On Sun, Feb 28, 2010 at 7:24 AM, Friedrich Romstedt <friedrichromstedt@gmail.com> wrote:

2010/2/28 Robert Love <rblove_lists@comcast.net>:

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What is the efficient numpy way to compare data from two different files?  For the nth line in each file I want to operate on the numbers.   I've been using loadtxt()

data_5x5 = N.loadtxt("file5")

data_8x8 = N.loadtxt("file8")

for line in data_5x5:        pos5 = N.array([line[0], line[1],  line[2]])

If you just want to compare row by row when you already have the arrays, you can just use numpy, e.g. based on first 3 columns: (data_8x8[:,:3] == data_5x5[:,:3]).all(1) but from your question it's not clear to me what you actually want to compare Josef

I believe there are several ways of doing that, and mine might not be the most efficient at all:

for line5, line8 in zip(data_5x5, data_8x8):    # line5 and line8 are row vectors of paired lines    pass

complete = numpy.hstack(data_5x5, data_8x8)  # If data_5x5.shape[0] == data_8x8.shape[0], i.e., same number of rows. for line in complete:    # complete is comprised of concatenated row vectors.    pass

for idx in xrange(0, min(data_5x5.shape[0], data_8x8.shape[0])):    line5 = data_5x5[idx]    line8 = data_8x8[idx]    # Do sth with the vectors. Or:    a1 = data_5x5[idx, (0, 1, 2)]  # Extract items 0, 1, 2 of line idx of first file.    a2 = data_8x8[idx, (0, 42)]   # Extract items 0, 42 of line idx of second file.

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