code/Ioannou_Georgios_Code.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2eedd3f9",
   "metadata": {},
   "source": [
    "# Ioannou_Georgios"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "156bb1f5",
   "metadata": {},
   "source": [
    "## Copyright © 2021 by Georgios Ioannou"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "64ed8b85",
   "metadata": {},
   "source": [
    "## <center>Libraries</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "68e47ce3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import libraries\n",
    "\n",
    "from math import log             # Provides the log function\n",
    "import time                      # Provides the time function\n",
    "import sys                       # Provides the float_info function\n",
    "import pandas as pd              # Provides the functions needed to create the tables\n",
    "import matplotlib.pyplot as plt  # Provides the functions needed to plot the graphs"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "23584915",
   "metadata": {},
   "source": [
    "## <center>Machine Environment</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9b6aa663",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Machine environment\n",
      "\n",
      " sys.float_info(max=1.7976931348623157e+308, max_exp=1024, max_10_exp=308, min=2.2250738585072014e-308, min_exp=-1021, min_10_exp=-307, dig=15, mant_dig=53, epsilon=2.220446049250313e-16, radix=2, rounds=1)\n",
      "\n",
      "machine epsilon= 2.220446049250313e-16\n"
     ]
    }
   ],
   "source": [
    "# machine environment\n",
    "\n",
    "print(\"Machine environment\")\n",
    "print(\"\\n\", sys.float_info)\n",
    "mepsilon = sys.float_info.epsilon\n",
    "print(\"\\nmachine epsilon=\", mepsilon)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b8cefcc6",
   "metadata": {},
   "source": [
    "## <center>Division Algorithms</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "aa0edaeb",
   "metadata": {},
   "source": [
    "### <center>For These Algorithms the Following Notation is Used:</center>\n",
    "#### n = numerator \n",
    "#### d = denominator \n",
    "#### q = quotient \n",
    "#### r = remainder "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a2fa309",
   "metadata": {},
   "source": [
    "## <center>Naive Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "034cc36e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def naive_division_algorithm(n, d):\n",
    "    r = n\n",
    "    q = 0.0\n",
    "    while r >= d:\n",
    "        r -= d\n",
    "        q += 1.0\n",
    "    return q, r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "881bc3cb",
   "metadata": {},
   "source": [
    "## <center>Long Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "74ae6c4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def first(quotient_multiply_by_divisor, numerator_list):\n",
    "    size = len(str(quotient_multiply_by_divisor))\n",
    "    subtraction_term_string = \"\"\n",
    "    for i in range(size):\n",
    "        subtraction_term_string += str(numerator_list[i])\n",
    "    if int(subtraction_term_string) < quotient_multiply_by_divisor:\n",
    "        size += 1\n",
    "    subtraction_term_string = \"\"\n",
    "    for i in range(size):\n",
    "        subtraction_term = numerator_list.pop(0)\n",
    "        subtraction_term_string += str(subtraction_term)\n",
    "        remainder = int(subtraction_term_string) - quotient_multiply_by_divisor\n",
    "    return remainder\n",
    "\n",
    "\n",
    "def long_division_algorithm(n, d):\n",
    "    numerator_list = [int(x) for x in str(n)]\n",
    "    i = 0\n",
    "    quotient = \"\"\n",
    "    first_turn = True\n",
    "\n",
    "    valid_dividend = numerator_list[i]\n",
    "    valid_dividend_string = str(valid_dividend)\n",
    "\n",
    "    while int(valid_dividend_string) < d:\n",
    "        i += 1\n",
    "        valid_dividend = numerator_list[i]\n",
    "        valid_dividend_string += str(valid_dividend)\n",
    "    while len(numerator_list) != 0:\n",
    "        j = 0\n",
    "        while (d * j) <= int(valid_dividend_string):\n",
    "            j += 1\n",
    "        quotient += str((j - 1))\n",
    "\n",
    "        quotient_multiply_by_divisor = int(quotient) % 10 * d\n",
    "        if first_turn:\n",
    "            remainder = first(quotient_multiply_by_divisor, numerator_list)\n",
    "            first_turn = False\n",
    "        else:\n",
    "            remainder = int(valid_dividend_string) - quotient_multiply_by_divisor\n",
    "            numerator_list.pop(0)\n",
    "        if len(numerator_list) == 0:\n",
    "            q = int(quotient)\n",
    "            r = n - (q * d)\n",
    "            return q, r\n",
    "        else:\n",
    "            valid_dividend_string = str(remainder) + str(numerator_list[0])\n",
    "    return int(quotient), n - (int(quotient) * d)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "826e8ec6",
   "metadata": {},
   "source": [
    "## <center>Restoring Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2f0ed410",
   "metadata": {},
   "outputs": [],
   "source": [
    "def complement(m):\n",
    "    d = {'0': '1', '1': '0'}\n",
    "    e = ''.join(d[x] for x in m)\n",
    "    l = len(e)\n",
    "    sum = bin(int(e, 2) + int('1', 2))\n",
    "    sum = sum[2:]\n",
    "    return sum.zfill(l)\n",
    "\n",
    "\n",
    "def shift_left(s):\n",
    "    s = s[1:]\n",
    "    s += '0'\n",
    "    return s\n",
    "\n",
    "\n",
    "def add_zero_to_string(x):\n",
    "    x = list(x)\n",
    "    x[-1] = '0'\n",
    "    return ''.join(x)\n",
    "\n",
    "\n",
    "def add_one_to_string(x):\n",
    "    x = list(x)\n",
    "    x[-1] = '1'\n",
    "    return ''.join(x)\n",
    "\n",
    "\n",
    "def restoring_division_algorithm(n, d):\n",
    "    q = bin(n)\n",
    "    m = bin(d)\n",
    "\n",
    "    q = q[2:]\n",
    "    m = m[2:]\n",
    "\n",
    "    if len(m) > len(q):\n",
    "        q = q.zfill(len(m))\n",
    "    else:\n",
    "        m = m.zfill(len(q))\n",
    "\n",
    "    l = len(q)\n",
    "    count = l\n",
    "    mc = complement(m)\n",
    "\n",
    "    a = '0'\n",
    "    for i in range(0, l - 1):\n",
    "        a = a + '0'\n",
    "\n",
    "    while count > 0:\n",
    "        s = a + q\n",
    "        value = shift_left(s)\n",
    "        a = value[0:l]\n",
    "        q = value[l:]\n",
    "        a = bin(int(a, 2) + int(mc, 2))\n",
    "        if len(a[2:]) == l + 1:\n",
    "            a = a[3:]\n",
    "        else:\n",
    "            a = a[2:]\n",
    "        if a[0] == '1':\n",
    "            a = bin(int(a, 2) + int(m, 2))\n",
    "            if len(a[2:]) == l + 1:\n",
    "                a = a[3:]\n",
    "            else:\n",
    "                a = a[2:]\n",
    "            q = add_zero_to_string(q)\n",
    "        else:\n",
    "            q = add_one_to_string(q)\n",
    "        count -= 1\n",
    "    return q, a"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "74a67ab9",
   "metadata": {},
   "source": [
    "## <center>Non-Restoring Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c1746f91",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add(a, m):\n",
    "    carry = 0\n",
    "    sum = \"\"\n",
    "\n",
    "    for i in range(len(a) - 1, -1, -1):\n",
    "        tmp = int(a[i]) + int(m[i]) + carry\n",
    "        if tmp > 1:\n",
    "            sum += str(tmp % 2)\n",
    "            carry = 1\n",
    "        else:\n",
    "            sum += str(tmp)\n",
    "            carry = 0\n",
    "    return sum[::-1]\n",
    "\n",
    "\n",
    "def complement_non_restoring_division(d):\n",
    "    return complement(d)\n",
    "\n",
    "\n",
    "def non_restoring_division_algorithm(n, d, accumulator):\n",
    "    q = n\n",
    "    count = len(d)\n",
    "    complement_m = complement_non_restoring_division(d)\n",
    "    flag = \"successful\"\n",
    "    while count:\n",
    "        accumulator = accumulator[1:] + q[0]\n",
    "        if flag == \"successful\":\n",
    "            accumulator = add(accumulator, complement_m)\n",
    "        else:\n",
    "            accumulator = add(accumulator, d)\n",
    "        if accumulator[0] == '1':\n",
    "            q = q[1:] + '0'\n",
    "            flag = \"unsuccessful\"\n",
    "        else:\n",
    "            q = q[1:] + '1'\n",
    "            flag = \"successful\"\n",
    "        count -= 1\n",
    "    return q, accumulator"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2de44186",
   "metadata": {},
   "source": [
    "## <center>Newton Raphson Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "21adddb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def size(x):\n",
    "    if isinstance(x, int):\n",
    "        return int(log(x, 2))\n",
    "    return x.numdigits(2)\n",
    "\n",
    "\n",
    "def newton_raphson_division_algorithm(n, d):\n",
    "    precision = 10**300\n",
    "    size_n = size(n)\n",
    "    size_d = size(d)\n",
    "    size_r = size_n - size_d\n",
    "    if min(size_n, size_d, size_r) < (2 * precision):\n",
    "        return n // d\n",
    "    r = (1 << (2 * precision)) // (d >> (size_d - precision))\n",
    "    last_precision = precision\n",
    "    for prec in range(precision, size_r + 1):\n",
    "        a = r << (prec - last_precision + 1)\n",
    "        b = (r**2 * (d >> size_d - prec)) >> (2 * last_precision)\n",
    "        r = a - b\n",
    "        last_precision = prec\n",
    "    return ((n >> size_d) * r) >> size_r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66db6fb7",
   "metadata": {},
   "source": [
    "## <center>Python's divmod Function</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b4fcc8af",
   "metadata": {},
   "outputs": [],
   "source": [
    "divmod_number_of_digits_list = []\n",
    "divmod_time_list = []\n",
    "for i in range(21):\n",
    "    j = 2**i\n",
    "    d = 10**j\n",
    "    n = d**2\n",
    "    start_time = time.time()\n",
    "    divmod(n, d)\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "    divmod_number_of_digits_list.append(j)\n",
    "    divmod_time_list.append(elapsed_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b304892c",
   "metadata": {},
   "source": [
    "## <center>Python's Integer Division</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "48bba690",
   "metadata": {},
   "outputs": [],
   "source": [
    "integer_division_number_of_digits_list = []\n",
    "integer_division_time_list = []\n",
    "for i in range(23):\n",
    "    j = 2**i\n",
    "    d = 10**j\n",
    "    n = d**2\n",
    "    start_time = time.time()\n",
    "    n//d\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "    integer_division_number_of_digits_list.append(j)\n",
    "    integer_division_time_list.append(elapsed_time)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6d9123e",
   "metadata": {},
   "source": [
    "## <center>Polynomial Long Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c447c945",
   "metadata": {},
   "outputs": [],
   "source": [
    "def degree_of_function(polynomial):\n",
    "    while polynomial[0] == 0:\n",
    "        polynomial.pop(0)\n",
    "    return len(polynomial) - 1\n",
    "\n",
    "\n",
    "def polynomial_long_division_algorithm(dividend, divisor):\n",
    "    dn = degree_of_function(dividend)\n",
    "    dd = degree_of_function(divisor)\n",
    "\n",
    "    q = []\n",
    "    r = dividend\n",
    "    dr = dn\n",
    "    i = 0\n",
    "    l = 0\n",
    "    while (len(r) != 0) and (dr >= dd):\n",
    "        leading_term_division = r[i] / divisor[0]\n",
    "        q.append(leading_term_division)\n",
    "\n",
    "        tmp = []\n",
    "        for j in range(len(divisor)):\n",
    "            tmp.append(leading_term_division * divisor[j])\n",
    "\n",
    "        for k in range(len(tmp)):\n",
    "            r[k + l] = r[k + l] - tmp[k]\n",
    "        dr -= 1\n",
    "        i += 1\n",
    "        l += 1\n",
    "    return q, r"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9a57a829",
   "metadata": {},
   "source": [
    "## <center>Polynomial Extended Synthetic Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "42d19af6",
   "metadata": {},
   "outputs": [],
   "source": [
    "def polynomial_extended_synthetic_division_algorithm(dividend, divisor):\n",
    "    normalizer = divisor[0]\n",
    "    for i in range(len(dividend) - len(divisor) + 1):\n",
    "        dividend[i] /= normalizer\n",
    "        coefficient = dividend[i]\n",
    "        if coefficient != 0:\n",
    "            for j in range(1, len(divisor)):\n",
    "                dividend[i + j] += -divisor[j] * coefficient\n",
    "    separator = 1 - len(divisor)\n",
    "    return dividend[:separator], dividend[separator:]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df4afc38",
   "metadata": {},
   "source": [
    "## <center>Calculate the Number of Digits, Highest Degree, and Time Used for Each Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9f6f266e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# naive_division_algorithm\n",
    "naive_division_number_of_digits_list = []\n",
    "naive_division_time_list = []\n",
    "for i in range(4):\n",
    "    j = 2**i\n",
    "    d = 10**j\n",
    "    n = d**2\n",
    "    start_time = time.time()\n",
    "    naive_division_algorithm(n, d)\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "    naive_division_number_of_digits_list.append(j)\n",
    "    naive_division_time_list.append(elapsed_time)\n",
    "\n",
    "\n",
    "# long_division_algorithm\n",
    "long_division_number_of_digits_list = []\n",
    "long_division_time_list = []\n",
    "for i in range(16):\n",
    "    j = 2**i\n",
    "    d = 10**j\n",
    "    n = d**2\n",
    "    start_time = time.time()\n",
    "    long_division_algorithm(n, d)\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "    long_division_number_of_digits_list.append(j)\n",
    "    long_division_time_list.append(elapsed_time)\n",
    "\n",
    "\n",
    "# restoring_division_algorithm\n",
    "restoring_division_number_of_digits_list = []\n",
    "restoring_division_time_list = []\n",
    "for i in range(14):\n",
    "    j = 2**i\n",
    "    d = 10**j\n",
    "    n = d**2\n",
    "    start_time = time.time()\n",
    "    restoring_division_algorithm(n, d)\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "    restoring_division_number_of_digits_list.append(j)\n",
    "    restoring_division_time_list.append(elapsed_time)\n",
    "\n",
    "\n",
    "# non_restoring_division_algorithm\n",
    "non_restoring_division_number_of_digits_list = []\n",
    "non_restoring_division_time_list = []\n",
    "for i in range(10):\n",
    "    j = 2**i\n",
    "    d = 10**j\n",
    "    d_binary = '{0:03934b}'.format(d)\n",
    "    n = d**2\n",
    "    n_binary = '{0:03934b}'.format(n)\n",
    "    accumulator = '0' * len(n_binary)\n",
    "    start_time = time.time()\n",
    "    non_restoring_division_algorithm(n_binary, d_binary, accumulator)\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "    non_restoring_division_number_of_digits_list.append(j)\n",
    "    non_restoring_division_time_list.append(elapsed_time)\n",
    "\n",
    "\n",
    "# non_restoring_division_algorithm\n",
    "non_restoring_division_2_number_of_digits_list = []\n",
    "non_restoring_division_2_time_list = []\n",
    "for i in range(11):\n",
    "    j = 2**i\n",
    "    d = 10**j\n",
    "    d_binary = '{0:07934b}'.format(d)\n",
    "    n = d**2\n",
    "    n_binary = '{0:07934b}'.format(n)\n",
    "    accumulator = '0' * len(n_binary)\n",
    "    start_time = time.time()\n",
    "    non_restoring_division_algorithm(n_binary, d_binary, accumulator)\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "    non_restoring_division_2_number_of_digits_list.append(j)\n",
    "    non_restoring_division_2_time_list.append(elapsed_time)\n",
    "\n",
    "\n",
    "# newton_raphson_division_algorithm\n",
    "newton_raphson_division_number_of_digits_list = []\n",
    "newton_raphson_division_time_list = []\n",
    "for i in range(21):\n",
    "    j = 2**i\n",
    "    d = 10**j\n",
    "    n = d**2\n",
    "    start_time = time.time()\n",
    "    newton_raphson_division_algorithm(n, d)\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "    newton_raphson_division_number_of_digits_list.append(j)\n",
    "    newton_raphson_division_time_list.append(elapsed_time)\n",
    "\n",
    "\n",
    "# polynomial_long_division_algorithm\n",
    "polynomial_long_division_highest_degree_list = []\n",
    "polynomial_long_division_time_list = []\n",
    "n = []\n",
    "d = []\n",
    "for i in range(5001, 300002, 29500):\n",
    "    for j in range(i + 1):\n",
    "        if j % 2 == 0:\n",
    "            n.append(10**100)\n",
    "        else:\n",
    "            n.append(-(10**100))\n",
    "    for j in range(i):\n",
    "        if j % 2 == 0:\n",
    "            d.append(15**85)\n",
    "        else:\n",
    "            d.append(-(15**85))\n",
    "    if len(d) != 0:\n",
    "        d.pop()\n",
    "    if len(d) == 0:\n",
    "        d.append(15 ** 85)\n",
    "    start_time = time.time()\n",
    "    q, r = polynomial_long_division_algorithm(n, d)\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "    polynomial_long_division_highest_degree_list.append(i)\n",
    "    polynomial_long_division_time_list.append(elapsed_time)\n",
    "    n.clear()\n",
    "    d.clear()\n",
    "\n",
    "\n",
    "# polynomial_extended_synthetic_division_algorithm\n",
    "polynomial_extended_synthetic_division_highest_degree_list = []\n",
    "polynomial_extended_synthetic_division_time_list = []\n",
    "for i in range(5001, 300002, 29500):\n",
    "    for j in range(i + 1):\n",
    "        if j % 2 == 0:\n",
    "            n.append(10**100)\n",
    "        else:\n",
    "            n.append(-(10**100))\n",
    "    for j in range(i):\n",
    "        if j % 2 == 0:\n",
    "            d.append(15**85)\n",
    "        else:\n",
    "            d.append(-(15**85))\n",
    "    if len(d) != 0:\n",
    "        d.pop()\n",
    "    if len(d) == 0:\n",
    "        d.append(15 ** 85)\n",
    "    start_time = time.time()\n",
    "    q, r = polynomial_extended_synthetic_division_algorithm(n, d)\n",
    "    end_time = time.time()\n",
    "    elapsed_time = end_time - start_time\n",
    "    polynomial_extended_synthetic_division_highest_degree_list.append(i)\n",
    "    polynomial_extended_synthetic_division_time_list.append(elapsed_time)\n",
    "    n.clear()\n",
    "    d.clear()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c77ac287",
   "metadata": {},
   "source": [
    "## <center>Create the Tables</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "60f2bdb1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the table with the results of the naive_division_algorithm\n",
    "\n",
    "data = [[naive_division_number_of_digits_list[0] * 2 + 1 , naive_division_number_of_digits_list[0] + 1, naive_division_time_list[0]],\n",
    "        [naive_division_number_of_digits_list[1] * 2 + 1 , naive_division_number_of_digits_list[1] + 1, naive_division_time_list[1]],\n",
    "        [naive_division_number_of_digits_list[2] * 2 + 1 , naive_division_number_of_digits_list[2] + 1, naive_division_time_list[2]],\n",
    "        [naive_division_number_of_digits_list[3] * 2 + 1 , naive_division_number_of_digits_list[3] + 1, naive_division_time_list[3]]\n",
    "       ]\n",
    "index = [\"1\", \"2\", \"3\", \"4\"]\n",
    "headings = [\"Numerator Digits\", \"Denominator Digits\", \"Time(s)\"]\n",
    "table_naive_division_algorithm = pd.DataFrame(data, index, headings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fd22f6fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the table with the results of the long_division_algorithm\n",
    "\n",
    "data = [[long_division_number_of_digits_list[0] * 2 + 1 , long_division_number_of_digits_list[0] + 1, long_division_time_list[0]],\n",
    "        [long_division_number_of_digits_list[1] * 2 + 1 , long_division_number_of_digits_list[1] + 1, long_division_time_list[1]],\n",
    "        [long_division_number_of_digits_list[2] * 2 + 1 , long_division_number_of_digits_list[2] + 1, long_division_time_list[2]],\n",
    "        [long_division_number_of_digits_list[3] * 2 + 1 , long_division_number_of_digits_list[3] + 1, long_division_time_list[3]],\n",
    "        [long_division_number_of_digits_list[4] * 2 + 1 , long_division_number_of_digits_list[4] + 1, long_division_time_list[4]],\n",
    "        [long_division_number_of_digits_list[5] * 2 + 1 , long_division_number_of_digits_list[5] + 1, long_division_time_list[5]],\n",
    "        [long_division_number_of_digits_list[6] * 2 + 1 , long_division_number_of_digits_list[6] + 1, long_division_time_list[6]],\n",
    "        [long_division_number_of_digits_list[7] * 2 + 1 , long_division_number_of_digits_list[7] + 1, long_division_time_list[7]],\n",
    "        [long_division_number_of_digits_list[8] * 2 + 1 , long_division_number_of_digits_list[8] + 1, long_division_time_list[8]],\n",
    "        [long_division_number_of_digits_list[9] * 2 + 1 , long_division_number_of_digits_list[9] + 1, long_division_time_list[9]],\n",
    "        [long_division_number_of_digits_list[10] * 2 + 1 , long_division_number_of_digits_list[10] + 1, long_division_time_list[10]],\n",
    "        [long_division_number_of_digits_list[11] * 2 + 1 , long_division_number_of_digits_list[11] + 1, long_division_time_list[11]],\n",
    "        [long_division_number_of_digits_list[12] * 2 + 1 , long_division_number_of_digits_list[12] + 1, long_division_time_list[12]],\n",
    "        [long_division_number_of_digits_list[13] * 2 + 1 , long_division_number_of_digits_list[13] + 1, long_division_time_list[13]],\n",
    "        [long_division_number_of_digits_list[14] * 2 + 1 , long_division_number_of_digits_list[14] + 1, long_division_time_list[14]],\n",
    "        [long_division_number_of_digits_list[15] * 2 + 1 , long_division_number_of_digits_list[15] + 1, long_division_time_list[15]]\n",
    "       ]\n",
    "index = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \"16\"]\n",
    "headings = [\"Numerator Digits\", \"Denominator Digits\", \"Time(s)\"]\n",
    "table_long_division_algorithm = pd.DataFrame(data, index, headings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "2f84b426",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the table with the results of the restoring_division_algorithm\n",
    "\n",
    "data = [[restoring_division_number_of_digits_list[0] * 2 + 1 , restoring_division_number_of_digits_list[0] + 1, restoring_division_time_list[0]],\n",
    "        [restoring_division_number_of_digits_list[1] * 2 + 1 , restoring_division_number_of_digits_list[1] + 1, restoring_division_time_list[1]],\n",
    "        [restoring_division_number_of_digits_list[2] * 2 + 1 , restoring_division_number_of_digits_list[2] + 1, restoring_division_time_list[2]],\n",
    "        [restoring_division_number_of_digits_list[3] * 2 + 1 , restoring_division_number_of_digits_list[3] + 1, restoring_division_time_list[3]],\n",
    "        [restoring_division_number_of_digits_list[4] * 2 + 1 , restoring_division_number_of_digits_list[4] + 1, restoring_division_time_list[4]],\n",
    "        [restoring_division_number_of_digits_list[5] * 2 + 1 , restoring_division_number_of_digits_list[5] + 1, restoring_division_time_list[5]],\n",
    "        [restoring_division_number_of_digits_list[6] * 2 + 1 , restoring_division_number_of_digits_list[6] + 1, restoring_division_time_list[6]],\n",
    "        [restoring_division_number_of_digits_list[7] * 2 + 1 , restoring_division_number_of_digits_list[7] + 1, restoring_division_time_list[7]],\n",
    "        [restoring_division_number_of_digits_list[8] * 2 + 1 , restoring_division_number_of_digits_list[8] + 1, restoring_division_time_list[8]],\n",
    "        [restoring_division_number_of_digits_list[9] * 2 + 1 , restoring_division_number_of_digits_list[9] + 1, restoring_division_time_list[9]],\n",
    "        [restoring_division_number_of_digits_list[10] * 2 + 1 , restoring_division_number_of_digits_list[10] + 1, restoring_division_time_list[10]],\n",
    "        [restoring_division_number_of_digits_list[11] * 2 + 1 , restoring_division_number_of_digits_list[11] + 1, restoring_division_time_list[11]],\n",
    "        [restoring_division_number_of_digits_list[12] * 2 + 1 , restoring_division_number_of_digits_list[12] + 1, restoring_division_time_list[12]],\n",
    "        [restoring_division_number_of_digits_list[13] * 2 + 1 , restoring_division_number_of_digits_list[13] + 1, restoring_division_time_list[13]]\n",
    "       ]\n",
    "index = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\"]\n",
    "headings = [\"Numerator Digits\", \"Denominator Digits\", \"Time(s)\"]\n",
    "table_restoring_division_algorithm = pd.DataFrame(data, index, headings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "da1125d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the first table with the results of the non_restoring_division_algorithm\n",
    "\n",
    "data = [[non_restoring_division_number_of_digits_list[0] * 2 + 1 , non_restoring_division_number_of_digits_list[0] + 1, non_restoring_division_time_list[0]],\n",
    "        [non_restoring_division_number_of_digits_list[1] * 2 + 1 , non_restoring_division_number_of_digits_list[1] + 1, non_restoring_division_time_list[1]],\n",
    "        [non_restoring_division_number_of_digits_list[2] * 2 + 1 , non_restoring_division_number_of_digits_list[2] + 1, non_restoring_division_time_list[2]],\n",
    "        [non_restoring_division_number_of_digits_list[3] * 2 + 1 , non_restoring_division_number_of_digits_list[3] + 1, non_restoring_division_time_list[3]],\n",
    "        [non_restoring_division_number_of_digits_list[4] * 2 + 1 , non_restoring_division_number_of_digits_list[4] + 1, non_restoring_division_time_list[4]],\n",
    "        [non_restoring_division_number_of_digits_list[5] * 2 + 1 , non_restoring_division_number_of_digits_list[5] + 1, non_restoring_division_time_list[5]],\n",
    "        [non_restoring_division_number_of_digits_list[6] * 2 + 1 , non_restoring_division_number_of_digits_list[6] + 1, non_restoring_division_time_list[6]],\n",
    "        [non_restoring_division_number_of_digits_list[7] * 2 + 1 , non_restoring_division_number_of_digits_list[7] + 1, non_restoring_division_time_list[7]],\n",
    "        [non_restoring_division_number_of_digits_list[8] * 2 + 1 , non_restoring_division_number_of_digits_list[8] + 1, non_restoring_division_time_list[8]],\n",
    "        [non_restoring_division_number_of_digits_list[9] * 2 + 1 , non_restoring_division_number_of_digits_list[9] + 1, non_restoring_division_time_list[9]]\n",
    "       ]\n",
    "index = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\"]\n",
    "headings = [\"Numerator Digits\", \"Denominator Digits\", \"Time(s)\"]\n",
    "table_non_restoring_division_algorithm = pd.DataFrame(data, index, headings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "d9c375ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the second table with the results of the non_restoring_division_algorithm\n",
    "\n",
    "data = [[non_restoring_division_2_number_of_digits_list[0] * 2 + 1 , non_restoring_division_2_number_of_digits_list[0] + 1, non_restoring_division_2_time_list[0]],\n",
    "        [non_restoring_division_2_number_of_digits_list[1] * 2 + 1 , non_restoring_division_2_number_of_digits_list[1] + 1, non_restoring_division_2_time_list[1]],\n",
    "        [non_restoring_division_2_number_of_digits_list[2] * 2 + 1 , non_restoring_division_2_number_of_digits_list[2] + 1, non_restoring_division_2_time_list[2]],\n",
    "        [non_restoring_division_2_number_of_digits_list[3] * 2 + 1 , non_restoring_division_2_number_of_digits_list[3] + 1, non_restoring_division_2_time_list[3]],\n",
    "        [non_restoring_division_2_number_of_digits_list[4] * 2 + 1 , non_restoring_division_2_number_of_digits_list[4] + 1, non_restoring_division_2_time_list[4]],\n",
    "        [non_restoring_division_2_number_of_digits_list[5] * 2 + 1 , non_restoring_division_2_number_of_digits_list[5] + 1, non_restoring_division_2_time_list[5]],\n",
    "        [non_restoring_division_2_number_of_digits_list[6] * 2 + 1 , non_restoring_division_2_number_of_digits_list[6] + 1, non_restoring_division_2_time_list[6]],\n",
    "        [non_restoring_division_2_number_of_digits_list[7] * 2 + 1 , non_restoring_division_2_number_of_digits_list[7] + 1, non_restoring_division_2_time_list[7]],\n",
    "        [non_restoring_division_2_number_of_digits_list[8] * 2 + 1 , non_restoring_division_2_number_of_digits_list[8] + 1, non_restoring_division_2_time_list[8]],\n",
    "        [non_restoring_division_2_number_of_digits_list[9] * 2 + 1 , non_restoring_division_2_number_of_digits_list[9] + 1, non_restoring_division_2_time_list[9]],\n",
    "        [non_restoring_division_2_number_of_digits_list[10] * 2 + 1 , non_restoring_division_2_number_of_digits_list[10] + 1, non_restoring_division_2_time_list[10]]\n",
    "       ]\n",
    "index = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\"]\n",
    "headings = [\"Numerator Digits\", \"Denominator Digits\", \"Time\"]\n",
    "table_2_non_restoring_division_algorithm = pd.DataFrame(data, index, headings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "89d0b69e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the table with the results of the newton_raphson_division_algorithm\n",
    "\n",
    "data = [[newton_raphson_division_number_of_digits_list[0] * 2 + 1 , newton_raphson_division_number_of_digits_list[0] + 1, newton_raphson_division_time_list[0]],\n",
    "        [newton_raphson_division_number_of_digits_list[1] * 2 + 1 , newton_raphson_division_number_of_digits_list[1] + 1, newton_raphson_division_time_list[1]],\n",
    "        [newton_raphson_division_number_of_digits_list[2] * 2 + 1 , newton_raphson_division_number_of_digits_list[2] + 1, newton_raphson_division_time_list[2]],\n",
    "        [newton_raphson_division_number_of_digits_list[3] * 2 + 1 , newton_raphson_division_number_of_digits_list[3] + 1, newton_raphson_division_time_list[3]],\n",
    "        [newton_raphson_division_number_of_digits_list[4] * 2 + 1 , newton_raphson_division_number_of_digits_list[4] + 1, newton_raphson_division_time_list[4]],\n",
    "        [newton_raphson_division_number_of_digits_list[5] * 2 + 1 , newton_raphson_division_number_of_digits_list[5] + 1, newton_raphson_division_time_list[5]],\n",
    "        [newton_raphson_division_number_of_digits_list[6] * 2 + 1 , newton_raphson_division_number_of_digits_list[6] + 1, newton_raphson_division_time_list[6]],\n",
    "        [newton_raphson_division_number_of_digits_list[7] * 2 + 1 , newton_raphson_division_number_of_digits_list[7] + 1, newton_raphson_division_time_list[7]],\n",
    "        [newton_raphson_division_number_of_digits_list[8] * 2 + 1 , newton_raphson_division_number_of_digits_list[8] + 1, newton_raphson_division_time_list[8]],\n",
    "        [newton_raphson_division_number_of_digits_list[9] * 2 + 1 , newton_raphson_division_number_of_digits_list[9] + 1, newton_raphson_division_time_list[9]],\n",
    "        [newton_raphson_division_number_of_digits_list[10] * 2 + 1 , newton_raphson_division_number_of_digits_list[10] + 1, newton_raphson_division_time_list[10]],\n",
    "        [newton_raphson_division_number_of_digits_list[11] * 2 + 1 , newton_raphson_division_number_of_digits_list[11] + 1, newton_raphson_division_time_list[11]],\n",
    "        [newton_raphson_division_number_of_digits_list[12] * 2 + 1 , newton_raphson_division_number_of_digits_list[12] + 1, newton_raphson_division_time_list[12]],\n",
    "        [newton_raphson_division_number_of_digits_list[13] * 2 + 1 , newton_raphson_division_number_of_digits_list[13] + 1, newton_raphson_division_time_list[13]],\n",
    "        [newton_raphson_division_number_of_digits_list[14] * 2 + 1 , newton_raphson_division_number_of_digits_list[14] + 1, newton_raphson_division_time_list[14]],\n",
    "        [newton_raphson_division_number_of_digits_list[15] * 2 + 1 , newton_raphson_division_number_of_digits_list[15] + 1, newton_raphson_division_time_list[15]],\n",
    "        [newton_raphson_division_number_of_digits_list[16] * 2 + 1 , newton_raphson_division_number_of_digits_list[16] + 1, newton_raphson_division_time_list[16]],\n",
    "        [newton_raphson_division_number_of_digits_list[17] * 2 + 1 , newton_raphson_division_number_of_digits_list[17] + 1, newton_raphson_division_time_list[17]],\n",
    "        [newton_raphson_division_number_of_digits_list[18] * 2 + 1 , newton_raphson_division_number_of_digits_list[18] + 1, newton_raphson_division_time_list[18]],\n",
    "        [newton_raphson_division_number_of_digits_list[19] * 2 + 1 , newton_raphson_division_number_of_digits_list[19] + 1, newton_raphson_division_time_list[19]],\n",
    "        [newton_raphson_division_number_of_digits_list[20] * 2 + 1 , newton_raphson_division_number_of_digits_list[20] + 1, newton_raphson_division_time_list[20]]\n",
    "       ]\n",
    "index = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \"16\", \"17\", \"18\", \"19\", \"20\", \"21\"]\n",
    "headings = [\"Numerator Digits\", \"Denominator Digits\", \"Time(s)\"]\n",
    "table_newton_raphson_division_algorithm = pd.DataFrame(data, index, headings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "9727f171",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the table with the results of Python's divmod function\n",
    "\n",
    "data = [[divmod_number_of_digits_list[0] * 2 + 1 , divmod_number_of_digits_list[0] + 1, divmod_time_list[0]],\n",
    "        [divmod_number_of_digits_list[1] * 2 + 1 , divmod_number_of_digits_list[1] + 1, divmod_time_list[1]],\n",
    "        [divmod_number_of_digits_list[2] * 2 + 1 , divmod_number_of_digits_list[2] + 1, divmod_time_list[2]],\n",
    "        [divmod_number_of_digits_list[3] * 2 + 1 , divmod_number_of_digits_list[3] + 1, divmod_time_list[3]],\n",
    "        [divmod_number_of_digits_list[4] * 2 + 1 , divmod_number_of_digits_list[4] + 1, divmod_time_list[4]],\n",
    "        [divmod_number_of_digits_list[5] * 2 + 1 , divmod_number_of_digits_list[5] + 1, divmod_time_list[5]],\n",
    "        [divmod_number_of_digits_list[6] * 2 + 1 , divmod_number_of_digits_list[6] + 1, divmod_time_list[6]],\n",
    "        [divmod_number_of_digits_list[7] * 2 + 1 , divmod_number_of_digits_list[7] + 1, divmod_time_list[7]],\n",
    "        [divmod_number_of_digits_list[8] * 2 + 1 , divmod_number_of_digits_list[8] + 1, divmod_time_list[8]],\n",
    "        [divmod_number_of_digits_list[9] * 2 + 1 , divmod_number_of_digits_list[9] + 1, divmod_time_list[9]],\n",
    "        [divmod_number_of_digits_list[10] * 2 + 1 , divmod_number_of_digits_list[10] + 1, divmod_time_list[10]],\n",
    "        [divmod_number_of_digits_list[11] * 2 + 1 , divmod_number_of_digits_list[11] + 1, divmod_time_list[11]],\n",
    "        [divmod_number_of_digits_list[12] * 2 + 1 , divmod_number_of_digits_list[12] + 1, divmod_time_list[12]],\n",
    "        [divmod_number_of_digits_list[13] * 2 + 1 , divmod_number_of_digits_list[13] + 1, divmod_time_list[13]],\n",
    "        [divmod_number_of_digits_list[14] * 2 + 1 , divmod_number_of_digits_list[14] + 1, divmod_time_list[14]],\n",
    "        [divmod_number_of_digits_list[15] * 2 + 1 , divmod_number_of_digits_list[15] + 1, divmod_time_list[15]],\n",
    "        [divmod_number_of_digits_list[16] * 2 + 1 , divmod_number_of_digits_list[16] + 1, divmod_time_list[16]],\n",
    "        [divmod_number_of_digits_list[17] * 2 + 1 , divmod_number_of_digits_list[17] + 1, divmod_time_list[17]],\n",
    "        [divmod_number_of_digits_list[18] * 2 + 1 , divmod_number_of_digits_list[18] + 1, divmod_time_list[18]],\n",
    "        [divmod_number_of_digits_list[19] * 2 + 1 , divmod_number_of_digits_list[19] + 1, divmod_time_list[19]],\n",
    "        [divmod_number_of_digits_list[20] * 2 + 1 , divmod_number_of_digits_list[20] + 1, divmod_time_list[20]]\n",
    "       ]\n",
    "index = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \"16\", \"17\", \"18\", \"19\", \"20\", \"21\"]\n",
    "headings = [\"Numerator Digits\", \"Denominator Digits\", \"Time(s)\"]\n",
    "table_divmod_function = pd.DataFrame(data, index, headings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c020b26d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the table with the results of Python's integer division\n",
    "\n",
    "data = [[integer_division_number_of_digits_list[0] * 2 + 1 , integer_division_number_of_digits_list[0] + 1, integer_division_time_list[0]],\n",
    "        [integer_division_number_of_digits_list[1] * 2 + 1 , integer_division_number_of_digits_list[1] + 1, integer_division_time_list[1]],\n",
    "        [integer_division_number_of_digits_list[2] * 2 + 1 , integer_division_number_of_digits_list[2] + 1, integer_division_time_list[2]],\n",
    "        [integer_division_number_of_digits_list[3] * 2 + 1 , integer_division_number_of_digits_list[3] + 1, integer_division_time_list[3]],\n",
    "        [integer_division_number_of_digits_list[4] * 2 + 1 , integer_division_number_of_digits_list[4] + 1, integer_division_time_list[4]],\n",
    "        [integer_division_number_of_digits_list[5] * 2 + 1 , integer_division_number_of_digits_list[5] + 1, integer_division_time_list[5]],\n",
    "        [integer_division_number_of_digits_list[6] * 2 + 1 , integer_division_number_of_digits_list[6] + 1, integer_division_time_list[6]],\n",
    "        [integer_division_number_of_digits_list[7] * 2 + 1 , integer_division_number_of_digits_list[7] + 1, integer_division_time_list[7]],\n",
    "        [integer_division_number_of_digits_list[8] * 2 + 1 , integer_division_number_of_digits_list[8] + 1, integer_division_time_list[8]],\n",
    "        [integer_division_number_of_digits_list[9] * 2 + 1 , integer_division_number_of_digits_list[9] + 1, integer_division_time_list[9]],\n",
    "        [integer_division_number_of_digits_list[10] * 2 + 1 , integer_division_number_of_digits_list[10] + 1, integer_division_time_list[10]],\n",
    "        [integer_division_number_of_digits_list[11] * 2 + 1 , integer_division_number_of_digits_list[11] + 1, integer_division_time_list[11]],\n",
    "        [integer_division_number_of_digits_list[12] * 2 + 1 , integer_division_number_of_digits_list[12] + 1, integer_division_time_list[12]],\n",
    "        [integer_division_number_of_digits_list[13] * 2 + 1 , integer_division_number_of_digits_list[13] + 1, integer_division_time_list[13]],\n",
    "        [integer_division_number_of_digits_list[14] * 2 + 1 , integer_division_number_of_digits_list[14] + 1, integer_division_time_list[14]],\n",
    "        [integer_division_number_of_digits_list[15] * 2 + 1 , integer_division_number_of_digits_list[15] + 1, integer_division_time_list[15]],\n",
    "        [integer_division_number_of_digits_list[16] * 2 + 1 , integer_division_number_of_digits_list[16] + 1, integer_division_time_list[16]],\n",
    "        [integer_division_number_of_digits_list[17] * 2 + 1 , integer_division_number_of_digits_list[17] + 1, integer_division_time_list[17]],\n",
    "        [integer_division_number_of_digits_list[18] * 2 + 1 , integer_division_number_of_digits_list[18] + 1, integer_division_time_list[18]],\n",
    "        [integer_division_number_of_digits_list[19] * 2 + 1 , integer_division_number_of_digits_list[19] + 1, integer_division_time_list[19]],\n",
    "        [integer_division_number_of_digits_list[20] * 2 + 1 , integer_division_number_of_digits_list[20] + 1, integer_division_time_list[20]],\n",
    "        [integer_division_number_of_digits_list[21] * 2 + 1 , integer_division_number_of_digits_list[21] + 1, integer_division_time_list[21]],\n",
    "        [integer_division_number_of_digits_list[22] * 2 + 1 , integer_division_number_of_digits_list[22] + 1, integer_division_time_list[22]]\n",
    "       ]\n",
    "index = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\", \"12\", \"13\", \"14\", \"15\", \"16\", \"17\", \"18\", \"19\", \"20\", \"21\", \"22\", \"23\"]\n",
    "headings = [\"Numerator Digits\", \"Denominator Digits\", \"Time(s)\"]\n",
    "table_integer_division = pd.DataFrame(data, index, headings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "2069d6d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the second table with the results of the polynomial_long_division_algorithm\n",
    "\n",
    "\n",
    "data = [[polynomial_long_division_highest_degree_list[0] + 1 , polynomial_long_division_highest_degree_list[0] - 1, polynomial_long_division_time_list[0]],\n",
    "        [polynomial_long_division_highest_degree_list[1] + 1 , polynomial_long_division_highest_degree_list[1] - 1, polynomial_long_division_time_list[1]],\n",
    "        [polynomial_long_division_highest_degree_list[2] + 1 , polynomial_long_division_highest_degree_list[2] - 1, polynomial_long_division_time_list[2]],\n",
    "        [polynomial_long_division_highest_degree_list[3] + 1 , polynomial_long_division_highest_degree_list[3] - 1, polynomial_long_division_time_list[3]],\n",
    "        [polynomial_long_division_highest_degree_list[4] + 1 , polynomial_long_division_highest_degree_list[4] - 1, polynomial_long_division_time_list[4]],\n",
    "        [polynomial_long_division_highest_degree_list[5] + 1 , polynomial_long_division_highest_degree_list[5] - 1, polynomial_long_division_time_list[5]],\n",
    "        [polynomial_long_division_highest_degree_list[6] + 1 , polynomial_long_division_highest_degree_list[6] - 1, polynomial_long_division_time_list[6]],\n",
    "        [polynomial_long_division_highest_degree_list[7] + 1 , polynomial_long_division_highest_degree_list[7] - 1, polynomial_long_division_time_list[7]],\n",
    "        [polynomial_long_division_highest_degree_list[8] + 1 , polynomial_long_division_highest_degree_list[8] - 1, polynomial_long_division_time_list[8]],\n",
    "        [polynomial_long_division_highest_degree_list[9] + 1 , polynomial_long_division_highest_degree_list[9] - 1, polynomial_long_division_time_list[9]],\n",
    "        [polynomial_long_division_highest_degree_list[10] + 1 , polynomial_long_division_highest_degree_list[10] - 1, polynomial_long_division_time_list[10]]\n",
    "       ]\n",
    "index = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\"]\n",
    "headings = [\"Numerator Highest Degree\", \"Denominator Highest Degree\", \"Time(s)\"]\n",
    "table_polynomial_long_division_algorithm = pd.DataFrame(data, index, headings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "60d298bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the second table with the results of the polynomial_extended_synthetic_division_algorithm\n",
    "\n",
    "data = [[polynomial_extended_synthetic_division_highest_degree_list[0] + 1 , polynomial_extended_synthetic_division_highest_degree_list[0] - 1, polynomial_extended_synthetic_division_time_list[0]],\n",
    "        [polynomial_extended_synthetic_division_highest_degree_list[1] + 1 , polynomial_extended_synthetic_division_highest_degree_list[1] - 1, polynomial_extended_synthetic_division_time_list[1]],\n",
    "        [polynomial_extended_synthetic_division_highest_degree_list[2] + 1 , polynomial_extended_synthetic_division_highest_degree_list[2] - 1, polynomial_extended_synthetic_division_time_list[2]],\n",
    "        [polynomial_extended_synthetic_division_highest_degree_list[3] + 1 , polynomial_extended_synthetic_division_highest_degree_list[3] - 1, polynomial_extended_synthetic_division_time_list[3]],\n",
    "        [polynomial_extended_synthetic_division_highest_degree_list[4] + 1 , polynomial_extended_synthetic_division_highest_degree_list[4] - 1, polynomial_extended_synthetic_division_time_list[4]],\n",
    "        [polynomial_extended_synthetic_division_highest_degree_list[5] + 1 , polynomial_extended_synthetic_division_highest_degree_list[5] - 1, polynomial_extended_synthetic_division_time_list[5]],\n",
    "        [polynomial_extended_synthetic_division_highest_degree_list[6] + 1 , polynomial_extended_synthetic_division_highest_degree_list[6] - 1, polynomial_extended_synthetic_division_time_list[6]],\n",
    "        [polynomial_extended_synthetic_division_highest_degree_list[7] + 1 , polynomial_extended_synthetic_division_highest_degree_list[7] - 1, polynomial_extended_synthetic_division_time_list[7]],\n",
    "        [polynomial_extended_synthetic_division_highest_degree_list[8] + 1 , polynomial_extended_synthetic_division_highest_degree_list[8] - 1, polynomial_extended_synthetic_division_time_list[8]],\n",
    "        [polynomial_extended_synthetic_division_highest_degree_list[9] + 1 , polynomial_extended_synthetic_division_highest_degree_list[9] - 1, polynomial_extended_synthetic_division_time_list[9]],\n",
    "        [polynomial_extended_synthetic_division_highest_degree_list[10] + 1 , polynomial_extended_synthetic_division_highest_degree_list[10] - 1, polynomial_extended_synthetic_division_time_list[10]]\n",
    "       ]\n",
    "index = [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"11\"]\n",
    "headings = [\"Numerator Highest Degree\", \"Denominator Highest Degree\", \"Time(s)\"]\n",
    "table_polynomial_extended_synthetic_division_algorithm = pd.DataFrame(data, index, headings)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dc6d43ea",
   "metadata": {},
   "source": [
    "## <center>Display the Tables and Plot the Graphs</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b00a8f17",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to increment the number_of_digits_list of each algorithm\n",
    "def increment(number_of_digits_list, i):\n",
    "    return [x + i for x in number_of_digits_list]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bebf473c",
   "metadata": {},
   "source": [
    "### <center>Naive Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "6a32b162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_7a721_ .col0 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_7a721_ .col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_7a721_ .col2 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_7a721_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Numerator Digits</th>\n",
       "      <th class=\"col_heading level0 col1\" >Denominator Digits</th>\n",
       "      <th class=\"col_heading level0 col2\" >Time(s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_7a721_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_7a721_row0_col0\" class=\"data row0 col0\" >3</td>\n",
       "      <td id=\"T_7a721_row0_col1\" class=\"data row0 col1\" >2</td>\n",
       "      <td id=\"T_7a721_row0_col2\" class=\"data row0 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a721_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_7a721_row1_col0\" class=\"data row1 col0\" >5</td>\n",
       "      <td id=\"T_7a721_row1_col1\" class=\"data row1 col1\" >3</td>\n",
       "      <td id=\"T_7a721_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a721_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_7a721_row2_col0\" class=\"data row2 col0\" >9</td>\n",
       "      <td id=\"T_7a721_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "      <td id=\"T_7a721_row2_col2\" class=\"data row2 col2\" >0.010991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_7a721_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_7a721_row3_col0\" class=\"data row3 col0\" >17</td>\n",
       "      <td id=\"T_7a721_row3_col1\" class=\"data row3 col1\" >9</td>\n",
       "      <td id=\"T_7a721_row3_col2\" class=\"data row3 col2\" >84.627516</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f9badd5c70>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_naive_division_algorithm.style.set_table_styles({\n",
    "    \"Numerator Digits\": [{'selector': '', 'props': 'text-align: center;'}],\n",
    "    \"Denominator Digits\": [{'selector':'','props': 'text-align: center ;'}],\n",
    "    \"Time(s)\": [{'selector':'','props': 'text-align: center ; '}]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "59949ed6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(increment(naive_division_number_of_digits_list, 1), naive_division_time_list,\n",
    "         color = \"blue\", marker = 'o', markersize = 7)\n",
    "plt.xlabel(\"Denominator Digits\", fontsize = 15)\n",
    "plt.ylabel(\"Time(s)\", fontsize = 15)\n",
    "plt.title(\"Naive Division Algorithm\", fontsize = 15)\n",
    "plt.gcf().set_size_inches(7, 7)\n",
    "plt.figtext(0.5, 0.01, \"The Numerator Digits are Always Twice the Denominator Digits\",\n",
    "            ha = \"center\", fontsize = 10, bbox = {\"facecolor\":\"white\", \"alpha\":0.3, \"pad\":3})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d554286",
   "metadata": {},
   "source": [
    "### <center>Long Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "f55a648d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_fc585_ .col0 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_fc585_ .col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_fc585_ .col2 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_fc585_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Numerator Digits</th>\n",
       "      <th class=\"col_heading level0 col1\" >Denominator Digits</th>\n",
       "      <th class=\"col_heading level0 col2\" >Time(s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_fc585_row0_col0\" class=\"data row0 col0\" >3</td>\n",
       "      <td id=\"T_fc585_row0_col1\" class=\"data row0 col1\" >2</td>\n",
       "      <td id=\"T_fc585_row0_col2\" class=\"data row0 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_fc585_row1_col0\" class=\"data row1 col0\" >5</td>\n",
       "      <td id=\"T_fc585_row1_col1\" class=\"data row1 col1\" >3</td>\n",
       "      <td id=\"T_fc585_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_fc585_row2_col0\" class=\"data row2 col0\" >9</td>\n",
       "      <td id=\"T_fc585_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "      <td id=\"T_fc585_row2_col2\" class=\"data row2 col2\" >0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_fc585_row3_col0\" class=\"data row3 col0\" >17</td>\n",
       "      <td id=\"T_fc585_row3_col1\" class=\"data row3 col1\" >9</td>\n",
       "      <td id=\"T_fc585_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_fc585_row4_col0\" class=\"data row4 col0\" >33</td>\n",
       "      <td id=\"T_fc585_row4_col1\" class=\"data row4 col1\" >17</td>\n",
       "      <td id=\"T_fc585_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "      <td id=\"T_fc585_row5_col0\" class=\"data row5 col0\" >65</td>\n",
       "      <td id=\"T_fc585_row5_col1\" class=\"data row5 col1\" >33</td>\n",
       "      <td id=\"T_fc585_row5_col2\" class=\"data row5 col2\" >0.000999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_fc585_row6_col0\" class=\"data row6 col0\" >129</td>\n",
       "      <td id=\"T_fc585_row6_col1\" class=\"data row6 col1\" >65</td>\n",
       "      <td id=\"T_fc585_row6_col2\" class=\"data row6 col2\" >0.001997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "      <td id=\"T_fc585_row7_col0\" class=\"data row7 col0\" >257</td>\n",
       "      <td id=\"T_fc585_row7_col1\" class=\"data row7 col1\" >129</td>\n",
       "      <td id=\"T_fc585_row7_col2\" class=\"data row7 col2\" >0.003997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_fc585_row8_col0\" class=\"data row8 col0\" >513</td>\n",
       "      <td id=\"T_fc585_row8_col1\" class=\"data row8 col1\" >257</td>\n",
       "      <td id=\"T_fc585_row8_col2\" class=\"data row8 col2\" >0.007992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_fc585_row9_col0\" class=\"data row9 col0\" >1025</td>\n",
       "      <td id=\"T_fc585_row9_col1\" class=\"data row9 col1\" >513</td>\n",
       "      <td id=\"T_fc585_row9_col2\" class=\"data row9 col2\" >0.022979</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "      <td id=\"T_fc585_row10_col0\" class=\"data row10 col0\" >2049</td>\n",
       "      <td id=\"T_fc585_row10_col1\" class=\"data row10 col1\" >1025</td>\n",
       "      <td id=\"T_fc585_row10_col2\" class=\"data row10 col2\" >0.084921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
       "      <td id=\"T_fc585_row11_col0\" class=\"data row11 col0\" >4097</td>\n",
       "      <td id=\"T_fc585_row11_col1\" class=\"data row11 col1\" >2049</td>\n",
       "      <td id=\"T_fc585_row11_col2\" class=\"data row11 col2\" >0.405623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
       "      <td id=\"T_fc585_row12_col0\" class=\"data row12 col0\" >8193</td>\n",
       "      <td id=\"T_fc585_row12_col1\" class=\"data row12 col1\" >4097</td>\n",
       "      <td id=\"T_fc585_row12_col2\" class=\"data row12 col2\" >2.424751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row13\" class=\"row_heading level0 row13\" >14</th>\n",
       "      <td id=\"T_fc585_row13_col0\" class=\"data row13 col0\" >16385</td>\n",
       "      <td id=\"T_fc585_row13_col1\" class=\"data row13 col1\" >8193</td>\n",
       "      <td id=\"T_fc585_row13_col2\" class=\"data row13 col2\" >17.200045</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row14\" class=\"row_heading level0 row14\" >15</th>\n",
       "      <td id=\"T_fc585_row14_col0\" class=\"data row14 col0\" >32769</td>\n",
       "      <td id=\"T_fc585_row14_col1\" class=\"data row14 col1\" >16385</td>\n",
       "      <td id=\"T_fc585_row14_col2\" class=\"data row14 col2\" >130.346088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_fc585_level0_row15\" class=\"row_heading level0 row15\" >16</th>\n",
       "      <td id=\"T_fc585_row15_col0\" class=\"data row15 col0\" >65537</td>\n",
       "      <td id=\"T_fc585_row15_col1\" class=\"data row15 col1\" >32769</td>\n",
       "      <td id=\"T_fc585_row15_col2\" class=\"data row15 col2\" >1007.628282</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f9bb4015e0>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_long_division_algorithm.style.set_table_styles({\n",
    "    \"Numerator Digits\": [{'selector': '', 'props': 'text-align: center;'}],\n",
    "    \"Denominator Digits\": [{'selector':'','props': 'text-align: center ;'}],\n",
    "    \"Time(s)\": [{'selector':'','props': 'text-align: center ; '}]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9a86c9de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(increment(long_division_number_of_digits_list, 1), long_division_time_list, \n",
    "         color = \"blue\", marker = 'o', markersize = 7)\n",
    "plt.xlabel(\"Denominator Digits\", fontsize = 15)\n",
    "plt.ylabel(\"Time(s)\", fontsize = 15)\n",
    "plt.title(\"Long Division Algorithm\", fontsize = 15)\n",
    "plt.gcf().set_size_inches(7, 7)\n",
    "plt.figtext(0.5, 0.01, \"The Numerator Digits are Always Twice the Denominator Digits\",\n",
    "            ha = \"center\", fontsize = 10, bbox = {\"facecolor\":\"white\", \"alpha\":0.3, \"pad\":3})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c589d31b",
   "metadata": {},
   "source": [
    "### <center>Restoring Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a923efa2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_6e446_ .col0 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_6e446_ .col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_6e446_ .col2 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_6e446_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Numerator Digits</th>\n",
       "      <th class=\"col_heading level0 col1\" >Denominator Digits</th>\n",
       "      <th class=\"col_heading level0 col2\" >Time(s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_6e446_row0_col0\" class=\"data row0 col0\" >3</td>\n",
       "      <td id=\"T_6e446_row0_col1\" class=\"data row0 col1\" >2</td>\n",
       "      <td id=\"T_6e446_row0_col2\" class=\"data row0 col2\" >0.001000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_6e446_row1_col0\" class=\"data row1 col0\" >5</td>\n",
       "      <td id=\"T_6e446_row1_col1\" class=\"data row1 col1\" >3</td>\n",
       "      <td id=\"T_6e446_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_6e446_row2_col0\" class=\"data row2 col0\" >9</td>\n",
       "      <td id=\"T_6e446_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "      <td id=\"T_6e446_row2_col2\" class=\"data row2 col2\" >0.000998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_6e446_row3_col0\" class=\"data row3 col0\" >17</td>\n",
       "      <td id=\"T_6e446_row3_col1\" class=\"data row3 col1\" >9</td>\n",
       "      <td id=\"T_6e446_row3_col2\" class=\"data row3 col2\" >0.000999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_6e446_row4_col0\" class=\"data row4 col0\" >33</td>\n",
       "      <td id=\"T_6e446_row4_col1\" class=\"data row4 col1\" >17</td>\n",
       "      <td id=\"T_6e446_row4_col2\" class=\"data row4 col2\" >0.003997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "      <td id=\"T_6e446_row5_col0\" class=\"data row5 col0\" >65</td>\n",
       "      <td id=\"T_6e446_row5_col1\" class=\"data row5 col1\" >33</td>\n",
       "      <td id=\"T_6e446_row5_col2\" class=\"data row5 col2\" >0.011989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_6e446_row6_col0\" class=\"data row6 col0\" >129</td>\n",
       "      <td id=\"T_6e446_row6_col1\" class=\"data row6 col1\" >65</td>\n",
       "      <td id=\"T_6e446_row6_col2\" class=\"data row6 col2\" >0.036967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "      <td id=\"T_6e446_row7_col0\" class=\"data row7 col0\" >257</td>\n",
       "      <td id=\"T_6e446_row7_col1\" class=\"data row7 col1\" >129</td>\n",
       "      <td id=\"T_6e446_row7_col2\" class=\"data row7 col2\" >0.133874</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_6e446_row8_col0\" class=\"data row8 col0\" >513</td>\n",
       "      <td id=\"T_6e446_row8_col1\" class=\"data row8 col1\" >257</td>\n",
       "      <td id=\"T_6e446_row8_col2\" class=\"data row8 col2\" >0.483552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_6e446_row9_col0\" class=\"data row9 col0\" >1025</td>\n",
       "      <td id=\"T_6e446_row9_col1\" class=\"data row9 col1\" >513</td>\n",
       "      <td id=\"T_6e446_row9_col2\" class=\"data row9 col2\" >1.853281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "      <td id=\"T_6e446_row10_col0\" class=\"data row10 col0\" >2049</td>\n",
       "      <td id=\"T_6e446_row10_col1\" class=\"data row10 col1\" >1025</td>\n",
       "      <td id=\"T_6e446_row10_col2\" class=\"data row10 col2\" >7.444095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
       "      <td id=\"T_6e446_row11_col0\" class=\"data row11 col0\" >4097</td>\n",
       "      <td id=\"T_6e446_row11_col1\" class=\"data row11 col1\" >2049</td>\n",
       "      <td id=\"T_6e446_row11_col2\" class=\"data row11 col2\" >28.663396</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
       "      <td id=\"T_6e446_row12_col0\" class=\"data row12 col0\" >8193</td>\n",
       "      <td id=\"T_6e446_row12_col1\" class=\"data row12 col1\" >4097</td>\n",
       "      <td id=\"T_6e446_row12_col2\" class=\"data row12 col2\" >114.108163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_6e446_level0_row13\" class=\"row_heading level0 row13\" >14</th>\n",
       "      <td id=\"T_6e446_row13_col0\" class=\"data row13 col0\" >16385</td>\n",
       "      <td id=\"T_6e446_row13_col1\" class=\"data row13 col1\" >8193</td>\n",
       "      <td id=\"T_6e446_row13_col2\" class=\"data row13 col2\" >462.880590</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f9badd5130>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_restoring_division_algorithm.style.set_table_styles({\n",
    "    \"Numerator Digits\": [{'selector': '', 'props': 'text-align: center;'}],\n",
    "    \"Denominator Digits\": [{'selector':'','props': 'text-align: center ;'}],\n",
    "    \"Time(s)\": [{'selector':'','props': 'text-align: center ; '}]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "b8e66049",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(increment(restoring_division_number_of_digits_list, 1), restoring_division_time_list,\n",
    "         color = \"blue\", marker = 'o', markersize = 7)\n",
    "plt.xlabel(\"Denominator Digits\", fontsize = 15)\n",
    "plt.ylabel(\"Time(s)\", fontsize = 15)\n",
    "plt.title(\"Restoring Division Algorithm\", fontsize = 15)\n",
    "plt.gcf().set_size_inches(7, 7)\n",
    "plt.figtext(0.5, 0.01, \"The Numerator Digits are Always Twice the Denominator Digits\",\n",
    "            ha = \"center\", fontsize = 10, bbox = {\"facecolor\":\"white\", \"alpha\":0.3, \"pad\":3})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0d2b153",
   "metadata": {},
   "source": [
    "### <center>Non-Restoring Division Algorithm Table 1 and Graph 1</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6e53562f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c1a58_ .col0 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_c1a58_ .col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_c1a58_ .col2 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c1a58_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Numerator Digits</th>\n",
       "      <th class=\"col_heading level0 col1\" >Denominator Digits</th>\n",
       "      <th class=\"col_heading level0 col2\" >Time(s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c1a58_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_c1a58_row0_col0\" class=\"data row0 col0\" >3</td>\n",
       "      <td id=\"T_c1a58_row0_col1\" class=\"data row0 col1\" >2</td>\n",
       "      <td id=\"T_c1a58_row0_col2\" class=\"data row0 col2\" >86.672612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1a58_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_c1a58_row1_col0\" class=\"data row1 col0\" >5</td>\n",
       "      <td id=\"T_c1a58_row1_col1\" class=\"data row1 col1\" >3</td>\n",
       "      <td id=\"T_c1a58_row1_col2\" class=\"data row1 col2\" >83.805259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1a58_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_c1a58_row2_col0\" class=\"data row2 col0\" >9</td>\n",
       "      <td id=\"T_c1a58_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "      <td id=\"T_c1a58_row2_col2\" class=\"data row2 col2\" >83.946112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1a58_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_c1a58_row3_col0\" class=\"data row3 col0\" >17</td>\n",
       "      <td id=\"T_c1a58_row3_col1\" class=\"data row3 col1\" >9</td>\n",
       "      <td id=\"T_c1a58_row3_col2\" class=\"data row3 col2\" >84.373748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1a58_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_c1a58_row4_col0\" class=\"data row4 col0\" >33</td>\n",
       "      <td id=\"T_c1a58_row4_col1\" class=\"data row4 col1\" >17</td>\n",
       "      <td id=\"T_c1a58_row4_col2\" class=\"data row4 col2\" >84.718411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1a58_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "      <td id=\"T_c1a58_row5_col0\" class=\"data row5 col0\" >65</td>\n",
       "      <td id=\"T_c1a58_row5_col1\" class=\"data row5 col1\" >33</td>\n",
       "      <td id=\"T_c1a58_row5_col2\" class=\"data row5 col2\" >84.425683</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1a58_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_c1a58_row6_col0\" class=\"data row6 col0\" >129</td>\n",
       "      <td id=\"T_c1a58_row6_col1\" class=\"data row6 col1\" >65</td>\n",
       "      <td id=\"T_c1a58_row6_col2\" class=\"data row6 col2\" >84.488609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1a58_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "      <td id=\"T_c1a58_row7_col0\" class=\"data row7 col0\" >257</td>\n",
       "      <td id=\"T_c1a58_row7_col1\" class=\"data row7 col1\" >129</td>\n",
       "      <td id=\"T_c1a58_row7_col2\" class=\"data row7 col2\" >83.954136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1a58_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_c1a58_row8_col0\" class=\"data row8 col0\" >513</td>\n",
       "      <td id=\"T_c1a58_row8_col1\" class=\"data row8 col1\" >257</td>\n",
       "      <td id=\"T_c1a58_row8_col2\" class=\"data row8 col2\" >93.925871</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c1a58_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_c1a58_row9_col0\" class=\"data row9 col0\" >1025</td>\n",
       "      <td id=\"T_c1a58_row9_col1\" class=\"data row9 col1\" >513</td>\n",
       "      <td id=\"T_c1a58_row9_col2\" class=\"data row9 col2\" >87.674854</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f9bc2e8cd0>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_non_restoring_division_algorithm.style.set_table_styles({\n",
    "    \"Numerator Digits\": [{'selector': '', 'props': 'text-align: center;'}],\n",
    "    \"Denominator Digits\": [{'selector':'','props': 'text-align: center ;'}],\n",
    "    \"Time(s)\": [{'selector':'','props': 'text-align: center ; '}]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "510263b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(increment(non_restoring_division_number_of_digits_list, 1),non_restoring_division_time_list, width = 5)\n",
    "plt.xlabel(\"Denominator Digits\", fontsize = 15)\n",
    "plt.ylabel(\"Time(s)\", fontsize = 15)\n",
    "plt.title(\"Non-Restoring Division Algorithm Graph 1\", fontsize = 15)\n",
    "plt.gcf().set_size_inches(7, 7)\n",
    "plt.figtext(0.5, 0.01, \"The Numerator Digits are Always Twice the Denominator Digits\",\n",
    "            ha = \"center\", fontsize = 10, bbox = {\"facecolor\":\"white\", \"alpha\":0.3, \"pad\":3})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0fc9cff6",
   "metadata": {},
   "source": [
    "### <center>Non-Restoring Division Algorithm Table 2 and Graph 2</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4af58f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_32cce_ .col0 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_32cce_ .col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_32cce_ .col-1 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_32cce_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Numerator Digits</th>\n",
       "      <th class=\"col_heading level0 col1\" >Denominator Digits</th>\n",
       "      <th class=\"col_heading level0 col2\" >Time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_32cce_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_32cce_row0_col0\" class=\"data row0 col0\" >3</td>\n",
       "      <td id=\"T_32cce_row0_col1\" class=\"data row0 col1\" >2</td>\n",
       "      <td id=\"T_32cce_row0_col2\" class=\"data row0 col2\" >370.069701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32cce_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_32cce_row1_col0\" class=\"data row1 col0\" >5</td>\n",
       "      <td id=\"T_32cce_row1_col1\" class=\"data row1 col1\" >3</td>\n",
       "      <td id=\"T_32cce_row1_col2\" class=\"data row1 col2\" >368.594196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32cce_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_32cce_row2_col0\" class=\"data row2 col0\" >9</td>\n",
       "      <td id=\"T_32cce_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "      <td id=\"T_32cce_row2_col2\" class=\"data row2 col2\" >376.286671</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32cce_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_32cce_row3_col0\" class=\"data row3 col0\" >17</td>\n",
       "      <td id=\"T_32cce_row3_col1\" class=\"data row3 col1\" >9</td>\n",
       "      <td id=\"T_32cce_row3_col2\" class=\"data row3 col2\" >370.354443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32cce_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_32cce_row4_col0\" class=\"data row4 col0\" >33</td>\n",
       "      <td id=\"T_32cce_row4_col1\" class=\"data row4 col1\" >17</td>\n",
       "      <td id=\"T_32cce_row4_col2\" class=\"data row4 col2\" >368.197445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32cce_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "      <td id=\"T_32cce_row5_col0\" class=\"data row5 col0\" >65</td>\n",
       "      <td id=\"T_32cce_row5_col1\" class=\"data row5 col1\" >33</td>\n",
       "      <td id=\"T_32cce_row5_col2\" class=\"data row5 col2\" >375.336798</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32cce_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_32cce_row6_col0\" class=\"data row6 col0\" >129</td>\n",
       "      <td id=\"T_32cce_row6_col1\" class=\"data row6 col1\" >65</td>\n",
       "      <td id=\"T_32cce_row6_col2\" class=\"data row6 col2\" >380.020501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32cce_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "      <td id=\"T_32cce_row7_col0\" class=\"data row7 col0\" >257</td>\n",
       "      <td id=\"T_32cce_row7_col1\" class=\"data row7 col1\" >129</td>\n",
       "      <td id=\"T_32cce_row7_col2\" class=\"data row7 col2\" >366.533988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32cce_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_32cce_row8_col0\" class=\"data row8 col0\" >513</td>\n",
       "      <td id=\"T_32cce_row8_col1\" class=\"data row8 col1\" >257</td>\n",
       "      <td id=\"T_32cce_row8_col2\" class=\"data row8 col2\" >371.276589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32cce_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_32cce_row9_col0\" class=\"data row9 col0\" >1025</td>\n",
       "      <td id=\"T_32cce_row9_col1\" class=\"data row9 col1\" >513</td>\n",
       "      <td id=\"T_32cce_row9_col2\" class=\"data row9 col2\" >370.073704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_32cce_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "      <td id=\"T_32cce_row10_col0\" class=\"data row10 col0\" >2049</td>\n",
       "      <td id=\"T_32cce_row10_col1\" class=\"data row10 col1\" >1025</td>\n",
       "      <td id=\"T_32cce_row10_col2\" class=\"data row10 col2\" >373.608426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f9bc1bd490>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_2_non_restoring_division_algorithm.style.set_table_styles({\n",
    "    \"Numerator Digits\": [{'selector': '', 'props': 'text-align: center;'}],\n",
    "    \"Denominator Digits\": [{'selector':'','props': 'text-align: center ;'}],\n",
    "    \"Time(s)\": [{'selector':'','props': 'text-align: center ; '}]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "66963a52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(increment(non_restoring_division_2_number_of_digits_list, 1),non_restoring_division_2_time_list, width = 5)\n",
    "plt.xlabel(\"Denominator Digits\", fontsize = 15)\n",
    "plt.ylabel(\"Time(s)\", fontsize = 15)\n",
    "plt.title(\"Non-Restoring Division Algorithm Graph 2\", fontsize = 15)\n",
    "plt.gcf().set_size_inches(7, 7)\n",
    "plt.figtext(0.5, 0.01, \"The Numerator Digits are Always Twice the Denominator Digits\",\n",
    "            ha = \"center\", fontsize = 10, bbox = {\"facecolor\":\"white\", \"alpha\":0.3, \"pad\":3})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d20dbb5",
   "metadata": {},
   "source": [
    "### <center>Newton Raphson Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7f764ff4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_4ed63_ .col0 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_4ed63_ .col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_4ed63_ .col2 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_4ed63_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Numerator Digits</th>\n",
       "      <th class=\"col_heading level0 col1\" >Denominator Digits</th>\n",
       "      <th class=\"col_heading level0 col2\" >Time(s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_4ed63_row0_col0\" class=\"data row0 col0\" >3</td>\n",
       "      <td id=\"T_4ed63_row0_col1\" class=\"data row0 col1\" >2</td>\n",
       "      <td id=\"T_4ed63_row0_col2\" class=\"data row0 col2\" >0.000999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_4ed63_row1_col0\" class=\"data row1 col0\" >5</td>\n",
       "      <td id=\"T_4ed63_row1_col1\" class=\"data row1 col1\" >3</td>\n",
       "      <td id=\"T_4ed63_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_4ed63_row2_col0\" class=\"data row2 col0\" >9</td>\n",
       "      <td id=\"T_4ed63_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "      <td id=\"T_4ed63_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_4ed63_row3_col0\" class=\"data row3 col0\" >17</td>\n",
       "      <td id=\"T_4ed63_row3_col1\" class=\"data row3 col1\" >9</td>\n",
       "      <td id=\"T_4ed63_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_4ed63_row4_col0\" class=\"data row4 col0\" >33</td>\n",
       "      <td id=\"T_4ed63_row4_col1\" class=\"data row4 col1\" >17</td>\n",
       "      <td id=\"T_4ed63_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "      <td id=\"T_4ed63_row5_col0\" class=\"data row5 col0\" >65</td>\n",
       "      <td id=\"T_4ed63_row5_col1\" class=\"data row5 col1\" >33</td>\n",
       "      <td id=\"T_4ed63_row5_col2\" class=\"data row5 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_4ed63_row6_col0\" class=\"data row6 col0\" >129</td>\n",
       "      <td id=\"T_4ed63_row6_col1\" class=\"data row6 col1\" >65</td>\n",
       "      <td id=\"T_4ed63_row6_col2\" class=\"data row6 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "      <td id=\"T_4ed63_row7_col0\" class=\"data row7 col0\" >257</td>\n",
       "      <td id=\"T_4ed63_row7_col1\" class=\"data row7 col1\" >129</td>\n",
       "      <td id=\"T_4ed63_row7_col2\" class=\"data row7 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_4ed63_row8_col0\" class=\"data row8 col0\" >513</td>\n",
       "      <td id=\"T_4ed63_row8_col1\" class=\"data row8 col1\" >257</td>\n",
       "      <td id=\"T_4ed63_row8_col2\" class=\"data row8 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_4ed63_row9_col0\" class=\"data row9 col0\" >1025</td>\n",
       "      <td id=\"T_4ed63_row9_col1\" class=\"data row9 col1\" >513</td>\n",
       "      <td id=\"T_4ed63_row9_col2\" class=\"data row9 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "      <td id=\"T_4ed63_row10_col0\" class=\"data row10 col0\" >2049</td>\n",
       "      <td id=\"T_4ed63_row10_col1\" class=\"data row10 col1\" >1025</td>\n",
       "      <td id=\"T_4ed63_row10_col2\" class=\"data row10 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
       "      <td id=\"T_4ed63_row11_col0\" class=\"data row11 col0\" >4097</td>\n",
       "      <td id=\"T_4ed63_row11_col1\" class=\"data row11 col1\" >2049</td>\n",
       "      <td id=\"T_4ed63_row11_col2\" class=\"data row11 col2\" >0.000999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
       "      <td id=\"T_4ed63_row12_col0\" class=\"data row12 col0\" >8193</td>\n",
       "      <td id=\"T_4ed63_row12_col1\" class=\"data row12 col1\" >4097</td>\n",
       "      <td id=\"T_4ed63_row12_col2\" class=\"data row12 col2\" >0.001998</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row13\" class=\"row_heading level0 row13\" >14</th>\n",
       "      <td id=\"T_4ed63_row13_col0\" class=\"data row13 col0\" >16385</td>\n",
       "      <td id=\"T_4ed63_row13_col1\" class=\"data row13 col1\" >8193</td>\n",
       "      <td id=\"T_4ed63_row13_col2\" class=\"data row13 col2\" >0.004995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row14\" class=\"row_heading level0 row14\" >15</th>\n",
       "      <td id=\"T_4ed63_row14_col0\" class=\"data row14 col0\" >32769</td>\n",
       "      <td id=\"T_4ed63_row14_col1\" class=\"data row14 col1\" >16385</td>\n",
       "      <td id=\"T_4ed63_row14_col2\" class=\"data row14 col2\" >0.019983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row15\" class=\"row_heading level0 row15\" >16</th>\n",
       "      <td id=\"T_4ed63_row15_col0\" class=\"data row15 col0\" >65537</td>\n",
       "      <td id=\"T_4ed63_row15_col1\" class=\"data row15 col1\" >32769</td>\n",
       "      <td id=\"T_4ed63_row15_col2\" class=\"data row15 col2\" >0.077929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row16\" class=\"row_heading level0 row16\" >17</th>\n",
       "      <td id=\"T_4ed63_row16_col0\" class=\"data row16 col0\" >131073</td>\n",
       "      <td id=\"T_4ed63_row16_col1\" class=\"data row16 col1\" >65537</td>\n",
       "      <td id=\"T_4ed63_row16_col2\" class=\"data row16 col2\" >0.310712</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row17\" class=\"row_heading level0 row17\" >18</th>\n",
       "      <td id=\"T_4ed63_row17_col0\" class=\"data row17 col0\" >262145</td>\n",
       "      <td id=\"T_4ed63_row17_col1\" class=\"data row17 col1\" >131073</td>\n",
       "      <td id=\"T_4ed63_row17_col2\" class=\"data row17 col2\" >1.243822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row18\" class=\"row_heading level0 row18\" >19</th>\n",
       "      <td id=\"T_4ed63_row18_col0\" class=\"data row18 col0\" >524289</td>\n",
       "      <td id=\"T_4ed63_row18_col1\" class=\"data row18 col1\" >262145</td>\n",
       "      <td id=\"T_4ed63_row18_col2\" class=\"data row18 col2\" >4.985398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row19\" class=\"row_heading level0 row19\" >20</th>\n",
       "      <td id=\"T_4ed63_row19_col0\" class=\"data row19 col0\" >1048577</td>\n",
       "      <td id=\"T_4ed63_row19_col1\" class=\"data row19 col1\" >524289</td>\n",
       "      <td id=\"T_4ed63_row19_col2\" class=\"data row19 col2\" >20.624876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_4ed63_level0_row20\" class=\"row_heading level0 row20\" >21</th>\n",
       "      <td id=\"T_4ed63_row20_col0\" class=\"data row20 col0\" >2097153</td>\n",
       "      <td id=\"T_4ed63_row20_col1\" class=\"data row20 col1\" >1048577</td>\n",
       "      <td id=\"T_4ed63_row20_col2\" class=\"data row20 col2\" >81.322561</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f9bb923bb0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_newton_raphson_division_algorithm.style.set_table_styles({\n",
    "    \"Numerator Digits\": [{'selector': '', 'props': 'text-align: center;'}],\n",
    "    \"Denominator Digits\": [{'selector':'','props': 'text-align: center ;'}],\n",
    "    \"Time(s)\": [{'selector':'','props': 'text-align: center ; '}]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "67f3522e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(increment(newton_raphson_division_number_of_digits_list, 1), newton_raphson_division_time_list,\n",
    "         color = \"blue\", marker = 'o', markersize = 7)\n",
    "plt.xlabel(\"Denominator Digits\", fontsize = 15)\n",
    "plt.ylabel(\"Time(s)\", fontsize = 15)\n",
    "plt.title(\"Newton Raphson Division Algorithm\", fontsize = 15)\n",
    "plt.gcf().set_size_inches(7, 7)\n",
    "plt.figtext(0.5, 0.01, \"The Numerator Digits are Always Twice the Denominator Digits\",\n",
    "            ha = \"center\", fontsize = 10, bbox = {\"facecolor\":\"white\", \"alpha\":0.3, \"pad\":3})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "40f915e9",
   "metadata": {},
   "source": [
    "### <center>Python's divmod Function</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "5fa249f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_0cbb9_ .col0 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_0cbb9_ .col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_0cbb9_ .col2 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_0cbb9_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Numerator Digits</th>\n",
       "      <th class=\"col_heading level0 col1\" >Denominator Digits</th>\n",
       "      <th class=\"col_heading level0 col2\" >Time(s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_0cbb9_row0_col0\" class=\"data row0 col0\" >3</td>\n",
       "      <td id=\"T_0cbb9_row0_col1\" class=\"data row0 col1\" >2</td>\n",
       "      <td id=\"T_0cbb9_row0_col2\" class=\"data row0 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_0cbb9_row1_col0\" class=\"data row1 col0\" >5</td>\n",
       "      <td id=\"T_0cbb9_row1_col1\" class=\"data row1 col1\" >3</td>\n",
       "      <td id=\"T_0cbb9_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_0cbb9_row2_col0\" class=\"data row2 col0\" >9</td>\n",
       "      <td id=\"T_0cbb9_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "      <td id=\"T_0cbb9_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_0cbb9_row3_col0\" class=\"data row3 col0\" >17</td>\n",
       "      <td id=\"T_0cbb9_row3_col1\" class=\"data row3 col1\" >9</td>\n",
       "      <td id=\"T_0cbb9_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_0cbb9_row4_col0\" class=\"data row4 col0\" >33</td>\n",
       "      <td id=\"T_0cbb9_row4_col1\" class=\"data row4 col1\" >17</td>\n",
       "      <td id=\"T_0cbb9_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "      <td id=\"T_0cbb9_row5_col0\" class=\"data row5 col0\" >65</td>\n",
       "      <td id=\"T_0cbb9_row5_col1\" class=\"data row5 col1\" >33</td>\n",
       "      <td id=\"T_0cbb9_row5_col2\" class=\"data row5 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_0cbb9_row6_col0\" class=\"data row6 col0\" >129</td>\n",
       "      <td id=\"T_0cbb9_row6_col1\" class=\"data row6 col1\" >65</td>\n",
       "      <td id=\"T_0cbb9_row6_col2\" class=\"data row6 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "      <td id=\"T_0cbb9_row7_col0\" class=\"data row7 col0\" >257</td>\n",
       "      <td id=\"T_0cbb9_row7_col1\" class=\"data row7 col1\" >129</td>\n",
       "      <td id=\"T_0cbb9_row7_col2\" class=\"data row7 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_0cbb9_row8_col0\" class=\"data row8 col0\" >513</td>\n",
       "      <td id=\"T_0cbb9_row8_col1\" class=\"data row8 col1\" >257</td>\n",
       "      <td id=\"T_0cbb9_row8_col2\" class=\"data row8 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_0cbb9_row9_col0\" class=\"data row9 col0\" >1025</td>\n",
       "      <td id=\"T_0cbb9_row9_col1\" class=\"data row9 col1\" >513</td>\n",
       "      <td id=\"T_0cbb9_row9_col2\" class=\"data row9 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "      <td id=\"T_0cbb9_row10_col0\" class=\"data row10 col0\" >2049</td>\n",
       "      <td id=\"T_0cbb9_row10_col1\" class=\"data row10 col1\" >1025</td>\n",
       "      <td id=\"T_0cbb9_row10_col2\" class=\"data row10 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
       "      <td id=\"T_0cbb9_row11_col0\" class=\"data row11 col0\" >4097</td>\n",
       "      <td id=\"T_0cbb9_row11_col1\" class=\"data row11 col1\" >2049</td>\n",
       "      <td id=\"T_0cbb9_row11_col2\" class=\"data row11 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
       "      <td id=\"T_0cbb9_row12_col0\" class=\"data row12 col0\" >8193</td>\n",
       "      <td id=\"T_0cbb9_row12_col1\" class=\"data row12 col1\" >4097</td>\n",
       "      <td id=\"T_0cbb9_row12_col2\" class=\"data row12 col2\" >0.001996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row13\" class=\"row_heading level0 row13\" >14</th>\n",
       "      <td id=\"T_0cbb9_row13_col0\" class=\"data row13 col0\" >16385</td>\n",
       "      <td id=\"T_0cbb9_row13_col1\" class=\"data row13 col1\" >8193</td>\n",
       "      <td id=\"T_0cbb9_row13_col2\" class=\"data row13 col2\" >0.005996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row14\" class=\"row_heading level0 row14\" >15</th>\n",
       "      <td id=\"T_0cbb9_row14_col0\" class=\"data row14 col0\" >32769</td>\n",
       "      <td id=\"T_0cbb9_row14_col1\" class=\"data row14 col1\" >16385</td>\n",
       "      <td id=\"T_0cbb9_row14_col2\" class=\"data row14 col2\" >0.024977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row15\" class=\"row_heading level0 row15\" >16</th>\n",
       "      <td id=\"T_0cbb9_row15_col0\" class=\"data row15 col0\" >65537</td>\n",
       "      <td id=\"T_0cbb9_row15_col1\" class=\"data row15 col1\" >32769</td>\n",
       "      <td id=\"T_0cbb9_row15_col2\" class=\"data row15 col2\" >0.104904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row16\" class=\"row_heading level0 row16\" >17</th>\n",
       "      <td id=\"T_0cbb9_row16_col0\" class=\"data row16 col0\" >131073</td>\n",
       "      <td id=\"T_0cbb9_row16_col1\" class=\"data row16 col1\" >65537</td>\n",
       "      <td id=\"T_0cbb9_row16_col2\" class=\"data row16 col2\" >0.361669</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row17\" class=\"row_heading level0 row17\" >18</th>\n",
       "      <td id=\"T_0cbb9_row17_col0\" class=\"data row17 col0\" >262145</td>\n",
       "      <td id=\"T_0cbb9_row17_col1\" class=\"data row17 col1\" >131073</td>\n",
       "      <td id=\"T_0cbb9_row17_col2\" class=\"data row17 col2\" >1.296821</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row18\" class=\"row_heading level0 row18\" >19</th>\n",
       "      <td id=\"T_0cbb9_row18_col0\" class=\"data row18 col0\" >524289</td>\n",
       "      <td id=\"T_0cbb9_row18_col1\" class=\"data row18 col1\" >262145</td>\n",
       "      <td id=\"T_0cbb9_row18_col2\" class=\"data row18 col2\" >5.649780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row19\" class=\"row_heading level0 row19\" >20</th>\n",
       "      <td id=\"T_0cbb9_row19_col0\" class=\"data row19 col0\" >1048577</td>\n",
       "      <td id=\"T_0cbb9_row19_col1\" class=\"data row19 col1\" >524289</td>\n",
       "      <td id=\"T_0cbb9_row19_col2\" class=\"data row19 col2\" >22.032585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_0cbb9_level0_row20\" class=\"row_heading level0 row20\" >21</th>\n",
       "      <td id=\"T_0cbb9_row20_col0\" class=\"data row20 col0\" >2097153</td>\n",
       "      <td id=\"T_0cbb9_row20_col1\" class=\"data row20 col1\" >1048577</td>\n",
       "      <td id=\"T_0cbb9_row20_col2\" class=\"data row20 col2\" >82.013895</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f9bb840940>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_divmod_function.style.set_table_styles({\n",
    "    \"Numerator Digits\": [{'selector': '', 'props': 'text-align: center;'}],\n",
    "    \"Denominator Digits\": [{'selector':'','props': 'text-align: center ;'}],\n",
    "    \"Time(s)\": [{'selector':'','props': 'text-align: center ; '}]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "1b44333d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(increment(divmod_number_of_digits_list, 1), divmod_time_list,\n",
    "         color = \"blue\", marker = 'o', markersize = 7)\n",
    "plt.xlabel(\"Denominator Digits\", fontsize = 15)\n",
    "plt.ylabel(\"Time(s)\", fontsize = 15)\n",
    "plt.title(\"Python's divmod Function\", fontsize = 15)\n",
    "plt.gcf().set_size_inches(7, 7)\n",
    "plt.figtext(0.5, 0.01, \"The Numerator Digits are Always Twice the Denominator Digits\",\n",
    "            ha = \"center\", fontsize = 10, bbox = {\"facecolor\":\"white\", \"alpha\":0.3, \"pad\":3})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c43969cd",
   "metadata": {},
   "source": [
    "### <center>Python's Integer Division</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6cf5e7af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_d6517_ .col0 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_d6517_ .col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_d6517_ .col2 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_d6517_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Numerator Digits</th>\n",
       "      <th class=\"col_heading level0 col1\" >Denominator Digits</th>\n",
       "      <th class=\"col_heading level0 col2\" >Time(s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_d6517_row0_col0\" class=\"data row0 col0\" >3</td>\n",
       "      <td id=\"T_d6517_row0_col1\" class=\"data row0 col1\" >2</td>\n",
       "      <td id=\"T_d6517_row0_col2\" class=\"data row0 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_d6517_row1_col0\" class=\"data row1 col0\" >5</td>\n",
       "      <td id=\"T_d6517_row1_col1\" class=\"data row1 col1\" >3</td>\n",
       "      <td id=\"T_d6517_row1_col2\" class=\"data row1 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_d6517_row2_col0\" class=\"data row2 col0\" >9</td>\n",
       "      <td id=\"T_d6517_row2_col1\" class=\"data row2 col1\" >5</td>\n",
       "      <td id=\"T_d6517_row2_col2\" class=\"data row2 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_d6517_row3_col0\" class=\"data row3 col0\" >17</td>\n",
       "      <td id=\"T_d6517_row3_col1\" class=\"data row3 col1\" >9</td>\n",
       "      <td id=\"T_d6517_row3_col2\" class=\"data row3 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_d6517_row4_col0\" class=\"data row4 col0\" >33</td>\n",
       "      <td id=\"T_d6517_row4_col1\" class=\"data row4 col1\" >17</td>\n",
       "      <td id=\"T_d6517_row4_col2\" class=\"data row4 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "      <td id=\"T_d6517_row5_col0\" class=\"data row5 col0\" >65</td>\n",
       "      <td id=\"T_d6517_row5_col1\" class=\"data row5 col1\" >33</td>\n",
       "      <td id=\"T_d6517_row5_col2\" class=\"data row5 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_d6517_row6_col0\" class=\"data row6 col0\" >129</td>\n",
       "      <td id=\"T_d6517_row6_col1\" class=\"data row6 col1\" >65</td>\n",
       "      <td id=\"T_d6517_row6_col2\" class=\"data row6 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "      <td id=\"T_d6517_row7_col0\" class=\"data row7 col0\" >257</td>\n",
       "      <td id=\"T_d6517_row7_col1\" class=\"data row7 col1\" >129</td>\n",
       "      <td id=\"T_d6517_row7_col2\" class=\"data row7 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_d6517_row8_col0\" class=\"data row8 col0\" >513</td>\n",
       "      <td id=\"T_d6517_row8_col1\" class=\"data row8 col1\" >257</td>\n",
       "      <td id=\"T_d6517_row8_col2\" class=\"data row8 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_d6517_row9_col0\" class=\"data row9 col0\" >1025</td>\n",
       "      <td id=\"T_d6517_row9_col1\" class=\"data row9 col1\" >513</td>\n",
       "      <td id=\"T_d6517_row9_col2\" class=\"data row9 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "      <td id=\"T_d6517_row10_col0\" class=\"data row10 col0\" >2049</td>\n",
       "      <td id=\"T_d6517_row10_col1\" class=\"data row10 col1\" >1025</td>\n",
       "      <td id=\"T_d6517_row10_col2\" class=\"data row10 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row11\" class=\"row_heading level0 row11\" >12</th>\n",
       "      <td id=\"T_d6517_row11_col0\" class=\"data row11 col0\" >4097</td>\n",
       "      <td id=\"T_d6517_row11_col1\" class=\"data row11 col1\" >2049</td>\n",
       "      <td id=\"T_d6517_row11_col2\" class=\"data row11 col2\" >0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row12\" class=\"row_heading level0 row12\" >13</th>\n",
       "      <td id=\"T_d6517_row12_col0\" class=\"data row12 col0\" >8193</td>\n",
       "      <td id=\"T_d6517_row12_col1\" class=\"data row12 col1\" >4097</td>\n",
       "      <td id=\"T_d6517_row12_col2\" class=\"data row12 col2\" >0.002991</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row13\" class=\"row_heading level0 row13\" >14</th>\n",
       "      <td id=\"T_d6517_row13_col0\" class=\"data row13 col0\" >16385</td>\n",
       "      <td id=\"T_d6517_row13_col1\" class=\"data row13 col1\" >8193</td>\n",
       "      <td id=\"T_d6517_row13_col2\" class=\"data row13 col2\" >0.007985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row14\" class=\"row_heading level0 row14\" >15</th>\n",
       "      <td id=\"T_d6517_row14_col0\" class=\"data row14 col0\" >32769</td>\n",
       "      <td id=\"T_d6517_row14_col1\" class=\"data row14 col1\" >16385</td>\n",
       "      <td id=\"T_d6517_row14_col2\" class=\"data row14 col2\" >0.025974</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row15\" class=\"row_heading level0 row15\" >16</th>\n",
       "      <td id=\"T_d6517_row15_col0\" class=\"data row15 col0\" >65537</td>\n",
       "      <td id=\"T_d6517_row15_col1\" class=\"data row15 col1\" >32769</td>\n",
       "      <td id=\"T_d6517_row15_col2\" class=\"data row15 col2\" >0.079931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row16\" class=\"row_heading level0 row16\" >17</th>\n",
       "      <td id=\"T_d6517_row16_col0\" class=\"data row16 col0\" >131073</td>\n",
       "      <td id=\"T_d6517_row16_col1\" class=\"data row16 col1\" >65537</td>\n",
       "      <td id=\"T_d6517_row16_col2\" class=\"data row16 col2\" >0.339664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row17\" class=\"row_heading level0 row17\" >18</th>\n",
       "      <td id=\"T_d6517_row17_col0\" class=\"data row17 col0\" >262145</td>\n",
       "      <td id=\"T_d6517_row17_col1\" class=\"data row17 col1\" >131073</td>\n",
       "      <td id=\"T_d6517_row17_col2\" class=\"data row17 col2\" >1.276824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row18\" class=\"row_heading level0 row18\" >19</th>\n",
       "      <td id=\"T_d6517_row18_col0\" class=\"data row18 col0\" >524289</td>\n",
       "      <td id=\"T_d6517_row18_col1\" class=\"data row18 col1\" >262145</td>\n",
       "      <td id=\"T_d6517_row18_col2\" class=\"data row18 col2\" >5.358030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row19\" class=\"row_heading level0 row19\" >20</th>\n",
       "      <td id=\"T_d6517_row19_col0\" class=\"data row19 col0\" >1048577</td>\n",
       "      <td id=\"T_d6517_row19_col1\" class=\"data row19 col1\" >524289</td>\n",
       "      <td id=\"T_d6517_row19_col2\" class=\"data row19 col2\" >21.516048</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row20\" class=\"row_heading level0 row20\" >21</th>\n",
       "      <td id=\"T_d6517_row20_col0\" class=\"data row20 col0\" >2097153</td>\n",
       "      <td id=\"T_d6517_row20_col1\" class=\"data row20 col1\" >1048577</td>\n",
       "      <td id=\"T_d6517_row20_col2\" class=\"data row20 col2\" >84.886254</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row21\" class=\"row_heading level0 row21\" >22</th>\n",
       "      <td id=\"T_d6517_row21_col0\" class=\"data row21 col0\" >4194305</td>\n",
       "      <td id=\"T_d6517_row21_col1\" class=\"data row21 col1\" >2097153</td>\n",
       "      <td id=\"T_d6517_row21_col2\" class=\"data row21 col2\" >334.145011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_d6517_level0_row22\" class=\"row_heading level0 row22\" >23</th>\n",
       "      <td id=\"T_d6517_row22_col0\" class=\"data row22 col0\" >8388609</td>\n",
       "      <td id=\"T_d6517_row22_col1\" class=\"data row22 col1\" >4194305</td>\n",
       "      <td id=\"T_d6517_row22_col2\" class=\"data row22 col2\" >1376.462213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f9bbb2b130>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_integer_division.style.set_table_styles({\n",
    "    \"Numerator Digits\": [{'selector': '', 'props': 'text-align: center;'}],\n",
    "    \"Denominator Digits\": [{'selector':'','props': 'text-align: center ;'}],\n",
    "    \"Time(s)\": [{'selector':'','props': 'text-align: center ; '}]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "c8a5c2ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(increment(integer_division_number_of_digits_list, 1), integer_division_time_list,\n",
    "         color = \"blue\", marker = 'o', markersize = 7)\n",
    "plt.xlabel(\"Denominator Digits\", fontsize = 15)\n",
    "plt.ylabel(\"Time(s)\", fontsize = 15)\n",
    "plt.title(\"Python's Integer Division\", fontsize = 15)\n",
    "plt.gcf().set_size_inches(7, 7)\n",
    "plt.figtext(0.5, 0.01, \"The Numerator Digits are Always Twice the Denominator Digits\",\n",
    "            ha = \"center\", fontsize = 10, bbox = {\"facecolor\":\"white\", \"alpha\":0.3, \"pad\":3})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a0f7fa3c",
   "metadata": {},
   "source": [
    "### <center>Polynomial Long Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "39c5ff5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_a0d14_ .col0 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_a0d14_ .col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_a0d14_ .col2 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_a0d14_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Numerator Highest Degree</th>\n",
       "      <th class=\"col_heading level0 col1\" >Denominator Highest Degree</th>\n",
       "      <th class=\"col_heading level0 col2\" >Time(s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_a0d14_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_a0d14_row0_col0\" class=\"data row0 col0\" >5002</td>\n",
       "      <td id=\"T_a0d14_row0_col1\" class=\"data row0 col1\" >5000</td>\n",
       "      <td id=\"T_a0d14_row0_col2\" class=\"data row0 col2\" >0.039962</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a0d14_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_a0d14_row1_col0\" class=\"data row1 col0\" >34502</td>\n",
       "      <td id=\"T_a0d14_row1_col1\" class=\"data row1 col1\" >34500</td>\n",
       "      <td id=\"T_a0d14_row1_col2\" class=\"data row1 col2\" >0.274746</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a0d14_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_a0d14_row2_col0\" class=\"data row2 col0\" >64002</td>\n",
       "      <td id=\"T_a0d14_row2_col1\" class=\"data row2 col1\" >64000</td>\n",
       "      <td id=\"T_a0d14_row2_col2\" class=\"data row2 col2\" >0.510526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a0d14_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_a0d14_row3_col0\" class=\"data row3 col0\" >93502</td>\n",
       "      <td id=\"T_a0d14_row3_col1\" class=\"data row3 col1\" >93500</td>\n",
       "      <td id=\"T_a0d14_row3_col2\" class=\"data row3 col2\" >0.747307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a0d14_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_a0d14_row4_col0\" class=\"data row4 col0\" >123002</td>\n",
       "      <td id=\"T_a0d14_row4_col1\" class=\"data row4 col1\" >123000</td>\n",
       "      <td id=\"T_a0d14_row4_col2\" class=\"data row4 col2\" >0.997053</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a0d14_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "      <td id=\"T_a0d14_row5_col0\" class=\"data row5 col0\" >152502</td>\n",
       "      <td id=\"T_a0d14_row5_col1\" class=\"data row5 col1\" >152500</td>\n",
       "      <td id=\"T_a0d14_row5_col2\" class=\"data row5 col2\" >1.326780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a0d14_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_a0d14_row6_col0\" class=\"data row6 col0\" >182002</td>\n",
       "      <td id=\"T_a0d14_row6_col1\" class=\"data row6 col1\" >182000</td>\n",
       "      <td id=\"T_a0d14_row6_col2\" class=\"data row6 col2\" >2.019138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a0d14_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "      <td id=\"T_a0d14_row7_col0\" class=\"data row7 col0\" >211502</td>\n",
       "      <td id=\"T_a0d14_row7_col1\" class=\"data row7 col1\" >211500</td>\n",
       "      <td id=\"T_a0d14_row7_col2\" class=\"data row7 col2\" >1.785330</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a0d14_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_a0d14_row8_col0\" class=\"data row8 col0\" >241002</td>\n",
       "      <td id=\"T_a0d14_row8_col1\" class=\"data row8 col1\" >241000</td>\n",
       "      <td id=\"T_a0d14_row8_col2\" class=\"data row8 col2\" >1.960184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a0d14_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_a0d14_row9_col0\" class=\"data row9 col0\" >270502</td>\n",
       "      <td id=\"T_a0d14_row9_col1\" class=\"data row9 col1\" >270500</td>\n",
       "      <td id=\"T_a0d14_row9_col2\" class=\"data row9 col2\" >2.251888</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_a0d14_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "      <td id=\"T_a0d14_row10_col0\" class=\"data row10 col0\" >300002</td>\n",
       "      <td id=\"T_a0d14_row10_col1\" class=\"data row10 col1\" >300000</td>\n",
       "      <td id=\"T_a0d14_row10_col2\" class=\"data row10 col2\" >2.590598</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f9bc43e460>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_polynomial_long_division_algorithm.style.set_table_styles({\n",
    "    \"Numerator Highest Degree\": [{'selector': '', 'props': 'text-align: center;'}],\n",
    "    \"Denominator Highest Degree\": [{'selector':'','props': 'text-align: center ;'}],\n",
    "    \"Time(s)\": [{'selector':'','props': 'text-align: center ; '}]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5c5cb40b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(polynomial_long_division_highest_degree_list, polynomial_long_division_time_list,\n",
    "         color = \"blue\", marker = 'o', markersize = 7)\n",
    "plt.xlabel(\"Average Highest Degree of Numerator and Denominator\", fontsize = 12)\n",
    "plt.ylabel(\"Time(s)\", fontsize = 15)\n",
    "plt.title(\"Polynomial Long Division Algorithm\", fontsize = 15)\n",
    "plt.gcf().set_size_inches(7, 7)\n",
    "plt.figtext(0.51, 0.01, \"The Average Highest Degree Also Represents the Number of Terms in the Polynomials\",\n",
    "            ha = \"center\", fontsize = 10, bbox = {\"facecolor\":\"white\", \"alpha\":0.3, \"pad\":3})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75f4a0e0",
   "metadata": {},
   "source": [
    "### <center>Polynomial Extended Synthetic Division Algorithm</center>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "b0e7f241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_79bf4_ .col0 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_79bf4_ .col1 {\n",
       "  text-align: center;\n",
       "}\n",
       "#T_79bf4_ .col2 {\n",
       "  text-align: center;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_79bf4_\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th class=\"col_heading level0 col0\" >Numerator Highest Degree</th>\n",
       "      <th class=\"col_heading level0 col1\" >Denominator Highest Degree</th>\n",
       "      <th class=\"col_heading level0 col2\" >Time(s)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_79bf4_level0_row0\" class=\"row_heading level0 row0\" >1</th>\n",
       "      <td id=\"T_79bf4_row0_col0\" class=\"data row0 col0\" >5002</td>\n",
       "      <td id=\"T_79bf4_row0_col1\" class=\"data row0 col1\" >5000</td>\n",
       "      <td id=\"T_79bf4_row0_col2\" class=\"data row0 col2\" >0.018982</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_79bf4_level0_row1\" class=\"row_heading level0 row1\" >2</th>\n",
       "      <td id=\"T_79bf4_row1_col0\" class=\"data row1 col0\" >34502</td>\n",
       "      <td id=\"T_79bf4_row1_col1\" class=\"data row1 col1\" >34500</td>\n",
       "      <td id=\"T_79bf4_row1_col2\" class=\"data row1 col2\" >0.140893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_79bf4_level0_row2\" class=\"row_heading level0 row2\" >3</th>\n",
       "      <td id=\"T_79bf4_row2_col0\" class=\"data row2 col0\" >64002</td>\n",
       "      <td id=\"T_79bf4_row2_col1\" class=\"data row2 col1\" >64000</td>\n",
       "      <td id=\"T_79bf4_row2_col2\" class=\"data row2 col2\" >0.254762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_79bf4_level0_row3\" class=\"row_heading level0 row3\" >4</th>\n",
       "      <td id=\"T_79bf4_row3_col0\" class=\"data row3 col0\" >93502</td>\n",
       "      <td id=\"T_79bf4_row3_col1\" class=\"data row3 col1\" >93500</td>\n",
       "      <td id=\"T_79bf4_row3_col2\" class=\"data row3 col2\" >0.382647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_79bf4_level0_row4\" class=\"row_heading level0 row4\" >5</th>\n",
       "      <td id=\"T_79bf4_row4_col0\" class=\"data row4 col0\" >123002</td>\n",
       "      <td id=\"T_79bf4_row4_col1\" class=\"data row4 col1\" >123000</td>\n",
       "      <td id=\"T_79bf4_row4_col2\" class=\"data row4 col2\" >0.490546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_79bf4_level0_row5\" class=\"row_heading level0 row5\" >6</th>\n",
       "      <td id=\"T_79bf4_row5_col0\" class=\"data row5 col0\" >152502</td>\n",
       "      <td id=\"T_79bf4_row5_col1\" class=\"data row5 col1\" >152500</td>\n",
       "      <td id=\"T_79bf4_row5_col2\" class=\"data row5 col2\" >0.616430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_79bf4_level0_row6\" class=\"row_heading level0 row6\" >7</th>\n",
       "      <td id=\"T_79bf4_row6_col0\" class=\"data row6 col0\" >182002</td>\n",
       "      <td id=\"T_79bf4_row6_col1\" class=\"data row6 col1\" >182000</td>\n",
       "      <td id=\"T_79bf4_row6_col2\" class=\"data row6 col2\" >0.728325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_79bf4_level0_row7\" class=\"row_heading level0 row7\" >8</th>\n",
       "      <td id=\"T_79bf4_row7_col0\" class=\"data row7 col0\" >211502</td>\n",
       "      <td id=\"T_79bf4_row7_col1\" class=\"data row7 col1\" >211500</td>\n",
       "      <td id=\"T_79bf4_row7_col2\" class=\"data row7 col2\" >0.934136</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_79bf4_level0_row8\" class=\"row_heading level0 row8\" >9</th>\n",
       "      <td id=\"T_79bf4_row8_col0\" class=\"data row8 col0\" >241002</td>\n",
       "      <td id=\"T_79bf4_row8_col1\" class=\"data row8 col1\" >241000</td>\n",
       "      <td id=\"T_79bf4_row8_col2\" class=\"data row8 col2\" >0.970101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_79bf4_level0_row9\" class=\"row_heading level0 row9\" >10</th>\n",
       "      <td id=\"T_79bf4_row9_col0\" class=\"data row9 col0\" >270502</td>\n",
       "      <td id=\"T_79bf4_row9_col1\" class=\"data row9 col1\" >270500</td>\n",
       "      <td id=\"T_79bf4_row9_col2\" class=\"data row9 col2\" >1.143923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_79bf4_level0_row10\" class=\"row_heading level0 row10\" >11</th>\n",
       "      <td id=\"T_79bf4_row10_col0\" class=\"data row10 col0\" >300002</td>\n",
       "      <td id=\"T_79bf4_row10_col1\" class=\"data row10 col1\" >300000</td>\n",
       "      <td id=\"T_79bf4_row10_col2\" class=\"data row10 col2\" >1.207880</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1f9bd716070>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "table_polynomial_extended_synthetic_division_algorithm.style.set_table_styles({\n",
    "    \"Numerator Highest Degree\": [{'selector': '', 'props': 'text-align: center;'}],\n",
    "    \"Denominator Highest Degree\": [{'selector':'','props': 'text-align: center ;'}],\n",
    "    \"Time(s)\": [{'selector':'','props': 'text-align: center ; '}]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "49d302d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(polynomial_extended_synthetic_division_highest_degree_list, polynomial_extended_synthetic_division_time_list,\n",
    "         color = \"blue\", marker = 'o', markersize = 7)\n",
    "plt.xlabel(\"Average Highest Degree of Numerator and Denominator\", fontsize = 12)\n",
    "plt.ylabel(\"Time(s)\", fontsize = 15)\n",
    "plt.title(\"Polynomial Extended Synthetic Division Algorithm\", fontsize = 15)\n",
    "plt.gcf().set_size_inches(7, 7)\n",
    "plt.figtext(0.51, 0.01, \"The Average Highest Degree Also represents the Number of Terms in the Polynomials\",\n",
    "            ha = \"center\", fontsize = 10, bbox = {\"facecolor\":\"white\", \"alpha\":0.3, \"pad\":3})\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}