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frictionlessdata
Data management framework for Python that provides functionality to describe, extract, validate, and transform tabular data
# Overview Currently, almost all the framework is nicely typed after the standards (e.g. Field -> IntegerField etc) and extensions for validate and transform (e.g. Check -> TableDimensionsCheck etc). For...
# Overview Currently, we basically don't do any non-tabular file validation in a normal validation flow. We just validate checksums afterward. As we're considering removing stats calculations from the normal...
# It's easy to vote for your favorite issue - Follow this [**LINK**](https://github.com/frictionlessdata/frictionless-py/issues?q=is%3Aissue+is%3Aopen+sort%3Areactions-%2B1-desc) where all the features are sorted by popularity - Open the issue you'd like to be implemented...
# Overview Across the codebase we use `'this style of "quoting" things'` but for metadata validation single and double quotes are inversed because of the JSONSchema validation library. It's a...
# Overview In v5 we have improved metadata validation errors but we can improve it more if we provide full JSON path to the error e.g. `at the resource "name"...
# Overview Here - https://framework.frictionlessdata.io/docs/errors/metadata.html - we want to have a separate `Hierarchy` page containing all the errors in a hierarchical view We can use `inspect.getclasstree` for it in `livemark.plugins.api`
# Overview For example here - https://framework.frictionlessdata.io/blog/2022/08-22-frictionless-framework-v5.html - we want to have `h2,h3` for TOC generation but withouth annoying expanding/collapsing
# Overview Currently, there `Detector.detect_descriptor` static method that returns a descriptor type (or None) based on the filename or dict contents (e.g. `resources` property -> data package). We want to...
# Overview For example, there is `ExcelParser` that inherits from `Parser`
# Overview It should be addressed in Livemark but we create it here for tracking simplicity (as it's this docs feature as well)
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Data management framework for Python that provides functionality to describe, extract, validate, and transform tabular data