Projects sharing features with Sklearn Pandas

A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner

Xarray is a Python multidimensional array library and labeled dataset framework. It extends the NumPy data structure by adding labels to arrays, allowing for the organization of complex N-dimensional data using named dimensions and coordinates. The library provides a NetCDF data interface for reading and writing scientific data formats such as NetCDF and Zarr. It enables scientific array computing by maintaining the relationship between data and physical coordinates during mathematical operations. The project covers multidimensional data analysis, geospatial data manipulation, and climate da

This project is an educational resource and a collection of instructional materials for performing data manipulation and statistical analysis using Python. It provides a comprehensive set of guides and code examples for using the Pandas, NumPy, and Matplotlib libraries to analyze structured data. The resource includes a dedicated guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas, alongside a reference for high-performance vectorized operations and linear algebra using NumPy. It also features tutorials for creating publication-quality charts, distribution p

Teable is a self-hosted relational data management tool and no-code PostgreSQL database. It provides a spreadsheet-like interface for managing and querying structured data, allowing users to interact with a professional database backend without writing manual SQL for every operation. The platform is an extensible low-code system that allows for the integration of custom plugins and extensions through a dedicated application bridge and marketplace. It enables the creation of tailored internal tools by adding new features or modifying behavior via these external extensions. The system covers a

This repository is a comprehensive collection of instructional guides and practical examples for Python development, focusing on machine learning, data science, and web scraping. It provides implementations for neural networks, reinforcement learning algorithms, and deep learning architectures using PyTorch, alongside detailed manuals for scientific computing and data visualization. The project distinguishes itself by offering specialized tutorials on concurrent programming to optimize CPU performance and guides for setting up Linux development environments. It covers the implementation of ad

DataFrame is a C++ tabular data library and manipulation engine designed for managing heterogeneous data in contiguous memory. It functions as a statistical analysis framework and time series analysis toolkit, providing the means to store, index, and transform multidimensional datasets. The project distinguishes itself through a high-performance execution model that utilizes column-major storage, SIMD-aligned memory allocation, and a thread-pool for parallel computations. It employs a visitor-based algorithm dispatch system and policy-driven transformations to decouple data processing logic f

This project is a comprehensive pandas data analysis tutorial and instructional guide designed for learning data manipulation and analysis. It serves as a tabular data processing guide and a manual for time series analysis, providing a structured approach to cleaning, merging, and transforming datasets. The repository functions as a data feature engineering course, providing tutorials on constructing and selecting dataset features to improve machine learning model performance. It also includes a vectorized data operations guide for performing element-wise mathematical computations and matrix

Scriban is a text templating library and .NET scripting engine used for dynamic text generation. It functions as a template processor and a safe scripting sandbox, providing a secure execution environment that restricts object exposure to prevent unauthorized code execution. The project also includes an abstract syntax tree template parser that allows for programmatic template analysis and modification. The engine features a dedicated Liquid template engine and compatibility mode, allowing it to parse, execute, and convert templates written in Liquid syntax. It distinguishes itself through a

Apache Hamilton helps data scientists and engineers define testable, modular, self-documenting dataflows, that encode lineage/tracing and metadata. Runs and scales everywhere python does.

Arrow is a Python date and time library that provides a simplified interface for creating and manipulating timestamps by wrapping the Python standard library. It serves as a tool for managing date objects, handling timezone offsets, and performing relative date calculations. The library is distinguished by its ability to humanize timestamps into natural language relative descriptions across multiple locales and parse human-readable time phrases back into precise date objects. It also features a specialized parser for converting ISO 8601 strings and custom formatted text into date objects. Br

Dask is a parallel computing framework and distributed task scheduler designed to scale Python data science workflows from single machines to large clusters. It functions as a cluster resource manager that orchestrates computational logic by representing tasks and their dependencies as directed acyclic graphs. This architecture allows the system to automate the distribution of workloads across available hardware while managing complex execution requirements. The project distinguishes itself through a lazy evaluation engine that defers data operations until they are explicitly requested, enabl

CuPy is a CUDA array computing library that implements a NumPy-compatible interface for executing array operations and numerical computing on NVIDIA GPUs. It serves as a GPU-accelerated numerical library and a CUDA-based SciPy implementation, offloading heavy calculations to graphics hardware to increase processing speed for scientific and engineering workloads. The library enables multi-framework tensor exchange, allowing data buffers to be shared between different deep learning frameworks using standardized memory layouts to avoid memory copies. It also supports custom GPU kernel integratio

functional data manipulation for pandas

@resultBuilder for building a Bool.

BatchFlow helps you conveniently work with random or sequential batches of your data and define data processing and machine learning workflows even for datasets that do not fit into memory.

Directions overlay for working with pandas in an analysis environment

vj - JSON Humanizer

dplyr for python

Bitwiser is a collection of methods and properties that makes you work with bits, bytes and nibbles a piece of cake

DuckDB is an in-process analytical database engine designed to run directly within an application process. As a zero-dependency, embedded system, it provides enterprise-grade SQL data processing capabilities without the overhead of managing a dedicated database server. It is built to handle complex analytical and aggregation tasks by storing and retrieving information in columns, allowing for high-performance relational data manipulation. The engine distinguishes itself through a columnar vectorized execution model that maximizes CPU cache efficiency during query operations. It employs adapti

DuckLake is an integrated data lake and catalog format

Clean APIs for data cleaning. Python implementation of R package Janitor

Daft is a distributed dataframe library and multimodal data processor designed to handle large-scale structured and unstructured data. It functions as a vectorized execution engine that processes tables alongside images, audio, and video, utilizing a unified schema to manage diverse data types. The project distinguishes itself by combining distributed data engineering with large-scale AI inference. It provides an AI data pipeline for batch-optimizing model prompts and generating high-dimensional text embeddings, while utilizing zero-copy memory sharing to execute custom Python functions witho

The goal of pandas-log is to provide feedback about basic pandas operations. It provides simple wrapper functions for the most common functions that add additional logs

Join tables together on inexact matching