calculate inventory turnover in python, taking difference using groupby?What is the difference between old style and new style classes in Python?What is the difference between Python's list methods append and extend?Use different Python version with virtualenvPeak detection in a 2D arrayUnicodeEncodeError: 'ascii' codec can't encode character u'xa0' in position 20: ordinal not in range(128)“Large data” work flows using pandasApply multiple functions to multiple groupby columnspandas create new column based on values from other columnsHow do I create a new column from the output of pandas groupby().sum()?replace certain number in DataFrame

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calculate inventory turnover in python, taking difference using groupby?

What is the difference between old style and new style classes in Python?What is the difference between Python's list methods append and extend?Use different Python version with virtualenvPeak detection in a 2D arrayUnicodeEncodeError: 'ascii' codec can't encode character u'xa0' in position 20: ordinal not in range(128)“Large data” work flows using pandasApply multiple functions to multiple groupby columnspandas create new column based on values from other columnsHow do I create a new column from the output of pandas groupby().sum()?replace certain number in DataFrame

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i have a dataframe which contains inventory information. One column is product type (i.e A,B,C), one column is the number of inventory for each product, one column records the date (i.e day 1, day 2, day 3). For example (see the table below), on day 1, we have 1 product A; on day 2, we have 3 product A and 2 product B; on day 3, we have 6 product B.

product | inventory | day
 A | 1 | 1 
 A | 3 | 2
 B | 2 | 2
 B | 6 | 3

I would like to calculate the product change for each day. The desired resulting table is shown below. The trick is that every day some products may be gone or some new product may come in. For example, on day 3, product A does not exist any more (in other words, all 3 product A on day 2 are gone on day 3). On day 2, new product B comes in as we do not have inventory for it on day 1. Remember, some product may disappear on certain day and come back later and then disappear again.

product | inventory_change | day
 A | 1 | 1 
 A | 2 | 2
 B | 2 | 2
 A | -3 | 3
 B | 4 | 3

I am sure i can use loop to go through each row to achieve it. I am wondering if there are any more efficient way to complete it without loop? I thinking use groupby, but still fail to come out a way without using loop, especially given available product list could be different every day (i.e day1:A, day2 A,B; day3: B) Any idea or suggestions?

asked Mar 27 at 0:21