Python in a Nutshell

Array Objects

Numeric provides an array type that represents a grid of items. An array object a has a specified number of dimensions, known as its rank, up to some arbitrarily high limit (normally 40, when Numeric is built with default options). A scalar (i.e., a single number) has rank 0, a vector has rank 1, a matrix has rank 2, and so forth.

Type Codes

The values that occupy cells in the grid of an array object, known as the elements of the array, are homogeneous, meaning they are all of the same type, and all element values are stored within one memory area. This contrasts with a list or tuple, where the items may be of different types and each is stored as a separate Python object. This means a Numeric array occupies far less memory than a Python list or tuple with the same number of items. The type of a’s elements is encoded as a’s type code, a one-character string, as shown in Table 15-3. Factory functions that build array instances, covered in Section 15.6.6 later in this chapter, take a typecode argument that is one of the values in Table 15-3.

Table 15-3. Type codes for Numeric arrays

Type code	C type	Python type	Synonym
'c'	char	str (length 1)	Character
'b'	unsigned char	int	UnsignedInt8
'1'	signed char	int	Int8
's'	short	int	Int16
'i'	int	int	Int32
'l'	long	int	Int
'f'	float	float	Float32
'F'	two floats	complex	Complex32
'd'	double	float	Float
'D'	two doubles	complex	Complex
'O'	PyObject*	any	PyObject

Numeric supplies ...