As mentionned earlier (see subsection 2.1.2), numpy.ndarray elements can be
of different kind, precision, and thus memory size. This is reflected through the
.dtype attribute of any numpy.ndarray. By default, integer elements are created
with the '<i4' datatype (standard integer), and floats with the '<f8'
one (double precision float). Nevertheless, not all SIC elements follows these
types. Attention have been paid to import SIC data into the correct type (see
|SIC type||Numeric typecode||# of bytes|
||'i'||sizeof(int) = 4|
|REAL*4||'f'||sizeof(float) = 4|
|REAL*8||'d'||sizeof(double) = 8|
|LOGICAL*4||'i'||sizeof(int) = 4|
|CHARACTER||'c'||sizeof(char) = 1|
Mixing arrays in Python formulas with element size different from the default Python behavior is not problematic. NumPy deals with all these types and applies coercion to the adequate type, and all is completely transparent for the user.