pint-pandas

library0.8.0pypypi✓ verified 84d ago

Pint-Pandas is a Python library that extends Pandas DataFrames and Series to seamlessly integrate physical quantities with units, leveraging the Pint library. It enables units-aware operations directly within Pandas data structures, ensuring dimensional consistency and preventing common unit errors. The library is actively maintained, with version 0.8.0 released on March 19, 2026. It's important to note that the Pandas integration is still considered experimental by the underlying Pint project.

data serialization

pip install pint-pandas

github ↗package ↗

INSTALL

IMPORT

SIG · PINT-PANDAS

pint-pandas

datapythonv0.8.0

Install

8.2s avg

Import

866ms

Disk

169MB

Pass rate

10/ 10

Env Coverage10 / 10

glibc

3.9–3.13

musl

3.9–3.13

Install & Compatibility

Where this runs

tested against v0.7.1 · 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 3.10–3.920 runs

installs and imports cleanly · install 0.0s · import 0.884s · 168.3MB

glibc

py 3.10–3.920 runs

installs and imports cleanly · install 8.2s · import 0.849s · 161MB

169MB installed

● package 169MB

Code

Verified usage

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

pandas

✓ import pandas as pd

pint

✓ import pint

pint_pandas

✓ import pint_pandas

✗ from pint import pandas

pint_pandas is a separate package, not a submodule of pint.

This quickstart demonstrates how to create a Pandas DataFrame with unit-aware columns using `pint[unit]` dtypes. It then shows how to perform arithmetic operations that automatically handle unit conversions and dimensional analysis. Finally, it illustrates how to convert units of a Series using the `.pint` accessor.

import pandas as pd import pint import pint_pandas # Create a DataFrame with PintArrays as columns df = pd.DataFrame({ "torque": pd.Series([1.0, 2.0, 2.0, 3.0], dtype="pint[lbf ft]"), "angular_velocity": pd.Series([1.0, 2.0, 2.0, 3.0], dtype="pint[rpm]"), }) print("Original DataFrame:\n", df) print("\nData Types:\n", df.dtypes) # Perform units-aware operations df['power'] = df['torque'] * df['angular_velocity'] print("\nDataFrame with calculated 'power':\n", df) print("\nData Types after calculation:\n", df.dtypes) # Access magnitudes or convert units print("\nPower in Watts:\n", df['power'].pint.to('W'))

Debug

Known issues

gotchaThe Pandas integration is officially considered 'experimental'. While functional, some edge cases or advanced Pandas features might not behave as expected or could change in future releases.

fix

Always refer to the latest documentation and test thoroughly, especially for critical applications.

affects: All versions

gotchaWhen downcasting a PintArray to a NumPy array (e.g., implicitly in some Pandas operations), the unit information will be stripped, resulting in a `UnitStrippedWarning`. This can lead to loss of unit context if not handled explicitly.

fix

If units are not needed, explicitly extract magnitudes using `.pint.magnitude`. If units are critical, ensure operations are units-aware or explicitly convert units before array conversion.

affects: All versions

breakingPint-Pandas has specific minimum version requirements for both `pandas` and `pint` that evolve with new releases. For instance, `pint-pandas` 0.6 requires `pandas` >= 2.2 and `pint` >= 0.24. Pandas 3.0 also introduced significant breaking changes and mandates Python 3.11+.

fix

Always check the `pint-pandas` changelog and documentation for the exact compatible versions of `pandas` and `pint` for your desired `pint-pandas` version. Ensure your Python environment meets the minimum `requires_python` of `pint-pandas` and `pandas`.

affects: All versions

Errors

Common errors & fixes

ModuleNotFoundError: No module named 'pint_pandas'

The `pint-pandas` package has not been installed. Users sometimes install `pint` but forget to install its Pandas integration package.

fix

Run `pip install pint-pandas` or `conda install -c conda-forge pint-pandas` to install the package.

DataFrame `dtype` is `object` instead of `pint[unit]` for columns expected to have units.

Columns were created or loaded (e.g., from CSV) containing `pint.Quantity` objects but were not explicitly cast to `PintArray` dtypes, leading Pandas to infer an `object` dtype. Operations may then fail to be units-aware.

fix

After loading data, use the `.pint.quantify()` or `.pint.convert_object_dtype()` accessor methods to correctly convert the `object` columns into `PintArray` dtypes. For example: `df = df.pint.quantify(level=-1)` if units are in the header, or `df = df.pint.convert_object_dtype()` for Series of Quantities.

In-place Series operations (e.g., `s.pint.ito('new_unit')`) appear to do nothing.

Due to how Pint-Pandas handles delegated methods for its Series accessor, direct in-place modification might not work as intuitively expected for all methods.

fix

Instead of in-place operations, reassign the result of the method. For example, use `s = s.pint.to('new_unit')` instead of `s.pint.ito('new_unit')`.

Upgrade

Version history

0.8.0latest on PyPI · released Mar 20, 2026

Audit

Dependencies

pandasrequiredCore dependency for DataFrame and Series extension. Pint-Pandas 0.6+ supports Pandas 3.0+.

pintrequiredCore dependency for physical quantity and unit management. Pint-Pandas 0.6+ requires Pint 0.24+.

Agent activity

9 hits · last 30 days

node

Resources

githubhttps://github.com/hgrecco/pint-pandas ↗

packagehttps://pypi.org/project/pint-pandas/ ↗