algopract 0.1.0

A learning-focused Python package implementing core algorithms with step-by-step explanations.

algopract

algopract is a learning-focused Python package that implements core algorithms with optional step-by-step explanations and execution-time profiling.

It is designed to help understand how algorithms work internally, not just produce final results.

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Installation

pip install algopract

For local development:

pip install -e .

Usage

Searching Example

result = run_binary_search(
    [1, 2, 3, 4, 5],
    3,
    explain=True,
    profile=True
)

print(result)

Output format:

{
  "algorithm": "Binary Search",
  "result": 2,
  "steps": [...],
  "execution_time_ms": 0.012,
  "expected_complexity": "O(log n)"
}

Sorting Example

from algopract import run_quick_sort

result = run_quick_sort([3, 1, 2])
print(result["result"])

Graph Traversal Example

from algopract import run_bfs

graph = {
    "A": ["B", "C"],
    "B": ["D"],
    "C": [],
    "D": []
}

result = run_bfs(graph, "A")
print(result["result"])

Design Principles

Algorithms return structured data, not printed output

Explanation and profiling are optional

Consistent API across all algorithms

Core logic is separated from presentation

This makes the library suitable for scripts, notebooks, CLI tools, and future UI or visualization layers.

Included Algorithms

Searching: Linear, Binary, Jump, Interpolation

Sorting: Bubble, Selection, Insertion, Merge, Quick

Data Structures: Stack, Queue

Graphs: BFS, DFS