frictionless 3.34.0

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

Frictionless Framework

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

• Getting Started
• Introduction Guide
• Describing Data
• Extracting Data
• Validating Data
• Transforming Data
• Extension Guide
• Migration Guide
• Schemes Reference
• Formats Reference
• Errors Reference
• API Reference
• Contributing
• Changelog
• Authors

Specific

• Working with AWS
• Working with BigQuery
• Working with CKAN
• Working with CSV
• Working with DataFlows
• Working with Elastic
• Working with Excel
• Working with GSheet
• Working with HTML
• Working with Inline
• Working with JSON
• Working with Local
• Working with ODS
• Working with Pandas
• Working with Remote
• Working with Server
• Working with SPSS
• Working with SQL
• Working with Stream
• Working with Text