DesafiosAula01_Eder.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "DesafiosAula01-Eder.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNCWv5f9FTKnWL5ThiNerrw",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/EderCruz/Alura-ImersaoDados/blob/main/DesafiosAula01_Eder.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FW-vAlZC_Zgf"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fonte = \"https://github.com/alura-cursos/imersao-dados-2-2020/blob/master/MICRODADOS_ENEM_2019_SAMPLE_43278.csv?raw=true\"\n",
        "\n",
        "dados = pd.read_csv(fonte)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmAxOE0J_tmc",
        "outputId": "4e8877a5-2a57-40b6-9c7f-a2debcb0c2b4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 217
        }
      },
      "source": [
        "#Desafio01: Proporção dos inscritos por idade.\n",
        "dados[\"NU_IDADE\"].value_counts()/len(dados)*100\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "18    16.840948\n",
              "17    16.687078\n",
              "19    11.318888\n",
              "20     8.174753\n",
              "16     6.146962\n",
              "        ...    \n",
              "75     0.001570\n",
              "73     0.001570\n",
              "82     0.000785\n",
              "77     0.000785\n",
              "76     0.000785\n",
              "Name: NU_IDADE, Length: 65, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "H8VCPny3Be9g",
        "outputId": "9d94a41a-96ca-4600-beb3-edcd6d3d9545",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 106
        }
      },
      "source": [
        "#Desafio02: Descobrir de quais estados são os inscritos com 13 anos.\n",
        "dados.query(\"NU_IDADE == 13\")[\"SG_UF_RESIDENCIA\"]\n",
        "#dados[[\"NU_IDADE\",\"SG_UF_RESIDENCIA\"]].value_counts().sort_index()"
      ],
      "execution_count": null,
      "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": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q7ePrvCn_pbu"
      },
      "source": [
        "Desafio01: Proporção dos inscritos por idade.\n",
        "\n",
        "Desafio02: Descobrir de quais estados são os inscritos com 13 anos.\n",
        "\n",
        "Desafio03: Adicionar título no gráfico\n",
        "\n",
        "Desafio04: Plotar os Histogramas das idades dos do treineiro e não treineiros.\n",
        "\n",
        "Desafio05: Comparar as distribuições das provas em inglês espanhol\n",
        "\n",
        "Desafio06: Explorar a documentações e visualizações com matplotlib ou pandas e gerar novas visualizações."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zp87V0yOCKSM",
        "outputId": "46410f1b-43d2-403e-bd69-bf66c17bed2f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        }
      },
      "source": [
        "#Desafio03: Adicionar título no gráfico\n",
        "plt.title(\"Histograma Idade Enem\")\n",
        "dados[\"NU_IDADE\"].hist( bins = 20, figsize = (8,6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f68c27c65c0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FjwkEc1XCt2z",
        "outputId": "5c86d67b-24b1-4544-be1a-efa4c3142415",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        }
      },
      "source": [
        "#Desafio04: Plotar os Histogramas das idades dos do treineiro e não treineiros.\n",
        "plt.figure(figsize = (8,6))\n",
        "plt.title(\"Treineiros\")\n",
        "plt.hist(dados.query(\"IN_TREINEIRO == 1\")[\"NU_IDADE\"], bins = 10)\n",
        "plt.grid()\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize = (8,6))\n",
        "plt.title(\"Não treineiros\")\n",
        "plt.hist(dados.query(\"IN_TREINEIRO != 1\")[\"NU_IDADE\"], bins = 10)\n",
        "plt.grid()\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YfwXwEPLHHVr",
        "outputId": "1071ace8-f199-40ba-ef6c-e285f217fee7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 765
        }
      },
      "source": [
        "#Desafio05: Comparar as distribuições das provas em inglês espanhol\n",
        "eng = dados.query('TP_LINGUA == 0')['NU_NOTA_LC']\n",
        "spa = dados.query('TP_LINGUA == 1')['NU_NOTA_LC']\n",
        "provaslng = [eng, spa]\n",
        "\n",
        "plt.figure(figsize = (8,6))\n",
        "plt.title(\"Notas de Inglês\")\n",
        "eng.plot.box(figsize=(8,6), grid = True  )\n",
        "plt.show()\n",
        "\n",
        "plt.figure(figsize = (8,6))\n",
        "plt.title(\"Notas de Inglês\")\n",
        "spa.plot.box(figsize=(8,6), grid = True  )\n",
        "plt.show()\n",
        "\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}