The parameter represents the delta degrees of freedom. That said, the function allows you to calculate both the sample and the population standard deviations using the ddof= parameter. NumPy, on the other hand, provides only a single function to calculate the standard deviation: np.std(). Using NumPy to Calculate the Standard Deviation As the number of data points grows, the difference between these two values will decrease. You can see from the sample datasets above, that the standard deviations are quite different. This can be beneficial for readers of your code, letting them know what type of data you’re working with. We can see that by using the statistics library, we’re able to explicitly identify what type of standard deviation we’re calculating. Print('Population standard deviation: ', population_std) Print('Sample standard deviation: ', sample_std) Let’s take a look at how we can calculate the standard deviation using both of these functions: # Calculating the Standard Deviation with statistics The library allows you to use two different functions: The Python statistics library is part of the standard library, which means that you don’t have to install anything additional. Let’s explore these three libraries! Using Python statistics to Calculate the Standard Deviation in Python
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