All ndarrays are homogenous: every item takes up the same size block of memory, and all blocks are interpreted in exactly the same way. How each item in the array is to be interpreted is specified by a separate data-type object, one of which is associated with every array. In addition to basic types (integers, floats, etc.), the data type objects can also represent data structures.
An item extracted from an array, e.g., by indexing, is represented by a Python object whose type is one of the array scalar types built in Numpy. The array scalars allow easy manipulation of also more complicated arrangements of data.
- The N-dimensional array (ndarray)
- Data type objects (dtype)
- Iterating Over Arrays
- Standard array subclasses
- Masked arrays
- The Array Interface
- Datetimes and Timedeltas