print row from a data in numpy structured array

Question

i have the next structured array in numpy:

>>> matriz
rec.array([('b8:27:eb:07:65:ad', '0.130s', 255), 
          ('b8:27:eb:07:65:ad', '0.120s', 215), 
          ('b8:27:eb:07:65:ad', '0.130s', 168) ],
  dtype=[('col1', '<U17'), ('col2', '<U17'), ('col3', '<i4'), 
   ('col4','<U17')])

i need to find in 'col3' the numbers < 179, but also i need to print the row where the number is.

for instance, in matriz the number lower than 179 is 168, then i need to print

('b8:27:eb:07:65:ad', '0.130s', 168)

i did,

for j in matriz['col3']:
         if j< 254:
                   print(j)

but i got 168 only the int, any idea?.

and, someone knows, if with pandas library, could i do that?..

thanks

ujhuyz0110 · Accepted Answer · 2018-07-11 00:19:17Z

1

You can do the following:

matrix = np.array([('b8:27:eb:07:65:ad', '0.130s', 255),
                   ('b8:27:eb:07:65:ad', '0.120s', 215),
                   ('b8:27:eb:07:65:ad', '0.130s', 168)],
                  dtype=[('col1', '<U17'), 
                         ('col2', '<U17'), 
                         ('col3', '<i4')])

for row in matrix:
    if row['col3'] < 254:
        print(row)

answered Jul 11, 2018 at 0:19

ujhuyz0110

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hpaulj · Accepted Answer · 2018-07-11 01:03:25Z

In [128]: arr=np.rec.array([('b8:27:eb:07:65:ad', '0.130s', 255), 
     ...:           ('b8:27:eb:07:65:ad', '0.120s', 215), 
     ...:           ('b8:27:eb:07:65:ad', '0.130s', 168) ],
     ...:   dtype=[('col1', '<U17'), ('col2', '<U17'), ('col3', '<i4')])

This is a 1d array with 3 fields:

In [129]: arr
Out[129]: 
rec.array([('b8:27:eb:07:65:ad', '0.130s', 255),
           ('b8:27:eb:07:65:ad', '0.120s', 215),
           ('b8:27:eb:07:65:ad', '0.130s', 168)],
          dtype=[('col1', '<U17'), ('col2', '<U17'), ('col3', '<i4')])

We can view one field with:

In [130]: arr['col3']
Out[130]: array([255, 215, 168], dtype=int32)

and get a boolean mask of its values:

In [131]: arr['col3']<179
Out[131]: array([False, False,  True])

and use that mask to select elements from the whole array:

In [132]: arr[arr['col3']<179]
Out[132]: 
rec.array([('b8:27:eb:07:65:ad', '0.130s', 168)],
          dtype=[('col1', '<U17'), ('col2', '<U17'), ('col3', '<i4')])

since it is a rec.array, not just a structured array, we can access the field as an attribute as well:

In [135]: print(arr[arr.col3<254])
[('b8:27:eb:07:65:ad', '0.120s', 215) ('b8:27:eb:07:65:ad', '0.130s', 168)]

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