Python Object-Oriented Programming with Constructors

Excited to share my recent mini project – a Mini Expense Tracker built using Python. Designed to record and manage daily expenses using a simple file-based approach, providing basic insights into spending patterns. Key Features: • Add, view, and delete expense records. • Calculate total expenditure. • Store and retrieve data using file handling. Key Learnings: • Python fundamentals • File handling • Lists, strings, and basic data processing • Exception handling This is a small step towards my journey in Data Analytics and Data Engineering. #Python #DataAnalytics #BeginnerProject #Learning #SoftwareDevelopment

DERA has published a set of Python code examples to make it easier for analysts, researchers, and developers to access and work with the SEC’s XBRL Financial Statement and Notes Data Sets: https://lnkd.in/gpWuXJZD   The GitHub repository walks through:  • Reading quarterly data into Pandas • Joining and analyzing numeric, dimensional, narrative, and custom facts • Visualizing results • Working with multiple datasets and exporting outputs   Code, notebooks, and setup instructions are all available in the link.

Great foundation from the SEC DERA team. I was able to modernize this in an afternoon, swapping Pandas for Polars with lazy evaluation, adding DuckDB for direct SQL queries on the TSV files, and a benchmark showing the speed difference on real XBRL data. 4.20.2026 1900 PST: This update improves integration with external data pipelines./Notes and R-based incremental downloader + DuckDB/Parquet workflow, which served as a strong reference point for data ingestion design patterns. Fork with improvements here: https://lnkd.in/g58ESerZ Happy to contribute anything back if useful. #Code #SEC #finance #data #AI #trading #Stockmarket #SQL #XBRL #fullstack #financialservices

While preparing a risk management module, I came across a very useful resource that deserves more visibility. This repository by the U.S. Securities and Exchange Commission (SEC) provides Python-based tools to work with structured financial datasets derived from company filings. What makes it valuable? Access to SEC Financial Statement datasets Structured data extracted from XBRL filings Ready-to-use Python workflows using Pandas Ideal for financial modeling, empirical research, and analytics For anyone working on financial research, sustainability reporting, valuation, or data-driven finance projects, this can significantly reduce the effort required to clean and structure raw filings. #Finance #FinancialModeling #DataAnalytics #Python #Research #SEC #XBRL #FinTech #OpenData

Good release from DERA. The broader point is not just access to data. It is making public market information more usable, more scalable, and easier to work with in modern analytical workflows. That is how transparency starts to compound.

Check it out: Automate Peer Benchmarking: Instantly extract and compare financial KPIs across entire industries to see how competitors stack up without manual data entry. Uncover Footnote Insights: Search thousands of narrative disclosures simultaneously to flag "hidden" risks like litigation, supply chain shifts, or aggressive accounting. Build Data-Driven Dashboards: Transform raw SEC filings into clean, visual trends to identify long-term sector shifts and high-growth opportunities.

📅 Day 9 of My Data Analytics Journey 🚀 Today I explored some important Python concepts that are essential for building strong programming fundamentals: 🔍 What I learned: • Iterating over dictionaries using ".keys()", ".values()", and ".items()" • Basics of Object-Oriented Programming (OOP) – classes, objects, and methods • How to import and use Python modules 🧠 Key Takeaways: • Iterating over dictionaries makes data handling more efficient • OOP helps in writing structured and reusable code • Modules allow us to use powerful built-in functionalities without rewriting code 💡 Slowly understanding how Python can be used to structure and manage real-world data. 📈 Building consistency and improving step by step. 🤝 If you're on a similar learning journey, let’s connect and grow together! #Python #DataAnalytics #OOP #LearningInPublic #Consistency #CareerGrowth

🚀 Built a School Management System using Python with Data Visualization! I recently developed a Python-based project where I applied Object-Oriented Programming concepts along with data visualization. 🔹 Key Highlights: - Designed classes: Person, Teacher, Student, and ClassTeacher - Implemented inheritance and constructor chaining - Used method overriding for better structure - Displayed organized data using class objects - 📊 Visualized data using a bar graph (Age comparison) The graph representation makes it easier to compare and understand the data, making the project more practical and interactive. 🛠️ Tech Used: Python, Matplotlib This project improved my understanding of OOP concepts and how visualization can enhance data interpretation. GitHub:https://lnkd.in/gYzzU5UC #Python #OOP #Matplotlib #Data_Visualization #Coding #StudentProject

🚀 Day 12 & 13 – Consistency is the Key! Still going strong on my Python learning journey, and these two days were all about revision + real application 💻 🔁 Quick Revision: Revisited core concepts like loops, functions, and conditionals — because strong basics = strong foundation. 💡 Mini Project: Bill Generator Built a simple yet practical Python project using: ✔️ if-elif-else statements ✔️ Operators (arithmetic & logical) ✔️ User inputs for dynamic calculations 🔹 Features included: - Item selection & pricing - Quantity-based calculations - Discount logic - Final bill generation 🧠 What I Improved: - Better problem-solving approach - Writing cleaner, more readable code - Debugging with more confidence - Thinking in a more structured, logical way Every small project is making me more confident and bringing me one step closer to becoming a skilled data professional 📈 🙏 Special thanks to Anurag Srivastava and the Data Engineering Bootcamp for the constant guidance and support! #Python #LearningJourney #100DaysOfCode #DataEngineering #Coding #BeginnerToPro #Consistency

Days 60–63 of the #three90challenge 📊 Started April 2026 by transitioning into Python — an essential tool for data analysis. This week was all about building the foundation. 📅 01-04-2026: Set up Python environment and tools 📅 02-04-2026: Learned variables & data types — the building blocks of any program 📅 03-04-2026: Worked with lists & dictionaries to store and manage data 📅 04-04-2026: Practiced loops to automate repetitive tasks Key Takeaways: • Python makes handling data more flexible compared to spreadsheets • Lists & dictionaries are powerful for structuring data • Loops help automate what would otherwise be manual work • Strong basics make advanced concepts easier later After SQL, stepping into Python feels like expanding from querying data → programming with data. Excited for what’s next 🚀 GeeksforGeeks #three90challenge #commitwithgfg #Python #DataAnalytics #LearningInPublic #Consistency #Upskilling #PythonBasics