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https://github.com/uwdata/arquero

Query processing and transformation of array-backed data tables.
https://github.com/uwdata/arquero

arrays data database dataframe query table transform

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Query processing and transformation of array-backed data tables.

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README 

        
# Arquero 



**Arquero** is a JavaScript library for query processing and transformation of array-backed data tables. Following the [relational algebra](https://en.wikipedia.org/wiki/Relational_algebra) and inspired by the design of [dplyr](https://dplyr.tidyverse.org/), Arquero provides a fluent API for manipulating column-oriented data frames. Arquero supports a range of data transformation tasks, including filter, sample, aggregation, window, join, and reshaping operations.



* Fast: process data tables with million+ rows.

* Flexible: query over arrays, typed arrays, array-like objects, or [Apache Arrow](https://arrow.apache.org/) columns.

* Full-Featured: perform a variety of wrangling and analysis tasks.

* Extensible: add new column types or functions, including aggregate & window operations.

* Lightweight: small size, minimal dependencies.



To get up and running, start with the [Introducing Arquero](https://observablehq.com/@uwdata/introducing-arquero) tutorial, part of the [Arquero notebook collection](https://observablehq.com/collection/@uwdata/arquero).



Have a question or need help? Post to the [Arquero GitHub Discussions board](https://github.com/uwdata/arquero/discussions).



Arquero is Spanish for "archer": if datasets are [arrows](https://arrow.apache.org/), Arquero helps their aim stay true. 🏹 Arquero also refers to a goalkeeper: safeguard your data from analytic "own goals"! 🥅 ✋ ⚽



## API Documentation



* [Top-Level API](https://uwdata.github.io/arquero/api) - All methods in the top-level Arquero namespace.

* [Table](https://uwdata.github.io/arquero/api/table) - Table access and output methods.

* [Verbs](https://uwdata.github.io/arquero/api/verbs) - Table transformation verbs.

* [Op Functions](https://uwdata.github.io/arquero/api/op) - All functions, including aggregate and window functions.

* [Expressions](https://uwdata.github.io/arquero/api/expressions) - Parsing and generation of table expressions.

* [Extensibility](https://uwdata.github.io/arquero/api/extensibility) - Extend Arquero with new expression functions or table verbs.



## Example



The core abstractions in Arquero are *data tables*, which model each column as an array of values, and *verbs* that transform data and return new tables. Verbs are table methods, allowing method chaining for multi-step transformations. Though each table is unique, many verbs reuse the underlying columns to limit duplication.



```js

import { all, desc, op, table } from 'arquero';



// Average hours of sunshine per month, from https://usclimatedata.com/.

const dt = table({

  'Seattle': [69,108,178,207,253,268,312,281,221,142,72,52],

  'Chicago': [135,136,187,215,281,311,318,283,226,193,113,106],

  'San Francisco': [165,182,251,281,314,330,300,272,267,243,189,156]

});



// Sorted differences between Seattle and Chicago.

// Table expressions use arrow function syntax.

dt.derive({

    month: d => op.row_number(),

    diff:  d => d.Seattle - d.Chicago

  })

  .select('month', 'diff')

  .orderby(desc('diff'))

  .print();



// Is Seattle more correlated with San Francisco or Chicago?

// Operations accept column name strings outside a function context.

dt.rollup({

    corr_sf:  op.corr('Seattle', 'San Francisco'),

    corr_chi: op.corr('Seattle', 'Chicago')

  })

  .print();



// Aggregate statistics per city, as output objects.

// Reshape (fold) the data to a two column layout: city, sun.

dt.fold(all(), { as: ['city', 'sun'] })

  .groupby('city')

  .rollup({

    min:  d => op.min(d.sun), // functional form of op.min('sun')

    max:  d => op.max(d.sun),

    avg:  d => op.average(d.sun),

    med:  d => op.median(d.sun),

    // functional forms permit flexible table expressions

    skew: ({sun: s}) => (op.mean(s) - op.median(s)) / op.stdev(s) || 0

  })

  .objects()

```



## Usage



### In Browser



To use in the browser, you can load Arquero from a content delivery network:



```html



```



Arquero will be imported into the `aq` global object. The default browser bundle does not include the [Apache Arrow](https://arrow.apache.org/) library. To perform Arrow encoding using [toArrow()](https://uwdata.github.io/arquero/api/#toArrow) or binary file loading using [loadArrow()](https://uwdata.github.io/arquero/api/#loadArrow), import Apache Arrow first:



```html



```



Alternatively, you can build and import `arquero.min.js` from the `dist` directory, or build your own application bundle. When building custom application bundles for the browser, the module bundler should draw from the `browser` property of Arquero's `package.json` file. For example, if using [rollup](https://rollupjs.org/), pass the `browser: true` option to the [node-resolve](https://github.com/rollup/plugins/tree/master/packages/node-resolve) plugin.



Arquero uses modern JavaScript features, and so will not work with some outdated browsers. To use Arquero with older browsers including Internet Explorer, set up your project with a transpiler such as [Babel](https://babeljs.io/).



### In Node.js or Application Bundles



First install `arquero` as a dependency, for example via `npm install arquero --save`. Arquero assumes Node version 12 or higher.



Import using CommonJS module syntax:



```js

const aq = require('arquero');

```



Import using ES module syntax, import all exports into a single object:



```js

import * as aq from 'arquero';

```



Import using ES module syntax, with targeted imports:



```js

import { op, table } from 'arquero';

```



## Build Instructions



To build and develop Arquero locally:



- Clone [https://github.com/uwdata/arquero](https://github.com/uwdata/arquero).

- Run `npm i` to install dependencies.

- Run `npm test` to run test cases, `npm run perf` to run performance benchmarks, and `npm run build` to build output files.