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

Project description

Frictionless Framework

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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


  • 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.


  • 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


$ 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"




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