从python中的大型csv数据文件中提取几行数据的有效方法

Question

I have a large number of csv data files, and each data file contains several days worth of tick data for one ticker in the following form :

 ticker  DD/MM/YYYY    time         bid      ask
  XXX,   19122014,  08:00:08.325,  9929.00,9933.00
  XXX,   19122014,  08:00:08.523,  9924.00,9931.00
  XXX,   19122014,  08:00:08.722,  9925.00,9930.50
  XXX,   19122014,  08:00:08.921,  9924.00,9928.00
  XXX,   19122014,  08:00:09.125,  9924.00,9928.00
  …
  XXX,   30122014,  21:56:25.181,  9795.50,9796.50
  XXX,   30122014,  21:56:26.398,  9795.50,9796.50
  XXX,   30122014,  21:56:26.598,  9795.50,9796.50
  XXX,   30122014,  21:56:26.798,  9795.50,9796.50
  XXX,   30122014,  21:56:28.896,  9795.50,9796.00
  XXX,   30122014,  21:56:29.096,  9795.50,9796.50
  XXX,   30122014,  21:56:29.296,  9795.50,9796.00
  …

I need to extract any lines of data whose time is within certain range, say: 09:00:00 to 09:15:00. My current solution is simply reading in each data file to a data frame, sorting it in order by time and then using searchsorted to find 09:00:00 to 09:15:00. It works fine if performance isn't an issue and I don't have 1000 files waiting to be processed. Any suggestions on how to boost the speed? Thanks for help in advance!!!

Answer 1

Short answer: put your data in an SQL database, and give the "time" column an index. You can't beat that with CSV files - using Pandas or not.

Without changing your CSV files, one thign a little bit faster, but not much would be to filter the rows as you read them - and have in memory just the ones that are interesting for you.

So instead of just getting the whole CSV into memory, a function like such could do the job:

import csv

def filter_time(filename, mintime, maxtime):
    timecol = 3
    reader = csv.reader(open(filename))
    next(reader)
    return [line for line in reader if mintime <= line[timecol] <= maxtime]

This task can be easilyt paralyzed - you could get some instances of this running concurrently before maxing the I/O on your device, I'd guess. One painless way to do that would be using the lelo Python package - it just provides you a @paralel decorator that makes the given function run in another process when called, and returns a lazy proxy for the results.

But that will still have to read everything in - I think the SQL solution should be about at least one order of magnitude faster.

Answer 2

My solution would be to read line by line and saving only what pass your filter:

with open('filename.csv') as fin:
    with open('fileout.csv', 'w') as fout:
        while True:
            line = fin.readline()
            if not line:
                break
            time_x = line.rstrip('\n').split(',')[2]
            # some parsing of time to do properly
            if a< time_x < b:
                fout.write(line)