cumulative-sum

问题 After trying a lot of approaches and formulas I decided to ask this question. See this Matrix visual: WeekDate is column in a table called Planning . It's related to another date time column in another table called Export . Export table only has values for the dates: 23-Dec-19, 30-Dec-19 and 06-Jan-20 whereas Planning table has dates spanning multiple weeks in the past and in the future. Cumulative Plan Count is calculating correctly as long as there are matching dates between the tables

问题 My dataset has as features: players IDs, weeks and points. I want to calculate the mean of points for previous weeks, but not all past weeks, just to the last 5 or less (if the current week is smaller than 5). Example: For player_id = 5, week = 7, the result will be the average of POINTS for player_id = 5 and weeks 2, 3, 4, 5 and 6. The following code already does the average for all previous week, so I need an adaptation to make it for just 5 previous week. player_id<-c(rep(1,30),rep(2,30)

问题 From every references that I search how to do cumulative sum / running total. they said it's better using windows function, so I did select grandtotal,sum(grandtotal)over(order by agentname) from call but I realize that the results are okay as long as the value of each rows are different. Here is the result : Is There anyway to fix this? 回答1: You might want to review the documentation on window specifications (which is here). The default is "range between" which defines the range by the

问题 I would like to do a cumulative sum on a column, but reset the aggregated value whenever a 0 is encountered Here is an example of what i try to do : This dataset : pk price 1 10 2 15 3 0 4 10 5 5 Gives this: pk price 1 10 2 25 3 0 4 10 5 15 回答1: In SQL Server 2008, you are severely limited because you cannot use analytic functions. The following is not efficient, but it will solve your problem: with tg as ( select t.*, g.grp from t cross apply (select count(*) as grp from t t2 where t2.pk <=

问题 A simplified version of my data set would look like: depth value 1 a 1 b 2 a 2 b 2 b 3 c I would like to make a new data set where, for each value of "depth", I would have the cumulative number of unique values, starting from the top. e.g. depth cumsum 1 2 2 2 3 3 Any ideas as to how to do this? I am relatively new to R. 回答1: I find this a perfect case of using factor and setting levels carefully. I'll use data.table here with this idea. Make sure your value column is character (not an

问题 This question already has answers here : Calculating cumulative sum for each row (5 answers) Vector of cumulative sums in R (1 answer) Closed 3 years ago . I have data containing columns biweek and Total , I want to get cumulative sum on biweek basis. My data is like: biweek Total 0 3060.913 1 4394.163 2 3413.748 3 2917.548 4 3442.055 5 3348.398 6 1771.722 and I want to get output like : biweek Total 0 3060.913 1 7455.076 2 10868.824 3 13786.372 4 17228.427 5 20576.825 6 22348.547 So it there

问题 This question already has answers here : Calculating cumulative sum for each row (5 answers) Vector of cumulative sums in R (1 answer) Closed 3 years ago . I have data containing columns biweek and Total , I want to get cumulative sum on biweek basis. My data is like: biweek Total 0 3060.913 1 4394.163 2 3413.748 3 2917.548 4 3442.055 5 3348.398 6 1771.722 and I want to get output like : biweek Total 0 3060.913 1 7455.076 2 10868.824 3 13786.372 4 17228.427 5 20576.825 6 22348.547 So it there

问题 Below is a test table for simplification of what I am looking to achieve in a query. I am attempting to create a query using a running sum which inserts into column b that last sum result that was not null. If you can imagine, i'm looking to have a cumulative sum the purchases of a customer every day, some days no purchases occurs for a particular customer thus I want to display the latest sum for that particular customer instead of 0/null. CREATE TABLE test (a int, b int); insert into test

问题 I have a forecast algorithm that works the trend of time series up to a given horizon using the following code: import numpy as np horizon = 91 phi = 0.2 trend = -0.004 trend_up_to_horizon = np.cumsum(phi ** np.arange(horizon) + 1) * self.trend In this example the first two trend_up_horizon values are: array([-0.008 , -0.0128]) Is there a computationally faster way to achieve this? At the moment this takes a long time as I guess using the np.cumsum method and ** operator are expensive. Thanks

问题 I've build an MDX calculated member that returns the Cumulative Sales over time. - the query returns correct results if no filters applied or the user filters with 1 month my problem is that when the user selects multiple months the query returns the cumulative for the whole year here is what i wrote using Dynamic Set, Calculated Member and Scope: Dynamic Set: PeriodToDate = EXISTING PeriodsToDate([Date].[Calendar - YQMD].[Year],[Date].[Calendar - YQMD].CurrentMember) Calculated Member: (