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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 Numpy frombuffer() is one of the predefined function that is used to create the array using the buffer storage with specific areas; mainly, this buffer function is creating the arrays with a different set of parameters it returns the array version of the buffer the python interpreter of the numpy frombuffer() ...
A buffer is a data area shared by hardware devices or program processes that operate at different speeds or with different sets of priorities. The buffer allows each device or process to operate without being held up by the other.
A memory view is a safe way to expose the buffer protocol in Python. It allows you to access the internal buffers of an object by creating a memory view object.
An example usage:
>>> s = 'Hello world'
>>> t = buffer(s, 6, 5)
>>> t
<read-only buffer for 0x10064a4b0, size 5, offset 6 at 0x100634ab0>
>>> print t
world
The buffer in this case is a sub-string, starting at position 6 with length 5, and it doesn't take extra storage space - it references a slice of the string.
This isn't very useful for short strings like this, but it can be necessary when using large amounts of data. This example uses a mutable bytearray:
>>> s = bytearray(1000000) # a million zeroed bytes
>>> t = buffer(s, 1) # slice cuts off the first byte
>>> s[1] = 5 # set the second element in s
>>> t[0] # which is now also the first element in t!
'\x05'
This can be very helpful if you want to have more than one view on the data and don't want to (or can't) hold multiple copies in memory.
Note that buffer has been replaced by the better named memoryview in Python 3, though you can use either in Python 2.7.
Note also that you can't implement a buffer interface for your own objects without delving into the C API, i.e. you can't do it in pure Python.
I think buffers are e.g. useful when interfacing python to native libraries. (Guido van Rossum explains buffer in this mailinglist post).
For example, numpy seems to use buffer for efficient data storage:
import numpy
a = numpy.ndarray(1000000)
the a.data is a:
<read-write buffer for 0x1d7b410, size 8000000, offset 0 at 0x1e353b0>
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