Python List vs Deque for Stack and Queue Operations

𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝘀 𝗼𝗳𝘁𝗲𝗻 𝗮𝗯𝗼𝘂𝘁 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗟𝗼𝗴𝗶𝗰. In backend development, a common bottleneck often hides in plain sight: Membership Testing. I frequently see code that validates inputs against a collection using a List. if item in large_list: While this works functionally, it forces Python to scan the entire list—an O(N) operation. As the dataset grows to 100,000+ records (common in e-commerce tags or user IDs), this creates significant latency. The Fix: Switching to a Set changes the game. if item in large_set: Since Sets use hash tables, the lookup becomes an O(1) operation, regardless of the dataset size. Writing production-grade Python isn't just about using the right syntax; it's about understanding the time complexity of the built-in tools we use daily. Small changes in data structure selection often yield the biggest performance gains. #Python #BackendEngineering #PerformanceOptimization #DataStructures #SoftwareDevelopment

🚀 From String Splits to Structured Data: A Quick Python Evolution Ever watched a simple Python script evolve? 😄 Started with extracting first names from a list: names = ["Charles Oladimeji", "Ken Collins"] fname = [] for i in names: fname.append(i.split()[0]) # Result: ['Charles', 'Ken'] Then flipped to last names: fname.append(i.split()[1]) # Result: ['Oladimeji', 'Collins'] Finally transformed it into clean, structured dictionaries: names = ["Charles Oladimeji", "Ken Collins", "John Smith"] fname = [] for i in names: parts = i.split() fname.append({"first": parts[0], "last": parts[1]}) # Result: [{'first': 'Charles', 'last': 'Oladimeji'}, ...] Why I love this progression: 1. Shows how small tweaks solve different problems 2. Demonstrates data structure thinking (list → list of dicts) 3. Real-world applicable for data cleaning/API responses 4. Sometimes the most satisfying code journeys start with a simple .split()! #DataEngineer #Python #Coding #DataTransformation #Programming

Exploring Python Context Managers under the hood Context managers are one of Python's most powerful features for resource management. I’ve been diving into how the context protocol works, and it’s fascinating to see how the with statement actually operates. To implement a context manager from scratch, you need two dunder methods: __enter__: Sets up the environment. If you’re opening a file or a database connection, this method prepares the object and returns it. __exit__: Handles the cleanup. It ensures that regardless of whether the code succeeds or crashes, the resources (like file handles or network sockets) are properly closed. As a fun experiment, I wrote this helper class to redirect print statements to a log file instead of the console: import sys class MockPrint: def __enter__(self): # Store the original write method to restore it later self.old_write = sys.stdout.write self.file = open('log.txt', 'a', encoding='utf-8') # Redirect stdout.write to our file logic sys.stdout.write = self.file.write return self def __exit__(self, exc_type, exc_value, traceback): # Restore the original functionality and close the file sys.stdout.write = self.old_write self.file.close() # Usage with MockPrint(): print("This goes to log.txt instead of the console!") While Python’s standard library has tools like contextlib.redirect_stdout for this exact purpose, building it manually really helped me understand how the protocol manages state and teardown. It’s a simple concept, but it's exactly what makes Python code so clean and safe. #Python #SoftwareEngineering #Backend

Redress: A Retry Library That Classifies Errors In Python Written by $DiligentTECH💀⚔️ Have you ever wondered why your Python scripts act like toddlers having a meltdown the moment a Wi-Fi signal flickers? Why does one tiny glitch in an Excel data pull cause your entire automation pipeline to commit digital stop? https://lnkd.in/dvspAHjD Let's talk about Redress. We’re moving past "turn it off and on again" and entering the world of intelligent, classified recovery. 1: The "What" and the "Why" (The Autopsy of a Crash) In the wild world of Python, most people use basic retry loops. They tell the code: "If you fail, try again three times." But that’s blind. If the server is dead, trying again 0.001 seconds later is just harassment. Redress is a sophisticated Python library designed to categorize all kinds of errors. It doesn't just "retry"; it classifies. https://lnkd.in/dyQkirMR

Redress: A Retry Library That Classifies Errors In Python Written by $DiligentTECH💀⚔️ Have you ever wondered why your Python scripts act like toddlers having a meltdown the moment a Wi-Fi signal flickers? Why does one tiny glitch in an Excel data pull cause your entire automation pipeline to commit digital stop? https://lnkd.in/dvspAHjD Let's talk about Redress. We’re moving past "turn it off and on again" and entering the world of intelligent, classified recovery. 1: The "What" and the "Why" (The Autopsy of a Crash) In the wild world of Python, most people use basic retry loops. They tell the code: "If you fail, try again three times." But that’s blind. If the server is dead, trying again 0.001 seconds later is just harassment. Redress is a sophisticated Python library designed to categorize all kinds of errors. It doesn't just "retry"; it classifies. https://lnkd.in/dyQkirMR

5 Useful DIY Python Functions for Parsing Dates and Times Image by Author   # Introduction  Parsing dates and times is one of those tasks that seems simple until you actually try to do it. Python's datetime module handles standard formats well, but real-world data is messy. User input, scraped web data, and legacy systems often throw curveballs. This article walks you through five practical functions for handling common date and time parsing tasks....

Dataclasses in Python: ClassVar, InitVar, slots, cached_property power tools with sharp edges ⚠️ I recently walked through this User dataclass and it’s a perfect example of how modern Python (2024/2025) gives us huge power but also demands clear intent. @dataclass(frozen=False, slots=True) class User:   ... This single line already tells a story: slots=True → memory-efficient, faster attribute access frozen=False → this is an entity, not a value object (it can change) 🧠 Key lessons hidden in this code 1️⃣ ClassVar is NOT instance state MAX_LOGIN_ATTEMPTS: ClassVar[int] = 5 This: Does NOT appear in __init__ Does NOT affect equality Is shared across all users 👉 Perfect for constants, limits, and rules not object identity. 2️⃣ InitVar is for construction only password_raw: InitVar[str | None] = None This value: Exists only during initialization Is passed to __post_init__ Is never stored on the object 👉 Ideal for passwords, secrets, or temporary setup data. 3️⃣ slots=True means no dynamic attributes Because of this line: _hashed_password: int | None = field(init=False, repr=False) We explicitly declare internal state. Without it, setting _hashed_password would crash at runtime. 👉 With slots, every attribute must be intentional. 4️⃣ cached_property + mutability = responsibility @cached_property def display_name(self):   ... This caches the value after first access. But since frozen=False, this is now possible: user.username = "new_name" 👉 Which means your cache can lie if you mutate fields carelessly. Caching + mutable objects = your responsibility. 5️⃣ Why frozen=False matters here If this were frozen=True: __post_init__ mutation would fail cached_property would break You’d need object.__setattr__ #Python #PythonDataclasses #SoftwareEngineering #BackendDevelopment #CleanCode #Architecture #DesignPatterns #PythonTips #SeniorEngineers

I just pushed a new PySpector release (v0.1.5-beta). This one, is a major architectural milestone, as we’ve officially transitioned from a partial pattern-matching engine to a full Graph-Based SAST engine! The core #Rust engine has been completely refactored, to support Inter-Procedural #Taint #Analysis. This allows PySpector (by SecurityCert) to track untrusted data across function boundaries, and between different files, something that was previously a blind spot. Some key technical updates, of this release, are: - Inter-Procedural Analysis: Propagation of taint through complex call chains and project-wide dependencies. - Flow-Sensitive CFG Engine: A new Control Flow Graph implementation that respects execution order to eliminate false positives. -Context-Aware Summaries: Precise mapping of how specific function parameters flow to return values. - Core Optimization: Maintained high-speed performance using Rayon parallelization despite the increased logic complexity. And how could i forget about the 6 new #contributors of PySpector, who in the last month decided to trust the project and dedicate their own free time to improve PySpector's #code and #documentation, your support is always much appreciated🫶 Doesn't matter if you're auditing standard Python libraries or specialized AI agents, v0.1.5 provides the depth needed to catch #critical #sinks (like Command Injections or Path Traversals) that local analysis misses, in your #Python codebase :) Check PySpector here: https://lnkd.in/dxsxJUDn

Python GIL explained in simple words Python has something called the Global Interpreter Lock (GIL). It means: only one thread can execute Python code at a time inside a single process. Now, why does Python do this? 🧠 The reason Python manages memory automatically (garbage collection, reference counting). If multiple threads modified memory at the same time, it could cause crashes and corrupted data. So the GIL: Protects memory Keeps Python simple and stable Makes single-thread execution very fast Yes, this safety comes with extra memory overhead, because Python needs bookkeeping to manage threads safely. ⚡ What about performance? Here’s the important part many people miss: I/O-bound tasks (API calls, database queries, file reads): 👉 Performance is excellent because threads release the GIL while waiting. CPU-bound tasks (heavy calculations, loops): 👉 Threads won’t scale — but Python gives alternatives: Multiprocessing Async programming Native extensions (C/C++) ✅ The takeaway The GIL is not a performance bug. It’s a design trade-off: Slight memory overhead In exchange for simplicity, safety, and great real-world performance Most backend systems are I/O-heavy — and for those, Python performs just fine 🚀 #Python #GIL #Concurrency #BackendEngineering #SoftwareDevelopment