Analise_dos_gastos_dos_deputados_estaduais_de_SP.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JoaoMouraSS/Projetos/blob/main/Analise_dos_gastos_dos_deputados_estaduais_de_SP.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yK8_KcxZ9ppo"
      },
      "source": [
        "# Introdução\n",
        "\n",
        "O seguinte projeto visa a análise exploratória dos gastos dos parlamentares do estado de São Paulo.\n",
        "\n",
        "## Fonte dos dados: \n",
        "https://www.al.sp.gov.br/dados-abertos/grupo/21;jsessionid=D351122785BD9D1FF13FCEE28C963181"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lco9EykR9gmp"
      },
      "source": [
        "# *Fluxo do Projeto*\n",
        "\n",
        "\n",
        "1.   Etapa API – Criação de uma API para consumir dados públicos da ALEPS.\n",
        "2.   Etapa Ingestão – Transferir os dados para  framework Pandas.\n",
        "3.   Etapa Modelagem – Cruzar os dados de 2 fontes da API e criar uma análise de despesas de gastos.\n",
        "4.   Etapa Armazenar – Criar um banco de dados e fazer a ingestão dos dados para futuras consultas.\n",
        "5.   Analisar os Dados – Utilizar frameworks de Data Visualization para entender o que contem nos dados\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "pZK85OQk-EYq"
      },
      "outputs": [],
      "source": [
        "# Consumir a api\n",
        "import requests\n",
        "\n",
        "# Ajustar os dados\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Armazenar os dados\n",
        "import sqlite3\n",
        "\n",
        "# Gerar gráficos\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "import plotly.graph_objects as go\n",
        "\n",
        "# Ignorar filtros\n",
        "from warnings import filterwarnings\n",
        "filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BrWbCr2SAgRd"
      },
      "source": [
        "# *Consumindo a api*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YO8HdxGLAeH7",
        "outputId": "893cb139-00e1-4420-9fbc-5f540686cf4f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<Response [200]> <Response [200]>\n"
          ]
        }
      ],
      "source": [
        "# Links\n",
        "despesas = 'http://www.al.sp.gov.br/repositorioDados/deputados/despesas_gabinetes.xml'\n",
        "cadastro = 'https://www.al.sp.gov.br/repositorioDados/deputados/deputados.xml'\n",
        "\n",
        "# Pegando as informações\n",
        "dados_despesas = requests.get(despesas)\n",
        "dados_cadastro = requests.get(cadastro)\n",
        "\n",
        "# Verificando o retorno da api\n",
        "print(dados_despesas, dados_cadastro)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vIsnTjFfC1yi"
      },
      "source": [
        "# *Transformando para o objeto pd.dataframe e fazendo alterações*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "2qAQ5ylJBrr1"
      },
      "outputs": [],
      "source": [
        "# Convertendo os dados para um dataframe\n",
        "df_despesas = pd.read_xml(dados_despesas.content)\n",
        "df_cadastro = pd.read_xml(dados_cadastro.content)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YtN101R3HEPI"
      },
      "source": [
        "# *Modelando os dados*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "cNRasvWLCg7n"
      },
      "outputs": [],
      "source": [
        "# Removendo colunas desnecessárias\n",
        "df_despesas = df_despesas[['Ano','Matricula','Valor']]\n",
        "df_cadastro = df_cadastro[['Matricula','Partido','NomeParlamentar']]\n",
        "\n",
        "filtro = df_despesas.loc[(df_despesas['Ano']>=2019)]\n",
        "\n",
        "# Agrupar o valor gasto de cada deputado por ano\n",
        "analise = filtro.groupby(by = ['Ano', 'Matricula']).sum()['Valor'].reset_index()\n",
        "analise = analise.pivot(index='Matricula',columns='Ano',values='Valor')\n",
        "analise = analise.dropna()\n",
        "\n",
        "# Fazer o cruzamento dos dados entre as tabelas\n",
        "df_analise = pd.merge(analise,df_cadastro,how='inner',on='Matricula')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Adicionando os dados sobre pib_per_capita\n",
        "data = pd.read_csv('pib_per_capita.csv',encoding='utf-8')\n",
        "\n",
        "# Deletando colunas que não serão usadas\n",
        "data.drop(['Municipio','Unnamed: 4'], axis=1, inplace=True)\n",
        "\n",
        "# Modificando a coluna PIB per capita - transformando em um int\n",
        "valores = []\n",
        "\n",
        "for i in data['PIB per Capita']:\n",
        "    separado = i.split()\n",
        "    sem_ponto = separado[1].replace('.','')\n",
        "    valores.append(sem_ponto)\n",
        "    \n",
        "data['PIB per Capita'] = valores\n",
        "data['PIB per Capita'] = data['PIB per Capita'].astype(int)\n",
        "\n",
        "# Somando os valores pelo ano\n",
        "pib_ano = (data.groupby(by='Ano').sum('PIB per Capita')/1000000).reset_index()"
      ],
      "metadata": {
        "id": "jZd0ocB3Ty0O"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c_Ktl4aYqkbd"
      },
      "source": [
        "# *Armazenando os dados*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vKM4U8aPDzMK"
      },
      "outputs": [],
      "source": [
        "# Criando o arquivo do banco de dados\n",
        "banco = sqlite3.connect('banco')\n",
        "\n",
        "# Criando um cursor\n",
        "curso_banco = banco.cursor()\n",
        "\n",
        "# Criando a tabela\n",
        "tabela1 = curso_banco.execute('create table Dados (Matricula,Gastos_2019,Gastos_2020,Gastos_2021, Gastos_2022)')\n",
        "\n",
        "# Enviando o comando\n",
        "banco.commit()\n",
        "\n",
        "# Adicionando dados para o banco de dados\n",
        "for i in df_analise.values:\n",
        "  curso_banco.execute(f'insert into Dados values ({i[0]},{i[1]},{i[2]},{i[3]},{i[4]})')\n",
        "\n",
        "# Consultando os valores do banco de dados\n",
        "curso_banco.execute('select * from Dados').fetchall()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YCsRcyW_yTEI"
      },
      "source": [
        "# *Visualizando os dados*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X4EzR67MykRN"
      },
      "source": [
        "### *Utilizando o Matplot Lib fiz um gráfico para analisar os gastos dos deputados do estado de São Paulo*"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "U3oPOmh1zVQL"
      },
      "outputs": [],
      "source": [
        "despesas_por_ano = (df_despesas.groupby(by=['Ano']).sum()['Valor']/1000000).reset_index()\n",
        "despesas_por_ano"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "2BQj0Qc8Fh41",
        "outputId": "5c6ac065-3ab3-4370-afae-9a3a9e858df8"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1296x576 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Configurando a área do gráfico \n",
        "plt.figure(figsize=(18,8))\n",
        "\n",
        "# Criando o gráfico\n",
        "plt.bar( despesas_por_ano['Ano'], despesas_por_ano['Valor'], width=0.7, color='#6A5ACD')\n",
        "\n",
        "#Ajustes\n",
        "plt.title('Despesas dos deputados de São Paulo entre os anos de 2002 até 2022', loc='left', fontsize = 16, fontweight='500')\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "# Adcionando valores as colunas\n",
        "for i,j in enumerate(despesas_por_ano['Valor']):\n",
        "  x = round(j,1)\n",
        "  valores = str(x)+'mi'\n",
        "  plt.text(x =despesas_por_ano['Ano'][i]-0.4,y = despesas_por_ano['Valor'][i]+0.5,s=valores, size = 'small')\n",
        "\n",
        "# \"Printando\" o gráfico\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### *Nesse gráfico, utilizando o plotly, estou querendo comparar a evoluação dos gastos e pib  per capita do estado*"
      ],
      "metadata": {
        "id": "21rTglFERH0o"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "1epagWrPy2YJ",
        "outputId": "6bf2c815-f803-4266-b1f8-b061387650ff"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "<html>\n",
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      "source": [
        "\n",
        "fig = go.Figure(data=[\n",
        "go.Line(name='Gasto dos deputados',y=despesas_por_ano['Valor'][0:18], x= pib_ano['Ano']), \n",
        "go.Line(name = 'PIB per capita',y=pib_ano['PIB per Capita'],x = pib_ano['Ano']),])\n",
        "\n",
        "fig.update_layout(title='Comparação entre os gastos dos deputados e o PIB per capita')\n",
        "\n",
        "fig.show()"
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