pyhelper-jkluess 0.3.0

A collection of Python data structures for educational purposes

PyHelper

Python implementations of fundamental data structures for learning and practical use.

What is this?

PyHelper provides clean, well-tested implementations of common data structures:

• Lists: Linked, Double, Circular
• Graphs: Unified Graph class + specialized types (Undirected, Directed, Weighted) with visualization & graph theory operations
• Trees: Hierarchical tree structures with comprehensive operations and traversals
• Skip Lists: Deterministic and Probabilistic

Prerequisites

pip install networkx matplotlib pytest

Installation

Install from local directory (for development)

pip install -e .

Install from local directory (regular installation)

pip install .

Install from GitHub

pip install git+https://github.com/Djey8/PyHelper.git

Install from PyPI

pip install pyhelper-jkluess

Quick Start

Linked List

from Basic.Lists.linked_list import LinkedList

ll = LinkedList()
ll.append(10)
ll.append(20)
ll.print_list()  # 10 -> 20 -> None

Graph (Unified Class - Recommended)

from Complex.Graphs.graph import Graph

# Create any graph type with parameters
g = Graph(directed=False, weighted=True)
g.add_edge("A", "B", 10)
g.add_edge("A", "C", 2)
g.add_edge("C", "B", 1)

# Find shortest path (uses Dijkstra for weighted graphs)
path, distance = g.find_shortest_path("A", "B")
print(f"Path: {path}, Distance: {distance}")  # Path: ['A', 'C', 'B'], Distance: 3

# Export/import adjacency list
adj_list = g.get_adjacency_list()
g2 = Graph(directed=False, weighted=True, data=adj_list)

# Visualize
g.visualize()  # Opens matplotlib window

Tree

from Complex.Trees.tree import Tree

# Create tree with root
tree = Tree("Root")

# Add children
child_a = tree.add_child(tree.root, "A")
child_b = tree.add_child(tree.root, "B")
tree.add_child(child_a, "A1")
tree.add_child(child_a, "A2")

# Print structure
tree.print_tree()

# Traversals
print(tree.traverse_preorder())    # ['Root', 'A', 'A1', 'A2', 'B']
print(tree.traverse_levelorder())  # ['Root', 'A', 'B', 'A1', 'A2']

# Statistics
stats = tree.get_statistics()
print(f"Nodes: {stats['node_count']}, Height: {stats['height']}")

Skip List

from Complex.SkipLists.probabilisticskiplist import ProbabilisticSkipList

sl = ProbabilisticSkipList()
sl.add(10)
sl.add(20)
print(sl.find(10))  # 10

Structure

PyHelper/
├── Grundlegende_Datenstrukuren/  # Basic data structures (Lists)
├── Complex/Graphs/                # Graph data structures
├── Complex/Trees/                 # Tree data structures
└── Complex/SkipLists/             # Skip list implementations

Documentation

• Basic Lists - LinkedList, DoubleLinkedList, CircularLinkedList
• Graphs - Graph (unified), UndirectedGraph, DirectedGraph, WeightedUndirectedGraph, WeightedDirectedGraph
  • Unified Graph class adapts to all 4 types based on initialization
  • Shortest path algorithms: BFS (unweighted) and Dijkstra (weighted)
  • Includes: Paths, cycles, connectivity, adjacency matrices/lists
  • 64% code reduction through inheritance architecture
• Trees - Tree, TreeNode
  • Hierarchical tree structures with parent-child relationships
  • Properties: m = n - 1, connected, acyclic, unique paths
  • Traversals: preorder, postorder, level-order
  • Operations: depth, height, levels, ancestors, descendants, path finding
• Skip Lists - SkipList, ProbabilisticSkipList

Testing

pytest tests/ -v  # 529 tests

License

MIT License