DendroPy is a cross-platform Python library for phylogenetic computing, providing object-oriented tools for reading, writing, simulating, and manipulating phylogenetic data. It emphasizes tree operations and uses a splits-hash mapping for efficient tree distance, similarity, and shape calculations. The library supports various phylogenetic data formats, including NEXUS, Newick, PHYLIP, FASTA, and NeXML, and offers rich simulation routines under different models. DendroPy's data model is flexible, allowing for complex datasets and supporting both rooted and unrooted trees. It handles diverse data sources and provides a framework for tree simulation under various branching models. The library includes a 'DataSet' object to manage multiple taxon sets and associated data. DendroPy supports tree manipulation, including adding/removing branches, re-rooting, and calculating various tree statistics. It also provides iterators for tree traversal and functions for tree comparison using metrics like weighted Robinson-Foulds distances. DendroPy includes applications like 'SumTrees' for summarizing bootstrap or Bayesian support for splits. It interoperates with other Python libraries like BioPython, PyCogent, and ETE, and supports data exchange with R libraries such as APE and Geiger. DendroPy complements other libraries by focusing on tree metrics, analysis, and simulation, while others focus on sequence-based operations or tree visualization. The library is designed to be used with or without programming expertise, offering a versatile tool for phyloinformatics and phylogeography.DendroPy is a cross-platform Python library for phylogenetic computing, providing object-oriented tools for reading, writing, simulating, and manipulating phylogenetic data. It emphasizes tree operations and uses a splits-hash mapping for efficient tree distance, similarity, and shape calculations. The library supports various phylogenetic data formats, including NEXUS, Newick, PHYLIP, FASTA, and NeXML, and offers rich simulation routines under different models. DendroPy's data model is flexible, allowing for complex datasets and supporting both rooted and unrooted trees. It handles diverse data sources and provides a framework for tree simulation under various branching models. The library includes a 'DataSet' object to manage multiple taxon sets and associated data. DendroPy supports tree manipulation, including adding/removing branches, re-rooting, and calculating various tree statistics. It also provides iterators for tree traversal and functions for tree comparison using metrics like weighted Robinson-Foulds distances. DendroPy includes applications like 'SumTrees' for summarizing bootstrap or Bayesian support for splits. It interoperates with other Python libraries like BioPython, PyCogent, and ETE, and supports data exchange with R libraries such as APE and Geiger. DendroPy complements other libraries by focusing on tree metrics, analysis, and simulation, while others focus on sequence-based operations or tree visualization. The library is designed to be used with or without programming expertise, offering a versatile tool for phyloinformatics and phylogeography.