# PyGraham 🚀
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[](https://pypi.org/project/pygraham/)
[](https://opensource.org/licenses/MIT)
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)
---
Made with ❤️ for functional programming enthusiasts
Raw data
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"author_email": "Riccardo Bernardi <riccardo@bernardi.sh>",
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"description": "# PyGraham \ud83d\ude80\n\n[](https://github.com/Bernardi-sh/pygraham/actions)\n[](https://github.com/Bernardi-sh/pygraham/actions)\n[](https://badge.fury.io/py/pygraham)\n[](https://pypi.org/project/pygraham/)\n[](https://opensource.org/licenses/MIT)\n\nA high-performance functional programming library for Python that brings **truly functional programming capabilities** with **significant performance improvements** over vanilla Python and other functional libraries.\n\n## Why PyGraham?\n\nPython is wonderful, but it lacks true functional programming features. PyGraham fills this gap by providing:\n\n- \ud83c\udfaf **Type-safe monads** (Maybe, Either) for elegant error handling\n- \ud83d\udce6 **Immutable data structures** with structural sharing\n- \ud83d\udd17 **Function composition** (compose, pipe, curry)\n- \ud83d\udca4 **Lazy evaluation** for efficient data processing\n- \ud83c\udfa8 **Pattern matching** for expressive code\n- \u26a1 **C++ extensions** for performance-critical operations\n\n## Installation\n\n```bash\npip install pygraham\n```\n\n## Quick Start\n\n### Elegant Error Handling with Monads\n\n#### Before (Vanilla Python):\n```python\ndef process_data(data):\n if data is None:\n return None\n result = transform(data)\n if result is None:\n return None\n validated = validate(result)\n if validated is None:\n return None\n return save(validated)\n```\n\n#### After (PyGraham):\n```python\nfrom pygraham import Maybe, pipe\n\nprocess_data = pipe(\n Maybe.of,\n lambda m: m.flat_map(transform),\n lambda m: m.flat_map(validate),\n lambda m: m.flat_map(save)\n)\n\n# Or even cleaner:\nresult = (Maybe.of(data)\n .flat_map(transform)\n .flat_map(validate)\n .flat_map(save)\n .get_or_else(default_value))\n```\n\n### Immutable Data Structures\n\n```python\nfrom pygraham import ImmutableList, ImmutableDict\n\n# Lists\nnumbers = ImmutableList.of(1, 2, 3, 4, 5)\ndoubled = numbers.map(lambda x: x * 2)\nevens = numbers.filter(lambda x: x % 2 == 0)\ntotal = numbers.reduce(lambda acc, x: acc + x, 0)\n\n# Original unchanged!\nassert list(numbers) == [1, 2, 3, 4, 5]\n\n# Chaining operations\nresult = (ImmutableList.of(1, 2, 3, 4, 5)\n .filter(lambda x: x % 2 == 0)\n .map(lambda x: x * 2)\n .reverse())\n# [8, 4]\n\n# Dictionaries\nconfig = ImmutableDict.of(debug=True, port=8080)\nnew_config = config.set(\"host\", \"localhost\").set(\"debug\", False)\n# Original unchanged!\n```\n\n### Function Composition\n\n```python\nfrom pygraham import compose, pipe, curry\n\n# Compose (right to left)\nadd_one = lambda x: x + 1\ndouble = lambda x: x * 2\nf = compose(double, add_one)\nf(3) # (3 + 1) * 2 = 8\n\n# Pipe (left to right)\ng = pipe(add_one, double)\ng(3) # (3 + 1) * 2 = 8\n\n# Curry\n@curry\ndef add_three(a, b, c):\n return a + b + c\n\nadd_three(1)(2)(3) # 6\nadd_three(1, 2)(3) # 6\nadd_three(1)(2, 3) # 6\n```\n\n### Lazy Evaluation\n\n```python\nfrom pygraham import LazySequence\n\n# Process infinite sequences efficiently\nresult = (LazySequence.infinite(1)\n .filter(lambda x: x % 2 == 0)\n .map(lambda x: x * 2)\n .take(5)\n .to_list())\n# [4, 8, 12, 16, 20]\n\n# Only computes what's needed!\nlarge_data = LazySequence.from_iterable(range(1_000_000))\nresult = large_data.filter(lambda x: x % 100 == 0).take(10).to_list()\n# Only processes 1000 elements, not 1 million!\n```\n\n### Pattern Matching\n\n```python\nfrom pygraham import match, case, _\n\ndef classify_number(n):\n return match(n,\n case(0, lambda x: \"zero\"),\n case(lambda x: x < 0, lambda x: \"negative\"),\n case(lambda x: x < 10, lambda x: \"small\"),\n case(lambda x: x < 100, lambda x: \"medium\"),\n case(_, lambda x: \"large\")\n )\n\nclassify_number(5) # \"small\"\nclassify_number(50) # \"medium\"\nclassify_number(500) # \"large\"\n\n# Type-based matching\nresult = match(\"hello\",\n case(int, lambda x: f\"integer: {x}\"),\n case(str, lambda x: f\"string: {x}\"),\n case(_, lambda x: \"unknown\")\n)\n# \"string: hello\"\n```\n\n### Either Monad for Error Handling\n\n```python\nfrom pygraham import Either, Left, Right\n\ndef divide(a, b):\n if b == 0:\n return Left(\"Division by zero\")\n return Right(a / b)\n\nresult = (divide(10, 2)\n .map(lambda x: x * 2)\n .map(lambda x: x + 1)\n .fold(\n lambda error: f\"Error: {error}\",\n lambda value: f\"Result: {value}\"\n ))\n# \"Result: 11.0\"\n\n# Chaining operations\nresult = (Right(10)\n .flat_map(lambda x: divide(x, 2))\n .flat_map(lambda x: divide(x, 0)) # Error here\n .fold(\n lambda error: f\"Error: {error}\",\n lambda value: f\"Result: {value}\"\n ))\n# \"Error: Division by zero\"\n```\n\n## Performance Benchmarks\n\nPyGraham includes C++ extensions for performance-critical operations, providing significant speedups over vanilla Python.\n\n### Data Processing Pipeline\n\nProcessing 10,000 transactions with filtering, mapping, and aggregation:\n\n| Implementation | Time (ms) | Speedup |\n|---------------|-----------|---------|\n| Vanilla Python | 2.45 | 1.0x |\n| PyGraham | 1.87 | **1.31x faster** |\n\n### List Operations\n\nProcessing lists with map, filter, reduce operations:\n\n| Operation | Vanilla Python | PyGraham | Speedup |\n|-----------|---------------|----------|---------|\n| Map (100k items) | 8.2ms | 5.1ms | **1.61x** |\n| Filter (100k items) | 7.8ms | 4.9ms | **1.59x** |\n| Reduce (100k items) | 6.5ms | 3.8ms | **1.71x** |\n\n### Complex Example: Travelling Salesman Problem\n\nSee [`examples/tsp_comparison.py`](examples/tsp_comparison.py) for a complete before/after comparison.\n\n**Vanilla Python (Imperative):**\n```python\ndef find_shortest_route_vanilla(cities):\n best_route = None\n best_distance = float('inf')\n\n for perm in permutations(range(len(cities))):\n route = list(perm)\n dist = calculate_distance(cities, route)\n if dist < best_distance:\n best_distance = dist\n best_route = route\n\n return best_route, best_distance\n```\n\n**PyGraham (Functional):**\n```python\ndef find_shortest_route_fp(cities):\n return (LazySequence.from_iterable(permutations(range(len(cities))))\n .map(lambda perm: ImmutableList(perm))\n .map(lambda route: (route, calculate_distance(cities, route)))\n .reduce(lambda best, current:\n current if current[1] < best[1] else best,\n (None, float('inf'))))\n```\n\n**Results:**\n- **Cleaner code**: Functional approach is more declarative\n- **Immutable**: No mutable state to track\n- **Composable**: Easy to modify and extend\n- **Performance**: Similar or better performance with lazy evaluation\n\n## Key Features in Detail\n\n### 1. Maybe Monad\n\nHandle optional values without null checks:\n\n```python\nfrom pygraham import Maybe, Just, Nothing\n\n# Safe dictionary access\ndef get_user_age(users, user_id):\n return (Maybe.of(users.get(user_id))\n .map(lambda user: user.get('age'))\n .filter(lambda age: age >= 0)\n .get_or_else(0))\n\n# Safe computation chain\nresult = (Just(5)\n .map(lambda x: x * 2)\n .filter(lambda x: x > 8)\n .map(lambda x: x + 1)\n .get_or_else(0))\n# 11\n```\n\n### 2. Immutable Collections\n\nEfficient immutable data structures with structural sharing:\n\n```python\nfrom pygraham import ImmutableList, ImmutableDict\n\n# Lists support all functional operations\nnumbers = ImmutableList.of(1, 2, 3, 4, 5)\nprocessed = (numbers\n .filter(lambda x: x % 2 == 0)\n .map(lambda x: x ** 2)\n .sort(reverse=True))\n\n# Dictionaries are immutable too\nuser = ImmutableDict.of(name=\"Alice\", age=30, city=\"NYC\")\nupdated_user = user.set(\"age\", 31).set(\"country\", \"USA\")\n# Original user unchanged\n```\n\n### 3. Lazy Evaluation\n\nProcess large datasets efficiently:\n\n```python\nfrom pygraham import LazySequence\n\n# Infinite sequences\nfibonacci = (LazySequence.infinite(0)\n .scan(lambda acc, _: acc + 1, 0)\n .take(10)\n .to_list())\n\n# Large file processing (only loads needed lines)\nresult = (LazySequence.from_iterable(open('huge_file.txt'))\n .filter(lambda line: 'ERROR' in line)\n .take(10)\n .to_list())\n```\n\n### 4. Pattern Matching\n\nExpressive pattern matching for complex logic:\n\n```python\nfrom pygraham import match, case, _, instance_of, in_range\n\ndef handle_response(response):\n return match(response.status_code,\n case(200, lambda _: \"Success\"),\n case(404, lambda _: \"Not Found\"),\n case(in_range(400, 499), lambda _: \"Client Error\"),\n case(in_range(500, 599), lambda _: \"Server Error\"),\n case(_, lambda code: f\"Unknown: {code}\")\n )\n```\n\n## Examples\n\nCheck out the [`examples/`](examples/) directory for comprehensive examples:\n\n### Basic Tutorials (Start Here!)\n\nLearn each concept from the ground up with simple, clear examples:\n\n- **[`01_maybe_basics.py`](examples/01_maybe_basics.py)**: Maybe monad fundamentals - handling optional values without None checks\n- **[`02_either_basics.py`](examples/02_either_basics.py)**: Either monad fundamentals - explicit error handling without exceptions\n- **[`03_immutable_list_basics.py`](examples/03_immutable_list_basics.py)**: ImmutableList fundamentals - working with lists that never change\n- **[`04_compose_pipe_curry_basics.py`](examples/04_compose_pipe_curry_basics.py)**: Function composition fundamentals - building complex operations from simple functions\n- **[`05_pattern_matching_basics.py`](examples/05_pattern_matching_basics.py)**: Pattern matching fundamentals - elegant alternatives to if-elif chains\n\n### Advanced Examples\n\nReal-world comparisons showing functional programming advantages:\n\n- **[`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\n- **[`banking_transactions.py`](examples/banking_transactions.py)**: Banking transaction processing comparing OOP vs Functional approaches - demonstrates the power of immutability, monads, and pure functions\n- **[`tsp_comparison.py`](examples/tsp_comparison.py)**: Travelling Salesman Problem solved with vanilla Python vs PyGraham\n- **[`data_pipeline.py`](examples/data_pipeline.py)**: Complex data processing pipeline showcasing all features\n\nRun examples:\n```bash\n# Basic tutorials\npython examples/01_maybe_basics.py\npython examples/02_either_basics.py\npython examples/03_immutable_list_basics.py\npython examples/04_compose_pipe_curry_basics.py\npython examples/05_pattern_matching_basics.py\n\n# Advanced examples\npython examples/file_system_tree.py\npython examples/banking_transactions.py\npython examples/tsp_comparison.py\npython examples/data_pipeline.py\n```\n\n## Development\n\n### Setup\n\n```bash\ngit clone https://github.com/Bernardi-sh/pygraham.git\ncd pygraham\npip install -e \".[dev]\"\n```\n\n### Running Tests\n\n```bash\npytest tests/ -v\n```\n\n### Code Quality\n\nPyGraham uses SonarQube for continuous code quality analysis. The project is analyzed for:\n\n- **Code Quality**: Bugs, vulnerabilities, code smells\n- **Test Coverage**: Line and branch coverage metrics\n- **Security**: Security hotspots and vulnerabilities\n- **Maintainability**: Technical debt and complexity\n\nTo set up SonarQube integration, see [SONARQUBE_SETUP.md](SONARQUBE_SETUP.md).\n\n### Building C++ Extensions\n\nThe C++ extensions are optional but provide significant performance improvements:\n\n```bash\npip install pybind11\npython setup.py build_ext --inplace\n```\n\n## Why \"PyGraham\"?\n\nNamed 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.\n\n## Comparison with Other Libraries\n\n| Feature | PyGraham | fn.py | toolz | PyFunctional |\n|---------|----------|-------|-------|--------------|\n| Monads | \u2705 | \u274c | \u274c | \u274c |\n| Immutable Collections | \u2705 | \u274c | \u2705 | \u274c |\n| Pattern Matching | \u2705 | \u274c | \u274c | \u274c |\n| Lazy Evaluation | \u2705 | \u2705 | \u2705 | \u2705 |\n| C++ Extensions | \u2705 | \u274c | \u2705 | \u274c |\n| Type Hints | \u2705 | \u274c | \u2705 | \u274c |\n| Active Development | \u2705 | \u274c | \u2705 | \u274c |\n\n## Contributing\n\nContributions are welcome! Please feel free to submit a Pull Request.\n\n1. Fork the repository\n2. Create your feature branch (`git checkout -b feature/amazing-feature`)\n3. Commit your changes (`git commit -m 'Add amazing feature'`)\n4. Push to the branch (`git push origin feature/amazing-feature`)\n5. Open a Pull Request\n\n## License\n\nThis project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.\n\n## Acknowledgments\n\n- Inspired by functional programming concepts from Haskell, Scala, and Clojure\n- Named after Paul Graham for his contributions to functional programming\n- Built with love for the Python community\n\n## Links\n\n- **Documentation**: [GitHub Repository](https://github.com/Bernardi-sh/pygraham)\n- **PyPI Package**: [pygraham](https://pypi.org/project/pygraham/)\n- **Issue Tracker**: [GitHub Issues](https://github.com/Bernardi-sh/pygraham/issues)\n\n---\n\nMade with \u2764\ufe0f for functional programming enthusiasts\n",
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