coola
Library to check equality between two complex/nested objects
Decision gist · record as of 2026-08-14
Yes. coola is actively maintained, has no core dependencies, supports current Python versions, carries no known vulnerabilities, and solves a real problem for anyone working with nested data structures in testing or validation. The permissive BSD-3-Clause license poses no restrictions. Install it if you regularly compare complex objects.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.10 or higher; optional dependencies must be installed separately to compare specific types like tensors or arrays.
- Low install friction with no runtime dependencies; actively maintained with a release 15 days ago and recent commits.
License · maintenance · safety
BSD-3-Clause (permissive) — BSD-3-Clause is permissive; you can use, modify, and distribute coola freely in commercial and private projects with minimal restrictions.
last release 2026-07-30 (15 days) · last repo commit 2026-08-14 · 1 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 1,006,325 downloads/mo, #4,525 on PyPI
Alternatives
Verify before relying
pip install coola
from coola.equality import objects_are_equal
data1 = {"key": "value1"}
data2 = {"key": "value1"}
result = objects_are_equal(data1, data2)
print(result) # True- Whether custom comparators can be registered for user-defined types beyond the documented supported types
- Performance characteristics when comparing very large nested structures or deeply nested hierarchies
- Whether the library handles circular references in nested structures
What it is and what it does
coola is a lightweight equality-checking library for scientific Python that solves the problem of comparing complex nested structures containing tensors, arrays, and DataFrames—objects where Python's native `==` operator fails or produces unexpected results. It provides three main functions: `objects_are_equal()` for strict comparison, `objects_are_allclose()` for numerical tolerance, and difference reporting to show exactly what differs between two structures.
Beyond equality checking, coola also offers utilities for data summarization (human-readable nested structure summaries), conversion between list-of-dicts and dict-of-lists formats, mapping utilities for flattening and filtering nested dictionaries, systematic traversal via depth-first and breadth-first search, and reduction operations (min, max, mean, median, quantile, std) on numeric sequences with pluggable backends. It has no core dependencies, making it lightweight, and supports modern Python versions (3.10 and above).
Use it for
- Unit testing: Compare expected and actual outputs in tests with clear failure messages showing structural differences.
- Numerical validation: Check if two nested structures are equal within a tolerance using `objects_are_allclose()`.
- Data pipeline debugging: Summarize and compare intermediate results to identify where data diverges.
- Configuration comparison: Flatten and compare nested configuration dictionaries or extract specific values.
- Batch processing: Traverse nested collections using DFS/BFS to filter or extract specific data types.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
coola is actively maintained, has no core dependencies, supports current Python versions, carries no known vulnerabilities, and solves a real problem for anyone working with nested data structures in testing or validation. The permissive BSD-3-Clause license poses no restrictions. Install it if you regularly compare complex objects.
Install
coola on PyPI
Before you install
Low install friction with no runtime dependencies; actively maintained with a release 15 days ago and recent commits.
Requires Python 3.10 or higher; optional dependencies must be installed separately to compare specific types like tensors or arrays.
License in practice
BSD-3-Clause is permissive; you can use, modify, and distribute coola freely in commercial and private projects with minimal restrictions.
Quickstart
pip install coola
from coola.equality import objects_are_equal
data1 = {"key": "value1"}
data2 = {"key": "value1"}
result = objects_are_equal(data1, data2)
print(result) # TrueVerify before relying
- Whether custom comparators can be registered for user-defined types beyond the documented supported types
- Performance characteristics when comparing very large nested structures or deeply nested hierarchies
- Whether the library handles circular references in nested structures
Package facts
| License | BSD-3-Clause permissive |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 15 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 1,006,325 / month, #4,525 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 4 - BetaIntended Audience :: DevelopersIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: MacOSOperating System :: POSIX :: LinuxProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14Topic :: Scientific/EngineeringTopic :: Software Development :: LibrariesTopic :: Software Development :: Testing |
Evidence: coola-1.1.10-py3-none-any.whl
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