Registry / testing / pyats-datastructures

pyats-datastructures

library26.5pypypiunverified

pyATS Datastructures provides extended data structures designed to enhance network automation and testing workflows. It offers specialized objects like `DataObject`, `DataList`, `DataDict`, and `MetaData` that allow for attribute-based access, schema validation, and meta-data handling, often used within the Cisco pyATS framework for managing configuration and state data. The current version is 26.3, and it follows the pyATS release cadence, typically releasing new versions quarterly or as needed with the main pyATS framework.

testing devops data

pip install pyats-datastructures

package ↗homepage ↗

INSTALL

IMPORT

SIG · PYATS-DATASTRUCTUR

pyats-datastructures

testingpythonv26.5

Install

1.8s avg

Import

—

Disk

19MB

Pass rate

7/ 10

Env Coverage7 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v26.5 · pip install

no network on importno background threads

Install × environment matrix

Each cell = how many times install + import succeeded across repeated harness runs. Partial = flaky.
glibc = Debian/Ubuntu slim · musl = Alpine Linux

musl

glibc

py 3.10

✓ —

✓ 1.78s

py 3.11

✕ build_error

✓ 1.75s

py 3.12

✕ build_error

✓ 1.6s

py 3.13

✕ build_error

✓ 1.58s

py 3.9

✓ —

✓ 2.1s

19MB installed

● package 19MB

Code

Verified usage

Verified import paths — ran on the pinned version, not inferred.

DataObject

✓ from pyats.datastructures import DataObject

✗ from pyats import DataObject

DataList

✓ from pyats.datastructures import DataList

DataDict

✓ from pyats.datastructures import DataDict

This quickstart demonstrates how to create and interact with a `DataObject`, assigning attributes and converting it to a standard Python dictionary. `DataObject` provides attribute-style access and allows for nesting other `DataObject` instances.

from pyats.datastructures import DataObject # Create a DataObject data = DataObject() # Assign attributes like a regular object data.attr = 'value' data.attr_2 = 123 data.nested = DataObject() data.nested.item = 'nested_value' # Access attributes print(f"data.attr: {data.attr}") print(f"data.nested.item: {data.nested.item}") # Convert to a standard Python dictionary plain_dict = data.to_dict() print(f"Converted to dict: {plain_dict}") assert plain_dict == {'attr': 'value', 'attr_2': 123, 'nested': {'item': 'nested_value'}}

Debug

Known issues

gotchaWhile `DataObject` provides dictionary-like and attribute-like access, it does not inherit directly from `dict` or `list`. Direct use of standard dictionary/list methods like `.keys()`, `.items()`, or `.append()` on a `DataObject` instance itself will result in an `AttributeError` (unless explicitly implemented or if it contains a `DataDict`/`DataList` instance).

fix

To perform iterable operations or convert to a native Python type, use specific methods like `DataObject.to_dict()` or `DataList.to_list()`. Interact with `DataDict` or `DataList` objects embedded within the `DataObject` for their respective operations.

affects: All versions

gotchaWhen assigning a plain Python `dict` or `list` to an attribute of a `DataObject`, `pyats-datastructures` will implicitly convert them into `DataDict` or `DataList` instances. While this offers convenience, it means subsequent operations on these nested structures will follow `DataDict`/`DataList` rules, not standard `dict`/`list` behavior.

fix

Be aware of this implicit conversion. If you require a strictly plain `dict` or `list` nested within, you may need to convert it back explicitly or manage it outside the `DataObject` context. Alternatively, embrace the `DataDict`/`DataList` behavior for enhanced features.

affects: All versions

Upgrade

Version history

26.5latest on PyPI · released May 28, 2026

Audit

Dependencies

pyyamlrequiredYAML serialization/deserialization.

schematicsrequiredSchema validation and model definition.

diff_match_patchrequiredString differencing capabilities.

coloramarequiredCross-platform colored terminal output.

jinja2requiredTemplating for string processing.

pyats.utilsrequiredCore utility functions from the pyATS framework.

Agent activity

15 hits · last 30 days

node

Resources

packagehttps://pypi.org/project/pyats-datastructures/ ↗

homepagehttps://developer.cisco.com/pyats/ ↗