ndarray 0.2.0

An N-dimensional array for general elements and for numerics. Lightweight array views and slicing. Supports both uniquely owned and shared copy-on-write arrays similar to numpy’s ndarray. `rblas` is an optional dependency.

The ndarray crate provides an N-dimensional container similar to numpy’s ndarray.

  • ArrayBase: The N-dimensional array type itself.
  • Array: An array where the data is shared and copy on write, it can act as both an owner of the data as well as a lightweight view.
  • OwnedArray: An array where the data is owned uniquely.
  • ArrayView, ArrayViewMut: Lightweight array views.


  • Generic N-dimensional array
  • Slicing, also with arbitrary step size, and negative indices to mean elements from the end of the axis.
  • There is both an easy to use copy on write array (Array), or a regular uniquely owned array (OwnedArray), and both can use read-only and read-write array views.
  • Iteration and most operations are very efficient on contiguous c-order arrays (the default layout, without any transposition or discontiguous subslicing), and on arrays where the lowest dimension is contiguous (contiguous block slicing).
  • Array views can be used to slice and mutate any [T] data.

Status and Lookout

  • Still iterating on the API
  • Performance status:
    • Arithmetic involving contiguous c-order arrays and contiguous lowest dimension arrays optimizes very well.
    • .fold() and .zip_mut_with() are the most efficient ways to perform single traversal and lock step traversal respectively.
    • Transposed arrays where the lowest dimension is not c-contiguous is still a pain point.
  • There is experimental bridging to the linear algebra package rblas.

Crate Feature Flags

  • assign_ops
    • Optional, requires nightly
    • Enables the compound assignment operators
  • rustc-serialize
    • Optional, stable
    • Enables serialization support
  • rblas
    • Optional, stable
    • Enables rblas integration