aggregate

问题 I have some tables that contain data about players and the games they have bowled this season in a bowling center during their leagues. What this particular query is used for is to sort the top X averages this year for men and for women. I have all of this down, but I still have a problem in some particular case when some players play in multiple leagues and have more than one of their averages in the top X. Obviously, I only want to list the best average for a given player, so if Player A

问题 I was developing a little application with spring data mongo and angularjs . Here is model : public class Lease { @Id private String id; private long created; private Lessor lessor; private Lessee lessee; } public class Payment { @Id private String id; private Integer month; private Integer year; private Long amount; private String leaseId; } I did not embed Payment model as I need it to have an id and edit it in its own form. On the main page of the app it represents a list of leases with

问题 I was developing a little application with spring data mongo and angularjs . Here is model : public class Lease { @Id private String id; private long created; private Lessor lessor; private Lessee lessee; } public class Payment { @Id private String id; private Integer month; private Integer year; private Long amount; private String leaseId; } I did not embed Payment model as I need it to have an id and edit it in its own form. On the main page of the app it represents a list of leases with

问题 Rails 2.3.4 I have searched google, and have not found an answer to my dilemma. For this discussion, I have two models. Users and Entries. Users can have many Entries (one for each day). Entries have values and sent_at dates. I want to query and display the average value of entries for a user BY DAY OF WEEK. So if a user has entered values for, say, the past 3 weeks, I want to show the average value for Sundays, Mondays, etc. In MySQL, it is simple: SELECT DAYOFWEEK(sent_at) as day, AVG(value

问题 I have a pandas data frame with a column 'id' and a column 'value'. It is already sorted by first id (ascending) and then value (descending). What I need is the top 10 values per id. I assumed that something like the following would work, but it doesn't: df.groupby("id", as_index=False).aggregate(lambda (index,rows) : rows.iloc[:10]) What I get is just a list of ids, the value column (and other columns that I omitted for the question) aren't there anymore. Any ideas how it might be done,

问题 I have a table that looks like this: Conversion_Date User_Name Last_Date_Touch Touch_Count 7/15/2017 A 6/17/2017 1 7/16/2017 B 6/24/2017 2 7/19/2017 A 6/20/2017 1 7/19/2017 C 6/29/2017 1 I want to get the sum of Touch_Count from the last 30 days for each Conversion_Date by User_Name in SQL. The only thing I could think of at first was to figure out the time window for each conversion date going back 30 days and then I'm not sure how to aggregate this to get the final result. First it was

问题 I am completely new to MongoDB and wanted to compare query performance of a NoSQL data model relative to its relational database counter part. I wrote this into MongoDB shell // Make 10 businesses // Each business has 10 locations // Each location has 10 departments // Each department has 10 teams // Each team has 100 employees (new Array(10)).fill(0).forEach(_=> db.businesses.insert({ "name":"Business Name", "locations":(new Array(10)).fill(0).map(_=>({ "name":"Office Location", "departments

问题 When I need to apply multiple functions to multiple columns sequentially and aggregate by multiple columns and want the results to be bound into a data frame I usually use aggregate() in the following manner: # bogus functions foo1 <- function(x){mean(x)*var(x)} foo2 <- function(x){mean(x)/var(x)} # for illustration purposes only npk$block <- as.numeric(npk$block) subdf <- aggregate(npk[,c("yield", "block")], by = list(N = npk$N, P = npk$P), FUN = function(x){c(col1 = foo1(x), col2 = foo2(x))

问题 I have a df in R in the following format. What could be the easiest way to aggregate this on an hourly time interval basis, currently it is every minute theTime24 Amount 988 2015-02-04 23:53:00 2 989 2015-02-04 23:55:00 1 990 2015-02-04 23:56:00 3 991 2015-02-04 23:57:00 2 992 2015-02-04 23:58:00 1 993 2015-02-04 23:59:00 2 回答1: (Since the user (@user20650) who answered added it as a comment, I am answering my own question) aggregate(Amount ~ format(as.POSIXct(countByCountryTime$theTime24), "

问题 How do I specify custom aggregating functions so that they behave correctly when used in list arguments of pandas.DataFrame.aggregate ? Given a two-column dataframe in pandas ... import pandas as pd import numpy as np df = pd.DataFrame(index=range(10)) df['a'] = [ 3 * x for x in range(10) ] df['b'] = [ 1 -2 * x for x in range(10) ] ... aggregating over a list of aggregation function specs is not a problem: def ok_mean(x): return x.mean() df.aggregate(['mean', np.max, ok_mean]) a b mean 13.5