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dsplot-logo

DSPlot - Data Structure Visualization

PyPI version Build Status Coverage Status License

Graph Pythonically. 📏📐🖍️🐍

DSPlot is a tool to simply visualize tree and graph data structures by serving as a Pythonic interface to the Graphviz layout. DSPlot allows you to easily draw trees, graphs (both directed and undirected), and matrices by passing data in primitive form and directly output an image.

📖 Table of Contents

⬇ Installation

0. Prerequisites

  • Python 3.9 or later
  • pip
  • virtualenv

1. Install Graphviz

  • MacOS:
brew install graphviz
  • Linux:
apt-get install graphviz libgraphviz-dev

2. Install package

$ pip install dsplot

📦 Features

  • Binary Tree:
from dsplot.tree import BinaryTree

tree = BinaryTree(nodes=[5, 4, 8, 11, None, 13, 4, 7, 2, None, None, 5, 1])
tree.plot()

tree

  • Graph:
from dsplot.graph import Graph

graph = Graph(
    {0: [1, 4, 5], 1: [3, 4], 2: [1], 3: [2, 4], 4: [], 5: []}, directed=True
)
graph.plot()

directed 

from dsplot.graph import Graph

graph = Graph(
    {1: [2, 4], 2: [1, 3], 3: [2, 4, 5], 4: [1, 3], 5: [3, 6, 7], 6: [5], 7: [5]}, directed=False
)
graph.plot()

undirected

  • Matrix:
from dsplot.matrix import Matrix

matrix = Matrix([[1, 2, 3], [4, 5, 6], [1, 2, 6]])
matrix.plot()

matrix

  • Customization:
    You can customize the border color, shape, style, and fill color of the nodes, and the orientation (left to right - LR, top to bottom - TB) of the graph.
from dsplot.graph import Graph

graph = Graph(
    {0: [1, 4, 5], 1: [3, 4], 2: [1], 3: [2, 4], 4: [], 5: []}, directed=True
)
graph.plot(fill_color='#aec6cf')

color_graph 

from dsplot.tree import BinaryTree

tree = BinaryTree(nodes=[5, 4, 8, 11, None, 13, 4, 7, 2, None, None, 5, 1])
tree.plot(orientation='LR', border_color='#FFCE30', fill_color='#aec6cf')

color_tree

  • Edge values for Graphs:
    For edge values, str and int data types are supported at the moment.
from dsplot.graph import Graph

graph = Graph(
    {0: [1, 4, 5], 1: [3, 4], 2: [1], 3: [2, 4], 4: [], 5: []},
    directed=True,
    edges={'01': 1, '04': 4, '05': 5, '13': 3, '14': 4, '21': 2, '32': 3, '34': 4},
)
graph.plot()

edge_graph

🎁 Bonus

1. Tree traversals:

from dsplot.tree import BinaryTree

tree = BinaryTree(nodes=[5, 4, 8, 11, None, 13, 4, 7, 2, None, None, 5, 1])

print(tree.preorder())
# [5, 4, 11, 7, 2, 8, 13, 4, 5, 1]

print(tree.inorder())
# [7, 11, 2, 4, 5, 13, 8, 5, 4, 1]

print(tree.postorder())
# [7, 2, 11, 4, 13, 5, 1, 4, 8, 5]

2. Graph traversals:

from dsplot.graph import Graph

graph = Graph(
    {0: [1, 4, 5], 1: [3, 4], 2: [1], 3: [2, 4], 4: [], 5: []}, directed=True
)

print(graph.bfs())
# [0, 1, 4, 5, 3, 2]

print(graph.dfs())
# [0, 1, 3, 2, 4, 5]

🤝 Contributing

Contributions, issues and feature requests are welcome!

Development setup

  • DSPlot uses poetry for dependency management. For guidance on how to install poetry, please refer to the official documentation.
  • After cloning the repo, run poetry install to install the dependencies.

Running tests

  • DSPlot uses pytest for testing. To run the tests, run poetry run pytest in the root directory.

📄 License

MIT