group-by

问题 Here is my query: User.where("created_at >= ? AND created_at <=?", date1,date2).order('DATE(created_at) DESC').group("DATE(created_at)").count and I get output as: {Thu, 15 May 2014=>1} But I want to get output as 0 for the rest of the days. For ex {Thu, 15 May 2014=>1,Fri, 15 May 2014=>0} What I want to get is Users created in a date range , ordered and grouped by created_at and number of such Users for each day. When no users are there for a particular day it should return 0, which the

问题 I have a list of students that shows whether they were present or absent from a particular class. CREATE TABLE classlist (`id` int, `studentid` int, `subjectid` int, `presentid` int) ; CREATE TABLE student (`id` int, `name` varchar(4)) ; CREATE TABLE subject (`id` int, `name` varchar(4)) ; CREATE TABLE classStatus (`id` int, `name` varchar(8)) ; INSERT INTO classlist (`id`, `studentid`, `subjectid`, `presentid`) VALUES (1, 111, 1, 1), (2, 222, 3, 0), (3, 333, 2, 1), (4, 111, 4, 0), (5, 111, 1

问题 In analytics processing there is often a need to collapse "unimportant" groups of data into a single row in the resulting table. One way to do this is to GROUP BY a CASE expression where unimportant groups are coalesced into a single row via the CASE expression returning a single value, e.g., NULL for the groups. This question is about efficient ways to perform this grouping in Amazon Redshift, which is based on ParAccel: close to PosgreSQL 8.0 in terms of functionality. As an example,

问题 I would like to assign unique IDs to rows of a data table per multiple column values. Let's consider a simple example: library(data.table) DT = data.table(a=c(4,2,NA,2,NA), b=c("a","b","c","b","c"), c=1:5) a b c 1: 4 a 1 2: 2 b 2 3: NA c 3 4: 2 b 4 5: NA c 5 I'd like to generate IDs based on columns a and b and expect to get three IDs where 2nd and 4th row IDs are identical and 3rd and 5th rows have the same ID as well. I have seen two solutions but each are slightly incomplete: 1) Solution

问题 I'm trying to spit out a django page which lists all entries by the year they were created. So, for example: 2010: Note 4 Note 5 Note 6 2009: Note 1 Note 2 Note 3 It's proving more difficult than I would have expected. The model from which the data comes is below: class Note(models.Model): business = models.ForeignKey(Business) note = models.TextField() created = models.DateTimeField(auto_now_add=True) updated = models.DateTimeField(auto_now=True) class Meta: db_table = 'client_note'

问题 I have the same problem posted here: LINQ to Entities group-by failure using .date However, the answer is not 100% correct. It works in all cases except when different timezones are used. When different timezones are used, it also groups on timezones. Why? I managed to bypass this by using many entity functions. int localOffset= Convert.ToInt32( TimeZone.CurrentTimeZone.GetUtcOffset(DateTime.Now).TotalMinutes); var results = ( from perfEntry in db.entry where (....) select new { perfEntry

问题 I have the following model in Django. class StoreVideoEventSummary(models.Model): Customer = models.ForeignKey(GlobalCustomerDirectory, null=True, db_column='CustomerID', blank=True, db_index=True) Store = models.ForeignKey(Store, null=True, db_column='StoreID', blank=True, related_name="VideoEventSummary") Timestamp = models.DateTimeField(null=True, blank=True, db_index=True) PeopleCount = models.IntegerField(null=True, blank=True) I would like to find out the number of people entering the

问题 I had asked this question before: python pandas: applying different aggregate functions to different columns but the latest changes to pandas https://github.com/pandas-dev/pandas/pull/15931 mean that what I thought was an elegant and pythonic solution is deprecated, for reasons I genuinely fail to understand. The question was, and still is: when doing a groupby, how can I apply different aggregate functions to different fields (e.g. sum of x, avg of x, min of y, max of z, etc.) and rename the

问题 I'm definitely a noob with SQL, I've been busting my head to write a complex query with the following table structure in Postgresql: CREATE TABLE reports ( reportid character varying(20) NOT NULL, userid integer NOT NULL, reporttype character varying(40) NOT NULL, ) CREATE TABLE users ( userid serial NOT NULL, username character varying(20) NOT NULL, ) The objective of the query is to fetch the amount of report types per user and display it in one column. There are three different types of

问题 I spend a few hours trying to translate simple SQL to lambda LINQ SELECT ID, AVG(Score) FROM myTable GROUP BY ID Any idea? 回答1: from t in myTable group t by new { t.ID } into g select new { Average = g.Average(p => p.Score), g.Key.ID } or Lambda myTable.GroupBy(t => new {ID = t.ID}) .Select (g => new { Average = g.Average (p => p.Score), ID = g.Key.ID }) 回答2: The equivalent in Linq-to-Objects would be something like the below. var results = from row in myTable group row by row.Id into rows