aggregate

问题 This question already has answers here : Only keep min value for each factor level (9 answers) Subset data based on Minimum Value (2 answers) Closed last year . I would like to select the youngest person in each group and categorize it by gender so this is my initial data data1 ID Age Gender Group 1 A01 25 m a 2 A02 35 f b 3 B03 45 m b 4 C99 50 m b 5 F05 60 f a 6 X05 65 f a I would like to have this Gender Group Age ID m a 25 A01 f a 60 F05 m b 45 B03 f b 35 A02 So I tried with aggraeate

问题 Below is my code: sFieldList.Select(y => "Sum(Convert.ToDouble(iif(it[\""+y+"\"] == @0,0,it[\""+y+"\"]))) as "+y)and then var newSort = dataTable .AsEnumerable() .AsQueryable() .GroupBy("new("+gField+")", "it") .Select("new("+sField+",it.Key as Key, it as Data)",DBNull.Value); I wish to round of the result of Sum() method above to 2 decimal digits. How can I add it in this query itself? 回答1: Hopefully I'm understanding your problem correctly, but as far as I can tell, you just need an

问题 I have looked at the aggregate data questions on this forum and elsewhere - but I don't quite see an answer that helps me but that may be non-understanding on my behalf.I apologize. I have a huge amount of raw data test findings and I want to aggregate certain scores into one mean score. I can do this by creating a new variable in compute > transform but I cannot do this for 330+ by adding it 1 + 2 + 3 all by hand. My question is: How can I aggregate hundreds of scores and calculate a new

问题 UserDetails { "_id" : "5c23536f807caa1bec00e79b", "UID" : "1", "name" : "A", }, { "_id" : "5c23536f807caa1bec00e78b", "UID" : "2", "name" : "B", } UserProducts { "_id" : "5c23536f807caa1bec00e79c", "UPID" : "100", "UID" : "1" }, { "_id" : "5c23536f807caa1bec00e79c", "UPID" : "200", "UID" : "2" } Groups { "_id" : "5bb20d7556db6915846da55f", "members" : { "regularStudent" : [ "200" // UPID ], } } Step 1 I have to take UID from UserDetails check with UserProducts then take UPID from UserProducts

问题 I would like to display price SUM of all products related to particular room. Model : class Item(models.Model): product = models.CharField(max_length=150) quantity = models.DecimalField(max_digits=8, decimal_places=3) price = models.DecimalField(max_digits=7, decimal_places=2) purchase_date = models.DateField(null=True, blank=True) warranty = models.DecimalField(max_digits=4, decimal_places=1) comment = models.TextField() room = models.ForeignKey(RoomList) user = models.ForeignKey(User) class

问题 this is my model: const calendarSchema = new mongoose.Schema( { hotel: { type: mongoose.Schema.ObjectId, ref: "Hotel", required: true }, city: { type: mongoose.Schema.ObjectId, ref: "City" }, calendar: [ { date: Date, rooms: [ { room: { type: mongoose.Schema.ObjectId, ref: "Room", required: true }, price: { type: Number }, total: { type: Number }, reserved: { type: Number } } ] } ] }, { timestamps: true, versionKey: false } ); and sample document: "calendar": { "_id":

问题 I am building upon this question find duplicate, compare a condition, erase one row r to solve a more complicated case. Using the following reproducible example: ID1<-c("a1","a4","a6","a6","a5", "a1",NA,"a3", "a2","a2", "a8", "a9", "a9") ID2<-c("b8","b99","b5","b5","b2","b8" , "b7","b7", "b6","b6",NA,"b9",NA) Value1<-c(2,5,6,6,2,7, NA,5,NA,4,4,6,6) Value2<- c(23,51,63,64,23,23,5,6,4,NA,NA,4,NA) Year<- c(2004,2004,2004,2004,2005,2004,2008,2009, 2008,2009,2014,2016,2016) df<-data.frame(ID1,ID2

问题 I am trying to compile the following example with ModelSim Microsemi 10.2c: architecture example of assignment_to_an_aggregate is type vowel_type is (a, e, i, o, u); type consonant_type is (b, c, d, f, g); signal my_vowel: vowel_type; signal my_consonant: consonant_type; begin (my_vowel, my_consonant) <= (a, b); end; And it gives the following error: ** Error: assignment_to_aggregates.vhdl(40): (vcom-1349) Ambiguous types in signal assignment statement. Possible target types are: std.STANDARD

问题 How to create dynamic nested aggregations query in Elastic search.net based on multiple fields. These are my fields. keyword 1 keyword 2 keyword 3 This is my static query. ISearchResponse<T> response = client.Search<T> ( s => s .Index(indexName) .Type(docType) .Size(0) .RequestConfiguration(r => r.DisableDirectStreaming()) .Aggregations ( a => a.Terms("result_by_keyword_1", t => t.Field("keyword 1").Missing("N/A").Size(10000) .Aggregations ( ag => ag.Terms("result_by_keyword_2", tg => tg

问题 I am having some difficulty counting non-missing values by group through the function below (which also gives sd, and mean): test <- do.call(data.frame, aggregate(. ~ treatment, have, function(x) c(n = sum(!is.na(x)), mean = mean(x), sd = sd(x)))) It ends up giving me the number of non-missing for all columns in the dataframe instead of just a single column. I have been looking through SO for some advice and found this, this, and this helpful, but I can't figure out why the aggregate with the