py-collections 0.1.4

A Python collections library providing enhanced collection types with additional functionality

py-collections

A Python collections library providing enhanced collection types with additional functionality, built using a modular mixin architecture.

Development Setup

This project uses uv for dependency management and Python environment management.

Prerequisites

• Python 3.13+
• uv installed
• taskipy (optional, for easier command execution)

Installation

1. Clone the repository

2. Install dependencies:

   uv sync
   

3. Install the package in development mode (for running examples):

   uv pip install -e .
   

4. (Optional) Install taskipy globally for easier command execution:

   pip install taskipy
   

Running Tests

To run all tests:

uv run pytest

To run tests with verbose output:

uv run pytest -v

To run tests with coverage:

uv run pytest --cov=src/py_collections --cov-report=term-missing

To generate HTML coverage report:

uv run pytest --cov=src/py_collections --cov-report=html --cov-report=term-missing

To run a specific test file:

uv run pytest tests/core/test_init.py

To run tests for a specific mixin:

uv run pytest tests/mixins/basic_operations/
uv run pytest tests/mixins/transformation/

To run tests in watch mode (re-runs on file changes):

uv run pytest --watch

Development Commands

Using taskipy (recommended)

If you have taskipy installed globally (pip install taskipy):

• Install dev dependencies: uv sync --group dev
• Run linting: task check
• Format code: task format
• Check formatting: task format-check
• Auto-fix linting issues: task check-fix
• Run tests: task test
• Run tests with verbose output: task test-verbose
• Run tests with coverage: task test-coverage
• Run tests with HTML coverage report: task test-coverage-html
• Run all checks: task lint
• Run linting, formatting, and tests: task all

If you don't have taskipy installed globally, use:

• Run linting: uv run python -c "from taskipy import cli; cli.main()" check
• Format code: uv run python -c "from taskipy import cli; cli.main()" format
• Check formatting: uv run python -c "from taskipy import cli; cli.main()" format-check
• Auto-fix linting issues: uv run python -c "from taskipy import cli; cli.main()" check-fix
• Run tests: uv run python -c "from taskipy import cli; cli.main()" test
• Run tests with verbose output: uv run python -c "from taskipy import cli; cli.main()" test-verbose
• Run tests with coverage: uv run python -c "from taskipy import cli; cli.main()" test-coverage
• Run tests with HTML coverage report: uv run python -c "from taskipy import cli; cli.main()" test-coverage-html
• Run all checks: uv run python -c "from taskipy import cli; cli.main()" lint
• Run linting, formatting, and tests: uv run python -c "from taskipy import cli; cli.main()" all

Using uv directly

• Run linting: uv run ruff check .
• Format code: uv run ruff format .
• Type checking: uv run mypy src/
• Run all checks: uv run ruff check . && uv run ruff format --check .

Project Structure

py-collections/
├── src/py_collections/     # Main package source code
│   ├── collection.py       # Main Collection class (combines all mixins)
│   ├── collection_map.py   # CollectionMap class
│   └── mixins/            # Modular mixin classes
│       ├── basic_operations.py    # append, extend, all, len, iteration
│       ├── element_access.py     # first, last, exists, first_or_raise
│       ├── navigation.py         # after, before
│       ├── transformation.py    # map, pluck, filter, reverse, clone
│       ├── grouping.py          # group_by, chunk
│       ├── removal.py           # remove, remove_one
│       └── utility.py           # take, dump_me, dump_me_and_die
├── tests/                  # Test files organized by functionality
│   ├── core/              # Core Collection tests
│   ├── collection_map/    # CollectionMap tests
│   └── mixins/           # Tests organized by mixin
│       ├── basic_operations/
│       ├── element_access/
│       ├── navigation/
│       ├── transformation/
│       ├── grouping/
│       ├── removal/
│       └── utility/
├── examples/               # Example usage and demonstrations
├── pyproject.toml         # Project configuration and dependencies
└── README.md              # This file

Architecture

The library uses a mixin-based architecture to provide modular, maintainable code:

Mixin Classes

Each mixin provides a focused set of related functionality:

• BasicOperationsMixin: Core collection operations (append, extend, all, len, iteration)
• ElementAccessMixin: Element retrieval and existence checking (first, last, exists, first_or_raise)
• NavigationMixin: Relative element access (after, before)
• TransformationMixin: Data transformation operations (map, pluck, filter, reverse, clone)
• GroupingMixin: Data grouping and chunking (group_by, chunk)
• RemovalMixin: Element removal operations (remove, remove_one)
• UtilityMixin: Utility and debugging methods (take, dump_me, dump_me_and_die)

Benefits of This Architecture

1. Modularity: Each mixin focuses on a specific domain of functionality
2. Maintainability: Changes to one area don't affect others
3. Testability: Tests are organized by functionality
4. Extensibility: New functionality can be added as new mixins
5. Reusability: Mixins can be used independently if needed

Features

• Enhanced collection types with additional utility methods
• Modular mixin architecture for maintainable code
• Type-safe implementations with full generic support
• 100% test coverage - All code paths tested
• Modern Python features (3.13+)
• Specialized CollectionMap for working with grouped data
• Code quality tools: Ruff (linting + formatting), MyPy (type checking)

Available Methods

The Collection class provides the following methods, organized by mixin:

Basic Operations (BasicOperationsMixin)

• append(item) - Add an item to the collection
• extend(items) - Add multiple items from a list or another collection
• all() - Get all items as a list
• len() - Get the number of items
• Iteration - Use in for loops and with built-in functions like sum(), max(), min(), any(), all(), etc.

Element Access (ElementAccessMixin)

• first(predicate=None) - Get the first element (optionally matching a predicate)
• first_or_raise(predicate=None) - Get the first element or raise exception if not found
• last() - Get the last element
• exists(predicate=None) - Check if an element exists (returns boolean)

Navigation (NavigationMixin)

• after(target) - Get the element after a target element or predicate match
• before(target) - Get the element before a target element or predicate match

Transformation (TransformationMixin)

• filter(predicate) - Filter elements based on a predicate
• map(func) - Apply a function to every item and return a new collection with the results
• pluck(key, value_key=None) - Extract values from items based on a key or attribute (inspired by Laravel)
• reverse() - Return a new collection with items in reverse order
• clone() - Return a new collection with the same items

Grouping (GroupingMixin)

• group_by(key) - Group items by a key or callback function
• chunk(size) - Split collection into smaller chunks

Removal (RemovalMixin)

• remove(target) - Remove all items that match the target element or predicate (modifies collection in-place)
• remove_one(target) - Remove the first occurrence of an item that matches the target element or predicate (modifies collection in-place)

Utility (UtilityMixin)

• take(count) - Return a new collection with the specified number of items (positive: from beginning, negative: from end)
• dump_me() - Debug method to print collection contents (doesn't stop execution)
• dump_me_and_die() - Debug method to print collection contents and stop execution
• to_dict(mode=None) - Convert items to plain Python structures. With mode="json", ensures JSON-serializable output (datetimes to ISO strings, Decimals to floats, UUIDs to strings, sets to lists, and dict keys to strings)
• to_json() - Return a JSON string using to_dict(mode="json")

CollectionMap Class

A specialized map that stores Collection instances as values, providing convenient methods for working with grouped data:

• Dictionary-like interface with string keys and Collection values
• Automatic conversion of lists/items to Collection instances
• get(key) - Returns empty Collection if key doesn't exist (no KeyError)
• add(key, items) - Add items to existing key or create new key
• flatten() - Combine all collections into one
• map(func) - Apply function to each collection
• filter(predicate) - Filter collections based on criteria
• filter_by_size(min_size, max_size) - Filter by collection size
• total_items() - Get total count across all collections
• largest_group() / smallest_group() - Find groups by size
• group_sizes() - Get size of each group

Usage Examples

from py_collections import Collection

# Basic usage
numbers = Collection([1, 2, 3, 4, 5])
numbers.append(6)

# Extending with multiple items
numbers.extend([7, 8, 9])
other_numbers = Collection([10, 11, 12])
numbers.extend(other_numbers)

# Reversing the collection
reversed_numbers = numbers.reverse()

# Cloning the collection
cloned_numbers = numbers.clone()  # Create a copy with the same items

# Taking items from the collection
first_two = numbers.take(2)  # Take first 2 items
last_three = numbers.take(-3)  # Take last 3 items

# Mapping elements
doubled = numbers.map(lambda x: x * 2)  # Double each number
squared = numbers.map(lambda x: x ** 2)  # Square each number
strings = numbers.map(str)  # Convert to strings

# Plucking values from objects/dictionaries
users = Collection([{"name": "Alice", "age": 25}, {"name": "Bob", "age": 30}])
names = users.pluck("name")  # ["Alice", "Bob"]
name_age_pairs = users.pluck("name", "age")  # [{"Alice": 25}, {"Bob": 30}]

# Nested key access with dot notation
nested_users = Collection([{"name": "Alice", "address": {"city": "NYC"}}, {"name": "Bob", "address": {"city": "LA"}}])
cities = nested_users.pluck("address.city")  # ["NYC", "LA"]
name_city_pairs = nested_users.pluck("name", "address.city")  # [{"Alice": "NYC"}, {"Bob": "LA"}]

# Removing elements
numbers.remove(1)  # Remove all occurrences of 1
numbers.remove(lambda x: x > 3)  # Remove all elements > 3
numbers.remove_one(lambda x: x == 2)  # Remove first occurrence of 2

# Check if elements exist
if numbers.exists(lambda x: x > 3):
    print("Found number greater than 3")

# Find elements
first_even = numbers.first(lambda x: x % 2 == 0)
after_three = numbers.after(3)

# Filter and chunk
evens = numbers.filter(lambda x: x % 2 == 0)
chunks = numbers.chunk(2)

# Group by
users = Collection([{"name": "Alice", "dept": "Eng"}, {"name": "Bob", "dept": "Sales"}])
by_dept = users.group_by("dept")
by_parity = numbers.group_by(lambda x: "even" if x % 2 == 0 else "odd")

# Iteration
for item in numbers:
    print(item)

# List comprehension
doubled = [item * 2 for item in numbers]

# Built-in functions
total = sum(item for item in numbers)
has_even = any(item % 2 == 0 for item in numbers)

# CollectionMap usage
# Serialization
from py_collections import Collection

data = Collection([
    {"name": "Alice", "age": 30},
    (1, 2, 3),
    {"tags": {"python", "collections"}},
])

# Plain Python structures
structure = data.to_dict()

# JSON-ready structure and JSON string
json_ready = data.to_dict(mode="json")
json_text = data.to_json()

### Pydantic Compatibility
If your items include Pydantic models, they are supported out of the box:

```python
from pydantic import BaseModel
from py_collections import Collection

class User(BaseModel):
    id: int
    name: str

users = Collection([User(id=1, name="Alice"), User(id=2, name="Bob")])

# Converts to list of dicts
users_dict = users.to_dict()

# JSON-ready and stringified
users_json_ready = users.to_dict(mode="json")
users_json = users.to_json()

from py_collections import CollectionMap

Create from group_by result

grouped = users.group_by("department") cmap = CollectionMap(grouped)

Work with groups

engineering = cmap["Engineering"] all_users = cmap.flatten() group_stats = cmap.map(lambda c: len(c))

Safe access and incremental building

missing = cmap.get("missing") # Returns empty Collection cmap.add("new_group", [1, 2, 3]) # Creates new group cmap.add("existing_group", [4, 5]) # Extends existing group

Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests for new functionality
5. Run tests: uv run pytest
6. Submit a pull request

License

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