7DaysOfCode_Python_Pandas.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMZWbR/kInm5QqzbuZh/dPq",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/christianalencar/7DaysOfCode-Pandas/blob/main/7DaysOfCode_Python_Pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Um dos objetivos de uma biblioteca é garantir que os materiais informacionais estejam sendo utilizados. Os empréstimos realizados podem ser um indicador,mesmo que de forma básica (pois você não consegue garantir que haja uma leitura ou utilização real).\n",
        "\n",
        "*   A quantidade de empréstimos está aumentando ou diminuindo ao decorrer dos últimos anos?\n",
        "*   Em quais bibliotecas do sistema estão a maior quantidade de empréstimos?\n",
        "*   Quais são os temas mais emprestados? E os menos?\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "_WsVWUhfbllc"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Primeiro Dia - Preparando e fazendo as primeiras analises"
      ],
      "metadata": {
        "id": "46_V8F8__ma_"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nHJNwCn2bXJL"
      },
      "outputs": [],
      "source": [
        "import pandas as pd"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/FranciscoFoz/7_Days_of_Code_Alura-Python-Pandas.git #importa todo o diretorio para dentro do google colab"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GS0D1Aozbc7O",
        "outputId": "2631cf7a-40f6-43c1-aa69-daf2ef70a245"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "fatal: destination path '7_Days_of_Code_Alura-Python-Pandas' already exists and is not an empty directory.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importar internamente os arquivos CSV\n",
        "\n"
      ],
      "metadata": {
        "id": "vBsfpzashC61"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "de01 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20101.csv')\n",
        "de02 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20102.csv')\n",
        "de03 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20111.csv')\n",
        "de04 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20112.csv')\n",
        "de05 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20121.csv')\n",
        "de06 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20122.csv')\n",
        "de07 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20131.csv')\n",
        "de08 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20132.csv')\n",
        "de09 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20141.csv')\n",
        "de10 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20142.csv')\n",
        "de11 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20151.csv')\n",
        "de12 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20152.csv')\n",
        "de13 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20161.csv')\n",
        "de14 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20162.csv')\n",
        "de15 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20171.csv')\n",
        "de16 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20172.csv')\n",
        "de17 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20181.csv')\n",
        "de18 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20182.csv')\n",
        "de19 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20191.csv')\n",
        "de20 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20192.csv')\n",
        "de21 = pd.read_csv('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_emprestimos/emprestimos-20201.csv')"
      ],
      "metadata": {
        "id": "7yRfwGBBglc9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Merge todos os arquivos importados em unico dataframe"
      ],
      "metadata": {
        "id": "eBH0Oxb9lwbf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "merged_de = pd.concat([de01, de02, de03, de04, de05, de06, de07, de08, de09, de10, de11, de12, de13, de14, de15, de16, de17, de18, de19, de20, de21])"
      ],
      "metadata": {
        "id": "p5MhGslbkhmb"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Criar um novo arquivo formado com este dataframe criado"
      ],
      "metadata": {
        "id": "4Pn2kKLhms6O"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "merged_de.to_csv('de_mesclado.csv', index=False)"
      ],
      "metadata": {
        "id": "7uIBVm3lm8-_"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(merged_de.head(20))"
      ],
      "metadata": {
        "id": "-hW5ekj7nS2n",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c184448f-30c2-4e77-cc49-c08c286ab85a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "    id_emprestimo codigo_barras                 data_renovacao  \\\n",
            "0          709684       L095049                            NaN   \n",
            "1          709685       L167050                            NaN   \n",
            "2          709686    2006017618  2010/01/26 08:07:01.738000000   \n",
            "3          709687       L184117  2010/01/18 11:07:46.470000000   \n",
            "4          709684       L095049                            NaN   \n",
            "5          709685       L167050                            NaN   \n",
            "6          709686    2006017618  2010/01/26 08:07:01.738000000   \n",
            "7          709687       L184117  2010/01/18 11:07:46.470000000   \n",
            "8          709688    2007027500  2010/01/19 15:59:26.464000000   \n",
            "9          709689       L157395  2010/01/19 15:59:26.447000000   \n",
            "10         709690    2007019312                            NaN   \n",
            "11         709691    2008046357  2010/01/19 11:50:13.404000000   \n",
            "12         709692       L187962                            NaN   \n",
            "13         709693       L192776                            NaN   \n",
            "14         709694       L202783                            NaN   \n",
            "15         709695    2007035128                            NaN   \n",
            "16         709696       L122346                            NaN   \n",
            "17         709697       L048797                            NaN   \n",
            "18         709698    2009047725  2010/01/18 14:44:41.163000000   \n",
            "19         709698    2009047725  2010/01/18 14:44:41.163000000   \n",
            "\n",
            "                  data_emprestimo                 data_devolucao  \\\n",
            "0   2010/01/04 07:44:10.721000000  2010/01/05 16:26:12.662000000   \n",
            "1   2010/01/04 07:44:10.750000000  2010/01/12 07:34:13.934000000   \n",
            "2   2010/01/04 08:08:44.081000000  2010/02/25 07:36:25.800000000   \n",
            "3   2010/01/04 08:24:21.284000000  2010/02/03 08:58:45.692000000   \n",
            "4   2010/01/04 07:44:10.721000000  2010/01/05 16:26:12.662000000   \n",
            "5   2010/01/04 07:44:10.750000000  2010/01/12 07:34:13.934000000   \n",
            "6   2010/01/04 08:08:44.081000000  2010/02/25 07:36:25.800000000   \n",
            "7   2010/01/04 08:24:21.284000000  2010/02/03 08:58:45.692000000   \n",
            "8   2010/01/04 08:27:33.569000000  2010/02/03 13:06:30.662000000   \n",
            "9   2010/01/04 08:27:33.596000000  2010/02/03 13:06:43.125000000   \n",
            "10  2010/01/04 08:49:57.857000000  2010/01/13 09:58:25.443000000   \n",
            "11  2010/01/04 09:02:30.576000000  2010/02/02 12:13:36.132000000   \n",
            "12  2010/01/04 09:07:06.131000000  2010/06/22 18:55:04.000000000   \n",
            "13  2010/01/04 09:07:06.159000000  2010/06/22 18:54:46.000000000   \n",
            "14  2010/01/04 09:09:38.329000000  2010/02/03 13:00:49.746000000   \n",
            "15  2010/01/04 09:14:55.681000000  2010/01/14 10:15:08.388000000   \n",
            "16  2010/01/04 09:14:55.721000000  2010/01/14 10:15:16.041000000   \n",
            "17  2010/01/04 09:18:17.514000000  2010/01/14 10:14:59.964000000   \n",
            "18  2010/01/04 09:21:19.099000000  2010/02/02 12:02:38.444000000   \n",
            "19  2010/01/04 09:21:19.099000000  2010/02/02 12:02:38.444000000   \n",
            "\n",
            "    matricula_ou_siape    tipo_vinculo_usuario  \n",
            "0         2.008023e+09      ALUNO DE GRADUAÇÃO  \n",
            "1         2.008023e+09      ALUNO DE GRADUAÇÃO  \n",
            "2         2.008112e+09  ALUNO DE PÓS-GRADUAÇÃO  \n",
            "3         2.007211e+08      ALUNO DE GRADUAÇÃO  \n",
            "4         2.008023e+09      ALUNO DE GRADUAÇÃO  \n",
            "5         2.008023e+09      ALUNO DE GRADUAÇÃO  \n",
            "6         2.008112e+09  ALUNO DE PÓS-GRADUAÇÃO  \n",
            "7         2.007211e+08      ALUNO DE GRADUAÇÃO  \n",
            "8         2.009046e+09      ALUNO DE GRADUAÇÃO  \n",
            "9         2.009046e+09      ALUNO DE GRADUAÇÃO  \n",
            "10        2.007014e+08      ALUNO DE GRADUAÇÃO  \n",
            "11        2.005075e+08      ALUNO DE GRADUAÇÃO  \n",
            "12        2.004044e+08      ALUNO DE GRADUAÇÃO  \n",
            "13        2.004044e+08      ALUNO DE GRADUAÇÃO  \n",
            "14        1.984206e+08                 DOCENTE  \n",
            "15        2.008005e+09      ALUNO DE GRADUAÇÃO  \n",
            "16        2.008005e+09      ALUNO DE GRADUAÇÃO  \n",
            "17        2.008005e+09      ALUNO DE GRADUAÇÃO  \n",
            "18        2.009047e+09      ALUNO DE GRADUAÇÃO  \n",
            "19        2.009047e+09      ALUNO DE GRADUAÇÃO  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(merged_de.info())"
      ],
      "metadata": {
        "id": "hss1Am_TnVhq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7b932348-a2fb-4181-d8af-78434d58ee60"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "Int64Index: 2258018 entries, 0 to 26560\n",
            "Data columns (total 7 columns):\n",
            " #   Column                Dtype  \n",
            "---  ------                -----  \n",
            " 0   id_emprestimo         int64  \n",
            " 1   codigo_barras         object \n",
            " 2   data_renovacao        object \n",
            " 3   data_emprestimo       object \n",
            " 4   data_devolucao        object \n",
            " 5   matricula_ou_siape    float64\n",
            " 6   tipo_vinculo_usuario  object \n",
            "dtypes: float64(1), int64(1), object(5)\n",
            "memory usage: 137.8+ MB\n",
            "None\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(merged_de.describe())"
      ],
      "metadata": {
        "id": "g26y0Bh-oS9V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "97f0d74b-7dd1-45d6-c806-cefff4c7024e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       id_emprestimo  matricula_ou_siape\n",
            "count   2.258018e+06        2.254848e+06\n",
            "mean    1.866667e+06        6.127611e+09\n",
            "std     6.642810e+05        7.827175e+09\n",
            "min     7.096840e+05        0.000000e+00\n",
            "25%     1.288985e+06        2.010057e+09\n",
            "50%     1.854210e+06        2.012975e+09\n",
            "75%     2.447154e+06        2.016069e+09\n",
            "max     2.989090e+06        2.022301e+10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_de"
      ],
      "metadata": {
        "id": "PvkpYvKvoaTP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47b322d1-000b-4dbe-e7c7-7645a49f3f63"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       id_emprestimo codigo_barras                 data_renovacao  \\\n",
              "0             709684       L095049                            NaN   \n",
              "1             709685       L167050                            NaN   \n",
              "2             709686    2006017618  2010/01/26 08:07:01.738000000   \n",
              "3             709687       L184117  2010/01/18 11:07:46.470000000   \n",
              "4             709684       L095049                            NaN   \n",
              "...              ...           ...                            ...   \n",
              "26556        2989086    2008047044                            NaN   \n",
              "26557        2989087    2008047047                            NaN   \n",
              "26558        2989088    2010052905                            NaN   \n",
              "26559        2989089    2010048023                            NaN   \n",
              "26560        2989090    2013106838                            NaN   \n",
              "\n",
              "                     data_emprestimo                 data_devolucao  \\\n",
              "0      2010/01/04 07:44:10.721000000  2010/01/05 16:26:12.662000000   \n",
              "1      2010/01/04 07:44:10.750000000  2010/01/12 07:34:13.934000000   \n",
              "2      2010/01/04 08:08:44.081000000  2010/02/25 07:36:25.800000000   \n",
              "3      2010/01/04 08:24:21.284000000  2010/02/03 08:58:45.692000000   \n",
              "4      2010/01/04 07:44:10.721000000  2010/01/05 16:26:12.662000000   \n",
              "...                              ...                            ...   \n",
              "26556  2020/05/05 19:51:06.059000000  2021/05/13 14:53:31.000000000   \n",
              "26557  2020/05/05 19:51:06.135000000  2021/05/13 14:53:38.000000000   \n",
              "26558  2020/05/05 19:51:06.164000000  2021/05/13 15:01:46.000000000   \n",
              "26559  2020/06/09 11:54:02.669000000  2021/07/21 15:33:55.000000000   \n",
              "26560  2020/06/09 11:54:02.852000000  2021/07/21 15:33:45.000000000   \n",
              "\n",
              "       matricula_ou_siape             tipo_vinculo_usuario  \n",
              "0            2.008023e+09               ALUNO DE GRADUAÇÃO  \n",
              "1            2.008023e+09               ALUNO DE GRADUAÇÃO  \n",
              "2            2.008112e+09           ALUNO DE PÓS-GRADUAÇÃO  \n",
              "3            2.007211e+08               ALUNO DE GRADUAÇÃO  \n",
              "4            2.008023e+09               ALUNO DE GRADUAÇÃO  \n",
              "...                   ...                              ...  \n",
              "26556        3.067431e+06  SERVIDOR TÉCNICO-ADMINISTRATIVO  \n",
              "26557        3.067431e+06  SERVIDOR TÉCNICO-ADMINISTRATIVO  \n",
              "26558        3.067431e+06  SERVIDOR TÉCNICO-ADMINISTRATIVO  \n",
              "26559        2.020101e+10           ALUNO DE PÓS-GRADUAÇÃO  \n",
              "26560        2.020101e+10           ALUNO DE PÓS-GRADUAÇÃO  \n",
              "\n",
              "[2258018 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-27e9e35d-8364-4f45-8aab-3352a67aa361\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>id_emprestimo</th>\n",
              "      <th>codigo_barras</th>\n",
              "      <th>data_renovacao</th>\n",
              "      <th>data_emprestimo</th>\n",
              "      <th>data_devolucao</th>\n",
              "      <th>matricula_ou_siape</th>\n",
              "      <th>tipo_vinculo_usuario</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>709684</td>\n",
              "      <td>L095049</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2010/01/04 07:44:10.721000000</td>\n",
              "      <td>2010/01/05 16:26:12.662000000</td>\n",
              "      <td>2.008023e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>709685</td>\n",
              "      <td>L167050</td>\n",
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              "      <td>2010/01/04 07:44:10.750000000</td>\n",
              "      <td>2010/01/12 07:34:13.934000000</td>\n",
              "      <td>2.008023e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>709686</td>\n",
              "      <td>2006017618</td>\n",
              "      <td>2010/01/26 08:07:01.738000000</td>\n",
              "      <td>2010/01/04 08:08:44.081000000</td>\n",
              "      <td>2010/02/25 07:36:25.800000000</td>\n",
              "      <td>2.008112e+09</td>\n",
              "      <td>ALUNO DE PÓS-GRADUAÇÃO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>709687</td>\n",
              "      <td>L184117</td>\n",
              "      <td>2010/01/18 11:07:46.470000000</td>\n",
              "      <td>2010/01/04 08:24:21.284000000</td>\n",
              "      <td>2010/02/03 08:58:45.692000000</td>\n",
              "      <td>2.007211e+08</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>709684</td>\n",
              "      <td>L095049</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2010/01/04 07:44:10.721000000</td>\n",
              "      <td>2010/01/05 16:26:12.662000000</td>\n",
              "      <td>2.008023e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>26556</th>\n",
              "      <td>2989086</td>\n",
              "      <td>2008047044</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020/05/05 19:51:06.059000000</td>\n",
              "      <td>2021/05/13 14:53:31.000000000</td>\n",
              "      <td>3.067431e+06</td>\n",
              "      <td>SERVIDOR TÉCNICO-ADMINISTRATIVO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26557</th>\n",
              "      <td>2989087</td>\n",
              "      <td>2008047047</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020/05/05 19:51:06.135000000</td>\n",
              "      <td>2021/05/13 14:53:38.000000000</td>\n",
              "      <td>3.067431e+06</td>\n",
              "      <td>SERVIDOR TÉCNICO-ADMINISTRATIVO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26558</th>\n",
              "      <td>2989088</td>\n",
              "      <td>2010052905</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020/05/05 19:51:06.164000000</td>\n",
              "      <td>2021/05/13 15:01:46.000000000</td>\n",
              "      <td>3.067431e+06</td>\n",
              "      <td>SERVIDOR TÉCNICO-ADMINISTRATIVO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26559</th>\n",
              "      <td>2989089</td>\n",
              "      <td>2010048023</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020/06/09 11:54:02.669000000</td>\n",
              "      <td>2021/07/21 15:33:55.000000000</td>\n",
              "      <td>2.020101e+10</td>\n",
              "      <td>ALUNO DE PÓS-GRADUAÇÃO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26560</th>\n",
              "      <td>2989090</td>\n",
              "      <td>2013106838</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020/06/09 11:54:02.852000000</td>\n",
              "      <td>2021/07/21 15:33:45.000000000</td>\n",
              "      <td>2.020101e+10</td>\n",
              "      <td>ALUNO DE PÓS-GRADUAÇÃO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2258018 rows × 7 columns</p>\n",
              "</div>\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Verificando Duplicatas"
      ],
      "metadata": {
        "id": "tConBkG-9dur"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "merged_de.value_counts()"
      ],
      "metadata": {
        "id": "cpWNqCZZpUuL",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2155f5c4-0005-4f71-962d-72880a8c412e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id_emprestimo  codigo_barras  data_renovacao                 data_emprestimo                data_devolucao                 matricula_ou_siape  tipo_vinculo_usuario  \n",
              "709704         2006021442     2010/01/17 18:56:43.923000000  2010/01/04 09:58:46.522000000  2010/02/01 13:59:02.528000000  2.008010e+09        ALUNO DE GRADUAÇÃO        7\n",
              "709705         2009032168     2010/01/17 18:56:43.909000000  2010/01/04 09:58:46.549000000  2010/01/20 13:14:34.464000000  2.008010e+09        ALUNO DE GRADUAÇÃO        7\n",
              "709698         2009047725     2010/01/18 14:44:41.163000000  2010/01/04 09:21:19.099000000  2010/02/02 12:02:38.444000000  2.009047e+09        ALUNO DE GRADUAÇÃO        3\n",
              "709686         2006017618     2010/01/26 08:07:01.738000000  2010/01/04 08:08:44.081000000  2010/02/25 07:36:25.800000000  2.008112e+09        ALUNO DE PÓS-GRADUAÇÃO    2\n",
              "2351392        2015008037     2016/01/17 16:03:38.705000000  2016/01/02 10:12:01.529000000  2016/01/27 16:37:59.000000000  2.014036e+09        ALUNO DE GRADUAÇÃO        2\n",
              "                                                                                                                                                                        ..\n",
              "1443389        2011018954     2013/03/22 13:18:55.037000000  2013/03/07 11:21:26.480000000  2013/04/08 20:26:07.000000000  2.010046e+09        ALUNO DE GRADUAÇÃO        1\n",
              "1443390        2009056314     2013/03/21 09:28:45.689000000  2013/03/07 11:21:28.899000000  2013/04/05 18:18:00.000000000  2.011015e+09        ALUNO DE GRADUAÇÃO        1\n",
              "1443393        2012037906     2013/03/20 20:17:10.574000000  2013/03/07 11:24:19.326000000  2013/03/25 08:58:44.000000000  2.011038e+09        ALUNO DE GRADUAÇÃO        1\n",
              "1443395        2011033069     2013/04/07 01:06:51.679000000  2013/03/07 11:25:22.661000000  2013/05/07 16:19:34.000000000  2.011106e+09        ALUNO DE PÓS-GRADUAÇÃO    1\n",
              "2989064        S023116        2021/07/19 16:17:24.750000000  2020/03/17 21:40:52.879000000  2021/10/18 10:32:30.000000000  2.018001e+10        ALUNO DE GRADUAÇÃO        1\n",
              "Length: 968028, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 26
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      ]
    },
    {
      "cell_type": "markdown",
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        "Excluindo Duplicatas\n"
      ],
      "metadata": {
        "id": "6Lz-F2WP9huT"
      }
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    {
      "cell_type": "code",
      "source": [
        "merged_de = merged_de.drop_duplicates()"
      ],
      "metadata": {
        "id": "IKz7kMvi9k77"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_de.value_counts()"
      ],
      "metadata": {
        "id": "-BeFGXwv9mpj",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id_emprestimo  codigo_barras  data_renovacao                 data_emprestimo                data_devolucao                 matricula_ou_siape  tipo_vinculo_usuario  \n",
              "709686         2006017618     2010/01/26 08:07:01.738000000  2010/01/04 08:08:44.081000000  2010/02/25 07:36:25.800000000  2.008112e+09        ALUNO DE PÓS-GRADUAÇÃO    1\n",
              "2336122        2011033079     2016/11/21 02:01:58.488000000  2016/11/10 17:00:12.989000000  2016/12/02 14:21:49.000000000  2.016023e+09        ALUNO DE GRADUAÇÃO        1\n",
              "2336103        2014085936     2016/11/25 19:25:36.215000000  2016/11/10 16:54:52.447000000  2016/12/13 14:30:52.000000000  2.014047e+09        ALUNO DE GRADUAÇÃO        1\n",
              "2336104        2011027960     2016/11/23 17:27:54.642000000  2016/11/10 16:55:17.956000000  2016/11/28 10:32:38.000000000  2.016083e+09        ALUNO DE GRADUAÇÃO        1\n",
              "2336105        2014000897     2016/12/02 15:02:53.356000000  2016/11/10 16:55:19.851000000  2016/12/23 13:53:23.000000000  2.014071e+09        ALUNO DE GRADUAÇÃO        1\n",
              "                                                                                                                                                                        ..\n",
              "1443383        2010043058     2013/03/21 14:06:09.837000000  2013/03/07 11:17:25.008000000  2013/03/22 11:31:20.000000000  2.010023e+09        ALUNO DE GRADUAÇÃO        1\n",
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              "1443390        2009056314     2013/03/21 09:28:45.689000000  2013/03/07 11:21:28.899000000  2013/04/05 18:18:00.000000000  2.011015e+09        ALUNO DE GRADUAÇÃO        1\n",
              "2989064        S023116        2021/07/19 16:17:24.750000000  2020/03/17 21:40:52.879000000  2021/10/18 10:32:30.000000000  2.018001e+10        ALUNO DE GRADUAÇÃO        1\n",
              "Length: 968028, dtype: int64"
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          "metadata": {},
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    {
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        "dados_exemplares = pd.read_parquet('/content/7_Days_of_Code_Alura-Python-Pandas/Dia_1-Importando_dados/Datasets/dados_exemplares.parquet')\n",
        "dados_exemplares"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        id_exemplar codigo_barras            colecao  \\\n",
              "index                                                  \n",
              "0                 5       L000003  Acervo Circulante   \n",
              "1                 4       L000002  Acervo Circulante   \n",
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              "\n",
              "                                               biblioteca status_material  \\\n",
              "index                                                                       \n",
              "0                          Biblioteca Central Zila Mamede         REGULAR   \n",
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              "...                                                   ...             ...   \n",
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              "568581                     Biblioteca Central Zila Mamede        ESPECIAL   \n",
              "568582                     Biblioteca Central Zila Mamede        ESPECIAL   \n",
              "568583                     Biblioteca Central Zila Mamede        ESPECIAL   \n",
              "568584                     Biblioteca Central Zila Mamede        ESPECIAL   \n",
              "\n",
              "        localizacao  registro_sistema  \n",
              "index                                  \n",
              "0               694                 1  \n",
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              "      <td>Biblioteca Setorial do Núcleo de Educação da I...</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>951</td>\n",
              "      <td>268231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568581</th>\n",
              "      <td>1353473</td>\n",
              "      <td>2019013454</td>\n",
              "      <td>Acervo Circulante</td>\n",
              "      <td>Biblioteca Central Zila Mamede</td>\n",
              "      <td>ESPECIAL</td>\n",
              "      <td>997</td>\n",
              "      <td>268233</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568582</th>\n",
              "      <td>1353474</td>\n",
              "      <td>2019012811</td>\n",
              "      <td>Acervo Circulante</td>\n",
              "      <td>Biblioteca Central Zila Mamede</td>\n",
              "      <td>ESPECIAL</td>\n",
              "      <td>987</td>\n",
              "      <td>268234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568583</th>\n",
              "      <td>1353475</td>\n",
              "      <td>2019013451</td>\n",
              "      <td>Acervo Circulante</td>\n",
              "      <td>Biblioteca Central Zila Mamede</td>\n",
              "      <td>ESPECIAL</td>\n",
              "      <td>764</td>\n",
              "      <td>268235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>568584</th>\n",
              "      <td>1353476</td>\n",
              "      <td>2019012276</td>\n",
              "      <td>Acervo Circulante</td>\n",
              "      <td>Biblioteca Central Zila Mamede</td>\n",
              "      <td>ESPECIAL</td>\n",
              "      <td>962</td>\n",
              "      <td>268236</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>546237 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed18bb3f-8651-48f6-8dfe-f78bfc1a634e')\"\n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Unindo DataFrames"
      ],
      "metadata": {
        "id": "72tJI9UAzTCH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_completo = merged_de.merge(dados_exemplares)\n",
        "emprestimos_completo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IwOOaxRmzaAO",
        "outputId": "eec4d3ab-2a48-4fa1-80a3-082647e3d569"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id_emprestimo codigo_barras                 data_renovacao  \\\n",
              "0               709684       L095049                            NaN   \n",
              "1               726965       L095049  2010/03/31 12:41:08.684000000   \n",
              "2               709685       L167050                            NaN   \n",
              "3               717763       L167050                            NaN   \n",
              "4               729996       L167050  2010/03/17 09:08:26.874000000   \n",
              "...                ...           ...                            ...   \n",
              "2072517        2989018    2009036492                            NaN   \n",
              "2072518        2989020    2014062640                            NaN   \n",
              "2072519        2989032    2018003324                            NaN   \n",
              "2072520        2989049    2006024313                            NaN   \n",
              "2072521        2989061    2018031352                            NaN   \n",
              "\n",
              "                       data_emprestimo                 data_devolucao  \\\n",
              "0        2010/01/04 07:44:10.721000000  2010/01/05 16:26:12.662000000   \n",
              "1        2010/03/01 12:40:56.212000000  2010/04/30 14:42:35.366000000   \n",
              "2        2010/01/04 07:44:10.750000000  2010/01/12 07:34:13.934000000   \n",
              "3        2010/02/11 11:16:30.335000000  2010/03/04 08:54:32.262000000   \n",
              "4        2010/03/04 13:36:26.599000000  2010/03/31 18:04:29.669000000   \n",
              "...                                ...                            ...   \n",
              "2072517  2020/03/17 19:49:10.187000000  2020/12/03 08:12:20.000000000   \n",
              "2072518  2020/03/17 19:51:15.700000000  2021/01/07 11:16:28.000000000   \n",
              "2072519  2020/03/17 20:00:11.416000000  2021/08/30 13:29:44.000000000   \n",
              "2072520  2020/03/17 20:47:18.741000000                            NaN   \n",
              "2072521  2020/03/17 21:22:22.138000000  2021/07/26 15:05:20.000000000   \n",
              "\n",
              "         matricula_ou_siape    tipo_vinculo_usuario  id_exemplar  \\\n",
              "0              2.008023e+09      ALUNO DE GRADUAÇÃO        13259   \n",
              "1              2.009122e+09  ALUNO DE PÓS-GRADUAÇÃO        13259   \n",
              "2              2.008023e+09      ALUNO DE GRADUAÇÃO        70865   \n",
              "3              2.008050e+09      ALUNO DE GRADUAÇÃO        70865   \n",
              "4              2.007226e+08      ALUNO DE GRADUAÇÃO        70865   \n",
              "...                     ...                     ...          ...   \n",
              "2072517        2.015011e+10      ALUNO DE GRADUAÇÃO       274820   \n",
              "2072518        2.019004e+10      ALUNO DE GRADUAÇÃO      1157915   \n",
              "2072519        2.019102e+10  ALUNO DE PÓS-GRADUAÇÃO      1317173   \n",
              "2072520        2.015013e+10      ALUNO DE GRADUAÇÃO       206073   \n",
              "2072521        2.020001e+10      ALUNO DE GRADUAÇÃO      1342535   \n",
              "\n",
              "                   colecao                                         biblioteca  \\\n",
              "0        Acervo Circulante                     Biblioteca Central Zila Mamede   \n",
              "1        Acervo Circulante                     Biblioteca Central Zila Mamede   \n",
              "2        Acervo Circulante                     Biblioteca Central Zila Mamede   \n",
              "3        Acervo Circulante                     Biblioteca Central Zila Mamede   \n",
              "4        Acervo Circulante                     Biblioteca Central Zila Mamede   \n",
              "...                    ...                                                ...   \n",
              "2072517  Acervo Circulante                     Biblioteca Central Zila Mamede   \n",
              "2072518  Acervo Circulante                     Biblioteca Central Zila Mamede   \n",
              "2072519  Acervo Circulante  Biblioteca Setorial da Faculdade de Ciências d...   \n",
              "2072520  Acervo Circulante                     Biblioteca Central Zila Mamede   \n",
              "2072521  Acervo Circulante                     Biblioteca Central Zila Mamede   \n",
              "\n",
              "        status_material  localizacao  registro_sistema  \n",
              "0               REGULAR          630              4225  \n",
              "1               REGULAR          630              4225  \n",
              "2               REGULAR          647             25009  \n",
              "3               REGULAR          647             25009  \n",
              "4               REGULAR          647             25009  \n",
              "...                 ...          ...               ...  \n",
              "2072517         REGULAR          676            120845  \n",
              "2072518         REGULAR          228            213248  \n",
              "2072519         REGULAR          784            255835  \n",
              "2072520         REGULAR          663             82178  \n",
              "2072521         REGULAR          355            174443  \n",
              "\n",
              "[2072522 rows x 13 columns]"
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              "      <td>2010/03/04 08:54:32.262000000</td>\n",
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              "<p>2072522 rows × 13 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5550585e-ce7b-428e-bb53-1dac7cacb2c6')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
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              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-5550585e-ce7b-428e-bb53-1dac7cacb2c6 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-5550585e-ce7b-428e-bb53-1dac7cacb2c6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Segundo dia - #7DaysOfCode\n",
        "\n",
        "Crie uma nova coluna com os valores da localização, para refletir a respectiva classe geral na CDU"
      ],
      "metadata": {
        "id": "AUBpOgWs2f9f"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "CDU_lista = []\n",
        "for CDU in emprestimos_completo['localizacao']:\n",
        "  if(CDU < 100):\n",
        "    CDU_lista.append('Generalidades')\n",
        "  elif(CDU < 200):\n",
        "    CDU_lista.append('Filosofia e psicologia')\n",
        "  elif(CDU < 300):\n",
        "    CDU_lista.append('Religião')\n",
        "  elif(CDU < 400):\n",
        "    CDU_lista.append('Ciências sociais')\n",
        "  elif(CDU < 500):\n",
        "    CDU_lista.append('Classe vaga')\n",
        "  elif(CDU < 600):\n",
        "    CDU_lista.append('Matemática e ciências naturais')\n",
        "  elif(CDU < 700):\n",
        "    CDU_lista.append('Ciências aplicadas')\n",
        "  elif(CDU < 800):\n",
        "    CDU_lista.append('Belas artes')\n",
        "  elif(CDU < 900):\n",
        "    CDU_lista.append('Linguagem')\n",
        "  else:\n",
        "    CDU_lista.append('Geografia. Biografia. História.')\n",
        "\n",
        "emprestimos_completo['CDU_geral'] = CDU_lista"
      ],
      "metadata": {
        "id": "jUZpaRN42lCA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_completo.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9kchGK43gJx",
        "outputId": "7502738a-cfcb-4aba-94a2-f830cb1a50c9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   id_emprestimo codigo_barras                 data_renovacao  \\\n",
              "0         709684       L095049                            NaN   \n",
              "1         726965       L095049  2010/03/31 12:41:08.684000000   \n",
              "2         709685       L167050                            NaN   \n",
              "3         717763       L167050                            NaN   \n",
              "4         729996       L167050  2010/03/17 09:08:26.874000000   \n",
              "\n",
              "                 data_emprestimo                 data_devolucao  \\\n",
              "0  2010/01/04 07:44:10.721000000  2010/01/05 16:26:12.662000000   \n",
              "1  2010/03/01 12:40:56.212000000  2010/04/30 14:42:35.366000000   \n",
              "2  2010/01/04 07:44:10.750000000  2010/01/12 07:34:13.934000000   \n",
              "3  2010/02/11 11:16:30.335000000  2010/03/04 08:54:32.262000000   \n",
              "4  2010/03/04 13:36:26.599000000  2010/03/31 18:04:29.669000000   \n",
              "\n",
              "   matricula_ou_siape    tipo_vinculo_usuario  id_exemplar            colecao  \\\n",
              "0        2.008023e+09      ALUNO DE GRADUAÇÃO        13259  Acervo Circulante   \n",
              "1        2.009122e+09  ALUNO DE PÓS-GRADUAÇÃO        13259  Acervo Circulante   \n",
              "2        2.008023e+09      ALUNO DE GRADUAÇÃO        70865  Acervo Circulante   \n",
              "3        2.008050e+09      ALUNO DE GRADUAÇÃO        70865  Acervo Circulante   \n",
              "4        2.007226e+08      ALUNO DE GRADUAÇÃO        70865  Acervo Circulante   \n",
              "\n",
              "                       biblioteca status_material  localizacao  \\\n",
              "0  Biblioteca Central Zila Mamede         REGULAR          630   \n",
              "1  Biblioteca Central Zila Mamede         REGULAR          630   \n",
              "2  Biblioteca Central Zila Mamede         REGULAR          647   \n",
              "3  Biblioteca Central Zila Mamede         REGULAR          647   \n",
              "4  Biblioteca Central Zila Mamede         REGULAR          647   \n",
              "\n",
              "   registro_sistema           CDU_geral  \n",
              "0              4225  Ciências aplicadas  \n",
              "1              4225  Ciências aplicadas  \n",
              "2             25009  Ciências aplicadas  \n",
              "3             25009  Ciências aplicadas  \n",
              "4             25009  Ciências aplicadas  "
            ],
            "text/html": [
              "\n",
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              "      <th></th>\n",
              "      <th>id_emprestimo</th>\n",
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              "      <th>data_renovacao</th>\n",
              "      <th>data_emprestimo</th>\n",
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              "      <th>id_exemplar</th>\n",
              "      <th>colecao</th>\n",
              "      <th>biblioteca</th>\n",
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              "      <th>localizacao</th>\n",
              "      <th>registro_sistema</th>\n",
              "      <th>CDU_geral</th>\n",
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              "  </thead>\n",
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              "      <th>0</th>\n",
              "      <td>709684</td>\n",
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              "      <td>2.008023e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>13259</td>\n",
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              "      <th>1</th>\n",
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              "      <td>2010/04/30 14:42:35.366000000</td>\n",
              "      <td>2.009122e+09</td>\n",
              "      <td>ALUNO DE PÓS-GRADUAÇÃO</td>\n",
              "      <td>13259</td>\n",
              "      <td>Acervo Circulante</td>\n",
              "      <td>Biblioteca Central Zila Mamede</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>630</td>\n",
              "      <td>4225</td>\n",
              "      <td>Ciências aplicadas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>709685</td>\n",
              "      <td>L167050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2010/01/04 07:44:10.750000000</td>\n",
              "      <td>2010/01/12 07:34:13.934000000</td>\n",
              "      <td>2.008023e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>70865</td>\n",
              "      <td>Acervo Circulante</td>\n",
              "      <td>Biblioteca Central Zila Mamede</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>647</td>\n",
              "      <td>25009</td>\n",
              "      <td>Ciências aplicadas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>717763</td>\n",
              "      <td>L167050</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2010/02/11 11:16:30.335000000</td>\n",
              "      <td>2010/03/04 08:54:32.262000000</td>\n",
              "      <td>2.008050e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>70865</td>\n",
              "      <td>Acervo Circulante</td>\n",
              "      <td>Biblioteca Central Zila Mamede</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>647</td>\n",
              "      <td>25009</td>\n",
              "      <td>Ciências aplicadas</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>729996</td>\n",
              "      <td>L167050</td>\n",
              "      <td>2010/03/17 09:08:26.874000000</td>\n",
              "      <td>2010/03/04 13:36:26.599000000</td>\n",
              "      <td>2010/03/31 18:04:29.669000000</td>\n",
              "      <td>2.007226e+08</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>70865</td>\n",
              "      <td>Acervo Circulante</td>\n",
              "      <td>Biblioteca Central Zila Mamede</td>\n",
              "      <td>REGULAR</td>\n",
              "      <td>647</td>\n",
              "      <td>25009</td>\n",
              "      <td>Ciências aplicadas</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ea0008bf-9d71-4703-8805-b381d0b66266')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ea0008bf-9d71-4703-8805-b381d0b66266 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-ea0008bf-9d71-4703-8805-b381d0b66266');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_completo.drop(columns=['registro_sistema'],inplace=True) #escluir a coluna \"regristo_sistema\""
      ],
      "metadata": {
        "id": "8ZB_3I3T4T6y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_completo['matricula_ou_siape'] = emprestimos_completo['matricula_ou_siape'].astype('string') #emprestimos_completo['matricula_ou_siape'] = emprestimos_completo['matricula_ou_siape'].astype('string')"
      ],
      "metadata": {
        "id": "hoyREqnb4l9Z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Terceiro Dia - #7DaysOfCode\n",
        "\n"
      ],
      "metadata": {
        "id": "4ljAdMtK6IUB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns #frameworks para visualização de resultados para análise.\n",
        "import matplotlib.ticker as ticker\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "wgKkTzO65rBD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_completo['id_emprestimo'].value_counts() #Verificando a relação entre empréstimo e empréstimo de exemplar"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RVnaxKTj6rfB",
        "outputId": "5da6f822-c811-49e9-800d-a922853f184e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2422542    6\n",
              "2434767    4\n",
              "2365995    4\n",
              "2393320    4\n",
              "2421605    4\n",
              "          ..\n",
              "1099303    1\n",
              "1073099    1\n",
              "1055189    1\n",
              "1009439    1\n",
              "2989061    1\n",
              "Name: id_emprestimo, Length: 1955945, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_completo.loc[emprestimos_completo['id_emprestimo']==2560028] #Filtrar as informações específicas de uma tabela e analisá-las separadamente."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiFmwsRE60Hi",
        "outputId": "9b166a92-21b7-4e16-8978-dd55d15f1342"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         id_emprestimo codigo_barras                 data_renovacao  \\\n",
              "1594547        2560028    2010082015  2017/11/22 09:44:18.435000000   \n",
              "1594548        2560028    2010082015  2017/11/22 09:44:18.435000000   \n",
              "1594549        2560028    2010082015  2017/11/22 09:44:18.435000000   \n",
              "\n",
              "                       data_emprestimo                 data_devolucao  \\\n",
              "1594547  2017/11/06 19:52:32.782000000  2017/12/07 19:52:23.000000000   \n",
              "1594548  2017/11/06 19:52:32.782000000  2017/12/07 19:52:23.000000000   \n",
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              "\n",
              "        matricula_ou_siape tipo_vinculo_usuario  id_exemplar  \\\n",
              "1594547       2015049280.0   ALUNO DE GRADUAÇÃO      1055921   \n",
              "1594548       2015049280.0   ALUNO DE GRADUAÇÃO      1055569   \n",
              "1594549       2015049280.0   ALUNO DE GRADUAÇÃO      1056359   \n",
              "\n",
              "                   colecao                      biblioteca status_material  \\\n",
              "1594547  Acervo Circulante  Biblioteca Central Zila Mamede         REGULAR   \n",
              "1594548  Acervo Circulante  Biblioteca Central Zila Mamede         REGULAR   \n",
              "1594549  Acervo Circulante  Biblioteca Central Zila Mamede         REGULAR   \n",
              "\n",
              "         localizacao           CDU_geral  \n",
              "1594547          671  Ciências aplicadas  \n",
              "1594548          699  Ciências aplicadas  \n",
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos = len(emprestimos_completo['id_emprestimo'].drop_duplicates()) #Retorna a quantidade de empréstimos que foram realizados ao total\n",
        "print(emprestimos) "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6IlTGgdu7MES",
        "outputId": "dd9672ab-0472-4d9c-8ca3-133d7a085627"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1955945\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "exemplares = len(emprestimos_completo) #Quantudade de exemplares que foram emprestados ao total\n",
        "exemplares"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WSY7ojlO7fl5",
        "outputId": "8397df0d-1de8-4627-9347-f508def53a55"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2072522"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_data = pd.DataFrame(emprestimos_completo['data_emprestimo'].value_counts()).reset_index() #Quantidade de exemplares alugados durante o ano\n",
        "emprestimos_data.columns = ['data','quantidade']\n",
        "emprestimos_data['data'] = pd.to_datetime(emprestimos_data['data'])\n",
        "emprestimos_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iGxst8ZL7pWD",
        "outputId": "4357258c-995d-438a-dc37-116355c807e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                           data  quantidade\n",
              "0       2017-04-20 21:07:44.036           3\n",
              "1       2014-05-12 09:56:18.787           3\n",
              "2       2015-05-05 20:37:19.551           3\n",
              "3       2018-02-22 10:45:11.523           3\n",
              "4       2014-01-28 14:49:50.505           3\n",
              "...                         ...         ...\n",
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              "2068690 2020-03-17 21:22:22.138           1\n",
              "\n",
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              "      <td>1</td>\n",
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              "<p>2068691 rows × 2 columns</p>\n",
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              "          const element = document.querySelector('#df-b862d24f-44a5-4699-b971-50ab623dd067');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_por_ano = emprestimos_data.groupby(by=emprestimos_data.data.dt.year).sum()\n",
        "emprestimos_por_ano.index.name = 'ano'\n",
        "emprestimos_por_ano"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "omh_bJ6MGy0H",
        "outputId": "0ac20ef6-1f22-4345-b275-4ad5bf302637"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      quantidade\n",
              "ano             \n",
              "2010      161394\n",
              "2011      196390\n",
              "2012      221365\n",
              "2013      234749\n",
              "2014      219189\n",
              "2015      213582\n",
              "2016      215900\n",
              "2017      218462\n",
              "2018      194080\n",
              "2019      171801\n",
              "2020       25610"
            ],
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              "      <th>2010</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>2011</th>\n",
              "      <td>196390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2012</th>\n",
              "      <td>221365</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2013</th>\n",
              "      <td>234749</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014</th>\n",
              "      <td>219189</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015</th>\n",
              "      <td>213582</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016</th>\n",
              "      <td>215900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017</th>\n",
              "      <td>218462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>194080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>171801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>25610</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1900af41-c172-49f3-bc86-fbb5012b6b68')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-1900af41-c172-49f3-bc86-fbb5012b6b68 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-1900af41-c172-49f3-bc86-fbb5012b6b68');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.set_theme(context='notebook', \n",
        "              style='darkgrid', \n",
        "              palette='deep', \n",
        "              font_scale=1.3, \n",
        "              rc={\"figure.figsize\":(15,8)}) #Configurando tema dos gráficos"
      ],
      "metadata": {
        "id": "N6CTWW-2G0p1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.lineplot(data=emprestimos_por_ano,x='ano',y='quantidade')\n",
        "ax.set(xlabel=None,ylabel=None)\n",
        "ax.tick_params(axis='x', rotation=30)\n",
        "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, p: format(int(x), ',').replace(',','.')))   \n",
        "\n",
        "ax.set_title('Quantidade de exemplares emprestados do SISBI por ano'+'\\n',size=20,loc='left',weight='bold')\n",
        "\n",
        "ax=ax  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y1wiPVOmHNT_",
        "outputId": "a3496e51-60b1-4ab8-9231-d8102c48e5b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_por_mes = emprestimos_data.groupby(by=emprestimos_data.data.dt.month).sum() #Quantos emprestimos foram por cada mês\n",
        "emprestimos_por_mes.index.name = 'mes'\n",
        "emprestimos_por_mes"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXLwe-SjHVcX",
        "outputId": "9980e675-71ab-40ef-c2d5-cfe69ff09841"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     quantidade\n",
              "mes            \n",
              "1         67514\n",
              "2        210200\n",
              "3        252185\n",
              "4        208725\n",
              "5        202530\n",
              "6        138123\n",
              "7        123790\n",
              "8        232793\n",
              "9        208234\n",
              "10       188410\n",
              "11       164222\n",
              "12        75796"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-018b61e0-8fb6-419c-918c-d801e0a7fcb8\">\n",
              "    <div class=\"colab-df-container\">\n",
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              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th></th>\n",
              "      <th>quantidade</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mes</th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>67514</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>210200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>252185</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>208725</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>202530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>138123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>123790</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>232793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>208234</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>188410</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>164222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>75796</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-018b61e0-8fb6-419c-918c-d801e0a7fcb8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-018b61e0-8fb6-419c-918c-d801e0a7fcb8 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-018b61e0-8fb6-419c-918c-d801e0a7fcb8');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dicionario_meses = {1:'Jan',2:'Fev',3:'Mar',4:'Abr',\n",
        "                    5:'Mai',6:'Jun',7:'Jul',8:'Ago',\n",
        "                    9:'Set',10:'Out',11:'Nov',12:'Dez'} #modificando os números por nomes dos meses"
      ],
      "metadata": {
        "id": "uunzK8kAHZSH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_por_mes.index = emprestimos_por_mes.index.map(dicionario_meses)"
      ],
      "metadata": {
        "id": "3Bqo4ra4HkHY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(emprestimos_por_mes)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OoDbTsEnHn5H",
        "outputId": "41fea219-c327-44cf-ea66-10935f13c683"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "     quantidade\n",
            "mes            \n",
            "Jan       67514\n",
            "Fev      210200\n",
            "Mar      252185\n",
            "Abr      208725\n",
            "Mai      202530\n",
            "Jun      138123\n",
            "Jul      123790\n",
            "Ago      232793\n",
            "Set      208234\n",
            "Out      188410\n",
            "Nov      164222\n",
            "Dez       75796\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.lineplot(data=emprestimos_por_mes,x='mes',y='quantidade')\n",
        "ax.set(xlabel=None,ylabel=None)\n",
        "ax.tick_params(axis='x',rotation=30)\n",
        "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, p: format(int(x), ',').replace(',','.')))                \n",
        "\n",
        "ax.set_title(\"Quantidade de exemplares emprestados do SISBI por mês\"+\"\\n\",size=20,loc='left',weight='bold')\n",
        "ax.text(s='Período de 2010 a 2020',x=-0.5,y=265000,fontsize=18, ha='left',color='gray')\n",
        "\n",
        "ax=ax"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCvjznUgHqao",
        "outputId": "465837df-df83-4e1f-cc3a-1c91b4fb54d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_data.data #Quantidade de empréstimos por hora ao longo do dia"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xS7T0YlBHtcX",
        "outputId": "c64a8d20-6f31-4e82-9db7-02f4676b705d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0         2017-04-20 21:07:44.036\n",
              "1         2014-05-12 09:56:18.787\n",
              "2         2015-05-05 20:37:19.551\n",
              "3         2018-02-22 10:45:11.523\n",
              "4         2014-01-28 14:49:50.505\n",
              "                    ...          \n",
              "2068686   2012-04-11 10:12:20.960\n",
              "2068687   2010-11-12 11:03:22.117\n",
              "2068688   2015-09-15 14:26:08.838\n",
              "2068689   2015-08-14 13:41:47.472\n",
              "2068690   2020-03-17 21:22:22.138\n",
              "Name: data, Length: 2068691, dtype: datetime64[ns]"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_por_hora = emprestimos_data.groupby(by=emprestimos_data.data.dt.hour).sum()\n",
        "emprestimos_por_hora.index.name = 'horas'\n",
        "emprestimos_por_hora = emprestimos_por_hora.reset_index()\n",
        "emprestimos_por_hora"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oCPHvjaUI38Y",
        "outputId": "cd3f6b2c-5263-40ec-9500-d985b31c8c6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    horas  quantidade\n",
              "0       0          17\n",
              "1       6          14\n",
              "2       7       18777\n",
              "3       8      103416\n",
              "4       9      131273\n",
              "5      10      185839\n",
              "6      11      180048\n",
              "7      12      148754\n",
              "8      13      126987\n",
              "9      14      150876\n",
              "10     15      166672\n",
              "11     16      210655\n",
              "12     17      185340\n",
              "13     18      182520\n",
              "14     19      102129\n",
              "15     20      127716\n",
              "16     21       51191\n",
              "17     22         247\n",
              "18     23          51"
            ],
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-7fc15f0e-b423-4fca-8772-34c3d264cbe3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7fc15f0e-b423-4fca-8772-34c3d264cbe3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_por_hora = emprestimos_por_hora.sort_values(ascending=True,by='quantidade')"
      ],
      "metadata": {
        "id": "Wr_xRo7uJQBA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "ax = sns.barplot(data=emprestimos_por_hora,y='quantidade',x='horas',\n",
        "                 palette='Blues',hue='quantidade',dodge=False)                  #Ordenar pela quantidade de exemplares emprestados\n",
        "plt.legend([],[], frameon=False)                                                #Excluir a legenda do gráfico\n",
        "\n",
        "ax.set(xlabel='Horário',ylabel=None)\n",
        "ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, p: format(int(x), ',').replace(',','.')))           \n",
        "ax.set_title(\"Quantidade de exemplares emprestados do SISBI por faixa horária\"+\"\\n\",size=20,loc='left',weight='bold')\n",
        "ax.text(s='Período de 2010 a 2020',x=-0.5,y=225000,fontsize=18, ha='left',color='gray')\n",
        "ax=ax"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nM2R8vqZJbFR",
        "outputId": "1d90e435-91ef-4b3f-d18f-839154adff43"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Quarto Dia - #7DaysOfCode"
      ],
      "metadata": {
        "id": "MbUesoQmK9BI"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "O objetivo será entender a quantidade de empréstimos a partir das variáveis categóricas do seu conjunto de dados.\n",
        "\n",
        "**Quantitativas:** são características numéricas que podemos enumerar:\n",
        "\n",
        "1) Discreta: possuem apenas valores finitos enumeráveis. Exemplo: número de empréstimos, número de filhos, número de páginas.\n",
        "\n",
        "2) Contínua: possuem valores dentro de um intervalo de números. Exemplo: altura, peso, notas.\n",
        "\n",
        "**Categóricas (ou qualitativas):** são características não-numéricas para as quais podemos definir categorias.\n",
        "\n",
        "1) Nominal: possuem categorias não ordenadas naturalmente. Exemplo: nomes, cores, tema do livro.\n",
        "\n",
        "2) Ordinal: são categorias onde está implícito um tipo de ordenamento ou hierarquia. Exemplo: classe social, nível de escolaridade.\n",
        "\n",
        "Como se distribuem os empréstimos de exemplares pelos tipos de vínculo dos usuários?\n",
        "\n",
        "Quais coleções são mais emprestadas ?\n",
        "\n",
        "Quais são as bibliotecas com mais ou menos quantidade de empréstimos ?\n",
        "\n",
        "De quais temas da CDU são os exemplares emprestados ?\n",
        "\n",
        "Gerar uma tabela de frequência e com o percentual para cada variável."
      ],
      "metadata": {
        "id": "FRkg6mHF19OV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_completo['data_emprestimo'] = pd.to_datetime(emprestimos_completo['data_emprestimo']) #Modificando tipo de dado da data de empréstimo do DataFrame"
      ],
      "metadata": {
        "id": "5z4DsE4rMN4i"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_completo.tipo_vinculo_usuario.unique() #Verificando valores únicos das variáveis"
      ],
      "metadata": {
        "id": "Njwqgk0e5MFe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "627af6e9-dd68-46d3-843e-1543663e65ff"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['ALUNO DE GRADUAÇÃO', 'ALUNO DE PÓS-GRADUAÇÃO', 'DOCENTE',\n",
              "       'DOCENTE EXTERNO', 'SERVIDOR TÉCNICO-ADMINISTRATIVO',\n",
              "       'ALUNO MÉDIO/TÉCNICO', 'USUÁRIO EXTERNO', 'OUTROS'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_completo.colecao.unique()"
      ],
      "metadata": {
        "id": "UuUBnHxU5VPW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26ac86d0-ba20-4f15-abd6-47fad6b11013"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Acervo Circulante', 'Folhetos', 'Teses', 'Dissertações',\n",
              "       'Monografias', 'Publicações de Autores do RN',\n",
              "       'Publicações da UFRN', 'Multimeios', 'Coleção Mossoroense',\n",
              "       'Eventos', 'Trabalho Acadêmico', 'Obras de Referência',\n",
              "       'Obras Raras', 'Literatura de Cordel', 'Coleção Zila Mamede',\n",
              "       'Necessidades Educacionais Específicas'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_completo.biblioteca.unique()"
      ],
      "metadata": {
        "id": "dL185ne05ba9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bc05459a-f73f-48fb-de05-02287fbc8b46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Biblioteca Central Zila Mamede',\n",
              "       'Biblioteca Setorial Prof. Rodolfo Helinski - Escola Agrícola de Jundiaí - EAJ  - Macaiba',\n",
              "       'Biblioteca Setorial Bertha Cruz Enders - \\xadEscola de Saúde da UFRN - ESUFRN',\n",
              "       'Biblioteca Setorial do Centro Ciências da Saúde - CCS',\n",
              "       'Biblioteca Setorial Prof. Alberto Moreira Campos - \\xadDepartamento de Odontologia',\n",
              "       'Biblioteca Setorial Prof. Ronaldo Xavier de Arruda - CCET',\n",
              "       'Biblioteca Setorial do Centro de Ciências Humanas, Letras e Artes - CCHLA',\n",
              "       'Biblioteca Setorial Prof. Horácio Nicolas Solimo - \\xad Engenharia Química - EQ - CT',\n",
              "       'Biblioteca Setorial Prof. Francisco Gurgel De Azevedo - Instituto Química - IQ',\n",
              "       'Biblioteca Setorial do Centro Ciências Sociais Aplicadas - CCSA',\n",
              "       'Biblioteca Setorial do Departamento de Artes - DEART',\n",
              "       'Biblioteca Setorial Prof. Dr. Marcelo Bezerra de Melo Tinôco - DARQ - \\xadCT',\n",
              "       'Biblioteca Setorial Árvore do Conhecimento - Instituto do Cérebro - ICe',\n",
              "       'Biblioteca Setorial Moacyr de Góes - CE',\n",
              "       'Biblioteca Setorial Prof. Leopoldo Nelson - \\xadCentro de Biociências - CB',\n",
              "       'Biblioteca Setorial Dr. Paulo Bezerra - EMCM/RN - Caicó',\n",
              "       'Biblioteca Setorial Pe. Jaime Diniz - Escola de Música - EMUFRN',\n",
              "       'Biblioteca Setorial Profª. Maria Lúcia da Costa Bezerra - \\xadCERES\\xad - Caicó',\n",
              "       'Biblioteca Setorial Profª. Maria José Mamede Galvão - FELCS - Currais Novos',\n",
              "       'Biblioteca Setorial do Núcleo de Educação da Infância - NEI',\n",
              "       'Biblioteca Setorial da Faculdade de Ciências da Saúde do Trairi - FACISA - Santa Cruz',\n",
              "       'Biblioteca Setorial do Núcleo de Ensino Superior do Agreste - NESA - Nova Cruz'],\n",
              "      dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_completo.CDU_geral.unique()"
      ],
      "metadata": {
        "id": "KBE0OkXm5eE9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d4ee3bdc-3741-49e1-bc67-be21786cdd35"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Ciências aplicadas', 'Linguagem', 'Ciências sociais',\n",
              "       'Geografia. Biografia. História.',\n",
              "       'Matemática e ciências naturais', 'Religião', 'Generalidades',\n",
              "       'Filosofia e psicologia', 'Belas artes'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def tabela_frequencia(variavel): #Criar a função para gerar a tabela de frequência com percentuais\n",
        "\n",
        "  dataframe = pd.DataFrame(emprestimos_completo[variavel].value_counts())                      \n",
        "  dataframe.columns = ['quantidade']\n",
        "  dataframe['percentual'] = round((dataframe.quantidade / dataframe.quantidade.sum())*100,1)\n",
        "\n",
        "  return dataframe"
      ],
      "metadata": {
        "id": "a01eICoo5y4l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tabela_frequencia('tipo_vinculo_usuario') #Quantos foram por tipo de vinculo do usuario ?"
      ],
      "metadata": {
        "id": "ZnFssJh88SRe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a9a908a6-a39a-4717-959f-a6f5d3ae0af4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 quantidade  percentual\n",
              "ALUNO DE GRADUAÇÃO                  1612324        77.8\n",
              "ALUNO DE PÓS-GRADUAÇÃO               302541        14.6\n",
              "DOCENTE                               70981         3.4\n",
              "SERVIDOR TÉCNICO-ADMINISTRATIVO       32184         1.6\n",
              "ALUNO MÉDIO/TÉCNICO                   28029         1.4\n",
              "DOCENTE EXTERNO                       23949         1.2\n",
              "USUÁRIO EXTERNO                        2509         0.1\n",
              "OUTROS                                    5         0.0"
            ],
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              "\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ALUNO DE GRADUAÇÃO</th>\n",
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              "      <td>77.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ALUNO DE PÓS-GRADUAÇÃO</th>\n",
              "      <td>302541</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>DOCENTE</th>\n",
              "      <td>70981</td>\n",
              "      <td>3.4</td>\n",
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              "      <th>SERVIDOR TÉCNICO-ADMINISTRATIVO</th>\n",
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              "      <td>28029</td>\n",
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              "      <th>DOCENTE EXTERNO</th>\n",
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              "      <td>1.2</td>\n",
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              "      <td>2509</td>\n",
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              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ec39a819-8490-48cf-80f2-a0a4421a0d34')\"\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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          },
          "metadata": {},
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tabela_frequencia('colecao') #Quantos foram por coleção ?"
      ],
      "metadata": {
        "id": "pVs2Glyp8jkf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d10899f9-55ee-4c68-b545-b45bcbd2bfd5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                       quantidade  percentual\n",
              "Acervo Circulante                         2053120        99.1\n",
              "Multimeios                                   7954         0.4\n",
              "Monografias                                  4021         0.2\n",
              "Dissertações                                 2406         0.1\n",
              "Obras de Referência                          1954         0.1\n",
              "Publicações de Autores do RN                  866         0.0\n",
              "Publicações da UFRN                           764         0.0\n",
              "Teses                                         714         0.0\n",
              "Folhetos                                      271         0.0\n",
              "Necessidades Educacionais Específicas         130         0.0\n",
              "Trabalho Acadêmico                            115         0.0\n",
              "Eventos                                       101         0.0\n",
              "Coleção Mossoroense                            44         0.0\n",
              "Coleção Zila Mamede                            30         0.0\n",
              "Literatura de Cordel                           24         0.0\n",
              "Obras Raras                                     8         0.0"
            ],
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              "      <th></th>\n",
              "      <th>quantidade</th>\n",
              "      <th>percentual</th>\n",
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              "      <th>Acervo Circulante</th>\n",
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              "      <th>Multimeios</th>\n",
              "      <td>7954</td>\n",
              "      <td>0.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Monografias</th>\n",
              "      <td>4021</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Dissertações</th>\n",
              "      <td>2406</td>\n",
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              "    <tr>\n",
              "      <th>Teses</th>\n",
              "      <td>714</td>\n",
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              "      <th>Folhetos</th>\n",
              "      <td>271</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
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              "      <th>Obras Raras</th>\n",
              "      <td>8</td>\n",
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              "\n",
              "        async function convertToInteractive(key) {\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tabela_frequencia('biblioteca') #Quantos foram por biblioteca ?"
      ],
      "metadata": {
        "id": "hV3F6gxS8scR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2eb06029-ae0c-4408-9f50-87c0c60dc7ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                    quantidade  percentual\n",
              "Biblioteca Central Zila Mamede                         1426122        68.8\n",
              "Biblioteca Setorial do Centro Ciências da Saúde...      118614         5.7\n",
              "Biblioteca Setorial da Faculdade de Ciências da...       77609         3.7\n",
              "Biblioteca Setorial Prof. Alberto Moreira Campo...       66447         3.2\n",
              "Biblioteca Setorial Profª. Maria Lúcia da Costa...       61478         3.0\n",
              "Biblioteca Setorial Bertha Cruz Enders - ­Escol...       46040         2.2\n",
              "Biblioteca Setorial do Centro de Ciências Human...       40744         2.0\n",
              "Biblioteca Setorial Dr. Paulo Bezerra - EMCM/RN...       32097         1.5\n",
              "Biblioteca Setorial Profª. Maria José Mamede Ga...       29089         1.4\n",
              "Biblioteca Setorial Prof. Rodolfo Helinski - Es...       27219         1.3\n",
              "Biblioteca Setorial Prof. Francisco Gurgel De A...       26923         1.3\n",
              "Biblioteca Setorial do Núcleo de Educação da In...       23656         1.1\n",
              "Biblioteca Setorial Prof. Ronaldo Xavier de Arr...       22749         1.1\n",
              "Biblioteca Setorial Pe. Jaime Diniz - Escola de...       21476         1.0\n",
              "Biblioteca Setorial do Departamento de Artes - ...       17099         0.8\n",
              "Biblioteca Setorial Prof. Leopoldo Nelson - ­Ce...       12900         0.6\n",
              "Biblioteca Setorial do Centro Ciências Sociais ...       10533         0.5\n",
              "Biblioteca Setorial Prof. Dr. Marcelo Bezerra d...        5057         0.2\n",
              "Biblioteca Setorial Prof. Horácio Nicolas Solim...        4098         0.2\n",
              "Biblioteca Setorial Moacyr de Góes - CE                   1036         0.0\n",
              "Biblioteca Setorial Árvore do Conhecimento - In...         919         0.0\n",
              "Biblioteca Setorial do Núcleo de Ensino Superio...         617         0.0"
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              "      <th>Biblioteca Central Zila Mamede</th>\n",
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              "      <th>Biblioteca Setorial do Centro Ciências da Saúde - CCS</th>\n",
              "      <td>118614</td>\n",
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              "      <th>Biblioteca Setorial da Faculdade de Ciências da Saúde do Trairi - FACISA - Santa Cruz</th>\n",
              "      <td>77609</td>\n",
              "      <td>3.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial Prof. Alberto Moreira Campos - ­Departamento de Odontologia</th>\n",
              "      <td>66447</td>\n",
              "      <td>3.2</td>\n",
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              "      <th>Biblioteca Setorial Profª. Maria Lúcia da Costa Bezerra - ­CERES­ - Caicó</th>\n",
              "      <td>61478</td>\n",
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              "      <th>Biblioteca Setorial Bertha Cruz Enders - ­Escola de Saúde da UFRN - ESUFRN</th>\n",
              "      <td>46040</td>\n",
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              "      <th>Biblioteca Setorial do Centro de Ciências Humanas, Letras e Artes - CCHLA</th>\n",
              "      <td>40744</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial Dr. Paulo Bezerra - EMCM/RN - Caicó</th>\n",
              "      <td>32097</td>\n",
              "      <td>1.5</td>\n",
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              "    <tr>\n",
              "      <th>Biblioteca Setorial Profª. Maria José Mamede Galvão - FELCS - Currais Novos</th>\n",
              "      <td>29089</td>\n",
              "      <td>1.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial Prof. Rodolfo Helinski - Escola Agrícola de Jundiaí - EAJ  - Macaiba</th>\n",
              "      <td>27219</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial Prof. Francisco Gurgel De Azevedo - Instituto Química - IQ</th>\n",
              "      <td>26923</td>\n",
              "      <td>1.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial do Núcleo de Educação da Infância - NEI</th>\n",
              "      <td>23656</td>\n",
              "      <td>1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial Prof. Ronaldo Xavier de Arruda - CCET</th>\n",
              "      <td>22749</td>\n",
              "      <td>1.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial Pe. Jaime Diniz - Escola de Música - EMUFRN</th>\n",
              "      <td>21476</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial do Departamento de Artes - DEART</th>\n",
              "      <td>17099</td>\n",
              "      <td>0.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial Prof. Leopoldo Nelson - ­Centro de Biociências - CB</th>\n",
              "      <td>12900</td>\n",
              "      <td>0.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial do Centro Ciências Sociais Aplicadas - CCSA</th>\n",
              "      <td>10533</td>\n",
              "      <td>0.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial Prof. Dr. Marcelo Bezerra de Melo Tinôco - DARQ - ­CT</th>\n",
              "      <td>5057</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial Prof. Horácio Nicolas Solimo - ­ Engenharia Química - EQ - CT</th>\n",
              "      <td>4098</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial Moacyr de Góes - CE</th>\n",
              "      <td>1036</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial Árvore do Conhecimento - Instituto do Cérebro - ICe</th>\n",
              "      <td>919</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Biblioteca Setorial do Núcleo de Ensino Superior do Agreste - NESA - Nova Cruz</th>\n",
              "      <td>617</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tabela_frequencia('CDU_geral') #Quantos foram pela CDU geral?"
      ],
      "metadata": {
        "id": "VQfU_ex28_93",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69e91ef4-1a3c-49a4-d2e3-3caf1dce8b07"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                 quantidade  percentual\n",
              "Ciências aplicadas                  1425473        68.8\n",
              "Ciências sociais                     369536        17.8\n",
              "Matemática e ciências naturais        68744         3.3\n",
              "Generalidades                         62521         3.0\n",
              "Religião                              62295         3.0\n",
              "Filosofia e psicologia                60563         2.9\n",
              "Geografia. Biografia. História.        7989         0.4\n",
              "Belas artes                            7911         0.4\n",
              "Linguagem                              7490         0.4"
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              "      <th>Ciências sociais</th>\n",
              "      <td>369536</td>\n",
              "      <td>17.8</td>\n",
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              "    <tr>\n",
              "      <th>Matemática e ciências naturais</th>\n",
              "      <td>68744</td>\n",
              "      <td>3.3</td>\n",
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              "      <td>62521</td>\n",
              "      <td>3.0</td>\n",
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              "      <th>Religião</th>\n",
              "      <td>62295</td>\n",
              "      <td>3.0</td>\n",
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              "      <th>Filosofia e psicologia</th>\n",
              "      <td>60563</td>\n",
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              "    <tr>\n",
              "      <th>Geografia. Biografia. História.</th>\n",
              "      <td>7989</td>\n",
              "      <td>0.4</td>\n",
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              "      <th>Belas artes</th>\n",
              "      <td>7911</td>\n",
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              "      <th>Linguagem</th>\n",
              "      <td>7490</td>\n",
              "      <td>0.4</td>\n",
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              "          const element = document.querySelector('#df-a2e42675-f07e-43fc-b8ae-9f88c28f3e31');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 62
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Quinto Dia - #7DaysOfCode\n"
      ],
      "metadata": {
        "id": "idSNFnHh-Dv5"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "Desenvolva a tarefa uma etapa por vez:\n",
        "\n",
        "Verifique qual é a coleção com maior frequência para cada tipo de usuário.\n",
        "Filtre os dados com condições solicitadas\n",
        "Selecione apenas os empréstimos\n",
        "Faça a contagem de empréstimos mensais por cada ano\n",
        "Crie uma função para gerar a visualização do gráfico de box plot por cada ano.\n",
        "Crie o gráfico de boxplot"
      ],
      "metadata": {
        "id": "H4aYDkWmGvkc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "TFVXoqaq-RiR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Alunos de graduação"
      ],
      "metadata": {
        "id": "fFvkEe-h_Aps"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alunos_graduacao = emprestimos_completo.query('tipo_vinculo_usuario == \"ALUNO DE GRADUAÇÃO\"')\n",
        "alunos_graduacao.colecao.value_counts()"
      ],
      "metadata": {
        "id": "kb-N4R2v-8Gy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2b30863-1164-4f7b-e40e-1f1310cc0afa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Acervo Circulante                        1600155\n",
              "Multimeios                                  4652\n",
              "Monografias                                 3572\n",
              "Dissertações                                1152\n",
              "Obras de Referência                         1047\n",
              "Publicações de Autores do RN                 595\n",
              "Publicações da UFRN                          466\n",
              "Teses                                        245\n",
              "Folhetos                                     180\n",
              "Trabalho Acadêmico                            81\n",
              "Necessidades Educacionais Específicas         73\n",
              "Eventos                                       57\n",
              "Coleção Mossoroense                           17\n",
              "Coleção Zila Mamede                           15\n",
              "Literatura de Cordel                          11\n",
              "Obras Raras                                    6\n",
              "Name: colecao, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "alunos_graduacao_acervo_circulante = alunos_graduacao.query('colecao == \"Acervo Circulante\"')\n",
        "alunos_graduacao_acervo_circulante = pd.DataFrame(alunos_graduacao_acervo_circulante)\n",
        "alunos_graduacao_acervo_circulante['data_emprestimo'] = pd.to_datetime(alunos_graduacao_acervo_circulante['data_emprestimo'])\n",
        "alunos_graduacao_acervo_circulante['ano'] = alunos_graduacao_acervo_circulante['data_emprestimo'].dt.year\n",
        "alunos_graduacao_acervo_circulante['mes'] = alunos_graduacao_acervo_circulante['data_emprestimo'].dt.month\n",
        "alunos_graduacao_acervo_circulante = alunos_graduacao_acervo_circulante.loc[:,['ano','mes']]\n",
        "alunos_graduacao_acervo_circulante = alunos_graduacao_acervo_circulante.value_counts().to_frame('quantidade').reset_index()\n",
        "alunos_graduacao_acervo_circulante \n",
        "\n",
        "#Criando tabela com a quantidade mensal por ano"
      ],
      "metadata": {
        "id": "VMBP68J5_FyS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7241d430-0127-472e-acaf-b7a2d7628129"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      ano  mes  quantidade\n",
              "0    2012    3       22797\n",
              "1    2015    3       22483\n",
              "2    2013    4       21853\n",
              "3    2017    3       21735\n",
              "4    2016    3       21692\n",
              "..    ...  ...         ...\n",
              "118  2016    1        2670\n",
              "119  2019    1        2276\n",
              "120  2019   12        1980\n",
              "121  2020    1        1781\n",
              "122  2011    1         809\n",
              "\n",
              "[123 rows x 3 columns]"
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "  #Função que vai gerar um boxplot\n",
        "\n",
        "def gera_box_plot(dataset,x,y,titulo,subtitulo):\n",
        "\n",
        "    sns.set_theme(style=\"darkgrid\", palette='Blues',font_scale=1.3)                    \n",
        "    plt.figure(figsize=(16,10))                                                           \n",
        "\n",
        "    ax = sns.boxplot(y= y, x= x, data=dataset, color='#4171EF')                                           \n",
        "    ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, p: format(int(x), ',').replace(',','.')))              \n",
        "\n",
        "    plt.ylim(0,max(dataset[y])*1.1)                                             \n",
        "    plt.xlabel(None)                                                                     \n",
        "    plt.ylabel(None)                                                                    \n",
        "\n",
        "    ax.set_title(titulo+\"\\n\",size=20,loc='left',weight='bold')\n",
        "    ax.text(s=subtitulo,x=-0.5,y=max(dataset[y])*1.11,fontsize=18, ha='left',color='gray')"
      ],
      "metadata": {
        "id": "ghfOksXS_Gfy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "gera_box_plot(alunos_graduacao_acervo_circulante,'ano','quantidade','Distribuição dos empréstimos mensais', 'Realizados pelos alunos de graduação no acervo ciruculante')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T2g-u6GlDqol",
        "outputId": "4197b87c-aa7f-49d1-9df4-f009f0e9b322"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Alunos de pós graduação"
      ],
      "metadata": {
        "id": "zfIu_HOCD8HC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "alunos_pos_graduacao = emprestimos_completo.query('tipo_vinculo_usuario == \"ALUNO DE PÓS-GRADUAÇÃO\"')\n",
        "alunos_pos_graduacao.colecao.value_counts()"
      ],
      "metadata": {
        "id": "WnjngKcZD_lS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c7d1fab2-7eb4-495c-98da-71bba27b5ffb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Acervo Circulante                        299201\n",
              "Dissertações                                934\n",
              "Obras de Referência                         700\n",
              "Multimeios                                  559\n",
              "Teses                                       367\n",
              "Monografias                                 278\n",
              "Publicações de Autores do RN                204\n",
              "Publicações da UFRN                         121\n",
              "Folhetos                                     60\n",
              "Necessidades Educacionais Específicas        30\n",
              "Eventos                                      26\n",
              "Coleção Mossoroense                          23\n",
              "Trabalho Acadêmico                           23\n",
              "Coleção Zila Mamede                          11\n",
              "Literatura de Cordel                          4\n",
              "Name: colecao, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Criando tabela com a quantidade mensal por ano\n",
        "\n",
        "alunos_pos_graduacao_acervo_circulante = alunos_pos_graduacao.query('colecao == \"Acervo Circulante\"')\n",
        "alunos_pos_graduacao_acervo_circulante = pd.DataFrame(alunos_pos_graduacao_acervo_circulante)\n",
        "alunos_pos_graduacao_acervo_circulante['data_emprestimo'] = pd.to_datetime(alunos_pos_graduacao_acervo_circulante['data_emprestimo'])\n",
        "alunos_pos_graduacao_acervo_circulante['ano'] = alunos_pos_graduacao_acervo_circulante['data_emprestimo'].dt.year\n",
        "alunos_pos_graduacao_acervo_circulante['mes'] = alunos_pos_graduacao_acervo_circulante['data_emprestimo'].dt.month\n",
        "alunos_pos_graduacao_acervo_circulante = alunos_pos_graduacao_acervo_circulante.loc[:,['ano','mes']]\n",
        "alunos_pos_graduacao_acervo_circulante = alunos_pos_graduacao_acervo_circulante.value_counts().to_frame('quantidade').reset_index()\n",
        "alunos_pos_graduacao_acervo_circulante"
      ],
      "metadata": {
        "id": "GavHEfLGECON",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8482a324-efc9-47b8-9c44-448071b60ff1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      ano  mes  quantidade\n",
              "0    2015    3        3927\n",
              "1    2017    8        3837\n",
              "2    2017    3        3722\n",
              "3    2016    3        3711\n",
              "4    2016    8        3662\n",
              "..    ...  ...         ...\n",
              "119  2020    1        1143\n",
              "120  2010    1        1105\n",
              "121  2019   12        1069\n",
              "122  2011    1         465\n",
              "123  2020    6           2\n",
              "\n",
              "[124 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-52073f4a-ec81-424e-a0a5-3ca3ed7cd3e9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>ano</th>\n",
              "      <th>mes</th>\n",
              "      <th>quantidade</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>3</td>\n",
              "      <td>3927</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2017</td>\n",
              "      <td>8</td>\n",
              "      <td>3837</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2017</td>\n",
              "      <td>3</td>\n",
              "      <td>3722</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016</td>\n",
              "      <td>3</td>\n",
              "      <td>3711</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016</td>\n",
              "      <td>8</td>\n",
              "      <td>3662</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>119</th>\n",
              "      <td>2020</td>\n",
              "      <td>1</td>\n",
              "      <td>1143</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>120</th>\n",
              "      <td>2010</td>\n",
              "      <td>1</td>\n",
              "      <td>1105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>121</th>\n",
              "      <td>2019</td>\n",
              "      <td>12</td>\n",
              "      <td>1069</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>122</th>\n",
              "      <td>2011</td>\n",
              "      <td>1</td>\n",
              "      <td>465</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>123</th>\n",
              "      <td>2020</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>124 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-52073f4a-ec81-424e-a0a5-3ca3ed7cd3e9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-52073f4a-ec81-424e-a0a5-3ca3ed7cd3e9 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-52073f4a-ec81-424e-a0a5-3ca3ed7cd3e9');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "gera_box_plot(alunos_pos_graduacao_acervo_circulante,'ano','quantidade','Distribuição dos empréstimos mensais', 'Realizados pelos alunos de pós graduação no acervo circulante')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wIZokIuNENzb",
        "outputId": "acb5f35b-f8cd-41db-965c-37db4b9876f3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "OBS: Que se observar que em ambos os casos, de alunos de graduação e pós-graduação, alguma ação foi feita durante o período composto entre 2010 e 2013 que provocou uma elevação.\n",
        "\n",
        "A partir de 2019 começou um forte declinio para ambos cenários."
      ],
      "metadata": {
        "id": "L5dKS0nQFdcd"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Sexto Dia - Novos dados, novas análises\n",
        "\n",
        "Você precisará de novas métricas em relação aos empréstimos, de acordo com os cursos.\n",
        "\n",
        "Você precisará calcular a quantidade de empréstimos realizados entre 2015 e 2020 por cada curso de graduação que passará pela avaliação.\n",
        "\n",
        "Os cursos serão:\n",
        "\n",
        "- Biblioteconomia\n",
        "- Ciências sociais\n",
        "- Comunicação social\n",
        "- Direito\n",
        "- Filosofia\n",
        "- Pedagogia\n",
        "\n",
        "Base de Dados:\n",
        "\n",
        "- Plainha de excell (https://github.com/FranciscoFoz/7_Days_of_Code_Alura-Python-Pandas/blob/main/Dia_6-Novos_dados_novas_analises/Datasets/matricula_alunos.xlsx)\n",
        "- Arquivo Json (https://raw.githubusercontent.com/FranciscoFoz/7_Days_of_Code_Alura-Python-Pandas/main/Dia_6-Novos_dados_novas_analises/Datasets/cadastro_alunos.json)"
      ],
      "metadata": {
        "id": "gUuQTehpHThc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_antes_2010 = pd.read_excel('https://github.com/FranciscoFoz/7_Days_of_Code_Alura-Python-Pandas/raw/main/Dia_6-Novos_dados_novas_analises/Datasets/matricula_alunos.xlsx',\n",
        "                                        sheet_name='Até 2010',skiprows=1)\n",
        "cadastro_usuarios_depois_2010 = pd.read_excel('https://github.com/FranciscoFoz/7_Days_of_Code_Alura-Python-Pandas/raw/main/Dia_6-Novos_dados_novas_analises/Datasets/matricula_alunos.xlsx',\n",
        "                                        sheet_name='Após 2010',skiprows=1)"
      ],
      "metadata": {
        "id": "-v5Y-qHVEZ0l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_antes_2010"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8uOCVZY7OZ5F",
        "outputId": "aab4ab34-052a-4fcc-a2fc-ec972a09b5cb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Número da matrícula     Tipo de vinculo  \\\n",
              "0            1.900017e+08  ALUNO DE GRADUAÇÃO   \n",
              "1            1.990145e+08  ALUNO DE GRADUAÇÃO   \n",
              "2            1.997357e+08  ALUNO DE GRADUAÇÃO   \n",
              "3            1.998124e+08  ALUNO DE GRADUAÇÃO   \n",
              "4            1.998237e+08  ALUNO DE GRADUAÇÃO   \n",
              "...                   ...                 ...   \n",
              "7721         2.009075e+09  ALUNO DE GRADUAÇÃO   \n",
              "7722         2.009075e+09  ALUNO DE GRADUAÇÃO   \n",
              "7723         2.009075e+09  ALUNO DE GRADUAÇÃO   \n",
              "7724         2.009075e+09  ALUNO DE GRADUAÇÃO   \n",
              "7725         2.009075e+09  ALUNO DE GRADUAÇÃO   \n",
              "\n",
              "                                      Curso  \n",
              "0                                MATEMÁTICA  \n",
              "1                       CIÊNCIAS BIOLÓGICAS  \n",
              "2                                MATEMÁTICA  \n",
              "3                                    LETRAS  \n",
              "4                                  ECOLOGIA  \n",
              "...                                     ...  \n",
              "7721                        BIBLIOTECONOMIA  \n",
              "7722  ANÁLISE E DESENVOLVIMENTO DE SISTEMAS  \n",
              "7723                          ADMINISTRAÇÃO  \n",
              "7724                                 MÚSICA  \n",
              "7725                                DIREITO  \n",
              "\n",
              "[7726 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3ff56f0e-10c8-49b2-badd-3fee085ba48e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Número da matrícula</th>\n",
              "      <th>Tipo de vinculo</th>\n",
              "      <th>Curso</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.900017e+08</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>MATEMÁTICA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.990145e+08</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>CIÊNCIAS BIOLÓGICAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.997357e+08</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>MATEMÁTICA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.998124e+08</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>LETRAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.998237e+08</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>ECOLOGIA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7721</th>\n",
              "      <td>2.009075e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>BIBLIOTECONOMIA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7722</th>\n",
              "      <td>2.009075e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>ANÁLISE E DESENVOLVIMENTO DE SISTEMAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7723</th>\n",
              "      <td>2.009075e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>ADMINISTRAÇÃO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7724</th>\n",
              "      <td>2.009075e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>MÚSICA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7725</th>\n",
              "      <td>2.009075e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>DIREITO</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>7726 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3ff56f0e-10c8-49b2-badd-3fee085ba48e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3ff56f0e-10c8-49b2-badd-3fee085ba48e button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3ff56f0e-10c8-49b2-badd-3fee085ba48e');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 72
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_antes_2010.columns = ['matricula_ou_siape','tipo_vinculo_usuario','curso']"
      ],
      "metadata": {
        "id": "_SaNdUmEOZw4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_depois_2010.columns= ['matricula_ou_siape','tipo_vinculo_usuario','curso']"
      ],
      "metadata": {
        "id": "U4P51c8oO1B0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_excel = pd.concat([cadastro_usuarios_antes_2010,cadastro_usuarios_depois_2010],ignore_index=True)\n",
        "cadastro_usuarios_excel.matricula_ou_siape = cadastro_usuarios_excel.matricula_ou_siape.astype('string')\n",
        "cadastro_usuarios_excel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jbg-OlOhO9Kx",
        "outputId": "c4a9d039-a28b-4b3d-8528-37224531e10f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     matricula_ou_siape tipo_vinculo_usuario                     curso\n",
              "0           190001688.0   ALUNO DE GRADUAÇÃO                MATEMÁTICA\n",
              "1           199014489.0   ALUNO DE GRADUAÇÃO       CIÊNCIAS BIOLÓGICAS\n",
              "2           199735674.0   ALUNO DE GRADUAÇÃO                MATEMÁTICA\n",
              "3           199812431.0   ALUNO DE GRADUAÇÃO                    LETRAS\n",
              "4           199823670.0   ALUNO DE GRADUAÇÃO                  ECOLOGIA\n",
              "...                 ...                  ...                       ...\n",
              "9995      20150116770.0   ALUNO DE GRADUAÇÃO                    LETRAS\n",
              "9996       2015074523.0   ALUNO DE GRADUAÇÃO                     DANÇA\n",
              "9997      20160002745.0   ALUNO DE GRADUAÇÃO  ENGENHARIA DA COMPUTAÇÃO\n",
              "9998      20170086533.0   ALUNO DE GRADUAÇÃO           BIBLIOTECONOMIA\n",
              "9999      20180090351.0   ALUNO DE GRADUAÇÃO                     DANÇA\n",
              "\n",
              "[10000 rows x 3 columns]"
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>matricula_ou_siape</th>\n",
              "      <th>tipo_vinculo_usuario</th>\n",
              "      <th>curso</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>190001688.0</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>MATEMÁTICA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>199014489.0</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>CIÊNCIAS BIOLÓGICAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>199735674.0</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>MATEMÁTICA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>199812431.0</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>LETRAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>199823670.0</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>ECOLOGIA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "      <th>9995</th>\n",
              "      <td>20150116770.0</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>LETRAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9996</th>\n",
              "      <td>2015074523.0</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>DANÇA</td>\n",
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              "    <tr>\n",
              "      <th>9997</th>\n",
              "      <td>20160002745.0</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>ENGENHARIA DA COMPUTAÇÃO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9998</th>\n",
              "      <td>20170086533.0</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>BIBLIOTECONOMIA</td>\n",
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              "    <tr>\n",
              "      <th>9999</th>\n",
              "      <td>20180090351.0</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>DANÇA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10000 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0ae3da6e-365f-4e92-bdbe-4f56895c107f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
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              "\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0ae3da6e-365f-4e92-bdbe-4f56895c107f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 75
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Trabalhando com o Arquivo JSON"
      ],
      "metadata": {
        "id": "p896IJdRRsev"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_json = pd.read_json('https://github.com/FranciscoFoz/7_Days_of_Code_Alura-Python-Pandas/raw/main/Dia_6-Novos_dados_novas_analises/Datasets/cadastro_alunos.json') #puxando o arquivo para dentro do colab\n",
        "cadastro_usuarios_json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "ZvWVpkozRoSS",
        "outputId": "fad17c90-70e8-4efa-abd1-8d0fef39826a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     tipo_curso                                          registros\n",
              "0     GRADUACAO  [{\"matricula_ou_siape\": \"2009029003.0\", \"tipo_...\n",
              "1  POSGRADUACAO  [{\"matricula_ou_siape\": \"2008112114.0\", \"tipo_..."
            ],
            "text/html": [
              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>tipo_curso</th>\n",
              "      <th>registros</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>GRADUACAO</td>\n",
              "      <td>[{\"matricula_ou_siape\": \"2009029003.0\", \"tipo_...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>POSGRADUACAO</td>\n",
              "      <td>[{\"matricula_ou_siape\": \"2008112114.0\", \"tipo_...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_graduacao_json = pd.read_json(cadastro_usuarios_json.registros[0]) #Identificando a variavel para trabalhar com o arquivo"
      ],
      "metadata": {
        "id": "SBUKkEzJRzfH"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_graduacao_json"
      ],
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        "colab": {
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          "height": 424
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        "id": "jOl5JKxaR0QV",
        "outputId": "531c3eb1-125a-4e95-aef6-4cc37c6a5424"
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              "       matricula_ou_siape tipo_vinculo_usuario                   curso\n",
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              "                                                     [key], {});\n",
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          "metadata": {},
          "execution_count": 78
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dados Matriculas\n",
        "\n",
        "Modificando para poder ter o formato correto para leitura e organização posterior"
      ],
      "metadata": {
        "id": "ewS1VBEAULXv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_graduacao_json.matricula_ou_siape = cadastro_usuarios_graduacao_json.matricula_ou_siape.astype('float')\n",
        "cadastro_usuarios_graduacao_json.matricula_ou_siape = cadastro_usuarios_graduacao_json.matricula_ou_siape.astype('string')"
      ],
      "metadata": {
        "id": "tabo4xw0T8h3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Concatenando os dados (Excell e Json)"
      ],
      "metadata": {
        "id": "Jog3oYXpUjJf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_cursos = pd.concat([cadastro_usuarios_excel,cadastro_usuarios_graduacao_json], ignore_index = True)\n",
        "cadastro_usuarios_cursos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ka4YnGuiUgIQ",
        "outputId": "84bd3d8f-b306-48f6-b933-7ed193423532"
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      "execution_count": null,
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              "      matricula_ou_siape tipo_vinculo_usuario                 curso\n",
              "0            190001688.0   ALUNO DE GRADUAÇÃO            MATEMÁTICA\n",
              "1            199014489.0   ALUNO DE GRADUAÇÃO   CIÊNCIAS BIOLÓGICAS\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Definindo os emprestimos a partir de 2015"
      ],
      "metadata": {
        "id": "I7QC2cibU1mw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matricula_data_de_emprestimo = emprestimos_completo.query(\"tipo_vinculo_usuario == 'ALUNO DE GRADUAÇÃO'\")\n",
        "matricula_data_de_emprestimo = matricula_data_de_emprestimo.loc[:,['matricula_ou_siape','data_emprestimo']]\n",
        "matricula_data_de_emprestimo = matricula_data_de_emprestimo.query('data_emprestimo > 2015')\n",
        "matricula_data_de_emprestimo = matricula_data_de_emprestimo.reset_index(drop=True)"
      ],
      "metadata": {
        "id": "JzZP6fj6UxuW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "matricula_data_de_emprestimo"
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              "                                                     [key], {});\n",
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              "\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matricula_data_de_emprestimo.isna().sum() #limpando as matriculas que possuem valores nulos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ruNFh6yVAtj",
        "outputId": "99fb338a-35a1-4115-ef55-c33641dc6726"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "matricula_ou_siape    94\n",
              "data_emprestimo        0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "matricula_data_de_emprestimo = matricula_data_de_emprestimo.dropna()"
      ],
      "metadata": {
        "id": "dsIbjuj5VSP0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_cursos_selecionados = cadastro_usuarios_cursos.query(\"curso == ['BIBLIOTECONOMIA','CIÊNCIAS SOCIAIS','COMUNICAÇÃO SOCIAL','DIREITO','FILOSOFIA','PEDAGOGIA']\") #selecionando apenas os cursos requisitados"
      ],
      "metadata": {
        "id": "92nGkWuQVoWo"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_cursos_selecionados"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "BeadslbHVt62",
        "outputId": "555e6fc9-54f0-4d60-a030-a678843928eb"
      },
      "execution_count": null,
      "outputs": [
        {
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            "text/plain": [
              "      matricula_ou_siape tipo_vinculo_usuario             curso\n",
              "8            200031011.0   ALUNO DE GRADUAÇÃO           DIREITO\n",
              "9            200045080.0   ALUNO DE GRADUAÇÃO   BIBLIOTECONOMIA\n",
              "15           200115053.0   ALUNO DE GRADUAÇÃO  CIÊNCIAS SOCIAIS\n",
              "18           200123382.0   ALUNO DE GRADUAÇÃO   BIBLIOTECONOMIA\n",
              "19           200123730.0   ALUNO DE GRADUAÇÃO   BIBLIOTECONOMIA\n",
              "...                  ...                  ...               ...\n",
              "72789      20200097481.0   ALUNO DE GRADUAÇÃO         PEDAGOGIA\n",
              "72796      20200107087.0   ALUNO DE GRADUAÇÃO         PEDAGOGIA\n",
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              "72800      20200009344.0   ALUNO DE GRADUAÇÃO   BIBLIOTECONOMIA\n",
              "72801      20200090284.0   ALUNO DE GRADUAÇÃO           DIREITO\n",
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              "      <th>9</th>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-53d7795e-56b4-43d6-8225-3cb4b2d3b6ec');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_cursos_selecionados = matricula_data_de_emprestimo.merge(cadastro_usuarios_cursos_selecionados) #unindo os dataframes de cadastrado de usuários e de empréstimos"
      ],
      "metadata": {
        "id": "Kw7P7zduVujF"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_cursos_selecionados.data_emprestimo = cadastro_usuarios_cursos_selecionados.data_emprestimo.dt.year"
      ],
      "metadata": {
        "id": "BGU4Fa9vV-xV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_cursos_selecionados = cadastro_usuarios_cursos_selecionados.iloc[:,[1,3]].value_counts().reset_index()\n",
        "emprestimos_cursos_selecionados.columns = ['ANO','CURSO','QUANTIDADE_EMPRESTIMOS']"
      ],
      "metadata": {
        "id": "uZLyRyt9WITa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
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        "emprestimos_cursos_selecionados"
      ],
      "metadata": {
        "id": "Nu-13oubWJyp",
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          "height": 1000
        },
        "outputId": "11ee2f3d-a522-4648-f067-1aa2b3882fcb"
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      "execution_count": null,
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            "text/plain": [
              "     ANO               CURSO  QUANTIDADE_EMPRESTIMOS\n",
              "0   2017  COMUNICAÇÃO SOCIAL                    8253\n",
              "1   2016  COMUNICAÇÃO SOCIAL                    7973\n",
              "2   2017             DIREITO                    7885\n",
              "3   2015     BIBLIOTECONOMIA                    7784\n",
              "4   2015             DIREITO                    7665\n",
              "5   2016           FILOSOFIA                    7576\n",
              "6   2015    CIÊNCIAS SOCIAIS                    7489\n",
              "7   2017           PEDAGOGIA                    7434\n",
              "8   2017           FILOSOFIA                    7389\n",
              "9   2016             DIREITO                    7363\n",
              "10  2017     BIBLIOTECONOMIA                    7307\n",
              "11  2015           PEDAGOGIA                    7189\n",
              "12  2016           PEDAGOGIA                    7153\n",
              "13  2015  COMUNICAÇÃO SOCIAL                    7147\n",
              "14  2016     BIBLIOTECONOMIA                    7044\n",
              "15  2018             DIREITO                    7027\n",
              "16  2016    CIÊNCIAS SOCIAIS                    6978\n",
              "17  2018  COMUNICAÇÃO SOCIAL                    6953\n",
              "18  2015           FILOSOFIA                    6950\n",
              "19  2017    CIÊNCIAS SOCIAIS                    6918\n",
              "20  2018           PEDAGOGIA                    6518\n",
              "21  2018     BIBLIOTECONOMIA                    6378\n",
              "22  2018           FILOSOFIA                    6296\n",
              "23  2018    CIÊNCIAS SOCIAIS                    6181\n",
              "24  2019     BIBLIOTECONOMIA                    5889\n",
              "25  2019  COMUNICAÇÃO SOCIAL                    5885\n",
              "26  2019           PEDAGOGIA                    5869\n",
              "27  2019             DIREITO                    5861\n",
              "28  2019    CIÊNCIAS SOCIAIS                    5788\n",
              "29  2019           FILOSOFIA                    5572\n",
              "30  2020             DIREITO                     905\n",
              "31  2020     BIBLIOTECONOMIA                     900\n",
              "32  2020           PEDAGOGIA                     866\n",
              "33  2020           FILOSOFIA                     832\n",
              "34  2020    CIÊNCIAS SOCIAIS                     825\n",
              "35  2020  COMUNICAÇÃO SOCIAL                     796"
            ],
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              "      <th>15</th>\n",
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              "      <th>16</th>\n",
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              "      <th>21</th>\n",
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              "      <td>BIBLIOTECONOMIA</td>\n",
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              "      <th>22</th>\n",
              "      <td>2018</td>\n",
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              "      <th>23</th>\n",
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              "      <td>CIÊNCIAS SOCIAIS</td>\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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          },
          "metadata": {},
          "execution_count": 90
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Criando a tabela pivotada"
      ],
      "metadata": {
        "id": "_GkYczhSWP-Y"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_tipo_usuario_curso_pivot = emprestimos_cursos_selecionados.pivot_table(\n",
        "        index = 'CURSO',\n",
        "        columns = 'ANO',\n",
        "        values = 'QUANTIDADE_EMPRESTIMOS',\n",
        "        fill_value = '-',\n",
        "        aggfunc= sum,\n",
        "        margins = True,\n",
        "        margins_name = 'TOTAL',\n",
        ")\n",
        "emprestimos_tipo_usuario_curso_pivot"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "gyHECbgQWRvO",
        "outputId": "d58c5bed-b84d-4040-b61a-fef92fa2500e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ANO                  2015   2016   2017   2018   2019  2020   TOTAL\n",
              "CURSO                                                              \n",
              "BIBLIOTECONOMIA      7784   7044   7307   6378   5889   900   35302\n",
              "CIÊNCIAS SOCIAIS     7489   6978   6918   6181   5788   825   34179\n",
              "COMUNICAÇÃO SOCIAL   7147   7973   8253   6953   5885   796   37007\n",
              "DIREITO              7665   7363   7885   7027   5861   905   36706\n",
              "FILOSOFIA            6950   7576   7389   6296   5572   832   34615\n",
              "PEDAGOGIA            7189   7153   7434   6518   5869   866   35029\n",
              "TOTAL               44224  44087  45186  39353  34864  5124  212838"
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              "      <th>CIÊNCIAS SOCIAIS</th>\n",
              "      <td>7489</td>\n",
              "      <td>6978</td>\n",
              "      <td>6918</td>\n",
              "      <td>6181</td>\n",
              "      <td>5788</td>\n",
              "      <td>825</td>\n",
              "      <td>34179</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>COMUNICAÇÃO SOCIAL</th>\n",
              "      <td>7147</td>\n",
              "      <td>7973</td>\n",
              "      <td>8253</td>\n",
              "      <td>6953</td>\n",
              "      <td>5885</td>\n",
              "      <td>796</td>\n",
              "      <td>37007</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DIREITO</th>\n",
              "      <td>7665</td>\n",
              "      <td>7363</td>\n",
              "      <td>7885</td>\n",
              "      <td>7027</td>\n",
              "      <td>5861</td>\n",
              "      <td>905</td>\n",
              "      <td>36706</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FILOSOFIA</th>\n",
              "      <td>6950</td>\n",
              "      <td>7576</td>\n",
              "      <td>7389</td>\n",
              "      <td>6296</td>\n",
              "      <td>5572</td>\n",
              "      <td>832</td>\n",
              "      <td>34615</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PEDAGOGIA</th>\n",
              "      <td>7189</td>\n",
              "      <td>7153</td>\n",
              "      <td>7434</td>\n",
              "      <td>6518</td>\n",
              "      <td>5869</td>\n",
              "      <td>866</td>\n",
              "      <td>35029</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TOTAL</th>\n",
              "      <td>44224</td>\n",
              "      <td>44087</td>\n",
              "      <td>45186</td>\n",
              "      <td>39353</td>\n",
              "      <td>34864</td>\n",
              "      <td>5124</td>\n",
              "      <td>212838</td>\n",
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              "  "
            ]
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          "metadata": {},
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EZLKjjZ9XKgC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Setimo Dia - Finaizando o projeto\n",
        "\n",
        "1) A diretoria da biblioteca está montando um plano de ação de marketing focado nos alunos da pós-graduação. Para isso, precisará analisar a diferença percentual de empréstimos realizados nos últimos anos (2017, 2018, 2019) para cada curso.\n",
        "\n",
        "2) Devido à pandemia do COVID-19, ela não poderá utilizar os dados de 2020 e 2021. Entretanto, seu colega de equipe já realizou uma análise de previsão de empréstimos de 2022 e a disponibilizou para que você também possa realizar o comparativo entre 2019-2022.\n",
        "\n",
        "3) Você precisará criar uma tabela com as diferenças percentuais de empréstimos entre 2017-2018, 2018-2019, 2019-2022.\n",
        "\n",
        "4) Porém, essa análise será disponibilizada em conjunto com outros dados, através de uma página da web, e a equipe de Front-end te solicitou que enviassem para eles o HTML da tabela."
      ],
      "metadata": {
        "id": "VjP0V_Vq_waO"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Importar o Banco de dados (Excell)"
      ],
      "metadata": {
        "id": "GnCgYQV_BmL3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_antes_2010 = pd.read_excel('https://github.com/FranciscoFoz/7_Days_of_Code_Alura-Python-Pandas/raw/main/Dia_6-Novos_dados_novas_analises/Datasets/matricula_alunos.xlsx',\n",
        "                                        sheet_name='Até 2010',skiprows=1)\n",
        "cadastro_usuarios_depois_2010 = pd.read_excel('https://github.com/FranciscoFoz/7_Days_of_Code_Alura-Python-Pandas/raw/main/Dia_6-Novos_dados_novas_analises/Datasets/matricula_alunos.xlsx',\n",
        "                                        sheet_name='Após 2010',skiprows=1)"
      ],
      "metadata": {
        "id": "8Euncx9pAwEs"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_antes_2010 #verificar o que consta no banco"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "ZANooCdIBy-E",
        "outputId": "011d3695-2306-4836-efef-6206225b734d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Número da matrícula     Tipo de vinculo  \\\n",
              "0            1.900017e+08  ALUNO DE GRADUAÇÃO   \n",
              "1            1.990145e+08  ALUNO DE GRADUAÇÃO   \n",
              "2            1.997357e+08  ALUNO DE GRADUAÇÃO   \n",
              "3            1.998124e+08  ALUNO DE GRADUAÇÃO   \n",
              "4            1.998237e+08  ALUNO DE GRADUAÇÃO   \n",
              "...                   ...                 ...   \n",
              "7721         2.009075e+09  ALUNO DE GRADUAÇÃO   \n",
              "7722         2.009075e+09  ALUNO DE GRADUAÇÃO   \n",
              "7723         2.009075e+09  ALUNO DE GRADUAÇÃO   \n",
              "7724         2.009075e+09  ALUNO DE GRADUAÇÃO   \n",
              "7725         2.009075e+09  ALUNO DE GRADUAÇÃO   \n",
              "\n",
              "                                      Curso  \n",
              "0                                MATEMÁTICA  \n",
              "1                       CIÊNCIAS BIOLÓGICAS  \n",
              "2                                MATEMÁTICA  \n",
              "3                                    LETRAS  \n",
              "4                                  ECOLOGIA  \n",
              "...                                     ...  \n",
              "7721                        BIBLIOTECONOMIA  \n",
              "7722  ANÁLISE E DESENVOLVIMENTO DE SISTEMAS  \n",
              "7723                          ADMINISTRAÇÃO  \n",
              "7724                                 MÚSICA  \n",
              "7725                                DIREITO  \n",
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              "      <td>1.990145e+08</td>\n",
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              "      <td>1.997357e+08</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_antes_2010.columns = ['matricula_ou_siape','tipo_vinculo_usuario','curso'] #renomear as colunas"
      ],
      "metadata": {
        "id": "pWaAycwQB65z"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_depois_2010"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "7Bw_UWZJB-20",
        "outputId": "25867fcf-d63e-46e3-901d-3bc113df5fc4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Matricula      Tipo de vinculo                    Cursos\n",
              "0     2.010000e+09  ALUNO DE GRADUAÇÃO                   DIREITO\n",
              "1     2.010000e+09  ALUNO DE GRADUAÇÃO                 AGRONOMIA\n",
              "2     2.010001e+09  ALUNO DE GRADUAÇÃO        ENGENHARIA QUÍMICA\n",
              "3     2.010003e+09  ALUNO DE GRADUAÇÃO      ENGENHARIA AMBIENTAL\n",
              "4     2.010003e+09  ALUNO DE GRADUAÇÃO                 GEOGRAFIA\n",
              "...            ...                 ...                       ...\n",
              "2269  2.015012e+10  ALUNO DE GRADUAÇÃO                    LETRAS\n",
              "2270  2.015075e+09  ALUNO DE GRADUAÇÃO                     DANÇA\n",
              "2271  2.016000e+10  ALUNO DE GRADUAÇÃO  ENGENHARIA DA COMPUTAÇÃO\n",
              "2272  2.017009e+10  ALUNO DE GRADUAÇÃO           BIBLIOTECONOMIA\n",
              "2273  2.018009e+10  ALUNO DE GRADUAÇÃO                     DANÇA\n",
              "\n",
              "[2274 rows x 3 columns]"
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              "      <th>Matricula</th>\n",
              "      <th>Tipo de vinculo</th>\n",
              "      <th>Cursos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2.010000e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>DIREITO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2.010000e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>AGRONOMIA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2.010001e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>ENGENHARIA QUÍMICA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2.010003e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>ENGENHARIA AMBIENTAL</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2.010003e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>GEOGRAFIA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2269</th>\n",
              "      <td>2.015012e+10</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>LETRAS</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2270</th>\n",
              "      <td>2.015075e+09</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>DANÇA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2271</th>\n",
              "      <td>2.016000e+10</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>ENGENHARIA DA COMPUTAÇÃO</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2272</th>\n",
              "      <td>2.017009e+10</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>BIBLIOTECONOMIA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2273</th>\n",
              "      <td>2.018009e+10</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>DANÇA</td>\n",
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              "<p>2274 rows × 3 columns</p>\n",
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    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_depois_2010.columns= ['matricula_ou_siape','tipo_vinculo_usuario','curso']"
      ],
      "metadata": {
        "id": "CuPdXK0uCA0H"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_excel = pd.concat([cadastro_usuarios_antes_2010,cadastro_usuarios_depois_2010],ignore_index=True)\n",
        "cadastro_usuarios_excel.matricula_ou_siape = cadastro_usuarios_excel.matricula_ou_siape.astype('string')\n",
        "cadastro_usuarios_excel"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "q-Hib9NaCG6b",
        "outputId": "92cf731e-cb06-4388-906c-525a55e8b79a"
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              "     matricula_ou_siape tipo_vinculo_usuario                     curso\n",
              "0           190001688.0   ALUNO DE GRADUAÇÃO                MATEMÁTICA\n",
              "1           199014489.0   ALUNO DE GRADUAÇÃO       CIÊNCIAS BIOLÓGICAS\n",
              "2           199735674.0   ALUNO DE GRADUAÇÃO                MATEMÁTICA\n",
              "3           199812431.0   ALUNO DE GRADUAÇÃO                    LETRAS\n",
              "4           199823670.0   ALUNO DE GRADUAÇÃO                  ECOLOGIA\n",
              "...                 ...                  ...                       ...\n",
              "9995      20150116770.0   ALUNO DE GRADUAÇÃO                    LETRAS\n",
              "9996       2015074523.0   ALUNO DE GRADUAÇÃO                     DANÇA\n",
              "9997      20160002745.0   ALUNO DE GRADUAÇÃO  ENGENHARIA DA COMPUTAÇÃO\n",
              "9998      20170086533.0   ALUNO DE GRADUAÇÃO           BIBLIOTECONOMIA\n",
              "9999      20180090351.0   ALUNO DE GRADUAÇÃO                     DANÇA\n",
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              "[10000 rows x 3 columns]"
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              "      <th>4</th>\n",
              "      <td>199823670.0</td>\n",
              "      <td>ALUNO DE GRADUAÇÃO</td>\n",
              "      <td>ECOLOGIA</td>\n",
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              "      <td>DANÇA</td>\n",
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    },
    {
      "cell_type": "markdown",
      "source": [
        "Importando Arquivos(JSON)"
      ],
      "metadata": {
        "id": "5NYWbu6KCP3X"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_json = pd.read_json('https://github.com/FranciscoFoz/7_Days_of_Code_Alura-Python-Pandas/raw/main/Dia_6-Novos_dados_novas_analises/Datasets/cadastro_alunos.json')\n",
        "cadastro_usuarios_json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "DH3z2t63CJd_",
        "outputId": "1176522d-41cd-45c9-d358-f4020f6af6fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     tipo_curso                                          registros\n",
              "0     GRADUACAO  [{\"matricula_ou_siape\": \"2009029003.0\", \"tipo_...\n",
              "1  POSGRADUACAO  [{\"matricula_ou_siape\": \"2008112114.0\", \"tipo_..."
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "\n",
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              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-e5c5bb2b-2ece-42ca-a42e-f6562e6719ee');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_posgraduacao_json = pd.read_json(cadastro_usuarios_json.registros[1])"
      ],
      "metadata": {
        "id": "fH1DqgnpCVX4"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_posgraduacao_json.info() #analisar formato dos itens do banco de dados json"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dHdFuaDGCXxh",
        "outputId": "10cdffb1-af2c-4058-dec1-6d6909528582"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19440 entries, 0 to 19439\n",
            "Data columns (total 3 columns):\n",
            " #   Column                Non-Null Count  Dtype \n",
            "---  ------                --------------  ----- \n",
            " 0   matricula_ou_siape    19440 non-null  int64 \n",
            " 1   tipo_vinculo_usuario  19440 non-null  object\n",
            " 2   curso                 19440 non-null  object\n",
            "dtypes: int64(1), object(2)\n",
            "memory usage: 455.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Modificando os tipos do dado de matricula"
      ],
      "metadata": {
        "id": "gz7o4dqSCivn"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_posgraduacao_json.matricula_ou_siape = cadastro_usuarios_posgraduacao_json.matricula_ou_siape.astype('float')\n",
        "cadastro_usuarios_posgraduacao_json.matricula_ou_siape = cadastro_usuarios_posgraduacao_json.matricula_ou_siape.astype('string')"
      ],
      "metadata": {
        "id": "5-WupEi8Ce_0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_posgraduacao_json.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BQRrPt3ZC1Hk",
        "outputId": "b5d570e8-7f0f-4d54-cb57-9bc52112c33d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 19440 entries, 0 to 19439\n",
            "Data columns (total 3 columns):\n",
            " #   Column                Non-Null Count  Dtype \n",
            "---  ------                --------------  ----- \n",
            " 0   matricula_ou_siape    19440 non-null  string\n",
            " 1   tipo_vinculo_usuario  19440 non-null  object\n",
            " 2   curso                 19440 non-null  object\n",
            "dtypes: object(2), string(1)\n",
            "memory usage: 455.8+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Conctenando os bancos de dados"
      ],
      "metadata": {
        "id": "L8K03H08DBsQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_cursos = pd.concat([cadastro_usuarios_excel,cadastro_usuarios_posgraduacao_json],ignore_index=True)\n",
        "cadastro_usuarios_cursos"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "rvgpU1LNC3r0",
        "outputId": "b7d4180c-1f8d-4f64-dc08-0a0ea381bcc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "      matricula_ou_siape    tipo_vinculo_usuario  \\\n",
              "0            190001688.0      ALUNO DE GRADUAÇÃO   \n",
              "1            199014489.0      ALUNO DE GRADUAÇÃO   \n",
              "2            199735674.0      ALUNO DE GRADUAÇÃO   \n",
              "3            199812431.0      ALUNO DE GRADUAÇÃO   \n",
              "4            199823670.0      ALUNO DE GRADUAÇÃO   \n",
              "...                  ...                     ...   \n",
              "29435      20201013501.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "29436      20201014062.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "29437      20201017690.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "29438      20201014465.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "29439      20201014993.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "\n",
              "                                        curso  \n",
              "0                                  MATEMÁTICA  \n",
              "1                         CIÊNCIAS BIOLÓGICAS  \n",
              "2                                  MATEMÁTICA  \n",
              "3                                      LETRAS  \n",
              "4                                    ECOLOGIA  \n",
              "...                                       ...  \n",
              "29435  GESTÃO DA INFORMAÇÃO E DO CONHECIMENTO  \n",
              "29436          CIÊNCIA, TECNOLOGIA E INOVAÇÃO  \n",
              "29437                       CIÊNCIAS DA SAÚDE  \n",
              "29438                                  DESIGN  \n",
              "29439                                  DESIGN  \n",
              "\n",
              "[29440 rows x 3 columns]"
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              "      <td>20201014062.0</td>\n",
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              "      <td>20201017690.0</td>\n",
              "      <td>ALUNO DE PÓS-GRADUAÇÃO</td>\n",
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              "      <td>ALUNO DE PÓS-GRADUAÇÃO</td>\n",
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              "    <tr>\n",
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              "      <td>ALUNO DE PÓS-GRADUAÇÃO</td>\n",
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              "                                                     [key], {});\n",
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              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cadastro_usuarios_cursos_pos_graduacao = cadastro_usuarios_posgraduacao_json.query(\"tipo_vinculo_usuario == 'ALUNO DE PÓS-GRADUAÇÃO'\")\n",
        "cadastro_usuarios_cursos_pos_graduacao"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "MutqNBlDDGgs",
        "outputId": "2e518648-a3f9-4a3b-e314-71fe648500ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      matricula_ou_siape    tipo_vinculo_usuario  \\\n",
              "0           2008112114.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "1           2008106583.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "2           2011101417.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "3           2011113122.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "4           2010110903.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "...                  ...                     ...   \n",
              "19435      20201013501.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "19436      20201014062.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "19437      20201017690.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "19438      20201014465.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "19439      20201014993.0  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "\n",
              "                                        curso  \n",
              "0                      CIÊNCIAS ODONTOLÓGICAS  \n",
              "1                      ENGENHARIA DE SOFTWARE  \n",
              "2                     ARQUITETURA E URBANISMO  \n",
              "3                                   FILOSOFIA  \n",
              "4                               ARTES CÊNICAS  \n",
              "...                                       ...  \n",
              "19435  GESTÃO DA INFORMAÇÃO E DO CONHECIMENTO  \n",
              "19436          CIÊNCIA, TECNOLOGIA E INOVAÇÃO  \n",
              "19437                       CIÊNCIAS DA SAÚDE  \n",
              "19438                                  DESIGN  \n",
              "19439                                  DESIGN  \n",
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              "[19440 rows x 3 columns]"
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    {
      "cell_type": "markdown",
      "source": [
        "Selecionando emprestismos (2017)"
      ],
      "metadata": {
        "id": "EeaCbuGHDTAp"
      }
    },
    {
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      "source": [
        "matricula_data_de_emprestimo_pos_graduacao = emprestimos_completo.loc[:,['matricula_ou_siape','data_emprestimo']]\n",
        "matricula_data_de_emprestimo_pos_graduacao = matricula_data_de_emprestimo_pos_graduacao.query('data_emprestimo > 2017')\n",
        "matricula_data_de_emprestimo_pos_graduacao = matricula_data_de_emprestimo_pos_graduacao.reset_index(drop=True)\n",
        "matricula_data_de_emprestimo_pos_graduacao"
      ],
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    {
      "cell_type": "code",
      "source": [
        "emprestimos_pos_graduacao_desde_2017 = matricula_data_de_emprestimo_pos_graduacao.merge(cadastro_usuarios_cursos_pos_graduacao)\n",
        "emprestimos_pos_graduacao_desde_2017"
      ],
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        "id": "x8qmscFgDWT0",
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      matricula_ou_siape         data_emprestimo    tipo_vinculo_usuario  \\\n",
              "0          20171029059.0 2017-03-28 09:04:09.781  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "1          20171029059.0 2017-05-09 15:07:27.165  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "2          20171029059.0 2017-06-12 08:37:05.048  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "3          20171029059.0 2018-03-19 10:10:03.552  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "4          20171029059.0 2018-08-24 19:36:53.956  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "...                  ...                     ...                     ...   \n",
              "92057      20201006310.0 2020-03-12 13:45:36.288  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "92058      20201006310.0 2020-03-12 13:45:36.326  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "92059      20191006490.0 2020-03-12 16:10:31.448  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "92060      20201011795.0 2020-03-16 12:24:44.550  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "92061      20201004360.0 2020-03-17 12:34:19.327  ALUNO DE PÓS-GRADUAÇÃO   \n",
              "\n",
              "                                        curso  \n",
              "0                                   FILOSOFIA  \n",
              "1                                   FILOSOFIA  \n",
              "2                                   FILOSOFIA  \n",
              "3                                   FILOSOFIA  \n",
              "4                                   FILOSOFIA  \n",
              "...                                       ...  \n",
              "92057                 ARQUITETURA E URBANISMO  \n",
              "92058                 ARQUITETURA E URBANISMO  \n",
              "92059                 ARQUITETURA E URBANISMO  \n",
              "92060  GESTÃO DA INFORMAÇÃO E DO CONHECIMENTO  \n",
              "92061                  CIÊNCIAS ODONTOLÓGICAS  \n",
              "\n",
              "[92062 rows x 4 columns]"
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              "      <td>2020-03-17 12:34:19.327</td>\n",
              "      <td>ALUNO DE PÓS-GRADUAÇÃO</td>\n",
              "      <td>CIÊNCIAS ODONTOLÓGICAS</td>\n",
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    {
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      "source": [
        "emprestimos_pos_graduacao_desde_2017.data_emprestimo = emprestimos_pos_graduacao_desde_2017.data_emprestimo.dt.year #modificando o tipo de data"
      ],
      "metadata": {
        "id": "6TWZy8YXEHZI"
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      "execution_count": null,
      "outputs": []
    },
    {
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      "source": [
        "emprestimos_pos_graduacao_desde_2017 = emprestimos_pos_graduacao_desde_2017.iloc[:,[1,3]].value_counts().reset_index() #fazendo a contagem dos emprestimos de 2017\n",
        "emprestimos_pos_graduacao_desde_2017.columns = ['ANO','CURSO','QUANTIDADE_EMPRESTIMOS']\n",
        "emprestimos_pos_graduacao_desde_2017.head()"
      ],
      "metadata": {
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              "    ANO                           CURSO  QUANTIDADE_EMPRESTIMOS\n",
              "0  2018          CIÊNCIAS ODONTOLÓGICAS                    2540\n",
              "1  2017                   ADMINISTRAÇÃO                    2538\n",
              "2  2017          ENGENHARIA DE SOFTWARE                    2477\n",
              "3  2017               CIÊNCIAS DA SAÚDE                    2434\n",
              "4  2017  CIÊNCIA, TECNOLOGIA E INOVAÇÃO                    2415"
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    {
      "cell_type": "markdown",
      "source": [
        "Pivotando a tabela"
      ],
      "metadata": {
        "id": "pBB3uJ79EUhO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_pos_graduacao_e_curso_pivot = emprestimos_pos_graduacao_desde_2017.pivot_table(\n",
        "        index = 'CURSO',\n",
        "        columns = 'ANO',\n",
        "        values = 'QUANTIDADE_EMPRESTIMOS'\n",
        ")\n",
        "emprestimos_pos_graduacao_e_curso_pivot #formato de exibição da tabela"
      ],
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          "height": 520
        },
        "id": "3cgdmSuyERne",
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              "ANO                                     2017  2018  2019  2020\n",
              "CURSO                                                         \n",
              "ADMINISTRAÇÃO                           2538  2316  1692   269\n",
              "ARQUITETURA E URBANISMO                 2209  1917  1818   257\n",
              "ARTES CÊNICAS                           2115  2082  1812   229\n",
              "BIOINFORMÁTICA                          2190  1914  1825   263\n",
              "BIOQUÍMICA                              2294  1944  2057   272\n",
              "CIÊNCIA E ENGENHARIA DE MATERIAIS       2316  2110  2008   328\n",
              "CIÊNCIA, TECNOLOGIA E INOVAÇÃO          2415  2365  2026   303\n",
              "CIÊNCIAS DA SAÚDE                       2434  1986  1825   234\n",
              "CIÊNCIAS ODONTOLÓGICAS                  2414  2540  2134   352\n",
              "DESIGN                                  2272  1838  1841   254\n",
              "ENGENHARIA CIVIL                        2399  2089  1989   254\n",
              "ENGENHARIA DE SOFTWARE                  2477  2222  1887   264\n",
              "FILOSOFIA                               2170  2048  1773   302\n",
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              "  "
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          },
          "metadata": {},
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        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dados de Previsão:\n",
        "\n",
        "- Importando;\n",
        "- Tratando;\n",
        "- Unindo tabelas."
      ],
      "metadata": {
        "id": "BeXq1FS0EvNZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "previsao_2022 = pd.read_table('https://github.com/FranciscoFoz/7_Days_of_Code_Alura-Python-Pandas/raw/main/Dia_7-Apresentando_resultados_em_HTML/Dataset/previsao')"
      ],
      "metadata": {
        "id": "EqUFqebaEkPO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "previsao_2022 = previsao_2022['curso previsao_2022'].str.split(' ',expand=True)\n",
        "previsao_2022"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "gN-xX7OrFC1p",
        "outputId": "b70f8a6f-9d96-4b37-acb8-c552772fc5c2"
      },
      "execution_count": null,
      "outputs": [
        {
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              "                                         0     1\n",
              "0                            administracao  1905\n",
              "1                  arquitetura_e_urbanismo  1896\n",
              "2                            artes_cenicas  1920\n",
              "3                           bioinformatica  2302\n",
              "4                               bioquimica  2090\n",
              "5        ciencia_e_engenharia_de_materiais  2010\n",
              "6            ciencia_tecnologia_e_inovacao  1960\n",
              "7                        ciencias_da_saude  1730\n",
              "8                   ciencias_odontologicas  2405\n",
              "9                                   design  1900\n",
              "10                        engenharia_civil  1920\n",
              "11                     engenharia_software  1890\n",
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        "id": "9UTZzMahHqd6",
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      "execution_count": null,
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            "text/plain": [
              "ANO                                     2017  2018  2019  2020\n",
              "CURSO                                                         \n",
              "ADMINISTRAÇÃO                           2538  2316  1692   269\n",
              "ARQUITETURA E URBANISMO                 2209  1917  1818   257\n",
              "ARTES CÊNICAS                           2115  2082  1812   229\n",
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              "BIOQUÍMICA                              2294  1944  2057   272\n",
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              "CIÊNCIAS ODONTOLÓGICAS                  2414  2540  2134   352\n",
              "DESIGN                                  2272  1838  1841   254\n",
              "ENGENHARIA CIVIL                        2399  2089  1989   254\n",
              "ENGENHARIA DE SOFTWARE                  2477  2222  1887   264\n",
              "FILOSOFIA                               2170  2048  1773   302\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "previsao_2022.index = emprestimos_pos_graduacao_e_curso_pivot.index #unindo as tabelas\n",
        "emprestimos_pos_graduacao_e_curso_pivot['2022'] = previsao_2022.iloc[:,1]\n",
        "emprestimos_pos_graduacao_e_curso_pivot"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "-CuX5lMDHtUi",
        "outputId": "387270df-1139-4b25-c242-62e7effc5263"
      },
      "execution_count": null,
      "outputs": [
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              "ANO                                     2017  2018  2019  2020  2022\n",
              "CURSO                                                               \n",
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              "ARQUITETURA E URBANISMO                 2209  1917  1818   257  1896\n",
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              "DESIGN                                  2272  1838  1841   254  1900\n",
              "ENGENHARIA CIVIL                        2399  2089  1989   254  1920\n",
              "ENGENHARIA DE SOFTWARE                  2477  2222  1887   264  1890\n",
              "FILOSOFIA                               2170  2048  1773   302  1903\n",
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              "      <td>1903</td>\n",
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              "      <td>2266</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "emprestimos_pos_graduacao_e_curso_pivot['2022'] = emprestimos_pos_graduacao_e_curso_pivot['2022'].astype('int') #modificando os dados para \"int\" para exibir melhor na tabela"
      ],
      "metadata": {
        "id": "NuvgH6KbHxeS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Pivotando a tabela dos emprestismos dos cursos e graduação\n"
      ],
      "metadata": {
        "id": "jAGESZ6mIMug"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def diferenca_percentual_ano_anterior(x,y): \n",
        "  return round(((x / y * 100) - 100),2) #função para diferença em percentual"
      ],
      "metadata": {
        "id": "MMTm5REMIFvY"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculando a diferença percentual"
      ],
      "metadata": {
        "id": "x9nx30VtJswM"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "percentual_2018 = diferenca_percentual_ano_anterior(emprestimos_pos_graduacao_e_curso_pivot.iloc[:,1],emprestimos_pos_graduacao_e_curso_pivot.iloc[:,0])\n",
        "percentual_2019 = diferenca_percentual_ano_anterior(emprestimos_pos_graduacao_e_curso_pivot.iloc[:,2],emprestimos_pos_graduacao_e_curso_pivot.iloc[:,1])\n",
        "percentual_2022 = diferenca_percentual_ano_anterior(emprestimos_pos_graduacao_e_curso_pivot.iloc[:,3],emprestimos_pos_graduacao_e_curso_pivot.iloc[:,2])"
      ],
      "metadata": {
        "id": "q5-dl1_VJivi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "percentual = pd.DataFrame({'2018':percentual_2018,\n",
        "                           '2019':percentual_2019,\n",
        "                           '2022':percentual_2022})\n",
        "percentual"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 520
        },
        "id": "-KC14h9xJsVR",
        "outputId": "6f0f85f3-b510-4015-c84a-16080d0b5a69"
      },
      "execution_count": null,
      "outputs": [
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              "                                         2018   2019   2022\n",
              "CURSO                                                      \n",
              "ADMINISTRAÇÃO                           -8.75 -26.94 -84.10\n",
              "ARQUITETURA E URBANISMO                -13.22  -5.16 -85.86\n",
              "ARTES CÊNICAS                           -1.56 -12.97 -87.36\n",
              "BIOINFORMÁTICA                         -12.60  -4.65 -85.59\n",
              "BIOQUÍMICA                             -15.26   5.81 -86.78\n",
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              "CIÊNCIAS DA SAÚDE                      -18.41  -8.11 -87.18\n",
              "CIÊNCIAS ODONTOLÓGICAS                   5.22 -15.98 -83.51\n",
              "DESIGN                                 -19.10   0.16 -86.20\n",
              "ENGENHARIA CIVIL                       -12.92  -4.79 -87.23\n",
              "ENGENHARIA DE SOFTWARE                 -10.29 -15.08 -86.01\n",
              "FILOSOFIA                               -5.62 -13.43 -82.97\n",
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              "      <td>5.22</td>\n",
              "      <td>-15.98</td>\n",
              "      <td>-83.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DESIGN</th>\n",
              "      <td>-19.10</td>\n",
              "      <td>0.16</td>\n",
              "      <td>-86.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENGENHARIA CIVIL</th>\n",
              "      <td>-12.92</td>\n",
              "      <td>-4.79</td>\n",
              "      <td>-87.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ENGENHARIA DE SOFTWARE</th>\n",
              "      <td>-10.29</td>\n",
              "      <td>-15.08</td>\n",
              "      <td>-86.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>FILOSOFIA</th>\n",
              "      <td>-5.62</td>\n",
              "      <td>-13.43</td>\n",
              "      <td>-82.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GESTÃO DA INFORMAÇÃO E DO CONHECIMENTO</th>\n",
              "      <td>-16.55</td>\n",
              "      <td>-6.35</td>\n",
              "      <td>-85.77</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Formantando os dados para a tabela"
      ],
      "metadata": {
        "id": "VgtfNQz7J36R"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "percentual.reset_index(inplace=True)\n",
        "percentual.columns = percentual.columns.str.capitalize()\n",
        "percentual.Curso = percentual.Curso.str.capitalize()"
      ],
      "metadata": {
        "id": "P9swmn8jJ1vJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "percentual"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "jZXWkCclJ7qc",
        "outputId": "1210c873-7072-456a-bbac-bf4a1040fb2c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                     Curso   2018   2019   2022\n",
              "0                            Administração  -8.75 -26.94 -84.10\n",
              "1                  Arquitetura e urbanismo -13.22  -5.16 -85.86\n",
              "2                            Artes cênicas  -1.56 -12.97 -87.36\n",
              "3                           Bioinformática -12.60  -4.65 -85.59\n",
              "4                               Bioquímica -15.26   5.81 -86.78\n",
              "5        Ciência e engenharia de materiais  -8.89  -4.83 -83.67\n",
              "6           Ciência, tecnologia e inovação  -2.07 -14.33 -85.04\n",
              "7                        Ciências da saúde -18.41  -8.11 -87.18\n",
              "8                   Ciências odontológicas   5.22 -15.98 -83.51\n",
              "9                                   Design -19.10   0.16 -86.20\n",
              "10                        Engenharia civil -12.92  -4.79 -87.23\n",
              "11                  Engenharia de software -10.29 -15.08 -86.01\n",
              "12                               Filosofia  -5.62 -13.43 -82.97\n",
              "13  Gestão da informação e do conhecimento -16.55  -6.35 -85.77"
            ],
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              "      <th></th>\n",
              "      <th>Curso</th>\n",
              "      <th>2018</th>\n",
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              "      <th>2022</th>\n",
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              "      <th>0</th>\n",
              "      <td>Administração</td>\n",
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              "      <td>-26.94</td>\n",
              "      <td>-84.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Arquitetura e urbanismo</td>\n",
              "      <td>-13.22</td>\n",
              "      <td>-5.16</td>\n",
              "      <td>-85.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Artes cênicas</td>\n",
              "      <td>-1.56</td>\n",
              "      <td>-12.97</td>\n",
              "      <td>-87.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Bioinformática</td>\n",
              "      <td>-12.60</td>\n",
              "      <td>-4.65</td>\n",
              "      <td>-85.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Bioquímica</td>\n",
              "      <td>-15.26</td>\n",
              "      <td>5.81</td>\n",
              "      <td>-86.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Ciência e engenharia de materiais</td>\n",
              "      <td>-8.89</td>\n",
              "      <td>-4.83</td>\n",
              "      <td>-83.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Ciência, tecnologia e inovação</td>\n",
              "      <td>-2.07</td>\n",
              "      <td>-14.33</td>\n",
              "      <td>-85.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Ciências da saúde</td>\n",
              "      <td>-18.41</td>\n",
              "      <td>-8.11</td>\n",
              "      <td>-87.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Ciências odontológicas</td>\n",
              "      <td>5.22</td>\n",
              "      <td>-15.98</td>\n",
              "      <td>-83.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Design</td>\n",
              "      <td>-19.10</td>\n",
              "      <td>0.16</td>\n",
              "      <td>-86.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Engenharia civil</td>\n",
              "      <td>-12.92</td>\n",
              "      <td>-4.79</td>\n",
              "      <td>-87.23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Engenharia de software</td>\n",
              "      <td>-10.29</td>\n",
              "      <td>-15.08</td>\n",
              "      <td>-86.01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Filosofia</td>\n",
              "      <td>-5.62</td>\n",
              "      <td>-13.43</td>\n",
              "      <td>-82.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Gestão da informação e do conhecimento</td>\n",
              "      <td>-16.55</td>\n",
              "      <td>-6.35</td>\n",
              "      <td>-85.77</td>\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-7b18e7a1-296c-4467-ad53-db4d349f3290 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-7b18e7a1-296c-4467-ad53-db4d349f3290');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Preparando a tabela para o Front End (em HMTL)"
      ],
      "metadata": {
        "id": "buzhTyUHJ-uR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Outra Opção para parte Html"
      ],
      "metadata": {
        "id": "2l-DvznVSn9N"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "th_props = [\n",
        "  ('font-size', '1.4rem'),\n",
        "  ('text-align', 'center'),\n",
        "  ('font-weight', 'bold'),\n",
        "  ('color', 'whitesmoke'),\n",
        "  ('background-color', '#001692'),\n",
        "  ('border-radius', '0.25rem'),\n",
        "  ('box-shadow','0 0 1rem gray')\n",
        "]\n",
        "\n",
        "td_props = [\n",
        "  ('font-size', '1rem'),\n",
        "  ('padding','0.5rem'),\n",
        "  ('text-align', 'left'),\n",
        "  ('border-bottom','0.1rem solid lightgray')\n",
        "]\n",
        "\n",
        "green_color = '#4CAF50'\n",
        "red_color = '#F44336'\n",
        "\n",
        "def color_negative_red(val):\n",
        "  color = red_color if val < 0 else 'black'\n",
        "  return 'color: %s' % color\n",
        "\n",
        "def color_positive_green(val):\n",
        "  color = green_color if val > 0 else 'black'\n",
        "  return 'color: %s' % color\n",
        "\n",
        "styles = [\n",
        "  dict(selector=\"th\", props=th_props),\n",
        "  dict(selector=\"td\", props=td_props),\n",
        "  dict(selector=\"td[colspan='5']\", props=[('text-align', 'center'), ('font-weight', 'bold')]),\n",
        "  dict(selector=\".dataframe tr:first-child\", props=[('display', 'none')]),\n",
        "  dict(selector=\".dataframe th:first-child\", props=[('display', 'none')]),\n",
        "  dict(selector=\".dataframe td:first-child\", props=[('text-transform', 'capitalize')]),\n",
        "  dict(selector=\".dataframe td.negative\", props=[('font-weight', 'bold'), ('color', red_color)]),\n",
        "  dict(selector=\".dataframe td.positive\", props=[('font-weight', 'bold'), ('color', green_color)]),\n",
        "]"
      ],
      "metadata": {
        "id": "UyP7UtRDKMZV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "percentual.style.text_gradient(cmap='RdYlGn',low=1, axis=1,vmax=0.1,vmin=0)\\\n",
        "    .format('{:.2f} %',subset=['2018','2019','2022'])\\\n",
        "    .hide_index()\\\n",
        "    .set_table_styles(styles)\\\n",
        "    .applymap(color_negative_red, subset=['2018','2019','2022'])\\\n",
        "    .applymap(color_positive_green, subset=['2018','2019','2022'])\\\n",
        "    .render()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 105
        },
        "id": "gViLvTzjVSS4",
        "outputId": "73d00449-2824-4108-be8c-7cd561dea435"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
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            "text/plain": [
              "'<style type=\"text/css\">\\n#T_3d332_ th {\\n  font-size: 1.4rem;\\n  text-align: center;\\n  font-weight: bold;\\n  color: whitesmoke;\\n  background-color: #001692;\\n  border-radius: 0.25rem;\\n  box-shadow: 0 0 1rem gray;\\n}\\n#T_3d332_ td {\\n  font-size: 1rem;\\n  padding: 0.5rem;\\n  text-align: left;\\n  border-bottom: 0.1rem solid lightgray;\\n}\\n#T_3d332_ td[colspan=\\'5\\'] {\\n  text-align: center;\\n  font-weight: bold;\\n}\\n#T_3d332_ .dataframe tr:first-child {\\n  display: none;\\n}\\n#T_3d332_ .dataframe th:first-child {\\n  display: none;\\n}\\n#T_3d332_ .dataframe td:first-child {\\n  text-transform: capitalize;\\n}\\n#T_3d332_ .dataframe td.negative {\\n  font-weight: bold;\\n  color: #F44336;\\n}\\n#T_3d332_ .dataframe td.positive {\\n  font-weight: bold;\\n  color: #4CAF50;\\n}\\n#T_3d332_row0_col1, #T_3d332_row0_col2, #T_3d332_row0_col3, #T_3d332_row1_col1, #T_3d332_row1_col2, #T_3d332_row1_col3, #T_3d332_row2_col1, #T_3d332_row2_col2, #T_3d332_row2_col3, #T_3d332_row3_col1, #T_3d332_row3_col2, #T_3d332_row3_col3, #T_3d332_row4_col1, #T_3d332_row4_col3, #T_3d332_row5_col1, #T_3d332_row5_col2, #T_3d332_row5_col3, #T_3d332_row6_col1, #T_3d332_row6_col2, #T_3d332_row6_col3, #T_3d332_row7_col1, #T_3d332_row7_col2, #T_3d332_row7_col3, #T_3d332_row8_col2, #T_3d332_row8_col3, #T_3d332_row9_col1, #T_3d332_row9_col3, #T_3d332_row10_col1, #T_3d332_row10_col2, #T_3d332_row10_col3, #T_3d332_row11_col1, #T_3d332_row11_col2, #T_3d332_row11_col3, #T_3d332_row12_col1, #T_3d332_row12_col2, #T_3d332_row12_col3, #T_3d332_row13_col1, #T_3d332_row13_col2, #T_3d332_row13_col3 {\\n  color: #a50026;\\n  color: #F44336;\\n  color: black;\\n}\\n#T_3d332_row4_col2, #T_3d332_row8_col1, #T_3d332_row9_col2 {\\n  color: #006837;\\n  color: black;\\n  color: #4CAF50;\\n}\\n</style>\\n<table id=\"T_3d332_\">\\n  <thead>\\n    <tr>\\n      <th class=\"col_heading level0 col0\" >Curso</th>\\n      <th class=\"col_heading level0 col1\" >2018</th>\\n      <th class=\"col_heading level0 col2\" >2019</th>\\n      <th class=\"col_heading level0 col3\" >2022</th>\\n    </tr>\\n  </thead>\\n  <tbody>\\n    <tr>\\n      <td id=\"T_3d332_row0_col0\" class=\"data row0 col0\" >Administração</td>\\n      <td id=\"T_3d332_row0_col1\" class=\"data row0 col1\" >-8.75 %</td>\\n      <td id=\"T_3d332_row0_col2\" class=\"data row0 col2\" >-26.94 %</td>\\n      <td id=\"T_3d332_row0_col3\" class=\"data row0 col3\" >-84.10 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row1_col0\" class=\"data row1 col0\" >Arquitetura e urbanismo</td>\\n      <td id=\"T_3d332_row1_col1\" class=\"data row1 col1\" >-13.22 %</td>\\n      <td id=\"T_3d332_row1_col2\" class=\"data row1 col2\" >-5.16 %</td>\\n      <td id=\"T_3d332_row1_col3\" class=\"data row1 col3\" >-85.86 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row2_col0\" class=\"data row2 col0\" >Artes cênicas</td>\\n      <td id=\"T_3d332_row2_col1\" class=\"data row2 col1\" >-1.56 %</td>\\n      <td id=\"T_3d332_row2_col2\" class=\"data row2 col2\" >-12.97 %</td>\\n      <td id=\"T_3d332_row2_col3\" class=\"data row2 col3\" >-87.36 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row3_col0\" class=\"data row3 col0\" >Bioinformática</td>\\n      <td id=\"T_3d332_row3_col1\" class=\"data row3 col1\" >-12.60 %</td>\\n      <td id=\"T_3d332_row3_col2\" class=\"data row3 col2\" >-4.65 %</td>\\n      <td id=\"T_3d332_row3_col3\" class=\"data row3 col3\" >-85.59 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row4_col0\" class=\"data row4 col0\" >Bioquímica</td>\\n      <td id=\"T_3d332_row4_col1\" class=\"data row4 col1\" >-15.26 %</td>\\n      <td id=\"T_3d332_row4_col2\" class=\"data row4 col2\" >5.81 %</td>\\n      <td id=\"T_3d332_row4_col3\" class=\"data row4 col3\" >-86.78 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row5_col0\" class=\"data row5 col0\" >Ciência e engenharia de materiais</td>\\n      <td id=\"T_3d332_row5_col1\" class=\"data row5 col1\" >-8.89 %</td>\\n      <td id=\"T_3d332_row5_col2\" class=\"data row5 col2\" >-4.83 %</td>\\n      <td id=\"T_3d332_row5_col3\" class=\"data row5 col3\" >-83.67 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row6_col0\" class=\"data row6 col0\" >Ciência, tecnologia e inovação</td>\\n      <td id=\"T_3d332_row6_col1\" class=\"data row6 col1\" >-2.07 %</td>\\n      <td id=\"T_3d332_row6_col2\" class=\"data row6 col2\" >-14.33 %</td>\\n      <td id=\"T_3d332_row6_col3\" class=\"data row6 col3\" >-85.04 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row7_col0\" class=\"data row7 col0\" >Ciências da saúde</td>\\n      <td id=\"T_3d332_row7_col1\" class=\"data row7 col1\" >-18.41 %</td>\\n      <td id=\"T_3d332_row7_col2\" class=\"data row7 col2\" >-8.11 %</td>\\n      <td id=\"T_3d332_row7_col3\" class=\"data row7 col3\" >-87.18 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row8_col0\" class=\"data row8 col0\" >Ciências odontológicas</td>\\n      <td id=\"T_3d332_row8_col1\" class=\"data row8 col1\" >5.22 %</td>\\n      <td id=\"T_3d332_row8_col2\" class=\"data row8 col2\" >-15.98 %</td>\\n      <td id=\"T_3d332_row8_col3\" class=\"data row8 col3\" >-83.51 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row9_col0\" class=\"data row9 col0\" >Design</td>\\n      <td id=\"T_3d332_row9_col1\" class=\"data row9 col1\" >-19.10 %</td>\\n      <td id=\"T_3d332_row9_col2\" class=\"data row9 col2\" >0.16 %</td>\\n      <td id=\"T_3d332_row9_col3\" class=\"data row9 col3\" >-86.20 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row10_col0\" class=\"data row10 col0\" >Engenharia civil</td>\\n      <td id=\"T_3d332_row10_col1\" class=\"data row10 col1\" >-12.92 %</td>\\n      <td id=\"T_3d332_row10_col2\" class=\"data row10 col2\" >-4.79 %</td>\\n      <td id=\"T_3d332_row10_col3\" class=\"data row10 col3\" >-87.23 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row11_col0\" class=\"data row11 col0\" >Engenharia de software</td>\\n      <td id=\"T_3d332_row11_col1\" class=\"data row11 col1\" >-10.29 %</td>\\n      <td id=\"T_3d332_row11_col2\" class=\"data row11 col2\" >-15.08 %</td>\\n      <td id=\"T_3d332_row11_col3\" class=\"data row11 col3\" >-86.01 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row12_col0\" class=\"data row12 col0\" >Filosofia</td>\\n      <td id=\"T_3d332_row12_col1\" class=\"data row12 col1\" >-5.62 %</td>\\n      <td id=\"T_3d332_row12_col2\" class=\"data row12 col2\" >-13.43 %</td>\\n      <td id=\"T_3d332_row12_col3\" class=\"data row12 col3\" >-82.97 %</td>\\n    </tr>\\n    <tr>\\n      <td id=\"T_3d332_row13_col0\" class=\"data row13 col0\" >Gestão da informação e do conhecimento</td>\\n      <td id=\"T_3d332_row13_col1\" class=\"data row13 col1\" >-16.55 %</td>\\n      <td id=\"T_3d332_row13_col2\" class=\"data row13 col2\" >-6.35 %</td>\\n      <td id=\"T_3d332_row13_col3\" class=\"data row13 col3\" >-85.77 %</td>\\n    </tr>\\n  </tbody>\\n</table>\\n'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from IPython.display import display, HTML"
      ],
      "metadata": {
        "id": "9VNaD9d9Tj2e"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "html = percentual.style.set_table_styles(styles).hide_index().format('{:.2f} %', subset=['2018', '2019', '2022']).render()\n",
        "display(HTML(html))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 594
        },
        "id": "DPdgMoO8UQy1",
        "outputId": "5a0d4f37-fdf9-4f18-dcb3-7e3dfebf7fd1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_affc6_ th {\n",
              "  font-size: 1.4rem;\n",
              "  text-align: center;\n",
              "  font-weight: bold;\n",
              "  color: whitesmoke;\n",
              "  background-color: #001692;\n",
              "  border-radius: 0.25rem;\n",
              "  box-shadow: 0 0 1rem gray;\n",
              "}\n",
              "#T_affc6_ td {\n",
              "  font-size: 1rem;\n",
              "  padding: 0.5rem;\n",
              "  text-align: left;\n",
              "  border-bottom: 0.1rem solid lightgray;\n",
              "}\n",
              "#T_affc6_ td[colspan='5'] {\n",
              "  text-align: center;\n",
              "  font-weight: bold;\n",
              "}\n",
              "#T_affc6_ .dataframe tr:first-child {\n",
              "  display: none;\n",
              "}\n",
              "#T_affc6_ .dataframe th:first-child {\n",
              "  display: none;\n",
              "}\n",
              "#T_affc6_ .dataframe td:first-child {\n",
              "  text-transform: capitalize;\n",
              "}\n",
              "#T_affc6_ .dataframe td.negative {\n",
              "  font-weight: bold;\n",
              "  color: #F44336;\n",
              "}\n",
              "#T_affc6_ .dataframe td.positive {\n",
              "  font-weight: bold;\n",
              "  color: #4CAF50;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_affc6_\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"col_heading level0 col0\" >Curso</th>\n",
              "      <th class=\"col_heading level0 col1\" >2018</th>\n",
              "      <th class=\"col_heading level0 col2\" >2019</th>\n",
              "      <th class=\"col_heading level0 col3\" >2022</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row0_col0\" class=\"data row0 col0\" >Administração</td>\n",
              "      <td id=\"T_affc6_row0_col1\" class=\"data row0 col1\" >-8.75 %</td>\n",
              "      <td id=\"T_affc6_row0_col2\" class=\"data row0 col2\" >-26.94 %</td>\n",
              "      <td id=\"T_affc6_row0_col3\" class=\"data row0 col3\" >-84.10 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row1_col0\" class=\"data row1 col0\" >Arquitetura e urbanismo</td>\n",
              "      <td id=\"T_affc6_row1_col1\" class=\"data row1 col1\" >-13.22 %</td>\n",
              "      <td id=\"T_affc6_row1_col2\" class=\"data row1 col2\" >-5.16 %</td>\n",
              "      <td id=\"T_affc6_row1_col3\" class=\"data row1 col3\" >-85.86 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row2_col0\" class=\"data row2 col0\" >Artes cênicas</td>\n",
              "      <td id=\"T_affc6_row2_col1\" class=\"data row2 col1\" >-1.56 %</td>\n",
              "      <td id=\"T_affc6_row2_col2\" class=\"data row2 col2\" >-12.97 %</td>\n",
              "      <td id=\"T_affc6_row2_col3\" class=\"data row2 col3\" >-87.36 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row3_col0\" class=\"data row3 col0\" >Bioinformática</td>\n",
              "      <td id=\"T_affc6_row3_col1\" class=\"data row3 col1\" >-12.60 %</td>\n",
              "      <td id=\"T_affc6_row3_col2\" class=\"data row3 col2\" >-4.65 %</td>\n",
              "      <td id=\"T_affc6_row3_col3\" class=\"data row3 col3\" >-85.59 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row4_col0\" class=\"data row4 col0\" >Bioquímica</td>\n",
              "      <td id=\"T_affc6_row4_col1\" class=\"data row4 col1\" >-15.26 %</td>\n",
              "      <td id=\"T_affc6_row4_col2\" class=\"data row4 col2\" >5.81 %</td>\n",
              "      <td id=\"T_affc6_row4_col3\" class=\"data row4 col3\" >-86.78 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row5_col0\" class=\"data row5 col0\" >Ciência e engenharia de materiais</td>\n",
              "      <td id=\"T_affc6_row5_col1\" class=\"data row5 col1\" >-8.89 %</td>\n",
              "      <td id=\"T_affc6_row5_col2\" class=\"data row5 col2\" >-4.83 %</td>\n",
              "      <td id=\"T_affc6_row5_col3\" class=\"data row5 col3\" >-83.67 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row6_col0\" class=\"data row6 col0\" >Ciência, tecnologia e inovação</td>\n",
              "      <td id=\"T_affc6_row6_col1\" class=\"data row6 col1\" >-2.07 %</td>\n",
              "      <td id=\"T_affc6_row6_col2\" class=\"data row6 col2\" >-14.33 %</td>\n",
              "      <td id=\"T_affc6_row6_col3\" class=\"data row6 col3\" >-85.04 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row7_col0\" class=\"data row7 col0\" >Ciências da saúde</td>\n",
              "      <td id=\"T_affc6_row7_col1\" class=\"data row7 col1\" >-18.41 %</td>\n",
              "      <td id=\"T_affc6_row7_col2\" class=\"data row7 col2\" >-8.11 %</td>\n",
              "      <td id=\"T_affc6_row7_col3\" class=\"data row7 col3\" >-87.18 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row8_col0\" class=\"data row8 col0\" >Ciências odontológicas</td>\n",
              "      <td id=\"T_affc6_row8_col1\" class=\"data row8 col1\" >5.22 %</td>\n",
              "      <td id=\"T_affc6_row8_col2\" class=\"data row8 col2\" >-15.98 %</td>\n",
              "      <td id=\"T_affc6_row8_col3\" class=\"data row8 col3\" >-83.51 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row9_col0\" class=\"data row9 col0\" >Design</td>\n",
              "      <td id=\"T_affc6_row9_col1\" class=\"data row9 col1\" >-19.10 %</td>\n",
              "      <td id=\"T_affc6_row9_col2\" class=\"data row9 col2\" >0.16 %</td>\n",
              "      <td id=\"T_affc6_row9_col3\" class=\"data row9 col3\" >-86.20 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row10_col0\" class=\"data row10 col0\" >Engenharia civil</td>\n",
              "      <td id=\"T_affc6_row10_col1\" class=\"data row10 col1\" >-12.92 %</td>\n",
              "      <td id=\"T_affc6_row10_col2\" class=\"data row10 col2\" >-4.79 %</td>\n",
              "      <td id=\"T_affc6_row10_col3\" class=\"data row10 col3\" >-87.23 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row11_col0\" class=\"data row11 col0\" >Engenharia de software</td>\n",
              "      <td id=\"T_affc6_row11_col1\" class=\"data row11 col1\" >-10.29 %</td>\n",
              "      <td id=\"T_affc6_row11_col2\" class=\"data row11 col2\" >-15.08 %</td>\n",
              "      <td id=\"T_affc6_row11_col3\" class=\"data row11 col3\" >-86.01 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row12_col0\" class=\"data row12 col0\" >Filosofia</td>\n",
              "      <td id=\"T_affc6_row12_col1\" class=\"data row12 col1\" >-5.62 %</td>\n",
              "      <td id=\"T_affc6_row12_col2\" class=\"data row12 col2\" >-13.43 %</td>\n",
              "      <td id=\"T_affc6_row12_col3\" class=\"data row12 col3\" >-82.97 %</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <td id=\"T_affc6_row13_col0\" class=\"data row13 col0\" >Gestão da informação e do conhecimento</td>\n",
              "      <td id=\"T_affc6_row13_col1\" class=\"data row13 col1\" >-16.55 %</td>\n",
              "      <td id=\"T_affc6_row13_col2\" class=\"data row13 col2\" >-6.35 %</td>\n",
              "      <td id=\"T_affc6_row13_col3\" class=\"data row13 col3\" >-85.77 %</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}