Why computing standard deviation in pandas and NumPy yields different results?

Why computing standard deviation in pandas and NumPy yields different results?

Curious? Let’s talk about statistics, populations, and samples…

How many of you have noticed that when you compute standard deviation using pandas and compare it to a result of NumPy function you will get different numbers?

I bet some of you did not realize this fact. And even if you did you’re maybe asking: Why?

In this short article, we will demonstrate that:

standard deviations results are indeed different using both libraries (at least at the first glance),
discuss why is that so (focusing on populations, samples, and how this influences calculation of standard deviation for each library)
and finally show you how to obtain same results using pandas and NumPy (in the end they should agree on such a simple computation that standard deviation is)

Let’s get started.