Figure: Hierarchy of type objects representing the array datatypes. Not shown are the two integer typesintp anduintp which just point to the integer type that holds apointer for the platform. All the number types can be obtainedusing bit-width names as well.

Built-in scalar types¶

The built-in scalar types are shown below. Along with their (mostly)C-derived names, the integer, float, and complex data-types are alsoavailable using a bit-width convention so that an array of the rightsize can always be ensured (e.g.int8,float64,complex128). Two aliases (intp anduintp)pointing to the integer type that is sufficiently large to hold a C pointerare also provided. The C-like names are associated with character codes,which are shown in the table. Use of the character codes, however,is discouraged.

Some of the scalar types are essentially equivalent to fundamentalPython types and therefore inherit from them as well as from thegeneric array scalar type:

Thebool_ data type is very similar to the PythonBooleanType but does not inherit from it because Python’sBooleanType does not allow itself to be inherited from, andon the C-level the size of the actual bool data is not the same as aPython Boolean scalar.

In the tables below,platform? means that the type may not beavailable on all platforms. Compatibility with different C or Pythontypes is indicated: two types are compatible if their data is of thesame size and interpreted in the same way.

Booleans:

Integers:

Unsigned integers:

Floating-point numbers:

Complex floating-point numbers:

Any Python object:

The following data types areflexible. They have no predefinedsize: the data they describe can be of different length in differentarrays. (In the character codes# is an integer denoting how manyelements the data type consists of.)

Attributes¶

The array scalar objects have anarraypriority ofNPY_SCALAR_PRIORITY(-1,000,000.0). They also do not (yet) have actypesattribute. Otherwise, they share the same attributes as arrays:

Indexing¶

Array scalars can be indexed like 0-dimensional arrays: ifx is anarray scalar,

• x[()] returns a copy of array scalar
• x[...] returns a 0-dimensionalndarray
• x['field-name'] returns the array scalar in the fieldfield-name.(x can have fields, for example, when it corresponds to a structured data type.)

Methods¶

Array scalars have exactly the same methods as arrays. The defaultbehavior of these methods is to internally convert the scalar to anequivalent 0-dimensional array and to call the corresponding arraymethod. In addition, math operations on array scalars are defined sothat the same hardware flags are set and used to interpret the resultsas forufunc, so that the error state used for ufuncsalso carries over to the math on array scalars.

The exceptions to the above rules are given below:

Defining new types¶

There are two ways to effectively define a new array scalar type(apart from composing structured typesdtypes fromthe built-in scalar types): One way is to simply subclass thendarray and overwrite the methods of interest. This will work toa degree, but internally certain behaviors are fixed by the data type ofthe array. To fully customize the data type of an array you need todefine a new data-type, and register it with NumPy. Such new types can onlybe defined in C, using theNumPy C-API.