Frictionless is a data framework
Project description
Frictionless for Python
Frictionless is a framework to describe, extract, validate, and transform tabular data. 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 Data Specifications.
Since version 3
goodtableswas renamed tofrictionless. The framework got various improvements and was extended to be a complete data solution. The change in not breaking for the existing software so no actions are required. We continue to maintain Goodtables Branch in this repo in bug-fixing mode as well as it's available on PyPi asgoodtables. Please read the Migration Guide to start working with the newer Frictionless for Python. Please note thatfrictionless@3.xversion's API, available at the moment, is not stable. We will releasefrictionless@4by the end of 2020 to be the first SemVer/stable version of the framework.
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 protocols 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
- 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
$ 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
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
Built Distribution
Hashes for frictionless-3.9.1-py2.py3-none-any.whl
| Algorithm | Hash digest | |
|---|---|---|
| SHA256 | 121113b3182568712797450a2df63c7423dba0fb2e23ed12b8dbbf7372c8a191 |
|
| MD5 | ba9d8c75de291bc4e89aa82737ab0cc6 |
|
| BLAKE2b-256 | ff4ce4697942194848c256762d1a998b78c1a27e926f984814299f0095aacbb0 |
