The NumPy function np.std takes an optional parameter ddof: “Delta Degrees of Freedom”. By default, this is 0. Set it to 1 to get the MATLAB result: >>> np.std([1,3,4,6], ddof=1) 2.0816659994661326 To add a little more context, in the calculation of the variance (of which the standard deviation is the square root) we typically divide … Read more
A solution with ggplot2 : qplot(x,y)+geom_errorbar(aes(x=x, ymin=y-sd, ymax=y+sd), width=0.25)
Try this R> sd(c(2,4,4,4,5,5,7,9)) * sqrt(7/8) [1] 2 R> and see the rest of the Wikipedia article for the discussion about estimation of standard deviations. Using the formula employed ‘by hand’ leads to a biased estimate, hence the correction of sqrt((N-1)/N). Here is a key quote: The term standard deviation of the sample is used … Read more
Since you asked for a one-liner: rnorm2 <- function(n,mean,sd) { mean+sd*scale(rnorm(n)) } r <- rnorm2(100,4,1) mean(r) ## 4 sd(r) ## 1
You can make your own extension calculating it public static class Extensions { public static double StdDev(this IEnumerable<double> values) { double ret = 0; int count = values.Count(); if (count > 1) { //Compute the Average double avg = values.Average(); //Perform the Sum of (value-avg)^2 double sum = values.Sum(d => (d – avg) * (d … Read more
How about the following short “manual calculation”? def weighted_avg_and_std(values, weights): “”” Return the weighted average and standard deviation. values, weights — Numpy ndarrays with the same shape. “”” average = numpy.average(values, weights=weights) # Fast and numerically precise: variance = numpy.average((values-average)**2, weights=weights) return (average, math.sqrt(variance))