Buffer Protocol

Certain objects available in Python wrap access to an underlying memory array or buffer. Such objects include the built-in bytes and bytearray, and some extension types like array.array. Third-party libraries may define their own types for special purposes, such as image processing or numeric analysis.

While each of these types have their own semantics, they share the common characteristic of being backed by a possibly large memory buffer. It is then desireable, in some situations, to access that buffer directly and without intermediate copying.

Python provides such a facility at the C level in the form of the buffer protocol. This protocol has two sides:

  • on the producer side, a type can export a “buffer interface” which allows objects of that type to expose information about their underlying buffer. This interface is described in the section Buffer Object Structures;
  • on the consumer side, several means are available to obtain a pointer to the raw underlying data of an object (for example a method parameter).

Simple objects such as bytes and bytearray expose their underlying buffer in byte-oriented form. Other forms are possible; for example, the elements exposed by a array.array can be multi-byte values.

An example consumer of the buffer interface is the write() method of file objects: any object that can export a series of bytes through the buffer interface can be written to a file. While write() only needs read-only access to the internal contents of the object passed to it, other methods such as readinto() need write access to the contents of their argument. The buffer interface allows objects to selectively allow or reject exporting of read-write and read-only buffers.

There are two ways for a consumer of the buffer interface to acquire a buffer over a target object:

In both cases, PyBuffer_Release() must be called when the buffer isn’t needed anymore. Failure to do so could lead to various issues such as resource leaks.

The buffer structure

Buffer structures (or simply “buffers”) are useful as a way to expose the binary data from another object to the Python programmer. They can also be used as a zero-copy slicing mechanism. Using their ability to reference a block of memory, it is possible to expose any data to the Python programmer quite easily. The memory could be a large, constant array in a C extension, it could be a raw block of memory for manipulation before passing to an operating system library, or it could be used to pass around structured data in its native, in-memory format.

Contrary to most data types exposed by the Python interpreter, buffers are not PyObject pointers but rather simple C structures. This allows them to be created and copied very simply. When a generic wrapper around a buffer is needed, a memoryview object can be created.

void *buf

A pointer to the start of the memory for the object.

Py_ssize_t len

The total length of the memory in bytes.

int readonly

An indicator of whether the buffer is read only.

const char *format

A NULL terminated string in struct module style syntax giving the contents of the elements available through the buffer. If this is NULL, "B" (unsigned bytes) is assumed.

int ndim

The number of dimensions the memory represents as a multi-dimensional array. If it is 0, strides and suboffsets must be NULL.

Py_ssize_t *shape

An array of Py_ssize_ts the length of ndim giving the shape of the memory as a multi-dimensional array. Note that ((*shape)[0] * ... * (*shape)[ndims-1])*itemsize should be equal to len.

Py_ssize_t *strides

An array of Py_ssize_ts the length of ndim giving the number of bytes to skip to get to a new element in each dimension.

Py_ssize_t *suboffsets

An array of Py_ssize_ts the length of ndim. If these suboffset numbers are greater than or equal to 0, then the value stored along the indicated dimension is a pointer and the suboffset value dictates how many bytes to add to the pointer after de-referencing. A suboffset value that it negative indicates that no de-referencing should occur (striding in a contiguous memory block).

Here is a function that returns a pointer to the element in an N-D array pointed to by an N-dimensional index when there are both non-NULL strides and suboffsets:

void *get_item_pointer(int ndim, void *buf, Py_ssize_t *strides,
    Py_ssize_t *suboffsets, Py_ssize_t *indices) {
    char *pointer = (char*)buf;
    int i;
    for (i = 0; i < ndim; i++) {
        pointer += strides[i] * indices[i];
        if (suboffsets[i] >=0 ) {
            pointer = *((char**)pointer) + suboffsets[i];
    return (void*)pointer;
Py_ssize_t itemsize

This is a storage for the itemsize (in bytes) of each element of the shared memory. It is technically un-necessary as it can be obtained using PyBuffer_SizeFromFormat(), however an exporter may know this information without parsing the format string and it is necessary to know the itemsize for proper interpretation of striding. Therefore, storing it is more convenient and faster.

void *internal

This is for use internally by the exporting object. For example, this might be re-cast as an integer by the exporter and used to store flags about whether or not the shape, strides, and suboffsets arrays must be freed when the buffer is released. The consumer should never alter this value.

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