kotcollections 0.1.0

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KotCollections - Kotlin Collections API for Python

kotcollections is a Python library that fully reproduces Kotlin's Collections interfaces. It brings Kotlin's rich collection operations to Python developers with List, Set, and Map implementations.

Installation

pip install kotcollections

Features

• Complete implementation of Kotlin's List, Set, and Map interfaces using Python's snake_case naming convention
• Pythonic _none aliases for all _null methods (e.g., both first_or_null() and first_or_none() are available)
• Provides read-only and mutable variants:
  • KotList and KotMutableList for list operations
  • KotSet and KotMutableSet for set operations
  • KotMap and KotMutableMap for map operations
• Full type safety with type hints
• Runtime type checking to ensure single element type (similar to Kotlin's generic type system)
• 100% test coverage

Type Safety

KotList and KotSet implement runtime type checking similar to Kotlin's type system. Once a collection is created with elements of a specific type, it can only contain elements of that same type.

How It Works

• The first element added to a collection determines its element type
• All subsequent elements must be of the same type
• Collections can be nested (similar to List<List<T>> or Set<Set<T>> in Kotlin)
• Type checking occurs on initialization and all modification operations

Examples

# Valid: All elements are the same type
lst = KotList([1, 2, 3])  # KotList[int]
s = KotSet(['a', 'b', 'c'])  # KotSet[str]

# Invalid: Mixed types will raise TypeError
try:
    lst = KotList([1, 'a', 2])  # TypeError!
except TypeError as e:
    print(e)  # Cannot add element of type 'str' to KotList[int]

# Valid: Nested collections
nested_lists = KotList(
    [
        KotList([1, 2]),
        KotList([3, 4])
    ]
)  # KotList[KotList]

nested_sets = KotSet(
    [
        KotSet([1, 2]),
        KotSet([3, 4])
    ]
)  # KotSet[KotSet]

# Mutable collections also enforce type safety
mutable_list = KotMutableList([1, 2, 3])
mutable_list.add(4)  # OK: same type

mutable_set = KotMutableSet(['a', 'b', 'c'])
mutable_set.add('d')  # OK: same type

try:
    mutable_list.add('string')  # TypeError!
except TypeError as e:
    print(e)  # Cannot add element of type 'str' to KotList[int]

# Empty collections determine type on first element
empty_list = KotMutableList()
empty_list.add('first')  # Now it's KotList[str]

empty_set = KotMutableSet()
empty_set.add(42)  # Now it's KotSet[int]

Comparison with Kotlin

This type safety implementation provides similar guarantees to Kotlin's generic type system:

Kotlin	kotcollections (Python)
List<Int>	KotList([1, 2, 3])
List<String>	KotList(['a', 'b', 'c'])
List<List<Int>>	KotList([KotList([1, 2]), KotList([3, 4])])
Set<Double>	KotSet([1.0, 2.0, 3.0])
Map<String, Int>	KotMap({"one": 1, "two": 2})
Map<Int, List<String>>	KotMap({1: KotList(["a", "b"]), 2: KotList(["c"])})

The main difference is that Kotlin performs compile-time type checking, while kotcollections performs runtime type checking.

Pythonic Aliases

To provide a more Pythonic API, all methods ending with _null have corresponding _none aliases:

# All these _null methods have _none aliases
lst = KotList([1, 2, None, 3, None])

# Access methods
print(lst.get_or_null(10))  # None
print(lst.get_or_none(10))  # None (same result)
print(lst.first_or_none())  # 1
print(lst.last_or_none())  # None

# Transformation methods  
result = lst.map_not_none(lambda x: x * 2 if x else None)  # [2, 4, 6]

# Filtering
non_empty = lst.filter_not_none()  # KotList([1, 2, 3])

# Aggregation
print(lst.max_or_none())  # 3
print(lst.min_or_none())  # 1

Both naming conventions are fully supported and can be used interchangeably based on your preference.

Quick Start

from kotcollections import KotList, KotMutableList, KotSet, KotMutableSet, KotMap, KotMutableMap

# Lists - ordered, allows duplicates
numbers = KotList([1, 2, 3, 2, 1])
print(numbers.distinct().to_list())  # [1, 2, 3]

# Sets - unordered, no duplicates
unique_numbers = KotSet([1, 2, 3, 2, 1])
print(unique_numbers.to_list())  # [1, 2, 3] (order not guaranteed)

# Maps - key-value pairs
scores = KotMap({"Alice": 90, "Bob": 85, "Charlie": 95})
print(scores.get("Alice"))  # 90

# Functional operations work on all
doubled_list = numbers.map(lambda x: x * 2)
doubled_set = unique_numbers.map(lambda x: x * 2)
high_scores = scores.filter(lambda k, v: v >= 90)

# Mutable variants allow modifications
mutable_list = KotMutableList([1, 2, 3])
mutable_list.add(4)

mutable_set = KotMutableSet([1, 2, 3])
mutable_set.add(4)

mutable_map = KotMutableMap({"a": 1})
mutable_map.put("b", 2)

Basic Usage

from kotcollections import KotList, KotMutableList, KotMap, KotMutableMap

# Create a read-only list
lst = KotList([1, 2, 3, 4, 5])

# Create a mutable list
mutable_lst = KotMutableList([1, 2, 3, 4, 5])

# Create a read-only map
m = KotMap({"a": 1, "b": 2, "c": 3})

# Create a mutable map
mutable_m = KotMutableMap({"x": 10, "y": 20})

Kotlin to Python Naming Convention

All Kotlin methods are available with Python's snake_case naming convention. Additionally, all methods ending with _null have Pythonic _none aliases:

Kotlin	Python (Primary)	Python (Alias)
getOrNull()	get_or_null()	get_or_none()
firstOrNull()	first_or_null()	first_or_none()
mapIndexed()	map_indexed()	-
filterNotNull()	filter_not_null()	filter_not_none()
associateBy()	associate_by()	-
joinToString()	join_to_string()	-

Note: Both naming styles (_null and _none) can be used interchangeably based on your preference.

API Reference

For detailed documentation of all available methods, please refer to the API Reference.

Performance Considerations

• KotList internally uses Python's standard list, so basic operation performance is equivalent to standard lists
• KotSet internally uses Python's standard set, providing O(1) average case for add, remove, and contains operations
• KotMap internally uses Python's standard dict, providing O(1) average case for get, put, and contains operations
• When using method chaining extensively, be aware that each method creates a new collection, which may impact memory usage
• For large datasets, consider generator-based implementations

License

MIT License