Ndarrays

An ndarray, short for n-dimensional array, is a core data structure in numerical computing libraries such as NumPy. It represents a homogeneous, multi-dimensional grid of elements stored in a contiguous block of memory and designed for efficient mathematical operations.

An ndarray has metadata attributes: shape, a tuple describing the size along each axis; ndim, the number of dimensions; dtype, the data type of the elements; size, the total number of elements; strides and itemsize describing the memory layout. The memory layout is described by these attributes and is typically contiguous in memory, with the default being C order.

Operations on ndarrays are typically vectorized; arithmetic, comparisons, and many functions are implemented in optimized C. Broadcasting rules allow operations between arrays of different shapes, expanding smaller arrays as needed. Indexing supports slicing, boolean indexing, and advanced (fancy) indexing. Views and copies determine whether operations share memory with the original array.

Creation and memory management: arrays are created from Python sequences or by specialized constructors such as zeros, ones, empty, or arange. Most operations return new arrays; however, views can share data with the original without copying. Copying can be forced with explicit methods.

Usage and limitations: ndarrays are central to scientific computing in Python, enabling efficient data handling and interoperability with libraries such as SciPy and pandas. They require homogeneous element types, which can limit flexibility compared with Python lists, but improve performance. Converting to lists or other formats is supported when needed.