Remove Element from Array in Place with Two-Pointer Technique

Why manually check Fire Ratings when the model can do it for you?  I wrote a Python script to automate this check.It finds every door in the project.It identifies the "Host" wall for each door.It pulls the Fire Rating from the Wall Type and pushes it directly to the Door's parameters.In the video, you can see it syncing the data instantly. If the wall changes, I just run the script again. Zero manual entry. #RevitAPI #Python#Day 6-100Days Challenge

# The Python Range Object # Arguments: <start number>, <end number - don't include>, <steps> # <steps> defaults to incrementing by 1 (no skipping numbers) # <steps> can be a negative number to decrement values. # When using negative steps, the start number should be larger #  than the end number. range(3, 23, 2) # result: 3, 5, 7, 9, 11, 13, 15, 17, 19, 21 # This is creating a number factory that starts at 3, #  and returns incremental numbers according to the #  <steps> value. # In this case, after returning the initial start value of 3, #  it will return 5, then 7, then 9, and so on, until 21. # It excludes the end number from the count. # Frequently, another object is used with range, to process #  the entire returned number set. # For example, the "List" object can be used to convert the range #  of numbers into an equivalent list of numbers. list(range(3, 15, 2)) # result: [3, 5, 7, 9, 11, 13]

When you start writing a system, the easiest thing to say is, “I'll tidy up the code later” In practice, that “later” almost never comes. The result is a lot of mess and bugs that could have been avoided. In the system I'm currently building in Python, I'm using two tools that help me solve this from the outset: Ruff - a tool that runs on the code and automatically formats it (consistent line length, spacing, brackets, etc) and catches problems before they become bugs. Two useful rules: 1. builtin variable shadowing* Checks for variable (and function) assignments that use the same names as builtins 2. Mutable argument default** Checks for uses of mutable objects as function argument defaults. There are similar tools for every programming language called linters.*** Pyright - static type checker: Errors that would occur during runtime are caught already during writing. The experience of seeing a type error before I run the code saves a lot of debugging time. The autocompletion and insights it provides make it easier to work with complex code. These two tools don't make the code perfect, but they allow me to catch problems earlier and write more consistently. This is much more worthwhile than trying to “fix it later.” relevant links: *https://lnkd.in/e5zNv6UZ **https://lnkd.in/ecg-fwT9 ***https://lnkd.in/eV35fKWU

Solved LeetCode 944 – Delete Columns to Make Sorted 🧠📊 This problem looks simple at first, but it’s a great reminder that clear thinking beats complex logic. 🔍 Problem in short: - You’re given multiple strings of equal length. Imagine them stacked one below another, forming a grid. Your task is to delete columns that are NOT lexicographically sorted from top to bottom and return how many such columns exist. 🧠 How I approached it: 1️⃣ First, I visualized the strings as a table where: - Each row is a string - Each column contains characters from all strings at that position 2️⃣ Then, I checked each column independently: - Start from the top row - Compare every character with the one directly below it 3️⃣ If at any point: - The upper character is greater than the one below → That column is not sorted 4️⃣ The moment a column fails this condition: - I mark it for deletion - Move on to the next column (no need to check further for that one) 5️⃣ Finally, I count how many columns were marked for deletion. ✨ Key takeaway - You don’t always need advanced data structures. - Sometimes, a simple comparison + clean iteration is all it takes. #LeetCode #DSA #ProblemSolving #Python #LearningInPublic #Consistency

🐍 𝐃𝐚𝐲 𝟖 (𝐄𝐯𝐞𝐧𝐢𝐧𝐠) 𝐨𝐟 𝐌𝐲 𝟏𝟓-𝐃𝐚𝐲 𝐏𝐲𝐭𝐡𝐨𝐧 𝐂𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞 — 𝐃𝐚𝐭𝐞 & 𝐓𝐢𝐦𝐞 𝐇𝐚𝐧𝐝𝐥𝐢𝐧𝐠 In today’s evening session, I focused on date and time handling — a critical skill for logging, analytics, scheduling, and reporting. Python’s datetime module makes working with dates and times straightforward. 🔹 𝐖𝐡𝐚𝐭 𝐈 𝐂𝐨𝐯𝐞𝐫𝐞𝐝 𝐓𝐨𝐝𝐚𝐲 ✅ Getting Current Date & Time from datetime import datetime now = datetime.now() print(now) ✅ Working with Dates from datetime import date today = date.today() print(today) ✅ Formatting Dates formatted = now.strftime("%Y-%m-%d %H:%M:%S") print(formatted) ✅ Date Arithmetic with timedelta from datetime import timedelta future_date = today + timedelta(days=7) print(future_date) ✅ Difference Between Dates start_date = date(2024, 1, 1) end_date = date.today() difference = end_date - start_date print(difference.days) 🎯 𝐊𝐞𝐲 𝐓𝐚𝐤𝐞𝐚𝐰𝐚𝐲 Date and time handling is essential for real-world applications like reports, logs, and automation. Mastering datetime and timedelta makes time-based logic much easier. ✅ Day 8 Completed 𝐓𝐨𝐦𝐨𝐫𝐫𝐨𝐰: 𝐃𝐚𝐲 𝟗 (𝐌𝐨𝐫𝐧𝐢𝐧𝐠) — 𝐎𝐎𝐏 𝐁𝐚𝐬𝐢𝐜𝐬 (𝐂𝐥𝐚𝐬𝐬𝐞𝐬 & 𝐎𝐛𝐣𝐞𝐜𝐭𝐬) Let’s keep moving #Python #DateTime #15DaysOfPython #LearningInPublic #Programming

🚀 Day 53 of #100DaysOfCode — Making Array Sum Divisible by K Hey everyone! 👋 Today’s challenge focused on a smart math-based optimization problem where the goal was to make the sum of an array divisible by a given number k using the minimum number of operations. 👨💻 What I practiced today: ✅ Understanding modulo (%) operations deeply ✅ Translating a problem into a simple mathematical observation ✅ Optimizing brute-force thinking into an O(n) solution ✅ Writing clean and minimal Python code 📌 Today’s Task: ✔ Given an integer array nums and an integer k ✔ In one operation, decrement any element by 1 ✔ Find the minimum operations required so that sum(nums) is divisible by k 🧠 Key Insight: If the sum of the array is S, then the minimum number of operations required is simply: S % k Because each operation reduces the sum by 1. 💡 Example: Input: nums = [3, 9, 7], k = 5 Sum = 19 → 19 % 5 = 4 Output: 4 ✨ Key Takeaway: Sometimes the best solution isn’t complex logic or loops — it’s recognizing a simple mathematical pattern. Mastering such observations can drastically reduce code complexity and runtime. #100DaysOfCode #Day53 #Python #LeetCode #ProblemSolving #MathInProgramming #CodingJourney #DSA #CleanCode

I’ve been working on a small Python library, stat-guard, and I just published v0.2.0 on PyPI. pip install stat-guard The idea is simple: Most statistical mistakes don’t come from wrong formulas. They come from bad data and violated assumptions that go unnoticed until after results are shipped. Things like: duplicated users counted multiple times users leaking across control and treatment sample sizes too small to justify comparisons extreme imbalance between groups metrics with zero variance stat-guard is a pre-analysis validation layer. You run it before doing any stats, and it tells you whether your data violates basic assumptions that would invalidate the analysis. It does not run hypothesis tests. It does not fix data. It only makes assumptions explicit and enforceable. Right now it’s intentionally minimal: – one validate() entry point – deterministic rules – clear errors vs warnings – usable in notebooks or pipelines This is still early and opinionated, but I wanted to get it out, document it clearly, and get feedback from people who care about experiment quality. PyPI: https://lnkd.in/dARZ2XS3 GitHub: https://lnkd.in/dU-5-xsk If you’ve ever been burned by “statistically valid” results that later turned out to be based on broken assumptions, this might be useful — or at least a starting point. #statistics #datascience #experimentation #python #opensource

Practised Python list manipulation methods to work with dynamic data efficiently. Used methods like append(), insert(), and extend() to add elements in different ways. Understood the difference between reference copy and shallow copy using copy(). Worked with pop() and remove() to delete elements by index and value. Implemented logic to remove duplicates, find item positions using index(), and frequency using count(). These exercises improved my understanding of list behavior, memory handling, and data operations. #Python #Lists #DataStructures #PythonBasics #ListMethods #LearningJourney

Not all attributes in Python should be plain data @property lets you expose a method like an attribute: compute values on access, control read/write, keep a clean API without breaking callers. Benefits: 1/ Encapsulation → hide implementation, validate on set, control access. 2/ Computed attributes → derive values on-the-fly (no stored redundant state). 3/ Backward compatibility & readability → switch a public attribute to a property without changing callers. 4/ Clear intent → callers read obj.value instead of obj.get_value() when it’s conceptually an attribute.

Observation.org just released `euring`, an open-source Python library and CLI for working with EURING ringing and recovery records. It lets you decode, validate and convert records, export records and code tables as JSON, and work with stable, machine-readable field keys in your software and data pipelines. Built for anyone developing or maintaining software around bird ringing data. Link: https://lnkd.in/ezzfS-sv EURING Codes are published and maintained by EURING.