Frictionless Data Frictionless Data
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  • Frictionless Framework

    • Purpose
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        # 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 [email protected] in this branch as well as it’s available on PyPi as it was before
        • please note that [email protected] version’s API, we’re working on at the moment, is not stable
        • we will release [email protected] 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 Excel
        • Working with Filelike
        • Working with GSheet
        • 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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        Last Updated: 12/2/2020, 11:16:38 AM