Solution

class normalPlotter:
    def __init__(self,dist):
        self.dist = dist
    def plot(self,xPoint,end,zScore = False):
        xVals = list(range(end+1))
        yVals = [self.dist.pdf(x) for x in xVals]
        xVals2 = list(range(xPoint+1))
        yVals2 = [self.dist.pdf(x) for x in xVals2]
        if zScore:
            xVals = [(x-self.dist.mean())/self.dist.std() for x in xVals]
            xVals2 = [(x-self.dist.mean())/self.dist.std() for x in xVals2]
        plt.plot(xVals,yVals)
        if zScore:
            plt.xlabel("Z Score")
        else:
            plt.xlabel("Value")
        plt.ylabel("Density")
        plt.fill_between(xVals2, yVals2)
        plt.show()

        xVals = list(range(end+1))
        yVals = [self.dist.cdf(x) for x in xVals]
        if zScore:
            xVals = [(x-self.dist.mean())/self.dist.std() for x in xVals]
        plt.plot(xVals,yVals)
        plt.xlabel("Z Score")
        plt.ylabel("Cummulative Percentage")

        if zScore:
            plt.xlabel("Z Score")
            plt.plot((xPoint-self.dist.mean())/self.dist.std(),dist.cdf(xPoint),"ro")

        else:
            plt.xlabel("Value")
            plt.plot(xPoint,dist.cdf(xPoint),"ro")

        plt.show()

x = normalPlotter(scipy.stats.norm(400,100))
x.plot(350,800)
x.plot(350,800,zScore=True)

The only new thing here may be how we use zScore = False in the plot function. This sets up an optional parameter, it is by default false, but the user could change it to true.

Source Code