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-5.14.1.tar.gz
(74.6 MB
view details)
Built Distribution
frictionless-5.14.1-py3-none-any.whl
(308.2 kB
view details)
File details
Details for the file frictionless-5.14.1.tar.gz.
File metadata
- Download URL: frictionless-5.14.1.tar.gz
- Upload date:
- Size: 74.6 MB
- Tags: Source
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.11.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | ba3630fdf57ce99dcf0b62dd001b25ee0b98dc2fdd86ad5f3b04bc267df95abb |
|
| MD5 | 0d9b03981d879f73c53e2c0a540bd7d1 |
|
| BLAKE2b-256 | d206ea8c100c96a8386bd2655df149b4113acda04ad7a7a4141fb19528f9222b |
Provenance
File details
Details for the file frictionless-5.14.1-py3-none-any.whl.
File metadata
- Download URL: frictionless-5.14.1-py3-none-any.whl
- Upload date:
- Size: 308.2 kB
- Tags: Python 3
- Uploaded using Trusted Publishing? No
- Uploaded via: twine/4.0.1 CPython/3.11.4
File hashes
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | f34dea8f33a8ea9688c25d41be611bf4269a2c5b880acd91892329e380d2b9ae |
|
| MD5 | af498044e389c1b9bba8d02b1eff017f |
|
| BLAKE2b-256 | 0c875c01d4282337fce501b98486a5677585bbd6c6673c3cbc19886fa5631905 |
