PhyloFrame: A DataFrame-based Library for Fast, Flexible Phylogenetic Computation
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| Authors | Matthew Andres Moreno, Jeet Sukumaran, Luis Zaman, Emily Dolson |
| Date | May 27th, 2026 |
| DOI | 10.48550/arXiv.2605.28545 |
| Venue | arXiv |
Abstract
PhyloFrame is a Python library for phylogenetic computation targeting the gap between specialist, compiler-optimized operations and flexible, script-based workflows — with emphasis on fast, memory-efficient operations for very large tree sizes (e.g., ≥ 300,000 taxa). PhyloFrame is built around a DataFrame-based tree representation, where each row corresponds to a node and columns record ancestor relationships, branch lengths, taxon labels, and any user-defined attributes. Crucial for scalability, such array-backed storage allows both library and end-user code alike to seamlessly harness Just-in-Time (JIT) compilation (e.g., Numba) and vectorized execution (e.g., NumPy, Polars). At large tree sizes, performance generally matches or exceeds Python libraries backed by native code – notably, achieving strong performance in topological-order traversals and Newick I/O.
DataFrame-based representation affords several additional conveniences, including:
- succinct bulk operations (e.g., NumPy);
- powerful queries and transformations (e.g., Polars expressions, Pandas indexing, SQL-style joins and merges);
- compatibility with modern tabular data formats that are compression-friendly, type-aware, nullable, and highly portable (e.g., Parquet); and
- broad interoperation with table-oriented data science tools (e.g., Seaborn, Plotly, Vega-Altair, tidyverse, Excel).
Current library features include tree input/output, synthetic tree generation, taxon-based queries, tree traversals, tree metrics, tree manipulation, tree downsampling, and tree comparison. Most functionality supports both Pandas and Polars DataFrames, and is available through programmatic and CLI-based interfaces.
BibTeX
@misc{moreno2026phyloframe,
doi={10.48550/arXiv.2605.28545},
url={https://arxiv.org/abs/2605.28545},
title={PhyloFrame: A DataFrame-based Library for Fast, Flexible Phylogenetic Computation},
author={Matthew Andres Moreno and Jeet Sukumaran and Luis Zaman and Emily Dolson},
year={2026},
eprint={2605.28545},
archivePrefix={arXiv},
primaryClass={q-bio.PE},
}
Citation
Moreno M. A., Sukumaran J., Zaman L., & Dolson E. (2026). PhyloFrame: A DataFrame-based Library for Fast, Flexible Phylogenetic Computation. arXiv preprint arXiv:2605.28545. https://doi.org/10.48550/arXiv.2605.28545