Big Tree Python Package

Tree Implementation and Methods for Python, integrated with list, dictionary, pandas and polars DataFrame.

It is pythonic, making it easy to learn and extendable to many types of workflows.

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Components

There are 3 segments to Big Tree consisting of Tree, Binary Tree, and Directed Acyclic Graph (DAG) implementation.

For Tree implementation, there are 12 main components.

🌺 Node
`BaseNode, extendable class

Node, BaseNode with node name attribute


🎄 Tree

Tree, wrapper around Node, providing high-level APIs to build, iterate, query, and export the entire tree structure


✨ Constructing Tree

From Node, using parent and children constructors


   From str, using tree display or Newick string notation
   From list, using paths or parent-child tuples
   From nested dictionary, using path-attribute key-value pairs or recursive structure
   From pandas DataFrame, using paths or parent-child columns
   From polars DataFrame, using paths or parent-child columns
   From interactive UI
   Add nodes to existing tree using path string
   Add nodes and attributes to existing tree using dictionary, pandas DataFrame, or polars DataFrame, using path
   Add only attributes to existing tree using dictionary, pandas DataFrame, or polars DataFrame, using node name
➰ Traversing Tree
   Pre-Order Traversal
   Post-Order Traversal
   Level-Order Traversal
   Level-Order-Group Traversal
   ZigZag Traversal
   ZigZag-Group Traversal
🧩 Parsing Tree
   Get common ancestors between nodes
   Get path from one node to another node
📝 Modifying Tree
   Copy nodes from location to destination
   Shift nodes from location to destination
   Shift and replace nodes from location to destination
   Copy nodes from one tree to another
   Copy and replace nodes from one tree to another
📌 Querying Tree
   Filter tree using Tree Query Language
🔍 Tree Search
   Find multiple nodes based on name, partial path, relative path, attribute value, user-defined condition
   Find single nodes based on name, partial path, relative path, full path, attribute value, user-defined condition
   Find multiple child nodes based on user-defined condition
   Find single child node based on name, user-defined condition
🔧 Helper Function

Cloning tree to another Node type


   Get subtree (smaller tree with different root)
   Prune tree (smaller tree with same root)
   Get difference between two trees
📊 Plotting Tree
   Enhanced Reingold Tilford Algorithm to retrieve (x, y) coordinates for a tree structure
   Plot tree using matplotlib (optional dependency)
🔨 Exporting Tree
   Print to console, in vertical or horizontal orientation
   Export to Newick string notation, dictionary, nested dictionary, pandas DataFrame, or polars DataFrame
   Export tree to dot (can save to .dot, .png, .svg, .jpeg files)
   Export tree to Pillow (can save to .png, .jpg)
   Export tree to Mermaid Flowchart (can display on .md)
   Export tree to Pyvis Network (can display interactive .html)
✔️ Workflows
   Sample workflows for tree demonstration!

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For Binary Tree implementation, there are 4 main components.
Binary Node inherits from Node, so the components in Tree implementation are also available in Binary Tree.

🌿 Node

BinaryNode, Node with binary tree rules


🎄 Binary Tree

BinaryTree, wrapper around BinaryNode, providing high-level APIs to build, iterate, query, and export the entire tree structure


✨ Constructing Binary Tree
   From list, using flattened list structure
➰ Traversing Binary Tree
   In-Order Traversal

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For Directed Acyclic Graph (DAG) implementation, there are 6 main components.

🌼 Node

DAGNode, extendable class for constructing Directed Acyclic Graph (DAG)


🎄 DAG

DAG, wrapper around DAGNode, providing high-level APIs to build, export, and iterate the entire DAG


✨ Constructing DAG
   From list, containing parent-child tuples
   From nested dictionary
   From pandas DataFrame
➰ Traversing DAG
   Generic traversal method
🧩 Parsing DAG
   Get possible paths from one node to another node
🔨 Exporting DAG
   Export to list, dictionary, or pandas DataFrame
   Export DAG to dot (can save to .dot, .png, .svg, .jpeg files)

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Installation

bigtree requires Python 3.10+. There are two ways to install bigtree, with pip (recommended) or conda.

`a) Installation with pip`

#### Basic Installation

To install bigtree, run the following line in command prompt:

$ pip install bigtree

#### Installing optional dependencies

bigtree has a number of optional dependencies, which can be installed using the "extras" syntax.

$ pip install 'bigtree[extra_1, extra_2]'

Examples of extra packages include:

all: include all optional dependencies

image: for exporting tree to image

matplotlib: for plotting trees

pandas: for pandas methods

polars: for polars methods

query for tree query methods

vis: for pyvis visualization

For image

 extra dependency, you may need to install more plugins.
$ brew install gprof2dot  # for MacOS
$ conda install graphviz  # for Windows
b) Installation with conda

To install bigtree` with conda, run the following line in the command prompt:

$ conda install -c conda-forge bigtree

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Star History

--- Tranlated By Open Ai Tx | Last indexed: 2025-12-16 ---