Different std in pandas vs numpy

Question

The standard deviation differs between pandas and numpy. Why and which one is the correct one? (the relative difference is 3.5% which should not come from rounding, this is

Answer 1

In a nutshell, neither is "incorrect". Pandas uses the unbiased estimator (N-1 in the denominator), whereas Numpy by default does not.

To make them behave the same, pass ddof=1 to numpy.std().

For further discussion, see

• Can someone explain biased/unbiased population/sample standard deviation?
• Population variance and sample variance.
• Why divide by n-1?

Answer 2

For pandas to performed the same as numpy, you can pass in the ddof=0 parameter, so df.std(ddof=0).

This short video explains quite well why n-1 might be preferred for samples. https://www.youtube.com/watch?v=Cn0skMJ2F3c