caged_data_speedup2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyNXBA9G3w9CE4C8XPUo5em/",
      "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/rodrigo-koblitz/Caged_data/blob/main/caged_data_speedup2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "A informação dos empregos formais no Brasil são fontes de inúmeras análises. Aqui pretendemos demonstrar uma forma de extração e apresentação desses dados. \n",
        "Para obter os dados faz-se necessário baixar as planilhas mês a mês no site do sistema do CAGED. Cada planilha tem um tamanho entre 8 e 22 MB. Portanto, o processo pode ser um pouco lento. A cada planilha lida voce poderá ver essa informação.\n",
        "Criaremos inicialmente um DataFrame pandas e depois uma visualização simples dos dados."
      ],
      "metadata": {
        "id": "TDdkEm91RzXN"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para facilitar o uso do mês em portugues, conforme é apresentado no CAGED, instalamos o 'language-pack pt_BR' no Colab."
      ],
      "metadata": {
        "id": "X-WWLRlRGbtE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "!/usr/share/locales/install-language-pack pt_BR\n",
        "!dpkg-reconfigure locales\n",
        "os.kill(os.getpid(), 9)\n"
      ],
      "metadata": {
        "id": "M4ufKt4twqrj",
        "outputId": "b9794b59-9fa3-473f-e9f5-e6ff24593540",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Generating locales (this might take a while)...\n",
            "  pt_BR.ISO-8859-1... done\n",
            "Generation complete.\n",
            "\u001b[1mdpkg-trigger:\u001b[0m \u001b[1;31merror:\u001b[0m must be called from a maintainer script (or with a --by-package option)\n",
            "\n",
            "Type dpkg-trigger --help for help about this utility.\n",
            "Generating locales (this might take a while)...\n",
            "  en_US.UTF-8... done\n",
            "  pt_BR.ISO-8859-1... done\n",
            "Generation complete.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import io\n",
        "from openpyxl import load_workbook\n",
        "import datetime #import datetime\n",
        "import pandas as pd\n",
        "import os\n",
        "import locale\n",
        "import matplotlib.pyplot as plt\n",
        "from matplotlib.pyplot import figure\n",
        "import requests\n",
        "import time\n",
        "import openpyxl\n",
        "from io import BytesIO\n",
        "import concurrent.futures\n",
        "import threading\n",
        "import time\n",
        "import asyncio\n",
        "import aiohttp\n",
        "def range_month(dt_ini=None,dt_fim=None):\n",
        " if not dt_fim:\n",
        "  dt_fim=datetime.datetime.now()\n",
        " if not dt_ini:\n",
        "  dt_ini='122019'\n",
        " lt_alltime=pd.date_range(datetime.datetime.strptime(dt_ini, \"%m%Y\") - datetime.timedelta(days=10),\n",
        "                          dt_fim, freq='MS')\n",
        " t_lim=datetime.datetime.strptime('122019','%m%Y')\n",
        " lt_before=[i for i in lt_alltime if i <= t_lim]\n",
        " lt_after=[i for i in lt_alltime if i > t_lim]\n",
        " return lt_before,lt_after\n",
        "def get_links(dt_ini=None,dt_fim=None):\n",
        " lt_before,lt_after=range_month(dt_ini,dt_fim)\n",
        " links_before=['ftp://ftp.mtps.gov.br/pdet/caged/%s/%s/nacionais/4-tabelas.xls'\\\n",
        "          %(i.strftime('%Y'),i.strftime('%B').replace('ç','c')) for i in lt_before]\n",
        " links_after=['http://pdet.mte.gov.br/images/Novo_CAGED/%s/3-tabelas.xlsx'\\\n",
        "          %i.strftime('%b%Y').capitalize() for i in lt_after]\n",
        " #return links_before+links_after\n",
        " return links_after\n",
        "def get_from_worksheet1(wd,muni,mesano):\n",
        "  #wd=pd.DataFrame(ws.values)\n",
        "  row=wd.loc[wd.iloc[:,2]==muni].values[0]\n",
        "  municipio=row[3]\n",
        "  saldo=row[6]\n",
        "  admissoes=row[4]\n",
        "  desligamentos=row[5]\n",
        "  var_relativa=row[7]\n",
        "  mes_anots=datetime.datetime.strptime(mesano[:7], \"%b%Y\")\n",
        "  return [municipio, saldo, admissoes, desligamentos, var_relativa, mesano, mes_anots]"
      ],
      "metadata": {
        "id": "KX4k-yqElm5S"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def download_ulr(url, municipios,session):\n",
        "  #session=requests.Session()\n",
        "  with session.get(url) as r:\n",
        "   with io.BytesIO(r.content) as sheet:\n",
        "    try:\n",
        "      dfg=pd.io.excel.read_excel(sheet, sheet_name=sheet_nameN)\n",
        "    except:\n",
        "      dfg=None\n",
        "    return dfg\n",
        "def processing_df(url,municipios,session,columns):\n",
        "  print('processing: %s'%url)\n",
        "  all_values=[]\n",
        "  mesano=\"%s\"%url.split('/')[5]\n",
        "  if type(municipios)!=list:\n",
        "    municipios=[municipios]\n",
        "  df=download_ulr(url, municipios,session)\n",
        "  if df is None:\n",
        "    return None\n",
        "  for muni in municipios:\n",
        "    values=get_from_worksheet1(df,muni,mesano)\n",
        "    all_values.append(values)\n",
        "  print('url processed')\n",
        "  return pd.DataFrame(all_values, columns=columns)\n",
        "\n",
        "def get_all_urls(urls, municipios,df1):\n",
        "  dfs=[]\n",
        "  if type(urls)!=list:\n",
        "    urls=[urls]\n",
        "  with requests.Session() as session:\n",
        "   for url in urls:\n",
        "    dft=processing_df(url,municipios,session, df1.columns)\n",
        "    if dft is None:\n",
        "      return None\n",
        "    dfs.append(pd.DataFrame(dft))\n",
        "  return pd.concat(dfs,ignore_index=True)\n",
        "if __name__=='__main__':\n",
        " sheet_nameN='Tabela 3'\n",
        " columnsN=['municipio','admissoes', 'desligamentos', 'saldo', 'var_relativa', 'mes_ano', 'mes_anots']\n",
        " df1=pd.DataFrame(columns=columnsN)\n",
        " links=get_links()\n",
        " municipios=[330340, 330390,330580]\n",
        " urls=links\n",
        " start_time = time.time()\n",
        " #  for url in links[:3]:\n",
        " #   df1=add_in_df(url,municipios,df1)\n",
        " df2=get_all_urls(urls, municipios,df1)\n",
        " duration = time.time() - start_time\n",
        " print(f\"Downloaded {len(urls)} in {duration} seconds\")"
      ],
      "metadata": {
        "id": "jLcOtIbNeQqY",
        "outputId": "806048f2-16ae-402f-c963-e49b1dbd4632",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Jan2020/3-tabelas.xlsx\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/openpyxl/worksheet/header_footer.py:48: UserWarning: Cannot parse header or footer so it will be ignored\n",
            "  warn(\"\"\"Cannot parse header or footer so it will be ignored\"\"\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Fev2020/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Mar2020/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Abr2020/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Mai2020/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Jun2020/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Jul2020/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Ago2020/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Set2020/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Out2020/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Nov2020/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Dez2020/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Jan2021/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Fev2021/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Mar2021/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Abr2021/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Mai2021/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Jun2021/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Jul2021/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Ago2021/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Set2021/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Out2021/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Nov2021/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Dez2021/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Jan2022/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Fev2022/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Mar2022/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Abr2022/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Mai2022/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Jun2022/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Jul2022/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Ago2022/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Set2022/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Out2022/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Nov2022/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Dez2022/3-tabelas.xlsx\n",
            "url processed\n",
            "processing: http://pdet.mte.gov.br/images/Novo_CAGED/Jan2023/3-tabelas.xlsx\n",
            "Downloaded 38 in 329.14598631858826 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now, let's use the threading strategy"
      ],
      "metadata": {
        "id": "e2qXv9KVeJFR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def download_ulr(url, municipios,session):\n",
        "  #session=requests.Session()\n",
        "  with session.get(url) as r:\n",
        "   with io.BytesIO(r.content) as sheet:\n",
        "     try:\n",
        "       dfg=pd.io.excel.read_excel(sheet, sheet_name=sheet_nameN)\n",
        "     except:\n",
        "       dfg=None\n",
        "     return dfg\n",
        "def processing_df(url,municipios,session, columns):\n",
        "  all_values=[]\n",
        "  mesano=\"%s\"%url.split('/')[5]\n",
        "  df=download_ulr(url, municipios,session)\n",
        "  if df is None:\n",
        "    return None\n",
        "  if type(municipios)!=list:\n",
        "    municipios=[municipios]\n",
        "  for muni in municipios:\n",
        "   all_values.append(get_from_worksheet1(df,muni,mesano))\n",
        "  return pd.DataFrame(all_values, columns=columns)\n",
        "def get_all_urls(urls, municipios, df1):\n",
        "  dfs = []\n",
        "  if type(urls) != list:\n",
        "    urls = [urls]\n",
        "  columns = df1.columns\n",
        "  with requests.Session() as session:\n",
        "    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executor:\n",
        "      futures = []\n",
        "      for url in urls:\n",
        "        print(url)\n",
        "        futures.append(executor.submit(processing_df, url, municipios, session, columns))\n",
        "\n",
        "        # with concurrent.futures.ThreadPoolExecutor(max_workers=5) as executor:\n",
        "        # executor.map(download_site, sites)\n",
        "\n",
        "\n",
        "      for future in concurrent.futures.as_completed(futures):\n",
        "        dft = future.result()\n",
        "        if dft is not None:\n",
        "          dfs.append(dft)\n",
        "  return pd.concat(dfs, ignore_index=True)\n",
        "if __name__=='__main__':\n",
        " sheet_nameN='Tabela 3'\n",
        " columnsN=['municipio','admissoes', 'desligamentos', 'saldo', 'var_relativa', 'mes_ano', 'mes_anots']\n",
        " df1=pd.DataFrame(columns=columnsN)\n",
        " links=get_links()\n",
        " municipios=[330340, 330390,330580]\n",
        " urls=links\n",
        " start_time = time.time()\n",
        " #  for url in links[:3]:\n",
        " #   df1=add_in_df(url,municipios,df1)\n",
        " df2=get_all_urls(urls, municipios,df1)\n",
        " duration = time.time() - start_time\n",
        " print(f\"Downloaded {len(urls)} in {duration} seconds\")"
      ],
      "metadata": {
        "id": "NM4tZcsBf6kZ",
        "outputId": "cc786b4f-04ce-48ee-afb2-0bfd6702abc3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "http://pdet.mte.gov.br/images/Novo_CAGED/Jan2020/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Fev2020/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Mar2020/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Abr2020/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Mai2020/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Jun2020/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Jul2020/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Ago2020/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Set2020/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Out2020/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Nov2020/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Dez2020/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Jan2021/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Fev2021/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Mar2021/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Abr2021/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Mai2021/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Jun2021/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Jul2021/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Ago2021/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Set2021/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Out2021/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Nov2021/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Dez2021/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Jan2022/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Fev2022/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Mar2022/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Abr2022/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Mai2022/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Jun2022/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Jul2022/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Ago2022/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Set2022/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Out2022/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Nov2022/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Dez2022/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Jan2023/3-tabelas.xlsx\n",
            "http://pdet.mte.gov.br/images/Novo_CAGED/Fev2023/3-tabelas.xlsx\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/openpyxl/worksheet/header_footer.py:48: UserWarning: Cannot parse header or footer so it will be ignored\n",
            "  warn(\"\"\"Cannot parse header or footer so it will be ignored\"\"\")\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloaded 38 in 91.82796049118042 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Now we test asyncio "
      ],
      "metadata": {
        "id": "exM01jY3sNHi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "async def download_ulr(url, municipios,session):\n",
        "  #session=requests.Session()\n",
        "  async with session.get(url) as r:\n",
        "   with io.BytesIO(r.content) as sheet:\n",
        "    if sheet is not None:\n",
        "      return await pd.io.excel.read_excel(sheet, sheet_name=sheet_nameN)\n",
        "    else:\n",
        "      return None\n",
        "\n",
        "def processing_df(url,municipios,session,columns):\n",
        "  print('processing: %s'%url)\n",
        "  all_values=[]\n",
        "  mesano=\"%s\"%url.split('/')[5]\n",
        "  if type(municipios)!=list:\n",
        "    municipios=[municipios]\n",
        "  df=download_ulr(url, municipios,session)\n",
        "  if df is None:\n",
        "    return None\n",
        "  for muni in municipios:\n",
        "    values=get_from_worksheet1(df,muni,mesano)\n",
        "    all_values.append(values)\n",
        "  print('url processed')\n",
        "  return pd.DataFrame(all_values, columns=columns)\n",
        "async def download_all_sites(sites):\n",
        "    async with aiohttp.ClientSession() as session:\n",
        "        tasks = []\n",
        "        for url in sites:\n",
        "            task = asyncio.ensure_future(download_site(session, url))\n",
        "            tasks.append(task)\n",
        "        await asyncio.gather(*tasks, return_exceptions=True)\n",
        "async def get_all_urls(urls, municipios,df1):\n",
        "  if type(urls)!=list:\n",
        "    urls=[urls]\n",
        "  async with aiohttp.ClientSession() as session:\n",
        "    dfs=[]\n",
        "    for url in urls:\n",
        "      task = asyncio.ensure_future(processing_df(url,municipios,session, df1.columns))\n",
        "      tasks.append(task)\n",
        "    results = await asyncio.gather(*tasks)\n",
        "    for data in results:\n",
        "      if data is not None:\n",
        "        df = pd.read_html(data)[0]\n",
        "        dfs.append(df)\n",
        "  return pd.concat(dfs,ignore_index=True)\n",
        "\n",
        "if __name__=='__main__':\n",
        " sheet_nameN='Tabela 3'\n",
        " columnsN=['municipio','admissoes', 'desligamentos', 'saldo', 'var_relativa', 'mes_ano', 'mes_anots']\n",
        " df1=pd.DataFrame(columns=columnsN)\n",
        " links=get_links()\n",
        " municipios=[330340, 330390,330580]\n",
        " urls=links\n",
        " start_time = time.time()\n",
        " #  for url in links[:3]:\n",
        " #   df1=add_in_df(url,municipios,df1)\n",
        " df2 =asyncio.get_event_loop().run_until_complete((get_all_urls(urls, municipios,df1)))\n",
        " #df2 = asyncio.run((get_all_urls(urls, municipios,df1)))\n",
        " duration = time.time() - start_time\n",
        " print(f\"Downloaded {len(urls)} in {duration} seconds\")\n",
        "\n"
      ],
      "metadata": {
        "id": "Cc-nup4_D_LG",
        "outputId": "097f4fa5-78d6-4666-bbef-a158bbf2c419",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-07892ac4d84c>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     54\u001b[0m  \u001b[0;31m#  for url in links[:3]:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     55\u001b[0m  \u001b[0;31m#   df1=add_in_df(url,municipios,df1)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 56\u001b[0;31m  \u001b[0mdf2\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0masyncio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_event_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_until_complete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mget_all_urls\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmunicipios\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     57\u001b[0m  \u001b[0;31m#df2 = asyncio.run((get_all_urls(urls, municipios,df1)))\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     58\u001b[0m  \u001b[0mduration\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mstart_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/asyncio/base_events.py\u001b[0m in \u001b[0;36mrun_until_complete\u001b[0;34m(self, future)\u001b[0m\n\u001b[1;32m    590\u001b[0m         \"\"\"\n\u001b[1;32m    591\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 592\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m         \u001b[0mnew_task\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfutures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfuture\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfuture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/asyncio/base_events.py\u001b[0m in \u001b[0;36m_check_running\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    550\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'This event loop is already running'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_running_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m             raise RuntimeError(\n",
            "\u001b[0;31mRuntimeError\u001b[0m: This event loop is already running"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "async def fetch(url):\n",
        "    async with aiohttp.ClientSession() as session:\n",
        "        async with session.get(url) as response:\n",
        "            return await response.text()\n",
        "\n",
        "async def main():\n",
        "    urls = ['https://www.example.com', 'https://www.google.com', 'https://www.python.org']\n",
        "    tasks = []\n",
        "    for url in urls:\n",
        "        tasks.append(asyncio.create_task(fetch(url)))\n",
        "    results = await asyncio.gather(*tasks)\n",
        "    print(results)\n",
        "\n",
        "loop = asyncio.get_event_loop()\n",
        "loop.run_until_complete(main())\n"
      ],
      "metadata": {
        "id": "8x_h6_EgJhq4",
        "outputId": "449ba75e-c508-4a5f-ffb2-09322cb6d102",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 348
        }
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-2-694a0bf9a431>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     14\u001b[0m \u001b[0mloop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masyncio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_event_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 15\u001b[0;31m \u001b[0mloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_until_complete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmain\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/lib/python3.8/asyncio/base_events.py\u001b[0m in \u001b[0;36mrun_until_complete\u001b[0;34m(self, future)\u001b[0m\n\u001b[1;32m    590\u001b[0m         \"\"\"\n\u001b[1;32m    591\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 592\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m         \u001b[0mnew_task\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfutures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfuture\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfuture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/asyncio/base_events.py\u001b[0m in \u001b[0;36m_check_running\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    550\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'This event loop is already running'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_running_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m             raise RuntimeError(\n",
            "\u001b[0;31mRuntimeError\u001b[0m: This event loop is already running"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "try:\n",
        "    loop = asyncio.get_running_loop()\n",
        "    print(f\"An event loop is already running: {loop}\")\n",
        "except RuntimeError:\n",
        "    print(\"No event loop is running\")"
      ],
      "metadata": {
        "id": "E89CUVbELhah",
        "outputId": "208e86a0-561c-4ba5-f232-c753a79eb159",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "An event loop is already running: <_UnixSelectorEventLoop running=True closed=False debug=False>\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "async def my_coroutine():\n",
        "    # This coroutine will run indefinitely until the event loop is stopped\n",
        "    while True:\n",
        "        print(\"Waiting for 1 second...\")\n",
        "        await asyncio.sleep(1)\n",
        "\n",
        "# Start the event loop and run the coroutine\n",
        "loop = asyncio.get_event_loop()\n",
        "task = loop.create_task(my_coroutine())\n",
        "\n",
        "# ...do other stuff...\n",
        "\n",
        "# Stop the event loop\n",
        "loop.stop()\n",
        "\n",
        "# Wait for the coroutine to finish\n",
        "try:\n",
        "    loop.run_until_complete(task)\n",
        "except asyncio.CancelledError:\n",
        "    pass"
      ],
      "metadata": {
        "id": "ijUDiI5bL5L3",
        "outputId": "b597a07e-4d09-4f2a-ee89-2b76ff1687a4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 383
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "RuntimeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-6-dd000a6cf6db>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;31m# Wait for the coroutine to finish\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m     \u001b[0mloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_until_complete\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtask\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0masyncio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mCancelledError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m     \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/asyncio/base_events.py\u001b[0m in \u001b[0;36mrun_until_complete\u001b[0;34m(self, future)\u001b[0m\n\u001b[1;32m    590\u001b[0m         \"\"\"\n\u001b[1;32m    591\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 592\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    593\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    594\u001b[0m         \u001b[0mnew_task\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mfutures\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0misfuture\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfuture\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/lib/python3.8/asyncio/base_events.py\u001b[0m in \u001b[0;36m_check_running\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    550\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 552\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'This event loop is already running'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    553\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_running_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    554\u001b[0m             raise RuntimeError(\n",
            "\u001b[0;31mRuntimeError\u001b[0m: This event loop is already running"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "async def my_coroutine():\n",
        "    # This coroutine will run indefinitely until the event loop is stopped\n",
        "    while True:\n",
        "        print(\"Waiting for 1 second...\")\n",
        "        await asyncio.sleep(1)\n",
        "\n",
        "# Start the event loop and run the coroutine\n",
        "loop = asyncio.get_event_loop()\n",
        "task = loop.create_task(my_coroutine())\n",
        "\n",
        "# ...do other stuff...\n",
        "\n",
        "# Stop the event loop\n",
        "loop.call_soon_threadsafe(loop.stop)\n",
        "\n",
        "# Wait for the coroutine to finish\n",
        "try:\n",
        "    loop.run_until_complete(task)\n",
        "except asyncio.CancelledError:\n",
        "    pass\n"
      ],
      "metadata": {
        "id": "I6-WlRecMKX0",
        "outputId": "45a7b857-078c-415d-9b8b-50c1619278a2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-d3b576b41128>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# Start the event loop and run the coroutine\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mloop\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0masyncio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_event_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mtask\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcreate_task\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_coroutine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'asyncio' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Selcionamos os município de Petrópolis, Nova Frburgo e Teresópolis para essa análise desde o início do novo CAGED."
      ],
      "metadata": {
        "id": "9hMqN-zkVGAC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "municipios=[330340, 330390,330580]"
      ],
      "metadata": {
        "id": "HLj7CGvoD27Q"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1=create_df(municipios,'012020')"
      ],
      "metadata": {
        "id": "TAou5rV8RXDp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Esse programa sequencialmente demorou xxx minutos para se completar. Vamos testar agora com diferentes processos concorrentes. Multiprocessing e multi threding para otimizá-lo e identificar qual o mais rápido. Como esse é um problema de IO bound, a limitação não é da CPU, que fica parada muito tempo, mas uma restrição de rede, a multitrheading deve ser a melhor opção. \n",
        "Começamos por pegar os links a serem baixados."
      ],
      "metadata": {
        "id": "VZhr5y3OrCcp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "links=get_links('012020')"
      ],
      "metadata": {
        "id": "z0ayel5NqTOL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "links"
      ],
      "metadata": {
        "id": "AMe17u44tJ64"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "OEYhrU0tFscr",
        "outputId": "9c765e7c-d56e-44bc-fe7e-2532ff9f0300"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            municipio saldo   ano  mes  mes_ano  mes_anots\n",
              "1    Rj-Nova Friburgo   -97  2020  Jan  Jan2020 2020-01-01\n",
              "2       Rj-Petropolis  -177  2020  Jan  Jan2020 2020-01-01\n",
              "3      Rj-Teresopolis    43  2020  Jan  Jan2020 2020-01-01\n",
              "4    Rj-Nova Friburgo   220  2020  Fev  Fev2020 2020-02-01\n",
              "5       Rj-Petropolis   300  2020  Fev  Fev2020 2020-02-01\n",
              "..                ...   ...   ...  ...      ...        ...\n",
              "98      Rj-Petropolis   396  2022  Set  Set2022 2022-09-01\n",
              "99     Rj-Teresopolis    33  2022  Set  Set2022 2022-09-01\n",
              "100  Rj-Nova Friburgo   103  2022  Out  Out2022 2022-10-01\n",
              "101     Rj-Petropolis   203  2022  Out  Out2022 2022-10-01\n",
              "102    Rj-Teresopolis     6  2022  Out  Out2022 2022-10-01\n",
              "\n",
              "[102 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-47deb797-c618-48cb-9fae-9c5d5f6cf1f9\">\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>municipio</th>\n",
              "      <th>saldo</th>\n",
              "      <th>ano</th>\n",
              "      <th>mes</th>\n",
              "      <th>mes_ano</th>\n",
              "      <th>mes_anots</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Rj-Nova Friburgo</td>\n",
              "      <td>-97</td>\n",
              "      <td>2020</td>\n",
              "      <td>Jan</td>\n",
              "      <td>Jan2020</td>\n",
              "      <td>2020-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rj-Petropolis</td>\n",
              "      <td>-177</td>\n",
              "      <td>2020</td>\n",
              "      <td>Jan</td>\n",
              "      <td>Jan2020</td>\n",
              "      <td>2020-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Rj-Teresopolis</td>\n",
              "      <td>43</td>\n",
              "      <td>2020</td>\n",
              "      <td>Jan</td>\n",
              "      <td>Jan2020</td>\n",
              "      <td>2020-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Rj-Nova Friburgo</td>\n",
              "      <td>220</td>\n",
              "      <td>2020</td>\n",
              "      <td>Fev</td>\n",
              "      <td>Fev2020</td>\n",
              "      <td>2020-02-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rj-Petropolis</td>\n",
              "      <td>300</td>\n",
              "      <td>2020</td>\n",
              "      <td>Fev</td>\n",
              "      <td>Fev2020</td>\n",
              "      <td>2020-02-01</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Rj-Petropolis</td>\n",
              "      <td>396</td>\n",
              "      <td>2022</td>\n",
              "      <td>Set</td>\n",
              "      <td>Set2022</td>\n",
              "      <td>2022-09-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>Rj-Teresopolis</td>\n",
              "      <td>33</td>\n",
              "      <td>2022</td>\n",
              "      <td>Set</td>\n",
              "      <td>Set2022</td>\n",
              "      <td>2022-09-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Rj-Nova Friburgo</td>\n",
              "      <td>103</td>\n",
              "      <td>2022</td>\n",
              "      <td>Out</td>\n",
              "      <td>Out2022</td>\n",
              "      <td>2022-10-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>Rj-Petropolis</td>\n",
              "      <td>203</td>\n",
              "      <td>2022</td>\n",
              "      <td>Out</td>\n",
              "      <td>Out2022</td>\n",
              "      <td>2022-10-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>Rj-Teresopolis</td>\n",
              "      <td>6</td>\n",
              "      <td>2022</td>\n",
              "      <td>Out</td>\n",
              "      <td>Out2022</td>\n",
              "      <td>2022-10-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>102 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-47deb797-c618-48cb-9fae-9c5d5f6cf1f9')\"\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-47deb797-c618-48cb-9fae-9c5d5f6cf1f9 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-47deb797-c618-48cb-9fae-9c5d5f6cf1f9');\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": 137
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Para fazer o download do dataframe df1 que contém toda a informação obtida:"
      ],
      "metadata": {
        "id": "qbybFSXSVsrp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files\n",
        "df1.to_csv('df1.csv')\n",
        "files.download(\"df1.csv\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "LQWGEiqmXMa1",
        "outputId": "e24a545b-148f-4df4-e4fe-91c18e58f55f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_33a0933d-6cbb-4635-b10e-8131c444031a\", \"df1.csv\", 3229)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df1"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "n7Cs90e2oQrC",
        "outputId": "6b7d2c20-8b6d-46c0-c3f6-88e7ce693631"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            municipio saldo   ano  mes  mes_ano  mes_anots\n",
              "1    Rj-Nova Friburgo   -97  2020  Jan  Jan2020 2020-01-01\n",
              "2       Rj-Petropolis  -177  2020  Jan  Jan2020 2020-01-01\n",
              "3      Rj-Teresopolis    43  2020  Jan  Jan2020 2020-01-01\n",
              "4    Rj-Nova Friburgo   220  2020  Fev  Fev2020 2020-02-01\n",
              "5       Rj-Petropolis   300  2020  Fev  Fev2020 2020-02-01\n",
              "..                ...   ...   ...  ...      ...        ...\n",
              "98      Rj-Petropolis   396  2022  Set  Set2022 2022-09-01\n",
              "99     Rj-Teresopolis    33  2022  Set  Set2022 2022-09-01\n",
              "100  Rj-Nova Friburgo   103  2022  Out  Out2022 2022-10-01\n",
              "101     Rj-Petropolis   203  2022  Out  Out2022 2022-10-01\n",
              "102    Rj-Teresopolis     6  2022  Out  Out2022 2022-10-01\n",
              "\n",
              "[102 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1ebdd308-3dac-4bf0-8f7b-76fbee67095d\">\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>municipio</th>\n",
              "      <th>saldo</th>\n",
              "      <th>ano</th>\n",
              "      <th>mes</th>\n",
              "      <th>mes_ano</th>\n",
              "      <th>mes_anots</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Rj-Nova Friburgo</td>\n",
              "      <td>-97</td>\n",
              "      <td>2020</td>\n",
              "      <td>Jan</td>\n",
              "      <td>Jan2020</td>\n",
              "      <td>2020-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Rj-Petropolis</td>\n",
              "      <td>-177</td>\n",
              "      <td>2020</td>\n",
              "      <td>Jan</td>\n",
              "      <td>Jan2020</td>\n",
              "      <td>2020-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Rj-Teresopolis</td>\n",
              "      <td>43</td>\n",
              "      <td>2020</td>\n",
              "      <td>Jan</td>\n",
              "      <td>Jan2020</td>\n",
              "      <td>2020-01-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Rj-Nova Friburgo</td>\n",
              "      <td>220</td>\n",
              "      <td>2020</td>\n",
              "      <td>Fev</td>\n",
              "      <td>Fev2020</td>\n",
              "      <td>2020-02-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Rj-Petropolis</td>\n",
              "      <td>300</td>\n",
              "      <td>2020</td>\n",
              "      <td>Fev</td>\n",
              "      <td>Fev2020</td>\n",
              "      <td>2020-02-01</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>98</th>\n",
              "      <td>Rj-Petropolis</td>\n",
              "      <td>396</td>\n",
              "      <td>2022</td>\n",
              "      <td>Set</td>\n",
              "      <td>Set2022</td>\n",
              "      <td>2022-09-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>99</th>\n",
              "      <td>Rj-Teresopolis</td>\n",
              "      <td>33</td>\n",
              "      <td>2022</td>\n",
              "      <td>Set</td>\n",
              "      <td>Set2022</td>\n",
              "      <td>2022-09-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>100</th>\n",
              "      <td>Rj-Nova Friburgo</td>\n",
              "      <td>103</td>\n",
              "      <td>2022</td>\n",
              "      <td>Out</td>\n",
              "      <td>Out2022</td>\n",
              "      <td>2022-10-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>101</th>\n",
              "      <td>Rj-Petropolis</td>\n",
              "      <td>203</td>\n",
              "      <td>2022</td>\n",
              "      <td>Out</td>\n",
              "      <td>Out2022</td>\n",
              "      <td>2022-10-01</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>102</th>\n",
              "      <td>Rj-Teresopolis</td>\n",
              "      <td>6</td>\n",
              "      <td>2022</td>\n",
              "      <td>Out</td>\n",
              "      <td>Out2022</td>\n",
              "      <td>2022-10-01</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>102 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1ebdd308-3dac-4bf0-8f7b-76fbee67095d')\"\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",
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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",
              "      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-1ebdd308-3dac-4bf0-8f7b-76fbee67095d 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-1ebdd308-3dac-4bf0-8f7b-76fbee67095d');\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": 105
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Uma opção de visualização"
      ],
      "metadata": {
        "id": "TXxrIWLlVilE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "figure(figsize=(10, 15), dpi=80)\n",
        "col1 = 'steelblue'\n",
        "d1=df1.loc[df1.municipio=='Rj-Nova Friburgo']\n",
        "col2 = 'red'\n",
        "d2=df1.loc[df1.municipio=='Rj-Petropolis']\n",
        "col3='black'\n",
        "d3=df1.loc[df1.municipio=='Rj-Teresopolis']\n",
        "plt.plot(d1.mes_ano, d1.saldo, color=col1, marker='o', linewidth=3,\n",
        "         label='Nova Friburgo')\n",
        "plt.xticks(rotation=90)\n",
        "plt.plot(d2.mes_ano, d2.saldo, color=col2, marker='o', linewidth=3,\n",
        "         label='Petrópolis')\n",
        "plt.plot(d3.mes_ano, d3.saldo, color=col3, marker='o', linewidth=3,\n",
        "         label='Teresópolis')\n",
        "plt.legend()#loc=\"upper left\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "mdRiPNcvuJsT",
        "outputId": "ed118a02-c026-47a4-b1cf-c2c1b853e0de"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7efc03449be0>"
            ]
          },
          "metadata": {},
          "execution_count": 136
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x1200 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Bom trabalho com suas análises! "
      ],
      "metadata": {
        "id": "vVHzS9iGPDp2"
      }
    }
  ]
}