A feature of compilation in object mode where a loop can be
automatically extracted and compiled in nopython mode. This
allows functions with operations unsupported in nopython mode (such as
array creation) to see significant performance improvements if they
contain loops with only nopython-supported operations.
A Numba compilation mode that generates code that does not access the
Python C API. This compilation mode produces the highest performance
code, but requires that the native types of all values in the function
can be inferred. Unless otherwise instructed,
the @jit decorator will automatically fall back to object
mode if nopython mode cannot be used.
A Numba compilation mode that generates code that handles all values
as Python objects and uses the Python C API to perform all operations
on those objects. Code compiled in object mode will often run
no faster than Python interpreted code, unless the Numba compiler can
take advantage of loop-jitting.
The process by which Numba determines the specialized types of all
values within a function being compiled. Type inference can fail
if arguments or globals have Python types unknown to Numba, or if
functions are used that are not recognized by Numba. Sucessful
type inference is a prerequisite for compilation in
A NumPy universal function.
Numba can create new compiled ufuncs with the @vectorize decorator.