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miller - Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON
Miller is like awk, sed, cut, join, and sort for name-indexed data such as CSV, TSV, and tabular JSON. With Miller, you get to use named fields without needing to count positional indices, using familiar formats such as CSV, TSV, JSON, and positionally-indexed.
data-processing data-cleaning csv csv-files csv-format csv-reader streaming-data streaming-algorithms tsv json json-data data-reduction data-regression statistics statistical-analysis devops devops-tools tabular-data command-line command-line-toolsHandsontable - JavaScript/HTML5 UI Spreadsheet library for web apps. Available for React, Vue and Angular
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.
spreadsheet data-grid grid-editor dynamic-table component data-binding data grid table editor data-table excel tabular-data edit-cell editable-table data-spreadsheettad - A desktop application for viewing and analyzing tabular data
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.
desktop-application pivots tabular-data csv pivot-tables data-science data-analysisReact Virtualized - React components for efficiently rendering large lists and tabular data
React components for efficiently rendering large, scrollable lists and tabular data.
react react-component windowing virtualization list grid listview tabular-data performance reactjs virtual scrolling infinite virtualized table fixed header flexbox spreadsheet table-view infinite-scrollvaex - Out-of-Core hybrid Apache Arrow/NumPy DataFrame for Python, ML, visualize and explore big tabular data at a billion rows per second 🚀
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.
visualization machine-learning bigdata tabular-data hdf5 machinelearning dataframe memory-mapped-filealibi-detect - Algorithms for outlier, adversarial and drift detection
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.
time-series text images detection tabular-data semi-supervised-learning anomaly unsupervised-learning adversarial concept-drift outlier drift-detection data-drifttui
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.
typescript grid preact excel tabular-data spreadsheet datatable treegrid reactivity datagrid toast-uiautogluon - AutoGluon: AutoML for Text, Image, and Tabular Data
AutoGluon automates machine learning tasks enabling you to easily achieve strong predictive performance in your applications. With just a few lines of code, you can train and deploy high-accuracy machine learning and deep learning models on text, image, and tabular data. Announcement for previous users: The AutoGluon codebase has been modularized into namespace packages, which means you now only need those dependencies relevant to your prediction task of interest! For example, you can now work with tabular data without having to install dependencies required for AutoGluon's computer vision tasks (and vice versa). Unfortunately this improvement required a minor API change (eg. instead of from autogluon import TabularPrediction, you should now do: from autogluon.tabular import TabularPredictor), for all versions newer than v0.0.15. Documentation/tutorials under the old API may still be viewed for version 0.0.15 which is the last released version under the old API.
data-science machine-learning natural-language-processing computer-vision deep-learning mxnet scikit-learn tabular-data pytorch hyperparameter-optimization image-classification ensemble-learning object-detection transfer-learning structured-data gluon automl automated-machine-learning neural-architecture-search autogluonrows - A common, beautiful interface to tabular data, no matter the format
Want to learn more? Read the documentation.
tabular-data convert-data data-science csv excel xlsx xls table datapytorch_tabular - A standard framework for modelling Deep Learning Models for tabular data
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.
deep-learning tabular-data pytorch pytorch-lightningdeltapy - DeltaPy - Tabular Data Augmentation (by @firmai)
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.
finance data-science machine-learning time-series tabular-data feature-extraction feature-engineering data-augmentation augmentationdaff - align and compare tables
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.
csv csv-diffs tabular-data comparing-tables diff-format table diff patch mergeactive_importer - Define importers that load tabular data from spreadsheets or CSV files into any ActiveRecord-like ORM
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.
tabular-data spreadsheet csv-files activerecord orm data-import importermeza - A Python toolkit for processing tabular data
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.
pandas csv xml xlsx excel data tabular-data library functional-programming featuredvaex - Lazy Out-of-Core DataFrames for Python, visualize and explore big tabular data at a billion rows per second
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).
dataframe bigdata tabular-data visualization memory-mapped-file hdf5TableQA - AI Tool for querying natural language on tabular data.
AI Tool for querying natural language on tabular data.Built using QA models from transformers.
nlp qa machine-learning csv sql database ai tabular-data sql-query question-answering sql-generation nl2sql tableqa table-qa querying-natural-languagehandsontable-pro - Handsontable Pro - JavaScript/HTML5 Spreadsheet Component with Premium Features
Spreadsheet-like data grid editor that provides copy/paste functionality compatible with Excel/Google Docs
data grid table editor grid-editor data-grid data-table spreadsheet excel tabular-data edit-cell editable-table data-spreadsheetTabula - :u7533: Pretty printer for maps/structs collections (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.
elixir pretty-printer tabular-datawq
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.
data-processing pythonic iterable pandas import export spreadsheet tabular-data
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