2.8. Deviations from Python Semantics¶

2.8.1. Exceptions and Memory Allocation¶

Due to limitations in the current compiler when handling exceptions, memory allocated (almost always NumPy arrays) within a function that raises an exception will leak. This is a known issue that will be fixed, but in the meantime, it is best to do memory allocation outside of functions that can also raise exceptions.

2.8.2. Integer width¶

While Python has arbitrary-sized integers, integers in Numba-compiled functions get a fixed size through type inference (usually, the size of a machine integer). This means that arithmetic operations can wrapround or produce undefined results or overflow.

Type inference can be overridden by an explicit type specification, if fine-grained control of integer width is desired.

2.8.3. Boolean inversion¶

Calling the bitwise complement operator (the ~ operator) on a Python boolean returns an integer, while the same operator on a Numpy boolean returns another boolean:

>>> ~True
-2
>>> ~np.bool_(True)
False

Numba follows the Numpy semantics.

2.8.4. Global and closure variables¶

In nopython mode, global and closure variables are frozen by Numba: a Numba-compiled function sees the value of those variables at the time the function was compiled. Also, it is not possible to change their values from the function.

Numba may or may not copy global variables referenced inside a compiled function. Small global arrays are copied for potential compiler optimization with immutability assumption. However, large global arrays are not copied to conserve memory. The definition of “small” and “large” may change.