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POMost efficient way to parse a large .csv in python?
 

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  1. POMost efficient way to parse a large .csv in python?
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    <p>I tried to look on other answers but I am still not sure the right way to do this. I have a number of really large .csv files (could be a gigabyte each), and I want to first get their column labels, cause they are not all the same, and then according to user preference extract some of this columns with some criteria. Before I start the extraction part I did a simple test to see what is the fastest way to parse this files and here is my code:</p> <pre><code>def mmapUsage(): start=time.time() with open("csvSample.csv", "r+b") as f: # memory-mapInput the file, size 0 means whole file mapInput = mmap.mmap(f.fileno(), 0) # read content via standard file methods L=list() for s in iter(mapInput.readline, ""): L.append(s) print "List length: " ,len(L) #print "Sample element: ",L[1] mapInput.close() end=time.time() print "Time for completion",end-start def fileopenUsage(): start=time.time() fileInput=open("csvSample.csv") M=list() for s in fileInput: M.append(s) print "List length: ",len(M) #print "Sample element: ",M[1] fileInput.close() end=time.time() print "Time for completion",end-start def readAsCsv(): X=list() start=time.time() spamReader = csv.reader(open('csvSample.csv', 'rb')) for row in spamReader: X.append(row) print "List length: ",len(X) #print "Sample element: ",X[1] end=time.time() print "Time for completion",end-start </code></pre> <p>And my results:</p> <pre><code>======================= Populating list from Mmap List length: 1181220 Time for completion 0.592000007629 ======================= Populating list from Fileopen List length: 1181220 Time for completion 0.833999872208 ======================= Populating list by csv library List length: 1181220 Time for completion 5.06700015068 </code></pre> <p>So it seems that the csv library most people use is really alot slower than the others. Maybe later it proves to be faster when I start extracting data from the csv file but I cannot be sure for that yet. Any suggestions and tips before I start implementing? Thanks alot!</p>
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    1. COCode for correctness first and then for speed -- ie. start with the `csv` module. If you want to do more performance testing instead of starting with `csv`, add a check to make sure your other methods are parsing the data correctly. Your fileopenUsage() looks like a good place to add a test for correctness.
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    2. COYour first 2 methods are NOT parsing each line into fields. Therefore, your benchmarks don't measure what you claim they measure.
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    3. COTry the `timeit` module in the future for benchmarks like this.
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