numpy structured array with bytes field

Question

I'm trying to build a structured numpy array with a field that is a named bytes field (type 4b, not unicode).

import numpy as np 
dtype = np.dtype([('count', 'u8'), ('name', '4b')], align=True)
a = np.asarray([(10, b'test')], dtype=dtype)
print(a.dtype)

I receive the error:

ValueError: invalid literal for int() with base 10: b'test'

Now, if i change the bytes field to unicode,

import numpy as np 
dtype = np.dtype([('count', 'u8'), ('name', 'U4')], align=True)
a = np.asarray([(10, 'test')], dtype=dtype)
print(a.dtype)

this does not result in an error and I get the output:

{'names':['count','name'], 'formats':['<u8','<U4'], 'offsets':[0,8], 'itemsize':24, 'aligned':True}

But to me this is a hack since I specifically just want bytes.

Question: How can I get a named bytes field on my structured numpy array?

juanpa.arrivillaga · Accepted Answer · 2021-05-05 21:08:46Z

2

You can use the S type string to work with byte strings:

>>> dtype = np.dtype([('count', 'u8'), ('name', 'S4')], align=True)
>>> a = np.array([(10, b'test')], dtype=dtype)
>>> a
array([(10, b'test')],
      dtype={'names':['count','name'], 'formats':['<u8','S4'], 'offsets':[0,8], 'itemsize':16, 'aligned':True})

answered May 5, 2021 at 21:08

juanpa.arrivillaga

97.6k14 gold badges141 silver badges190 bronze badges

Sign up to request clarification or add additional context in comments.

Collectives™ on Stack Overflow

numpy structured array with bytes field

1 Answer 1

Comments

Your Answer

Hot Network Questions

Collectives™ on Stack Overflow

1 Answer 1

Comments

Your Answer

Sign up or log in

Post as a guest

Related