Metadata-Version: 2.1
Name: frictionless
Version: 5.8.1
Summary: Data management framework for Python that provides functionality to describe, extract, validate, and transform tabular data
Home-page: https://github.com/frictionlessdata/frictionless-py
Author: Open Knowledge Foundation
Author-email: info@okfn.org
License: MIT
Keywords: data validation,frictionless data,open data,json schema,json table schema,data package,tabular data package
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Description-Content-Type: text/markdown
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# Frictionless Framework

[![Build](https://img.shields.io/github/actions/workflow/status/frictionlessdata/framework/general.yaml?branch=main)](https://github.com/frictionlessdata/framework/actions)
[![Coverage](https://img.shields.io/codecov/c/github/frictionlessdata/framework/main)](https://codecov.io/gh/frictionlessdata/framework)
[![Release](https://img.shields.io/pypi/v/frictionless.svg)](https://pypi.python.org/pypi/frictionless)
[![Citation](https://zenodo.org/badge/28409905.svg)](https://zenodo.org/badge/latestdoi/28409905)
[![Codebase](https://img.shields.io/badge/codebase-github-brightgreen)](https://github.com/frictionlessdata/framework)
[![Support](https://img.shields.io/badge/support-slack-brightgreen)](https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg)

```markdown remark type=primary
Migrating from an older version? Please read **[v5](blog/2022/08-22-frictionless-framework-v5.html)** announcement and migration guide.
```

Data management framework for Python that provides functionality to describe, extract, validate, and transform tabular data (DEVT Framework). 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 Standards](https://specs.frictionlessdata.io/).

## 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 schemes 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

- Open Source (MIT)
- 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

```bash
$ 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

Please visit our documentation portal:
- https://framework.frictionlessdata.io

