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.

• 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.

• 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

Please visit our documentation portal:

• https://framework.frictionlessdata.io