The ndarray is an object that provide a python array interface to data in memory.
It often happens that the memory that you want to view with an array is not of the same byte ordering as the computer on which you are running Python.
For example, I might be working on a computer with a little-endian CPU - such as an Intel Pentium, but I have loaded some data from a file written by a computer that is big-endian. Let’s say I have loaded 4 bytes from a file written by a Sun (big-endian) computer. I know that these 4 bytes represent two 16-bit integers. On a big-endian machine, a two-byte integer is stored with the Most Significant Byte (MSB) first, and then the Least Significant Byte (LSB). Thus the bytes are, in memory order:
Let’s say the two integers were in fact 1 and 770. Because 770 = 256 * 3 + 2, the 4 bytes in memory would contain respectively: 0, 1, 3, 2. The bytes I have loaded from the file would have these contents:
>>> big_end_str = chr(0) + chr(1) + chr(3) + chr(2) >>> big_end_str '\x00\x01\x03\x02'
We might want to use an ndarray to access these integers. In that case, we can create an array around this memory, and tell numpy that there are two integers, and that they are 16 bit and big-endian:
>>> import numpy as np >>> big_end_arr = np.ndarray(shape=(2,),dtype='>i2', buffer=big_end_str) >>> big_end_arr 1 >>> big_end_arr 770
Note the array dtype above of >i2. The > means ‘big-endian’ (< is little-endian) and i2 means ‘signed 2-byte integer’. For example, if our data represented a single unsigned 4-byte little-endian integer, the dtype string would be <u4.
In fact, why don’t we try that?
>>> little_end_u4 = np.ndarray(shape=(1,),dtype='<u4', buffer=big_end_str) >>> little_end_u4 == 1 * 256**1 + 3 * 256**2 + 2 * 256**3 True
Returning to our big_end_arr - in this case our underlying data is big-endian (data endianness) and we’ve set the dtype to match (the dtype is also big-endian). However, sometimes you need to flip these around.
As you can imagine from the introduction, there are two ways you can affect the relationship between the byte ordering of the array and the underlying memory it is looking at:
The common situations in which you need to change byte ordering are:
We make something where they don’t match:
>>> wrong_end_dtype_arr = np.ndarray(shape=(2,),dtype='<i2', buffer=big_end_str) >>> wrong_end_dtype_arr 256
The obvious fix for this situation is to change the dtype so it gives the correct endianness:
>>> fixed_end_dtype_arr = wrong_end_dtype_arr.newbyteorder() >>> fixed_end_dtype_arr 1
Note the the array has not changed in memory:
>>> fixed_end_dtype_arr.tostring() == big_end_str True
You might want to do this if you need the data in memory to be a certain ordering. For example you might be writing the memory out to a file that needs a certain byte ordering.
>>> fixed_end_mem_arr = wrong_end_dtype_arr.byteswap() >>> fixed_end_mem_arr 1
Now the array has changed in memory:
>>> fixed_end_mem_arr.tostring() == big_end_str False
You may have a correctly specified array dtype, but you need the array to have the opposite byte order in memory, and you want the dtype to match so the array values make sense. In this case you just do both of the previous operations:
>>> swapped_end_arr = big_end_arr.byteswap().newbyteorder() >>> swapped_end_arr 1 >>> swapped_end_arr.tostring() == big_end_str False