Numpy Genfromtxt Memory Error
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million programmers, just like you, helping each other. Join them; it only takes a minute: Sign up Memory error: numpy.genfromtxt() up vote 2 down vote favorite I have a 50,000x5,000 matrix(float) file. when use x = pandas large csv np.genfromtxt(readFrom, dtype=float) to load the file into memory, I am getting the following error message: File "C:\Python27\lib\site-packages\numpy\lib\npyio.py", line 1583, in genfromtxt for (i, converter) in enumerate(converters)]) MemoryError I want to load the whole file into memory because I am calculating the euclidean distance between each vectors using Scipy. dis = scipy.spatial.distance.euclidean(x[row1], x[row2]) Is there any efficient way to load a huge matrix file into memory. Thank you. Update: I managed to solve pandas.read_csv memory error the problem. Here is my solution. I am not sure whether it's efficient or logically correct but works fine for me: x = open(readFrom, 'r').readlines() y = np.asarray([np.array(s.split()).astype('float32') for s in x], dtype=np.float32) .... dis = scipy.spatial.distance.euclidean(y[row1], y[row2]) Please help me to improve my solution. python memory numpy scipy share|improve this question edited Jul 14 '12 at 17:54 asked Jul 14 '12 at 16:18 Maggie 1,34421743 1 Calculating the distance for all pairs of vectors will take much longer than loading the file. Recheck if you really need all vector pairs. Also, you are going to need at least 25 * 10^7 * 4 = 10^9 bytes, perhaps 2*10^9 bytes -- the latter would be infeasible on a 32-bit system. –krlmlr Jul 14 '12 at 16:28 have a look at stackoverflow.com/q/1896674/1301710 –bmu Jul 14 '12 at 17:49 add a comment| 2 Answers 2 active oldest votes up vote 1 down vote Depending on your OS and Python version, it's quite likely that you'll never be able to allocate a 1GB array (mgilson's answer is spot on here). The problem is not that you're running out of memory, but that you're running out of contiguous memory. If you're on a 32-bit machine (especially running Windows), it will not help to add more memory. Mo
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Users Badges Ask Question x Dismiss Join the Stack Overflow Community Stack Overflow is a community of 6.2 million programmers, just like you, helping each other. Join them; it only http://stackoverflow.com/questions/11485369/memory-error-numpy-genfromtxt takes a minute: Sign up Getting numpy Memory Error on (relatively) small matrix up vote 0 down vote favorite I am trying to create a numpy array of certain dimensions however I am getting a Memory Error. no_of_frames = 1404 no_of_cells = 136192 original_vals = np.zeros((no_of_frames, no_of_cells), dtype=np.float32) The error I am getting is: difference_array = np.zeros((no_of_frames, no_of_cells), dtype=np.float32) MemoryError According http://stackoverflow.com/questions/32789331/getting-numpy-memory-error-on-relatively-small-matrix to my calculations 1404 x 136192 * 4 is ~729 MB. Which seems pretty reasonable. The machine I am running this code on has 8 GB of RAM. So why am I getting this error? I would greatly appreciate any help with this. Thanks! python numpy memory share|improve this question asked Sep 25 '15 at 19:28 user3057470 31 add a comment| 2 Answers 2 active oldest votes up vote 1 down vote accepted if you are working in 32bit python you are limited to 32bit address space (~2GB) if you have other stuff going on it you maybe are exceeding this limit in addition numpy requires contiguous memory space in order to create its lists ... this means it has to find an uninterupted 768MB block of ram (which is kind of hard) share|improve this answer answered Sep 25 '15 at 19:43 Joran Beasley 54.7k34979 Thank you I guess I will have to shift to 64-bit Python. –user3057470 Sep 25 '15 at 22:48 add a comment| up vote 1 down vote I just tried the code you gav
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| Report Content as Inappropriate ♦ ♦ Memory usage of numpy-arrays Dear NumPy developers, I have to process some big data files with high-frequency financial data. I am trying to load a delimited text file having ~700 MB with ~ 10 million lines using numpy.genfromtxt(). The machine is a Debian Lenny server 32bit with 3GB of memory. Since the file is just 700MB I am naively assuming that it should fit into memory in whole. However, when I attempt to load it, python fills the entire available memory and then fails with Traceback (most recent call last): File "