SQLAlchemy Solution for Many-to-Many Database Challenge

🐍📰 Polars vs pandas: What's the Difference? Understand the key differences between Polars and pandas to choose the right Python library for faster and more efficient data analysis. #python

Day 37 – Understanding How Python Stores Data Today, I am continuing on hash tables in Python, which is what powers dictionaries (dict). In simple terms: Python uses a smart system to store and find data almost instantly, instead of searching line by line. That’s why dictionaries are fast and used everywhere — from logins to APIs to caching. Today, I didn’t just read about how Python dictionaries work — I built a simple hash table from scratch in VS Code. What I did: Created a basic HashTable class Used Python’s hash() function to decide where data should live Stored values in buckets (lists) to safely handle collisions Retrieved values using keys, just like a real Python dict Even tested collisions by inserting keys that land in the same bucket I learned: Why dictionary keys must not change What a hash is (Python’s way of knowing where to store data) Why this concept matters for building fast and scalable systems This might look small, but it’s one of the ideas behind efficient backend and full-stack development. Slow progress is still progress. Understanding beats rushing. Which of the terms or concepts used here sounds too scary and unusual for you? Let me know, let's learn together 😊 #Day37 #LearningInPublic #Python #DataStructures #BackendBasics #Consistency

Day 7 of Python 🐍 | Understanding Lists & Memory Today I dove deep into one of Python's most powerful data structures - Lists! Here's what I explored today :✅ 📌 Indexing - Accessing elements is easier than I thought. Python's zero-based indexing means the first element is at index [0], and negative indexing lets you work backwards from the end . 📌 List Operations - Lists are incredibly flexible. Unlike some languages, Python lists can hold different data types in one container, making them super versatile for real-world applications. 📌 Memory Allocation - This was eye-opening! Python allocates memory dynamically for lists. When a list grows, Python doesn't just add one slot - it over-allocates to optimize performance. Understanding this helps write more efficient code. 📌 The len() Function - Simple but essential. len() returns the number of elements, and it's O(1) time complexity because Python stores the list size internally. 🎯Key Takeaway: Lists aren't just arrays - they're dynamic, flexible, and optimized for Python's philosophy of making code readable and efficient. What's your favorite Python data structure? Drop it in the comments! 👇 #Python #100DaysOfCode #DataStructures #PythonProgramming #LearnInPublic #CodingJourney #TechLearning #DeveloperCommunity

Day 36 – Hash Tables in Python (What’s really behind dict) 🐍 Today, we’re starting with Hash Tables — the idea behind one of Python’s most-used tools: the dict. If you’ve ever written: user = {"name": "John", "age": 25} then you’ve already used a hash table (even if you didn’t realize it). So why start here? Because hash tables help us store and retrieve data fast. Instead of looping through a list item by item, we can jump straight to what we need. That’s why they show up everywhere: user profiles settings and configurations caching quick lookups in real applications Why Python? Python makes this concept very approachable. Dictionaries look simple on the surface, but there’s a lot of smart engineering underneath. Once you understand how they work, you stop writing “just working” code and start writing efficient, intentional code. And yes — this matters for full-stack development too: Backends use hash tables to manage users, sessions, and data Frontends rely on key-value structures for state and UI logic Performance often comes down to how well you organize and access data We’re starting here because this is foundational. When this clicks, many other data structures and algorithms start to make sense. More coming from tomorrow — challenges, breakdowns, and practical thinking. 🚀 #Day36 #Python #DataStructures #HashTables #SoftwareEngineering #FullStackDevelopment #LearningInPublic

Honest thought: Getting yesterday’s date feels more straightforward in SQL than in Python. SQL: DATE_SUB(CURRENT_DATE, INTERVAL 1 DAY) Python (Pandas): import pandas as pd yesterday = pd.Timestamp("today").normalize() - pd.Timedelta(days=1) Both are valid, but the SQL version almost reads like plain English. Python gives you more control, but you need a bit more code to get there. Curious how others see it, is this just preference, or does SQL genuinely win on readability? #SQL #Python #Pandas #DataAnalytics

When I review Python code, I often look past syntax and focus on decisions. Take this line: if user_id in users: grant_access() It works. But what matters is what users actually is. A list → Python checks items one by one A set or dict → Python jumps straight to the answer Same line of code. Very different performance. With large data, these choices decide whether a system feels instant or slow. This is the kind of detail that separates: • someone who writes Python • from someone who understands how Python behaves I recently wrote a complete breakdown of how Python searches data internally—linear search, binary search, and hash lookup—using real examples and benchmarks. It’s not about algorithms. It’s about choosing the right data structure upfront. Full breakdown 👇 https://lnkd.in/gT2uaZER #Python #SoftwareEngineering #BackendEngineering #Performance #CodeQuality

21th's Python Class – Built-in Functions & Utilities In a recent Python session, we explored several built-in functions that help inspect, combine, and manipulate data efficiently. 🔹 dir() & __builtins__ Used dir() to inspect available names in the current scope Learned about __builtins__ and how Python provides default functions automatically 🔹 dict.fromkeys() Created dictionaries using keys from strings Assigned default values to all keys Updated individual key values after dictionary creation 🔹 eval() & Input Handling Compared how int, float, and input() handle user input Understood how input types affect program output and behavior 🔹 zip() Combined multiple collections into: List Tuple Set Dictionary Learned how zip() pairs elements index-wise 🔹 enumerate() Added counters to collections Generated indexed data using different starting values Converted enumerated output into list, tuple, and dictionary 🔹 ASCII Operations (chr() & ord()) Converted ASCII values to characters using chr() Converted characters to ASCII values using ord() Generated alphabet lists using ASCII ranges and list comprehension This class improved my understanding of Python’s built-in power tools, making code more readable, efficient, and expressive 🚀 #Python #BuiltInFunctions #zip #enumerate #ASCII #PythonLearning #CodingPractice Pooja Chinthakayala

Doubling down on database ease! Did you know Dyne’s CRUDMixin brings Active Record style utilities to your SQLAlchemy models? ⚡️ It provides explicit, async helpers for common tasks like get, create, find, patch, and destroy, so you can stop writing repetitive query logic for simple operations. Clean code, powerful models: https://lnkd.in/eqEc4Tuw #Python #SQLAlchemy #Backend #DyneFramework #CleanCode