group-by

问题 I have a table that looks like this: id | SubjectCode | Grade | DateApproved | StudentId 1 SUB123 1.25 1/4/2012 2012-12345 2 SUB123 2.00 1/5/2012 2012-12345 3 SUB123 3.00 1/5/2012 2012-98765 I'm trying to GROUP BY SubjectCode but i'd like it to display the most recent DateApproved so it will look like: id | SubjectCode | Grade | DateApproved | StudentId 2 SUB123 2.00 1/5/2012 2012-12345 3 SUB123 3.00 1/5/2012 2012-98765 I'm a little bit lost on how to do it? EDIT: Ok guys now im on my real PC

问题 I use TimeGrouper from pandas.tseries.resample to sum monthly return to 6M as follows: 6m_return = monthly_return.groupby(TimeGrouper(freq='6M')).aggregate(numpy.sum) where monthly_return is like: 2008-07-01 0.003626 2008-08-01 0.001373 2008-09-01 0.040192 2008-10-01 0.027794 2008-11-01 0.012590 2008-12-01 0.026394 2009-01-01 0.008564 2009-02-01 0.007714 2009-03-01 -0.019727 2009-04-01 0.008888 2009-05-01 0.039801 2009-06-01 0.010042 2009-07-01 0.020971 2009-08-01 0.011926 2009-09-01 0.024998

问题 I need to group by a Calculated field ins SQL Server 2005/2008. I have the following sql: select dateadd(day, -7, Convert(DateTime, mwspp.DateDue) + (7 - datepart(weekday, mwspp.DateDue))), sum(mwspp.QtyRequired) from manufacturingweekshortagepartpurchasing mwspp where mwspp.buildScheduleSimID = 10109 and mwspp.partID = 8366 group by mwspp.DateDue order by mwspp.DateDue Instead of group by mwspp.DateDue I need to group by the result of the calculation. Is it possible ? Thanks in advance 回答1:

问题 I have a field in a table which contains bitwise flags. Let's say for the sake of example there are three flags: 4 => read, 2 => write, 1 => execute and the table looks like this * : user_id | file | permissions -----------+--------+--------------- 1 | a.txt | 6 ( <-- 6 = 4 + 2 = read + write) 1 | b.txt | 4 ( <-- 4 = 4 = read) 2 | a.txt | 4 2 | c.exe | 1 ( <-- 1 = execute) I'm interested to find all users who have a particular flag set (eg: write) on ANY record. To do this in one query, I

问题 I have a field in a table which contains bitwise flags. Let's say for the sake of example there are three flags: 4 => read, 2 => write, 1 => execute and the table looks like this * : user_id | file | permissions -----------+--------+--------------- 1 | a.txt | 6 ( <-- 6 = 4 + 2 = read + write) 1 | b.txt | 4 ( <-- 4 = 4 = read) 2 | a.txt | 4 2 | c.exe | 1 ( <-- 1 = execute) I'm interested to find all users who have a particular flag set (eg: write) on ANY record. To do this in one query, I

问题 I've got table a table with different records for publishers, each record have a date in a column of type timestamp. id | id_publisher | date 1 1 11/2012 03:09:40 p.m. 2 1 12/2012 03:09:40 p.m. 3 2 01/2013 03:09:40 p.m. 4 3 01/2013 03:09:40 p.m. 5 4 11/2012 03:09:40 p.m. 6 4 02/2013 03:09:40 p.m. 7 4 02/2012 03:09:40 p.m. I need a count for number of records published by each publisher for each month. For example Month | id_publisher | num 11/2012 | 1 | 1 11/2012 | 2 | 0 11/2012 | 3 | 0 11

问题 When using dplyr's "group_by" and "mutate", if I understand correctly, the data frame is split in different sub-dataframes according to the group_by argument. For example, with the following code : set.seed(7) df <- data.frame(x=runif(10),let=rep(letters[1:5],each=2)) df %>% group_by(let) %>% mutate(mean.by.letter = mean(x)) mean() is applied successively to the column x of 5 sub-dfs corresponding to a letter between a & e. So you can manipulate the columns of the sub-dfs but can you access

问题 When using dplyr's "group_by" and "mutate", if I understand correctly, the data frame is split in different sub-dataframes according to the group_by argument. For example, with the following code : set.seed(7) df <- data.frame(x=runif(10),let=rep(letters[1:5],each=2)) df %>% group_by(let) %>% mutate(mean.by.letter = mean(x)) mean() is applied successively to the column x of 5 sub-dfs corresponding to a letter between a & e. So you can manipulate the columns of the sub-dfs but can you access

问题 From a Pandas newbie: I have data that looks essentially like this - data1=pd.DataFrame({'Dir':['E','E','W','W','E','W','W','E'], 'Bool':['Y','N','Y','N','Y','N','Y','N'], 'Data':[4,5,6,7,8,9,10,11]}, index=pd.DatetimeIndex(['12/30/2000','12/30/2000','12/30/2000','1/2/2001','1/3/2001','1/3/2001','12/30/2000','12/30/2000'])) data1 Out[1]: Bool Data Dir 2000-12-30 Y 4 E 2000-12-30 N 5 E 2000-12-30 Y 6 W 2001-01-02 N 7 W 2001-01-03 Y 8 E 2001-01-03 N 9 W 2000-12-30 Y 10 W 2000-12-30 N 11 E And I

问题 I'm a MySQL user who is trying to port some things over to MS SQL Server. I'm joining a couple of tables, and aggregating some of the columns via GROUP BY. A simple example would be employees and projects: select empID, fname, lname, title, dept, count(projectID) from employees E left join projects P on E.empID = P.projLeader group by empID ...that would work in MySQL, but MS SQL is stricter and requires that everything is either enclosed in an aggregate function or is part of the GROUP BY