jsonschema
pandera
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jsonschema | pandera | |
---|---|---|
4 | 7 | |
4,432 | 2,994 | |
1.2% | 4.8% | |
8.8 | 8.9 | |
2 days ago | 7 days ago | |
Python | Python | |
MIT License | MIT License |
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jsonschema
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Forced to move away from Django template because of nested forms ?
Forms are hard. We use python jsonschema to write our form schemas and validation and use react json schema form for the front end. It's a long time in the making and we still have to write widgets and extensions to get everything we need. Good luck.
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I wrote okjson - A fast, simple, and pythonic JSON Schema Validator
I had a requirement to process and validate large payloads of JSON concurrently for a web service, initially I implemented it using jsonschema and fastjsonschema but I found the whole JSON Schema Specification to be confusing at times and on top of that wanted better performance. Albeit there are ways to compile/cache the schema, I wanted to move away from the schema specification so I wrote a validation library inspired by the design of tiangolo/sqlmodel (type hints) to solve this problem easier.
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Validating a YAML file
This is a hard problem, because, if I understand you correctly, you issue is that you are using something like this: https://github.com/Julian/jsonschema, but want to make the error messages more specific. That means you will need to understand the package sufficiently to find out where it is encountering issues and then provide a more human readable error. Definitely doable, but the first piece, understanding the package enough to revise the messages is difficult.
- Simple method for JSON body minimum required keys checking
pandera
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Unit testing functions that input/output dataframes?
I use Pandera, so I just need to define the expected input/output schemas (i.e. column names, types, and constraints on them), and Pandera automatically generates fake data for the unit tests, and validates the result: https://github.com/unionai-oss/pandera
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Great Expectations is annoyingly cumbersome
Please DM me! Or we can discuss in this issue which I just created: https://github.com/unionai-oss/pandera/issues/1042
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Data validation for dashboards
In my opinion for simple data validation tasks the best solution is always Pandera.
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Show HN: Pandera 0.8.0 – validate pandas, dask, modin, and koalas dataframes
* adds support for mypy static type-linting if you need that extra type safety
Repo: https://github.com/pandera-dev/pandera
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Pandera 0.8.0: Schema Validation for Pandas, Dask, Modin, and Koalas DataFrames. Oh, and also out-of-the-box Pydantic and Mypy support :)
Repo: https://github.com/pandera-dev/pandera
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How heavily do you use Great Expectations?
pandera
What are some alternatives?
Cerberus - Lightweight, extensible data validation library for Python
soda-sql - Data profiling, testing, and monitoring for SQL accessible data.
schema - Schema validation just got Pythonic
Schematics - Python Data Structures for Humans™.
voluptuous - CONTRIBUTIONS ONLY: Voluptuous, despite the name, is a Python data validation library.
pointblank - Data quality assessment and metadata reporting for data frames and database tables
swifter - A package which efficiently applies any function to a pandas dataframe or series in the fastest available manner
colander - A serialization/deserialization/validation library for strings, mappings and lists.
dbt-expectations - Port(ish) of Great Expectations to dbt test macros
valideer - Lightweight data validation and adaptation Python library.
sweetviz - Visualize and compare datasets, target values and associations, with one line of code.