Frictionless is a framework to describe, extract, validate, and transform tabular data
Project description
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
goodtablestofrictionlesssince 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 fromgoodtablesto Frictionless Framework.
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 Excel
- Working with Filelike
- Working with GSheets
- Working with HTML
- Working with Inline
- Working with JSON
- Working with Local
- Working with Multipart
- Working with ODS
- Working with Pandas
- Working with Remote
- Working with Server
- Working with SPSS
- Working with SQL
- Working with Text
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