https://github.com/shapelets/shapelets-demos

https://github.com/shapelets/shapelets-demos

data-science data-structures data-visualization python shapelets time-series

Last synced: about 2 months ago
JSON representation

• Host: GitHub
• URL: https://github.com/shapelets/shapelets-demos
• Owner: shapelets
• License: mit
• Created: 2022-11-08T11:07:24.000Z (about 2 years ago)
• Default Branch: main
• Last Pushed: 2024-07-22T06:57:30.000Z (5 months ago)
• Last Synced: 2024-07-22T08:16:51.713Z (5 months ago)
• Topics: data-science, data-structures, data-visualization, python, shapelets, time-series
• Language: Python
• Size: 26.5 MB
• Stars: 4
• Watchers: 3
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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# Shapelets Demos

[![PyPI Latest Release](https://img.shields.io/pypi/v/shapelets-platform)](https://pypi.org/project/shapelets-platform/)

[![Documentation](https://img.shields.io/badge/docs-Shapelets-lightgrey)](https://shapelets.io/doc/)

Shapelets is an integrated platform for data scientists that provides significant speedups and greater efficiency to help data scientists

extract insights from data, create powerful visualizations and share them instantly with the business.

Shapelets Core incorporates an efficient data engine, implemented in C++ but controlled through a Python API, that can be connected to

multiple data stores (Azure Blob, S3, SMB, FTP, etc.) to load various types of data files (parquet, arrow, CSV, excel) in an extremely

efficient way and keeping a minimum memory usage. This data engine relies on bitmap indexing technology to optimize time series storage and query times in large databases.

Shapelets Data Apps can be used to build data apps. Data apps are web applications with professional visualizations that be quickly prototyped by data scientists and shared with business stakeholders across an organization, allowing to quickly validate the insights found by data scientist. These data apps can seamlessly scale from prototypes to production-ready applications. In order to build data apps, the data scientist simply uses Shapelets API to create visual components (buttons, tabs, line charts, etc.) and the interactions between those components. This is done using a simple, intuitive syntax.

You can check the __User's Guide__ and __Documentation__ [here](https://shapelets.io/doc/)