels of abstraction, and can modify plans, I think it can be a lot better. Also implementation sub-agents have much too narrow objectives and tend to lose the big picture. For instance, it will constantly forget that the SFrame design goals is to maximize parallelism, minimize memory utilization, and will keep writing code that reads everything into memory. It will also forget that the query executor has parallel streaming capabilities. It makes sense to minimize the amount of context the implementation agent needs to do its job, but on the other hand that also means that in a large project it does not see enough code patterns to understand the broad design intentions. Anyway, as an experiment, I consider this to be quite a success. I have learnt a lot about working with AI, got a better sense of its capabilities and its limits, and most importantly... scratched an itch (That's basically all my personal AI projects right now). I have no idea what to do with the outcome though :-)

I built a programming language from scratch in Rust. Then I benchmarked it against Python. The results broke my expectations. 🔬 THE BENCHMARK We ran 74 functions across 15 categories, testing Vitalis v9.0 against pure Python on identical workloads (100K elements, zero overhead). Vitalis won 13/13 benchmarks, averaging 7.5x faster than Python. The highlights: 🚀 Cosine Distance → 29.1x faster 🚀 Batch ReLU → 10.3x faster 🚀 Std Deviation → 9.2x faster 🚀 MSE Loss → 8.1x faster Even with Python→Rust FFI overhead included, our Science and Math modules are hitting over 1.5 Million ops/sec. ⚡ WHAT IS VITALIS? It is not a wrapper around an LLM. It is a full, hand-written JIT-compiled language pipeline: Source (.sl) → Lexer → Parser → AST → Type Checker → SSA IR → Cranelift JIT → Native x86-64 We bypassed LLVM and GCC entirely, emitting native machine code through Cranelift. 🏗️ THE ECOSYSTEM We built 14 algorithm libraries right into the language, fully equipped with FFI and Python bindings: 🔐 Cryptography (SHA-256, HMAC) ⚛️ Quantum Simulator (Statevector, Bell states) 🕸️ Graph Algorithms (Dijkstra, PageRank) 📈 Statistics & Advanced Math ...plus Signal Processing, Compression, Security, and more. 💡 WHY THIS MATTERS Most modern "AI languages" are just prompt wrappers. Vitalis actually compiles to native x86-64 machine code, evolves its own functions at runtime, and radically outperforms Python on real ML/AI workloads. It’s cross-platform, CI/CD ready, and fully documented. If you are interested in compiler design, code evolution, or what a hand-crafted Rust compiler looks like, the repo is live and open-source. 🔗 https://lnkd.in/eXcnej-m #RustLang #CompilerDesign #MachineLearning #Python #OpenSource #DeepTech #SoftwareEngineering #Cranelift

🐍 Python Cheat Sheet Every Developer Should Bookmark. Python is powerful not because it is complex — but because it is simple, readable, and incredibly versatile. From data science and automation to AI and backend development, Python continues to dominate the programming world. Here are some core concepts every Python developer should master: 📌 Data Types – Numbers, Strings, Lists, Tuples, Dictionaries, Sets 📌 Operators – Comparison & Logical operations 📌 Functions – Writing reusable and efficient code 📌 Loops & Conditions – Automating repetitive tasks 📌 Error Handling – Using exceptions to manage failures 📌 Modules & Imports – Expanding Python’s capabilities The beauty of Python lies in how quickly you can move from idea → prototype → real solution. Whether you're starting your programming journey or sharpening your development skills, mastering these fundamentals creates a strong foundation for building powerful applications. 💡 Remember: Great developers don’t memorize everything — they understand the fundamentals and know where to look. Save this cheat sheet for quick reference. #Python #Programming #Coding #SoftwareDevelopment #DataScience #MachineLearning #Developer #TechSkills

🔹 Understanding Python Memory One important concept is how Python stores data in memory. When we write: a = 10 Most people think variable a stores the value 10. But in reality, Python variables store references to objects. Here 10 is the object, and a simply points to that object in memory. Multiple variables can reference the same object: a = 10 b = a Both a and b point to the same object in memory. 🔹 Mutable vs Immutable Objects Understanding this difference is very important in backend development. Immutable objects (cannot change after creation) ✴️ int ✴️ float ✴️ bool ✴️ str ✴️ tuple Example: a = 10 a = 20 Python creates a new object instead of modifying the old one. Mutable objects (can change after creation) ✴️ list ✴️ dictionary ✴️ set ✴️ custom classes Example: a = [1, 2] b = a b.append(3) Now a becomes: [1, 2, 3] Because both variables point to the same mutable object. This is a very common source of bugs in backend systems when shared state is not handled properly. 🔹 Generators in Python Generators are extremely useful for handling large data efficiently. A generator produces values one at a time instead of loading everything into memory. Example: def numbers(): for i in range(5): yield i for n in numbers(): print(n) Here, values are generated only when needed. 💡 Why generators are important in backend systems Generators are widely used for: ✴️ Streaming large API responses ✴️ Processing logs ✴️ Reading millions of database rows ✴️ Background workers ✴️ Data pipelines ✴️ Async streaming They help save memory and improve performance, especially when working with large datasets. #Python #BackendEngineering #SoftwareDevelopment