Automate Peer Benchmarking with SEC Filings Data

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.

I’m excited to share my latest project: a comprehensive Descriptive Statistics Suite built in Python! 🚀 Before jumping into complex Machine Learning models, every great data story starts with a deep dive into the data's "personality." This project automates that process using the industry-standard stack: NumPy, Pandas, and SciPy. Key highlights of what I’ve built: 🔹 Central Tendency: Automated calculation of Mean, Median, and Mode to find the "heart" of the data. 🔹 Dispersion Analysis: Measuring Variance, Standard Deviation, and IQR to quantify data spread and volatility. 🔹 Distribution Shape: Using Skewness and Kurtosis to identify symmetry and the likelihood of extreme outliers. 🔹 Visualizations: Clean, publication-ready Histograms, Frequency Polygons, and Pie Charts for intuitive storytelling. This repository is designed to be a "one-click" solution for anyone performing initial Exploratory Data Analysis (EDA). 📂 Check out the full code and documentation on GitHub: https://lnkd.in/gBPsc95s I’d love to hear your thoughts or any suggestions for future statistical features! #DataScience #Python #DataAnalytics #Statistics #GitHub #Pandas #NumPy #DataVisualization #MachineLearning #Coding

Your Python Code Consuming Too Much Memory? Today, I explored a fundamental concept in NumPy that many of us often overlook: manual data type (dtype) . While NumPy is naturally more efficient than standard Python arrays, the way we define our data plays a massive role in actual performance. I recently followed a lecture by Respected Sir Zafar Iqbal on this topic, and it changed how I look at memory management in Data Science/ML. Here are my three key takeaways from today's practice: 1. The "Default" Memory Waste When we create an array without specifying a data type, Python often assigns the maximum possible size, such as int64, by default. If your data consists of small numbers (like 1 to 100), using int64 is a waste of resources. By simply defining dtype=np.int8, you can perform the same operations while using significantly less memory. 2. The Out-of-Bounds Trap Every data type has a specific boundary. For instance, int8 can only store values between -128 and 127. If you try to store a number like 130 in an int8 array, you will encounter an "out of bounds" error. In such cases, moving to int16 or int32 provides the necessary range while still being more efficient than the 64-bit default. 3. The Cost of "Object" Flexibility NumPy allows us to mix different types, like strings, integers, and floats, by using dtype=object. While this offers flexibility, it comes at a price: you lose the famous speed advantage that makes NumPy so powerful. For high-performance computing, keeping your data homogeneous is essential. Pro Tip: When working with large datasets, always use the .nbytes attribute to check exactly how much memory your array is consuming. Making small adjustments to your data types can transform a heavy, slow program into a super-efficient one. I am curious to hear from other data professionals: Do you usually stick with the default settings, or do you prefer manual control over your memory usage? Let me know in the comments. #Python #DataScience #NumPy #CodingLife #LearningEveryday #MachineLearning #Efficiency

Working on Real World Data Problems Using Pure Python Recently worked on a project focused on handling and analyzing structured data using core Python without relying on libraries like NumPy or Pandas. The goal was to understand the logic from the ground up. Cleaned and structured raw JSON data Built logic for “People You May Know” (mutual connections) Implemented “Pages You Might Like” recommendations Focused on problem-solving using basic data structures This approach helped me strengthen my core data handling and logical thinking, rather than depending on pre-built tools. Late nights after work, but worth it for the growth. #Python #DataProcessing #DataScience #ProblemSolving #CorePython #Algorithms #NumPy #pandas

Mastering Data Ingestion: Why NumPy is the Standard For anyone working with numerical data in Python, the transition from built-in functions to NumPy is a game-changer. While Python’s open() function handles basics, NumPy arrays offer a level of efficiency and speed that standard lists simply cannot match. Why use NumPy for flat files? The Industry Standard: NumPy arrays are the backbone of the Python data ecosystem. Essential for ML: If you plan to use libraries like scikit-learn, your data needs to be in a NumPy format. Built-in Efficiency: Functions like loadtxt() and genfromtxt() make importing arrays seamless. Pro-Tips for np.loadtxt() When importing data, the real power lies in the customization arguments: delimiter: Remember that the default is whitespace. For CSVs, always specify delimiter=','. skiprows: Perfect for bypassing headers (e.g., skiprows=1) so string labels don't break your numerical array. usecols: Optimization starts at ingestion. Only grab what you need by passing a list of indices, like usecols=[0, 2]. dtype: Control your data types from the start (e.g., dtype='str'). The Catch While loadtxt() is excellent for clean, uniform datasets, it hits a wall with mixed data types (like the Titanic dataset). When your columns vary between strings and floats, it’s time to level up to genfromtxt() or move into the world of Pandas. #DataEngineering #python #Numpy #Learninginpublic

Most Python classes I've seen in DS projects do too much! They load data, clean it, transform it, run the model, and log results... all in one place. It feels efficient until you need to change one thing and have to re-test everything else. That's the cost of ignoring the Single Responsibility Principle. 🐍 In my latest article, I break down what SRP actually means for Python data pipelines: https://lnkd.in/esKz_ARk This is post 1 of 5 in a series on SOLID principles applied to Data Science code. What's the messiest class you've inherited on a DS project? 👇 #Python #DataScience #SoftwareEngineering #SOLID #DataEngineering

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