Data management framework for Python that provides functionality to describe, extract, validate, and transform tabular data
Project description
Frictionless Framework
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.
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
$ pip install frictionless
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
Please visit our documentation portal:
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
frictionless-5.13.1.tar.gz
(2.6 MB
view details)
Built Distribution
File details
Details for the file frictionless-5.13.1.tar.gz.
File metadata
- Download URL: frictionless-5.13.1.tar.gz
- Upload date:
- Size: 2.6 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.11.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 78ee7c4ea784e85ef7117f17165baf4f2728790ba9406693df8370e812726c4a |
|
| MD5 | 1dc69d4470ecc2090eff26a616a61153 |
|
| BLAKE2b-256 | 3f70b1eec97b15e22188e79d02a6e15ad03f29dd32090a6d6974d35d79244850 |
Provenance
File details
Details for the file frictionless-5.13.1-py2.py3-none-any.whl.
File metadata
- Download URL: frictionless-5.13.1-py2.py3-none-any.whl
- Upload date:
- Size: 2.8 MB
- Tags: Python 2, Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.11.3
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 8e5afc7c08364d4563797944920e044a8d37970c767c0c6920ac804a05c5f1a9 |
|
| MD5 | a36dac85ffe9dac1b46fb218d2dc223e |
|
| BLAKE2b-256 | 3d00067d25bda8d84804b87c6ba75a52f6ce56a20767e47fb87655ee653079f4 |
