Metadata-Version: 2.4
Name: devank-paandass
Version: 0.1.0
Summary: A Python package with graph algorithms and pandas utilities
Home-page: https://github.com/DevankU/pandass
Author: Your Name
Author-email: Your Name <your.email@example.com>
License: MIT
Project-URL: Homepage, https://github.com/DevankU/pandass
Project-URL: Bug Reports, https://github.com/DevankU/pandass/issues
Project-URL: Source, https://github.com/DevankU/pandass
Keywords: graph,algorithms,pandas,bfs,dfs,astar,best-first-search
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Topic :: Software Development :: Libraries :: Python Modules
Classifier: Topic :: Scientific/Engineering
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Requires-Python: >=3.7
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: pandas>=1.3.0
Requires-Dist: networkx>=2.6
Dynamic: author
Dynamic: home-page
Dynamic: license-file
Dynamic: requires-python

# Devank-Paandass

A Python package containing graph algorithms implementations and utilities for working with pandas and NetworkX.

## Features

- **Graph Algorithms**: Implementations of DFS, BFS, Best-First Search, and A* algorithms
- **Pandas Integration**: Read graph data from CSV files using pandas
- **Educational Resources**: Includes text files with algorithm implementations
- **Easy to Use**: Simple API to access algorithm code and examples

## Installation

```bash
pip install devank-paandass
```

## What Gets Installed

When you install `devank-paandass`, you get:
- **algorithms.txt**: Comprehensive implementations of graph algorithms (DFS, BFS, Best-First, A*)
- **python1.txt**: Additional graph algorithms code with NetworkX examples

## Usage

### Accessing Data Files

```python
import pandass

# List all available data files
files = pandass.list_data_files()
print(files)

# Read a specific data file
algorithms_code = pandass.read_data_file('algorithms.txt')
print(algorithms_code)

# Get the path to a data file
file_path = pandass.get_data_file('python1.txt')
print(f"File located at: {file_path}")
```

### Using the Algorithm Implementations

The package includes implementations of:

1. **Depth-First Search (DFS)**
   - Recursive traversal
   - Path finding

2. **Breadth-First Search (BFS)**
   - Level-order traversal
   - Shortest path finding

3. **Best-First Search (Greedy)**
   - Heuristic-based search
   - Priority queue implementation

4. **A* Search Algorithm**
   - Optimal pathfinding
   - Combines cost and heuristic

### Reading Graphs from CSV

The algorithms work with CSV files containing graph data:

```csv
source,target,weight
A,B,1
A,C,4
B,C,2
B,D,5
C,D,1
```

## Example

```python
import pandass
import pandas as pd
import networkx as nx

# Read the algorithms code
algorithms = pandass.read_data_file('algorithms.txt')

# The algorithms.txt file contains complete implementations
# that can be executed or studied

# You can also use the code as a reference for your own implementations
```

## Dependencies

- pandas >= 1.3.0
- networkx >= 2.6

## Package Contents

- `algorithms.txt`: Complete implementations of graph algorithms with pandas CSV reading
- `python1.txt`: NetworkX-based graph algorithm examples

## Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

## License

This project is licensed under the MIT License - see the LICENSE file for details.

## Author

Your Name (your.email@example.com)

## Links

- **GitHub**: https://github.com/DevankU/pandass
- **PyPI**: https://pypi.org/project/devank-paandass/
- **Issues**: https://github.com/DevankU/pandass/issues

## Educational Purpose

This package is designed for educational purposes, providing clear implementations of classic graph algorithms that can be studied and used as examples.

## Keywords

graph algorithms, DFS, BFS, A*, best-first search, pandas, networkx, pathfinding, graph theory
