TimeCopilot’s Post

🚀 pandas 3.0 is here! The long-awaited major release brings game-changing improvements: ✨ Dedicated string dtype by default, better type safety & performance ✨ Copy-on-Write (CoW), consistent copy/view behaviour, no more SettingWithCopyWarning ✨ Improved date time handling, microsecond resolution, avoiding out-of-bounds errors Read on for more: https://lnkd.in/eUzg9mEH #Pandas #Python #DataScience #Pandas3

𝐂𝐒𝐕 𝐟𝐢𝐥𝐞 → 𝐃𝐚𝐭𝐚𝐅𝐫𝐚𝐦𝐞 → 𝐈𝐧𝐝𝐞𝐱𝐢𝐧𝐠 𝐚𝐧𝐝 𝐬𝐞𝐥𝐞𝐜𝐭𝐢𝐧𝐠 𝐝𝐚𝐭𝐚. This is Day 4 of #1000DaysOfLearning Yesterday I practiced querying with conditions. Today I learned how indexing works in DataFrames. I understood that the index is separate from columns. Once a column is set as an index, it becomes a row label and still appears on the left even after selecting specific columns. Understanding indexing makes querying feel cleaner. #Python #Pandas #DataScience #LearningInPublic #1000DaysOfLearning

🐍 Day 60 — Bar Charts in Matplotlib Day 60 of #python365ai 📊 Bar charts compare categories. Example: plt.bar(["A", "B", "C"], [5, 7, 3]) plt.show() 📌 Why this matters: Bar charts are common in reports and dashboards. 📘 Practice task: Create a bar chart for three products. #python365ai #BarChart #DataAnalysis #Python

While working with datasets in Pandas, one small thing that made a big difference for me was understanding vectorization. In the beginning, I used apply() for many transformations. It worked — but as datasets got bigger, I noticed things slowing down. Then I started using column-wise operations instead of row-wise logic, and my code became both simpler and faster. Now, apply() is something I use only when there’s no easier alternative. Still learning something new with every dataset I work on. What’s one Pandas habit or trick that improved your workflow? #Pandas #Python #DataEngineering #DataAnalysis

This is demo for my Algorithm class where we would take a couple of algorithms and visualize them in a python program to demonstrate how fast they each run both on runtime and with visual feedback. The main goal was to demonstrate big O notation through visuals and including the time each one took. We would randomize the numbers and pass them through the algorithms to see how they ran, how fast they ran and organized them. It was clear that with big data sets Quick and merge were the best at handling larger and more scattered sets. One of the key takeaways was seeing just how each was useful in its own way and how even one that is clearly much faster than the rest like merge or quick would maybe be overkill when it comes to smaller changes which bubble sort would be better at #CSUF #AlgorithmEngineering #Spring2026

🚀 Day-56 of #100DaysOfCode 📊 NumPy Practice – Finding Unique Values & Frequency Today I practiced identifying unique elements and counting their occurrences using NumPy. 🔹 Concepts Practiced: ✔ np.unique() ✔ Frequency counting ✔ Handling duplicate values ✔ Efficient array analysis 🔹 Key Learning: Using return_counts=True makes frequency analysis simple and efficient without loops — very useful in data preprocessing. Slowly stepping into data analysis concepts using NumPy 💡🔥 #Python #NumPy #DataAnalysis #ArrayOperations #100DaysOfCode #LearnPython #CodingPractice #PythonDeveloper

Python Tip of the Day 🐍 sep() and end() in print() help control how output is formatted. sep changes the separator between values, and end controls how the line finishes. Small parameters — but very useful for clean output formatting. Day 5 of building Python basics. #PythonDaily #PythonBasics #DataAnalytics #LearningPython

I recently tackled the "Count Triplets with Sum Smaller than X" problem. While a Brute Force approach is the most intuitive, it's often the least efficient. Here’s how I optimized the logic from O(n^3) to O(n^2). The goal is to find triplets (i, j, k) such that arr[i] + arr[j] + arr[k] < target By sorting the array first (O(n \log n)), we gain a predictable structure. I fix the first element (i) and then use two pointers (left and right) for the remaining part of the array. If the current triplet sum is less than the target, then every element between left and right will also form a valid triplet with i and left.  This allows us to add (right - left) to our count instantly, instead of checking each one individually. #DSA #Python

LeetCode Problem 83: Given the head of a sorted linked list, delete all duplicates such that each element appears only once. Return the linked list sorted as well. The below implementation in Python successfully resolves this in time complexity of O(n) where n is length of the list with constant space complexity. The approach is simple, handle the base case first like list having 0 or 1 number of nodes and then write the logic of handling lists of length greater than one. Maintain two pointers, one to keep track of present node and other to keep track of previous node. Check if the value of both nodes match or not, if match update the pointing of previous node, point its next to the present.next. #LeetCode #LinkedList #Python #CompetitiveProgramming #Algorithms #DataStructures #ProblemSolving

Exploring Linear Regression using Scikit-Learn Today I implemented a simple Linear Regression model using Python and Scikit-learn to predict house prices based on area. 🔹 Learned how to: ->Import and use LinearRegression ->Prepare data ->Train the model using fit() ->Predict outputs using predict() #SIC_INDIA_2025 #Linearregression