Optimize Python Code with cProfile and snakeviz

The Hidden Cost of Python Dictionaries (And 3 Safer Alternatives) This article compares and contrasts: dictionaries, named tuples, dataclasses and Pydantic. #python #pythonhowtos #programming #pydantic #pythondataclasses #pythondicts https://lnkd.in/e2NqHfCB

If you’ve ever found yourself stuck in loop-heavy Python code, this one’s for you 🙂 While learning Python, I realized that many problems I was solving with long for loops could be written in a much cleaner and more expressive way using higher-order functions. I’ve shared this learning in a Medium article 📖 Beyond Loops: Leveraging Higher-Order Functions in Python ✔️ What I cover: > map(), filter(), lambda, reduce() > When to use them (and why it matters) > Easy-to-follow examples Writing this helped me better understand how thinking functionally can simplify everyday Python problems. I am really grateful to Harsha Mg for his support and feedback. 👉 Check it out here: https://lnkd.in/gefA4VEm 💬 I’d love to know — do you usually prefer traditional for loops, or higher-order functions in Python? #Python #HigherOrderFunctions #map() #reduce() #filter() #lambda #MediumBlog #InnomaticsResearchLabs

Why Is My Code So Slow? A Guide to Py-Spy Python Profiling frustrating issues to debug in data science code aren’t syntax errors or logical mistakes. Rather, they come from code that does exactly what it is supposed to do, but takes its sweet time doing it. Functional but inefficient code can be a massive bottleneck in a data science workflow. In this article, I will provide a brief introduction and walk-through of…...

Check out my latest article published on Towards Data Science. It gives a walkthrough on how the Py-spy profiler can be used to diagnose inefficiencies in python code, helping make your code run much faster. https://lnkd.in/gEaZ7smH

Python’s 🐍 standard library has a hidden gem: difflib With it, you can catch near-duplicate user input like: • Almost identical form submissions • Repeated comments with tiny typos • Duplicate product listings • "Did You Mean…?" Suggestions ✅ No ML, no extra libraries; just clean, fast, production-safe Python. 💡 Save yourself from messy string comparisons and spammy inputs. I wrote a short, practical post with real API examples. 👉 https://lnkd.in/ge9-k3Z5 #Python #Backend #APIs #Django #CleanCode

In the Metasploit Wrap-Up from last week, a new Python Site-Specific Hook Persistence module was released. [1] I wrote a detailed blog about this persistence, which I think is pretty cool. [2] If you have never heard of this technique, you might want to read up on it. [1] https://lnkd.in/ei_C5TgQ [2] https://lnkd.in/eYRmWrx8

🚀 Python Tip: Using default_factory in Dataclasses While working on a data quality framework in Python, I encountered an interesting scenario with timestamps. I wanted every new instance of my dataclass to have a fresh, current timestamp. At first, I tried this: from dataclasses import dataclass, field from datetime import datetime, timezone @dataclass class QualityCheckResult:   timestamp: datetime = datetime.now(timezone.utc) # ❌ The problem? Every instance got the same timestamp — Python evaluated it once when the class was defined. Not what I wanted! The solution: default_factory @dataclass class QualityCheckResult:   timestamp: datetime = field(default_factory=lambda: datetime.now(timezone.utc)) # ✅ Why this works: default_factory expects a callable (like a lambda or function) Python stores the callable, but doesn’t run it immediately Every time you create a new object, Python calls the lambda, producing a fresh timestamp 💡 Think of it as keeping a “recipe” instead of the finished product. Each object gets its own freshly baked “timestamp” instead of reusing the same one. This small tweak solves a subtle bug and ensures my data quality logs always reflect the exact creation time. Python’s dataclasses + default_factory = cleaner, bug-free defaults! ⚡

Most Python code works. Very little Python code scales. The difference? 👉 Object-Oriented Programming (OOPS). As part of rebuilding my Python foundations for Data, ML, and AI, I’m now focusing on OOPS — the layer that turns scripts into maintainable systems. Below are short, practical notes on OOPS — explained the way I wish I learned it 👇 (No theory overload, only what actually matters) 🧠 Python OOPS — Short Notes (Practical First) 🔹 1. Class & Object A class is a blueprint. An object is a real instance. class User: def __init__(self, name): self.name = name u = User("Anurag") Used to model real-world entities (User, File, Model, Pipeline) 🔹 2. __init__ (Constructor) Runs automatically when an object is created. Used to initialize data. def __init__(self, x, y): self.x = x self.y = y 🔹 3. Encapsulation Keep data + logic together. Control access using methods. class Account: def get_balance(self): return self.__balance Improves safety & maintainability 🔹 4. Inheritance Reuse existing code instead of rewriting. class Admin(User): pass Used heavily in frameworks & libraries 🔹 5. Polymorphism Same method name, different behavior. obj.process() Makes systems flexible and extensible 🔹 6. Abstraction Expose what a class does, hide how it does it. from abc import ABC, abstractmethod Critical for large codebases & APIs OOPS isn’t about syntax. It’s about thinking in systems, not scripts. #Python #OOPS #DataEngineering #LearningInPublic #SoftwareEngineering #AIJourney

PEP-800: typing.disjoint_base PEP: https://lnkd.in/eHzws5n6 Discussion: https://lnkd.in/ewrrUSKY Implementation in typing_extensions: https://lnkd.in/e-pZED3M In python, you can inherit from several classes (and generally with the __mro_entries__ method, yes). Which is sometimes quite convenient if used without fanaticism. But the problem is that not all sets of classes are suitable for multiple inheritance. For example: >>> class What(str, int): ... Traceback (most recent call last): TypeError: multiple bases have instance lay-out conflict Why this happens, you can read here (https://lnkd.in/eNd2Mxhc). But, it is important for us to know that CPython has the concept of "solid base", which is considered for all created classes here. (https://lnkd.in/e3vZ_s_9). In short, CPython should be able to build a proper memory layout for all descendant classes. For example, all int descendants should be stored in memory like this: Otherwise, the basic int logic will stop working. But strs are arranged differently. Details (https://lnkd.in/eMUeHaGT ) from the ashes about "solid bases". Previously, this code was used in some typecheckers. After all, they thought that a type subclass of int and str could exist. And now they will know that this is impossible at the definition level and throw out the right mistake. Excellent PEP, the type system has become a little better. Discussion: Did you know about solid and disjoint bases in python? Have you shot yourself in the foot like that?