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
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: