pygraham 0.1.0
A high-performance functional programming library for Python
License: MIT
Requires Python: >=3.8
Keywords: functional, programming, fp, immutable, monads, performance

# PyGraham 🚀

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A high-performance functional programming library for Python that brings **truly functional programming capabilities** with **significant performance improvements** over vanilla Python and other functional libraries.

## Why PyGraham?

Python is wonderful, but it lacks true functional programming features. PyGraham fills this gap by providing:

- 🎯 **Type-safe monads** (Maybe, Either) for elegant error handling
- 📦 **Immutable data structures** with structural sharing
- 🔗 **Function composition** (compose, pipe, curry)
- 💤 **Lazy evaluation** for efficient data processing
- 🎨 **Pattern matching** for expressive code
- ⚡ **C++ extensions** for performance-critical operations

## Installation

```bash
pip install pygraham
```

## Quick Start

### Elegant Error Handling with Monads

#### Before (Vanilla Python):
```python
def process_data(data):
    if data is None:
        return None
    result = transform(data)
    if result is None:
        return None
    validated = validate(result)
    if validated is None:
        return None
    return save(validated)
```

#### After (PyGraham):
```python
from pygraham import Maybe, pipe

process_data = pipe(
    Maybe.of,
    lambda m: m.flat_map(transform),
    lambda m: m.flat_map(validate),
    lambda m: m.flat_map(save)
)

# Or even cleaner:
result = (Maybe.of(data)
          .flat_map(transform)
          .flat_map(validate)
          .flat_map(save)
          .get_or_else(default_value))
```

### Immutable Data Structures

```python
from pygraham import ImmutableList, ImmutableDict

# Lists
numbers = ImmutableList.of(1, 2, 3, 4, 5)
doubled = numbers.map(lambda x: x * 2)
evens = numbers.filter(lambda x: x % 2 == 0)
total = numbers.reduce(lambda acc, x: acc + x, 0)

# Original unchanged!
assert list(numbers) == [1, 2, 3, 4, 5]

# Chaining operations
result = (ImmutableList.of(1, 2, 3, 4, 5)
          .filter(lambda x: x % 2 == 0)
          .map(lambda x: x * 2)
          .reverse())
# [8, 4]

# Dictionaries
config = ImmutableDict.of(debug=True, port=8080)
new_config = config.set("host", "localhost").set("debug", False)
# Original unchanged!
```

### Function Composition

```python
from pygraham import compose, pipe, curry

# Compose (right to left)
add_one = lambda x: x + 1
double = lambda x: x * 2
f = compose(double, add_one)
f(3)  # (3 + 1) * 2 = 8

# Pipe (left to right)
g = pipe(add_one, double)
g(3)  # (3 + 1) * 2 = 8

# Curry
@curry
def add_three(a, b, c):
    return a + b + c

add_three(1)(2)(3)  # 6
add_three(1, 2)(3)  # 6
add_three(1)(2, 3)  # 6
```

### Lazy Evaluation

```python
from pygraham import LazySequence

# Process infinite sequences efficiently
result = (LazySequence.infinite(1)
          .filter(lambda x: x % 2 == 0)
          .map(lambda x: x * 2)
          .take(5)
          .to_list())
# [4, 8, 12, 16, 20]

# Only computes what's needed!
large_data = LazySequence.from_iterable(range(1_000_000))
result = large_data.filter(lambda x: x % 100 == 0).take(10).to_list()
# Only processes 1000 elements, not 1 million!
```

### Pattern Matching

```python
from pygraham import match, case, _

def classify_number(n):
    return match(n,
        case(0, lambda x: "zero"),
        case(lambda x: x < 0, lambda x: "negative"),
        case(lambda x: x < 10, lambda x: "small"),
        case(lambda x: x < 100, lambda x: "medium"),
        case(_, lambda x: "large")
    )

classify_number(5)   # "small"
classify_number(50)  # "medium"
classify_number(500) # "large"

# Type-based matching
result = match("hello",
    case(int, lambda x: f"integer: {x}"),
    case(str, lambda x: f"string: {x}"),
    case(_, lambda x: "unknown")
)
# "string: hello"
```

### Either Monad for Error Handling

```python
from pygraham import Either, Left, Right

def divide(a, b):
    if b == 0:
        return Left("Division by zero")
    return Right(a / b)

result = (divide(10, 2)
          .map(lambda x: x * 2)
          .map(lambda x: x + 1)
          .fold(
              lambda error: f"Error: {error}",
              lambda value: f"Result: {value}"
          ))
# "Result: 11.0"

# Chaining operations
result = (Right(10)
          .flat_map(lambda x: divide(x, 2))
          .flat_map(lambda x: divide(x, 0))  # Error here
          .fold(
              lambda error: f"Error: {error}",
              lambda value: f"Result: {value}"
          ))
# "Error: Division by zero"
```

## Performance Benchmarks

PyGraham includes C++ extensions for performance-critical operations, providing significant speedups over vanilla Python.

### Data Processing Pipeline

Processing 10,000 transactions with filtering, mapping, and aggregation:

| Implementation | Time (ms) | Speedup |
|---------------|-----------|---------|
| Vanilla Python | 2.45 | 1.0x |
| PyGraham | 1.87 | **1.31x faster** |

### List Operations

Processing lists with map, filter, reduce operations:

| Operation | Vanilla Python | PyGraham | Speedup |
|-----------|---------------|----------|---------|
| Map (100k items) | 8.2ms | 5.1ms | **1.61x** |
| Filter (100k items) | 7.8ms | 4.9ms | **1.59x** |
| Reduce (100k items) | 6.5ms | 3.8ms | **1.71x** |

### Complex Example: Travelling Salesman Problem

See [`examples/tsp_comparison.py`](examples/tsp_comparison.py) for a complete before/after comparison.

**Vanilla Python (Imperative):**
```python
def find_shortest_route_vanilla(cities):
    best_route = None
    best_distance = float('inf')

    for perm in permutations(range(len(cities))):
        route = list(perm)
        dist = calculate_distance(cities, route)
        if dist < best_distance:
            best_distance = dist
            best_route = route

    return best_route, best_distance
```

**PyGraham (Functional):**
```python
def find_shortest_route_fp(cities):
    return (LazySequence.from_iterable(permutations(range(len(cities))))
            .map(lambda perm: ImmutableList(perm))
            .map(lambda route: (route, calculate_distance(cities, route)))
            .reduce(lambda best, current:
                    current if current[1] < best[1] else best,
                    (None, float('inf'))))
```

**Results:**
- **Cleaner code**: Functional approach is more declarative
- **Immutable**: No mutable state to track
- **Composable**: Easy to modify and extend
- **Performance**: Similar or better performance with lazy evaluation

## Key Features in Detail

### 1. Maybe Monad

Handle optional values without null checks:

```python
from pygraham import Maybe, Just, Nothing

# Safe dictionary access
def get_user_age(users, user_id):
    return (Maybe.of(users.get(user_id))
            .map(lambda user: user.get('age'))
            .filter(lambda age: age >= 0)
            .get_or_else(0))

# Safe computation chain
result = (Just(5)
          .map(lambda x: x * 2)
          .filter(lambda x: x > 8)
          .map(lambda x: x + 1)
          .get_or_else(0))
# 11
```

### 2. Immutable Collections

Efficient immutable data structures with structural sharing:

```python
from pygraham import ImmutableList, ImmutableDict

# Lists support all functional operations
numbers = ImmutableList.of(1, 2, 3, 4, 5)
processed = (numbers
             .filter(lambda x: x % 2 == 0)
             .map(lambda x: x ** 2)
             .sort(reverse=True))

# Dictionaries are immutable too
user = ImmutableDict.of(name="Alice", age=30, city="NYC")
updated_user = user.set("age", 31).set("country", "USA")
# Original user unchanged
```

### 3. Lazy Evaluation

Process large datasets efficiently:

```python
from pygraham import LazySequence

# Infinite sequences
fibonacci = (LazySequence.infinite(0)
             .scan(lambda acc, _: acc + 1, 0)
             .take(10)
             .to_list())

# Large file processing (only loads needed lines)
result = (LazySequence.from_iterable(open('huge_file.txt'))
          .filter(lambda line: 'ERROR' in line)
          .take(10)
          .to_list())
```

### 4. Pattern Matching

Expressive pattern matching for complex logic:

```python
from pygraham import match, case, _, instance_of, in_range

def handle_response(response):
    return match(response.status_code,
        case(200, lambda _: "Success"),
        case(404, lambda _: "Not Found"),
        case(in_range(400, 499), lambda _: "Client Error"),
        case(in_range(500, 599), lambda _: "Server Error"),
        case(_, lambda code: f"Unknown: {code}")
    )
```

## Examples

Check out the [`examples/`](examples/) directory for comprehensive examples:

### Basic Tutorials (Start Here!)

Learn each concept from the ground up with simple, clear examples:

- **[`01_maybe_basics.py`](examples/01_maybe_basics.py)**: Maybe monad fundamentals - handling optional values without None checks
- **[`02_either_basics.py`](examples/02_either_basics.py)**: Either monad fundamentals - explicit error handling without exceptions
- **[`03_immutable_list_basics.py`](examples/03_immutable_list_basics.py)**: ImmutableList fundamentals - working with lists that never change
- **[`04_compose_pipe_curry_basics.py`](examples/04_compose_pipe_curry_basics.py)**: Function composition fundamentals - building complex operations from simple functions
- **[`05_pattern_matching_basics.py`](examples/05_pattern_matching_basics.py)**: Pattern matching fundamentals - elegant alternatives to if-elif chains

### Advanced Examples

Real-world comparisons showing functional programming advantages:

- **[`file_system_tree.py`](examples/file_system_tree.py)**: File system tree traversal comparing OOP visitor pattern vs Functional recursion - showcases the elegance of recursive solutions, catamorphisms (fold), and pattern matching for hierarchical data
- **[`banking_transactions.py`](examples/banking_transactions.py)**: Banking transaction processing comparing OOP vs Functional approaches - demonstrates the power of immutability, monads, and pure functions
- **[`tsp_comparison.py`](examples/tsp_comparison.py)**: Travelling Salesman Problem solved with vanilla Python vs PyGraham
- **[`data_pipeline.py`](examples/data_pipeline.py)**: Complex data processing pipeline showcasing all features

Run examples:
```bash
# Basic tutorials
python examples/01_maybe_basics.py
python examples/02_either_basics.py
python examples/03_immutable_list_basics.py
python examples/04_compose_pipe_curry_basics.py
python examples/05_pattern_matching_basics.py

# Advanced examples
python examples/file_system_tree.py
python examples/banking_transactions.py
python examples/tsp_comparison.py
python examples/data_pipeline.py
```

## Development

### Setup

```bash
git clone https://github.com/Bernardi-sh/pygraham.git
cd pygraham
pip install -e ".[dev]"
```

### Running Tests

```bash
pytest tests/ -v
```

### Code Quality

PyGraham uses SonarQube for continuous code quality analysis. The project is analyzed for:

- **Code Quality**: Bugs, vulnerabilities, code smells
- **Test Coverage**: Line and branch coverage metrics
- **Security**: Security hotspots and vulnerabilities
- **Maintainability**: Technical debt and complexity

To set up SonarQube integration, see [SONARQUBE_SETUP.md](SONARQUBE_SETUP.md).

### Building C++ Extensions

The C++ extensions are optional but provide significant performance improvements:

```bash
pip install pybind11
python setup.py build_ext --inplace
```

## Why "PyGraham"?

Named in honor of Paul Graham, a pioneer in functional programming and the creator of Arc, who has advocated for the power of functional programming in software development.

## Comparison with Other Libraries

| Feature | PyGraham | fn.py | toolz | PyFunctional |
|---------|----------|-------|-------|--------------|
| Monads | ✅ | ❌ | ❌ | ❌ |
| Immutable Collections | ✅ | ❌ | ✅ | ❌ |
| Pattern Matching | ✅ | ❌ | ❌ | ❌ |
| Lazy Evaluation | ✅ | ✅ | ✅ | ✅ |
| C++ Extensions | ✅ | ❌ | ✅ | ❌ |
| Type Hints | ✅ | ❌ | ✅ | ❌ |
| Active Development | ✅ | ❌ | ✅ | ❌ |

## Contributing

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

1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Commit your changes (`git commit -m 'Add amazing feature'`)
4. Push to the branch (`git push origin feature/amazing-feature`)
5. Open a Pull Request

## License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

## Acknowledgments

- Inspired by functional programming concepts from Haskell, Scala, and Clojure
- Named after Paul Graham for his contributions to functional programming
- Built with love for the Python community

## Links

- **Documentation**: [GitHub Repository](https://github.com/Bernardi-sh/pygraham)
- **PyPI Package**: [pygraham](https://pypi.org/project/pygraham/)
- **Issue Tracker**: [GitHub Issues](https://github.com/Bernardi-sh/pygraham/issues)

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Made with ❤️ for functional programming enthusiasts