Python vs Java for AI and LLM Development

Day 15/365: Merging Two Dictionaries with Summed Values in Python 🧮🔗 Today I worked on a very common real-world task: merging two dictionaries where overlapping keys should have their values added together. 🧠 What this code does: I start with two dictionaries: d1 = {1: 10, 2: 20, 3: 30} d2 = {3: 40, 5: 50, 6: 60} Each key can represent something like: a product ID with its total sales, a student ID with total marks, a user ID with total points. The goal is to combine d2 into d1: If a key from d2 already exists in d1, I add the values. If the key doesn’t exist in d1, I insert it. Step by step: I loop over each key i in d2: for i in d2: For each key: If i is already a key in d1: I update d1[i] by adding d2[i] to it. Otherwise: I create a new entry in d1 with that key and its value from d2. After the loop finishes, d1 contains the merged result. For the given dictionaries: Key 3 exists in both, so its values are added: 30 + 40 = 70. Keys 5 and 6 only exist in d2, so they are added as new keys. Final output: {1: 10, 2: 20, 3: 70, 5: 50, 6: 60} 💡 What I learned: How to merge two dictionaries manually using a loop and conditions. How to update values in a dictionary when keys overlap. How this pattern appears in real data tasks like: combining monthly reports, merging user activity stats, aggregating counts from multiple sources. Next, I’d like to explore: Handling much larger dictionaries efficiently. Using dictionary methods like update() or Counter from collections to compare approaches. Trying the same logic with string keys (like product names) instead of numbers. Day 15 done ✅ 350 more to go. Got any other dictionary + loop problems (like counting frequencies from multiple sources or merging configs)? Drop them in the comments—I’d love to try them next. #100DaysOfCode #365DaysOfCode #Python #Dictionaries #DataStructures #LogicBuilding #CodingJourney #LearnInPublic #AspiringDeveloper

🚀 Day 5/30 of My LeetCode Journey (Python + SQL) Staying consistent and pushing forward every single day! 💻🔥 🔹 **SQL Problem of the Day** 👉 *Customers Who Never Order* Given two tables `Customers` and `Orders`, write a query to find all customers who never placed any order. 💡 *Key Concept:* LEFT JOIN + filtering NULL values to identify missing relationships. 🔹 **Python Problem of the Day** 👉 *Search Insert Position* Given a sorted array of distinct integers and a target value, return the index if found. If not, return the index where it would be inserted. 💡 *Key Concept:* Binary Search (O(log n)) for efficient searching. Understanding patterns like joins and binary search is making problem-solving faster and cleaner 📈 Day 5 done ✅ Let’s keep going! #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #BinarySearch #ProblemSolving

Technical post: I've been posting some graphs on here, talking about functions and "equivalence". This was all started by working on porting an MLOPs framework from python 3.10 to 3.12, and all the "dependency hell" one has to go through. Then naturally the question arose "What are the boundaries of one project to another, in terms of functions being called etc.,?" This led me down the rabbit hole (not too deep) of what happens when I do something like python -m <module> <somescript>. Specifically, what is a "no op" module, and what kind of ops can we inject, thanks to python being an interpreted language. A few years ago I'd worked on something along similar lines called TracePath, which provided a decorator to do something similar (e.g. who called who, how long it took, etc.). So I merged these two ideas (avoid decorating every function, have an "inspector" module) and ran this on a simple pandas dataframe creation. The resulting function invocation graph is the image attached to this post. When I ran it across the whole workflow (create, load, transform data etc.,), the graph had ~9000 connections. The nice thing is I can specify which modules (e.g. only pandas, or pandas and numpy) should be added to the graph etc. What do you think is the next logical thing to do with something like this? What kind of graphs would well structured software produce? How about badly written software? #graphs #swe #dependencyhell #python

A Python function that checks stationarity across multiple financial symbols—a critical step anyone serious about algorithmic trading should master. Why does it matter? Many profitable strategies (mean reversion, pairs trading, cointegration) assume stationary data. If your underlying series is non-stationary (trending), your strategy will fail spectacularly. The approach: I'm using dual statistical tests—ADF and KPSS—because relying on just one can be misleading. Both must agree for a true verdict. What it does: ✓ Downloads price data from any symbol ✓ Tests both raw prices and log returns ✓ Returns p-values + actionable verdict ✓ Works with local CSV files too This is table-stakes for quant work. Whether you're building mean-reversion bots or testing factor strategies, validating stationarity upfront saves months of debugging DOA strategies. github: Chinedum14/Quant-Dev Happy building! 📈 #QuantTrading #AlgorithmicTrading #DataScience #Python #TimeSeries #SoftwareEngineering #Developer

🐍 **Python support is coming to AI MR Reviewer — this Sunday.** After rolling out Java and JavaScript, the next major milestone is here. This Sunday, Python joins the lineup — bringing the same fast, inline, severity-based PR reviews your team already relies on, now for Python codebases. No setup. No delays. Just actionable feedback the moment you open a PR. **Here's what the Python analyzer covers in v1:** 🔴 **HIGH — Security & Critical Issues** → SQL injection via string formatting or concatenation → Use of `eval()` / `exec()` → Hardcoded secrets — API keys, tokens, passwords → `subprocess` calls with `shell=True` → Insecure deserialization (`pickle` / `yaml.load` without safe loader) → Bare `except:` blocks — silent failure risks → Debug mode left enabled in production frameworks 🟡 **MID — Code Quality & Maintainability** → `print()` statements in production code → Broad exception handling without specificity → Mutable default arguments in functions → Long functions or too many parameters → Missing context managers (`open()` without `with`) → Deprecated libraries or patterns (basic detection) 🔵 **LOW — Clean Code & Hygiene** → `TODO` / `FIXME` / `HACK` comments → Magic numbers without named constants → Non-descriptive variable or function names → Unused imports (basic detection) → Inconsistent naming — PEP8 signal detection **Every rule is built around one principle: low noise, high signal.** Your team only sees what truly matters. ⚡ **Same experience. Extended to Python.** ✅ Inline comments directly on PR diffs ✅ Clear severity levels — HIGH / MID / LOW ✅ Instant feedback within seconds of opening a PR 🚀 Going live this Sunday, InshaAllah. If your team works with Python daily — this is built for you. 👉 Install now: https://lnkd.in/dNaHtm2J 🌐 Learn more: primeoctopus.com #Python #CodeReview #AI #DeveloperTools #StaticAnalysis #DevEx #Automation #GitHub #OpenSource

Your 2020 Python skills are becoming a 2026 bottleneck. I’ve seen brilliant analysts struggle with memory errors and 10-minute wait times for simple joins. The problem isn't their logic; it’s their toolkit. The "Modern Python Stack" for Analysts has fundamentally shifted. If you are still relying 100% on Pandas and Matplotlib, you are leaving performance and interactivity on the table. I’ve fact-checked the production environments of top data teams this year. Here is the Save-Worthy 2026 Python for Analysts Cheat Sheet. 🚀 Polars: The multi-threaded engine that handles 10GB+ datasets on a laptop. 🦆 DuckDB: Run high-speed SQL directly on your local Parquet files. 📊 Plotly Express: Interactive charts that stakeholders can actually explore. ✅ Pydantic V2: Automated data cleaning that's 20x faster than traditional methods. 👇 The Big Debate: Is it finally time to retire import pandas as pd for good, or is it still the king of small-scale EDA? Let’s settle it in the comments. #Python #DataAnalytics #Polars #DuckDB #DataScience #MicrosoftFabric #2026Trends #Coding

☕ Why Choose JSONata? 📺 JSONata is a lightweight, open-source query and transformation language designed specifically to navigate, manipulate, and restructure JSON data. It uses a compact, declarative syntax to extract nested values, filter data, and restructure payloads into new formats, often acting as a powerful alternative to JavaScript or Python for data processing. 🗒️ Summary, JSONata is the best powerful query and transform the JSON structure. #jsonata #KPI #dashboard #design

🚀 Day 8/30 of My LeetCode Journey (Python + SQL) Staying consistent and leveling up every day! 💻🔥 🔹 **SQL Problem of the Day** 👉 *Find Customer Referee* Given a `Customer` table, write a query to find the names of customers who are either: • Not referred by customer with id = 2 • OR not referred by anyone 💡 *Key Concept:* Filtering with conditions (`!= 2` OR `IS NULL`). 🔹 **Python Problem of the Day** 👉 *Merge Sorted Array* Given two sorted arrays, merge them into a single sorted array in-place without returning a new array. 💡 *Key Concept:* Two-pointer approach from the end for efficient in-place merging. Every problem is helping me think more efficiently and write better code ⚡ Day 8 done ✅ #LeetCode #30DaysChallenge #Python #SQL #CodingJourney #Consistency #ProblemSolving #Learning

Most “slow APIs” in Python aren’t CPU-bound. They’re blocking the event loop without realizing it. Classic FastAPI mistake: @app.get("/users") async def get_users(): users = db.fetch_all() # blocking call return users Looks async. Isn’t. Result: * event loop stalls * requests queue up * latency spikes under load Fix → respect async boundaries @app.get("/users") async def get_users(): users = await db.fetch_all() return users Or offload properly: from asyncio import to_thread users = await to_thread(sync_db_call) Advanced production pattern: * separate sync + async layers clearly * use connection pools (asyncpg, aiomysql) * never mix blocking ORM calls inside async routes Hidden issue: One blocking call can freeze thousands of concurrent requests. Build-in-public lesson: Async isn’t about syntax. It’s about protecting the event loop at all costs. AI can convert code to async— but only experience catches where it’s still secretly blocking. #Python #BackendEngineering #FastAPI #Scalability #SystemDesign