Skip to main content

Frictionless is a framework to describe, extract, validate, and transform tabular data

Project description

Frictionless Framework

Travis Coveralls PyPi Github Discord

Frictionless is a framework to describe, extract, validate, and transform tabular data. It supports a great deal of data sources and formats, as well as provides popular platforms integrations. The framework is powered by the lightweight yet comprehensive Frictionless Data Specifications.

[Important Notice] We have renamed goodtables to frictionless since version 3. The framework got various improvements and was extended to be a complete data solution. The change in not breaking for the existing software so no actions are required. Please read the Migration Guide from goodtables to Frictionless Framework.

  • we continue to bug-fix goodtables@2.x in this branch as well as it's available on PyPi as it was before
  • please note that frictionless@3.x version's API, we're working on at the moment, is not stable
  • we will release frictionless@4.x by the end of 2020 to be the first SemVer/stable version

Purpose

  • Describe your data: You can infer, edit and save metadata of your data tables. It's a first step for ensuring data quality and usability. Frictionless metadata includes general information about your data like textual description, as well as, field types and other tabular data details.
  • Extract your data: You can read your data using a unified tabular interface. Data quality and consistency are guaranteed by a schema. Frictionless supports various file protocols like HTTP, FTP, and S3 and data formats like CSV, XLS, JSON, SQL, and others.
  • Validate your data: You can validate data tables, resources, and datasets. Frictionless generates a unified validation report, as well as supports a lot of options to customize the validation process.
  • Transform your data: You can clean, reshape, and transfer your data tables and datasets. Frictionless provides a pipeline capability and a lower-level interface to work with the data.

Features

  • Powerful Python framework
  • Convenient command-line interface
  • Low memory consumption for data of any size
  • Reasonable performance on big data
  • Support for compressed files
  • Custom checks and formats
  • Fully pluggable architecture
  • The included API server
  • More than 1000+ tests

Example

$ frictionless validate data/invalid.csv
[invalid] data/invalid.csv

  row    field  code              message
-----  -------  ----------------  --------------------------------------------
             3  blank-header      Header in field at position "3" is blank
             4  duplicate-header  Header "name" in field "4" is duplicated
    2        3  missing-cell      Row "2" has a missing cell in field "field3"
    2        4  missing-cell      Row "2" has a missing cell in field "name2"
    3        3  missing-cell      Row "3" has a missing cell in field "field3"
    3        4  missing-cell      Row "3" has a missing cell in field "name2"
    4           blank-row         Row "4" is completely blank
    5        5  extra-cell        Row "5" has an extra value in field  "5"

Documentation

General

Specific

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Files for frictionless, version 3.34.0
Filename, size File type Python version Upload date Hashes
Filename, size frictionless-3.34.0-py2.py3-none-any.whl (215.4 kB) File type Wheel Python version py2.py3 Upload date Hashes View
Filename, size frictionless-3.34.0.tar.gz (155.2 kB) File type Source Python version None Upload date Hashes View

Supported by

Pingdom Pingdom Monitoring Google Google Object Storage and Download Analytics Sentry Sentry Error logging AWS AWS Cloud computing DataDog DataDog Monitoring Fastly Fastly CDN DigiCert DigiCert EV certificate StatusPage StatusPage Status page