Pint-Pandas
Extend Pandas Dataframe with Physical quantities module
Decision gist · record as of 2026-08-14
Yes, if you work with physical quantities in pandas. Low install friction, active maintenance, no known vulnerabilities, and permissive licensing make it a safe choice. The package solves a real problem—unit tracking in data analysis—that would otherwise require manual bookkeeping or separate unit columns. Suitable for scientific, engineering, and research workflows where dimensional consistency is important.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires Python 3.11 or later.
- Low friction install with active maintenance.
- Last commit 2026-03-20, repository not archived, and requires only three runtime dependencies (pint, pandas, packaging).
License · maintenance · safety
BSD (permissive) — BSD permissive license allows commercial and private use with minimal restrictions—suitable for most projects that can accommodate attribution.
last release 2026-03-20 (147 days) · last repo commit 2026-03-20 · 189 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 507,903 downloads/mo, #6,282 on PyPI
Alternatives
Verify before relying
pip install pint-pandas
import pandas as pd
import pint_pandas
df = pd.DataFrame({
"torque": pd.Series([1, 2, 3], dtype="pint[lbf ft]"),
"velocity": pd.Series([1, 2, 3], dtype="pint[rpm]")
})
df['power'] = df['torque'] * df['velocity']- Whether unit-aware operations (arithmetic, aggregation, groupby) work seamlessly with all pandas methods or only a subset.
- Performance impact of unit tracking on large DataFrames compared to plain numeric operations.
- Compatibility with pandas extension array ecosystem and third-party libraries that expect standard dtypes.
What it is and what it does
Pint-Pandas bridges pandas DataFrames and the Pint unit library, letting you define DataFrame columns with physical units (e.g., "pint[lbf ft]" for torque) and perform arithmetic operations that propagate and combine units correctly. When you multiply a torque column by an angular velocity column, the result automatically carries the correct combined unit. This eliminates the need to track units separately or convert everything to a common base unit before analysis.
The package is built on top of pandas' extension array system, so unit-aware columns behave like standard DataFrame columns in most contexts. It's aimed at scientific and engineering workflows where dimensional consistency matters—simulations, data analysis, and reporting where mixing incompatible units would be a silent error without this library.
Use it for
- Store and manipulate engineering measurements (force, torque, velocity) in a DataFrame while preserving unit information throughout calculations.
- Perform dimensional analysis on scientific datasets to catch unit mismatches early and ensure results have the correct derived units.
- Build data pipelines for physics simulations or experimental data where automatic unit propagation reduces manual conversion overhead.
- Generate reports with physical quantities that display units alongside values, improving clarity and reducing interpretation errors.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you work with physical quantities in pandas.
Low install friction, active maintenance, no known vulnerabilities, and permissive licensing make it a safe choice. The package solves a real problem—unit tracking in data analysis—that would otherwise require manual bookkeeping or separate unit columns. Suitable for scientific, engineering, and research workflows where dimensional consistency is important.
Install
pint-pandas on PyPI
Before you install
Low friction install with active maintenance. Last commit 2026-03-20, repository not archived, and requires only three runtime dependencies (pint, pandas, packaging). Supports current Python versions (3.11, 3.12, 3.13).
Requires Python 3.11 or later.
License in practice
BSD permissive license allows commercial and private use with minimal restrictions—suitable for most projects that can accommodate attribution.
Quickstart
pip install pint-pandas
import pandas as pd
import pint_pandas
df = pd.DataFrame({
"torque": pd.Series([1, 2, 3], dtype="pint[lbf ft]"),
"velocity": pd.Series([1, 2, 3], dtype="pint[rpm]")
})
df['power'] = df['torque'] * df['velocity']Verify before relying
- Whether unit-aware operations (arithmetic, aggregation, groupby) work seamlessly with all pandas methods or only a subset.
- Performance impact of unit tracking on large DataFrames compared to plain numeric operations.
- Compatibility with pandas extension array ecosystem and third-party libraries that expect standard dtypes.
Package facts
| License | BSD permissive |
| Python support | Supports the current Python release >=3.11 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 3 packagespintpandaspackaging |
| Maintenance | Actively maintained 147 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 507,903 / month, #6,282 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 :: MacOS :: MacOS XOperating System :: Microsoft :: WindowsOperating System :: POSIXProgramming Language :: PythonProgramming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Topic :: Scientific/EngineeringTopic :: Software Development :: Libraries |
Evidence: pint_pandas-0.8.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “pandas dataframe units”
- Pint-PandasPint-Pandas adds physical unit support to pandas DataFrames, allowing…
- gspread-dataframeConverts between Google Sheets worksheets and pandas DataFrames,…
- sklearn-pandasBridges pandas DataFrames and scikit-learn by mapping DataFrame…
Give your agent the search over MCP, or paste the wish link into any chat.
More Libraries packages
urllib3 is an HTTP client library that provides thread-safe connection pooling, SSL/TLS verification, multipart file uploads, request retries, compression support, and proxy handling for Python applications.
Requests is a Python HTTP library that simplifies sending HTTP/1.1 requests with automatic handling of headers, authentication, cookies, and response parsing.
Pluggy provides a plugin system that lets you define hook specifications and register implementations to be called in sequence, enabling extensible Python applications without tight coupling.
Install it if you're building an extensible application or framework.
Provides parsing, arithmetic, and recurrence rule computation for dates and times, with timezone support and iCalendar RFC compliance.
Install it if you need to parse flexible date strings, compute relative dates, handle timezones, or work with recurrence rules—it's the de facto choice for these tasks.
Six provides utility functions to write Python code that runs on both Python 2.7 and Python 3.3+, smoothing over language differences between the two versions.
pytest is a testing framework that lets you write test functions using plain assert statements and automatically discovers and runs them, with detailed failure reporting.
See also Pint · isqx · pint-xarray · ansys-units · hepunits · quantities · ucumvert · unyt · mendeleev · pandas-flavor