percentile

I've been surprisingly unable to find an nth percentile function for postgresql. I am using this via mondrian olap tool so i just need an aggregate function which returns a 95th percentile. I did find this link: http://www.postgresql.org/message-id/162867790907102334r71db0227jfa0e4bd96f48b8e4@mail.gmail.com But for some reason the code in that percentile function is returning nulls in some cases with certain queries. I've checked the data and there's nothing odd in the data that would seem to cause that! alfonx With PostgreSQL 9.4 there is native support for percentiles now, implemented in

A quick one for you, dearest R gurus: I'm doing an assignment and I've been asked, in this exercise, to get basic statistics out of the infert dataset (it's in-built), and specifically one of its columns, infert$age . For anyone not familiar with the dataset: > table_ages # Which is just subset(infert, select=c("age")); age 1 26 2 42 3 39 4 34 5 35 6 36 7 23 8 32 9 21 10 28 11 29 ... 246 35 247 29 248 23 I've had to find median values of the column, variance, skewness, standard deviation which were all okay, until I was asked to find the column "percentiles" . I haven't been able to find

I have a program that needs to repeatedly compute the approximate percentile (order statistic) of a dataset in order to remove outliers before further processing. I'm currently doing so by sorting the array of values and picking the appropriate element; this is doable, but it's a noticable blip on the profiles despite being a fairly minor part of the program. More info: The data set contains on the order of up to 100000 floating point numbers, and assumed to be "reasonably" distributed - there are unlikely to be duplicates nor huge spikes in density near particular values; and if for some odd

In an algorithm I have to calculate the 75th percentile of a data set whenever I add a value. Right now I am doing this: Get value x Insert x in an already sorted array at the back swap x down until the array is sorted Read the element at position array[array.size * 3/4] Point 3 is O(n), and the rest is O(1), but this is still quite slow, especially if the array gets larger. Is there any way to optimize this? UPDATE Thanks Nikita! Since I am using C++ this is the solution easiest to implement. Here is the code: template<class T> class IterativePercentile { public: /// Percentile has to be in

I'm plotting some data from various tests. Sometimes in a test I happen to have one outlier (say 0.1), while all other values are three orders of magnitude smaller. With matplotlib, I plot against the range [0, max_data_value] How can I just zoom into my data and not display outliers, which would mess up the x-axis in my plot? Should I simply take the 95 percentile and have the range [0, 95_percentile] on the x-axis? There's no single "best" test for an outlier. Ideally, you should incorporate a-priori information (e.g. "This parameter shouldn't be over x because of blah..."). Most tests for

Is there a way to use the numpy.percentile function to compute weighted percentile? Or is anyone aware of an alternative python function to compute weighted percentile? thanks! Unfortunately, numpy doesn't have built-in weighted functions for everything, but, you can always put something together. def weight_array(ar, weights): zipped = zip(ar, weights) weighted = [] for i in zipped: for j in range(i[1]): weighted.append(i[0]) return weighted np.percentile(weight_array(ar, weights), 25) Alleo Completely vectorized numpy solution Here is the code I'm using. It's not an optimal one (which I'm