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pane
is a modern Python library for dataclasses and data conversion, aiming
for speed and expressiveness.
pane
draws heavy inspiration from Pydantic (among others), but its goals
are quite different. For example, pane
has first-class conversion to and from
all JSON datatypes, not just mappings (and no 'root' hacks necessary).
In this sense pane
is a library for general data conversion & validation,
while Pydantic is a dataclass-first library. In addition, pane
is stricter
than Pydantic in several cases. For instance, pane
will not attempt to coerce
3.14
or "3"
to an integer.
pane
is designed to be used to create complex declarative configuration languages
in formats like JSON, TOML, and YAML. This requires full support for complex, nested
types like t.Union[int, t.Tuple[int, str], list[int]]
. It also requires
useful error messages:
>>> pane.convert('fail', t.Union[int, t.Tuple[int, str], list[int]])
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "pane/convert.py", line 489, in convert
return from_data(data, ty)
^^^^^^^^^^^^^^^^^^^
File "pane/convert.py", line 484, in from_data
return converter.convert(val)
^^^^^^^^^^^^^^^^^^^^^^
File "pane/convert.py", line 128, in convert
raise ConvertError(node)
pane.convert.ConvertError: Expected one of:
- an int
- tuple of length 2
- sequence
Instead got `fail` of type `str`
pane
is a work in progress. The following is a roadmap of features:
Feature | State |
---|---|
Basic type conversions | Done |
Numeric array types (numpy) | Done |
Sum & product type support | Done |
Tagged unions | Partial |
'Flattened' fields | Planned |
Basic dataclasses | Done |
Dataclass helpers | Partial |
Generic & inherited dataclasses | Done |
Parameter aliases & renaming | Done |
Arbitrary validation logic | Done |
Schema import/export | Not planned |
FAQs
A modern dataclass & data conversion library, focused on speed and expressiveness.
We found that py-pane demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
Did you know?
Socket for GitHub automatically highlights issues in each pull request and monitors the health of all your open source dependencies. Discover the contents of your packages and block harmful activity before you install or update your dependencies.
Product
Socket now supports Scala and Kotlin, bringing AI-powered threat detection to JVM projects with easy manifest generation and fast, accurate scans.
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