Schema validation for Python data structures
Basic validation for Python data structures in a mostly declarative form (there is an escape hatch in “extraValidation” callables).
Validation errors are reported as both a path within the data structure (sequence of indices or keys) and a descriptive message (string).
data = json.load(some_file) # or pickle, or ... errors = dataschema.Validator(my_schema).validate(data) if errors: for path, message in errors: # Report error `message` at path `path`. else: # Any data access or application-specific validation can now # rely on properties of my_schema (e.g. minimum number of # elements in a sequence, data types of elements, presence of # certain keys in a dict, etc.).
See the unit tests for schema examples.
There are a few limitations (only string keys for any dictionaries in data) and a more fully Pythonic validator might focus on interfaces and abstract base classes over concrete types. However, dataschema is a great improvement over ad hoc validation code for many uses today.
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
|Filename, size||File type||Python version||Upload date||Hashes|
|Filename, size dataschema-0.1.tar.gz (7.1 kB)||File type Source||Python version None||Upload date||Hashes View hashes|