User guide

NumPy User Guide, Release 1.9.0
>>> x.shape
(4,)
>>> z.shape
(3, 4)
>>> (x + z).shape
(3, 4)
>>> x + z
array([[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.],
[ 1., 2., 3., 4.]])
Broadcasting provides a convenient way of taking the outer product (or any other outer operation) of two arrays. The
following example shows an outer addition operation of two 1-d arrays:
>>> a = np.array([0.0, 10.0, 20.0, 30.0])
>>> b = np.array([1.0, 2.0, 3.0])
>>> a[:, np.newaxis] + b
array([[ 1., 2., 3.],
[ 11., 12., 13.],
[ 21., 22., 23.],
[ 31., 32., 33.]])
Here the newaxis index operator inserts a new axis into a, making it a two-dimensional 4x1 array. Combining the
4x1 array with b, which has shape (3,), yields a 4x3 array.
See this article for illustrations of broadcasting concepts.
2.6 Byte-swapping
2.6.1 Introduction to byte ordering and ndarrays
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:
1. MSB integer 1
2. LSB integer 1
3. MSB integer 2
4. LSB integer 2
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:
2.6. Byte-swapping 29