np.average takes an optional weight parameter. If it is not supplied they are equivalent. Take a look at the source code: Mean, Average np.mean: try: mean = a.mean except AttributeError: return _wrapit(a, ‘mean’, axis, dtype, out) return mean(axis, dtype, out) np.average: … if weights is None : avg = a.mean(axis) scl = avg.dtype.type(a.size/avg.size) else: #code … Read more
In MariaDB / MySQL: SELECT AVG(dd.val) as median_val FROM ( SELECT d.val, @rownum:=@rownum+1 as `row_number`, @total_rows:=@rownum FROM data d, (SELECT @rownum:=0) r WHERE d.val is NOT NULL — put some where clause here ORDER BY d.val ) as dd WHERE dd.row_number IN ( FLOOR((@total_rows+1)/2), FLOOR((@total_rows+2)/2) ); Steve Cohen points out, that after the first pass, … Read more
You might be interested in the SciPy Stats package. It has the percentile function you’re after and many other statistical goodies. percentile() is available in numpy too. import numpy as np a = np.array([1,2,3,4,5]) p = np.percentile(a, 50) # return 50th percentile, e.g median. print p 3.0 This ticket leads me to believe they won’t … Read more
If you’re using scikit-learn you can use sklearn.preprocessing.normalize: import numpy as np from sklearn.preprocessing import normalize x = np.random.rand(1000)*10 norm1 = x / np.linalg.norm(x) norm2 = normalize(x[:,np.newaxis], axis=0).ravel() print np.all(norm1 == norm2) # True
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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
From Wikipedia: Studies by IBM in the 1990s suggest that computers typically experience about one cosmic-ray-induced error per 256 megabytes of RAM per month.[15] This means a probability of 3.7 × 10-9 per byte per month, or 1.4 × 10-15 per byte per second. If your program runs for 1 minute and occupies 20 MB … Read more