I'm excited to share my latest personal project: Data Plot It's a data visualization tool made with Python that lets users quickly plot their data into interactive graphs, which can be useful for anyone who needs fast insights from a dataset. How it works: Upload a dataset (.csv, .xlsx, .xls, or .json), choose your X and Y axis columns, and instantly generate an interactive histogram. Built with: Pandas for data handling Plotly Express for interactive charts Streamlit for web interface The project also includes a CLI version for terminal users, featuring input validation and support for selection by file name or index number, although contains more limitations, such as not being able to handle non-CSV files. This was a great exercise in building both a web application and a command line tool from the same core logic. I focused on clean code, reusable functions, and supporting multiple file formats. Check it out: https://lnkd.in/d2cu2EaQ Feedback is always welcome! #Python #DataAnalysis #Streamlit #Plotly #OpenSource #Portfolio #DataVisualization

Replit doesn’t care what language or framework you want. It just builds it. Python, Node, Go, your stack, your rules, instant results. ⚡ New to Replit? Now’s the time to experiment. #Askraa #Replit #BuildInPublic #AI #Python #RapidIteration #DevTools #StartupWins

🚀 New Episode: Web Scraper Builder Extract the data you need—automatically. In this episode, we build a Python-powered web scraper with two practical applications: 📈 Use case 1: Scrape real-time prices of trading pairs from TradingView 💼 Use case 2: Extract latest job listings with details and links into an Excel file 📘 Full tutorial here: 👉 https://lnkd.in/dgc8KF4V 📘 Access the codes/scripts here: 👉 https://lnkd.in/dCh4-py2 Automate data collection. Save hours of manual work. #DBSTechnologies #TechCatalyst #WebScraping #Python #Automation #TradingView #JobSearch

Topic 5/100 🚀 🧠 Topic 5 — Iterators Ever wondered how a for loop actually works behind the scenes? 🤔 This is the concept powering it. 👉 What is it? Iterators are objects that allow you to traverse through data step-by-step using __iter__() and __next__() methods. 👉 Use Case: Used in real-world applications for: Custom data pipelines Streaming data Building your own iterable objects 👉 Why it’s Helpful: Gives full control over iteration Enables custom looping logic Foundation for generators 💻 Example: class Counter: def __init__(self, max): self.max = max self.current = 0 def __iter__(self): return self def __next__(self): if self.current < self.max: self.current += 1 return self.current raise StopIteration for num in Counter(3): print(num) 🧠 What’s happening here? We created a custom object that behaves like a loop by controlling how values are returned one by one. ⚡ Pro Tip: If you understand iterators, you’ll unlock how Python handles loops internally. 💬 Follow this series for more Topics #Python #BackendDevelopment #100TopicOfCode #SoftwareEngineering #LearnInPublic

🚀 Day 6: Decoding Maximum Consecutive One's 💡 How I solved it: *Maintained a running counter that increments every time I encounter a 1. *Used a global maximum variable to capture the highest streak reached before hitting a 0. *The Reset: Every time a 0 appeared, I reset the current counter to zero to begin tracking the next potential streak. 🧠 Key Takeaway: *Efficiency: Achieved O(n) time complexity and O(1) space—optimal for large datasets. *State Tracking: Learned the importance of maintaining a "local" vs. "global" state. It’s a foundational logic used in many sliding window and greedy algorithm problems. One step closer to mastering Data Structures and Algorithms! 💻🔥 The logic is getting sharper every day! 📈🤝 #100DaysOfCode #DSA #Python #ProblemSolving #StriverA2ZSheet #CodingJourney

I share open-source projects in my newsletter every week. Last week, the focus was on the freestiler project - a new geospatial library for R and Python by Kyle Walker. Key Features ⚙️ ✅ Generates PMTiles vector tilesets from spatial data objects, files, or database queries. ✅ Works with R and Python, enabling flexible integration into data science and geospatial workflows. ✅ Accepts multiple input sources, including sf objects, GeoParquet files, Shapefiles, GeoPackages, and DuckDB queries. ✅ Uses a Rust-based tiling engine that runs in-process, avoiding the need for external tile-building tools. ✅ Supports large-scale datasets through streaming pipelines that process data without loading everything into memory. More details are available in the project documentation. 🔗: https://lnkd.in/gS-kR7F8 License: MIT 🦄 📌 Subscribe to receive weekly updates: https://lnkd.in/gb3P8YdE #rstats #python #datascience

Coding agents for data analysis This post walks through the complete materials from a hands-on workshop on agent-assisted data workflows, covering querying, cleaning, scraping, and visualization with Claude Code and Codex. The focus is on end-to-end interaction patterns, grounded in Python and SQLite. https://lnkd.in/db3iJSJb

Thinking in Systems, Not Tables 🐍 Rows and columns don't show you the system — they hide it. You analyze a drop in conversions. The table shows a number. But the system shows a story: 🔹 A pricing change last week 🔹 A marketing campaign shift 🔹 Slower page load times 🔹 A change in user onboarding The table isolates. The system connects. The best analysts don't just ask what does the data say? They ask: 👉 Where did this data come from? 👉 What decisions will this analysis influence? 👉 What else could be affecting this number? Spreadsheets give you structure. Systems give you understanding. Stop thinking in tables. Start thinking in systems. #DataAnalytics #Python #AnalyticsThinking #SystemsThinking

Trying to simplify Pandas data exploration & filtering in my own way 📊 - Quick look → "head()", "tail()" - Overview → "info()", "describe()" - Selecting data → columns & rows - Filtering → conditions using masks One thing that confused me earlier: 👉 "iloc" is similar to "loc", but it uses index positions (numbers), and the stop index is not included. 👉 In practice, "loc" is used more often because it’s label-based and easier to read. Refer the below carousel for better understanding. #Python #Pandas #DataAnalytics

I've been spending a lot of time watching matches and thinking — why do I still build my tactical reports manually? So I decided to fix that. I built Football-Match-Analysis, a Python tool that pulls match data from WhoScored and automatically generates 39 tactical visualizations — shot maps, pass networks, xG timelines, defensive heatmaps, progressive passes, zone 14 actions, box entries, and more — then compiles everything into a single PDF report. No copy-pasting. No manual charting. Just paste the match URL and let it run. It's not perfect, and there's still a lot I want to add. But it already saves me hours of work after every game. If you're into football analytics or just curious about the code, everything is open-source on GitHub: 👉 https://lnkd.in/d4mcUUEZ Would love to hear your thoughts or ideas for new features! #FootballAnalytics #DataScience #Python #OpenSource #TacticalAnalysis