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frictionlessdata / frictionless-js

A lightweight, standardized library accessing files and datasets, especially tabular ones (CSV, Excel).
https://frictionlessdata.io
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frictionless.js is a lightweight, standardized "stream-plus-metadata" interface for accessing files and datasets, especially tabular ones (CSV, Excel).

frictionless.js follows the "Frictionless Data Lib Pattern".

Build Status Gitter

A line of code is worth a thousand words ...

const {open} = require('frictionless.js')

var file = open('path/to/ons-mye-population-totals.xls')

file.descriptor
  {
    path: '/path/to/ons-mye-population-totals.xls',
    pathType: 'local',
    name: 'ons-mye-population-totals',
    format: 'xls',
    mediatype: 'application/vnd.ms-excel',
    encoding: 'windows-1252'
  }

file.size
  67584

file.rows() => stream object for rows
  // keyed by header row by default ...
  { 'col1': 1, 'col2': 2, ... }
  { 'col1': 10, 'col2': 20, ... }

Table of Contents

Motivation

frictionless.js is motivated by the following use cases:

Features

Installation

npm install frictionless.js

Browser

If you want to use the it in the browser, first you need to build the bundle.

Run the following command to generate the bundle for the necessary JS targets

yarn build

This will create two bundles in the dist folder. node sub-folder contains build for node environment, while browser sub-folder contains build for the browser. In a simple html file you can use it like this:

<head>
  <script src="https://github.com/frictionlessdata/frictionless-js/raw/master/dist/browser/bundle.js"></script>
  <script>
    // Global data lib is available here...

    const file = data.open('path/to/file')
    ...
  </script>
</head>
<body></body>

Usage

With a simple file:

const data = require('frictionless.js')

// path can be local or remote
const file = data.open(path)

// descriptor with metadata e.g. name, path, format, (guessed) mimetype etc
console.log(file.descriptor)

// returns promise with raw stream
const stream = await file.stream()

// let's get an object stream of the rows
// (assuming it is tabular i.e. csv, xls etc)
const rows = await file.rows()

// entire file as a buffer
const buffer = await file.buffer

//for large files you can return in chunks
await file.bufferInChunks((chunk, progress)=>{
  console.log(progress, chunk)
})

With a Dataset:

const { Dataset } = require('frictionless.js')

const path = '/path/to/directory/' // must have datapackage.json in the directory atm

Dataset.load(path).then(dataset => {
  // get a data file in this dataset
  const file = dataset.resources[0]

  const data = file.stream()
})

API

open

Load a file from a path or descriptor.

load(pathOrDescriptor, {basePath, format}={})

There are 3 types of file source we support:

const data = require('frictionless.js')

const file = data.open('/path/to/file.csv')

const file = data.open('https://example.com/data.xls')

// loading raw data
const file = data.open({
  name: 'mydata',
  data: { // can be any javascript - an object, an array or a string or ...
    a: 1,
    b: 2
  }
})

// Loading with a descriptor - this allows more fine-grained configuration
// The descriptor should follow the Frictionless Data Resource model
// http://specs.frictionlessdata.io/data-resource/
const file = data.open({
  // file or url path
  path: 'https://example.com/data.csv',
  // a Table Schema - https://specs.frictionlessdata.io/table-schema/
  schema: {
    fields: [
      ...
    ]
  }
  // CSV dialect - https://specs.frictionlessdata.io/csv-dialect/
  dialect: {
    // this is tab separated CSV/DSV
    delimiter: '\t'
  }
})

basePath: use in cases where you want to create a File with a path that is relative to a base directory / path e.g.

const file = data.open('data.csv', {basePath: '/my/base/path'})

Will open the file: /my/base/path/data.csv

This functionality is primarily useful when using Files as part of Datasets where it can be convenient for a File to have a path relative to the directory of the Dataset. (See also Data Package and Data Resource in the Frictionless Data specs).

Files

A single data file - local or remote.

load

DEPRECATED. Use simple open.

Metadata

Main metadata is available via the descriptor:

file.descriptor

This metadata is a combination of the metadata passed in at File creation (if you created the File with a descriptor object) and auto-inferred information from the File path. This is the info that is auto-inferred:

path: path this was instantiated with - may not be same as file.path (depending on basePath)
pathType: remote | local
name:   file name (without extension)
format: the extension
mediatype: mimetype based on file name and extension

In addition to this metadata there are certain properties which are computed on demand:

// the full path to the file (using basepath)
const path = file.path

const size = file.size

// md5 hash of the file
const hash = file.hash()

// sha256 hash of the file
const hash256 = file.hash(hashType='sha256')

// file encoding
const encoding = file.encoding

Note: size, hash are not available for remote Files (those created from urls).

stream

stream()

Get readable stream

@returns Promise with readable stream object on resolve

buffer

File.buffer

Get this file as a buffer (async)

@returns: promise which resolves to the buffer

rows

rows({keyed}={})

Get the rows for this file as a node object stream (assumes underlying data is tabular!)

@returns Promise with rows as parsed JS objects (depends on file format)

TODO: casting (does data get cast automatically for you or not ...)

What formats are supported?

The rows functionality is currently available for CSV and Excel files. The Tabular support incorporates supports for Table Schema and CSV Dialect e.g. you can do:


// load a CSV with a non-standard dialect e.g. tab separated or semi-colon separated
const file = data.open({
  path: 'mydata.tsv'
  // Full support for http://specs.frictionlessdata.io/csv-dialect/
  dialect: {
    delimiter: '\t' // for tabs or ';' for semi-colons etc
  }
})

// open a CSV with a Table Schema
const file = data.open({
  path: 'mydata.csv'
  // Full support for Table Schema https://specs.frictionlessdata.io/table-schema/
  schema: {
    fields: [
      {
        name: 'Column 1',
        type: 'integer'
      },
      ...
    ]
  }
})

Datasets

A collection of data files with optional metadata.

Under the hood it heavily uses Data Package formats and it natively supports Data Package formats including loading from datapackage.json files. However, it does not require knowledge or use of Data Packages.

A Dataset has four primary properties:

In addition we provide the convenience attributes:

load

To create a new Dataset object use Dataset.load. It takes descriptor Object or identifier string:

async Dataset.load(pathOrDescriptor, {owner = null} = {})

For example:

const data = require('frictionless.js')

const pathOrDescriptor = 'https://raw.githubusercontent.com/datasets/co2-ppm/master/datapackage.json'
const dataset = await data.Dataset.load(pathOrDescriptor)

addResource

Add a resource to the Dataset:

addResource(resource)

Utilities

isDataset

// seeks to guess whether a given path is the path to a Dataset or a File
// (i.e. a directory or datapackage.json)
data.isDataset(path)

parseDatasetIdentifier

// parses dataset path and returns identifier dictionary
// handles local paths, remote URLs as well as DataHub and GitHub specific URLs
// (e.g., https://datahub.io/core/finance-vix or https://github.com/datasets/finance-vix
const identifier = data.parseDatasetIdentifier(path)

console.log(identifier)

and it prints out:

{
    name: <name>,
    owner: <owner>,
    path: <path>,
    type: <type>,
    original: <path>,
    version: <version>
}

Developers

Requirements:

Test

We have two type of tests Karma based for browser testing and Mocha with Chai for Node. All node tests are in datajs/test folder. Since Mocha is sensitive to test namings, we have separate the folder /browser-test for only Karma.

Setup

  1. Git clone the repo

  2. Install dependencies: yarn

  3. To make the browser and node test work, first run the build: yarn build

  4. Run tests: yarn test

  5. Do some dev work

  6. Once done, make sure tests are passing. Then build distribution version of the app - yarn build.

    Run yarn build to compile using webpack and babel for different node and web target. To watch the build run: yarn build:watch.

  7. Now proceed to "Deployment" stage

Deployment

  1. Update version number in package.json.
  2. Git commit: git commit -m "some message, eg, version".
  3. Release: git tag -a v0.12.0 -m "some message".
  4. Push: git push origin master --tags
  5. Publish to NPM: npm publish