累计和（熊猫）

Question

Apologies if this has been asked already.

I am trying to create a yearly cumulative sum for all order-points within a certain customer account, and am struggling.

Essentially, I want to create `YearlyTotal' below:

Customer   Year   Date       Order   PointsPerOrder   YearlyTotal
123456     2016   11/2/16    A939    1                 20
123456     2016   3/13/16    A102    19                19
789089     2016   7/15/16    A123    7                 7

I've tried:

df['YEARLYTOTAL'] = df.groupby(by=['Customer','Year'])['PointsPerOrder'].cumsum()

But this produces YearlyTotal in the wrong order (ie, YearlyTotal of A939 is 1 instead of 20.

Not sure if this matters, but Customer is a string (the database has leading zeroes -- don't get me started). sort_values(by=['Customer','Year','Date'],ascending=True) at the front also produces an error.

Help?

Answer 1

Use [::-1] for reversing dataframe:

df['YEARLYTOTAL'] = df[::-1].groupby(by=['Customer','Year'])['PointsPerOrder'].cumsum()

print (df)
   Customer  Year     Date Order  PointsPerOrder  YearlyTotal  YEARLYTOTAL
0    123456  2016  11/2/16  A939               1           20           20
1    123456  2016  3/13/16  A102              19           19           19
2    789089  2016  7/15/16  A123               7            7            7

Answer 2

first make sure Date is a datetime column:

In [35]: df.Date = pd.to_datetime(df.Date)

now we can do:

In [36]: df['YearlyTotal'] = df.sort_values('Date').groupby(['Customer','Year'])['PointsPerOrder'].cumsum()

In [37]: df
Out[37]:
   Customer  Year       Date Order  PointsPerOrder  YearlyTotal
0    123456  2016 2016-11-02  A939               1           20
1    123456  2016 2016-03-13  A102              19           19
2    789089  2016 2016-07-15  A123               7            7

PS this solution will NOT depend on the order of records...