apply

问题 I have a array with following content: > head(MEAN) 1901DJF 1901JJA 1901MAM 1901SON 1902DJF 1902JJA -0.45451556 -0.72922229 -0.17669396 -1.12095590 -0.86523850 -0.04031273 This should be a time series with seasonal mean values from 1901 to 2009. The problem is that the generated column heads are strictly alphabetically ordered. However, in terms of season this doesn't make to much sense, e.g. JJA (june, july, august) is leading MAM (march, april, may). How could I switch each MAM and JJA

问题 Is there a way to apply rollapply() backwards say from 100 to 1 instead of 1 to 100 or should I sort my data first and then apply rollaplly()? 回答1: Try this z = 1:100 #Normal rollapply rollapply(z, 2, mean) #Reverese rollapply(z[length(z):1], 2, mean) 来源： https://stackoverflow.com/questions/24056809/rollapply-backwards-in-r

问题 Hi I have a data frame as follow: df = pd.DataFrame() df['Team1'] = ['A','B','C','D','E','F','A','B','C','D','E','F'] df['Score1'] = [1,2,3,1,2,4,1,2,3,1,2,4] df['Team2'] = ['U','V','W','X','Y','Z','U','V','W','X','Y','Z'] df['Score2'] = [2,1,2,2,3,3,2,1,2,2,3,3] df['Match'] = df['Team1'] + ' Vs '+ df['Team2'] df['Match_no']= [1,2,3,4,5,6,1,2,3,4,5,6] df['model'] = ['ELO','ELO','ELO','ELO','ELO','ELO','xG','xG','xG','xG','xG','xG'] winner = df.Score1>df.Score2 df['winner'] = np.where(winner

问题 I want to fit models and pull out specific parameters split by grouping factors (fac1 and fac2 below) or subsets. My problem is that when sapply outputs the correct parameters, I'm stuck with a list where the elements are named as combinations. What I want to get is a data.frame where I have a column for each factor with the appropriate label. I want to do this in base R . Notice, the answer needs to be general and not for the specific names used in this case. The answer shouldn't be hindered

问题 I would like to use a function from the apply family (in R) to apply a function of two arguments to two matrices. I assume this is possible. Am I correct? Otherwise, it would seem that I have to put the two matrices into one, and redefine my function in terms of the new matrix. Here's an example of what I'd like to do: a <- matrix(1:6,nrow = 3,ncol = 2) b <- matrix(7:12,nrow = 3,ncol = 2) foo <- function(vec1,vec2){ d <- sample(vec1,1) f <- sample(vec2,1) result <- c(d,f) return(result) } I

问题 I have two data frames --- table A is the pattern table, and table B is the name table. I want to subset table B, where it matches the pattern in table a. A <- data.frame(pattern = c("aa", "bb", "cc", "dd")) B <- data.frame(name = "aa1", "bb1", "abc", "def" ,"ddd") I'm trying to do a for loop looks like: for (i in 1:nrow(A)){ for (j in 1:nrow(B)){ DT <- data.frame(grep(A$pattern[i], B$name[j], ignore.case = T, value = T)) }} And I want my resulting table DT to only contains aa1 , bb1 , and

问题 I have two panda dataframe: price and sales dataframe. price dataframe records price for each product (columns) in each year (index) |a |b |c |d |e | 2018|3.2|4.5|5.6|7.8|8.1| 2017|6.2|1.5|2.6|7.8|2.1| 2016|2.2|9.5|0.6|6.8|4.1| 2015|2.2|6.5|7.6|7.8|2.1| sales dataframe (see below) records sales for each product (columns) in each year (index) |a |b |c |d |e | 2018|101|405|526|108|801| 2017|601|105|726|308|201| 2016|202|965|856|408|411| 2015|322|615|167|458|211| I would like to calculate

问题 I need to compute the running cumsum per group in R but the window over which to cumsum must only be the last 3 observations: If for example I have a table with a person's name, a date and a score as follow: Name Date Score 1 John 2017-01-01 4 2 John 2017-01-02 5 3 John 2017-01-03 3 4 John 2017-01-04 1 5 John 2017-01-05 4 6 John 2017-01-06 4 7 Ben 2017-01-01 4 8 Ben 2017-01-02 4 9 Ben 2017-01-03 5 10 Ben 2017-01-04 2 11 Ben 2017-01-05 3 12 Ben 2017-01-06 4 13 Ben 2017-01-07 4 14 Ben 2017-01

问题 A normal matrix would be 2-dimension matrix. But, I can initialise: a<-array(0,dim=c(2,3,4,5)) Which is a 2*4*5*3 matrix, or array. Command apply(a,c(2,3),sum) will give a 4*5 array, contain the sum over elements in the 1st and 4th dimension. Why it that? As far as I know, in the apply function, 1 indicates rows, 2 indicates columns, but what does 3 mean here? We need some abstraction here. 回答1: The easiest way to understand apply on an array is to try some examples. Here's some data modified

问题 I have a dataframe in pandas with four columns. The data consists of strings. Sample: A B C D 0 2 asicdsada v:cVccv u 1 4 ascccaiiidncll v:cVccv:ccvc u 2 9 sca V:c u 3 11 lkss v:cv u 4 13 lcoao v:ccv u 5 14 wuduakkk V:ccvcv: u I want to replace the string 'u' in Col D with the string 'a' if Col C in that row contains the substring 'V' (case sensitive). Desired outcome: A B C D 0 2 asicdsada v:cVccv a 1 4 ascccaiiidncll v:cVccv:ccvc a 2 9 sca V:c a 3 11 lkss v:cv u 4 13 lcoao v:ccv u 5 14