If the sampling is with replacement, you can use this algorithm (implemented here in Python): import random items = [(10, “low”), (100, “mid”), (890, “large”)] def weighted_sample(items, n): total = float(sum(w for w, v in items)) i = 0 w, v = items[0] while n: x = total * (1 – random.random() ** (1.0 / … Read more
I generally break my projects into 4 pieces: load.R clean.R func.R do.R load.R: Takes care of loading in all the data required. Typically this is a short file, reading in data from files, URLs and/or ODBC. Depending on the project at this point I’ll either write out the workspace using save() or just keep things … Read more
One more solution, which works for both numeric & character/factor data: Mode <- function(x) { ux <- unique(x) ux[which.max(tabulate(match(x, ux)))] } On my dinky little machine, that can generate & find the mode of a 10M-integer vector in about half a second. If your data set might have multiple modes, the above solution takes the … Read more
Simple way to calculate median with MySQL
Distribution Fitting with Sum of Square Error (SSE) This is an update and modification to Saullo’s answer, that uses the full list of the current scipy.stats distributions and returns the distribution with the least SSE between the distribution’s histogram and the data’s histogram. Example Fitting Using the El Niño dataset from statsmodels, the distributions are … Read more