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Groups > comp.lang.python > #90615
| Date | 2015-05-14 10:08 -0700 |
|---|---|
| From | Albert-Jan Roskam <fomcl@yahoo.com> |
| Subject | Re: Looking for direction |
| Newsgroups | comp.lang.python |
| Message-ID | <mailman.6.1431623438.17265.python-list@python.org> (permalink) |
----------------------------- On Thu, May 14, 2015 3:35 PM CEST Dennis Lee Bieber wrote: >On Wed, 13 May 2015 16:24:30 -0700, 20/20 Lab <lab@pacbell.net> declaimed >the following: > >>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. >> > > This appears to be a matter of algorithm development -- there won't be >an pre-made "function" for it. The closest would be the summing functions >(control break http://en.wikipedia.org/wiki/Control_break ) of a report >writer application. > > The short gist would be: > > SORT the data by the account field > Initialize sum using first record > loop > read next record > if end of data > output sum record > exit > if record is same account as sum > add quantity to sum > else > output sum record > reset sum to the new record > > Granted -- loading the data into an SQL capable database would make >this simple... > > select account, sum(quantity) from table > order by account You could also use pandas. Read the data in a DataFrame, create a groupby object, use the sum() and the first() methods. http://pandas.pydata.org/pandas-docs/version/0.15.2/groupby.html
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Re: Looking for direction Albert-Jan Roskam <fomcl@yahoo.com> - 2015-05-14 10:08 -0700
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