Looking for direction

[Ziqi Xiong]

maybe we can change this list to dict, using item[0] and item[1] as keys,
the whole item as value . then you can update by the same key i think
Tim Chase <python.list at tim.thechases.com>于2015年5月15日 周五01:17写道：

> On 2015-05-14 09:57, 20/20 Lab wrote:
> > On 05/13/2015 06:23 PM, Steven D'Aprano wrote:
> >>> I have a LARGE csv file that I need to process.  110+ columns,
> >>> 72k rows.  I managed to write enough to reduce it to a few
> >>> hundred rows, and the five columns I'm interested in.
> > I actually stumbled across the csv module after coding enough to
> > make a list of lists.  So that is more the reason I approached the
> > list; Nothing like spending hours (or days) coding something that
> > already exists and just dont know about.
> >>> Now is were I have my problem:
> >>>
> >>> myList = [ [123, "XXX", "Item", "Qty", "Noise"],
> >>>              [72976, "YYY", "Item", "Qty", "Noise"],
> >>>              [123, "XXX" "ItemTypo", "Qty", "Noise"]    ]
> >>>
> >>> Basically, I need to check for rows with duplicate accounts
> >>> row[0] and staff (row[1]), and if so, remove that row, and add
> >>> it's Qty to the original row. I really dont have a clue how to
> >>> go about this.
> >>
> >> processed = {}  # hold the processed data in a dict
> >>
> >> for row in myList:
> >>      account, staff = row[0:2]
> >>      key = (account, staff)  # Put them in a tuple.
> >>      if key in processed:
> >>          # We've already seen this combination.
> >>          processed[key][3] += row[3]  # Add the quantities.
> >>      else:
> >>          # Never seen this combination before.
> >>          processed[key] = row
> >>
> >> newlist = list(processed.values())
> >>
> > It does, immensely.  I'll make this work.  Thank you again for the
> > link from yesterday and apologies for hitting the wrong reply
> > button.  I'll have to study more on the usage and implementations
> > of dictionaries and tuples.
>
> In processing the initial CSV file, I suspect that using a
> csv.DictReader would make the code a bit cleaner.  Additionally,
> as you're processing through the initial file, unless you need
> the intermediate data, you should be able to do it in one pass.
> Something like
>
>   HEADER_ACCOUNT = "account"
>   HEADER_STAFF = "staff"
>   HEADER_QTY = "Qty"
>
>   processed = {}
>   with open("data.csv") as f:
>     reader = csv.DictReader(f)
>     for row in reader:
>       if should_process_row(row):
>         account = row[HEADER_ACCOUNT]
>         staff = row[HEADER_STAFF]
>         qty = row[HEADER_QTY]
>         try:
>           row[HEADER_QTY] = qty = int(qty)
>         except Exception:
>           # not a numeric quantity?
>           continue
>         # from Steven's code
>         key = (account, staff)
>         if key in processed:
>           processed[key][HEADER_QTY] += qty
>         else:
>           processed[key][HEADER_QTY] = row
>   so_something_with(processed.values())
>
> I find that using names is a lot clearer than using arbitrary
> indexing.  Barring that, using indexes-as-constants still would
> add further clarity.
>
> -tkc
>
>
>
>
> .
> --
> https://mail.python.org/mailman/listinfo/python-list
>
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