# frictionless-py
[](https://github.com/frictionlessdata/frictionless-py/actions)
[](https://codecov.io/gh/frictionlessdata/frictionless-py)
[](https://pypi.python.org/pypi/frictionless)
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[](https://github.com/frictionlessdata/frictionless-py)
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```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
## Installation
```bash
$ pip install frictionless
```
## 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
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"description": "# frictionless-py\n\n[](https://github.com/frictionlessdata/frictionless-py/actions)\n[](https://codecov.io/gh/frictionlessdata/frictionless-py)\n[](https://pypi.python.org/pypi/frictionless)\n[](https://zenodo.org/badge/latestdoi/28409905)\n[](https://github.com/frictionlessdata/frictionless-py)\n[](https://join.slack.com/t/frictionlessdata/shared_invite/zt-17kpbffnm-tRfDW_wJgOw8tJVLvZTrBg)\n\n```markdown remark type=primary\nMigrating from an older version? Please read **[v5](blog/2022/08-22-frictionless-framework-v5.html)** announcement and migration guide.\n```\n\nData 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/).\n\n## Purpose\n\n- **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.\n- **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.\n- **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.\n- **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.\n\n## Features\n\n- Open Source (MIT)\n- Powerful Python framework\n- Convenient command-line interface\n- Low memory consumption for data of any size\n- Reasonable performance on big data\n- Support for compressed files\n- Custom checks and formats\n- Fully pluggable architecture\n- The included API server\n- More than 1000+ tests\n\n## Installation\n\n```bash\n$ pip install frictionless\n```\n\n## Example\n\n```bash\n$ frictionless validate data/invalid.csv\n[invalid] data/invalid.csv\n\n row field code message\n----- ------- ---------------- --------------------------------------------\n 3 blank-header Header in field at position \"3\" is blank\n 4 duplicate-header Header \"name\" in field \"4\" is duplicated\n 2 3 missing-cell Row \"2\" has a missing cell in field \"field3\"\n 2 4 missing-cell Row \"2\" has a missing cell in field \"name2\"\n 3 3 missing-cell Row \"3\" has a missing cell in field \"field3\"\n 3 4 missing-cell Row \"3\" has a missing cell in field \"name2\"\n 4 blank-row Row \"4\" is completely blank\n 5 5 extra-cell Row \"5\" has an extra value in field \"5\"\n```\n\n## Documentation\n\nPlease visit our documentation portal:\n- https://framework.frictionlessdata.io\n",
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