On 2015-05-14 01:06, Ethan Furman wrote:
> On 05/13/2015 04:24 PM, 20/20 Lab wrote:
>> I'm a beginner to python. Reading here and there. Written a couple of
>> short and simple programs to make life easier around the office.
>>
>> That being said, I'm not even sure what I need to ask for. I've never
>> worked with external data before.
>>
>> 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.
>>
>> 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. The
>> number of rows change based on which run it is, so I couldnt even get
>> away with using hundreds of compare loops.
>>
>> If someone could point me to some documentation on the functions I would
>> need, or a tutorial it would be a great help.
>
> You could try using a dictionary, combining when needed:
>
> # untested
> data = {}
> for row in all_rows:
> key = row[0], row[1]
> if key in data:
> item, qty, noise = data[key]
> qty += row[3]
> else:
> item, qty, noise = row[2:]
> data[key] = item, qty, noise
>
> for (account, staff), (item, qty, noise) in data.items():
> do_stuff_with(account, staff, item, qty, noise)
>
> At the end, data should have what you want. It won't, however, be in
> the same order, so hopefully that's not an issue for you.
>
Starting from that, if the order matters, you can do it this way:
data = {}
order = {}
for index, row in enumerate(all_rows):
key = row[0], row[1]
if key in data:
item, qty, noise = data[key]
qty += row[3]
else:
item, qty, noise = row[2:]
data[key] = item, qty, noise
order.setdefault(key, index)
merged_rows = [(account, staff, item, qty, noise) for (account, staff),
(item, qty, noise) in data.items()]
def original_order(row):
key = row[0], row[1]
return order[key]
merged_rows.sort(key=original_order)