Why is this python code taking so long?

Alright, I have this python code that compares merge sort and selection sort, but it is taking forever. When done from n = 0 to 90,000 (the size of the list), it only takes about 3 seconds to sort the list. By this logic, it would take about 10 * 3 * 9 seconds (number of run throughs * duration * incremented run throughs [ we start with 10,000 then do 20,000, then 30,000, etc ] ). However, it takes far longer than that.

import time
import random

# Selection Sort Code #
def maxIndex(J):
    return J.index(max(J))

def swap(LCopy, i, j):
    temp = LCopy[i]
    LCopy[i] = LCopy[j]
    LCopy[j] = temp

# Implementation of selection sort
def selectionSort(L):
    for i in range(len(L)-1, 1, -1):
        j = maxIndex(L[0:i+1])
        swap(L, i, j)

# Merge Sort Code #
# Assumes that L[first:mid+1] is sorted and also
# that L[mid: last+开发者_StackOverflow中文版1] is sorted. Returns L with L[first: last+1] sorted

def merge(L, first, mid, last):

    i = first # index into the first half
    j = mid + 1 # index into the second half

    tempList = []

    # This loops goes on as long as BOTH i and j stay within their
    # respective sorted blocks
    while (i <= mid) and (j <= last):
        if L[i] <= L[j]:
            tempList.append(L[i])
            #print L[i], "from the first block"
            i += 1
        else:
            tempList.append(L[j])
            #print L[j], "from the second block"
            j += 1

    # If i goes beyond the first block, there may be some elements
    # in the second block that need to be copied into tempList.
    # Similarly, if j goes beyond the second block, there may be some
    # elements in the first block that need to be copied into tempList
    if i == mid + 1:
        tempList.extend(L[j:last+1])
        #print L[j:last+1], "some elements in second block are left over"
    elif j == last+1:
        tempList.extend(L[i:mid+1])
        #print L[i:mid+1], "some elements from first block are left over"

    L[first:last+1] = tempList
    #print tempList


# The merge sort function; sorts the sublist L[first:last+1]    
def generalMergeSort(L, first, last):
    # Base case: if first == last then it is already sorted

    # Recursive case: L[first:last+1] has size 2 or more
    if first < last:
        # divide step
        mid = (first + last)/2

        # conquer step
        generalMergeSort(L, first, mid)
        generalMergeSort(L, mid+1, last)

        # combine step
        merge(L, first, mid, last)

# Wrapper function
def mergeSort(L):
    generalMergeSort(L, 0, len(L)-1)



m = 10
n = 100000
n_increments = 9
baseList = [ random.randint(0,100) for r in range(n) ]


i = 0

while i < n_increments:
    j = 0
    sel_time = 0
    mer_time = 0

    while j < m:
        # Do a Selection Sort #
        x = time.clock()

        selectionSort( baseList)

        y = time.clock()

        sel_time += ( y - x )

        random.shuffle( baseList )

        # Do a Merge Sort #

        x = time.clock()

        mergeSort( baseList )

        y = time.clock()

        mer_time += ( y - x )

        random.shuffle( baseList )

        j += 1
    print "average select sort time for a list of", n, "size:", sel_time / m
    print "average merge sort time for a list of", n, "size:", mer_time / m

    j = 0
    i += 1
    n += 10000

Because you are using O(n^2) sorting algorithms. This means that if you double n, the algorithm takes 4 times longer to run. Note that you are starting at 100,000 not 10,000