{"id":5514,"date":"2023-03-28T06:12:25","date_gmt":"2023-03-28T14:12:25","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=5514"},"modified":"2025-05-08T09:49:15","modified_gmt":"2025-05-08T09:49:15","slug":"kangas-2-0-exploratory-data-analysis-for-computer-vision-datasets","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/kangas-2-0-exploratory-data-analysis-for-computer-vision-datasets\/","title":{"rendered":"Kangas 2.0: Exploratory Data Analysis for Computer Vision"},"content":{"rendered":"\n

Today, we\u2019re excited to release version 2.0 of Kangas<\/a>, our open-source platform for exploring, analyzing, and visualizing multi-media data. Whether your data lives in a .csv file or a pandas dataframe, with just a couple lines of code, you can quickly import your data into a Kangas DataGrid and start exploring. <\/span><\/p>\n\n\n\n

To get started, you can install Kangas via pip.<\/span><\/p>\n\n\n\n

pip<\/span> install<\/span> kangas<\/span><\/code><\/p>\n\n\n\n

import<\/span> kangas<\/span> as<\/span> kg<\/span>\n\n# Load an existing DataGrid<\/span>\n\ndg<\/span> =<\/span> kg.read_datagrid(<\/span>\"https:\/\/github.com\/caleb-kaiser\/\" +<\/span>\n\n                    <\/span>\"kangas_examples\/raw\/master\/\" + <\/span>\n\n                    \"<\/span>coco-500.datagrid.zip\"<\/span>)<\/span>\n\ndg.show()<\/span>\n<\/pre>\n\n\n\n
As part of our release of Kangas 2.0, we want to introduce some of the key features and design principles of Kangas.<\/span><\/p>\n\n\n\n
1. Fast Rendering at Scale<\/span><\/h1>\n\n\n\n
Kangas is designed to generate robust, interactive visualizations over large multimedia datasets. The complexity of Kangas\u2019 UI, and the need for it to run across environments, precluded us from building it on top of tools like Jupyter Widgets or Matplotlib.<\/span><\/p>\n\n\n\n
\"\"\/<\/figure>\n\n\n\n
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Instead of building yet-another-Python-charting-library, we designed Kangas as a standalone web application, capable of processing and rendering thousands of cells of multimedia data quickly, interactively, and within many different environments.<\/span><\/p>\n\n\n\n
At a high level, Kangas has the following architecture. A Flask server stores DataGrids (which are SQLLite databases, under the hood) and serves queries. In front of this server sits a Node-based rendering engine, which generates the bulk of the frontend. Finally, a client-side React app syncs with the rendering engine, displaying the Kangas UI.<\/span><\/p>\n\n\n\n
\"Kangas<\/figure>\n\n\n\n
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This architecture gives Kangas several critical benefits:<\/span><\/p>\n\n\n\n