Showing 4 open source projects for "sql command line"

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  • 1
    Quadratic

    Quadratic

    Data science spreadsheet with Python & SQL

    Quadratic enables your team to work together on data analysis to deliver better results, faster. You already know how to use a spreadsheet, but you’ve never had this much power before. Quadratic is a Web-based spreadsheet application that runs in the browser and as a native app (via Electron). Our goal is to build a spreadsheet that enables you to pull your data from its source (SaaS, Database, CSV, API, etc) and then work with that data using the most popular data science tools today...
    Downloads: 15 This Week
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  • 2
    Data Science at the Command Line

    Data Science at the Command Line

    Data science at the command line

    Command Line by Jeroen Janssens, published by O’Reilly Media in October 2021. Obtain, scrub, explore, and model data with Unix Power Tools. This repository contains the full text, data, and scripts used in the second edition of the book Data Science at the Command Line by Jeroen Janssens. This thoroughly revised guide demonstrates how the flexibility of the command line can help you become a more efficient and productive data scientist. ...
    Downloads: 0 This Week
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  • 3
    xsv

    xsv

    A fast CSV command line toolkit written in Rust

    xsv is a command line program for indexing, slicing, analyzing, splitting and joining CSV files. Commands should be simple, fast and composable. Simple tasks should be easy. Performance trade offs should be exposed in the CLI interface. Composition should not come at the expense of performance. Let's say you're playing with some of the data from the Data Science Toolkit, which contains several CSV files.
    Downloads: 0 This Week
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  • 4

    slycat

    Web-based data science analysis and visualization platform.

    This is Slycat - a web-based data science analysis and visualization platform, created at Sandia National Laboratories. The goal of the Slycat project is to develop processes, tools and techniques to support data science, particularly analysis of large, high-dimensional data.
    Downloads: 0 This Week
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