User guide

NumPy User Guide, Release 1.9.0

Note the the array has not changed in memory:

>>> fixed_end_dtype_arr.tobytes() == big_end_str

True

Data and type endianness don’t match, change data to match dtype

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[0]

Now the array has changed in memory:

>>> fixed_end_mem_arr.tobytes() == big_end_str

False

Data and dtype endianness match, swap data and dtype

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[0]

>>> swapped_end_arr.tobytes() == big_end_str

False

An easier way of casting the data to a speciﬁc dtype and byte ordering can be achieved with the ndarray astype method:

>>> swapped_end_arr = big_end_arr.astype(’<i2’)

>>> swapped_end_arr[0]

>>> swapped_end_arr.tobytes() == big_end_str

False

2.7 Structured arrays (aka “Record arrays”)

2.7.1 Structured Arrays (and Record Arrays)

Introduction

Numpy provides powerful capabilities to create arrays of structs or records. These arrays permit one to manipulate the

data by the structs or by ﬁelds of the struct. A simple example will show what is meant.:

>>> x = np.zeros((2,),dtype=(’i4,f4,a10’))

>>> x[:] = [(1,2.,’Hello’),(2,3.,"World")]

>>> x

array([(1, 2.0, ’Hello’), (2, 3.0, ’World’)],

dtype=[(’f0’, ’>i4’), (’f1’, ’>f4’), (’f2’, ’|S10’)])

2.7. Structured arrays (aka “Record arrays”) 31