I do it for my cat

It's definitely been a bit since I've seen this graphy. Anyone who has learnt about standard deviation knows this graph. Standard Deviation Standard deviation shows us how spread out all the values in a set are from the mean. The higher the standard deviation, the more spread out the values are over a wider range and the flatter this curve. In a normal distribution, most values are within 1 standard deviation from the mean(the green part of the graph). Apparently NumPy can calculate standard deviation too! import numpy numSet = [ *lots of numbers* ] numSetStdDev = numpy.std(numSet) Variance The variance also indicates how spread out the values in a set are. It measures the average degree to which each value differs from the mean. variance = standard deviation ^2 import numpy numSet = [ *lots of numbers * ] numSetVar = numpy.var(numSet) Source:  https://www.w3schools.com/python/python_ml_standard_deviation.asp

It's been quite a while since I last had to calculate the mean, median or mode of any set of numbers. But here I am, giving myself a refresher on statistical calculations. I'm learning some basics with the help of W3 schools. They always break concepts down so well :)  Calculating Mean We can do this the hard way, which sucks and I'm lazy and is ridiculous when we're looking at incredible large sets of numbers anyway. So instead, we're going to do it the easy way using the NumPy module in Python. import numpy numSet = [ *lots of numbers *] numSetMean = numpy.mean(numSet) Calculating Median This is even more annoying to calculate manually. You have to sort all the values from smallest to largest and search for the value in the middle. No one has time for that. Numpy can do this too. import numpy numSet = [ *lots of numbers* ] numSetMedian = numpy.median(numSet) Mode This is just as troublesome to calculate as the median. You're trying to get the value that appear...