scooby

library0.11.2pypypi✓ verified 23d ago

Scooby is a lightweight Python library designed to report on your Python environment's package versions and hardware resources. It generates nicely formatted reports (HTML for Jupyter notebooks, plain text otherwise) to aid in debugging and reproducibility. Currently at version 0.11.0, it is actively maintained with a consistent release cadence.

observability devops

pip install scooby

github ↗package ↗

INSTALL

IMPORT

SIG · SCOOBY

scooby

observabilitypythonv0.11.2

Install

1.5s avg

Import

96ms

Disk

16MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.11.2 · 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

py 3.10–3.95 runs

installs and imports cleanly · install 0.0s · import 0.100s · 17.9MB

glibc

py 3.10–3.95 runs

installs and imports cleanly · install 1.5s · import 0.092s · 18MB

16MB installed

● package 16MB

Code

Verified usage

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

Report

✓ from scooby import Report

doo

✓ from scooby import doo

An alias for `Report()` for convenience.

AutoReport

✓ from scooby import AutoReport

For generating reports based on a package's distribution dependencies.

track_imports

✓ from scooby import track_imports

Enables tracking of imported modules during a Python session for reporting.

get_version

✓ from scooby import get_version

Retrieves the version string for a single package.

Instantiate `scooby.Report()` to generate a comprehensive report of your Python environment, including system information, Python version, and versions of specified (or default) packages. The report is automatically formatted for Jupyter notebooks or as plain text when printed.

import scooby # Generate and print a basic environment report report = scooby.Report() print(report) # You can also use the 'doo' alias for fun # print(scooby.doo()) # To report on specific packages (e.g., numpy, matplotlib) and define core/optional # my_report = scooby.Report(additional=['numpy', 'matplotlib'], core=['my_package'], optional=['another_package']) # print(my_report)

scooby --version

Debug

Known issues

breakingAs of v0.8.0, Scooby primarily uses `importlib.metadata` (standard in Python 3.8+) for retrieving package versions, replacing `pkg_resources`. While fallbacks may exist, applications relying on `scooby` for versioning, especially in older Python environments or with unusual package distributions, should verify compatibility.

fix

Ensure your Python environment is 3.8+ for full `importlib.metadata` compatibility. If integrating `scooby` into your own package, test its version reporting across target Python versions.

affects: >=0.8.0

gotchaWhen integrating Scooby as a 'soft dependency' within another package, directly importing `scooby` might raise an `ImportError` if it's not installed. It's recommended to wrap the import in a `try-except ImportError` block.

fix

Use a `try-except ImportError` block when importing `scooby` if it's an optional dependency for your project, providing a graceful fallback or error message.

affects: All

gotchaIn vanilla Python scripts (outside of Jupyter/IPython environments), simply calling `scooby.Report()` will not display the report. The `Report` object must be explicitly printed to show its plain-text representation.

fix

Always use `print(scooby.Report())` in standard Python scripts to ensure the environment report is displayed.

affects: All

gotchaBy default, `scooby.Report()` includes `numpy`, `scipy`, `IPython`, and `matplotlib` (and `scooby` itself) in its 'optional' package list if found. If these packages are crucial 'core' dependencies for the project being reported, they should be explicitly passed to the `core` argument of `scooby.Report()` for clear distinction.

fix

When creating a `Report` for your own package, use `scooby.Report(core=['your_package', 'numpy'])` to explicitly define core dependencies, allowing users to differentiate essential vs. optional components.

affects: All

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'scooby'

The 'scooby' library has not been installed in the current Python environment.

fix

pip install scooby

AttributeError: module 'scooby' has no attribute 'Report'

The main function name for generating a report is case-sensitive and must be 'report' (lowercase), not 'Report'.

fix

scooby.report()

AttributeError: module 'scooby' has no attribute 'version'

The version of the 'scooby' module is exposed through the special dunder attribute `__version__` or the `get_version()` function, not a direct `version` attribute.

fix

scooby.__version__

TypeError: 'int' object is not iterable

The 'core' or 'optional' arguments of `scooby.report()` expect a string (for a single package) or a list/tuple of strings (for multiple packages), but an uniterable type like an integer or boolean was provided.

fix

scooby.report(core=['numpy', 'scipy'])

Upgrade

Version history

0.11.2latest on PyPI · released Apr 22, 2026

Audit

Dependencies

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Agent activity

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OpenAI (training)

Resources

githubhttps://github.com/banesullivan/scooby ↗

packagehttps://pypi.org/project/scooby/ ↗