Converting a 2D numpy array to a structured array

Question

I'm trying to convert a two-dimensional array into a structured array with named fields. I want each row in the 2D array to be a new record in the structured array. Unfortunately, nothing I've tried is working the way I expect.

I'm starting with:

>>> myarray = numpy.array([("Hello",2.5,3),("World",3.6,2)]) >>> print myarray [['Hello' '2.5' '3']  ['World' '3.6' '2']] 

I want to convert to something that looks like this:

>>> newarray = numpy.array([("Hello",2.5,3),("World",3.6,2)], dtype=[("Col1","S8"),("Col2","f8"),("Col3","i8")]) >>> print newarray [('Hello', 2.5, 3L) ('World', 3.6000000000000001, 2L)] 

What I've tried:

>>> newarray = myarray.astype([("Col1","S8"),("Col2","f8"),("Col3","i8")]) >>> print newarray [[('Hello', 0.0, 0L) ('2.5', 0.0, 0L) ('3', 0.0, 0L)]  [('World', 0.0, 0L) ('3.6', 0.0, 0L) ('2', 0.0, 0L)]]  >>> newarray = numpy.array(myarray, dtype=[("Col1","S8"),("Col2","f8"),("Col3","i8")]) >>> print newarray [[('Hello', 0.0, 0L) ('2.5', 0.0, 0L) ('3', 0.0, 0L)]  [('World', 0.0, 0L) ('3.6', 0.0, 0L) ('2', 0.0, 0L)]] 

Both of these approaches attempt to convert each entry in myarray into a record with the given dtype, so the extra zeros are inserted. I can't figure out how to get it to convert each row into a record.

Another attempt:

>>> newarray = myarray.copy() >>> newarray.dtype = [("Col1","S8"),("Col2","f8"),("Col3","i8")] >>> print newarray [[('Hello', 1.7219343871178711e-317, 51L)]  [('World', 1.7543139673493688e-317, 50L)]] 

This time no actual conversion is performed. The existing data in memory is just re-interpreted as the new data type.

The array that I'm starting with is being read in from a text file. The data types are not known ahead of time, so I can't set the dtype at the time of creation. I need a high-performance and elegant solution that will work well for general cases since I will be doing this type of conversion many, many times for a large variety of applications.

Thanks!

Answer 1

You can "create a record array from a (flat) list of arrays" using numpy.core.records.fromarrays as follows:

>>> import numpy as np >>> myarray = np.array([("Hello",2.5,3),("World",3.6,2)]) >>> print myarray [['Hello' '2.5' '3']  ['World' '3.6' '2']]   >>> newrecarray = np.core.records.fromarrays(myarray.transpose(),                                               names='col1, col2, col3',                                              formats = 'S8, f8, i8')  >>> print newrecarray [('Hello', 2.5, 3) ('World', 3.5999999046325684, 2)] 

I was trying to do something similar. I found that when numpy created a structured array from an existing 2D array (using np.core.records.fromarrays), it considered each column (instead of each row) in the 2-D array as a record. So you have to transpose it. This behavior of numpy does not seem very intuitive, but perhaps there is a good reason for it.