Projects sharing features with PigPen

Human interpretation of data is inherently susceptible to cognitive biases. While Large Language Models (LLMs) act as automated data analysts, they often mirror user biases or training artifacts. This project introduces a "Bias-Contrastive" Agentic Framework that goes beyond simple text analysis.

Experiments in Streaming

ParaMonte: Parallel Monte Carlo and Machine Learning Library for Python, MATLAB, Fortran, C++, C.

Hadoop MapReduce in idiomatic Clojure.

This project is a data profiling and exploratory data analysis tool designed to generate automated quality reports for Pandas and Spark dataframes. It serves as a system for computing descriptive statistics, identifying correlations, and analyzing univariate and multivariate data patterns. The tool provides specialized capabilities for comparing different versions of datasets to identify changes in data quality and distributions. It includes a dedicated profiler for time-dependent data to extract statistical information such as seasonality and auto-correlation. The software covers a broad an

Tuple MapReduce for Hadoop: Hadoop API made easy

Desbordante is a high-performance data profiler that is capable of discovering many different patterns in data using various algorithms. It also allows to run data cleaning scenarios using these algorithms. Desbordante has a console version and an easy-to-use web application.

Esper Complex Event Processing, Streaming SQL and Event Series Analysis

Fast primitive based math library

Gota: DataFrames and data wrangling in Go (Golang)

Gonum is a numerical computing library for the Go programming language, providing a collection of packages for scientific computing, linear algebra, statistics, and optimization. It functions as a framework for performing complex numerical computations and solving systems of linear equations. The project includes a dedicated graph analysis framework for modeling network graphs and solving connectivity and pathfinding problems. It also provides a statistical analysis toolkit for computing descriptive and inferential statistics and estimating mixture entropy. The library's capability surface c

Ibis is a portable Python dataframe library and multi-backend query engine that provides a unified interface for executing data transformations across diverse compute engines. It functions as a Python SQL expression compiler and dialect transpiler, allowing users to define data logic once and execute it across cloud warehouses, embedded databases, and distributed clusters without rewriting code. The project distinguishes itself through a database backend abstraction that decouples transformation logic from the underlying execution engine. It enables polyglot data workflows by mixing raw SQL s

Develop streaming applications for IBM Streams in Python, Java & Scala.

This Python script is designed to scrape articles from The Guardian's technology section using their API. It fetches article data, extracts the titles and content, and then saves each article's content to separate text files. The text files are organized in a folder named with the current date…

The "Image Scraper" is a Flask web application that allows users to search for images on Google and download them directly to their local machines. The project leverages web scraping techniques to fetch the image URLs from Google search results and then download the images to a specified directory.

This repository contains a Power BI data analysis project on Indian restaurants, enabling you to delve into restaurant data, customer preferences, and regional trends.

This project leverages Power BI to create a dynamic and visually appealing analytics dashboard focused on the cricket performances of the legendary Virat Kohli. Gain insights into his batting trends, run-scoring patterns, and statistical analysis over time.

An implementation of JSON-LD for Elixir

An implementation of RDF for Elixir

An implementation of SPARQL for Elixir

A library of statistical distribution sampling and transducing functions

Modin is a distributed dataframe library and parallel data processing engine designed to handle large datasets that exceed system memory. It functions as a distributed computing framework that parallelizes data manipulation tasks across multiple CPU cores or clusters to increase throughput and avoid memory errors. The project mirrors the Pandas API, allowing for the distribution of data workflows without changing core code logic. It utilizes a pluggable backend interface, which enables users to switch between different distributed execution engines to optimize performance based on available h