PhyloFrame: A DataFrame-based Library for Fast, Flexible Phylogenetic Computation

arXiv:2605.28545v1 Announce Type: new
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., $geq$ 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.