|Project Name||Stars||Downloads||Repos Using This||Packages Using This||Most Recent Commit||Total Releases||Latest Release||Open Issues||License||Language|
|Pachyderm||5,979||1||12 hours ago||504||August 04, 2023||882||apache-2.0||Go|
|Data-Centric Pipelines and Data Versioning|
|Root||2,233||20||6 hours ago||16||October 24, 2022||898||other||C++|
|The official repository for ROOT: analyzing, storing and visualizing big data, scientifically|
|Spark Py Notebooks||1,515||6 months ago||9||other||Jupyter Notebook|
|Apache Spark & Python (pySpark) tutorials for Big Data Analysis and Machine Learning as IPython / Jupyter notebooks|
|Optimus||1,406||11 days ago||32||June 19, 2022||27||apache-2.0||Python|
|:truck: Agile Data Preparation Workflows made easy with Pandas, Dask, cuDF, Dask-cuDF, Vaex and PySpark|
|Scikit Learn Intelex||1,047||17||7 hours ago||21||July 21, 2023||54||apache-2.0||Python|
|Intel(R) Extension for Scikit-learn is a seamless way to speed up your Scikit-learn application|
|Dataflowjavasdk||853||249||14||3 years ago||38||June 26, 2018||54|
|Google Cloud Dataflow provides a simple, powerful model for building both batch and streaming parallel data processing pipelines.|
|GUI-based Python code generator for data science, extension to Jupyter Lab, Jupyter Notebook and Google Colab.|
|Arcticdb||600||6 hours ago||216||other||C++|
|ArcticDB is a high performance, serverless DataFrame database built for the Python Data Science ecosystem.|
|Wedatasphere||593||4 months ago||24|
|WeDataSphere is a financial grade, one-stop big data platform suite.|
|Courses||590||4 months ago||8||apache-2.0||Jupyter Notebook|
|Answers for Quizzes & Assignments that I have taken|
The ROOT system provides a set of modules with all the functionality needed to handle and analyze large amounts of data in a very efficient way. Having the data defined as a set of objects, specialized storage methods are used to get direct access to the separate attributes of the selected objects, without having to touch the bulk of the data. Included are histograming methods in an arbitrary number of dimensions, curve fitting, function evaluation, minimization, graphics and visualization classes to allow the easy setup of an analysis system that can query and process the data interactively or in batch mode, as well as a general parallel processing framework, PROOF, that can considerably speed up an analysis.
Thanks to the built-in C++ interpreter cling, the command, the scripting and the programming language are all C++. The interpreter allows for fast prototyping of the macros since it removes the time consuming compile/link cycle. It also provides a good environment to learn C++. If more performance is needed the interactively developed macros can be compiled using a C++ compiler via a machine independent transparent compiler interface called ACliC.
The system has been designed in such a way that it can query its databases in parallel on clusters of workstations or many-core machines. ROOT is an open system that can be dynamically extended by linking external libraries. This makes ROOT a premier platform on which to build data acquisition, simulation and data analysis systems.
When citing ROOT, please use both the reference reported below and the DOI specific to your ROOT version available on Zenodo . For example, you can copy-paste and fill in the following citation:
Rene Brun and Fons Rademakers, ROOT - An Object Oriented Data Analysis Framework, Proceedings AIHENP'96 Workshop, Lausanne, Sep. 1996, Nucl. Inst. & Meth. in Phys. Res. A 389 (1997) 81-86. See also "ROOT" [software], Release vX.YY/ZZ, dd/mm/yyyy, (Select the right link for your release here: https://zenodo.org/search?page=1&size=20&q=conceptrecid:848818&all_versions&sort=-version).
These screenshots shows some of the plots (produced using ROOT) presented when the Higgs boson discovery was announced at CERN:
See more screenshots on our gallery.
Our "Getting started with ROOT" page is then the perfect place to get familiar with ROOT.