cumsum

问题 I currently am trying to graph 2 columns in a data frame I created using ggplot I am graphing date vs. numeric value. I used dplyr library to create the dataframe: is_china <- confirmed_cases_worldwide %>% filter(country == "China", type=='confirmed') %>% mutate(cumu_cases = cumsum(cases)) I believe the reason is due to the y value being a result column of cumsum function, but am unsure The table looks something like this, the last column being the targeted y value: 2020-01-22 NA China 31

问题 I have a pandas dataframe that looks as follows: ID round player1 player2 1 1 A B 1 2 A C 1 3 B D 2 1 B C 2 2 C D 2 3 C E 3 1 B C 3 2 C D 3 3 C A The dataframe contains sport match results, where the ID column denotes one tournament, the round column denotes the round for each tournament, and player1 and player2 columns contain the names of players that played against eachother in the respective round . I now want to cumulatively count the tournament participations for, say, player A . In

问题 I have a numpy boolean array w=np.array([True,False,True,True,False,False,False]) I would like to get the index of the first time there are at n_at_least false values. For instance here `n_at_least`=1 -> desired_index=1 `n_at_least`=3 -> desired_index=4 I have tried np.cumsum(~w) which does increase every time a False value is encountered. However, when True is encountered the counter is not starting from 0 again so I only get the total count of False elements rather than the count of the

问题 Suppose I have a scipy.sparse.csr_matrix representing the values below [[0 0 1 2 0 3 0 4] [1 0 0 2 0 3 4 0]] I want to calculate the cumulative sum of non-zero values in-place, which would change the array to: [[0 0 1 3 0 6 0 10] [1 0 0 3 0 6 10 0]] The actual values are not 1, 2, 3, ... The number of non-zero values in each row are unlikely to be the same. How to do this fast? Current program: import scipy.sparse import numpy as np # sparse data a = scipy.sparse.csr_matrix( [[0,0,1,2,0,3,0,4

问题 I'm trying to follow some rules about when to group data to chart. How would I go from this data frame: # A tibble: 11 x 8 assay year qtr invalid valid total_assays hfr predicted_inv <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 test_case 2016. 1. 2. 36. 38. 0.0350 1.33 2 test_case 2016. 2. 1. 34. 35. 0.0350 1.23 3 test_case 2016. 3. 0. 25. 25. 0.0350 0.875 4 test_case 2016. 4. 2. 23. 25. 0.0350 0.875 5 test_case 2017. 1. 1. 29. 30. 0.0350 1.05 6 test_case 2017. 2. 2. 24. 26. 0.0350 0.910

问题 I'm trying to follow some rules about when to group data to chart. How would I go from this data frame: # A tibble: 11 x 8 assay year qtr invalid valid total_assays hfr predicted_inv <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> 1 test_case 2016. 1. 2. 36. 38. 0.0350 1.33 2 test_case 2016. 2. 1. 34. 35. 0.0350 1.23 3 test_case 2016. 3. 0. 25. 25. 0.0350 0.875 4 test_case 2016. 4. 2. 23. 25. 0.0350 0.875 5 test_case 2017. 1. 1. 29. 30. 0.0350 1.05 6 test_case 2017. 2. 2. 24. 26. 0.0350 0.910

问题 This is code for i = 1 : 5 b = i; a=cumsum(b); end fprintf('%f \n', a); I expected 1 + 2 + 3 + 4 + 5 = 15 so I would print 15 at the end. But it output 5.000000. If i code "a = cumsum (b)" outside the for loop, it will not be calculated How can I get the value I want 1 + 2 + 3 + 4 + 5? Thanks you 回答1: cumsum performs something like integration, where each element of the output is the sum of all elements up to that position (including) of the input vector. Your code doesn't work because you

问题 Adding on to what's already being said or commented on this post: Cumulative sum until maximum reached, then repeat from zero in the next row I've a similar dataframe which has about 50k+ observations. This dataframe was being read from a csv file and is an outcome of several operations already performed on it. Pasting a sample here: Home Date Time Appliance Run value 679 2 1/21/2017 1:30:00 0 1 0 680 2 1/21/2017 1:45:00 0 1 0 681 2 1/21/2017 2:00:00 0 1 0 682 2 1/21/2017 2:15:00 0 1 0 683 2

问题 I have a pandas df with word counts corresponding to articles. I want to be able to be able to add another column MERGED that is based on groups of articles that have a minimum cumulative sum of 'min_words'. df = pd.DataFrame([[ 0, 6], [ 1, 10], [ 3, 5], [ 4, 7], [ 5, 26], [ 6, 7], [ 9, 4], [ 10, 133], [ 11, 42], [ 12, 1]], columns=['ARTICLE', 'WORD_COUNT']) df Out[15]: ARTICLE WORD_COUNT 0 0 6 1 1 10 2 3 5 3 4 7 4 5 26 5 6 7 6 9 4 7 10 133 8 11 42 9 12 1 So then if min_words = 20 this is the

问题 Sample data dfData <- data.frame(ID = c(1, 2, 3, 4, 5), DistA = c(10, 8, 15, 22, 15), DistB = c(15, 35, 40, 33, 20), DistC = c(20,40,50,45,30), DistD = c(60,55,55,48,50)) ID DistA DistB DistC DistD 1 1 10 15 20 60 2 2 8 35 40 55 3 3 15 40 50 55 4 4 22 33 45 48 5 5 15 20 30 50 I have some IDs for which there are four columns which measure cumulative distance. I want to create new column that gives the actual distance for each column i.e. subtract the next column from previous column. For e.g.