Aula_04_Pos_Ciencia_De_Dados.ipynb

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  "metadata": {
    "colab": {
      "name": "Aula 04 - Pos_Ciencia_De_Dados.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MTFG2t4kzJy9"
      },
      "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": "TRZsB5SBzN73"
      },
      "source": [
        "# **Aula 04**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5waBSQP6zdSq"
      },
      "source": [
        "# **Solução do Exercício da Aula 03**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15-7fg6n1je4"
      },
      "source": [
        "import pandas as pd"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "slnuX6Xmyf3B",
        "outputId": "febcac0d-c07c-4297-aeb8-28e20df2267e"
      },
      "source": [
        "dados = {'Ativo': ['A01', 'A02', 'A03', 'A04']}\n",
        "df = pd.DataFrame(data = dados)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ativo</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A04</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Ativo\n",
              "0   A01\n",
              "1   A02\n",
              "2   A03\n",
              "3   A04"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_uiUQgHX1qzv",
        "outputId": "a1f5a29d-b98e-4db0-a061-c28fc50b50bd"
      },
      "source": [
        "n = len(df.Ativo)\n",
        "n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Tmp-nwt32jxw"
      },
      "source": [
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TNUGbbZ11ZxZ",
        "outputId": "4c6a5e89-72d2-4b75-d7bb-73bb436e2db4"
      },
      "source": [
        "media, desvio_padrao = 100, 7\n",
        "preco = np.random.normal(media, desvio_padrao, n)\n",
        "preco"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([104.24775983,  87.45187988, 102.70877347,  98.99663273])"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "9WWxlKst3Qnr",
        "outputId": "9b3c0e70-9d44-4cf2-b2fb-5faf659ee57d"
      },
      "source": [
        "df.insert(loc=1, column=\"Preço\", value =preco)\n",
        "df"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Ativo</th>\n",
              "      <th>Preço</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A01</td>\n",
              "      <td>104.247760</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>A02</td>\n",
              "      <td>87.451880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>A03</td>\n",
              "      <td>102.708773</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>A04</td>\n",
              "      <td>98.996633</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Ativo       Preço\n",
              "0   A01  104.247760\n",
              "1   A02   87.451880\n",
              "2   A03  102.708773\n",
              "3   A04   98.996633"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "DHmYKPHs3iz3",
        "outputId": "ab886229-9b1a-4f32-ef38-1e507e993e8e"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "plt.bar(df.Ativo,df.Preço, color = \"red\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8bKwa14X4f4u"
      },
      "source": [
        "#**Vamos conhecer mais sobre o Pandas**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y1pCdknk4nbN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "outputId": "0d784a57-1fac-477a-e5a7-6530ddc41cfc"
      },
      "source": [
        "dados_2 = {'Produto': ['A', 'B'], 'Preço': [100, 500]}\n",
        "df_2 = pd.DataFrame(data = dados_2)\n",
        "df_2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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>Produto</th>\n",
              "      <th>Preço</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>A</td>\n",
              "      <td>100</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>B</td>\n",
              "      <td>500</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  Produto  Preço\n",
              "0       A    100\n",
              "1       B    500"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IOA9qIfg9PBN",
        "outputId": "c34b66c9-79be-437f-b863-685b41872b89"
      },
      "source": [
        "df_2.iloc[0]['Preço']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "5F-R_Vto9ScE",
        "outputId": "e30fe228-591f-4fe8-bc4c-32af214cdee0"
      },
      "source": [
        "df_2.loc[0]['Produto']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'A'"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eUU6yqFe9UlE",
        "outputId": "bac9fa51-ad39-4db5-a22c-59b3a05a4d82"
      },
      "source": [
        "df_2.loc[0]['Preço']"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "100"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "4m1G_JUC9WoE",
        "outputId": "186db70f-dde4-4b2e-a5a4-11d72ffd838b"
      },
      "source": [
        "df_2.Produto[0]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'A'"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCGW5jjuzf_5"
      },
      "source": [
        "# **Visualização de Dados**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zAE1uOlIzwHx"
      },
      "source": [
        "**Gráfico de Pizza**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "wJoSf7Lczgud",
        "outputId": "e5b345bc-8b1a-418b-d5fc-dd4dcac2e9b8"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "y = np.array([35, 25, 25, 15])\n",
        "\n",
        "plt.pie(y)\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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "PCS8L2VIz4QI",
        "outputId": "0a513270-3a78-4089-c22a-e587f2f65e2a"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "y = np.array([35, 25, 25, 15])\n",
        "rotulos = [\"Maçãs\", \"Bananas\", \"Cerejas\", \"Tâmaras\"]\n",
        "plt.pie(y, labels = rotulos)\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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "L_x14aglz8Kq",
        "outputId": "cd782b3f-2ad4-4772-a7df-2ec258e49733"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "y = np.array([35, 25, 25, 15])\n",
        "rotulos = [\"Maçãs\", \"Bananas\", \"Cerejas\", \"Tâmaras\"]\n",
        "\n",
        "myexplode = [0.2, 0.2, 0.1, 0]\n",
        "\n",
        "plt.pie(y, labels = rotulos, explode = myexplode)\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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "UqXcclE_0AF5",
        "outputId": "0db69560-9c27-4425-c0bd-c815121f1c93"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "y = np.array([35, 25, 25, 15])\n",
        "rotulos = [\"Maçãs\", \"Bananas\", \"Cerejas\", \"Tâmaras\"]\n",
        "minhas_cores = [\"black\", \"hotpink\", \"b\", \"#4CAF50\"]\n",
        "\n",
        "#plt.pie(y, labels = rotulos, colors = minhas_cores)\n",
        "plt.pie(y, labels = rotulos, explode = myexplode, colors = minhas_cores)\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 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "8rxedK3b0Drw",
        "outputId": "b1b9be8f-9c88-4fff-dd69-1e186471c437"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "y = np.array([35, 25, 25, 15])\n",
        "rotulos = [\"Maçãs\", \"Bananas\", \"Cerejas\", \"Tâmaras\"]\n",
        "\n",
        "plt.pie(y, labels = rotulos)\n",
        "\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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "9CviXedo0HCg",
        "outputId": "37b49edf-17ab-417d-a503-f057476e26d4"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "y = np.array([35, 25, 25, 15])\n",
        "rotulos = [\"Maçãs\", \"Bananas\", \"Cerejas\", \"Tâmaras\"]\n",
        "\n",
        "plt.pie(y, labels = rotulos)\n",
        "plt.legend(title = \"Quatro frutas:\")\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": {}
        }
      ]
    }
  ]
}