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    <p>This is what I think you're trying to accomplish based on your edit: for every date in your CSV file, group the date along with a list of prices for each item with a signal of "S".</p> <p>You didn't include any sample data in your question, so I made a test one that I hope matches the format you described:</p> <pre><code>12/28/2012,1:30,10.00,"foo","S" 12/28/2012,2:15,11.00,"bar","N" 12/28/2012,3:00,12.00,"baz","S" 12/28/2012,4:45,13.00,"fibble","N" 12/28/2012,5:30,14.00,"whatsit","S" 12/28/2012,6:15,15.00,"bobs","N" 12/28/2012,7:00,16.00,"widgets","S" 12/28/2012,7:45,17.00,"weevils","N" 12/28/2012,8:30,18.00,"badger","S" 12/28/2012,9:15,19.00,"moose","S" 11/29/2012,1:30,10.00,"foo","N" 11/29/2012,2:15,11.00,"bar","N" 11/29/2012,3:00,12.00,"baz","S" 11/29/2012,4:45,13.00,"fibble","N" 11/29/2012,5:30,14.00,"whatsit","N" 11/29/2012,6:15,15.00,"bobs","N" 11/29/2012,7:00,16.00,"widgets","S" 11/29/2012,7:45,17.00,"weevils","N" 11/29/2012,8:30,18.00,"badger","N" 11/29/2012,9:15,19.00,"moose","N" 12/29/2012,1:30,10.00,"foo","N" 12/29/2012,2:15,11.00,"bar","N" 12/29/2012,3:00,12.00,"baz","S" 12/29/2012,4:45,13.00,"fibble","N" 12/29/2012,5:30,14.00,"whatsit","N" 12/29/2012,6:15,15.00,"bobs","N" 12/29/2012,7:00,16.00,"widgets","S" 12/29/2012,7:45,17.00,"weevils","N" 12/29/2012,8:30,18.00,"badger","N" 12/29/2012,9:15,19.00,"moose","N" 8/9/2008,1:30,10.00,"foo","N" 8/9/2008,2:15,11.00,"bar","N" 8/9/2008,3:00,12.00,"baz","S" 8/9/2008,4:45,13.00,"fibble","N" 8/9/2008,5:30,14.00,"whatsit","N" 8/9/2008,6:15,15.00,"bobs","N" 8/9/2008,7:00,16.00,"widgets","S" 8/9/2008,7:45,17.00,"weevils","N" 8/9/2008,8:30,18.00,"badger","N" 8/9/2008,9:15,19.00,"moose","N" </code></pre> <p>And here's a method using Python 2.7 and built-in libraries to group it in the way it sounds like you want:</p> <pre><code>import csv import itertools import time from collections import OrderedDict with open("sample.csv", "r") as file: reader = csv.DictReader(file, fieldnames=["date", "time", "price", "mag", "signal"]) # Reduce the size of the data set by filtering out the non-"S" rows. filterFunc = lambda row: row["signal"] == "S" filteredData = itertools.ifilter(filterFunc, reader) # Sort by date so we can use the groupby function. dateKeyFunc = lambda row: time.strptime(row["date"], r"%m/%d/%Y") sortedData = sorted(filteredData, key=dateKeyFunc) # Group by date: create a new dictionary of date to a list of prices. datePrices = OrderedDict((date, [row["price"] for row in rows]) for date, rows in itertools.groupby(sortedData, dateKeyFunc)) for date, prices in datePrices.iteritems(): print "{0}: {1}".format(time.strftime(r"%m/%d/%Y", date), ", ".join(str(price) for price in prices)) &gt;&gt;&gt; 08/09/2008: 12.00, 16.00 &gt;&gt;&gt; 11/29/2012: 12.00, 16.00 &gt;&gt;&gt; 12/28/2012: 10.00, 12.00, 14.00, 16.00, 18.00, 19.00 &gt;&gt;&gt; 12/29/2012: 12.00, 16.00 </code></pre> <p>The type conversions are up to you, since you may be using other libraries to do your CSV reading, but that should hopefully get you started -- and take careful note of @DSM's comment about import *.</p>
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    1. COI ran your example; it provides all the 'S' prices with the dates in a peculiar order: 3/4/2009, 3/4/2008,1/14/2009,1/14/2008, 5/1/2012,3/5/2008,3/5/2009,...,etc. Would you comment on why the order of the dates is like so? I ask because, it will be important for what I really need to do: Write a P&L function which computes, for each day, the sum(S_i-B_i) for all i, in a given date. And return the P&L number for all dates in the file. (In the case that num ('B') != num ('S'), for some day, I would like to consider subtracting to/from the close price (here 1620).
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    2. COWhere do you define 'rows', where you call 'rows' to construct datePrices? Thanks very much.
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    3. COEdited my response to include date conversion -- the odd order was because I had originally left out type conversions, so it was being sorted in string order (and then the dict at the end destroyed the order) - although after the extra detail in your comment, there are better ways of accomplishing your end goal; you might consider loading your CSV file into a database and querying it. See http://docs.python.org/2/library/time.html for docs on the time conversion functions.
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