Data management framework for Python that provides functionality to describe, extract, validate, and transform tabular data
Project description
frictionless-py
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-5.15.3.tar.gz
(74.6 MB
view details)
Built Distribution
frictionless-5.15.3-py3-none-any.whl
(309.0 kB
view details)
File details
Details for the file frictionless-5.15.3.tar.gz.
File metadata
- Download URL: frictionless-5.15.3.tar.gz
- Upload date:
- Size: 74.6 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 325e2c66a39240046840efd5e3764ec36bafaf542abf443129291293be9483ae |
|
| MD5 | 7978af9187929a05c4d09c48f9f50d7d |
|
| BLAKE2b-256 | 340fb1bb50c9fb68029b4945ff71e654bd6bee78c06d750ffa7cac585ebf0a40 |
Provenance
File details
Details for the file frictionless-5.15.3-py3-none-any.whl.
File metadata
- Download URL: frictionless-5.15.3-py3-none-any.whl
- Upload date:
- Size: 309.0 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.2 CPython/3.11.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 1f99ed0a5a73a23e9246c10559be4e8f7bcd02524257c3dc8a3b4f0b0a70d5f1 |
|
| MD5 | bccda0446e0f89f86e387468d805ed9f |
|
| BLAKE2b-256 | beb9c78e3c4efc221a046073e4ae80d5b3c0c9d3be2a3ee2935453bee308e2fc |
