create structured numpy array in python with strings and int

i have this:

>>> matriz
[['b8:27:eb:d6:e3:10', '0.428s', '198'],
['b8:27:eb:d6:e3:10', '0.428s', '232'],
['b8:27:eb:07:65:ad', '0.796s', '180'],
['b8:27:eb:07:65:ad', '0.796s', '255'],
dtype='<U17']`


but i need the column

`matriz[:, [2]] :
[['198'],
['232'],
['180'],
['255']]`


to be int and the other columns to be strings, i was trying with structured numpy array but i have this error message,

ValueError: invalid literal for int() with base 10: 'b8:27:eb:d6:e3:10'
TypeError: a bytes-like object is required, not 'str'


i used

matriz=np.array(matriz, dtype='U17,U17,i4')


i'm using numpy version '1.12.1' for raspberry pi 3, i don't know what i'm doing wrong.
thanks a lot

pandas

2 Answers
2

In [484]: x = np.array([['b8:27:eb:d6:e3:10', '0.428s', '198'],
...: ['b8:27:eb:d6:e3:10', '0.428s', '232'],
...: ['b8:27:eb:07:65:ad', '0.796s', '180'],
...: ['b8:27:eb:07:65:ad', '0.796s', '255']],
...: dtype='<U17')
...:


You could fetch the last column with an astype conversion:

astype

In [485]: x[:,2].astype(int)
Out[485]: array([198, 232, 180, 255])
In [486]: x[:,[2]].astype(int)
Out[486]:
array([[198],
[232],
[180],
[255]])


To construct a structured array, you need to provide a list of tuples. A list of lists or non-structured array with the compound dtype will produce your kind of error.

In [487]: np.array([tuple(i) for i in x],'U17,U10,int')
Out[487]:
array([('b8:27:eb:d6:e3:10', '0.428s', 198),
('b8:27:eb:d6:e3:10', '0.428s', 232),
('b8:27:eb:07:65:ad', '0.796s', 180),
('b8:27:eb:07:65:ad', '0.796s', 255)],
dtype=[('f0', '<U17'), ('f1', '<U10'), ('f2', '<i8')])
In [488]: _['f2']
Out[488]: array([198, 232, 180, 255])


Fields of the structured array are fetched by name.

NumPy works best with homogeneous dtype arrays. Pandas is a good alternative if you have different types.

However, what you ask is possible with NumPy structured arrays:

import numpy as np

x = np.array([['b8:27:eb:d6:e3:10', '0.428s', '198'],
['b8:27:eb:d6:e3:10', '0.428s', '232'],
['b8:27:eb:07:65:ad', '0.796s', '180'],
['b8:27:eb:07:65:ad', '0.796s', '255']],
dtype='<U17')

arr = np.core.records.fromarrays(x.transpose(),
formats='<U17,<U17,i4',
names='col1,col2,col3')

print(arr)

rec.array([('b8:27:eb:d6:e3:10', '0.428s', 198),
('b8:27:eb:d6:e3:10', '0.428s', 232),
('b8:27:eb:07:65:ad', '0.796s', 180),
('b8:27:eb:07:65:ad', '0.796s', 255)],
dtype=[('col1', '<U17'), ('col2', '<U17'), ('col3', '<i4')])


arr['col3']

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