dsa-builder 1.0.0

A complete Python library for Data Structures, Algorithms, Graphs, Searching, Sorting & Utilities — beginner-friendly, production-ready.

📦 dsa_builder

A complete Python library for Data Structures, Algorithms, Graphs, Searching, Sorting & Utilities — beginner-friendly, production-ready.

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🧠 What is dsa_builder?

dsa_builder is a lightweight yet complete Python package designed for:

• Students learning Data Structures & Algorithms
• Developers who want reusable DSA utilities
• Interview preparation
• Teaching / classroom demos
• Research prototypes
• Quick scripting requiring fast DSA helpers

It includes search algorithms, sorting algorithms, graph algorithms, stacks, queues, linked lists, pathfinding, cycle detection, MST, SCC, and more.

It also includes Rich-powered automatic pretty-printing, so all print() outputs from the package or user code are formatted in rich, readable style without importing anything manually.

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⭐ Key Features

✔ Complete DSA Suite

• Stacks (array & linked)
• Queues (deque & linked)
• Singly, doubly, circular linked lists
• Searching: linear, binary, bounds, rotated array search
• Sorting: basic, intermediate, advanced (merge, quick, heap, counting, radix, timsort)
• Graph class + advanced algorithms
• Dijkstra, Bellman-Ford, DFS, BFS
• MST: Kruskal, Prim
• SCC (Kosaraju)
• Cycle detection with cycle path
• Shortest path reconstruction

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✔ Automatic Rich Output (No Import Needed)

print() automatically becomes Rich's colorful pretty printer.

Example:

print({"a": 1, "b": [1,2,3]})

Prints beautifully formatted output without importing Rich manually.

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✔ Modern Python API

Every module is accessible through:

from dsa_builder import *

or specifically:

from dsa_builder import Graph, merge_sort, binary_search_iterative

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✔ Easy to Extend

All modules are independent, readable, and documented. New data structures or algorithms can be added easily.

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📁 Directory Structure

dsa_builder/
│
├── __init__.py
├── rich_print.py
├── stack.py
├── queue.py
├── linked_list.py
├── search.py
├── sorting.py
├── graph.py
├── graph_algorithms.py
├── complexity.py
└── main.py  (example usage)

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🔧 Installation

pip install dsa_builder

(If publishing later, you will replace with the real PyPI command.)

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🚀 Getting Started

1. Import the library

import dsa_builder

Everything prints in Rich style automatically.

2. Graph Example

from dsa_builder import Graph

g = Graph(directed=False)
g.add_edge("A", "B")
g.add_edge("B", "C")

print(g.to_adj_list())

Output (auto-rich):

{
    "A": [("B", 1.0)],
    "B": [("A", 1.0), ("C", 1.0)],
    "C": [("B", 1.0)],
}

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🔎 Searching Algorithms

Supported:

• linear_search
• binary_search_iterative
• binary_search_recursive
• lower_bound
• upper_bound
• find_first_last
• search_in_rotated_array

Example

from dsa_builder import binary_search_iterative

nums = [1,3,5,7,9]
print(binary_search_iterative(nums, 7))  # 3

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🔢 Sorting Algorithms

Supported sorts include:

Basic Sorting

• selection_sort
• bubble_sort
• insertion_sort

Advanced Sorting

• merge_sort
• quick_sort
• heap_sort
• counting_sort
• shell_sort
• radix_sort
• bucket_sort
• tim_sort (custom implementation)

Example

from dsa_builder import merge_sort

arr = [5,2,9,1]
print(merge_sort(arr))

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📚 Linked List Structures

Supported:

• SinglyLL
• DoublyLinkedList
• CircularLinkedList

Example

from dsa_builder import SinglyLL

ll = SinglyLL()
ll.insert_end(10)
ll.insert_end(20)
print(ll)

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🧱 Stack & Queue

Stack

• Stack
• LinkedStack

Queue

• DequeQueue
• LinkedQueue

Example:

from dsa_builder import Stack

s = Stack()
s.push(10)
s.push(20)
print(s.pop())  # 20

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🌐 Graph Algorithms

Included Algorithms:

Algorithm	Purpose
BFS	traversal / shortest unweighted
DFS	traversal / cycle detection
Dijkstra	shortest path (positive weights)
Bellman-Ford	negative weights + cycle detection
Prim MST	minimum spanning tree
Kruskal MST	minimum spanning tree
SCC	strongly connected components
detect_cycle_directed	detects cycle & returns cycle path
reconstruct_path	recreate shortest path from parent map

Example: Dijkstra

from dsa_builder import Graph, shortest_path_dijkstra, reconstruct_path

g = Graph(directed=True, weighted=True)
g.add_edge("s","a",1)
g.add_edge("a","b",2)
g.add_edge("s","b",5)

dist, parent = shortest_path_dijkstra(g, "s")
print(dist)                    # {'s':0, 'a':1, 'b':3}
print(reconstruct_path(parent, "s", "b"))  # ['s','a','b']

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📊 Complexity Module

complexity.py provides easy lookup tables:

from dsa_builder import COMPLEXITY_SORT

print(COMPLEXITY_SORT["merge_sort"])  # O(n log n)

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🎨 Auto Rich Printing Behavior

This package overrides Python’s print globally:

Inside rich_print.py:

import builtins
from rich import print as rich_print
builtins.print = rich_print

Once import dsa_builder happens:

• All print() inside the package = Rich
• All print() inside user code = Rich
• No extra imports needed

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🧪 Example Project (main.py)

Your included main.py demonstrates combining DSA operations:

from dsa_builder import Graph, merge_sort, binary_search_iterative

g = Graph(directed=False)
g.add_edge(1,2)
g.add_edge(2,3)

print("Adjacency List:", g.to_adj_list())
print("Sorted:", merge_sort([9,1,3,7]))
print("Search:", binary_search_iterative([1,3,5,7], 5))

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🛠 Contributing

Pull requests are welcome. To contribute:

1. Fork the repository
2. Create a new branch
3. Add features or fix issues
4. Update documentation
5. Submit PR

All code should follow PEP8 and include tests where appropriate.

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📄 License

MIT License — simple and open. You may freely use, modify, or distribute this library.

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📌 Versioning

We follow Semantic Versioning:

MAJOR.MINOR.PATCH

Example:

• 1.0.0 — Initial release
• 1.1.0 — Added algorithms
• 1.1.1 — Bug fixes

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🎉 Final Notes

dsa_builder aims to be:

• beginner-friendly
• powerful
• easy to extend
• rich-printed by default
• fully modular

It’s a perfect starting point for learning, building projects, interview prep, and classroom use.