bigtree 1.5.1

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

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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Related Links:

• Documentation
• GitHub
• Community
  • Issues / Discussions / Changelog / Contributing
• Package
  • PyPI / Conda
• Articles
  • Python Tree Implementation with BigTree
  • The Reingold Tilford Algorithm Explained, with Walkthrough

• If you want to support bigtree,

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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.

1. 🌺 Node
   1. BaseNode, extendable class
   2. Node, BaseNode with node name attribute
2. 🎄 Tree
   1. Tree, wrapper around Node, providing high-level APIs to build, iterate, query, and export the entire tree structure
3. 🎬 Studio
   1. Construct, modify, search, export tree with a Terminal interface
4. ✨ Constructing Tree
   1. From Node, using parent and children constructors
   2. From str, using tree display or Newick string notation
   3. From list, using paths or parent-child tuples
   4. From nested dictionary, using path-attribute key-value pairs or recursive structure
   5. From pandas DataFrame, using paths or parent-child columns
   6. From polars DataFrame, using paths or parent-child columns
   7. From interactive UI
   8. From rich trees
   9. Add nodes to existing tree using path string
   10. Add nodes and attributes to existing tree using dictionary, pandas DataFrame, or polars DataFrame, using path
   11. Add only attributes to existing tree using dictionary, pandas DataFrame, or polars DataFrame, using node name
5. ➰ Traversing Tree
   1. Pre-Order Traversal
   2. Post-Order Traversal
   3. Level-Order Traversal
   4. Level-Order-Group Traversal
   5. ZigZag Traversal
   6. ZigZag-Group Traversal
6. 🧩 Parsing Tree
   1. Get common ancestors between nodes
   2. Get path from one node to another node
7. 📝 Modifying Tree
   1. Copy nodes from location to destination
   2. Shift nodes from location to destination
   3. Shift and replace nodes from location to destination
   4. Copy nodes from one tree to another
   5. Copy and replace nodes from one tree to another
8. 📌 Querying Tree
   1. Filter tree using Tree Query Language
9. 🔍 Tree Search
   1. Find multiple nodes based on name, partial path, relative path, attribute value, user-defined condition
   2. Find single nodes based on name, partial path, relative path, full path, attribute value, user-defined condition
   3. Find multiple child nodes based on user-defined condition
   4. Find single child node based on name, user-defined condition
10. 🔧 Helper Function
    1. Cloning tree to another Node type
    2. Get subtree (smaller tree with different root)
    3. Prune tree (smaller tree with same root)
    4. Get difference between two trees
11. 📊 Plotting Tree
    1. Enhanced Reingold Tilford Algorithm to retrieve (x, y) coordinates for a tree structure
    2. Plot tree using matplotlib (optional dependency)
12. 🔨 Exporting Tree
    1. Print to console, in vertical or horizontal orientation
    2. Display on jupyter notebook
    3. Export to html, Newick string notation, dictionary, nested dictionary, pandas DataFrame, or polars DataFrame
    4. Export tree to dot (can save to .dot, .png, .svg, .jpeg files)
    5. Export tree to Pillow (can save to .png, .jpg)
    6. Export tree to Mermaid Flowchart (can display on .md)
    7. Export tree to Pyvis Network (can display interactive .html)
13. ✔️ Workflows
    1. 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.

1. 🌿 Node
   1. BinaryNode, Node with binary tree rules
2. 🎄 Binary Tree
   1. BinaryTree, wrapper around BinaryNode, providing high-level APIs to build, iterate, query, and export the entire tree structure
3. ✨ Constructing Binary Tree
   1. From list, using flattened list structure
4. ➰ Traversing Binary Tree
   1. In-Order Traversal

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

1. 🌼 Node
   1. DAGNode, extendable class for constructing Directed Acyclic Graph (DAG)
2. 🎄 DAG
   1. DAG, wrapper around DAGNode, providing high-level APIs to build, export, and iterate the entire DAG
3. ✨ Constructing DAG
   1. From list, containing parent-child tuples
   2. From nested dictionary
   3. From pandas DataFrame
4. ➰ Traversing DAG
   1. Generic traversal method
5. 🧩 Parsing DAG
   1. Get possible paths from one node to another node
6. 🔨 Exporting DAG
   1. Export to list, dictionary, or pandas DataFrame
   2. 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 have a number of optional dependencies, which can be installed using "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
• rich: for printing tree in rich format
• vis: for pyvis visualisation

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 command prompt:

$ conda install -c conda-forge bigtree

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