Master Python Dictionary Methods for Cleaner Code

Your Python API might be slow for a very simple reason: N + 1 queries. This happens a lot when using SQLAlchemy/SQLModel with FastAPIor Flask applications. You run one query to fetch a list of records... and then you accidentally trigger one extra query per item when accessing relationships (Lazy loading). Example: 1 query to load 50 students +50 queries when you do student.teacher.name in a loop But isn’t it just one more query?! Look at this: N = number of students returned R = 1 relationship (teacher) students returned (N) | total queries 10 | 1 + 10 = 11 50 | 1 + 50 = 51 200 | 1 + 200 = 201 1000 | 1 + 1000 = 1001 but now R = 2 without selectinload students returned (N) | total queries 10 | 1 + 20 = 21 50 | 1 + 100 = 101 200 | 1 + 400 = 401 1000 | 1 + 2000 = 2001 with selectinload 1(student) + 1(teacher) + 1(lessons) = 3 queries Now look at what happens in terms of time. If each extra query costs only ~5ms (network + DB time + parsing): Example: N=200 students, R=3 relationships Without selectinload → 601 queries extra queries = 600 600 × 5ms = 3,000ms (3 seconds) of overhead with selectinload: overhead ≈ 20ms So the trade-off is absolutely worth it.

🧠 Python Concept That Saves You from Bugs: dict.get() Most beginners write this 👇 if "age" in data: age = data["age"] else: age = 0 Python says… simplify 😌 ✅ Pythonic Way age = data.get("age", 0) 🧒 Simple Explanation Imagine asking a friend: 👉 “Do you have a pencil?” ✏️ If yes → give pencil If no → give eraser (default) That’s dict.get(). 💡 Why This Is Important ✔ Avoids KeyError ✔ Cleaner code ✔ Perfect for APIs & JSON ✔ Frequently asked in interviews ⚡ More Examples user = {"name": "Asha"} print(user.get("name")) # Asha print(user.get("age")) # None print(user.get("age", 18)) # 18 💻 Clean code isn’t about writing less. 💻 It’s about writing safer code 🐍✨ 💻 dict.get() is one of those small habits that matter. #Python #PythonTips #CleanCode #LearnPython #DeveloperLife #Programming

🧠 Python Feature That Makes Functions Smarter: functools.singledispatch 💫 One function. 💫 Multiple behaviors. 💫 No ugly if isinstance() chains 😌 ❌ Old Way def process(data): if isinstance(data, int): return data * 2 elif isinstance(data, str): return data.upper() ✅ Pythonic Way from functools import singledispatch @singledispatch def process(data): raise NotImplementedError @process.register def _(data: int): return data * 2 @process.register def _(data: str): return data.upper() 🧒 Simple Explanation Imagine one teacher 👩🏫 💻 If a number comes → do math 💻 If a word comes → read loudly 💻 Same teacher. 💻 Different rules.. 💡 Why This Is Powerful ✔ Cleaner logic ✔ Easy to extend ✔ Great for APIs & libraries ✔ Real-world Python feature ⚠️ Important Note Dispatch happens on the first argument only. 🐍 Python lets your code decide what to do based on the data. 🐍 singledispatch keeps logic clean and extensible #Python #PythonTips #PythonTricks #AdvancedPython #CleanCode #LearnPython #Programming #DeveloperLife #DailyCoding #100DaysOfCode

🧠 SQL vs Python It’s Not a Competition A lot of people treat SQL and Python like they’re competing with each other. But in real work, they usually work together. Most people start by asking, “Which one should I learn?” But over time, the question changes to, “Which one makes sense for this problem?” SQL is great when you need to work directly with data inside databases pulling data, filtering it, joining tables, and getting exactly what you need 📊 Python usually comes in when you need to go further automation, data processing, complex transformations, or building something on top of that data And honestly, most real-world workflows use both. The real confidence doesn’t come from knowing tools. It comes from understanding where each tool fits. That’s when things start feeling less confusing and a lot more practical. #SQL #Python #DataEngineering

Python looks simple, but getting job-ready needs depth. Here’s a short checklist of 140 important Python topics you should cover 👇 Basics, syntax, variables, data types, operators Conditions, loops, control flow Functions, arguments, recursion, lambda Lists, tuples, sets, dictionaries, comprehensions Strings, formatting, regex, Unicode Modules, packages, virtual environments File handling, CSV, JSON, logging Exception handling, custom errors OOP concepts, inheritance, polymorphism Iterators, generators, decorators Multithreading, multiprocessing, GIL Standard library tools and utilities Databases, SQL, ORM, APIs Testing, profiling, PEP 8, deployment This single list can guide your Python preparation for interviews, projects, and real-world work. To get complete Machine Learning guide for free: 1. Connect with me 2. Like this post 3. Comment “ML" below, and I’ll send it to you! Pdf credit goes to respective owner. Follow Pratham Uday Chandratre for more!

Stop letting your code trip over its own feet. If you are working with Python, you are likely dealing with expensive function calls or repetitive data processing. The secret to shaving off hours of computation time often lies in caching. Here is how to supercharge your productivity using Python’s built-in `@lru_cache` and other speed-boosting methods: 🚀 **1. The Magic of `functools.lru_cache`** This is the low-hanging fruit of optimization. If you have a pure function (one that always returns the same output for the same input), simply adding the `@lru_cache` decorator stores the results in memory. * **How it works:** The first time you run the function, it does the work. Every subsequent time? It grabs the result from a high-speed dictionary. * **The Fix:** Stop recalculating Fibonacci sequences or database fetches inside loops. Decorate it, and watch the execution time drop to near zero. 🧠 **2. `@cached_property` for Heavy Objects** If you are working with classes and have attributes that take time to compute (like parsing a large file or a complex calculation), use `@cached_property` from `functools`. It ensures the calculation runs only once per instance, saving resources without manual attribute checks. ⚡ **3. Generator Expressions** Stop building massive lists in memory just to iterate over them once. Swapping list comprehensions `[]` for generator expressions `()` can drastically reduce memory footprint and increase speed when handling large datasets. 🛠️ **4. Profiling Before Optimizing** You can't speed up what you don't understand. Use `cProfile` or the `timeit` module to find exactly where your bottlenecks are before you start refactoring. 💡 **The Takeaway:** Productivity isn't just about typing faster; it's about writing smarter code. Utilize the tools Python gives you (like `lru_cache`) to handle the heavy lifting so you can focus on building logic, not waiting for scripts to finish. 👇 **What is your favorite Python trick for performance? Let me know in the comments!** #Python #CodingTips #Programming #Developer #TechTips

How does multi threading work in python if GIL locks everything? TL;DR: The trick is when the GIL actually holds the lock and when it lets go. Our code usually does two kind of tasks - 1. CPU-bound (thinking) - Heavy stuff like parsing large JSON, resizing images. This needs the GIL. 2. I/O-bound (waiting) - Waiting for a database, an API call, S3, etc. This does not need the GIL. How it works in Python? Lets take an example of a thread making an API call using requests.get(). -> Python sends the request -> Now it’s just waiting to get a response. At this point, Python knows there’s nothing to “compute”, so it releases the GIL automatically. While Thread A is waiting on the network, Thread B can takeover the GIL and handle another user request. When the response comes, thread A reacquires the GIL, parses the response and completes the API call. This is why Django, Flask, and FastAPI servers can handle hundreds of requests at once. Fun fact - In a typical web request: ~90% of the time is spent waiting (DB, APIs, storage) ~10% is actual computation The GIL makes Python slower for heavy computation. But in most backend systems, database query is usually much slower than any GIL switching delay. Have you ever actually hit a performance issue where the GIL was the real problem? I am trying to learn Python Internals in detail and will share my learnings. Do follow along and tell your experiences in comments. #Python #PythonInternals #SoftwareEngineering #BackendDevelopment

📘 Python Dictionary: One of the Most Powerful Data Structures 🐍 If you work with Python, you work with dictionaries — directly or indirectly. A dictionary stores data in key–value pairs, making it fast, flexible, and perfect for real-world applications. 🔑 Why dictionaries are so powerful: - Instant access to data using keys - Easy to model real-world objects (user, order, response, config) - Widely used in API responses (JSON → dict) - Supports nested and complex structures 🧠 Common real-world uses: - API testing → validating response fields - Automation frameworks → config & test data handling - Storing dynamic values during runtime - Mapping roles, permissions, and states 💡 Example: user = { "name": "Nishant", "role": "QA", "skills": ["Python", "API Testing"] } 👉 If you understand dictionaries well, half of Python testing becomes easier. Master dictionaries, and Python will start making sense everywhere. #Python #Dictionary #CorePython #QATesting #AutomationTesting #PythonQA #LearningPython #SDET #SoftwareTesting

💡 SQL is better than Python No, Python is better than SQL The debate is useless. It's like comparing a husband to a wife, LOL. They are different tools designed for different purposes, and both are exceptionally good at what they do. SQL washes the dishes, and Python brings in the money. 😀 They are complementary. Look at the code below: ▶️ SQL does the data work. ▶️ Python does the system work. 💨 Now go learn Python and some SQL 📙 SQL Essentials for Data Analysis is now available 🔗 https://lnkd.in/erNbWQJi

💥 Mastering Data Structures in Python! Understanding data structures is essential for any programmer. This visual guide simplifies the basics, making it easy to understand how different data structures work and when to use them. Here’s a quick breakdown: 🔹 Types of Data Structures Lists, Dictionaries, Sets, Tuples Each has unique characteristics and use cases 🔹 Lists Mutable: You can modify them! Indexed: Access elements by index Methods: Use handy functions like append() and sort() to manage list items 🔹 Dictionaries Store data in key-value pairs Ideal for quick lookups and organizing data 🔹 Sets Hold unique elements only, no duplicates! Great for membership testing and removing duplicates 🔹 Tuples Immutable: Once created, they can’t be changed Use them for fixed data that doesn’t need modification 🔹 Loops & Indexing Iterate through elements using loops like "for elem in mylist" Indexing starts from "0 to length -1", allowing specific element access These fundamental structures are the building blocks of efficient Python programming. Save this post for a quick reminder, and start applying these concepts to write cleaner, faster code! [Explore More In The Post] Don’t Forget to save this post for later and follow Future Tech Skills for more such information. #DataAnalytics #BusinessIntelligence #DataDriven #AnalyticsStrategy #DecisionMaking #MachineLearning #BigData #DataScie #Python #DataStructures #Programming #PythonTips #Coding #TechLearning