uncertainties
calculations with values with uncertainties, error propagation
Decision gist · record as of 2026-08-14
Yes. The package is actively maintained, has no dependencies, installs easily, carries no security vulnerabilities, and solves a genuine problem in scientific and engineering workflows. It's production-stable (since 2010) and widely used. Install it if you work with measurements or experimental data where uncertainty quantification matters.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction installation with no runtime dependencies.
- Active maintenance status with recent commits and a stable release history since 2010.
License · maintenance · safety
Revised BSD License (permissive) — Released under the Revised BSD License (permissive), allowing use in commercial and proprietary projects with minimal restrictions.
last release 2025-04-21 (480 days) · last repo commit 2026-07-01 · 675 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 2,595,858 downloads/mo, #2,976 on PyPI
Alternatives
Verify before relying
pip install uncertainties
from uncertainties import ufloat
x = ufloat(2, 0.25)
square = x**2
print(square) # 4.0+/-1.0
print(square.nominal_value, square.std_dev)- Whether the package supports symbolic uncertainty propagation or only numerical methods
- Performance characteristics when working with very large arrays or matrices of uncertain values
What it is and what it does
The uncertainties package lets you perform calculations on numbers that have measurement errors or uncertainties attached to them, automatically computing how those errors propagate through your math. Instead of manually tracking error bars at each step, you work with uncertain values directly—operations like addition, multiplication, and trigonometric functions all handle error propagation transparently. The package correctly accounts for correlations, so expressions like x - x evaluate to exactly zero rather than showing spurious uncertainty.
It supports most standard mathematical operations including functions from the math module, comparison operators, and array operations through a NumPy-like interface. You can also extract derivatives of any expression automatically, which the package uses internally for error propagation but exposes for your own use. The package is designed to require minimal changes to existing code—you typically just replace float literals with uncertain values and the rest works as expected.
Use it for
- Physics or chemistry experiments: track measurement uncertainties through multi-step calculations and report final results with error bars
- Engineering design: propagate component tolerances through system models to predict overall system uncertainty
- Data analysis: compute statistics on datasets where each point has an associated measurement error
- Educational demonstrations: show students how errors accumulate through calculations without manual error propagation formulas
- Calibration and metrology: combine multiple uncertain measurements and track how uncertainty changes through transformations
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes.
The package is actively maintained, has no dependencies, installs easily, carries no security vulnerabilities, and solves a genuine problem in scientific and engineering workflows. It's production-stable (since 2010) and widely used. Install it if you work with measurements or experimental data where uncertainty quantification matters.
Install
uncertainties on PyPI
Before you install
Low friction installation with no runtime dependencies. Active maintenance status with recent commits and a stable release history since 2010.
License in practice
Released under the Revised BSD License (permissive), allowing use in commercial and proprietary projects with minimal restrictions.
Quickstart
pip install uncertainties
from uncertainties import ufloat
x = ufloat(2, 0.25)
square = x**2
print(square) # 4.0+/-1.0
print(square.nominal_value, square.std_dev)Verify before relying
- Whether the package supports symbolic uncertainty propagation or only numerical methods
- Performance characteristics when working with very large arrays or matrices of uncertain values
Package facts
| License | Revised BSD License permissive |
| Python support | Supports the current Python release >=3.8 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Actively maintained 480 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 2,595,858 / month, #2,976 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Development Status :: 5 - Production/StableIntended Audience :: DevelopersIntended Audience :: EducationIntended Audience :: Other AudienceIntended Audience :: Science/ResearchLicense :: OSI Approved :: BSD LicenseOperating System :: OS IndependentProgramming Language :: PythonProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.8Programming Language :: Python :: 3.9Programming Language :: Python :: Implementation :: JythonProgramming Language :: Python :: Implementation :: PyPyTopic :: EducationTopic :: Scientific/EngineeringTopic :: Scientific/Engineering :: MathematicsTopic :: Scientific/Engineering :: PhysicsTopic :: Software DevelopmentTopic :: Software Development :: LibrariesTopic :: Software Development :: Libraries :: Python ModulesTopic :: Utilities |
Evidence: uncertainties-3.2.3-py3-none-any.whl
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See also sigfig · numdifftools · math-verify · Pint · lmfit · MAPIE · autograd · autograd-gamma · statistics · riskfolio-lib