# Deviations from Python Semantics

## Bounds Checking

By default, instead of causing an `IndexError`, accessing an out-of-bound index of an array in a Numba-compiled function will return invalid values or lead to an access violation error (it’s reading from invalid memory locations). Bounds checking can be enabled on a specific function via the boundscheck option of the jit decorator. Additionally, the `NUMBA_BOUNDSCHECK` can be set to 0 or 1 to globally override this flag.

Note

Bounds checking will slow down typical functions so it is recommended to only use this flag for debugging purposes.

## 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.

## 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.

See also

Enhancement proposal 1: Changes in integer typing

## 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.

## 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.

## Zero initialization of variables

Numba does not track variable liveness at runtime. For simplicity of implementation, all variables are zero-initialized. Example:

    from numba import njit

    @njit
    def foo():
        for i in range(0):
            pass
        print(i) # will print 0 and not raise UnboundLocalError

    foo()