Algorítimos de Ordenação O(n log n).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Algoritmos de Ordenação O (n log n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importando bibliotecas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import time \n",
    "import math\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Definindo vetores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TAMANHO VETOR 5K - 20K\n",
    "# ELEMENTOS DE 0 À 1M\n",
    "\n",
    "vetorzin6 = np.random.randint(1000000, size=(20000))\n",
    "vetorzin5 = np.random.randint(1000000, size=(17500))\n",
    "vetorzin4 = np.random.randint(1000000, size=(15000))\n",
    "vetorzin3 = np.random.randint(1000000, size=(12500))\n",
    "vetorzin2 = np.random.randint(1000000, size=(10000))\n",
    "vetorzin1 = np.random.randint(1000000, size=(5000))\n",
    "\n",
    "# TAMANHO VETOR 100K - 10M\n",
    "# ELEMENTOS DE 0 À 10M\n",
    "\n",
    "vetorzao6 = np.random.randint(10000000, size=(10000000))\n",
    "vetorzao5 = np.random.randint(10000000, size=(5000000))\n",
    "vetorzao4 = np.random.randint(10000000, size=(2000000))\n",
    "vetorzao3 = np.random.randint(10000000, size=(1000000))\n",
    "vetorzao2 = np.random.randint(10000000, size=(500000))\n",
    "vetorzao1 = np.random.randint(10000000, size=(100000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# TAMANHO VETOR 20K\n",
    "vet_ordenado = np.arange(20000)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algoritmos de Ordenação O (n²)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Selection Sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def selection_sort(vector):\n",
    "\n",
    "    for i in range(0, len(vector) - 1):\n",
    "        less = i;\n",
    "        for j in range(i+1, len(vector)):\n",
    "            if vector[j] < vector[less]:\n",
    "                less = j\n",
    "            \n",
    "        vector[i],vector[less] = vector[less],vector[i]    \n",
    "    return vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chamando o selection para vetores de tamanho 5k até 20k e calculando o tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VETOR 20K\n",
    "start6_selection = time.time()\n",
    "selection_sort(vetorzin6)\n",
    "end6_selection = time.time()\n",
    "\n",
    "#VETOR 17,5K\n",
    "start5_selection = time.time()\n",
    "selection_sort(vetorzin5)\n",
    "end5_selection = time.time()\n",
    "\n",
    "#VETOR 15K\n",
    "start4_selection = time.time()\n",
    "selection_sort(vetorzin4)\n",
    "end4_selection = time.time()\n",
    "\n",
    "#VETOR 12,5K\n",
    "start3_selection = time.time()\n",
    "selection_sort(vetorzin3)\n",
    "end3_selection = time.time()\n",
    "\n",
    "#VETOR 10K\n",
    "start2_selection = time.time()\n",
    "selection_sort(vetorzin2)\n",
    "end2_selection = time.time()\n",
    "\n",
    "#VETOR 5K\n",
    "start1_selection = time.time()\n",
    "selection_sort(vetorzin1)\n",
    "end1_selection = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chama o Selection Sort em um vetor ordenado de 20k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "tos = time.time()\n",
    "selection_sort(vet_ordenado)\n",
    "toe = time.time()\n",
    "\n",
    "o = toe - tos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Colocando os valores de tempo encontrados em um vetor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12c60e198>]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "s6 = end6_selection - start6_selection\n",
    "s5 = end5_selection - start5_selection\n",
    "s4 = end4_selection - start4_selection\n",
    "s3 = end3_selection - start3_selection\n",
    "s2 = end2_selection - start2_selection\n",
    "s1 = end1_selection - start1_selection\n",
    "\n",
    "selection = [s1,s2,s3,s4,s5,s6]\n",
    "plt.plot(selection, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bubble Sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bubble_sort(vector):\n",
    "    for i in range(0, len(vector) - 1):\n",
    "        for j in range(0, len(vector) - 1):\n",
    "            if vector[j] > vector[j+1]:\n",
    "                vector[j], vector[j+1] = vector[j+1], vector[j]\n",
    "        \n",
    "    return vector"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chamando o bubble para vetores de tamanho 5k até 20k e calculando o tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VETOR 20K\n",
    "start6_bubble = time.time()\n",
    "bubble_sort(vetorzin6)\n",
    "end6_bubble = time.time()\n",
    "\n",
    "#VETOR 17,5K\n",
    "start5_bubble = time.time()\n",
    "bubble_sort(vetorzin5)\n",
    "end5_bubble = time.time()\n",
    "\n",
    "#VETOR 15K\n",
    "start4_bubble = time.time()\n",
    "bubble_sort(vetorzin4)\n",
    "end4_bubble = time.time()\n",
    "\n",
    "#VETOR 12,5K\n",
    "start3_bubble = time.time()\n",
    "bubble_sort(vetorzin3)\n",
    "end3_bubble = time.time()\n",
    "\n",
    "#VETOR 10K\n",
    "start2_bubble = time.time()\n",
    "bubble_sort(vetorzin2)\n",
    "end2_bubble = time.time()\n",
    "\n",
    "#VETOR 5K\n",
    "start1_bubble = time.time()\n",
    "bubble_sort(vetorzin1)\n",
    "end1_bubble = time.time()\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chama o Bubble Sort em um vetor ordenado de 20k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "tos1 = time.time()\n",
    "bubble_sort(vet_ordenado)\n",
    "toe1 = time.time()\n",
    "\n",
    "o1 = toe1 - tos1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Colocando os valores de tempo encontrados em um vetor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12c660c50>]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "b6 = end6_bubble - start6_bubble\n",
    "b5 = end5_bubble - start5_bubble\n",
    "b4 = end4_bubble - start4_bubble\n",
    "b3 = end3_bubble - start3_bubble\n",
    "b2 = end2_bubble - start2_bubble\n",
    "b1 = end1_bubble - start1_bubble\n",
    "\n",
    "bubble = [b1,b2,b3,b4,b5,b6]\n",
    "plt.plot(bubble, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Algoritmos de Ordenação O (n log n)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quick Sort"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Características:\n",
    "\n",
    "* Instável \n",
    "* Operações Locais (in-place)\n",
    "* Dividir e Conquistar \n",
    "* O(n log n)\n",
    "\n",
    "#### Definição e Lógica:\n",
    "\n",
    "Usando a abordegam de dividir e conquistar o Quick Sort usa da partição como lógica principal do algorítimo. Uma das característica do Quick Sort é a instabilidade que não pode ser ajustada com mudanças no código.\n",
    "\n",
    "A estratégia consiste em rearranjar as chaves de modo que as chaves menores fiquem antes das chaves maiores. Em seguida o quicksort ordena as duas sublistas de chaves menores e maiores recursivamente até que a lista completa se encontre ordenada. Os passos são:\n",
    "\n",
    "1. Escolher pivô;\n",
    "\n",
    "2. Particionar: rearranjar a lista de forma que todos os elementos anteriores ao pivô sejam menores que ele, e todos os elementos posteriores ao pivô sejam maiores que ele. Ao fim do processo o pivô estará em sua posição final e haverá duas sublistas não ordenadas;\n",
    "\n",
    "3. Recursivamente ordenar a sublista dos elementos menores e a sublista dos elementos maiores;\n",
    "\n",
    "4. O caso base da recursão são as listas de tamanho zero ou um, que estão sempre ordenadas. O processo é finito, pois a cada iteração pelo menos um elemento é posto em sua posição final e não será mais manipulado na iteração seguinte.\n",
    "\n",
    "A escolha do pivô e os passos do Particionar podem ser feitos de diferentes formas e a escolha de uma implementação específica afeta fortemente a performance do algoritmo. A escolha de um pivo ruim pode implicar a mudança de complexidade do algoritmo de O(n log n) para O(n²), por isso escolhemos o Quick Sort usando a mediana de três que consiste em achar qual o melhor pivo entre o primeiro o último e o índice do méio, sendo ele, o pivo, a mediana entre eles, fazendo assim com que mesmo que o vetor esteja ordenado o Quick Sort não caia no pior caso O(n²).\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Algoritmo Quick Sort Mediana de Três"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def quick_sort(array, begin,end):\n",
    "    if begin < end:\n",
    "        new_begin_end = particion(array, begin, end)\n",
    "        \n",
    "        #Chamada recursiva\n",
    "        quick_sort(array, begin, new_begin_end - 1) # Quick Sort para subarray de zero a pivo - 1\n",
    "        quick_sort(array, new_begin_end + 1, end) # Quick Sort para subarray de pivo + 1 a fim \n",
    "\n",
    "        \n",
    "def particion(array, begin, end) -> (int):\n",
    "    \n",
    "    # Observe que os nomes em português são valores e os inglês são índices\n",
    "    middle = (begin + end) // 2\n",
    "    inicio = array[begin]\n",
    "    meio = array[middle]\n",
    "    final = array[end]\n",
    "    \n",
    "    # Comparação entre entre inicio, meio e fim para achar a mediana\n",
    "    if inicio < meio:\n",
    "        if meio < final:\n",
    "            mediana_index = middle\n",
    "        else:\n",
    "            if inicio < final:\n",
    "                mediana_index = end\n",
    "            else:\n",
    "                mediana_index = begin\n",
    "    else:\n",
    "        if final < meio:\n",
    "            mediana_index = middle\n",
    "        else:\n",
    "            if final < inicio:\n",
    "                mediana_index = end\n",
    "            else: \n",
    "                mediana_index = begin\n",
    "                \n",
    "    # Swap\n",
    "    array[mediana_index], array[end] = array[end], array[mediana_index]\n",
    "    \n",
    "    pivo = array[end]\n",
    "    i = begin - 1\n",
    "    \n",
    "    for j in range(begin, end+1):\n",
    "        if array[j] <= pivo: # Fonte da instabilidade\n",
    "            i += 1\n",
    "            array[i], array[j] = array[j], array[i]\n",
    "                     \n",
    "    return i \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chamando o quick para vetores de tamanho 5k até 20k e calculando o tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#VETOR 20K\n",
    "start6_quick = time.time()\n",
    "quick_sort(vetorzin6, 0, len(vetorzin6) - 1)\n",
    "end6_quick = time.time()\n",
    "\n",
    "#VETOR 17,5K\n",
    "start5_quick = time.time()\n",
    "quick_sort(vetorzin5, 0, len(vetorzin5) - 1)\n",
    "end5_quick = time.time()\n",
    "\n",
    "#VETOR 15K\n",
    "start4_quick = time.time()\n",
    "quick_sort(vetorzin4, 0, len(vetorzin4) - 1)\n",
    "end4_quick = time.time()\n",
    "\n",
    "#VETOR 12,5K\n",
    "start3_quick = time.time()\n",
    "quick_sort(vetorzin3, 0, len(vetorzin3) - 1)\n",
    "end3_quick = time.time()\n",
    "\n",
    "#VETOR 10K\n",
    "start2_quick = time.time()\n",
    "quick_sort(vetorzin2, 0, len(vetorzin2) - 1)\n",
    "end2_quick = time.time()\n",
    "\n",
    "#VETOR 5K\n",
    "start1_quick = time.time()\n",
    "quick_sort(vetorzin1, 0, len(vetorzin1) - 1)\n",
    "end1_quick = time.time()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chama o Quick Sort em um vetor ordenado de 20K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "tos2 = time.time()\n",
    "quick_sort(vet_ordenado, 0, len(vet_ordenado) - 1)\n",
    "toe2 = time.time()\n",
    "\n",
    "o2 = toe2 - tos2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Colocando os valores de tempo encontrados para vetores de tamanho 5k até 20k em um vetor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12c87b0f0>]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "q6 = end6_quick - start6_quick\n",
    "q5 = end5_quick - start5_quick\n",
    "q4 = end4_quick - start4_quick\n",
    "q3 = end3_quick - start3_quick\n",
    "q2 = end2_quick - start2_quick\n",
    "q1 = end1_quick - start1_quick\n",
    "\n",
    "quick_vetorzin = [q1,q2,q3,q4,q5,q6]\n",
    "\n",
    "plt.plot(quick_vetorzin, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chamando o quick para vetores de tamanho 100k até 10M e calculando o tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VETOR 10M\n",
    "start12_quick = time.time()\n",
    "quick_sort(vetorzao6, 0, len(vetorzao6) - 1)\n",
    "end12_quick = time.time()\n",
    "\n",
    "#VETOR 5M\n",
    "start11_quick = time.time()\n",
    "quick_sort(vetorzao5, 0, len(vetorzao5) - 1)\n",
    "end11_quick = time.time()\n",
    "\n",
    "#VETOR 2M\n",
    "start10_quick = time.time()\n",
    "quick_sort(vetorzao4, 0, len(vetorzao4) - 1)\n",
    "end10_quick = time.time()\n",
    "\n",
    "#VETOR 1M\n",
    "start9_quick = time.time()\n",
    "quick_sort(vetorzao3, 0, len(vetorzao3) - 1)\n",
    "end9_quick = time.time()\n",
    "\n",
    "#VETOR 500K\n",
    "start8_quick = time.time()\n",
    "quick_sort(vetorzao2, 0, len(vetorzao2) - 1)\n",
    "end8_quick = time.time()\n",
    "\n",
    "#VETOR 100K\n",
    "start7_quick = time.time()\n",
    "quick_sort(vetorzao1, 0, len(vetorzao1) - 1)\n",
    "end7_quick = time.time()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Colocando os valores de tempo encontrados para vetores de tamanho 100k até 10M em um vetor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12c7f0128>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "q7 = end7_quick - start7_quick\n",
    "q8 = end8_quick - start8_quick\n",
    "q9 = end9_quick - start9_quick\n",
    "q10 = end10_quick - start10_quick\n",
    "q11 = end11_quick - start11_quick\n",
    "q12 = end12_quick - start12_quick\n",
    "\n",
    "quick_vetorzao = [q7,q8,q9,q10,q11,q12]\n",
    "\n",
    "plt.plot(quick_vetorzao, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Integrando os 2 vetores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12ca53588>]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "quick_total = [q1,q2,q3,q4,q5,q6,q7,q8,q9,q10,q11,q12]\n",
    "\n",
    "plt.plot(quick_total, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Merge Sort"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Características:\n",
    "\n",
    "* Características:\n",
    "* Dividir e conquistar\n",
    "* Não local\n",
    "* O(n log n)\n",
    "* Alto consumo de memória\n",
    "\n",
    "#### Definição e Lógica:\n",
    "\n",
    "A ideia é dividir a lista não ordenada em grupos menores até que haja apenas um elemento em um grupo. Em seguida,\n",
    "agrupar dois elementos na ordem correta e gradualmente criar uma lista do tamanho original. Toda vez que a junção\n",
    "acontece, os elementos nos grupos devem ser comparados um por um e combinados em uma única lista em ordem. Esse\n",
    "processo continua até que todos os elementos sejam mesclados e ordenados. Observe que, quando o reagrupamento\n",
    "acontece, a ordem deve sempre ser mantida."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Algoritmo Merge Sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "def merge_sort(vector):\n",
    "\n",
    "    if len(vector)>1:\n",
    "        mid = len(vector)//2\n",
    "        left_half = vector[:mid]\n",
    "        right_half = vector[mid:]\n",
    "\n",
    "        merge_sort(left_half)\n",
    "        merge_sort(right_half)\n",
    "\n",
    "        i=0\n",
    "        j=0\n",
    "        k=0\n",
    "\n",
    "        while i < len(left_half) and j < len(right_half):\n",
    "            if left_half[i] < right_half[j]:\n",
    "                vector[k]=left_half[i]\n",
    "                i=i+1\n",
    "            else:\n",
    "                vector[k]=right_half[j]\n",
    "                j=j+1\n",
    "            k=k+1\n",
    "\n",
    "        while i < len(left_half):\n",
    "            vector[k]=left_half[i]\n",
    "            i=i+1\n",
    "            k=k+1\n",
    "\n",
    "        while j < len(right_half):\n",
    "            vector[k]=right_half[j]\n",
    "            j=j+1\n",
    "            k=k+1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chamando o merge para vetores de tamanho 5k até 20k e calculando o tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VETOR 20K\n",
    "start6_merge = time.time()\n",
    "merge_sort(vetorzin6)\n",
    "end6_merge = time.time()\n",
    "\n",
    "#VETOR 17,5K\n",
    "start5_merge = time.time()\n",
    "merge_sort(vetorzin5)\n",
    "end5_merge = time.time()\n",
    "\n",
    "#VETOR 15K\n",
    "start4_merge = time.time()\n",
    "merge_sort(vetorzin4)\n",
    "end4_merge = time.time()\n",
    "\n",
    "#VETOR 12,5K\n",
    "start3_merge = time.time()\n",
    "merge_sort(vetorzin3)\n",
    "end3_merge = time.time()\n",
    "\n",
    "#VETOR 10K\n",
    "start2_merge = time.time()\n",
    "merge_sort(vetorzin2)\n",
    "end2_merge = time.time()\n",
    "\n",
    "#VETOR 5K\n",
    "start1_merge = time.time()\n",
    "merge_sort(vetorzin1)\n",
    "end1_merge = time.time()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chama o Merge Sort em um vetor ordenado de 20K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "tos3 = time.time()\n",
    "merge_sort(vet_ordenado)\n",
    "toe3 = time.time()\n",
    "\n",
    "o3 = toe3 - tos3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Colocando os valores de tempo encontrados para vetores de tamanho 5k até 20k em um vetor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12c9acdd8>]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m6 = end6_merge - start6_merge\n",
    "m5 = end5_merge - start5_merge\n",
    "m4 = end4_merge - start4_merge\n",
    "m3 = end3_merge - start3_merge\n",
    "m2 = end2_merge - start2_merge\n",
    "m1 = end1_merge - start1_merge\n",
    "\n",
    "merge_vetorzin = [m1,m2,m3,m4,m5,m6]\n",
    "\n",
    "plt.plot(merge_vetorzin, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chamando o merge para vetores de tamanho 100k até 10M e calculando o tempo de execução¶\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VETOR 10M\n",
    "start12_merge = time.time()\n",
    "merge_sort(vetorzao6)\n",
    "end12_merge = time.time()\n",
    "\n",
    "#VETOR 5M\n",
    "start11_merge = time.time()\n",
    "merge_sort(vetorzao5)\n",
    "end11_merge = time.time()\n",
    "\n",
    "#VETOR 2M\n",
    "start10_merge = time.time()\n",
    "merge_sort(vetorzao4)\n",
    "end10_merge = time.time()\n",
    "\n",
    "#VETOR 1M\n",
    "start9_merge = time.time()\n",
    "merge_sort(vetorzao3)\n",
    "end9_merge = time.time()\n",
    "\n",
    "#VETOR 500K\n",
    "start8_merge = time.time()\n",
    "merge_sort(vetorzao2)\n",
    "end8_merge = time.time()\n",
    "\n",
    "#VETOR 100K\n",
    "start7_merge = time.time()\n",
    "merge_sort(vetorzao1)\n",
    "end7_merge = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Colocando os valores de tempo encontrados para vetores de tamanho 100k até 10M em um vetor¶\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12cc1c278>]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5bn3uEx5duJWrvzCYW8/PU295jyjoRSTomnyOm5/+mP8sLeLaSUP5ydSRCnkPKehFJKgam3z8+D8reHZ5MddPHs4NU4Yr5D2moBeRoGlo8nHD48t5cWUJN00dyXVnDvO6JEFBLyJBsr+xie8/uozXCkr5n2l5XHP6EK9LEj8FvYgErK6hiWsfWcqba8qZPX0MM0/J9bokaUFBLyIBqa1v4pp/LuG9DRX85ovHc+nJA70uSQ4T8P3HZhZrZsvM7AX/88FmttDM1pnZE2YWH3iZItIVVe9v5KqHFvHehgp+/+XPKeS7qGA0mrgeKGzx/HfAn5xzw4HdwNVBuIaIdDF76xqY+cAiFm/ezZ+/foK6T3ZhAQW9mfUHzgfu9z834CzgKf8pDwMzArmGiHQ9VTUNXD53Ecu3VfLXS8Zx0QnqHd+VBTpH/2fgJ0BP//N0oNI51+h/XgTovwCRCLK7up7L5y5kXek+/n755zl7dJbXJclRdHhEb2YXAGXOuaUtD7dyqvuM188ysyVmtqS8vLyjZYhIJ6rYt59L7vuQdWX7mPMNhXy4CGREfyow3cymAQlAL5pH+KlmFucf1fcHilt7sXNuDjAHID8/v9UfBiLSdZTtqePS+xdStLuGB688iVOHZXhdkrRTh0f0zrlbnHP9nXO5wMXAG865y4A3ga/4T5sJPBdwlSLiqZKqWr4+50OKK2t56KrxCvkwE4rtXX4K3Ghm62mes58bgmuISCfZtquGr/3jAyr27udfV49nwpB0r0uSYxSUG6acc28Bb/m/3giMD8b7ioi3tuys5tL7FrK3roFHvnUyYwekel2SdIDujBWRVm0o38el931IfaOPR6+ZwHE5KV6XJB2koBeRI6wt3cul9y0EHI/PmsjIvj2P+hrpuhT0InKIguI9XD53IXExxqPXTGRYnx5elyQBCsUvY0UkTK0squKS+z6ke1wMT3xbIR8pNKIXEQA+2rqbmQ8sIiWxG49dM4EBaUlelyRBohG9iLBo0y6uuH8hacnxPPHtiQr5CKMRvUiUe399BVc/vIR+qQk8es0EsnoleF2SBJlG9CJR7O215Vz10GIGpiXx+KyJCvkIpRG9SJRaUFjKtY98xLA+PXjkWyeTlqw9giKVRvQiUeiVT3bwnUeWMiq7J49eo5CPdBrRi0SZ51cUc8MTyxnbP4WHvjmeXgndvC5JQkxBLxJFnvmoiB//ZwX5uWk8cOVJ9OiuCIgG+lsWiRJPLN7Kzc+sZOKQdO6fmU9SvP75Rwv9TYtEgX99sJmfP7eKM0Zk8o8rPk9Ct1ivS5JOpKAXiXBz393Er14oYEpeFn+7bBzd4xTy0UZBLxLB/v7WBn73ymrOO64vf7l4HPFxWmgXjRT0IhHq7gXruGv+WqaP7cddXxtLXKxCPlop6EUijHOOP762lnveXM+XT+zP77/yOWJjzOuyxEMKepEI4pzjty+vZs47G7lk/AB+PeN4YhTyUU9BLxIhnHPMfr6Ah97fzMyJg7jtwjEKeQEU9CIRwedz/M+zn/DYoq1cc9pgfjYtDzOFvDRT0IuEuSaf46dPf8xTS4u47syh/PickQp5OYSCXiSMNTb5+NF/VvDc8mJ+OGUEP5g8TCEvR1DQi4SphiYfNzy+nBdXlvCTc0fy3UnDvC5JuigFvUgY2t/YxPceXcb8glJuPT+Pb502xOuSpAtT0IuEmbqGJq59ZClvrinnlxeN4RsTc70uSbo4Bb1IGKmtb+Kafy7hvQ0V3PGl47l4/ECvS5IwoKAXCRPV+xv55kOLWbx5F3/4yli+/Pn+XpckYUJBLxIG9tQ1cNWDi1m+rZI/XzyO6WP7eV2ShJEOdzkyswFm9qaZFZrZKjO73n88zczmm9k6/2Pv4JUrEn2qahq44v6FrNhWyT2XKOTl2AXSzq4R+JFzLg+YAFxnZqOBm4EFzrnhwAL/cxHpgF3V9Vx6/4cUluzl3ss/z3nHZ3tdkoShDk/dOOdKgBL/13vNrBDIAS4CJvlPexh4C/hpQFWKRJFnl23nzlfXUFxZS2yM4Zxj7pUnMWlkH69LkzAVlDl6M8sFxgELgSz/DwGccyVm1up/nWY2C5gFMHCgVg6IQHPI3/LMSmobmgBo9DniY2OorGnwuDIJZwHvRGBmPYCngRucc3va+zrn3BznXL5zLj8zMzPQMkTCXlVNA7OfX3Uw5A+ob/Jx56trPKpKIkFAI3oz60ZzyP/bOfeM/3CpmWX7R/PZQFmgRYpEqq07a5hfWMrrBaUs2ryLJp9r9bziytpOrkwiSYeD3po7J80FCp1zd7X41jxgJnCH//G5gCoUiSA+n2NFUSWvF5byekEZa0r3AjAyqyffOWMITy4uonzf/iNe1y81sbNLlQgSyIj+VOAKYKWZLfcf+xnNAf+kmV0NbAW+GliJIuGttr6J99ZXNId7YRkV+/YTG2OcPDiNX5w0mil5WQxMTwJgeJ+eh8zRAyR2i+WmqSO9Kl8iQCCrbt4FPqsf6uSOvq9IJCjfu583Vpcyv6CMd9eXU9fgo2f3OM4YmcnZo7OYNKIPKUndjnjdjHE5AAdX3fRLTeSmqSMPHhfpCN0ZKxIEzjnWl+07ON++bFslzkFOaiIXnzSQKXlZjB+cRnzc0dc/zBiXo2CXoFLQi3RQY5OPxZt3+6dkStmyswaAsf1TuHHKCKaMzmJU357aCEQ8p6AXOQZ76xp4Z23zfPsbq8uoqm0gPi6GU4emM+v0IUwelUXflASvyxQ5hIJe5Ci2V9ayoLCU+QWlfLhxJw1NjrTkeM4encWUvCxOG55Bcnf9U5KuS/91ihzGOccn2/ccnG8vKGm+D3BIZjLf/MJgzs7LYtzA3sTGaEpGwoOCXoTmrfk+2LDz4Pr2HXvqiDHIH5TGz6aNYnJeFkMze3hdpkiHKOglau2qrufN1WW8XljKO2vLqa5vIik+ljNGZDIlL4szR/UhLTne6zJFAqagl6iyqaKa+QU7eL2gjCVbduFzkNWrOzPG5XD26CwmDEknoVus12WKBJWCXiJak8+xbOvug/PtG8qrARid3YvvnTWcs/OyOC6nl5ZASkRT0EvEqd7fyH/XfboEcld1Pd1ijQlD0pl5Si6T87LIUe8YiSIKeokIpXvqWFBYxvyCHby3YSf1jT56JcRx1qg+TBmdxekjMumVcGTLAZFooKCXLq3lbkst+74451i9Yy+vFzTflbqiqAqAgWlJXDFhEFPyssjP7U232IC3XBAJe+Zc6/2vO1N+fr5bsmSJ12VIF3P4bksA8bExnDwkjY3l1WyvrMUMThiQypS8LM4encXwPj003y5Rw8yWOufyj3aeRvTSJTX5HL99ubDV3Zb+u66CKXlZ/GDyMM4c1Yc+PdVyQKQtCnrxjM/nKK6qZcvOGjZVVLO5oprNO6vZvLOGrTtrqG/ytfo6A+6fedRBjIj4KeglpHw+R8meuk9DvKKaTRU1bNlZzZZdNdQ3fhrm3eNiyE1PZmhmMpNH9eGJxduorD1yU2zttiRybBT0EjCfz7HjYJjXsHlnNZsqqpvDfGcN+w8L80HpSQzOSObMUX3ITU8mN6P5eVbPBGJa9I/Jy+6l3ZZEgkBBL+3i8zlK99b5A7zGPzL3f72z+pAwj4+LYVBaErkZyZwxIpPcjGQGpyeTm5FM316HhnlbtNuSSHAo6OUg5xyle/YfHI1v8k+1bK6oYcuuauoaWoR5bAwD05PITU/m9BEZDEpPZnBGc5hnH0OYH412WxIJnII+yjjnKNvbIswrag7On2/ZWXPEUsYBaYkMzkjmtOEZDDo4Mk8iOyVRbXpFwoSCPox81s1Dh3POUe4P8wOrWFpOtbQM826xxoC0JAanJ3PqsAxy05unXHLTk+mXqjAXiQQK+jBx+M1D2ytrufnpj9lQvo8BvZP8gf7pipaa+k/DPC7GGOifMz9laAa5Gc1TLoMzkslOSSBOd4+KRDQFfRfV2OSjbO9+SqpqKa6s4xfPfXLEzUN1jT7++sZ6oDnMB6QlkZuexIQhaf7VLM1TLf1SFeYi0UxB7wGfz7Gzup7iytqDQV5SVUtxVR0llbWUVNVRuqcOXzu7U7x90yRyUhMV5iLSKgV9kDnnqKptaDW8i/2PO6rqjrjrMz4uhn4pCWSnJDJxaDo5qYlkpySSnZpAv5RErnxwESVVdUdcLyc1kUHpyZ318UQkDCnoj1H1/sZDR+EtHouraimprDtiiiU2xujbK4F+qQmcMCCV7OObwzs7JYF+qc2PacnxbTbj+um5o3TzkIh0iIK+hbqGJnZUfRrYrY3I99Q1HvIaM8js0Z3s1ERGZvVk0og+9EtNOGQ0ntmze8CrV3TzkIh0VNgGfXuXGh7Q2OSjdO9+Sipbn04pqaqlYl/9Ea/rndSN7JRE+vdO5KTcNLJTEz6dVklJIKtXAvFxnTM3rpuHRKQjQhL0ZnYu8BcgFrjfOXdHMN//s5Yabt1VzYisns3TKAeC3D86L9t75C83e3SPOzj6Pi6n18HwPjCdkp2SSGK8NooWkfAW9KA3s1jgb8DZQBGw2MzmOecKgnWNO19d0+pSw7vmrzv4vHtczMHAPnVYxhHTKdmpCdpaTkSiQihG9OOB9c65jQBm9jhwERC0oC+urP3M773w/S/QLzWR3kndtNOQiAgQisnlHGBbi+dF/mOHMLNZZrbEzJaUl5cf0wU+qx95Tmoix+WkHHUFi4hINAlF0LeWsEfc+uOcm+Ocy3fO5WdmZh7TBW6aOpLEbofOnWupoYhI60IxdVMEDGjxvD9QHMwLaKmhiEj7hSLoFwPDzWwwsB24GLg02BfRUkMRkfYJetA75xrN7HvAqzQvr3zAObcq2NcREZH2Cck6eufcS8BLoXhvERE5Nmp3KCIS4RT0IiIRTkEvIhLhzLl27m4RyiLMyoEtHXx5BlARxHLCgT5zdNBnjg6BfOZBzrmj3ojUJYI+EGa2xDmX73UdnUmfOTroM0eHzvjMmroREYlwCnoRkQgXCUE/x+sCPKDPHB30maNDyD9z2M/Ri4hI2yJhRC8iIm0I66A3s3PNbI2ZrTezm72uJ9TM7AEzKzOzT7yupbOY2QAze9PMCs1slZld73VNoWZmCWa2yMxW+D/zbK9r6gxmFmtmy8zsBa9r6QxmttnMVprZcjNbEtJrhevUjX/LwrW02LIQuCSYWxZ2NWZ2OrAP+Kdz7jiv6+kMZpYNZDvnPjKznsBSYEaE/z0bkOyc22dm3YB3geudcx96XFpImdmNQD7Qyzl3gdf1hJqZbQbynXMhv28gnEf0B7csdM7VAwe2LIxYzrl3gF1e19GZnHMlzrmP/F/vBQppZceySOKa7fM/7eb/E54jsnYys/7A+cD9XtcSicI56Nu1ZaFEDjPLBcYBC72tJPT80xjLgTJgvnMu0j/zn4GfAD6vC+lEDnjNzJaa2axQXiicg75dWxZKZDCzHsDTwA3OuT1e1xNqzrkm59wJNO/QNt7MInaqzswuAMqcc0u9rqWTneqcOxE4D7jOPzUbEuEc9CHfslC6Bv889dPAv51zz3hdT2dyzlUCbwHnelxKKJ0KTPfPWT8OnGVmj3hbUug554r9j2XA/9E8HR0S4Rz0B7csNLN4mrcsnOdxTRJk/l9MzgUKnXN3eV1PZzCzTDNL9X+dCEwBVntbVeg4525xzvV3zuXS/O/4Defc5R6XFVJmluxfXICZJQPnACFbTRe2Qe+cawQObFlYCDwZ6VsWmtljwAfASDMrMrOrva6pE5wKXEHzKG+5/880r4sKsWzgTTP7mOYBzXznXFQsOYwiWcC7ZrYCWAS86Jx7JVQXC9vllSIi0j5hO6IXEZH2UdCLiEQ4Bb2ISIRT0IuIRDgFvYhIhFPQi4hEOAW9iEiEU9CLiES4/wffNYdaye2dkAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "m7 = end7_merge - start7_merge\n",
    "m8 = end8_merge - start8_merge\n",
    "m9 = end9_merge - start9_merge\n",
    "m10 = end10_merge - start10_merge\n",
    "m11 = end11_merge - start11_merge\n",
    "m12 = end12_merge - start12_merge\n",
    "\n",
    "merge_vetorzao = [m7,m8,m9,m10,m11,m12]\n",
    "\n",
    "plt.plot(merge_vetorzao, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Integrando os 2 vetores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12cb79400>]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "merge_total = [m1,m2,m3,m4,m5,m6,m7,m8,m9,m10,m11,m12]\n",
    "\n",
    "plt.plot(merge_total, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bucket Sort"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Características:\n",
    "\n",
    "* Estavel\n",
    "* Dividir e Conquistar\n",
    "* O(n log n)\n",
    "\n",
    "\n",
    "\n",
    "#### Definição e Lógica:\n",
    "\n",
    "Bucket sort, é um algoritmo de ordenação que funciona dividindo um vetor em um número finito de baldes. Cada balde é então ordenado individualmente, seja usando um algoritmo de ordenação diferente, ou usando o algoritmo bucket sort recursivamente, algorítimo tem complexidade linear O(n log n).\n",
    "\n",
    "A estratégia consiste em dividir o array em vários subgrupos chamados de baldes, onde só elementos do mesmo \"tipo\" estão nesse balde. Após essa divisão é aplicado outro método de ordenação para cada um dos baldes e os baldes se juntam novamente formando um vetor ordenado.\n",
    "\n",
    "Bucket sort funciona do seguinte modo:\n",
    "\n",
    "1. Inicialize um vetor de \"baldes\", inicialmente vazios.\n",
    "2. Vá para o vetor original, incluindo cada elemento em um balde.\n",
    "3. Ordene todos os baldes não vazios.\n",
    "4. Coloque os elementos dos baldes que não estão vazios no vetor original.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Algoritmo Bucket Sort utilizando o Quick Sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bucket_sort_quick( A ):\n",
    "\n",
    "    code = hashing( A )\n",
    "    buckets = [list() for _ in range( code[1] )]\n",
    "    \n",
    "    for i in A:\n",
    "        x = re_hashing( i, code )\n",
    "        buck = buckets[x]\n",
    "        buck.append( i )\n",
    " \n",
    "    for bucket in buckets:\n",
    "        quick_sort(bucket, 0, len(bucket)-1)\n",
    "        ndx = 0\n",
    "        \n",
    "    for b in range( len( buckets ) ):\n",
    "        for v in buckets[b]:\n",
    "            A[ndx] = v\n",
    "            ndx += 1\n",
    "\n",
    "def hashing( A ):\n",
    "    m = A[0]\n",
    "    for i in range( 1, len( A ) ):\n",
    "        if ( m < A[i] ):\n",
    "            m = A[i]\n",
    "    result = [m, int( math.sqrt( len( A ) ) )]\n",
    "    return result\n",
    " \n",
    "def re_hashing( i, code ):\n",
    "    return int( i / code[0] * ( code[1] - 1 ) )\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Algoritmo Bucket Sort utilizando o Merge Sort"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bucket_sort_merge( A ):\n",
    "\n",
    "    code = hashing( A )\n",
    "    buckets = [list() for _ in range( code[1] )]\n",
    "    \n",
    "    for i in A:\n",
    "        x = re_hashing( i, code )\n",
    "        buck = buckets[x]\n",
    "        buck.append( i )\n",
    " \n",
    "    for bucket in buckets:\n",
    "        merge_sort(bucket)\n",
    "        ndx = 0\n",
    "        \n",
    "    for b in range( len( buckets ) ):\n",
    "        for v in buckets[b]:\n",
    "            A[ndx] = v\n",
    "            ndx += 1\n",
    "\n",
    "def hashing( A ):\n",
    "    m = A[0]\n",
    "    for i in range( 1, len( A ) ):\n",
    "        if ( m < A[i] ):\n",
    "            m = A[i]\n",
    "    result = [m, int( math.sqrt( len( A ) ) )]\n",
    "    return result\n",
    " \n",
    "def re_hashing( i, code ):\n",
    "    return int( i / code[0] * ( code[1] - 1 ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chamando o bucket com quick para vetores de tamanho 5k até 20k e calculando o tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VETOR 20K\n",
    "start6_bucket_quick = time.time()\n",
    "bucket_sort_quick(vetorzin6)\n",
    "end6_bucket_quick = time.time()\n",
    "\n",
    "#VETOR 17,5K\n",
    "start5_bucket_quick = time.time()\n",
    "bucket_sort_quick(vetorzin5)\n",
    "end5_bucket_quick = time.time()\n",
    "\n",
    "#VETOR 15K\n",
    "start4_bucket_quick = time.time()\n",
    "bucket_sort_quick(vetorzin4)\n",
    "end4_bucket_quick = time.time()\n",
    "\n",
    "#VETOR 12,5K\n",
    "start3_bucket_quick = time.time()\n",
    "bucket_sort_quick(vetorzin3)\n",
    "end3_bucket_quick = time.time()\n",
    "\n",
    "#VETOR 10K\n",
    "start2_bucket_quick = time.time()\n",
    "bucket_sort_quick(vetorzin2)\n",
    "end2_bucket_quick = time.time()\n",
    "\n",
    "#VETOR 5K\n",
    "start1_bucket_quick = time.time()\n",
    "bucket_sort_quick(vetorzin1)\n",
    "end1_bucket_quick = time.time()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chama o Bucket Sort com Quick em um vetor ordenado de 20K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "tos4 = time.time()\n",
    "bucket_sort_quick(vet_ordenado)\n",
    "toe4 = time.time()\n",
    "\n",
    "o4 = toe4 - tos4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chamando o bucket com merge para vetores de tamanho 5k até 20k e calculando o tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VETOR 20K\n",
    "start6_bucket_merge = time.time()\n",
    "bucket_sort_merge(vetorzin6)\n",
    "end6_bucket_merge = time.time()\n",
    "\n",
    "#VETOR 17,5K\n",
    "start5_bucket_merge = time.time()\n",
    "bucket_sort_merge(vetorzin5)\n",
    "end5_bucket_merge = time.time()\n",
    "\n",
    "#VETOR 15K\n",
    "start4_bucket_merge = time.time()\n",
    "bucket_sort_merge(vetorzin4)\n",
    "end4_bucket_merge = time.time()\n",
    "\n",
    "#VETOR 12,5K\n",
    "start3_bucket_merge = time.time()\n",
    "bucket_sort_merge(vetorzin3)\n",
    "end3_bucket_merge = time.time()\n",
    "\n",
    "#VETOR 10K\n",
    "start2_bucket_merge = time.time()\n",
    "bucket_sort_merge(vetorzin2)\n",
    "end2_bucket_merge = time.time()\n",
    "\n",
    "#VETOR 5K\n",
    "start1_bucket_merge = time.time()\n",
    "bucket_sort_merge(vetorzin1)\n",
    "end1_bucket_merge = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chama o Bucket Sort com Merge em um vetor ordenado de 20K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "tos5 = time.time()\n",
    "bucket_sort_merge(vet_ordenado)\n",
    "toe5 = time.time()\n",
    "\n",
    "o5 = toe5 - tos5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Colocando os valores de tempo encontrados para vetores de tamanho 5k até 20k em um vetor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12cde8f60>]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bck6_quick = end6_bucket_quick - start6_bucket_quick\n",
    "bck5_quick = end5_bucket_quick - start5_bucket_quick\n",
    "bck4_quick = end4_bucket_quick - start4_bucket_quick\n",
    "bck3_quick = end3_bucket_quick - start3_bucket_quick\n",
    "bck2_quick = end2_bucket_quick - start2_bucket_quick\n",
    "bck1_quick = end1_bucket_quick - start1_bucket_quick\n",
    "\n",
    "bucket_vetorzin_quick = [bck1_quick,bck2_quick,bck3_quick,bck4_quick,bck5_quick,bck6_quick]\n",
    "\n",
    "plt.plot(bucket_vetorzin_quick, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12cd4f898>]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bck6_merge = end6_bucket_merge - start6_bucket_merge\n",
    "bck5_merge = end5_bucket_merge - start5_bucket_merge\n",
    "bck4_merge = end4_bucket_merge - start4_bucket_merge\n",
    "bck3_merge = end3_bucket_merge - start3_bucket_merge\n",
    "bck2_merge = end2_bucket_merge - start2_bucket_merge\n",
    "bck1_merge = end1_bucket_merge - start1_bucket_merge\n",
    "\n",
    "bucket_vetorzin_merge = [bck1_merge,bck2_merge,bck3_merge,bck4_merge,bck5_merge,bck6_merge]\n",
    "\n",
    "plt.plot(bucket_vetorzin_merge, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chamando o bucket com quick  para vetores de tamanho 100k até 10M e calculando o tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VETOR 10M\n",
    "start12_bucket_quick = time.time()\n",
    "bucket_sort_quick(vetorzao6)\n",
    "end12_bucket_quick = time.time()\n",
    "\n",
    "#VETOR 5M\n",
    "start11_bucket_quick = time.time()\n",
    "bucket_sort_quick(vetorzao5)\n",
    "end11_bucket_quick = time.time()\n",
    "\n",
    "#VETOR 2M\n",
    "start10_bucket_quick = time.time()\n",
    "bucket_sort_quick(vetorzao4)\n",
    "end10_bucket_quick = time.time()\n",
    "\n",
    "#VETOR 1M\n",
    "start9_bucket_quick = time.time()\n",
    "bucket_sort_quick(vetorzao3)\n",
    "end9_bucket_quick = time.time()\n",
    "\n",
    "#VETOR 500K\n",
    "start8_bucket_quick = time.time()\n",
    "bucket_sort_quick(vetorzao2)\n",
    "end8_bucket_quick = time.time()\n",
    "\n",
    "#VETOR 100K\n",
    "start7_bucket_quick = time.time()\n",
    "bucket_sort_quick(vetorzao1)\n",
    "end7_bucket_quick = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Chamando o bucket com merge  para vetores de tamanho 100k até 10M e calculando o tempo de execução"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "#VETOR 10M\n",
    "start12_bucket_merge = time.time()\n",
    "bucket_sort_merge(vetorzao6)\n",
    "end12_bucket_merge = time.time()\n",
    "\n",
    "#VETOR 5M\n",
    "start11_bucket_merge = time.time()\n",
    "bucket_sort_merge(vetorzao5)\n",
    "end11_bucket_merge = time.time()\n",
    "\n",
    "#VETOR 2M\n",
    "start10_bucket_merge = time.time()\n",
    "bucket_sort_merge(vetorzao4)\n",
    "end10_bucket_merge = time.time()\n",
    "\n",
    "#VETOR 1M\n",
    "start9_bucket_merge = time.time()\n",
    "bucket_sort_merge(vetorzao3)\n",
    "end9_bucket_merge = time.time()\n",
    "\n",
    "#VETOR 500K\n",
    "start8_bucket_merge = time.time()\n",
    "bucket_sort_merge(vetorzao2)\n",
    "end8_bucket_merge = time.time()\n",
    "\n",
    "#VETOR 100K\n",
    "start7_bucket_merge = time.time()\n",
    "bucket_sort_merge(vetorzao1)\n",
    "end7_bucket_merge = time.time()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Colocando os valores de tempo encontrados para vetores de tamanho 100k até 10M em um vetor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12cfa97b8>]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ETEllDVc9uIyy/bU89OWp7DlYzz1LiimtrGFwRiq3zBij8fMwUqGLSERsrjjIVQ8uo6qukYevn8rpBS3zzVXgkaNCF5GwW7/zAFfPX4Y7PHrDdCbk9w86Uo+gQheRsFq5fR9fWrCc9OTe/GHONEbm9gk6Uo+hQheRsHlz026u/30ROX2TeXjONIZmpgUdqUdRoYtIWLy4bhdfeWQlw7PS+cOcqeT2Swk6Uo+jQheRLnv67RJufvwdJgzux0PXTWVAelLQkXokFbqIdMkjy7bzvadWM7Uwk/lfOoM+yaqVoOhPXkQ67Tevvc/dizZwwdhc/u+Vp5GSmBB0pB5NhS4iHebu3L90I//18iZmn5LH/Z8/laTeuvA8aCp0EemQ5mbnrufW8dCbW/niGUP5yacnasWhKKFCF5F2a2xq5jtPrubPK3Zw/dnD+d5l4zBTmUcLFbqItEtdYxPffOxtFq0p418vGs03LhypMo8yKnQRaVNNfRM3PryC1zdWcPvs8cw5e3jQkaQVKnQROaEDtQ3Meegtirbt42efncgXzhgWdCQ5DhW6iBzX3kP1XLNgGRt2VvFfV0xm9imDg44kJ6BCF5FWle2v5ar5y/hgbzW/vWYK54/NDTqStEGFLiIfsX1PNVfO/wd7D9az8MtTmT4iK+hI0g4qdBE5ynu7qrjywWXUNzXzyA3TmTQ0I+hI0k4qdBE5bPWO/VyzYBm9E3rx/+aeyZhBfYOOJB2gQhcRAJZt3sOchUVkpCXyx+unUZCVHnQk6aA2b75gZgvMrNzM1hyxLdPMlprZe6HvAyIbU0Qi6dXicq5ZsJyB/ZL507+cqTKPUe25m85DwMxjtn0HeMndRwEvhZ6LSAx6fvVObvh9ESNz+/D4jWeS1z816EjSSW0Wuru/Duw9ZvOngIWhxwuBy8OcS0S6weNFH/C1R1YyaUgGj9wwnaw+yUFHki7o7Bj6QHffCeDuO81ME1RFYszv/raFO59dxzmjsvnN1aeTlqSP1GJdxP8GzWwuMBdg2DBdMiwSNHfnv1/exH1LNzLj5IH88orJJPfWwhTxoLN3pN9lZnkAoe/lx9vR3ee5+xR3n5KTk9PJw4lIOLg7dy/awH1LN/KZ0/J54J9PU5nHkc4W+jPAtaHH1wJPhyeOiERKU7Pz3b+sYd7rm7nmzALu/dwkeidolaF40uaQi5k9CpwHZJvZDuCHwE+Bx81sDrAd+KdIhhSRrmloaubmx9/h2XdK+er5J/HtS8boXuZxqM1Cd/crjvOjC8OcRUQioLahia/+cSUvbSjn1plj+T/nnRR0JIkQfawtEscO1jVyw8Ii/rFlDz+6fAJXTy8IOpJEkApdJE5VVtfzpd+9xeqS/dz/+Ul8evKQoCNJhKnQReJQeVUt18xfzuaKQ/zqytO45ORBQUeSbqBCF4kzO/ZVc9WDy9h1oI4FXzqDs0dlBx1JuokKXSSObK44yFUPLuNgXSMPXz+N0wt037yeRIUuEifWlR7gmgXLcIdH507n5MH9g44k3UyFLhIHVmzbx3W/W056cm8evn4aJ+X0CTqSBECFLhLj/rZpNzf8vojcvsk8fP00hgxICzqSBESFLhLDXlhbxtceWcXw7HT+cP1UcvumBB1JAqRCF4lRT60q4Vt/eocJ+f1ZeN0ZZKQlBR1JAqZCF4lBD/9jG7c/vYZpwzN58Noz6JOs/5RFhS4Sc3792vv8dNEGLhybywNXnkZKom5/Ky1U6CIxwt2594ViHnjlfT4xaTD3f34Sibr9rRxBhS4SA5qbnTufXcvCv2/jiqlD+fHlE0nopdvfytFU6CJRrrGpmVufWM0TK3dwwznD+e6l43Qvc2mVCl0kitU1NnHTo2+zeG0ZN188mq9fMFJlLselQheJUtX1jdz4hxX8z3u7+cHs8Xz57OFBR5Iop0IXiUIHahv48u/eYuX2ffzHZ0/h82cMDTqSxAAVukiU2XOwjmsWLGfjrir+64rTuOyUvKAjSYxQoYtEkbL9tVz54D/Ysa+GeddM4fwxuUFHkhiiQheJEtv2HOLKB5dRWd3A7788lWkjsoKOJDFGhS4SBTbuquKqB5fR0NTMIzdM45QhGUFHkhikQhcJyFOrSrhnSTGllTVg0CcpgSe+chajB/YNOprEKF03LBKAp1aVcNuTqymprMEBd6hvctaVHgg6msQwFbpIN3J3isuquP3pNdQ0NB31s7rGZu5ZUhxQMokHXRpyMbOtQBXQBDS6+5RwhBKJJ+7OmpIDLFqzk8Vryti8+9Bx9y2trOnGZBJvwjGGfr677w7DP0ckbjQ3Oyu372PxmjIWry1jx74aEnoZ00dkct3Zw3ng5U2UHaj9yOsGZ6QGkFbihT4UFQmTxqZmlm/Zy6I1ZSxZW0Z5VR1JCb04e1Q237hwFBePG8iA9JZVhfom9+a2J1cfNeySmpjALTPGBBVf4kBXC92BF8zMgd+4+7xjdzCzucBcgGHDhnXxcCLRpa6xiTc37WHRmp0sXbeLfdUNpCT24vwxucycMIgLxubSNyXxI6+7fHI+wOFZLoMzUrllxpjD20U6w9y98y82G+zupWaWCywFvu7urx9v/ylTpnhRUVGnjycSDWrqm3htYzmL15Tx0vpyquoa6ZvcmwvG5TJrwiA+PjqX1CStIiThY2Yr2vMZZZfO0N29NPS93Mz+AkwFjlvoIrGqqraBlze0lPirxRXUNDQxIC2RWRMHMWtCHh8bmUVyb5W4BKvThW5m6UAvd68KPb4EuCtsyUQCtu9QPUvX72LxmjLeeG839U3N5PRN5rOn5zNrQh7ThmfSW0vASRTpyhn6QOAvoZvt9wYecffFYUklEpDyqlpeWNtS4n/fvIemZic/I5Wrzyxg1oRBnDZsAL209JtEqU4XurtvBiaFMYtIIEoqa1qmF67ZSdG2fbjDiOx0bjx3BDMnDGJifn+tEiQxQdMWpUfasvvQ4Qt93t2xH4Cxg/py04WjmDUhj9ED+6jEJeao0KVHcHeKd1WFzsTL2FBWBcCkIf25deZYZk4YxPDs9IBTinSNCl3ilruzumQ/i0IlvmX3IczgjIJMbp89npkTBpGvKzMljqjQJa40Nzsrtu9j0eqWqzVLKlsuuT9zRBZzzh7OJScPJLdvStAxRSJChS4xr7GpmWVb9rJozU6WrN1FReiS+3NGZfPNi0Zx0RGX3IvEMxW6xKS6xib+tmk3i1aXsXT9LiqrG0hNTOC8MTknvOReJJ6p0CVmVNc38lpxBYvWlPHyhnIOhi65v3BcLjMn5PHx0Tm65F56NBW6RIUjl2M78kZVB2obeGVDOYtWl/HqxnJqG5oZkJbIZRPzmDlhkC65FzlCl27O1VG6OZe05sPl2I68lWxigjEypw/vVxyivqmZ3L7JzDh5ELMmDGKqLrmXHqZbbs4l0lXV9Y38+/PrP7IcW0OTs3HXQa47q5BZEwcxeaguuRdpiwpdIm5/TQPb9hxi255qtu05xNY91WzfU83WPYcor6o77uua3fn+7PHdmFQktqnQpcvcnT2H6g+X9tZQcX9Y4PuqG47af2C/ZAoy0/n46BwKs9N58H82f2Qf0HJsIh2lQpd2aW52dlXVsnV3Ndv3HjqmtKs5WNd4eN9e1lLGBVlpzJqYR2FWGgVZ6RRkpTEsM420pKP/tcvPSNVybCJhoEKXwxqbmimtrGXrnkNs21vNtt3/W9zb91ZT19h8eN/EBGPogDQKstI4ozCTgqw0CrPSGZaVxpABqR2aeaLl2ETCQ4Xew9Q1NvHB3pojxrL/t7R37Kuhsfl/Zz2lJPaiIDOd4dnpnDcmh4KsdApDZ9p5/VPCOtPk8sn5KnCRLlKhx6Hq+sajPoDcdsTwSOn+Go6cqdo3uTcF2WmcnN+fSyfmHS7sgqx0cvsma2aJSAxRoUeh411kc6T91Q1s+3Ase3doiCRU4BXHzBzJTE+iICuNqcMzGZaZRmF2aEw7M43M9CTd91skTqjQo8yxF9mUVNZwy5/fYdHqUlKSeh8eHqlsbeZIVjrnh4ZGjhzT7qd7moj0CCr0gNQ3NlO2v5aSyhpKQ18llTX8ZVXJUR8+QstFNkvWlTNkQCqFWelcNjHv8LBIYVY6wzLTdA8TEVGhR4K7s7+mgZLKGkr2hQr7mPIur6rj2LsuZPdJ/kiZf8iAN269IPLhRSRmqdA7oaGptbPro59X1x99KXtS717kZ6QyOCOFc0flkD8glcEZqaFtqeT1TyElMYGzfvoyJZU1HzmmLrIRkbao0I/h7hyoaTxczsd+L62sZVdV7UfOrrPSkxickcrInD6cOyqHwRkph8s6f0AqWe388PGWGWN0kY2IdEqPK/SGpmZ2HahtGQrZ31LQh0s7NDxy6Niz64ReDM5IYXBGKmePyg6dWaeQn5F2eHtKYnjGsHWRjYh0VtQXenum8B1pf03D4WGP1oZCdh2opfmYs+vM9CQGZ6QwPDuds0ZmMyQ0HDI4NESSnd6987F1kY2IdEaXCt3MZgL/CSQAD7r7T8OSKqS1KXy3PvEum3cfZER2n6OKurSyltLKGqqOuKcItFyinte/Zaz6Yydlkx86o/5wKGRw/1TNEBGRuNDpQjezBOAB4GJgB/CWmT3j7uvCFe6eJcUfuU92XWMzv3xp0+HnA9ISGZyRyrCsNM48KSs0dp12eAw7u4+udhSRnqErZ+hTgU3uvhnAzB4DPgWErdBLW5nt8aEXbz6XvP6ppCdH/aiRiEi36MrdlfKBD454viO0LWyON1UvPyOVkbl9VeYiIkfoSqG3No7xkQVKzWyumRWZWVFFRUWHDnDLjDGkHjN7RFP4RERa15VC3wEMPeL5EKD02J3cfZ67T3H3KTk5OR06wOWT87n7MxPJz0jFaDkzv/szEzUDRESkFV0Zs3gLGGVmw4ES4IvAP4cl1RE0hU9EpH06Xeju3mhmXwOW0DJtcYG7rw1bMhER6ZAufaro7s8Dz4cpi4iIdEH41hATEZFAqdBFROKECl1EJE6YH3sf2EgezKwC2NbJl2cDu8MYJxboPfcMes89Q1fec4G7tznvu1sLvSvMrMjdpwSdozvpPfcMes89Q3e8Zw25iIjECRW6iEiciKVCnxd0gADoPfcMes89Q8Tfc8yMoYuIyInF0hm6iIicQEwUupnNNLNiM9tkZt8JOk+kmdkCMys3szVBZ+kOZjbUzF4xs/VmttbMbgo6U6SZWYqZLTezd0Lv+c6gM3UXM0sws1Vm9lzQWbqDmW01s9Vm9raZFUX0WNE+5BJa6m4jRyx1B1wRzqXuoo2ZnQscBH7v7hOCzhNpZpYH5Ln7SjPrC6wALo/zv2MD0t39oJklAm8AN7n7PwKOFnFmdjMwBejn7rODzhNpZrYVmOLuEZ93Hwtn6IeXunP3euDDpe7ilru/DuwNOkd3cfed7r4y9LgKWE+YV7+KNt7iYOhpYugrus+uwsDMhgCXAQ8GnSUexUKhR3ypO4keZlYITAaWBZsk8kJDD28D5cBSd4/79wz8Avg3oDnoIN3IgRfMbIWZzY3kgWKh0Nu11J3EPjPrAzwBfNPdDwSdJ9LcvcndT6Vlta+pZhbXw2tmNhsod/cVQWfpZme5+2nALOCroSHViIiFQm/XUncS20LjyE8Af3T3J4PO053cvRJ4FZgZcJRIOwv4ZGhM+THgAjN7ONhIkefupaHv5cBfaBlGjohYKPTDS92ZWRItS909E3AmCaPQB4TzgfXufn/QebqDmeWYWUbocSpwEbAh2FSR5e63ufsQdy+k5b/jl939qoBjRZSZpYc+6MfM0oFLgIjNXov6Qnf3RuDDpe7WA4/H+1J3ZvYo8HdgjJntMLM5QWeKsLOAq2k5Y3s79HVp0KEiLA94xczepeWkZam794hpfD3MQOANM3sHWA781d0XR+pgUT9tUURE2ifqz9BFRKR9VOgiInFChS4iEidU6CIicUKFLiISJ1ToIiJxQoUuIhInVOgiInHi/wOft7fAt9JJeAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bck7_quick = end7_bucket_quick - start7_bucket_quick\n",
    "bck8_quick = end8_bucket_quick - start8_bucket_quick\n",
    "bck9_quick = end9_bucket_quick - start9_bucket_quick\n",
    "bck10_quick = end10_bucket_quick - start10_bucket_quick\n",
    "bck11_quick = end11_bucket_quick - start11_bucket_quick\n",
    "bck12_quick = end12_bucket_quick - start12_bucket_quick\n",
    "\n",
    "bucket_vetorzao_quick = [bck7_quick,bck8_quick,bck9_quick,bck10_quick,bck11_quick,bck12_quick]\n",
    "\n",
    "plt.plot(bucket_vetorzao_quick, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12d6cbb00>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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il45Z6M65t4Gj3X3+fP/GEZFQUVRWw+X3rQYcj10zVRs8BwGtFBWR47Zh136uXLyaqCjjsWumMnqwyjwY6F4uInJcCkqrueK+VcRER/HEtSrzYKIRuoh020c7q7jq/jUkxfXh0WumMGJQoteRpBON0EWkW9Z/uo/vLl5Nv759ePzaqSrzIKQRuogc09rte/neg+8xKCmWR6+ZSpbuYR6UNEIXkS+0ungPVz+whsH94nji2jNU5kFMI3QROap3tlQyd8lasgbG8+i8KQzu39frSPIFNEIXkS69ubmC7z/0HsNTEnjsmqkq8xCgEbqIHOGNjeX88OF1jEpL4uG5kxmUFOd1JOkGFbqIHGJFYRnXPfI+Y4ck8fDcKSQnxHodSbpJUy4i0uGVT3bxfx5ex/iMfjwyd6rKPMRohC4iALzwUSk3PP4BE4YO4KEfTKZ/3xivI8lxUqGLCM+uL+GmJz8gf0QKD3z/dJLiVA2hSH9qIhHuqXU7mf/Uh0zJSeGB751OQqxqIVTpT04kgj3x3qfc+szHnDUqlfuuzic+NtrrSHICdFJUJEI9vGoHtzz9MeeMSWPxHJV5ONAIXSQCPfSvbSx4vpDzxw3mz1eeRlwflXk4UKGLRJjFbxXzixc3MCMvnf+64jRi++h/1MOFCl0kgvxl5VZ+/cpGvnbyEO75zkRiolXm4USFLhIh7n2tiN+t2MwlEzL5/aUT6KMyDzsqdJEw55zjD68W8Z+vFfGNiVks/PYEoqOOtu+7hDIVukgYc86xcNkm/rxyK9+eNJS7v3mKyjyMqdBFwpRzjl+9vJFFbxZz+eTh3DX7S0SpzMOaCl0kDDnn+P8vFPLgv7Zz9RkjuPOSkzBTmYc7FbpImGlrc9yxtIC/r9rBD87K4WcXjVeZRwgVukgYaWtz/PTZj3lszWf88JyR3PrVcSrzCKJCFwkTrW2OW57+iKfW7eS680bx4xm5KvMIo0IXCQMtrW3Mf+oj/rG+hBsvGMMN549RmUcgFbpIiGtpbeNHT37I8x+W8uMZY7l+2hivI4lHVOgiIay5tY1/f2w9L3+ym1u/Oo7/fe4oryOJh1ToIiGqsaWV6x9dz4rCMv7frPHMO3uk15HEYyp0kRDU0NzKvz3yPq9vLOfOS05izpnZXkeSIKBCFwkxDc2tXPv3dby5uYK7/teX+O6UEV5HkiChQhcJIfVNrcz723u8s3UPv/nmKVx6+jCvI0kQUaGLhIi6xhZ+8NB7vLd9L7/91gS+OWmo15EkyBzzhshm9oCZlZvZJ51eSzGzFWZW5Pt5YGBjikS22sYWvvfgGt7bvpc/XHaqyly61J073D8EzDzstVuB15xzY4DXfM9FJAD2NzRz1f2ref/TKu69/DS+fmqW15EkSB2z0J1zbwJ7D3v568AS3+MlwGw/5xIRoPpAM1cuXs0nJdX86YrTmHVKhteRJIj1dA493Tm3C8A5t8vMBvsxk4gA++qauPL+1RSV1fKX707igrx0ryNJkAv4poJmdq2ZrTWztRUVFYE+nEhY2FPbyOX3raKovJa/Xq0yl+7paaGXmVkGgO/n8qO90Tm3yDmX75zLT0tL6+HhRCJHRU17mW+rrOP+Ofmcl6v/AZbu6WmhLwXm+B7PAZ7zTxyRyFa+v4HvLHqXz/bW8+D3T+fsMRoESfd157LFx4B3gVwz22lmc4G7gelmVgRM9z0XkROwq7qeyxatYld1Aw99/3TOHJXqdSQJMcc8Keqcu/wov3S+n7OIRKyd+w5wxX2r2VvXxN/nTmbSiBSvI0kI0kpREY88u76Ehcs2UVpVT5QZfaLhiR+eyanDkr2OJiEq4Fe5iMiRnl1fwm3PfExJVT0OaHUOMLZX1nkdTUKYCl2kl7W0tvGLFwupb2495PXGljYWLtvkUSoJB5pyEekFB5paeHNzBcsLynh9UzlVB5q7fF9pVX0vJ5NwokIXCZCKmkZe21DGisIy3tpSSVNLGwPiYzh/3GBWbi5nb92RpZ6ZHO9BUgkXKnQRPyquqGVFYRnLC8t4/9N9OAdZyfF8d8pwpuelMzk7hT7RUR1z6J2nXeJjopl/Ya6H6SXUqdBFTkBbm+PDnVUsL2wfiW8prwXgpMz+3Hj+WKbnpTM+ox9mdsjnZk9sv2PiwatcMpPjmX9hbsfrIj2hQhc5To0trby7dQ/LC8t4tbCM8ppGoqOMqSNTuHLKcC7IS2fowIRj/j6zJ2apwMWvVOgi3VBd38zKTeUsLyjjn5srqG1sITE2mnNz05iRN4TzcgczICHG65gS4VToIkdRWlXPCt9UyqriPbS0OVKT4rh4QgYz8oZwxqhB9I2J9jqmSAcVuoiPc46Nu2t8JzV380nJfgBGpSUy7+yRTM9LZ+KwZKKi7Bi/k4g3VOgS0Vpa21i7Yx/LC8pYsWE3n+2txwwmDkvm1q+OY3peOqPSkryOKdItKnSJOO2LfCpZUVjG6xvL2Hegmdg+UXx5dCr/9pXRnD9+MIP79fU6pshxU6FLRKisbeT1DeUsL9zNW0WVNPoW+UwbN5gZeemcMzaNxDj9dZDQpn+DJWxtr6xjeeFuVhSWsXbH54t8Lp88nBl56Zyek0JMtG5nJOFDhS5ho63N8VFJNSsKd7O8oIwi3yKfvIz+/Pu0Mcw4KZ28jP5HLPIRCRcqdAlpTS1tvFu8h+UFu3l1Qxll+9sX+UzOTuGKKcO5YHw6w1KOvchHJByo0CXk7G9o5o2N5awoLGPlpvZFPgmx0Zw7No3peelMGzeY5IRYr2OK9DoVuoSEXdX1vOq76dWq4j00tzpSk2K56JQMZpyUzpmjUrXIRyKeCl2CQuft2DKT4/nxjLHkZQ5gecFuVmwo46Od1QCMTE3kB1/OYUZeOqcOG0i0FvmIdFChi+cOv5VsSVU9Nz35Ic736xOHJ3PzzFxm5A1h9GAt8hE5GhW69Lqahma2VdaxrbKO4oo6Fr1ZfMR2bA5Ijo9h+Y/OYXB/LfIR6Q4VugREY0srn+09QHFF3efl7SvwytrGjveZgXNd/x7V9c0qc5HjoEKXHmtrc+za38C2ijq2VdZSXPl5eX+29wBtnYo6NSmWnNREpo1LIyc1iZzUREamJTI8JYHzf/dPSrrYS1PbsYkcHxW6HFPVgaaO0fW2ytqOqZLte+poaG7reF98TDQ5qYmcnDWAr0/IJCctsb28ByV+4b3C51+Yq+3YRPxAhS4ANDS3sn1PHdsq6o4o732ddqiPjjKGpySQk5rIl0en+ko7kZGpSaT3j+vRKkxtxybiHyr0CNLa5ijZV0+xr6g7n5gsra4/ZC47vX8cOamJzPxSBiN90yM5qYkMS0kIyP1PtB2byIlToYcZ5xx76po6RtjFlXW+Oe46duw5QFPr51Mk/eL6MDItkdOzB5KTOoyctERGpiaSnZpIku48KBJy9Lc2CB2+yKar6Ye6xpZDRtgdc9uVddQ0tHS8LybaGDGovainjR/MyNTEjpOSqUmxulGVSBhRoQeZrhbZ3PzUR6zcVE5CXB+KK9qLu2x/4yGfy0qOJyc1kdmnZnVMj4xMTSIzuS99dItYkYigQvdQbWMLu6rqKamqZ1d1A6VV9Sx+a9sRi2yaWtt49oNSBibE+E5Gpn1e2mmJjEhJJD5W9zERiXQq9ABpaW2jrKaR0qp6Sg+WdlVDx+PSqnr2d5oaAYgyDrl2uzMD1t8+I/DBRSRkqdB7wDlH1YHmQ0bWpYeNtMv2NxxRzgPiY8hMjmfowHhOz04hMzmezOS+ZCXHk5EcT3q/OM5duFKLbESkR1ToXWhobu0o5s4j69Lqz58fPi0SGx1FZnJfMgbEc+aoVLKS+5LpK+os3+vd2bNSi2xEpKcirtDb2hwVtQenQjqVdvXnz/fUNR3xubR+cWQmxzNuSD/Oyx1Mpq+oM5PjyRgQz6DEWKL8cCtXLbIRkZ4K+kLvziV8ndU0NHcUc2l1fUdxHyzt3dUNNLceOheSGBtN1sD2Yv5S1oCOEXV7aceTPiCOuD69d9JRi2xEpCdOqNDNbCZwDxANLHbO3e2XVD5dXcJ3y9MfsaW8hlGDkz4v7k6j7ZrGQ0809oky0vu3z1NPGj6QjOT4I0bX/fv20fXYIhLyelzoZhYN/AmYDuwE3jOzpc65Qn+FW7hs0xFz1Y0tbfzXG1s7nqckxpKZ3JfhgxI4Y9Sgjnnsg6PrtH5x2tVGRCLCiYzQJwNbnHPFAGb2OPB1wG+FXtrF1R4HvfYf55I5IF7XX4uI+JzIEsIs4LNOz3f6XvObo12ql5Ucz6i0JJW5iEgnJ1LoXc1jHLEsxsyuNbO1Zra2oqLiuA4w/8Jc4g/byV2X8ImIdO1ECn0nMKzT86FA6eFvcs4tcs7lO+fy09LSjusAsydm8atvnExWcjxG+8j8V984WVeAiIh04UTm0N8DxphZDlACfAe4wi+pOtElfCIi3dPjQnfOtZjZ9cAy2i9bfMA5V+C3ZCIiclxO6Dp059xLwEt+yiIiIidAN8oWEQkTKnQRkTChQhcRCRPm3FF2VAjEwcwqgB09/HgqUOnHOKFA3zky6DuHvxP9viOcc8e87rtXC/1EmNla51y+1zl6k75zZNB3Dn+99X015SIiEiZU6CIiYSKUCn2R1wE8oO8cGfSdw1+vfN+QmUMXEZEvFkojdBER+QIhUehmNtPMNpnZFjO71es8gWZmD5hZuZl94nWW3mBmw8zsDTPbYGYFZnaD15kCzcz6mtkaM/vQ953v9DpTbzGzaDNbb2YveJ2lN5jZdjP72Mw+MLO1AT1WsE+5+La620ynre6Ay/251V2wMbNzgFrgb865L3mdJ9DMLAPIcM69b2b9gHXA7DD/MzYg0TlXa2YxwNvADc65VR5HCzgzuwnIB/o75y7yOk+gmdl2IN85F/Dr7kNhhN6x1Z1zrgk4uNVd2HLOvQns9TpHb3HO7XLOve97XANswM+7XwUb167W9zTG9yO4R1d+YGZDgVnAYq+zhKNQKPSAb3UnwcPMsoGJwGpvkwSeb+rhA6AcWOGcC/vvDPwRuBlo8zpIL3LAcjNbZ2bXBvJAoVDo3drqTkKfmSUBTwM3Ouf2e50n0Jxzrc65U2nf7WuymYX19JqZXQSUO+fWeZ2ll53lnDsN+CpwnW9KNSBCodC7tdWdhDbfPPLTwCPOuWe8ztObnHNVwEpgpsdRAu0s4BLfnPLjwDQze9jbSIHnnCv1/VwO/IP2aeSACIVC79jqzsxiad/qbqnHmcSPfCcI7wc2OOd+73We3mBmaWaW7HscD1wAbPQ2VWA5525zzg11zmXT/vf4defclR7HCigzS/Sd6MfMEoEZQMCuXgv6QnfOtQAHt7rbADwZ7lvdmdljwLtArpntNLO5XmcKsLOAq2gfsX3g+/E1r0MFWAbwhpl9RPugZYVzLiIu44sw6cDbZvYhsAZ40Tn3SqAOFvSXLYqISPcE/QhdRES6R4UuIhImVOgiImFChS4iEiZU6CIiYUKFLiISJlToIiJhQoUuIhIm/gewfBf1B7fI0QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bck7_merge = end7_bucket_merge - start7_bucket_merge\n",
    "bck8_merge = end8_bucket_merge - start8_bucket_merge\n",
    "bck9_merge = end9_bucket_merge - start9_bucket_merge\n",
    "bck10_merge = end10_bucket_merge - start10_bucket_merge\n",
    "bck11_merge = end11_bucket_merge - start11_bucket_merge\n",
    "bck12_merge = end12_bucket_merge - start12_bucket_merge\n",
    "\n",
    "bucket_vetorzao_merge = [bck7_merge,bck8_merge,bck9_merge,bck10_merge,bck11_merge,bck12_merge]\n",
    "\n",
    "plt.plot(bucket_vetorzao_merge, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Integrando os 2 vetores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12e321f28>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bucket_total_quick = [bck1_quick,bck2_quick,bck3_quick,bck4_quick,bck5_quick,bck6_quick,bck7_quick,bck8_quick,bck9_quick,bck10_quick,bck11_quick,bck12_quick]\n",
    "\n",
    "plt.plot(bucket_total_quick, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12fb05320>]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXQAAAD8CAYAAABn919SAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAG7BJREFUeJzt3Xl0XGeZ5/Hvo9WSN9myYsfyvmbBiR2c4Dg2CQ5ZIEDcNFuGod0DTIAD3TDQAYfmDIfTzCGMe1h6Dp2QIUD6hD0YJxNIDBOSWHISJ07seInjaPEqbyrJ8iLLWqqe+UMlR7a1q6pu3arf5xyfqnvrlt7nyvLPV2/d933N3RERkfDLCboAERFJDAW6iEiGUKCLiGQIBbqISIZQoIuIZAgFuohIhlCgi4hkCAW6iEiGUKCLiGSIvFQ2NmHCBJ8xY0YqmxQRCb1XXnkl4u5l/R2X0kCfMWMGmzdvTmWTIiKhZ2b7BnKculxERDKEAl1EJEMo0EVEMoQCXUQkQyjQRUQyRErvchERySbrttSxZv1uDjW1MLmkiHtum8/KReVJa0+BLiKSBOu21HHv2u20tEcBqGtq4d612wGSFurqchERSYI163efC/MuLe1R1qzfnbQ2FegiIklwqKllUPsTQYEuIpIEk0uKBrU/ERToIiJJcM9t8xmRf37EFuXncs9t85PWpgJdRCQJVi4q55PLZp7bLi8p4jsfXKC7XEREwsgd8nKMrd+8lVGFyY9bXaGLiCRJZVWERdNKUhLmoEAXEUmKxuY2dhw6wfK5/U5jnjAKdBGRJNhYHcEdls2dkLI2FegiIklQWRVh9Ig8riofm7I2FegiIgnm7lRWR1g6u5S83NTFrAJdRCTB9kSaqWtqSWn/OSjQRUQSrqIqAsDyFPafgwJdRCThKqoiTB1fxPTSkSltV4EuIpJA7dEYL9Y2pLy7BRToIiIJtfVAE6dbO1g+J7XdLaBAFxFJqIqqCDkGS2cr0EVEQq2yqp6rppQwtjg/5W0r0EVEEuRESztbDzSl/O6WLgMOdDPLNbMtZvZEfHummW0ysyoz+42ZFSSvTBGR9PdCTQMxh2UB9J/D4K7Qvwjs6rb9XeD77j4XOA58KpGFiYiETWV1PSMLclk0bVwg7Q8o0M1sCnAH8JP4tgErgEfjhzwMrExGgSIiYVFRFWHJrFIK8oLpzR5oqz8AvgrE4tulQJO7d8S3DwLJW4ZDRCTNHWg8w76GMymdXfFC/Qa6mb0POObur3Tf3cOh3sv77zazzWa2ub6+fohlioikt7eG+6d+QFGXgVyh3wB8wMz2Ar+ms6vlB0CJmXUtwzEFONTTm939QXdf7O6Ly8qCO1ERkWSqqKrn0rEjmF2W2uH+3fUb6O5+r7tPcfcZwMeAv7r7x4FngA/FD1sFPJa0KkVE0lg05jxf08CyORPo/IgxGMPpuf8a8GUzq6azT/2hxJQkIhIu2+tOcKKlneXzgu2FGNTKpe7+LPBs/HktcF3iSxIRCZfKqs7PB2+YXRpoHRopKiIyTBuqIlw5eQylowoDrUOBLiIyDM2tHWzZfzzQu1u6KNBFRIZh054G2qMe2Pwt3SnQRUSGYcObEQrzcnj79GCG+3enQBcRGYbK6gjvmFXKiPzcoEtRoIuIDNXhEy1UHzsdyOpEPVGgi4gMUddw/yDnb+lOgS4iMkSVVREmjCrkskmjgy4FUKCLiAxJLOZsrI6wfG6ww/27U6CLiAzB64dP0tDcFtjqRD1RoIuIDEFldXr1n4MCXURkSCqrIsyfOJqJY0YEXco5CnQRkUE62x7lpb2NaXV1Dgp0EZFBe2lPI20dMQW6iEjYVVZHKMjN4R0zxwddynkU6CIig7ThzXrePn0cxQWDWlIi6RToIiKDUH+qlTeOnEq77hZQoIuIDMrG+O2K70yD+c8vpEAXERmEiqoI44rzuXLymKBLuYgCXURkgNydiqp6ls6ZQE5Oegz3706BLiIyQFXHTnPsVCvvTMP+c1Cgi4gM2FvT5aZf/zko0EVEBqyiqp5ZE0ZSXlIUdCk9UqCLiAxAa0eUTbWNabEYdG8U6CIiA/DqviZa2qNp290CCnQRkQGpqKonN8dYMiu9hvt3p0AXERmAyuoI10wrYfSI/KBL6ZUCXUSkH8eb29hed4Jlc9K3uwUU6CIi/dpYE8E9vVYn6okCXUSkH5VVEUaPyOPqKWODLqVPCnQRkT50DvePsHR2KXm56R2Z6V2diEjA9kSaqWtqSevbFbso0EVE+lAZny53+Zz07j8HBbqISJ8qqiJMHV/E9NLioEvplwJdRKQX7dEYL9Y0sGxOGWbpN13uhfoNdDMbYWYvmdlrZrbTzL4V3z/TzDaZWZWZ/cbMCpJfrohI6rx2oIlTrR1pPX9LdwO5Qm8FVrj71cBC4HYzWwJ8F/i+u88FjgOfSl6ZIiKpV1EVIcdg6ezSoEsZkH4D3Tudjm/mx/84sAJ4NL7/YWBlUioUEQlIZXWEBVNKKCkORwfEgPrQzSzXzLYCx4C/ADVAk7t3xA85CJQnp0QRkdQ7ebadrQeaQnF3S5cBBbq7R919ITAFuA64vKfDenqvmd1tZpvNbHN9ff3QKxURSaEXahqIxjw0/ecwyLtc3L0JeBZYApSYWV78pSnAoV7e86C7L3b3xWVl6X9jvogIdA73Ly7IZdG0cUGXMmADuculzMxK4s+LgHcDu4BngA/FD1sFPJasIkVEUq2iqp4ls0opyAvP3d0DqfRS4Bkz2wa8DPzF3Z8AvgZ82cyqgVLgoeSVKSKSOgcaz7C34UyoulsA8vo7wN23AYt62F9LZ3+6iEhGOTfcP2SBHp7fJUREUqSiqp5JY0Ywu2xU0KUMigJdRKSbaMzZWN3A8rkTQjHcvzsFuohINzvqTnCipT3tVyfqiQJdRKSbiqrO8TI3hGhAURcFuohINxVVEa6cPIYJowqDLmXQFOgiInHNrR28uv94KLtbQIEuInLOS3saaY86y+eEc1S7Al1EJG5DVT2FeTksnhGe4f7dKdBFROIqqyJcN3M8I/Jzgy5lSBToIiLAkRNnqTp2OnSjQ7tToIuI8NbtisvnhrP/HBToIiJA5/wtE0YVctmk0UGXMmQKdBHJerGYs7E6wrI5paEb7t+dAl1Est6uIyeJnG5jWYi7W0CBLiJCZVU4p8u9kAJdRLJeZXWEeRNHMXHMiKBLGRYFuohktbPtUTbtaWRZSEeHdqdAF5Gs9vLeRto6YiyfF+7uFlCgi0iWq6yKUJCbwztmjg+6lGFToItIVttQFeGa6SUUF/S7xHLaU6CLSNaqP9XKrsMnQz06tDsFuohkredrMuN2xS4KdBHJWhVVEUqK87ly8tigS0kIBbqIZCV3p6KqnhvmTCA3J7zD/btToItIVqo+dpqjJ1tZHsLFoHujQBeRrFQRH+4f1vVDe6JAF5GsVFFVz6wJI5kyrjjoUhJGgS4iWaetI9Y53D+Drs5BgS4iWWbdljqW3vc0Z9qiPLHtMOu21AVdUsKEf2iUiMgArdtSx71rt9PSHgWgsbmNe9duB2DlovIgS0sIXaGLSNZYs373uTDv0tIeZc363QFVlFgKdBHJGoeaWga1P2wU6CKSNSaXFA1qf9go0EUka3zmxpkX7SvKz+We2+YHUE3iKdBFJGscPtEKwMQxhRhQXlLEdz64ICM+EIUB3OViZlOB/wAmATHgQXf/oZmNB34DzAD2Ah9x9+PJK1VEZOhOnm3nkRf2cceCS/nRx68JupykGMgVegfwFXe/HFgCfN7MrgBWA0+7+1zg6fi2iEha+uWm/Zxq7eCzN84OupSk6TfQ3f2wu78af34K2AWUA3cCD8cPexhYmawiRUSG42x7lIcq97BszgQWTMmMqXJ7Mqg+dDObASwCNgET3f0wdIY+cEmiixMRSYQ/bKmj/lQrn7spc6/OYRCBbmajgN8DX3L3k4N4391mttnMNtfX1w+lRhGRIYvGnB8/V8NVU8aydHZp0OUk1YAC3czy6QzzX7j72vjuo2Z2afz1S4FjPb3X3R9098XuvrisLDPW7ROR8HhqxxH2NpzhszfOxiwzFrLoTb+Bbp3fgYeAXe7+vW4vPQ6sij9fBTyW+PJERIbO3XnguRpmThjJbVdOCrqcpBvIFfoNwCeAFWa2Nf7nvcB9wC1mVgXcEt8WEUkbG6sb2F53gs+8c1bGLDPXl37vQ3f3SqC378TNiS1HRCRx7n+umktGF/I312TGwKH+aKSoiGSkbQeb2FjdwKeWzaQwLzfoclJCgS4iGemB52oYPSKP//SOaUGXkjIKdBHJOLX1p3lyxxH+7vrpjB6RH3Q5KaNAF5GM8+CGWvJzc/j7pRfPrpjJFOgiklGOnjzL2lfr+MjiKZSNLgy6nJRSoItIRvlp5R46YjHuXp7Zw/x7okAXkYxxoqWdX2zazx1XTWZaaXHQ5aScAl1EMsYjL+7jdGsHn71xVtClBEKBLiIZ4Wx7lJ9t3MON88q4cnLmTpHbFwW6iGSE371ykMjptoxewKI/CnQRCb2OaIz/s6GWhVNLWDJrfNDlBEaBLiKh96cdR9jfeIbP3ZT5U+T2RYEuIqHm7tz/bA2zy0Zyy+UTgy4nUAp0EQm1596sZ9fhk3zmxtnkZMEUuX1RoItIqD3wXA2Txoxg5cLsmCK3Lwp0EQmtV/cf58XaRj69fCYFeYozfQdEJLQeeLaGsUX53HVd9kyR2xcFuoiEUvWxU/z59aOsun46Iwv7XXwtKyjQRSSUfvxcLSPyc1i1dEbQpaQNBbqIhM7hEy2s21rHRxdPpXRUdk2R2xcFuoiEzkMVe4g5fHp5dk7C1RsFuoiEStOZNn750n4+cPVkpo7Pvily+6JAF5FQ+Y8X9nGmLcpnsnSK3L4o0EUkNFraovz8+b2suOwSLps0Juhy0o4CXURC47ebD9DY3MbnbsreKXL7okAXkVBoj8Z4cEMti6eP49oZ2TtFbl8U6CISCk9sO0RdU0tWL2DRHwW6iKQ9d+eBZ2uZN3EUKy67JOhy0pYCXUTS3jO7j7H76Ck+qyly+6RAF5G0d/+zNZSXFPH+qycHXUpaU6CLSFrbvLeRl/ce59PLZ5Kfq8jqi747IpLWHniuhnHF+Xz02qlBl5L2FOgikrZ2HznF/9t1jL9fOpPiAk2R2x8FuoikrR9vqKEoP5e/u3560KWEggJdRNLSweNneHzrIe66bhrjRhYEXU4o9BvoZvZTMztmZju67RtvZn8xs6r447jkliki2eYnFXsA+PTymQFXEh4DuUL/OXD7BftWA0+7+1zg6fi2iEhCNDa38ZuXD3DnwnImlxQFXU5o9Bvo7r4BaLxg953Aw/HnDwMrE1yXiGSxh5/fS0t7lM9qitxBGWof+kR3PwwQf9RYXBFJiObWDh5+YS+3XDGRuRNHB11OqCT9Q1Ezu9vMNpvZ5vr6+mQ3JyIh9+uXD9B0pl1T5A7BUAP9qJldChB/PNbbge7+oLsvdvfFZWVlQ2xORLJBW0eMhypquW7meK6ZpnstBmuogf44sCr+fBXwWGLKEZFs9vhrhzh04qyuzodoILct/gp4AZhvZgfN7FPAfcAtZlYF3BLfFhEZsljMeeC5Gi6bNJqb5um3+aHodyytu9/Vy0s3J7gWEcliT79xjOpjp/nhxxZipilyh0KTI4hIoNZtqWPN+jeoazpLbo4RjcaCLim0FOgiEph1W+q4d+12WtqjAERjzj+v20lOTg4rF5UHXF34aC4XEQnMmvW7z4V5l5b2KGvW7w6oonBToItIIM62R6lraunxtUO97Je+KdBFJOW27D/Oe/+totfXNX/L0CjQRSRlWjui/M+n3uBv73+es21RPnfjbIryc887pig/l3tumx9QheGmD0VFJCV2HjrBV377Gm8cOcVHFk/hG++7gjEj8pk/aTRr1u/mUFMLk0uKuOe2+fpAdIgU6CKSVO3RGPc/W8O/PV3FuJEFPLRqMTdfPvHc6ysXlSvAE0SBLiJJU3X0FF/53WtsO3iCD1w9mW994EqtPpRECnQRSbhozHmospZ//fObjCrM498/fg3vXXBp0GVlPAW6iCTU3kgz//S719i87zi3XjGR//E3CygbXRh0WVlBgS4iCRGLOY9s2sd3/vQGebnG9z96NSsXlmtelhRSoIvIsB08foavPrqN52sauHFeGd/926uYNHZE0GVlHQW6iAyZu/PbzQf4lyd24e5854ML+Ni1U3VVHhAFuogMydGTZ1n9+208s7ueJbPGs+ZDVzN1fHHQZWU1BbqIDIq78/hrh/jvj+2ktSPKN99/Bauun0FOjq7Kg6ZAF5EBi5xu5Rt/2MFTO4+waFoJ/+vDVzOrbFTQZUmcAl1EBuSpHYf55z/s4NTZDla/5zL+6/JZ5OqqPK0o0EXkPJ0rCL01t8rn3zWbl/Y0sm7rId5WPoZffngh8yeNDrpM6YECXUTOuXAFobqmFr7+hx0Y8KV3z+Xz75pDfq4maU1XCnQROaenFYQAJowu5EvvnhdARTIY+q9WRACoqT/d6wpCkVOtKa5GhkJX6CJZyt158+hp/rT9ME/tOMLuo6d6PVYrCIWDAl0ki7g7Ow+dPBfitZFmzODaGeP55vuvwAy+++T53S5aQSg8FOgiGS4Wc7YebOLJ7Yd5cscRDh5vITfHuH5WKZ9cNpNbr5zIJaPfmnelpKhAKwiFlAJdJANFY87mvY08ueMIT+04wpGTZ8nPNW6YM4F/XDGXd18xkfG9LDShFYTCS4EukiE6ojFerG3kyR2HWb/zKJHTrRTk5XDjvDK+tmA+Ky6byNii/KDLlCRSoIuEwIWDfbq6Qdo6YmysjvDkjsP8+fWjNJ1ppyg/lxWXXcJ7FkziXfMvYWSh/plnC/1Ni6S5ngb7fPXR13jkxb3sPnqaU2c7GF2Yx82XX8Ltb7uUG+eVUVSQG3DVEgQFukia62mwT1vUeWV/Ex+6ZgrvWTCJG+ZMoDBPIZ7tFOgiaeh4cxub9jTwQk1Dr4N9cFjz4atTW5ikNQW6SBo4caa9M8BrO0P8jSOdg3yK8nMpzMuhtSN20Xs02EcupEAXCcDJs+28vKeRF2s7Q3znoZO4Q2FeDotnjOOfbp3H9bNLWVBewp+2Hz6vDx002Ed6pkAXSYHm1g5e3tvIC7UNvFjTwPa6E8QcCnJzWDSthC/ePJfrZ5WycFrJRX3hXfeEa7CP9EeBLjIEvd1G2KWlLcrmfY28UNPAi7UNbDt4go6Yk59rLJxawhfeNYcls0u5Zto4RuT3/2GmBvvIQAwr0M3sduCHQC7wE3e/LyFViaSxnm4jXL12G28eO0WeGS/UNrD1QBPtUScvx7hqyljufucsrp9dytunj6O4QNdRkhxD/skys1zgR8AtwEHgZTN73N1fT1Rx0P+VUKKlsr1MPrdUt5estqIxp6U9ypnWDprbojS3dvDtP75+0W2EZ9tj/PszNeQYLCgfyyeXzeT6WaVcO2O8BvZIygznJ+06oNrdawHM7NfAnUDCAr2nK6F7124HSEowpLK9sJ2bu1+w3e15D8c+vrWOr6/bwdn22Ln2Vq/dRkc0xvsXTr7oa1z4dT3+VXtqp6uWru0/bjvEt/7v6+e19bXfb2NfYzPXTh9Pc1uUM20dNLde8NjWwZnWaOdjPKzPtHVut7RFaW6N9rjYQ1+2fvNWxozQ8HoJhl34D3XAbzT7EHC7u386vv0J4B3u/oXe3rN48WLfvHnzgNu44b6/9ngPbl6OMW18Mc75/7i7h8G5590Dwb1bKJwfGg40nG4l1sO3I8dgXHHBecF1UcCd91rPx3U/5vTZjouCEMCA4oLc8+p86/29hxw9HNf92I6eTqyrTbu4rUxXmJfDyMI8igtyGVmQR3Fh/LEg9639vbx+79rtNDS3XfQ1y0uK2Lh6RQBnI5nOzF5x98X9HTecK/Selvu+KBLM7G7gboBp06YNqoFDvQyo6Ig5V5aPPVdEVyB1Pre3iju33y44pof9Br966UCP7cUc3rNg0rn3vHVu5x/XfdMufPGC9/xs494eX3fgruumnXds969lFz15qyY7b9/5bf7omZoe2wP4h3fNubjAXr7O+a/1/r343l/e7LW97rfbdf+7uHjfxV+7p3P99h939drWr+9ecn4gF+ZSnJ9L3jDWxTzTFtVthJKWhhPoB4Gp3banAIcuPMjdHwQehM4r9ME0MLmkqMcr9PKSIv73XYsGVexAbHgz0mt73165IKFt/Xnn0V7b+sb7rkhoWwDrthzqtb0v35r4IPrNywd6be/z3f8DSYCfbdzba1tLZpUmtC3QbYSSvoazpujLwFwzm2lmBcDHgMcTU1ane26bT9EFt3Ql80oole1l8rmlur1Unxt0hvrG1SvYc98dbFy9QmEuaWHIV+ju3mFmXwDW03nb4k/dfWfCKiP1V0KpbC+Tzy3V7emKWaTTkD8UHYrBfigqIiID/1B0OF0uIiKSRhToIiIZQoEuIpIhFOgiIhlCgS4ikiFSepeLmdUD+4b49glAJIHlpJNMPjfI7PPTuYVXmM5vuruX9XdQSgN9OMxs80Bu2wmjTD43yOzz07mFVyaen7pcREQyhAJdRCRDhCnQHwy6gCTK5HODzD4/nVt4Zdz5haYPXURE+hamK3QREelDKALdzG43s91mVm1mq4OuJ1HMbKqZPWNmu8xsp5l9MeiaEs3Mcs1si5k9EXQtiWZmJWb2qJm9Ef87vD7omhLFzP5b/Gdyh5n9ysxGBF3TcJjZT83smJnt6LZvvJn9xcyq4o/jgqwxEdI+0LstRv0e4ArgLjNL/AoQwegAvuLulwNLgM9n0Ll1+SLQ+5JC4fZD4Cl3vwy4mgw5TzMrB/4RWOzub6NzeuyPBVvVsP0cuP2CfauBp919LvB0fDvU0j7Q6bYYtbu3AV2LUYeeux9291fjz0/RGQgZM4m3mU0B7gB+EnQtiWZmY4B3Ag8BuHubuzcFW1VC5QFFZpYHFNPDamRh4u4bgMYLdt8JPBx//jCwMqVFJUEYAr0c6L7Y50EyKPS6mNkMYBGwKdhKEuoHwFeBWNCFJMEsoB74WbxL6SdmNjLoohLB3euAfwX2A4eBE+7+52CrSoqJ7n4YOi+ugEsCrmfYwhDoA1qMOszMbBTwe+BL7n4y6HoSwczeBxxz91eCriVJ8oBrgPvdfRHQTAb8yg4Q70u+E5gJTAZGmtl/DrYqGYgwBPqAFqMOKzPLpzPMf+Hua4OuJ4FuAD5gZnvp7CZbYWaPBFtSQh0EDrp7129Uj9IZ8Jng3cAed69393ZgLbA04JqS4aiZXQoQfzwWcD3DFoZAT/pi1EExM6OzD3aXu38v6HoSyd3vdfcp7j6Dzr+zv7p7xlzlufsR4ICZda1EfTPweoAlJdJ+YImZFcd/Rm8mQz7wvcDjwKr481XAYwHWkhBDXiQ6VVKxGHWAbgA+AWw3s63xfV939z8FWJMM3D8Av4hfaNQC/yXgehLC3TeZ2aPAq3TeibWFkI+qNLNfATcBE8zsIPBN4D7gt2b2KTr/E/twcBUmhkaKiohkiDB0uYiIyAAo0EVEMoQCXUQkQyjQRUQyhAJdRCRDKNBFRDKEAl1EJEMo0EVEMsT/B9zYY+O2/q2+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bucket_total_merge = [bck1_merge,bck2_merge,bck3_merge,bck4_merge,bck5_merge,bck6_merge,bck7_merge,bck8_merge,bck9_merge,bck10_merge,bck11_merge,bck12_merge]\n",
    "\n",
    "plt.plot(bucket_total_merge, linestyle='-', marker='o')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### BENCHMARKING"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparação entre algoritmos O (n²) e O (n log n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x13b631710>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(selection, linestyle='-', marker='o', color='blue')\n",
    "plt.plot(bubble, linestyle='-', marker='o',color='red')\n",
    "plt.plot(quick_vetorzin, linestyle='-', marker='o',color='purple')\n",
    "plt.plot(bucket_vetorzin_merge, linestyle='-', marker='o',color='black')\n",
    "plt.plot(bucket_vetorzin_quick, linestyle='-', marker='o',color='magenta')\n",
    "plt.plot(merge_vetorzin, linestyle='-', marker='o',color='cyan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparação entre Buckets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12d114240>]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(bucket_total_quick, linestyle='-', marker='o',color='black')\n",
    "plt.plot(bucket_total_merge, linestyle='-', marker='o',color='magenta')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Comparação entre algoritmos O (n log n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x12e422550>]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(quick_total, linestyle='-', marker='o',color='purple')\n",
    "plt.plot(bucket_total_quick, linestyle='-', marker='o',color='black')\n",
    "plt.plot(bucket_total_merge, linestyle='-', marker='o',color='magenta')\n",
    "plt.plot(merge_total, linestyle='-', marker='o',color='cyan')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Criando DataFrame com tempo dos vetores até 20K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'Selection':selection,'Bubble':bubble, 'Quick': quick_vetorzin, 'Merge':merge_vetorzin,'Bucket com Quick':bucket_vetorzin_quick,'Bucket com Merge':bucket_vetorzin_merge}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_menor = pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Selection</th>\n",
       "      <th>Bubble</th>\n",
       "      <th>Quick</th>\n",
       "      <th>Merge</th>\n",
       "      <th>Bucket com Quick</th>\n",
       "      <th>Bucket com Merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.940558</td>\n",
       "      <td>6.295169</td>\n",
       "      <td>0.025971</td>\n",
       "      <td>0.029095</td>\n",
       "      <td>0.010165</td>\n",
       "      <td>0.015031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11.617864</td>\n",
       "      <td>23.769561</td>\n",
       "      <td>0.056130</td>\n",
       "      <td>0.063121</td>\n",
       "      <td>0.021475</td>\n",
       "      <td>0.031115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>18.130004</td>\n",
       "      <td>38.950604</td>\n",
       "      <td>0.078411</td>\n",
       "      <td>0.082224</td>\n",
       "      <td>0.026941</td>\n",
       "      <td>0.039230</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>26.093152</td>\n",
       "      <td>53.010315</td>\n",
       "      <td>0.074379</td>\n",
       "      <td>0.098441</td>\n",
       "      <td>0.032511</td>\n",
       "      <td>0.048894</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>35.642405</td>\n",
       "      <td>73.078233</td>\n",
       "      <td>0.090414</td>\n",
       "      <td>0.118422</td>\n",
       "      <td>0.037953</td>\n",
       "      <td>0.056542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>46.633024</td>\n",
       "      <td>94.474575</td>\n",
       "      <td>0.105978</td>\n",
       "      <td>0.141091</td>\n",
       "      <td>0.045011</td>\n",
       "      <td>0.066728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Selection     Bubble     Quick     Merge  Bucket com Quick  \\\n",
       "0   2.940558   6.295169  0.025971  0.029095          0.010165   \n",
       "1  11.617864  23.769561  0.056130  0.063121          0.021475   \n",
       "2  18.130004  38.950604  0.078411  0.082224          0.026941   \n",
       "3  26.093152  53.010315  0.074379  0.098441          0.032511   \n",
       "4  35.642405  73.078233  0.090414  0.118422          0.037953   \n",
       "5  46.633024  94.474575  0.105978  0.141091          0.045011   \n",
       "\n",
       "   Bucket com Merge  \n",
       "0          0.015031  \n",
       "1          0.031115  \n",
       "2          0.039230  \n",
       "3          0.048894  \n",
       "4          0.056542  \n",
       "5          0.066728  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_menor"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Criando DataFrame com tempo dos vetores de 100K até 10M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "d = {'Quick':quick_vetorzao,'Merge': merge_vetorzao,'Bucket com Quick':bucket_vetorzao_quick,'Bucket com Merge':bucket_vetorzao_merge}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_maior = pd.DataFrame(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Quick</th>\n",
       "      <th>Merge</th>\n",
       "      <th>Bucket com Quick</th>\n",
       "      <th>Bucket com Merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.635234</td>\n",
       "      <td>0.769252</td>\n",
       "      <td>0.239841</td>\n",
       "      <td>0.359997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3.500676</td>\n",
       "      <td>4.489455</td>\n",
       "      <td>1.321987</td>\n",
       "      <td>1.949775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7.651786</td>\n",
       "      <td>9.188939</td>\n",
       "      <td>2.601251</td>\n",
       "      <td>3.993317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20.925130</td>\n",
       "      <td>19.184328</td>\n",
       "      <td>5.568054</td>\n",
       "      <td>8.246443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>46.311996</td>\n",
       "      <td>51.055491</td>\n",
       "      <td>14.216187</td>\n",
       "      <td>21.665621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>92.231926</td>\n",
       "      <td>107.151176</td>\n",
       "      <td>30.029845</td>\n",
       "      <td>44.837451</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Quick       Merge  Bucket com Quick  Bucket com Merge\n",
       "0   0.635234    0.769252          0.239841          0.359997\n",
       "1   3.500676    4.489455          1.321987          1.949775\n",
       "2   7.651786    9.188939          2.601251          3.993317\n",
       "3  20.925130   19.184328          5.568054          8.246443\n",
       "4  46.311996   51.055491         14.216187         21.665621\n",
       "5  92.231926  107.151176         30.029845         44.837451"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_maior"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Criando um DataFrame de comparação entre um vetor ordenado e desordenado de 20k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[46.660804986953735,\n",
       " 100.43103909492493,\n",
       " 0.10148501396179199,\n",
       " 0.13492107391357422,\n",
       " 0.04381585121154785,\n",
       " 0.06492090225219727]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec_ord = [o,o1,o2,o3,o4,o5]\n",
    "vec_ord"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[46.63302397727966,\n",
       " 94.47457504272461,\n",
       " 0.10597825050354004,\n",
       " 0.14109110832214355,\n",
       " 0.045011043548583984,\n",
       " 0.06672787666320801]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vec_desor = [s6,b6,q6,m6,bck6_quick,bck6_merge]\n",
    "vec_desor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_comp = {'Ordenado':vec_ord,'Desordenado': vec_desor}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_comp = pd.DataFrame(df_comp)\n",
    "del df_comp.index.name "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ordenado</th>\n",
       "      <th>Desordenado</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>46.660805</td>\n",
       "      <td>46.633024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>100.431039</td>\n",
       "      <td>94.474575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.101485</td>\n",
       "      <td>0.105978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.134921</td>\n",
       "      <td>0.141091</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.043816</td>\n",
       "      <td>0.045011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.064921</td>\n",
       "      <td>0.066728</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ordenado  Desordenado\n",
       "0   46.660805    46.633024\n",
       "1  100.431039    94.474575\n",
       "2    0.101485     0.105978\n",
       "3    0.134921     0.141091\n",
       "4    0.043816     0.045011\n",
       "5    0.064921     0.066728"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_comp"
   ]
  }
 ],
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