Efficient matrix representations for working with tabular data

Installation

Simply install via conda-forge!

conda install -c conda-forge tabmat

Getting Started

The easiest way to start with tabmat is to use the convenience constructor tabmat.from_pandas.

import tabmat as tm
import numpy as np

dense_array = np.random.normal(size=(100, 1))

Use case

TL;DR: We provide matrix classes for efficiently building statistical algorithms with data that is partially dense, partially sparse and partially categorical.

Data used in economics, actuarial science, and many other fields is often tabular, containing rows and columns. Further common properties are also common:

It often is very sparse.
It often contains a mix of dense and sparse columns.
It often contains categorical data, processed into many columns of indicator values created by "one-hot encoding."

High-performance statistical applications often require fast computation of certain operations, such as

Computing sandwich products of the data, transpose(X) @ diag(d) @ X. A sandwich product shows up in the solution to weighted least squares, as well as in the Hessian of the likelihood in generalized linear models such as Poisson regression.
Matrix-vector products, possibly on only a subset of the rows or columns. For example, when limiting computation to an "active set" in a L1-penalized coordinate descent implementation, we may only need to compute a matrix-vector product on a small subset of the columns.
Computing all operations on standardized predictors which have mean zero and standard deviation one. This helps with numerical stability and optimizer efficiency in a wide range of machine learning algorithms.

This library and its design

We designed this library with the above use cases in mind. We built this library first for estimating generalized linear models, but expect it will be useful in a variety of econometric and statistical use cases. This library was borne out of our need for speed, and its unified API is motivated by the desire to work with a unified matrix API internal to our statistical algorithms.

Design principles:

Speed and memory efficiency are paramount.
You don't need to sacrifice functionality by using this library: DenseMatrix and SparseMatrix subclass np.ndarray and scipy.sparse.csc_matrix respectively, and inherit behavior from those classes wherever it is not improved on.
As much as possible, syntax follows NumPy syntax, and dimension-reducing operations (like sum) return NumPy arrays, following NumPy dimensions about the dimensions of results. The aim is to make these classes as close as possible to being drop-in replacements for numpy.ndarray. This is not always possible, however, due to the differing APIs of numpy.ndarray and scipy.sparse.
Other operations, such as toarray, mimic Scipy sparse syntax.
All matrix classes support matrix-vector products, sandwich products, and getcol.

Individual subclasses may support significantly more operations.

Matrix types

DenseMatrix represents dense matrices, subclassing numpy nparray. It additionally supports methods getcol, toarray, sandwich, standardize, and unstandardize.
SparseMatrix represents column-major sparse data, subclassing scipy.sparse.csc_matrix. It additionally supports methods sandwich and standardize.
CategoricalMatrix represents one-hot encoded categorical matrices. Because all the non-zeros in these matrices are ones and because each row has only one non-zero, the data can be represented and multiplied much more efficiently than a generic sparse matrix.
SplitMatrix represents matrices with both dense, sparse and categorical parts, allowing for a significant speedup in matrix multiplications.
StandardizedMatrix efficiently and sparsely represents a matrix that has had its column normalized to have mean zero and variance one. Even if the underlying matrix is sparse, such a normalized matrix will be dense. However, by storing the scaling and shifting factors separately, StandardizedMatrix retains the original matrix sparsity.

Wide data set

Benchmarks

See here for detailed benchmarking.

API documentation

See here for detailed API documentation.

Release files for tabmat 4.2.1

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for tabmat 4.2.1
File	Size	Uploaded
tabmat-4.2.1.tar.gz	2.3 MB	Feb 4, 2026	Details

Built distributions (wheels)

Table of built distributions (wheels) for tabmat 4.2.1
File	Interpreter	ABI	Platform	Reset
tabmat-4.2.1-cp314-cp314t-win_amd64.whl 676.2 kB Feb 4, 2026	CPython 3.14	CPython 3.14 free-threading	Windows x86-64	Details
tabmat-4.2.1-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl 7.0 MB Feb 4, 2026	CPython 3.14	CPython 3.14 free-threading	Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64	Details
tabmat-4.2.1-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl 6.9 MB Feb 4, 2026	CPython 3.14	CPython 3.14 free-threading	Linux glibc 2.24+ ARM64, Linux glibc 2.28+ ARM64	Details
tabmat-4.2.1-cp314-cp314t-macosx_12_0_x86_64.whl 1.8 MB Feb 4, 2026	CPython 3.14	CPython 3.14 free-threading	macOS 12.0+ x86-64	Details
tabmat-4.2.1-cp314-cp314t-macosx_12_0_arm64.whl 1.6 MB Feb 4, 2026	CPython 3.14	CPython 3.14 free-threading	macOS 12.0+ ARM64	Details
tabmat-4.2.1-cp314-cp314-win_amd64.whl 561.8 kB Feb 4, 2026	CPython 3.14	CPython 3.14	Windows x86-64	Details
tabmat-4.2.1-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl 6.9 MB Feb 4, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.24+ x86-64, Linux glibc 2.28+ x86-64	Details
tabmat-4.2.1-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl 6.8 MB Feb 4, 2026	CPython 3.14	CPython 3.14	Linux glibc 2.24+ ARM64, Linux glibc 2.28+ ARM64	Details
tabmat-4.2.1-cp314-cp314-macosx_12_0_x86_64.whl 1.8 MB Feb 4, 2026	CPython 3.14	CPython 3.14	macOS 12.0+ x86-64	Details
tabmat-4.2.1-cp314-cp314-macosx_12_0_arm64.whl 1.6 MB Feb 4, 2026	CPython 3.14	CPython 3.14	macOS 12.0+ ARM64	Details
tabmat-4.2.1-cp313-cp313-win_amd64.whl 548.3 kB Feb 4, 2026	CPython 3.13	CPython 3.13	Windows x86-64	Details
tabmat-4.2.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl 7.0 MB Feb 4, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.24+ x86-64, Linux glibc 2.28+ x86-64	Details
tabmat-4.2.1-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl 6.9 MB Feb 4, 2026	CPython 3.13	CPython 3.13	Linux glibc 2.24+ ARM64, Linux glibc 2.28+ ARM64	Details
tabmat-4.2.1-cp313-cp313-macosx_12_0_x86_64.whl 1.8 MB Feb 4, 2026	CPython 3.13	CPython 3.13	macOS 12.0+ x86-64	Details
tabmat-4.2.1-cp313-cp313-macosx_12_0_arm64.whl 1.6 MB Feb 4, 2026	CPython 3.13	CPython 3.13	macOS 12.0+ ARM64	Details
tabmat-4.2.1-cp312-cp312-win_amd64.whl 548.7 kB Feb 4, 2026	CPython 3.12	CPython 3.12	Windows x86-64	Details
tabmat-4.2.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl 7.0 MB Feb 4, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.24+ x86-64, Linux glibc 2.28+ x86-64	Details
tabmat-4.2.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl 6.9 MB Feb 4, 2026	CPython 3.12	CPython 3.12	Linux glibc 2.24+ ARM64, Linux glibc 2.28+ ARM64	Details
tabmat-4.2.1-cp312-cp312-macosx_12_0_x86_64.whl 1.7 MB Feb 4, 2026	CPython 3.12	CPython 3.12	macOS 12.0+ x86-64	Details
tabmat-4.2.1-cp312-cp312-macosx_12_0_arm64.whl 1.6 MB Feb 4, 2026	CPython 3.12	CPython 3.12	macOS 12.0+ ARM64	Details
tabmat-4.2.1-cp311-cp311-win_amd64.whl 557.0 kB Feb 4, 2026	CPython 3.11	CPython 3.11	Windows x86-64	Details
tabmat-4.2.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl 7.0 MB Feb 4, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.24+ x86-64, Linux glibc 2.28+ x86-64	Details
tabmat-4.2.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl 7.0 MB Feb 4, 2026	CPython 3.11	CPython 3.11	Linux glibc 2.24+ ARM64, Linux glibc 2.28+ ARM64	Details
tabmat-4.2.1-cp311-cp311-macosx_12_0_x86_64.whl 1.7 MB Feb 4, 2026	CPython 3.11	CPython 3.11	macOS 12.0+ x86-64	Details
tabmat-4.2.1-cp311-cp311-macosx_12_0_arm64.whl 1.5 MB Feb 4, 2026	CPython 3.11	CPython 3.11	macOS 12.0+ ARM64	Details
tabmat-4.2.1-cp310-cp310-win_amd64.whl 557.3 kB Feb 4, 2026	CPython 3.10	CPython 3.10	Windows x86-64	Details
tabmat-4.2.1-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl 6.9 MB Feb 4, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.28+ x86-64, Linux glibc 2.24+ x86-64	Details
tabmat-4.2.1-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl 6.8 MB Feb 4, 2026	CPython 3.10	CPython 3.10	Linux glibc 2.28+ ARM64, Linux glibc 2.24+ ARM64	Details
tabmat-4.2.1-cp310-cp310-macosx_12_0_x86_64.whl 1.7 MB Feb 4, 2026	CPython 3.10	CPython 3.10	macOS 12.0+ x86-64	Details
tabmat-4.2.1-cp310-cp310-macosx_12_0_arm64.whl 1.5 MB Feb 4, 2026	CPython 3.10	CPython 3.10	macOS 12.0+ ARM64	Details

Total release size: 108.7 MB

Release files / tabmat-4.2.1.tar.gz

Download URL	tabmat-4.2.1.tar.gz
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Release files / tabmat-4.2.1-cp314-cp314t-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl

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Publishing platform: GitHub Actions
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Release files / tabmat-4.2.1-cp314-cp314t-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl

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Release files / tabmat-4.2.1-cp314-cp314t-macosx_12_0_x86_64.whl

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Release files / tabmat-4.2.1-cp314-cp314t-macosx_12_0_arm64.whl

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Release files / tabmat-4.2.1-cp314-cp314-win_amd64.whl

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Release files / tabmat-4.2.1-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl

Download URL	tabmat-4.2.1-cp314-cp314-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
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Subject: tabmat-4.2.1-cp314-cp314-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl
SHA-256 checksum: 8f76c56322fcd9f64db3ca598464a3cd29445d897e19f1b8f297df88c6e32233 Verifying attestations

Release files / tabmat-4.2.1-cp314-cp314-macosx_12_0_x86_64.whl

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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

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Publishing logs: github.com/Quantco/tabmat/actions/runs/21678275918/attempts/1

Artifact

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SHA-256 checksum: 39985225c4dd2fa2368411a96c5a6ab9f67d913bc048f13290712c8a75cca37d Verifying attestations

Release files / tabmat-4.2.1-cp314-cp314-macosx_12_0_arm64.whl

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Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

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Artifact

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Release files / tabmat-4.2.1-cp313-cp313-win_amd64.whl

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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

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Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

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Artifact

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SHA-256 checksum: 24830127fdae27b9f8e21896c3f8ccfd608db7153ed111f23d6dd72fb08eb3f4 Verifying attestations

Release files / tabmat-4.2.1-cp313-cp313-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl

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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

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Artifact

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SHA-256 checksum: 770dcb04a38029255f1384701bec30d125dcd9463a225bbe696aad22f830cea6 Verifying attestations

Release files / tabmat-4.2.1-cp313-cp313-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl

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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

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Artifact

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SHA-256 checksum: 90433d463c527d6a509901a0d3829606dd76d785f994dd1383024b10e9b550bb Verifying attestations

Release files / tabmat-4.2.1-cp313-cp313-macosx_12_0_x86_64.whl

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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

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Artifact

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Release files / tabmat-4.2.1-cp313-cp313-macosx_12_0_arm64.whl

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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

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Artifact

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SHA-256 checksum: 0f9425ccf4bdcbce5c9f6c1f9dbddb9148311a315077c3533cb026bfe79d5e8f Verifying attestations

Release files / tabmat-4.2.1-cp312-cp312-win_amd64.whl

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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

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Artifact

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Release files / tabmat-4.2.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl

Download URL	tabmat-4.2.1-cp312-cp312-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

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Artifact

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SHA-256 checksum: 36dedd4312408b06ef5d1c697408bccf68d9d7b616681d62fadf0303c6449dac Verifying attestations

Release files / tabmat-4.2.1-cp312-cp312-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl

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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

Publishing configuration: github.com/Quantco/tabmat/blob/d919b46e9153b65286e01c7d11131af844b7e13e/.github/workflows/build-wheels.yml
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Artifact

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Release files / tabmat-4.2.1-cp312-cp312-macosx_12_0_x86_64.whl

Download URL	tabmat-4.2.1-cp312-cp312-macosx_12_0_x86_64.whl
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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

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Artifact

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Release files / tabmat-4.2.1-cp312-cp312-macosx_12_0_arm64.whl

Download URL	tabmat-4.2.1-cp312-cp312-macosx_12_0_arm64.whl
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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

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Artifact

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SHA-256 checksum: cd4bbba1f3cc5171c67809838f97917fdbe895f5dcf5c7031a966f7ce09e962a Verifying attestations

Release files / tabmat-4.2.1-cp311-cp311-win_amd64.whl

Download URL	tabmat-4.2.1-cp311-cp311-win_amd64.whl
Size	557.0 kB
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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

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Artifact

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Release files / tabmat-4.2.1-cp311-cp311-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl

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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

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Artifact

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SHA-256 checksum: 34b7be87684c31bb6ad5b3211370903b19f69258e425b7934d37666d74eeb22b Verifying attestations

Release files / tabmat-4.2.1-cp311-cp311-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl

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Tags	CPython 3.11 Linux glibc 2.24+ ARM64 Linux glibc 2.28+ ARM64
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Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

Publishing configuration: github.com/Quantco/tabmat/blob/d919b46e9153b65286e01c7d11131af844b7e13e/.github/workflows/build-wheels.yml
Publishing logs: github.com/Quantco/tabmat/actions/runs/21678275918/attempts/1

Artifact

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SHA-256 checksum: 730d8ebf130c471beb5196a37136a57642d20fa34f15e47e98ac80b08ce99ead Verifying attestations

Release files / tabmat-4.2.1-cp311-cp311-macosx_12_0_x86_64.whl

Download URL	tabmat-4.2.1-cp311-cp311-macosx_12_0_x86_64.whl
Size	1.7 MB
Tags	CPython 3.11 macOS 12.0+ x86-64
SHA-256 checksum How to use checksums	`d0039d5dc3f27268cfb965862c6b2c039902bc65f96a5809a90d1a9ca85ec4df`
BLAKE2b-256 checksum How to use checksums	`8c5a1af6b0109cc128eb5e193acfa928c44a58092b68648df6c3f266995c7568`
Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

Publishing configuration: github.com/Quantco/tabmat/blob/d919b46e9153b65286e01c7d11131af844b7e13e/.github/workflows/build-wheels.yml
Publishing logs: github.com/Quantco/tabmat/actions/runs/21678275918/attempts/1

Artifact

Subject: tabmat-4.2.1-cp311-cp311-macosx_12_0_x86_64.whl
SHA-256 checksum: d0039d5dc3f27268cfb965862c6b2c039902bc65f96a5809a90d1a9ca85ec4df Verifying attestations

Release files / tabmat-4.2.1-cp311-cp311-macosx_12_0_arm64.whl

Download URL	tabmat-4.2.1-cp311-cp311-macosx_12_0_arm64.whl
Size	1.5 MB
Tags	CPython 3.11 macOS 12.0+ ARM64
SHA-256 checksum How to use checksums	`4bd57f726cf6ce2cf27cc08a4e9f352a8cbb7c0db04132d5d7174b7e0833f48f`
BLAKE2b-256 checksum How to use checksums	`1357a35730545fefea893fedd3332b86e1ee8e55df7778b4eef5ac3ddfcfb0aa`
Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

Publishing configuration: github.com/Quantco/tabmat/blob/d919b46e9153b65286e01c7d11131af844b7e13e/.github/workflows/build-wheels.yml
Publishing logs: github.com/Quantco/tabmat/actions/runs/21678275918/attempts/1

Artifact

Subject: tabmat-4.2.1-cp311-cp311-macosx_12_0_arm64.whl
SHA-256 checksum: 4bd57f726cf6ce2cf27cc08a4e9f352a8cbb7c0db04132d5d7174b7e0833f48f Verifying attestations

Release files / tabmat-4.2.1-cp310-cp310-win_amd64.whl

Download URL	tabmat-4.2.1-cp310-cp310-win_amd64.whl
Size	557.3 kB
Tags	CPython 3.10 Windows x86-64
SHA-256 checksum How to use checksums	`4f4645c025258fe5729dcea95d6c123368c0717b13a280c17365da5f75319e83`
BLAKE2b-256 checksum How to use checksums	`c215244b87efb78adb7ee70663c54907bad751c99659986dd80993b18652f907`
Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

Publishing configuration: github.com/Quantco/tabmat/blob/d919b46e9153b65286e01c7d11131af844b7e13e/.github/workflows/build-wheels.yml
Publishing logs: github.com/Quantco/tabmat/actions/runs/21678275918/attempts/1

Artifact

Subject: tabmat-4.2.1-cp310-cp310-win_amd64.whl
SHA-256 checksum: 4f4645c025258fe5729dcea95d6c123368c0717b13a280c17365da5f75319e83 Verifying attestations

Release files / tabmat-4.2.1-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl

Download URL	tabmat-4.2.1-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
Size	6.9 MB
Tags	CPython 3.10 Linux glibc 2.24+ x86-64 Linux glibc 2.28+ x86-64
SHA-256 checksum How to use checksums	`a9e3022c7646feb927beafce2fccf37808c0cbbcc5a97976d3d54bdea7ff79b3`
BLAKE2b-256 checksum How to use checksums	`12c0585b533216df7563fcadc9c82775c7bc119f2fb3a0bf6e0ab207e7b004eb`
Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

Publishing configuration: github.com/Quantco/tabmat/blob/d919b46e9153b65286e01c7d11131af844b7e13e/.github/workflows/build-wheels.yml
Publishing logs: github.com/Quantco/tabmat/actions/runs/21678275918/attempts/1

Artifact

Subject: tabmat-4.2.1-cp310-cp310-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
SHA-256 checksum: a9e3022c7646feb927beafce2fccf37808c0cbbcc5a97976d3d54bdea7ff79b3 Verifying attestations

Release files / tabmat-4.2.1-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl

Download URL	tabmat-4.2.1-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl
Size	6.8 MB
Tags	CPython 3.10 Linux glibc 2.24+ ARM64 Linux glibc 2.28+ ARM64
SHA-256 checksum How to use checksums	`b0053bfda22af2f826519195a9e7a58ddcbd03a4834a58488cd4b60e713a0d92`
BLAKE2b-256 checksum How to use checksums	`023cd16d7a4c83008bbc93c2cc41d6f2a22029b485a6ed4dd29396cd239b71bc`
Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

Publishing configuration: github.com/Quantco/tabmat/blob/d919b46e9153b65286e01c7d11131af844b7e13e/.github/workflows/build-wheels.yml
Publishing logs: github.com/Quantco/tabmat/actions/runs/21678275918/attempts/1

Artifact

Subject: tabmat-4.2.1-cp310-cp310-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl
SHA-256 checksum: b0053bfda22af2f826519195a9e7a58ddcbd03a4834a58488cd4b60e713a0d92 Verifying attestations

Release files / tabmat-4.2.1-cp310-cp310-macosx_12_0_x86_64.whl

Download URL	tabmat-4.2.1-cp310-cp310-macosx_12_0_x86_64.whl
Size	1.7 MB
Tags	CPython 3.10 macOS 12.0+ x86-64
SHA-256 checksum How to use checksums	`9ee6630060ca174de8c02f2010bcd79cfb8c4b13a0f2791641c531e5c73bf991`
BLAKE2b-256 checksum How to use checksums	`aac4913941fca073d66007623e2acd21ff184ea33525775cd90c121e0cabab91`
Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

Publishing configuration: github.com/Quantco/tabmat/blob/d919b46e9153b65286e01c7d11131af844b7e13e/.github/workflows/build-wheels.yml
Publishing logs: github.com/Quantco/tabmat/actions/runs/21678275918/attempts/1

Artifact

Subject: tabmat-4.2.1-cp310-cp310-macosx_12_0_x86_64.whl
SHA-256 checksum: 9ee6630060ca174de8c02f2010bcd79cfb8c4b13a0f2791641c531e5c73bf991 Verifying attestations

Release files / tabmat-4.2.1-cp310-cp310-macosx_12_0_arm64.whl

Download URL	tabmat-4.2.1-cp310-cp310-macosx_12_0_arm64.whl
Size	1.5 MB
Tags	CPython 3.10 macOS 12.0+ ARM64
SHA-256 checksum How to use checksums	`e553a66b6350cc39609c71a6a3502c2c5c7b8f5624758c80cfe8793a8f8fa9d4`
BLAKE2b-256 checksum How to use checksums	`f685aae6f34157596cb57547a35ffc7a63d69f4386a757a8992dd111b584d8d8`
Upload date	Feb 4, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/6.1.0 CPython/3.13.7`

Provenance

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/Quantco/tabmat
Publishing commit: github.com/Quantco/tabmat/tree/d919b46e9153b65286e01c7d11131af844b7e13e

Workflow

Publishing configuration: github.com/Quantco/tabmat/blob/d919b46e9153b65286e01c7d11131af844b7e13e/.github/workflows/build-wheels.yml
Publishing logs: github.com/Quantco/tabmat/actions/runs/21678275918/attempts/1

Artifact

Subject: tabmat-4.2.1-cp310-cp310-macosx_12_0_arm64.whl
SHA-256 checksum: e553a66b6350cc39609c71a6a3502c2c5c7b8f5624758c80cfe8793a8f8fa9d4 Verifying attestations