Issue
Say, I have an array:
import numpy as np
x = np.array([0, 1, 2, 5, 6, 7, 8, 8, 8, 10, 29, 32, 45])
How can I calculate the 3rd standard deviation for it, so I could get the value of +3sigma
as shown on the picture below?
Typically, I use std = np.std(x)
, but to be honest, I don't know if it returns the 1sigma
value or maybe 2sigma
, or whatever. I'll very grateful for you help. Thank you in advance.
Solution
NumPy's std
yields the standard deviation, which is usually denoted with "sigma". To get the 2-sigma or 3-sigma ranges, you can simply multiply sigma with 2 or 3:
print [x.mean() - 3 * x.std(), x.mean() + 3 * x.std()]
Output:
[-27.545797458510656, 52.315028227741429]
For more detailed information, you might refer to the documentation, which states:
The standard deviation is the square root of the average of the squared deviations from the mean, i.e., std = sqrt(mean(abs(x - x.mean())**2)).
http://docs.scipy.org/doc/numpy/reference/generated/numpy.std.html
Answered By - Falko
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