Aula_02_Pos_Ciencia_De_Dados.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Aula 02 - Pos_Ciencia_De_Dados.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "cRCVb7zmGNJs",
        "xW3dVSqhGQ4y",
        "wr5sYuVjpeRz",
        "PJsiMznDFHnU",
        "hzsi4Hj_p1uA",
        "hooGauJm--1o"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cRCVb7zmGNJs"
      },
      "source": [
        "# **Turma de Pós-Graduação de Ciência de Dados**\n",
        "\n",
        "**Disciplina: Linguagem de Programação Python**\n",
        "\n",
        "**prof: Sérgio Assunção Monteiro, DSc**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xW3dVSqhGQ4y"
      },
      "source": [
        "# **Aula 02**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wr5sYuVjpeRz"
      },
      "source": [
        "# **Solução do Exercício da Aula 01**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f4Ssxier4w5-"
      },
      "source": [
        "x = [1, 5, 7, -1, 5] \n",
        "soma = 6"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vlKBdkLNpj6J"
      },
      "source": [
        "n = len(x)\n",
        "lista_pares = []\n",
        "for i in range(n):\n",
        "  for j in range(i + 1, n):\n",
        "    if x[i] + x[j] == soma:\n",
        "      par=(x[i],x[j])\n",
        "      lista_pares.append(par)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qZ8IIRpu5F3k",
        "outputId": "935c4c23-068d-4e6b-e636-abc7af667a02"
      },
      "source": [
        "print(\"pares:[{}]\".format(lista_pares))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pares:[[(1, 5), (1, 5), (7, -1)]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PJsiMznDFHnU"
      },
      "source": [
        "# **Funções**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cSMXk_syFNVb"
      },
      "source": [
        "**Exemplos**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bfkWjt-PFRuK"
      },
      "source": [
        "def funcao_somatorio_impares(lista):\n",
        "  soma_impar = 0\n",
        "  for elem in lista:\n",
        "    if(elem % 2 == 1):\n",
        "      soma_impar = soma_impar + elem            \n",
        "  return soma_impar"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FrxenjsbFXmT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ec93dc17-1eba-4399-919f-950586e0e791"
      },
      "source": [
        "lista_teste = [1,2,3,4,5]\n",
        "somat = funcao_somatorio_impares(lista_teste)\n",
        "print(\"O somatório dos elementos ímpares da lista é:[{}]\".format(somat))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "O somatório dos elementos ímpares da lista é:[9]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cOpRndtX7bCy"
      },
      "source": [
        "**outro exemplo ...**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bMXMRXyV7t-g"
      },
      "source": [
        "def obter_total_pares(lista, soma):\n",
        "  n = len(lista)\n",
        "  lista_pares = []\n",
        "  for i in range(n):\n",
        "    for j in range(i + 1, n):\n",
        "      if x[i] + x[j] == soma:\n",
        "        par=(x[i],x[j])\n",
        "        lista_pares.append(par)\n",
        "  return lista_pares"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qSz9CO6f7_R5",
        "outputId": "2f0d0f2a-718c-4df3-fe90-176f4d7b64e6"
      },
      "source": [
        "# Chamada para função \n",
        "x = [1, 5, 7, -1, 5] \n",
        "soma = 6\n",
        "lista_pares=obter_total_pares(x, soma)\n",
        "print(\"pares:{}\".format(lista_pares))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "pares:[(1, 5), (1, 5), (7, -1)]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hzsi4Hj_p1uA"
      },
      "source": [
        "## **Package Numpy**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7wwKK9Us82Io",
        "outputId": "0ef35e9c-50bc-4c15-a731-c5160e97f5a0"
      },
      "source": [
        "!pip install numpy"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (1.19.5)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rnnNWMnnM-Ih"
      },
      "source": [
        "**Trabalhar com vetor**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S8TJZMJi-FBW",
        "outputId": "c3444e08-a0cb-4ca7-8611-2a56aa69d3d0"
      },
      "source": [
        "import numpy as np\n",
        "vetor_teste = np.array([1,2,3,4,5])\n",
        "print(\"vetor:{}\".format(vetor_teste))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vetor:[1 2 3 4 5]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yC2sZamBR-1r",
        "outputId": "09390fa1-a60c-470c-f506-dd6ccd48e16b"
      },
      "source": [
        "import numpy as np\n",
        "vetor = np.array([5,21,33,54,85])\n",
        "posicao = 3\n",
        "print(\"vetor[{}]={}\".format(posicao,vetor[posicao]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vetor[3]=54\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NADVsS0_PRVl",
        "outputId": "4b1e9d71-09b5-47ec-da51-728412847959"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "vetor1 = np.array([5,21,33,54,85])\n",
        "vetor2 = np.array([6,7,8,9,10])\n",
        "vetor3 = vetor1 + vetor2\n",
        "print(\"soma dos vetores:{}\".format(vetor3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "soma dos vetores:[11 28 41 63 95]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tTOedlxYNDjt"
      },
      "source": [
        "**Trabalhar com matriz**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qlH80Owr_OGp",
        "outputId": "1b69f3d3-1dc2-428b-cbab-8977d37148f8"
      },
      "source": [
        "import numpy as np\n",
        "matriz2D = np.array([[1,2,3,4,5],[6,7,8,9,10]])\n",
        "print(\"matriz2D:{}\".format(matriz2D))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "matriz2D:[[ 1  2  3  4  5]\n",
            " [ 6  7  8  9 10]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SmK4gsgxAI-f",
        "outputId": "15ebbaad-99cd-4092-a0f7-6b6e214889c3"
      },
      "source": [
        "import numpy as np\n",
        "matriz2D = np.array([[1,2,3,4,5],[6,7,8,9,10]])\n",
        "for vetor in matriz2D:\n",
        "  print(\"vetor: {}\".format(vetor))\n",
        "  for elem in vetor:\n",
        "    print(\"{}\".format(elem))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vetor: [1 2 3 4 5]\n",
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "vetor: [ 6  7  8  9 10]\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SbAf2gCOA95f",
        "outputId": "d6895db1-4b2e-442a-bccc-dd02b24e32cf"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "matriz2D = np.array([[1,2,3,4,5],[6,7,8,9,10]])\n",
        "nlinhas, ncolunas = matriz2D.shape\n",
        "print(\"nlinhas={}, ncolunas={}\".format(nlinhas, ncolunas))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "nlinhas=2, ncolunas=5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G4QH8qbifXau",
        "outputId": "20034f5e-e19b-40db-a319-a33ce5591922"
      },
      "source": [
        "import numpy as np\n",
        "matriz2D = np.array([[1,2,3,4,5],[6,7,8,9,10]])\n",
        "linhas, colunas = matriz2D.shape\n",
        "for i in range(linhas):\n",
        "  for j in range(colunas):\n",
        "    print(\"{}\".format(matriz2D[i,j]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1\n",
            "2\n",
            "3\n",
            "4\n",
            "5\n",
            "6\n",
            "7\n",
            "8\n",
            "9\n",
            "10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CDiY5_KaRFqG",
        "outputId": "bfe374b8-26e1-4035-d9c6-a037c2344486"
      },
      "source": [
        "import numpy as np\n",
        "\n",
        "matriz2D = np.arange(15).reshape(3, 5)\n",
        "print('matriz: {}'.format(matriz2D))\n",
        "print('formato: {}'.format(matriz2D.shape))\n",
        "print('dimensões: {}'.format(matriz2D.ndim))\n",
        "print('tipo dos dados da matriz: {}'.format(matriz2D.dtype.name))\n",
        "print('tipo do dado: {}'.format(type(matriz2D)))\n",
        "print('quantidade de bytes do dado: {}'.format(matriz2D.itemsize))\n",
        "print('quantidade de elementos: {}'.format(matriz2D.size))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "matriz: [[ 0  1  2  3  4]\n",
            " [ 5  6  7  8  9]\n",
            " [10 11 12 13 14]]\n",
            "formato: (3, 5)\n",
            "dimensões: 2\n",
            "tipo dos dados da matriz: int64\n",
            "tipo do dado: <class 'numpy.ndarray'>\n",
            "quantidade de bytes do dado: 8\n",
            "quantidade de elementos: 15\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qo7djgh2A0--"
      },
      "source": [
        "**Exemplo: gerar valores randômicos de 1 a 100**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A7AWrYnQAyvy",
        "outputId": "303c344f-16cb-451d-d6c5-136120fcd95e"
      },
      "source": [
        "import numpy as np\n",
        "x = np.random.randint(100)\n",
        "print(\"x:{}\".format(x))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:55\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1GiFVfWADDQ1",
        "outputId": "1c9f6189-1958-4861-fe0b-34a2cbfb42f2"
      },
      "source": [
        "import numpy as np\n",
        "x = np.random.randint(100,size=5)\n",
        "print(\"x:{}\".format(x))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "x:[84 86 88 34 89]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZZqtnH49KoeG",
        "outputId": "cbba4b9e-54f2-4bf7-89d3-8261430cf7ea"
      },
      "source": [
        "from numpy.random import seed\n",
        "from numpy.random import normal\n",
        "\n",
        "seed(1)\n",
        "dados = normal(loc=0, scale=1, size=200)\n",
        "print(\"dados: {}\".format(dados))\n",
        "\n",
        "#loc: Média da distribuição. O padrão é 0.\n",
        "#scale: Desvio padrão da distribuição. O padrão é 1.\n",
        "#size: Tamanho dos dados.\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dados: [ 1.62434536 -0.61175641 -0.52817175 -1.07296862  0.86540763 -2.3015387\n",
            "  1.74481176 -0.7612069   0.3190391  -0.24937038  1.46210794 -2.06014071\n",
            " -0.3224172  -0.38405435  1.13376944 -1.09989127 -0.17242821 -0.87785842\n",
            "  0.04221375  0.58281521 -1.10061918  1.14472371  0.90159072  0.50249434\n",
            "  0.90085595 -0.68372786 -0.12289023 -0.93576943 -0.26788808  0.53035547\n",
            " -0.69166075 -0.39675353 -0.6871727  -0.84520564 -0.67124613 -0.0126646\n",
            " -1.11731035  0.2344157   1.65980218  0.74204416 -0.19183555 -0.88762896\n",
            " -0.74715829  1.6924546   0.05080775 -0.63699565  0.19091548  2.10025514\n",
            "  0.12015895  0.61720311  0.30017032 -0.35224985 -1.1425182  -0.34934272\n",
            " -0.20889423  0.58662319  0.83898341  0.93110208  0.28558733  0.88514116\n",
            " -0.75439794  1.25286816  0.51292982 -0.29809284  0.48851815 -0.07557171\n",
            "  1.13162939  1.51981682  2.18557541 -1.39649634 -1.44411381 -0.50446586\n",
            "  0.16003707  0.87616892  0.31563495 -2.02220122 -0.30620401  0.82797464\n",
            "  0.23009474  0.76201118 -0.22232814 -0.20075807  0.18656139  0.41005165\n",
            "  0.19829972  0.11900865 -0.67066229  0.37756379  0.12182127  1.12948391\n",
            "  1.19891788  0.18515642 -0.37528495 -0.63873041  0.42349435  0.07734007\n",
            " -0.34385368  0.04359686 -0.62000084  0.69803203 -0.44712856  1.2245077\n",
            "  0.40349164  0.59357852 -1.09491185  0.16938243  0.74055645 -0.9537006\n",
            " -0.26621851  0.03261455 -1.37311732  0.31515939  0.84616065 -0.85951594\n",
            "  0.35054598 -1.31228341 -0.03869551 -1.61577235  1.12141771  0.40890054\n",
            " -0.02461696 -0.77516162  1.27375593  1.96710175 -1.85798186  1.23616403\n",
            "  1.62765075  0.3380117  -1.19926803  0.86334532 -0.1809203  -0.60392063\n",
            " -1.23005814  0.5505375   0.79280687 -0.62353073  0.52057634 -1.14434139\n",
            "  0.80186103  0.0465673  -0.18656977 -0.10174587  0.86888616  0.75041164\n",
            "  0.52946532  0.13770121  0.07782113  0.61838026  0.23249456  0.68255141\n",
            " -0.31011677 -2.43483776  1.0388246   2.18697965  0.44136444 -0.10015523\n",
            " -0.13644474 -0.11905419  0.01740941 -1.12201873 -0.51709446 -0.99702683\n",
            "  0.24879916 -0.29664115  0.49521132 -0.17470316  0.98633519  0.2135339\n",
            "  2.19069973 -1.89636092 -0.64691669  0.90148689  2.52832571 -0.24863478\n",
            "  0.04366899 -0.22631424  1.33145711 -0.28730786  0.68006984 -0.3198016\n",
            " -1.27255876  0.31354772  0.50318481  1.29322588 -0.11044703 -0.61736206\n",
            "  0.5627611   0.24073709  0.28066508 -0.0731127   1.16033857  0.36949272\n",
            "  1.90465871  1.1110567   0.6590498  -1.62743834  0.60231928  0.4202822\n",
            "  0.81095167  1.04444209]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PC1pUe9G9jQa",
        "outputId": "682d31a6-c082-4f96-abac-1e20dfd1d55a"
      },
      "source": [
        "import numpy as np\n",
        "a = np.arange(15).reshape(3, 5)\n",
        "print(\"vetor:{}\".format(a))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "vetor:[[ 0  1  2  3  4]\n",
            " [ 5  6  7  8  9]\n",
            " [10 11 12 13 14]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dGKKowCa6cTj",
        "outputId": "612af034-1f63-4c5f-c777-f1c5bab4cf1c"
      },
      "source": [
        "import numpy as np\n",
        "vetor = np.array([10,5,3,9,2,1])\n",
        "matriz = vetor.reshape(3,2)\n",
        "print(\"matriz:{}\".format(matriz))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "matriz:[[10  5]\n",
            " [ 3  9]\n",
            " [ 2  1]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5LIe5AOKptr4"
      },
      "source": [
        "**Análise Exploratória de Dados**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jx4XmZEXpz7t"
      },
      "source": [
        "import numpy as np\n",
        "from scipy import stats\n",
        "\n",
        "dados = np.array([3,4,45,4,5,1,0,2,2,45,1,45,45,1,0,5,5,10,45,6,7,10.3,45,4.3,2])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hV23diOu7Yvi",
        "outputId": "bb6ed80f-73c6-4e34-e69d-bcded63a15e5"
      },
      "source": [
        "media = dados.mean()\n",
        "desvio_padrao=dados.std()\n",
        "variancia=dados.var()\n",
        "minimo = dados.min()\n",
        "maximo = dados.max()\n",
        "moda = stats.mode(dados)\n",
        "print(\"media:{}\".format(media))\n",
        "print(\"desvio padrão:{}\".format(desvio_padrao))\n",
        "print(\"variância:{}\".format(variancia))\n",
        "print(\"mínimo:{}\".format(minimo))\n",
        "print(\"máximo:{}\".format(maximo))\n",
        "print(\"moda:{}; quantidade:{}\".format(moda.mode,moda.count))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "media:13.704\n",
            "desvio padrão:17.771426054202852\n",
            "variância:315.823584\n",
            "mínimo:0.0\n",
            "máximo:45.0\n",
            "moda:[45.]; quantidade:[6]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hooGauJm--1o"
      },
      "source": [
        "# **Visualizar Dados**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "CS5b1iIlAL3S",
        "outputId": "c2220938-2a8a-45d4-dc62-be6461285d02"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.linspace(0, 10, 100)\n",
        "\n",
        "plt.plot(x, x**2, label='quadrática teste')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "57hJtmg4y0ci",
        "outputId": "1ad5b696-3ee8-4213-ffe2-20b8232cebbe"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.linspace(0, 10, 100)\n",
        "\n",
        "plt.plot(x, x**2, label='quadrática')\n",
        "plt.legend()\n",
        "plt.grid()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ib2QTN0YzCBi",
        "outputId": "96c7bdea-d03d-4fe4-87d0-3ef3fd6d372f"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.linspace(0, 10, 100)\n",
        "\n",
        "plt.plot(x, x**2, label='quadrática')\n",
        "plt.legend()\n",
        "plt.grid(axis = 'x')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 306
        },
        "id": "Oud8UEEWA7ph",
        "outputId": "5b1d7b0d-c66d-4278-c3cf-9e45c691daf8"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "font1 = {'family':'serif','color':'blue','size':20}\n",
        "font2 = {'family':'serif','color':'darkred','size':15}\n",
        "\n",
        "plt.title(\"Este é um gráfico exemplo\", fontdict = font1)\n",
        "plt.xlabel(\"dias\", fontdict = font2)\n",
        "plt.ylabel(\"valores\", fontdict = font2)\n",
        "\n",
        "x = np.linspace(0, 10, 100)\n",
        "dias = x\n",
        "funcao = x**2\n",
        "plt.plot(dias, funcao, label='quadrática')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "jXIhJUkeBISd",
        "outputId": "0a71ecd3-53b8-4c60-aa99-5cbcb1a0f7ff"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "x = np.array([0.3, 3.8, 1.2, 2.5, 3.1, 2.7, 1.9, 1.5, 0.5, 0.95])\n",
        "y = np.array([11, 25, 9, 26, 20, 15, 33, 8, 2, 17])\n",
        "plt.scatter(x, y, color='darkgreen', marker='x', label='pontos dispersos')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "dQ2-Fc8BALhZ",
        "outputId": "770d1acf-4173-4e0a-d7da-bf360b8ce0d8"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "x = np.linspace(0, 5, 100)\n",
        "plt.plot(x, x**2, color='lightblue', linewidth=3, label='quadrática')\n",
        "x = np.array([0.3, 3.8, 1.2, 2.5, 3.1, 2.7, 1.9, 1.5, 0.5, 0.95])\n",
        "y = np.array([11, 25, 9, 26, 20, 15, 33, 8, 2, 17])\n",
        "plt.scatter(x, y, color='darkgreen', marker='^', label='pontos dispersos')\n",
        "plt.legend()\n",
        "plt.xlim(0.5, 4.5)\n",
        "plt.ylim(0.0, 45)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "ymVJPhKCzfVB",
        "outputId": "cb74cbd8-e79d-4723-df19-c84c73035d6e"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "#plot 1:\n",
        "x = np.linspace(0, 5, 100)\n",
        "y = x**2\n",
        "plt.subplot(2,1,1)#a figura tem duas linhas, uma coluna e está no primeito gráfico.\n",
        "plt.plot(x, y, color='lightblue', linewidth=3, label='quadrática')\n",
        "plt.legend()\n",
        "\n",
        "#plot 2:\n",
        "x = np.array([0.3, 3.8, 1.2, 2.5, 3.1, 2.7, 1.9, 1.5, 0.5, 0.95])\n",
        "y = np.array([11, 25, 9, 26, 20, 15, 33, 8, 2, 17])\n",
        "plt.subplot(2,1,2) #a figura tem duas linhas, uma coluna e está no segundo gráfico.\n",
        "plt.scatter(x, y, color='darkgreen', marker='^', label='pontos dispersos')\n",
        "plt.legend()\n",
        "\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YSmTTfofFu-i"
      },
      "source": [
        "# **Exercícios:** Um empreendedor possui 6 investimentos e tem a percepção de que podem render melhor. Nesse sentido, ele resolveu contratar um dos cientistas de dados da turma de pós graduação. Agora, você precisa produzir os seguintes estudos:\n",
        "\n",
        "\n",
        "1.   Imprimir um gráfico de retorno de cada investimento;\n",
        "2.   Gerar uma análise de média, desvio-padrão e os valores mínimo e máximo de cada investimento.\n",
        "\n",
        "Observações:\n",
        "\n",
        "1.   Utilize funções\n",
        "2.   O período de estudo é de 365 dias\n",
        "3.   Gere valores aleatórios para os rendimentos seguindo a distribuição normal\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Solução: próxima aula**"
      ],
      "metadata": {
        "id": "H4sqaWQFol1l"
      }
    }
  ]
}