Aula01_Desafios_ImersaoDados.ipynb

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    "colab": {
      "name": "Aula01_Desafios_ImersaoDados.ipynb",
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      "authorship_tag": "ABX9TyNrTAzzDsvOGGVlHOKFQLOb",
      "include_colab_link": true
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      "name": "python3",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lhfranca/ImersaoDados/blob/main/Aula01_Desafios_ImersaoDados.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1WVAVrO2PGXo"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NJ09HJRpcKvo"
      },
      "source": [
        "#Importando as bibliotecas que serão utilizadas nos Desafios.\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ZQsazL9wBZd",
        "outputId": "4f267a37-26d0-4756-d0d7-8dc93f7d709a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 246
        }
      },
      "source": [
        "#Obtendo nossa fonte de dados (Amostra do ENEM)\n",
        "\n",
        "fonte = 'https://github.com/alura-cursos/imersao-dados-2-2020/blob/ddff4f29df0d6470dd59910553899e549d0b5806/MICRODADOS_ENEM_2019_SAMPLE_43278.csv?raw=true'\n",
        "dados = pd.read_csv(fonte)\n",
        "dados.head()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>16</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>1</td>\n",
              "      <td>1500800.0</td>\n",
              "      <td>Ananindeua</td>\n",
              "      <td>15.0</td>\n",
              "      <td>PA</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>CCBCBEACABACABBBBACCBCEBDCBABCADADCBCADBAADEA</td>\n",
              "      <td>EBCAB99999BBCECBCACBEACBDAABDBCBBDACEBBACDCAEC...</td>\n",
              "      <td>AECCBBCBBCBDEDECACBAABEDABBEDDADCEADDBEBDBBEB</td>\n",
              "      <td>0</td>\n",
              "      <td>DEADBAAAEBEECEBCBCBCBDADAEABCEDDDDADCBEECACBC</td>\n",
              "      <td>ACACEEBCCBABADBBBACDBBACCCCADCEBADCBEEDBBEADB</td>\n",
              "      <td>EBBADCABDABACBCEBDEEAAADDBECDECDDBADBCDAAECBCC...</td>\n",
              "      <td>AADDDBEEEBEDDBEBACABCDBABECECACAECDCBDCCEDCDA</td>\n",
              "      <td>1.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>140.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>160.0</td>\n",
              "      <td>780.0</td>\n",
              "      <td>E</td>\n",
              "      <td>E</td>\n",
              "      <td>B</td>\n",
              "      <td>D</td>\n",
              "      <td>3</td>\n",
              "      <td>E</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>C</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>D</td>\n",
              "      <td>A</td>\n",
              "      <td>A</td>\n",
              "      <td>B</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 136 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "   NU_INSCRICAO  NU_ANO  CO_MUNICIPIO_RESIDENCIA  ... Q023  Q024 Q025\n",
              "0  190001004661    2019                  1506138  ...    A     B    B\n",
              "1  190001004674    2019                  1504208  ...    A     A    B\n",
              "2  190001004722    2019                  1501402  ...    A     A    B\n",
              "3  190001004735    2019                  1507300  ...    A     A    B\n",
              "4  190001004776    2019                  1500800  ...    A     A    B\n",
              "\n",
              "[5 rows x 136 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdVYAaWIwVnP"
      },
      "source": [
        "#**Nota**\n",
        "\n",
        "Irei fazer minhas pesquisas a nível Nacional e também a nivel Municipal, analisando os dados de Sorocaba."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kV8VP3b2k6A"
      },
      "source": [
        "dados_br = dados\n",
        "dados_sor = dados.query('NO_MUNICIPIO_RESIDENCIA ==\"Sorocaba\"')"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t0sQRH6NPJYP"
      },
      "source": [
        "## **Desafio 01**\n",
        "Descobir qual a proporção de inscritos por idade."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jnM-iJKt2haP"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HWZZeIBIONvG",
        "outputId": "0d1ef583-6df5-4e40-a725-631abd754f05",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 216
        }
      },
      "source": [
        "#Aqui estou obtendo a quantidade de inscritos por idade e classificando da menor idade para a maior.\n",
        "\n",
        "idade_br = dados_br['NU_IDADE'].value_counts().sort_index()\n",
        "idade_sor = dados_sor['NU_IDADE'].value_counts().sort_index()\n",
        "\n",
        "#Aqui vamos obter a proporção, utilizando o método value_count e definindo a normalize como True, conseguimos esse resultado.\n",
        "prop_br = round(dados_br['NU_IDADE'].value_counts(normalize=True).sort_index() , 5) *100\n",
        "prop_sor = round(dados_sor['NU_IDADE'].value_counts(normalize=True).sort_index(), 5)*100\n",
        "\n",
        "print(prop_br.head())\n",
        "print(prop_sor.head())"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "13     0.003\n",
            "14     0.111\n",
            "15     1.577\n",
            "16     6.147\n",
            "17    16.687\n",
            "Name: NU_IDADE, dtype: float64\n",
            "15     0.627\n",
            "16     6.270\n",
            "17    32.288\n",
            "18    15.987\n",
            "19     8.150\n",
            "Name: NU_IDADE, dtype: float64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VU01L5pSKM9R"
      },
      "source": [
        "#Desafio 02\n",
        "\n",
        "Descobrir de quais Estados são os Inscritos com 13 anos."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QKYr5NW9PnOv",
        "outputId": "6ae79559-0802-4b9a-fa99-819c80ec3484",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 100
        }
      },
      "source": [
        "#Para isso, filtramos todo o nosso DataFrame, utilziando o método query, caso a idade fosse igual a 13. Logo após isso, selecionamos apenas a coluna da SF_UF_RESIDENCIA\n",
        "treze_br = dados_br.query('NU_IDADE == 13')\n",
        "treze_br = treze_br[\"SG_UF_RESIDENCIA\"]\n",
        "treze_br"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "57359     SP\n",
              "95630     MT\n",
              "98197     AP\n",
              "116501    BA\n",
              "Name: SG_UF_RESIDENCIA, dtype: object"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zZNxzwNPTZlX"
      },
      "source": [
        "# **Desafio 03**\n",
        "\n",
        "Adicionar título no gráfico."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xmC0WoXdTh2k",
        "outputId": "4ec2ee42-7297-4d8c-9ae8-d34bc8090cca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 548
        }
      },
      "source": [
        "plt.subplot(dados_br['NU_IDADE'].hist(legend = True, bins=80, figsize = (10,8)))\n",
        "plt.subplot(dados_sor['NU_IDADE'].hist(legend = True, bins=80, figsize = (10,8) ))\n",
        "\n",
        "#parâmetros do gráfico\n",
        "plt.xlabel('Idade',fontsize = 18)\n",
        "plt.ylabel('Número de Participantes',fontsize = 18)\n",
        "plt.title('Histograma:\\n Número de participantes por idade',fontsize = 20)\n",
        "plt.grid(True)\n",
        "\n",
        "#legenda do gráfico\n",
        "plt.legend(('Brasil', 'Sorocaba'),\n",
        "           loc='upper right', shadow = True,fontsize = 18)\n",
        "\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "for4vO3jgQB8",
        "outputId": "bacaeddb-e72c-40d8-a944-f2344fd52d67",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 544
        }
      },
      "source": [
        "#Como criar dois histogramas utilizando o método subplots. Dessa maneira não ficou muito visual os nossos dados, foi constatado que o histograma\n",
        "# não fica muito eficiente para esses dados.\n",
        "\n",
        "fig, ax = plt.subplots()\n",
        "fig1, ax1 = plt.subplots()\n",
        "ax.hist(dados_br['NU_IDADE'], bins = 25)\n",
        "ax1.hist(dados_sor['NU_IDADE'], bins = 25)\n",
        "ax.set_title(\"BRASIL\")\n",
        "ax1.set_title(\"SOROCABA\")\n",
        "plt.show()"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CM5DU_hmC6gG"
      },
      "source": [
        "# **Desafio 04**\n",
        "\n",
        "Criar 2 histogramas com as idades dos treineiros e não treineiros."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BRB-KhaBbb2D",
        "outputId": "c79f49c8-62c7-4265-c3ac-2a242efea0f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 561
        }
      },
      "source": [
        "#Não irei fazer outro histograma, pois já utilizei esse método duas vezes e não gostei muito do resultado.\n",
        "\n",
        "#Primeiro irei obter a quantidade de inscritos que fizeram a prova como treineiro, por idade.\n",
        "idade_trei_br = dados_br.query(\"IN_TREINEIRO == 1\")['NU_IDADE'].value_counts()\n",
        "idade_trei_sor = dados_sor.query(\"IN_TREINEIRO == 1\")['NU_IDADE'].value_counts()\n",
        "\n",
        "#Segundo irei obter a quantidade de inscritos que não fizeram a prova como treineiro, por idade.\n",
        "idade_n_trei_br = dados_br.query(\"IN_TREINEIRO == 0\")['NU_IDADE'].value_counts()\n",
        "idade_n_trei_sor = dados_sor.query(\"IN_TREINEIRO == 0\")['NU_IDADE'].value_counts()\n",
        "\n",
        "plt.figure(figsize=(100,100))\n",
        "fig, ax2 = plt.subplots()\n",
        "\n",
        "fig, ax3 = plt.subplots()\n",
        "\n",
        "\n",
        "\n",
        "ax2.bar(idade_n_trei_br.index, idade_n_trei_br , label = \"Brasil\", alpha = 0.45)\n",
        "ax2.bar(idade_trei_br.index, idade_trei_br , label = \"Brasil\", alpha = 0.45)\n",
        "plt.title(\"Distribuição de Treineiros por idade\\n Brasil\")\n",
        "\n",
        "ax3.bar(idade_n_trei_sor.index, idade_n_trei_sor , label = \"Sorocaba\", alpha = 0.45)\n",
        "ax3.bar(idade_trei_sor.index, idade_trei_sor , label = \"Sorocaba\", alpha = 0.45)\n",
        "plt.title(\"Distribuição de Treineiros por idade\\n Sorocaba\")\n",
        "\n",
        "plt.show()\n",
        "\n",
        "#Ainda não fiquei satistfeito com os gráficos gerados, mas isso é por conta da minha falta de experiência com o matplotlib e seaborn, os próximos serão mais detalhados."
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 7200x7200 with 0 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VaanKemxQHyU"
      },
      "source": [
        "# **Desafio 05**\n",
        "\n",
        "Comparar as distribuições das provas de inglês e espanhol."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Da6m2OblQjiv",
        "outputId": "64ecd4d3-2015-4e21-f716-e1042d38db28",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 515
        }
      },
      "source": [
        "#Obtendo os dados e plotando os histogramas.\n",
        "\n",
        "pe_br = dados_br.query(\"TP_LINGUA == 1\")['NU_NOTA_LC'].hist(bins = 25, figsize = (12,8), alpha = 0.6)\n",
        "in_br = dados_br.query(\"TP_LINGUA == 0\")['NU_NOTA_LC'].hist(bins = 25, figsize = (12,8), alpha = 0.6)\n",
        "\n",
        "plt.title(\"Distribuição das provas de Inglês e Espanhol\")\n",
        "plt.legend([\"Espanhol\", \"Inglês\"])"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f995ce95b70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}