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
array?
david
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
z=array([])
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()
fd.close()
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