Kangas: Effortless Navigation Through Your Vast Oceans of Big Data

Everything seems to be getting bigger and bigger: big drums, big basses, and big bugs! But perhaps the most overwhelming is the sheer volume of data that companies handle, reaching an enormous scale that understanding or processing it becomes a challenge without the assistance of learning tools, such as AI.

Kangas was developed to explore, analyze, and visualize this vast amount of big data. Kangas is a Python library capable of effortlessly processing very large data tables. It provides an interface to visualize complex queries for processing and understanding data.

This tool is powerful and “scalable,” meaning it can handle and store as much data as needed, allowing you to sort, filter, and group data in just a few seconds. Kangas also includes image processing features, enabling users to view, filter, and associate tags and metadata with images.

To install Kangas, you can use the following command:

pip install kangas

Note: You may also need to install IPython if it is not already installed.

Once installed, you can import the library into your Python code and load a datagrid (in this example, using sample data) like this:

import kangas as kg # Load an existing DataGrid dg = kg.read_datagrid("https://github.com/caleb-kaiser/kangas_examples/raw/master/coco-500.datagrid")

After loading the data source, you can display it using:

dg.show()

This command will open the Kangas Data Viewer in your browser, allowing you to easily navigate your big data.

Practical, isn’t it?