Looking for suggestions on improving numpy code

David Lees debl2NoSpam at verizon.net
Sat Feb 23 07:37:38 CET 2008

I am starting to use numpy and have written a hack for reading in a 
large data set that has 8 columns and millions of rows.  I want to read 
and process a single column.  I have written the very ugly hack below, 
but am sure there is a more efficient and pythonic way to do this.  The 
file is too big to read by brute force and select a column, so it is 
read in chunks and the column selected. Things I don't like in the code:
1. Performing a transpose on a large array
2. Uncertainty about numpy append efficiency

Is there a way to directly read every n'th element from the file into an 


from numpy import *
from scipy.io.numpyio import fread

fd = open('testcase.bin', 'rb')
datatype = 'h'
byteswap = 0
M = 1000000
N = 8
size = M*N
shape = (M,N)
colNum = 2
sf =1.645278e-04*10
for i in xrange(50):
     data = fread(fd, size, datatype,datatype,byteswap)
     data = data.reshape(shape)
     data = data.transpose()
     z = append(z,data[colNum]*sf)

print z.mean()


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