statistics.py

def getMean(numbers):
    if len(numbers) == 0:
        return -1
    sum = 0
    for i in range(len(numbers)):
        sum = numbers[i] + sum
    mean = sum / len(numbers)
    return mean


def getMedian(numbers):
    numbers.sort()

    if len(numbers) % 2 == 0:
        mid = len(numbers) // 2
        previous = mid - 1
        median = (numbers[mid] + numbers[previous]) / 2

        return median

    else:
        return numbers[len(numbers) // 2]


def getMode(lst):
    uniquelst = []

    for i in range(len(lst)):
        if lst[i] not in uniquelst:
            uniquelst.append(lst[i])

    maxrep = 0
    mode = uniquelst[0]
    for i in uniquelst:
        rep = lst.count(i)
        if rep > maxrep:
            maxrep = rep
            mode = i
    return mode


#main program
def getRange(numbers):
    numbers.sort()
    range = numbers[-1] - numbers[0]
    return range


def getMeanDeviation(numbers):
    mean = getMean(numbers)
    deviations = []
    for i in range(len(numbers)):
        deviation = abs(numbers[i] - mean)
        deviations.append(deviation)
    meanDeviation = getMean(deviations)
    return meanDeviation


def getMeanDeviationAboutMedian(numbers):
    deviations = []
    median = getMedian(numbers)
    for i in range(len(numbers)):
        deviation = abs(numbers[i] - median)
        deviations.append(deviation)
    meanDeviationAboutMedian = getMean(deviations)
    return meanDeviationAboutMedian


def getStandardDeviation(numbers):
    mean = getMean(numbers)
    deviations = []
    for i in range(len(numbers)):
        deviation = (numbers[i] - mean)**2
        deviations.append(deviation)
    standardDeviation = (getMean(deviations))**0.5
    return standardDeviation