Python Problem: Continue from (https://www.chegg.com/homework-help/questions-and-answers/problem-2-computing-standard-deviation-problem-write-function-standarddeviation-takes-list-q44265904?trackid=t95iz_bh)

Problem 3: A variance method for the EfficientAveragerator class Below is the EfficientAveragerator class we saw during lecture, with one new property for you to implement: variance. In EfficientAveragerator, we compute average and standard deviation in an incremental fashion, so that we don't have to recompute everything when a new value arrives in our data set. We want to compute variance using the same incremental approach. Fortunately, EfficientAveragerator already has everything we need to write such an incremental definition of variance. Fill in the definition of the variance property in the cell below. It should take no more than 1-2 lines of code, and does not require NumPy or any other fancy library. (Hint: it will look a lot like the definition of std.) [] import numpy as np class EfficientAveragerator: def _init__(self): self. sum_x = 0. self.sum_x_sq = 0. self.n = 0 def add(self, x): # We compute the sum of the x, to compute their average. self.sum_x += x # Sum of the x^2, so we can later compute the average of the x^2. self.sum_x_59 += x * x self.n += 1 @property def avg(self): return self.sum_x / self.n @property def variance(self): "Returns the variance of all the data points seen so far." # YOUR CODE HERE raise NotImplementedError() @property def std(self): average = self.avg # To avoid calling self.avg twice. return np.sqrt(self. sum_x_sq / self.n - average * average)