smartarr 0.1.1

Expressive array utilities with an optional C backend.

smartarr

smartarr is an expressive array utility layer for Python with a clean smart(arr) API and an optional C backend for fast core operations.

Why it exists

The goal is simple:

• one readable interface for lists, tuples, and NumPy arrays
• fast common operations when the C extension is available
• chainable transformations that still feel like Python

from smartarr import smart

value = smart([1, 2, 3, 4, 5]).rotate(2).reverse().middle()
print(value)  # 1

Install

Local install from this project:

python -m pip install .

Editable install for development:

python -m pip install -e .

Optional NumPy support:

python -m pip install ".[numpy]"

If a compiler is available, smartarr builds its C extension automatically. If not, installation still succeeds and falls back to the pure Python backend.

Quick start

from smartarr import smart

arr = smart([1, 2, 3, 4])

print(arr.first())                 # 1
print(arr.last())                  # 4
print(arr.middle())                # 3
print(arr.middle(mode="left"))     # 2
print(arr.middle(mode="avg"))      # 2.5
print(arr.size())                  # 4
print(len(arr))                    # 4
print(arr.rotate(1).to_list())     # [4, 1, 2, 3]
print(arr.reverse().to_list())     # [4, 3, 2, 1]
print(arr.chunk(2).to_list())      # [[1, 2], [3, 4]]
print(arr.window(2).to_list())     # [[1, 2], [2, 3], [3, 4]]

Design notes

• middle() defaults to the right-middle element for even-length arrays, matching arr[len(arr) // 2].
• SmartArray operations are non-mutating by default. Methods like rotate() and reverse() return a new wrapper and leave the original array unchanged.
• Prefer len(arr) or arr.size() for length checks. arr.len() remains as a compatibility alias.
• Structural methods return a new SmartArray, so chaining works naturally.
• Terminal methods return a final value, such as an element, a number, or a dictionary.
• Flat transformations preserve the original adapter where practical. Shape-changing operations return list-shaped data by default.
• random() returns one element when n is omitted, samples without replacement when replace=False, and samples with replacement when replace=True. A seed makes output deterministic.
• flatten() performs a deep recursive flatten.
• reshape() validates that the requested shape exactly matches the flattened element count.
• reduce() requires either a non-empty array or an explicit initial value.

Supported input types

• list
• tuple
• NumPy ndarray when NumPy is installed
• general iterables such as range

Current feature coverage

V1

• first
• last
• middle
• rotate
• reverse
• len
• is_empty

V2

• chunk
• window
• random
• find
• count
• contains
• is_sorted
• sorted

V3

• map
• filter
• reduce
• sum
• mean
• median
• min
• max
• peaks
• unique
• duplicates
• frequency
• flatten
• reshape

Development

Run tests:

python -m unittest discover -s tests -v

License

MIT