cumsum

I have a (long) list in which zeros and ones appear at random: list_a = [1, 1, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1] I want to get the list_b sum of the list up to where 0 appears where 0 appears, retain 0 in the list list_b = [1, 2, 3, 0, 1, 2, 0, 1, 0, 1, 2, 3] I can implement this as follows: list_b = [] for i, x in enumerate(list_a): if x == 0: list_b.append(x) else: sum_value = 0 for j in list_a[i::-1]: if j != 0: sum_value += j else: break list_b.append(sum_value) print(list_b) but the actual list's length is very long. So, I want to improve code for high speed. (if it is not readable) I change

I'm building a shiny app where I want to plot a dataset with one of the variables being a cumulative sum of another variable. The latter needs to be re-calculated every time the start date of dygraphs ' dyRangeSelector changes. Below is a basic code without cumsum calculations. Commented out code is what I tried, with no success. library(shinydashboard) library(stringr) library(zoo) library(dplyr) library(dygraphs) ui <-dashboardPage( dashboardHeader(), dashboardSidebar(), dashboardBody( uiOutput("Ui1") ) ) server <- function(input, output, session) { output$Ui1 <- renderUI({ # date range

I'd like to define the start and end indices of a moving integrator (sum) window based on the cumulative sum of the values of array . Each window should have a cumulative sum of less than or equal to a threshold. The end_index of this window is uniformly moving forward by 1 for the future windows, however the start_index is dependent on the values of array . start_index could move forward, stay the same or backwards (negative values), hence the size of this moving window is not fixed. For example: array = [ 1 0 2 1 1 2 0 0 1 2 0 1 0 1 1] ; With the start_index = 1 , the cumulative sum of array

I have a data frame df (which can be downloaded here ) referred to a register of companies that looks something like this: Provider.ID Local.Authority month year entry exit total 1 1-102642676 Warwickshire 10 2010 2 0 2 2 1-102642676 Bury 10 2010 1 0 1 3 1-102642676 Kent 10 2010 1 0 1 4 1-102642676 Essex 10 2010 1 0 1 5 1-102642676 Lambeth 10 2010 2 0 2 6 1-102642676 East Sussex 10 2010 5 0 5 7 1-102642676 Bristol, City of 10 2010 1 0 1 8 1-102642676 Liverpool 10 2010 1 0 1 9 1-102642676 Merton 10 2010 1 0 1 10 1-102642676 Cheshire East 10 2010 2 0 2 11 1-102642676 Knowsley 10 2010 1 0 1 12 1

If I have this I get an error sum(vector) == cumsum(vector)(length(vector)) >> Error: ()-indexing must appear last in an index expression. I know I can just do: Vec1 = cumsum(mat); sum(mat) == Vec1(length(mat)) which will return a logical 1. Is there an alternative to get everything on a single line? Well, if you are absolutely determined to do it in one line, sum(vec) == subsref(cumsum(vec),struct('type','()','subs',{{numel(vec)}})) But this is a borderline abuse of subsref , which is generally used for overloading the subscripting operators (i.e. {} , () , . ) in custom classes. 来源： https:/

I'm sort of newbie in numpy so I'm sorry if this question was already asked. I'm looking for a vectorization solution which enable to run multiple cumsum of different size within a one dimension numpy array. my_vector=np.array([1,2,3,4,5]) size_of_groups=np.array([3,2]) I would like something like np.cumsum.group(my_vector,size_of_groups) [1,3,6,4,9] I do not want a solution with loops. Either numpy functions or numpy operations. Not sure about numpy, but pandas can do this pretty easily with a groupby + cumsum : import pandas as pd s = pd.Series(my_vector) s.groupby(s.index.isin(size_of