rolling-computation

Consider a pandas DataFrame which looks like the one below A B C 0 0.63 1.12 1.73 1 2.20 -2.16 -0.13 2 0.97 -0.68 1.09 3 -0.78 -1.22 0.96 4 -0.06 -0.02 2.18 I would like to use the function .rolling() to perform the following calculation for t = 0,1,2 : Select the rows from t to t+2 Take the 9 values contained in those 3 rows, from all the columns. Call this set S Compute the 75th percentile of S (or other summary statistics about S ) For instance, for t = 1 we have S = { 2.2 , -2.16, -0.13, 0.97, -0.68, 1.09, -0.78, -1.22, 0.96 } and the 75th percentile is 0.97. I couldn't find a way to make

I have a function which takes an array and a value, and returns a value. I would like to apply it to my Series s on a rolling basis, so the array is always the rolling window. Here's a minimal example of what I've tried (unsuccessfully), using np.random.choice in place of my real function. I find lots of examples for finding rolling means and other built-in functions, but can't get it to work for my arbitrary lambda function. s = pd.Series([1,2,3,4,5,6,7,8,9]) rolling_window = s.rolling(3) First attempt: new_values = s.apply(lambda x: np.random.choice(rolling_window, size=1)) ValueError: a [a

I feel this should be easy in base R but I just can't figure it out. I have a simple dataframe, let's say it looks like this tbl <- read.table(text = "Field1 Field2 100 200 150 180 200 160 280 250 300 300 300 250", header = TRUE) Now, what I want to do is create a function that will apply a rolling % addition, something like: fn <- function(tbl, pct) {} which accepts the dataframe above as tbl . It adds a percentage fraction of the current row to the NEXT row down based on pct , and rolls this almost in a cumulative fashion. For example, fn(tbl$Field1, 0.1) would generate the following results

Say I want to run regressions per group whereby I want to use the last 5 year data as input for that regression. Then, for each next year, I would like to "shift" the input for that regression by one year (i.e., 4 observations). From those regressions I want to extract both the R2 and the fitted values/residuals, which I then need in subsequent regressions that follow similar notions. I have some code working using loops, but it is not really elegant nor efficient for large datasets. I assume there must be a nice plyr way for resolving this issue. # libraries # library(dplyr) library(broom) #

I have some real rainfall data recorded as the date and time, and the accumulated number of tips on a tipping bucket rain-gauge. The tipping bucket represents 0.5mm of rainfall. I want to cycle through the file and determine the variation in intensity (rainfall/time) So I need a rolling average over multiple fixed time frames: So I want to accumulate rainfall, until 5minutes of rain is accumulated and determine the intensity in mm/hour. So if 3mm is recorded in 5min it is equal to 3/5*60 = 36mm/hr. the same rainfall over 10 minutes would be 18mm/hr... So if I have rainfall over several hours I

I'm quite new to all the packages meant for calculating rolling averages in R and I hope you can show me in the right direction. I have the following data as an example: ms <- c(300, 300, 300, 301, 303, 305, 305, 306, 308, 310, 310, 311, 312, 314, 315, 315, 316, 316, 316, 317, 318, 320, 320, 321, 322, 324, 328, 329, 330, 330, 330, 332, 332, 334, 334, 335, 335, 336, 336, 337, 338, 338, 338, 340, 340, 341, 342, 342, 342, 342) correct <- c(1, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 0, 0) df <-