Displaying 1 to 20 from 28 results

Handsontable - JavaScript/HTML5 UI Spreadsheet library for web apps. Available for React, Vue and Angular

  •    Javascript

Handsontable Community Edition (CE) is an open source JavaScript/HTML5 UI Spreadsheet component for web apps. It easily integrates with any data source and comes with a variety of useful features like data binding, validation, sorting or powerful context menu. It is available for Vue, React, Angular and Polymer.

tad - A desktop application for viewing and analyzing tabular data

  •    Javascript

Tad is a desktop application for viewing and analyzing tabular data such as CSV files. The easiest way to install Tad is to use a pre-packaged binary release. See The Tad Landing Page for information on the latest release and a download link.

vaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀

  •    Python

Vaex is a high performance Python library for lazy Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It calculates statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid for more than a billion (10^9) samples/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted). HDF5 and Apache Arrow supported.




alibi-detect - Algorithms for outlier, adversarial and drift detection

  •    Python

Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection. For more background on the importance of monitoring outliers and distributions in a production setting, check out this talk from the Challenges in Deploying and Monitoring Machine Learning Systems ICML 2020 workshop, based on the paper Monitoring and explainability of models in production and referencing Alibi Detect.

tui

  •    TypeScript

The functionality of TOAST UI Grid is available when using the Plain javaScript, React, Vue Component. The TOAST UI Grid is a component that can display, edit, add, and delete multiple data. You can append units to the data shown and use html to represent images and links instead of textual data.


pytorch_tabular - A standard framework for modelling Deep Learning Models for tabular data

  •    Python

It has been built on the shoulders of giants like PyTorch(obviously), and PyTorch Lightning. Although the installation includes PyTorch, the best and recommended way is to first install PyTorch from here, picking up the right CUDA version for your machine.

deltapy - DeltaPy - Tabular Data Augmentation (by @firmai)

  •    Jupyter

Animated investment research at Sov.ai, sponsoring open source initiatives. Tabular augmentation is a new experimental space that makes use of novel and traditional data generation and synthesisation techniques to improve model prediction success. It is in essence a process of modular feature engineering and observation engineering while emphasising the order of augmentation to achieve the best predicted outcome from a given information set. DeltaPy was created with finance applications in mind, but it can be broadly applied to any data-rich environment.

daff - align and compare tables

  •    Java

This is a library for comparing tables, producing a summary of their differences, and using such a summary as a patch file. It is optimized for comparing tables that share a common origin, in other words multiple versions of the "same" table.

active_importer - Define importers that load tabular data from spreadsheets or CSV files into any ActiveRecord-like ORM

  •    Ruby

Define importers that load tabular data from spreadsheets or CSV files into any ActiveRecord-like ORM. Define classes that you instruct on how to import data into data models.

meza - A Python toolkit for processing tabular data

  •    Python

meza is a Python library for reading and processing tabular data. It has a functional programming style API, excels at reading/writing large files, and can process 10+ file types. meza has been tested and is known to work on Python 2.7, 3.5, and 3.6; PyPy2 5.8.0, and PyPy3 5.8.0.

vaex - Lazy Out-of-Core DataFrames for Python, visualize and explore big tabular data at a billion rows per second

  •    Python

Vaex is a python library for Out-of-Core DataFrames (similar to Pandas), to visualize and explore big tabular datasets. It can calculate statistics such as mean, sum, count, standard deviation etc, on an N-dimensional grid up to a billion (109) objects/rows per second. Visualization is done using histograms, density plots and 3d volume rendering, allowing interactive exploration of big data. Vaex uses memory mapping, zero memory copy policy and lazy computations for best performance (no memory wasted).

Tabula - :u7533: Pretty printer for maps/structs collections (Elixir)

  •    Elixir

Tabula can transform a list of maps (structs too, e.g. Ecto models) or Keywords into an ASCII/GitHub Markdown table. It's a weekend-over-beer-project of mine, loosely based on clojure.pprint.print-table.

wq

  •    Python

wq.io is a Pythonic library for consuming (input), iterating over, and generating (output) external data resources in various formats. wq.io facilitates interoperability between the wq framework and other systems and formats. wq.io is designed to be customized, with a base class and modular mixin classes that handle loading, parsing, and mapping external data to a convenient API.






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