Search Results for "python documentation"

LIFELINES is a pure Python library for survival analysis, a statistical field focused on modeling time until an event occurs. It can be used for traditional cases like medical survival time, but also for business and product questions such as churn, subscription length, equipment failure, and customer retention. The library includes estimators such as Kaplan-Meier, Nelson-Aalen, and regression-based survival models. It is designed to be accessible to Python users and works well with common...

...In addition to code editing and execution, RStudio offers extensive support for reproducible research via R Markdown, notebooks, and integration with version control systems like Git and SVN. Package development is built in, with tooling for building, checking, and testing R packages, plus integration with documentation tools, CRAN submission workflows, and project templates.

marimo is an open-source reactive notebook for Python, reproducible, git-friendly, executable as a script, and shareable as an app. marimo notebooks are reproducible, extremely interactive, designed for collaboration (git-friendly!), deployable as scripts or apps, and fit for modern Pythonista. Run one cell and marimo reacts by automatically running affected cells, eliminating the error-prone chore of managing the notebook state. marimo's reactive UI elements, like data frame GUIs and plots,...

Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. Software developers have long known that testing and documentation are essential for managing complex codebases. Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams. Expectations are assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues. Expectations...

...It relies on NVIDIA® CUDA® primitives for low-level compute optimization but exposing that GPU parallelism and high-bandwidth memory speed through user-friendly Python interfaces.

SynapseML (previously MMLSpark) is an open source library to simplify the creation of scalable machine learning pipelines. SynapseML builds on Apache Spark and SparkML to enable new kinds of machine learning, analytics, and model deployment workflows. SynapseML adds many deep learning and data science tools to the Spark ecosystem, including seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit,...

...It guides users through the full workflow across three tabs: Model setup (formula input with live parsing, CSV data upload with auto-detected variable types, effect size sliders, and correlation editing), Analysis configuration (find power for a given sample size or find the minimum sample size for a target power, with multiple testing correction and scenario analysis), and Results (interactive charts, exportable tables, and auto-generated Python replication scripts). Supports both standard linear models and mixed-effects models. Additional features include analysis history, configurable scenarios, and built-in documentation.

A data science IDE for Python. RODEO, that is an open-source python IDE and has been brought up by the folks at yhat, is a development environment that is lightweight, intuitive and yet customizable to its very core and also contains all the features mentioned above that were searched for so long. It is just like your very own personal home base for exploration and interpretation of data that aims at Data Scientists and answers the main question, "Is there anything like RStudio for Python?"...