We just finished the first YouTube series on py-dss-toolkit. If you use OpenDSS with Python, this series shows practical ways to explore models, extract results, and visualize studies more efficiently. You can use py-dss-toolkit to: ✅ work with OpenDSS data in a more structured way 📊 move results into pandas DataFrames for analysis 💻 create visual workflows, including in Google Colab I’d love your feedback: what should the next series focus on? 🔎 Plotting? 🔎 QSTS? 🔎 Model exploration? 🔎 Automation? #OpenDSS #Python #PowerSystems

Still slicing DataFrames the hard way? Here are the three essentials: grab one column with df['col'], pick multiple with df[['a','b']], then filter rows by building a boolean mask like df[df['age'] >= 18]. Master these and inspecting/subsetting data becomes effortless.#pandas #python #datascience #dataanalysis

🚀 Day 56 — LeetCode 451: Sort Characters by Frequency using HashMap + Sorting. 💡 Key Learnings: • Counting character frequency using dictionary • Custom sorting with key=lambda • Efficient string building using "".join() • Understanding time vs space complexity trade-offs ⏱️ Complexity: Time — O(n + k log k) Space — O(k) #Day56 #LeetCode #Python #DataStructures #DSA #CodingJourney #ProblemSolving

Solved an interesting array problem using prefix sums + hash maps 🚀 This solution efficiently finds the longest subarray based on a comparison condition (arr[i] > k) by converting the problem into a prefix sum balance and tracking first occurrences with a hashmap. ✅ O(n) time complexity ✅ Smart use of prefix sums ✅ Great example of turning a complex condition into a simple math problem Always fun when problem-solving meets optimization! 💻📊 #Python #DataStructures #Algorithms #CodingInterview #ProblemSolving #100DaysOfCode

Day 14 | Problem-Solving Practice Back to practice and worked on number system conversions today: • Binary to Decimal conversion • Decimal to Binary conversion Implemented both mathematical logic and built-in approaches to understand the underlying concept as well as practical shortcuts. Continuing to rebuild momentum step by step. GitHub: https://lnkd.in/g35tV9Gj #ProblemSolving #Python #LearningInPublic

🔁 Rotate Matrix by 90° — Clean In-Place Trick! Solved the classic Rotate Matrix by 90° problem using a simple observation: • Transpose the matrix • Reverse it column-wise This approach rotates the matrix in-place with O(n²) time and O(1) extra space. Sometimes the best solutions come from simple transformations on the matrix. 🚀 #DSA #ProblemSolving #Python #CodingInterview #Algorithms

#1 — Hardcoded / Magic Numbers It works. But where do 9, 5, and 32 come from? Part of the series: One Problem – Different Approaches Start here: https://lnkd.in/dU4kN-q8 #OneProblemDifferentApproaches  #CelsiusToFahrenheit  #Python #sedatçapar

✅Day 12 of #DSAPrep > Problem: Product of Array Except Self > Platform: LeetCode > Concept: Prefix Sum + Suffix Product Solved using an optimized approach by calculating the product of elements on the left and right side separately. First pass stores left products, second pass multiplies right products — without using division. > Time Complexity: O(n) > Space Complexity: O(1) #DataStructures #Algorithms #Python #ProblemSolving #CodingJourney

🐍 Ever wondered what "cannot pickle" actually means? When you run a deepcopy or try to save a complex object, Python uses a process called Serialization (or Pickling). It's the magic trick that turns an object into a stream of bytes. But the magic has limits. In our latest episode in The Secret Life of Python, Timothy and Margaret head to the whiteboard to demystify the pickle module. The Lesson: ✅ How Python translates objects into a Byte Stream. ✅ Why system resources (like database connections) can't be "pickled." ✅ Why you should NEVER unpickle data from untrusted sources. Understand how your data moves behind the scenes. 👉 Read the full story here: https://lnkd.in/gdPVgXcD #Python #Coding #SoftwareEngineering #Security