### Navigation

- index
- modules |
- next |
- previous |
- NumPy v1.3 Manual (DRAFT) »
- NumPy Reference »
- Array objects »

# The Array Interface

Warning

This page describes the old, deprecated array interface. Everything still works as described as of numpy 1.2 and on into the foreseeable future), but new development should target PEP 3118 – `The Revised Buffer Protocol`. PEP 3118 was incorporated into Python 2.6 and 3.0, and is additionally supported by Cython’s numpy buffer support. (See the Cython numpy tutorial.) Cython provides a way to write code that supports the buffer protocol with Python versions older than 2.6 because it has a backward-compatible implementation utilizing the legacy array interface described here.

version: | 3

The array interface (sometimes called array protocol) was created in 2005 as a means for array-like Python objects to re-use each other’s data buffers intelligently whenever possible. The homogeneous N-dimensional array interface is a default mechanism for objects to share N-dimensional array memory and information. The interface consists of a Python-side and a C-side using two attributes. Objects wishing to be considered an N-dimensional array in application code should support at least one of these attributes. Objects wishing to support an N-dimensional array in application code should look for at least one of these attributes and use the information provided appropriately.

This interface describes homogeneous arrays in the sense that each item of the array has the same “type”. This type can be very simple or it can be a quite arbitrary and complicated C-like structure.

There are two ways to use the interface: A Python side and a C-side. Both are separate attributes.

## Python side

This approach to the interface consists of the object having an `__array_interface__` attribute.

`__array_interface__`

  A dictionary of items (3 required and 5 optional). The optional keys in the dictionary have implied defaults if they are not provided.

  The keys are:

  shape (required)

  > Tuple whose elements are the array size in each dimension. Each entry is an integer (a Python int or long). Note that these integers could be larger than the platform “int” or “long” could hold (a Python int is a C long). It is up to the code using this attribute to handle this appropriately; either by raising an error when overflow is possible, or by using `Py_LONG_LONG` as the C type for the shapes.

  typestr (required)

  > A string providing the basic type of the homogenous array The basic string format consists of 3 parts: a character describing the byteorder of the data (`<`: little-endian, `>`: big-endian, `|`: not-relevant), a character code giving the basic type of the array, and an integer providing the number of bytes the type uses.

  > The basic type character codes are:

  > `t` | Bit field (following integer gives the number of bits in the bit field).
  > `b` | Boolean (integer type where all values are only True or False)
  > `i` | Integer
  > `u` | Unsigned integer
  > `f` | Floating point
  > `c` | Complex floating point
  > `O` | Object (i.e. the memory contains a pointer to `PyObject`)
  > `S` | String (fixed-length sequence of char)
  > `U` | Unicode (fixed-length sequence of `Py_UNICODE`)
  > `V` | Other (void * – each item is a fixed-size chunk of memory)

  descr (optional)

  > A list of tuples providing a more detailed description of the memory layout for each item in the homogeneous array. Each tuple in the list has two or three elements. Normally, this attribute would be used when typestr is `V[0-9]+`, but this is not a requirement. The only requirement is that the number of bytes represented in the typestr key is the same as the total number of bytes represented here. The idea is to support descriptions of C-like structs (records) that make up array elements. The elements of each tuple in the list are

  > 1. A string providing a name associated with this portion of the record. This could also be a tuple of `('full name', 'basic_name')` where basic name would be a valid Python variable name representing the full name of the field.
  > 2. Either a basic-type description string as in typestr or another list (for nested records)
  > 3. An optional shape tuple providing how many times this part of the record should be repeated. No repeats are assumed if this is not given. Very complicated structures can be described using this generic interface. Notice, however, that each element of the array is still of the same data-type. Some examples of using this interface are given below.

  > Default: `[('', typestr)]`

  data (optional)

  > A 2-tuple whose first argument is an integer (a long integer if necessary) that points to the data-area storing the array contents. This pointer must point to the first element of data (in other words any offset is always ignored in this case). The second entry in the tuple is a read-only flag (true means the data area is read-only).

  > This attribute can also be an object exposing the `buffer interface` which will be used to share the data. If this key is not present (or returns `None`), then memory sharing will be done through the buffer interface of the object itself. In this case, the offset key can be used to indicate the start of the buffer. A reference to the object exposing the array interface must be stored by the new object if the memory area is to be secured.

  > Default: `None`

  strides (optional)

  > Either `None` to indicate a C-style contiguous array or a Tuple of strides which provides the number of bytes needed to jump to the next array element in the corresponding dimension. Each entry must be an integer (a Python `int` or `long`). As with shape, the values may be larger than can be represented by a C “int” or “long”; the calling code should handle this appropiately, either by raising an error, or by using `Py_LONG_LONG` in C. The default is `None` which implies a C-style contiguous memory buffer. In this model, the last dimension of the array varies the fastest. For example, the default strides tuple for an object whose array entries are 8 bytes long and whose shape is (10,20,30) would be (4800, 240, 8)

  > Default: `None` (C-style contiguous)

  mask (optional)

  > `None` or an object exposing the array interface. All elements of the mask array should be interpreted only as true or not true indicating which elements of this array are valid. The shape of this object should be “broadcastable” to the shape of the original array.

  > Default: `None` (All array values are valid)

  offset (optional)

  > An integer offset into the array data region. This can only be used when data is `None` or returns a `buffer` object.

  > Default: 0.

  version (required)

  > An integer showing the version of the interface (i.e. 3 for this version). Be careful not to use this to invalidate objects exposing future versions of the interface.

## C-struct access

This approach to the array interface allows for faster access to an array using only one attribute lookup and a well-defined C-structure.

`__array_struct__`
  A `PyCObject` whose `voidptr` member contains a pointer to a filled `PyArrayInterface` structure. Memory for the structure is dynamically created and the `PyCObject` is also created with an appropriate destructor so the retriever of this attribute simply has to apply `Py_DECREF()` to the object returned by this attribute when it is finished. Also, either the data needs to be copied out, or a reference to the object exposing this attribute must be held to ensure the data is not freed. Objects exposing the `__array_struct__` interface must also not reallocate their memory if other objects are referencing them.

New since June 16, 2006:

In the past most implementations used the “desc” member of the `PyCObject` itself (do not confuse this with the “descr” member of the `PyArrayInterface` structure above — they are two separate things) to hold the pointer to the object exposing the interface. This is now an explicit part of the interface. Be sure to own a reference to the object when the `PyCObject` is created using `PyCObject_FromVoidPtrAndDesc`.

### Navigation

- index
- modules |
- next |
- previous |
- NumPy v1.3 Manual (DRAFT) »
- NumPy Reference »
- Array objects »