tabpfn-common-utils
Utilities shared between TabPFN codebases
Decision gist · record as of 2026-08-14
Yes, if you are working within the TabPFN ecosystem. The package is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. However, it is very new (first release September 2025) with minimal external adoption signals. Install it as a dependency of TabPFN or related tools, or directly if you need its specific data utilities; do not expect it as a general-purpose ML utility library.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Low friction install with a pure-Python wheel.
- Active maintenance (last commit 2026-07-21), though the repository is very new (first release 2025-09-05) and has not yet accumulated adoption signals.
License · maintenance · safety
permissive license (permissive) — Apache License 2.0 is permissive: you can use, modify, and distribute the package freely in commercial and private projects, provided you include a copy of the license and note any changes you make.
last release 2026-06-22 (53 days) · last repo commit 2026-07-21
0 known vulnerabilities (OSV.dev, 2026-08-14) · 243,171 downloads/mo, #8,821 on PyPI
Alternatives
Verify before relying
pip install tabpfn-common-utils
from tabpfn_common_utils.utils import get_example_dataset
X_train, X_test, y_train, y_test = get_example_dataset("iris")- Whether the included telemetry (posthog) can be fully disabled without side effects beyond the TABPFN_DISABLE_TELEMETRY environment variable.
- Performance characteristics when serializing large DataFrames or datasets.
- Whether nvidia-ml-py is required for all use cases or only when GPU cost estimation is needed.
What it is and what it does
TabPFN Common Utilities is a support library for the TabPFN tabular foundation model ecosystem. It provides a collection of utilities for handling regression prediction results (mean, median, mode, quantiles), converting data between pandas DataFrames, NumPy arrays, and CSV formats, and loading standard ML datasets with train/test splits. The package also includes cost estimation tools for planning computational resources and cloud pricing, with different models for classification versus regression tasks.
The package depends on numpy, pandas, scikit-learn, requests, filelock, platformdirs, typing-extensions, and includes optional telemetry via posthog and nvidia-ml-py for GPU monitoring. It supports Python 3.9 through 3.13 and is actively maintained. Telemetry is anonymous and aggregated, and can be disabled via environment variable.
Use it for
- Load and preprocess standard ML datasets (iris, etc.) with proper train/test splits for TabPFN experiments.
- Convert regression prediction outputs containing mean, median, mode, and quantiles into serializable formats.
- Estimate computational costs and cloud pricing for TabPFN classification or regression tasks before execution.
- Serialize pandas DataFrames to CSV-formatted bytes for transmission or storage in TabPFN workflows.
- Track anonymous usage patterns across TabPFN ecosystem tools via optional telemetry integration.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are working within the TabPFN ecosystem.
The package is actively maintained, has low install friction, carries no known vulnerabilities, and is licensed permissively. However, it is very new (first release September 2025) with minimal external adoption signals. Install it as a dependency of TabPFN or related tools, or directly if you need its specific data utilities; do not expect it as a general-purpose ML utility library.
Install
tabpfn-common-utils on PyPI
Before you install
Low friction install with a pure-Python wheel. Active maintenance (last commit 2026-07-21), though the repository is very new (first release 2025-09-05) and has not yet accumulated adoption signals.
License in practice
Apache License 2.0 is permissive: you can use, modify, and distribute the package freely in commercial and private projects, provided you include a copy of the license and note any changes you make.
Quickstart
pip install tabpfn-common-utils
from tabpfn_common_utils.utils import get_example_dataset
X_train, X_test, y_train, y_test = get_example_dataset("iris")Verify before relying
- Whether the included telemetry (posthog) can be fully disabled without side effects beyond the TABPFN_DISABLE_TELEMETRY environment variable.
- Performance characteristics when serializing large DataFrames or datasets.
- Whether nvidia-ml-py is required for all use cases or only when GPU cost estimation is needed.
Package facts
| License | permissive license permissive |
| Python support | Supports the current Python release >=3.9 |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | 9 packagesfilelocknumpynvidia-ml-pypandasplatformdirsposthogrequestsscikit-learntyping-extensions |
| Maintenance | Actively maintained 53 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 243,171 / month, #8,821 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | License :: OSI Approved :: Apache Software LicenseProgramming Language :: Python :: 3Programming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.9 |
Evidence: tabpfn_common_utils-0.2.23-py3-none-any.whl
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See also tabpfn · tabicl · braintrust-core · tablib · fastf1 · Geode-Common · linearmodels · pandas-summary · mltable · tabledata