T2/t2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fce125b9",
   "metadata": {},
   "source": [
    "# Trabalho 2 - Funções usadas na resolução dos exercícios\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "c462171a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import statistics as stat\n",
    "\n",
    "\n",
    "def ler_arquivo_csv(caminho: str = './', nome_arquivo: str = '', separador: str = \";\", decimal: str = \".\") -> pd.DataFrame:\n",
    "    return pd.read_csv(caminho + nome_arquivo, sep=separador, decimal=decimal)\n",
    "\n",
    "\n",
    "def gravar_arquivo_csv(caminho: str = './', nome_arquivo: str = '', dataframe: pd.DataFrame = None, separador: str = \";\", decimal: str = \".\") -> None:\n",
    "    dataframe.to_csv(caminho + nome_arquivo, index=False,\n",
    "                     sep=separador, decimal=decimal)\n",
    "\n",
    "\n",
    "def apagar_colunas(dataframe: pd.DataFrame, nome_colunas: list[str]) -> pd.DataFrame:\n",
    "    return dataframe.drop(nome_colunas, axis=1)\n",
    "\n",
    "\n",
    "def obter_limite_controle(media_dataframe: float, desvio_padrao_dataframe: float, limite_superior: bool) -> float:\n",
    "\n",
    "    if(limite_superior):\n",
    "        # limite superior de controle\n",
    "        lsc = media_dataframe + desvio_padrao_dataframe\n",
    "        print(\"Limite Superior de Controle = \", lsc)\n",
    "        return lsc\n",
    "    else:\n",
    "        # limite inferior de controle\n",
    "        lic = media_dataframe - desvio_padrao_dataframe\n",
    "        print(\"Limite Inferior de Controle = \", lic)\n",
    "        return lic\n",
    "\n",
    "\n",
    "def gerar_grafico_controle(dataframe: pd.DataFrame, dataframe_indices: list[str], rotulo_eixo_x: str, rotulo_eixo_y: str, titulo_grafico: str):\n",
    "\n",
    "    # médias\n",
    "    medias = dataframe.mean(numeric_only=True, skipna=True, axis=0)\n",
    "    media_geral = medias.mean()\n",
    "    print(\"Média Geral = \", media_geral)\n",
    "\n",
    "    # desvio padrão anuais\n",
    "    desvios_padrao = dataframe.std(numeric_only=True, skipna=True, axis=0)\n",
    "    desvio_padrao_geral = desvios_padrao.std()\n",
    "    print(\"Desvio Padrão Geral = \", desvio_padrao_geral)\n",
    "\n",
    "    # limite superior de controle\n",
    "    lsc = obter_limite_controle(media_dataframe=media_geral,\n",
    "                                desvio_padrao_dataframe=desvio_padrao_geral * 2, limite_superior=True)\n",
    "\n",
    "    # limite inferior de controle\n",
    "    lic = obter_limite_controle(media_dataframe=media_geral,\n",
    "                                desvio_padrao_dataframe=desvio_padrao_geral * 2, limite_superior=False)\n",
    "\n",
    "    # nome das colunas\n",
    "    eixo_x = dataframe.columns\n",
    "\n",
    "    lsc_vetor = [lsc] * len(eixo_x)\n",
    "    lic_vetor = [lic] * len(eixo_x)\n",
    "    media_vetor = [media_geral] * len(eixo_x)\n",
    "\n",
    "    plt.plot(eixo_x, lsc_vetor, 'r--', label='Limite Superior de Controle')\n",
    "    plt.plot(eixo_x, media_vetor, 'b--', label='Média Geral')\n",
    "    plt.plot(eixo_x, lic_vetor, 'r--', label='Limite Inferior de Controle')\n",
    "    plt.plot(eixo_x, medias, 'g-', label='Média Anual')\n",
    "\n",
    "    for index, linha, in dataframe.iterrows():\n",
    "        # transformar coluna em array\n",
    "        coluna_array: pd.Series = linha.values\n",
    "        # pegar o nome do país pelo índice da tabela original\n",
    "        nome_pais: str = dataframe_indices[index]\n",
    "        plt.plot(eixo_x, coluna_array, label=nome_pais)\n",
    "\n",
    "    fig = plt.gcf()\n",
    "    fig.set_size_inches(18.5, 10.5, forward=True)\n",
    "    plt.grid()\n",
    "    plt.title(titulo_grafico)\n",
    "    plt.xlabel(rotulo_eixo_x)\n",
    "    plt.ylabel(rotulo_eixo_y)\n",
    "    plt.tick_params(rotation=45)\n",
    "    plt.legend(bbox_to_anchor=(1.04, 1), loc=\"upper left\")\n",
    "\n",
    "    plt.show()\n",
    "\n",
    "\n",
    "def obter_elementos_saem_faixa_controle(dataframe: pd.DataFrame, limite_superior: bool, valor_limite: float) -> pd.DataFrame:\n",
    "    # monta a expressao conforme o tipo de limite, superior ou inferior\n",
    "    expressao = lambda x: x >= valor_limite if limite_superior else x <= valor_limite\n",
    "    \n",
    "    # retorna um dataframe de booleanos apenas nos valores existentes, mantendo NAN nas posições vazias\n",
    "    fora_faixa_controle = dataframe.applymap( expressao, na_action='ignore')\n",
    "\n",
    "    return fora_faixa_controle\n",
    "   \n",
    "def exibir_saida_faixa_controle(dataframe: pd.DataFrame, dataframe_indices: list[str], limite_superior: bool):\n",
    "\n",
    "    texto = 'acima' if limite_superior else 'abaixo'\n",
    "\n",
    "    for indice, linha in dataframe.iterrows():\n",
    "        nome_pais: str = dataframe_indices[indice]\n",
    "        fora_faixa = linha.isin([limite_superior]).sum()\n",
    "        periodo_fora_faixa: pd.Series = linha[linha == limite_superior].index\n",
    "        if(fora_faixa > 0):\n",
    "            print(\n",
    "                f\"{nome_pais} ficou {texto} da faixa de controle {fora_faixa} vezes, sendo os anos de:\")\n",
    "            display(periodo_fora_faixa.values)\n",
    "        else:\n",
    "            display(\n",
    "                f\"{nome_pais} não ficou {texto} da faixa de controle nenhuma vez.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "546f4733",
   "metadata": {},
   "source": [
    "## Atividade 1\n",
    "\n",
    "Crie um dataset que envolva apenas o indicador GDP e persista em um arquivo, sem as colunas \"Indicator Name\" e \"Indicator Code\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "2bbcd764",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Antes de remover haviam essas colunas.\n"
     ]
    },
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Indicator Name</th>\n",
       "      <th>Indicator Code</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
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       "Empty DataFrame\n",
       "Columns: [index, Country Name, Country Code, Indicator Name, Indicator Code, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 59 columns]"
      ]
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     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Depois de removido há as seguintes colunas.\n"
     ]
    },
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>1960</th>\n",
       "      <th>1961</th>\n",
       "      <th>1962</th>\n",
       "      <th>1963</th>\n",
       "      <th>1964</th>\n",
       "      <th>1965</th>\n",
       "      <th>1966</th>\n",
       "      <th>1967</th>\n",
       "      <th>...</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
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       "Empty DataFrame\n",
       "Columns: [Country Name, Country Code, 1960, 1961, 1962, 1963, 1964, 1965, 1966, 1967, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013]\n",
       "Index: []\n",
       "\n",
       "[0 rows x 56 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "df_dados = ler_arquivo_csv(\n",
    "    nome_arquivo='dataset_bancomundial.csv', decimal=',')\n",
    "\n",
    "# filtrar ocorrencias de um único termo em uma coluna\n",
    "filtro = 'GDP per capita'\n",
    "\n",
    "# filtrando apenas as linhas que contém o critério do filtro na coluna Indicator Name\n",
    "df_gpd = df_dados[df_dados['Indicator Name'].str.contains(filtro)]\n",
    "\n",
    "print(\"Antes de remover haviam essas colunas.\")\n",
    "display(df_gpd.head(0))\n",
    "\n",
    "# apagar colunas do dataframe\n",
    "df_gpd = apagar_colunas(dataframe=df_gpd, nome_colunas=[\n",
    "                        'Indicator Name', 'Indicator Code', 'index'])\n",
    "\n",
    "df_gpd = df_gpd.reset_index(drop=True)\n",
    "\n",
    "print(\"Depois de removido há as seguintes colunas.\")\n",
    "display(df_gpd.head(0))\n",
    "\n",
    "# gravando em um novo arquivo\n",
    "gravar_arquivo_csv(nome_arquivo='dataset_gpd_ex1.csv', dataframe=df_gpd)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fecdca33",
   "metadata": {},
   "source": [
    "## Atividade 2\n",
    "Selecione os países da América do Sul e crie um novo dataset, persistindo em arquivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "4e5fe372",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Short Name</th>\n",
       "      <th>Table Name</th>\n",
       "      <th>Long Name</th>\n",
       "      <th>2-alpha code</th>\n",
       "      <th>Currency Unit</th>\n",
       "      <th>Special Notes</th>\n",
       "      <th>Region</th>\n",
       "      <th>Income Group</th>\n",
       "      <th>WB-2 code</th>\n",
       "      <th>...</th>\n",
       "      <th>Government Accounting concept</th>\n",
       "      <th>IMF data dissemination standard</th>\n",
       "      <th>Latest population census</th>\n",
       "      <th>Latest household survey</th>\n",
       "      <th>Source of most recent Income and expenditure data</th>\n",
       "      <th>Vital registration complete</th>\n",
       "      <th>Latest agricultural census</th>\n",
       "      <th>Latest industrial data</th>\n",
       "      <th>Latest trade data</th>\n",
       "      <th>Latest water withdrawal data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentine Republic</td>\n",
       "      <td>AR</td>\n",
       "      <td>Argentine peso</td>\n",
       "      <td>The base year has changed to 2004.</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>AR</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2010</td>\n",
       "      <td>Multiple Indicator Cluster Survey (MICS), 2011/12</td>\n",
       "      <td>Integrated household survey (IHS), 2012</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2013</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BOL</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>Bolivia</td>\n",
       "      <td>Plurinational State of Bolivia</td>\n",
       "      <td>BO</td>\n",
       "      <td>Bolivian Boliviano</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Lower middle income; based on FY13 income clas...</td>\n",
       "      <td>BO</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>General Data Dissemination System (GDDS)</td>\n",
       "      <td>2012</td>\n",
       "      <td>Demographic and Health Survey (DHS), 2008</td>\n",
       "      <td>Integrated household survey (IHS), 2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Federative Republic of Brazil</td>\n",
       "      <td>BR</td>\n",
       "      <td>Brazilian real</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>BR</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2010</td>\n",
       "      <td>World Health Survey (WHS), 2003</td>\n",
       "      <td>Integrated household survey (IHS), 2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHL</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Chile</td>\n",
       "      <td>Republic of Chile</td>\n",
       "      <td>CL</td>\n",
       "      <td>Chilean peso</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>CL</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Integrated household survey (IHS), 2011</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COL</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Colombia</td>\n",
       "      <td>Republic of Colombia</td>\n",
       "      <td>CO</td>\n",
       "      <td>Colombian peso</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>CO</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2006</td>\n",
       "      <td>Demographic and Health Survey (DHS), 2010</td>\n",
       "      <td>Integrated household survey (IHS), 2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ECU</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Ecuador</td>\n",
       "      <td>Republic of Ecuador</td>\n",
       "      <td>EC</td>\n",
       "      <td>U.S. dollar</td>\n",
       "      <td>National accounts have been revised from 1965 ...</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>EC</td>\n",
       "      <td>...</td>\n",
       "      <td>Budgetary central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2010</td>\n",
       "      <td>Reproductive Health Survey (RHS), 2004</td>\n",
       "      <td>Integrated household survey (IHS), 2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013/15</td>\n",
       "      <td>2008.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2005.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GUY</td>\n",
       "      <td>Guyana</td>\n",
       "      <td>Guyana</td>\n",
       "      <td>Co-operative Republic of Guyana</td>\n",
       "      <td>GY</td>\n",
       "      <td>Guyana dollar</td>\n",
       "      <td>In 2010, the Bureau of Statistics introduced a...</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Lower middle income; based on FY13 income clas...</td>\n",
       "      <td>GY</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>General Data Dissemination System (GDDS)</td>\n",
       "      <td>2012</td>\n",
       "      <td>Multiple Indicator Cluster Survey (MICS), 2014</td>\n",
       "      <td>Integrated household survey (IHS), 1998</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PRY</td>\n",
       "      <td>Paraguay</td>\n",
       "      <td>Paraguay</td>\n",
       "      <td>Republic of Paraguay</td>\n",
       "      <td>PY</td>\n",
       "      <td>Paraguayan guarani</td>\n",
       "      <td>National accounts data have been revised from ...</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Lower middle income; based on FY13 income clas...</td>\n",
       "      <td>PY</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>General Data Dissemination System (GDDS)</td>\n",
       "      <td>2012</td>\n",
       "      <td>Reproductive Health Survey (RHS), 2008</td>\n",
       "      <td>Integrated household survey (IHS), 2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PER</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Peru</td>\n",
       "      <td>Republic of Peru</td>\n",
       "      <td>PE</td>\n",
       "      <td>Peruvian new sol</td>\n",
       "      <td>The new base year is 2007.</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>PE</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2007</td>\n",
       "      <td>Continuous Demographic and Health Survey (DHS)...</td>\n",
       "      <td>Integrated household survey (IHS), 2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2012</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2008.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SUR</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>Suriname</td>\n",
       "      <td>Republic of Suriname</td>\n",
       "      <td>SR</td>\n",
       "      <td>Suriname dollar</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>SR</td>\n",
       "      <td>...</td>\n",
       "      <td>Budgetary central government</td>\n",
       "      <td>General Data Dissemination System (GDDS)</td>\n",
       "      <td>2012</td>\n",
       "      <td>Multiple Indicator Cluster Survey (MICS), 2010</td>\n",
       "      <td>Expenditure survey/budget survey (ES/BS), 1999</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2008</td>\n",
       "      <td>2004.0</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2006.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>URY</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Oriental Republic of Uruguay</td>\n",
       "      <td>UY</td>\n",
       "      <td>Uruguayan peso</td>\n",
       "      <td>In 2011, the Central Bank revised national acc...</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>UY</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2011</td>\n",
       "      <td>Multiple Indicator Cluster Survey (MICS), 2012/13</td>\n",
       "      <td>Integrated household survey (IHS), 2013</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>VEN</td>\n",
       "      <td>Venezuela</td>\n",
       "      <td>Venezuela, RB</td>\n",
       "      <td>República Bolivariana de Venezuela</td>\n",
       "      <td>VE</td>\n",
       "      <td>Venezuelan bolivar fuerte</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>VE</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>General Data Dissemination System (GDDS)</td>\n",
       "      <td>2011</td>\n",
       "      <td>Multiple Indicator Cluster Survey (MICS), 2000</td>\n",
       "      <td>Integrated household survey (IHS), 2012</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2007</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>12 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Country Code Short Name     Table Name                           Long Name  \\\n",
       "0           ARG  Argentina      Argentina                  Argentine Republic   \n",
       "1           BOL    Bolivia        Bolivia      Plurinational State of Bolivia   \n",
       "2           BRA     Brazil         Brazil       Federative Republic of Brazil   \n",
       "3           CHL      Chile          Chile                   Republic of Chile   \n",
       "4           COL   Colombia       Colombia                Republic of Colombia   \n",
       "5           ECU    Ecuador        Ecuador                 Republic of Ecuador   \n",
       "6           GUY     Guyana         Guyana     Co-operative Republic of Guyana   \n",
       "7           PRY   Paraguay       Paraguay                Republic of Paraguay   \n",
       "8           PER       Peru           Peru                    Republic of Peru   \n",
       "9           SUR   Suriname       Suriname                Republic of Suriname   \n",
       "10          URY    Uruguay        Uruguay        Oriental Republic of Uruguay   \n",
       "11          VEN  Venezuela  Venezuela, RB  República Bolivariana de Venezuela   \n",
       "\n",
       "   2-alpha code              Currency Unit  \\\n",
       "0            AR             Argentine peso   \n",
       "1            BO         Bolivian Boliviano   \n",
       "2            BR             Brazilian real   \n",
       "3            CL               Chilean peso   \n",
       "4            CO             Colombian peso   \n",
       "5            EC                U.S. dollar   \n",
       "6            GY              Guyana dollar   \n",
       "7            PY         Paraguayan guarani   \n",
       "8            PE           Peruvian new sol   \n",
       "9            SR            Suriname dollar   \n",
       "10           UY             Uruguayan peso   \n",
       "11           VE  Venezuelan bolivar fuerte   \n",
       "\n",
       "                                        Special Notes  \\\n",
       "0                  The base year has changed to 2004.   \n",
       "1                                                 NaN   \n",
       "2                                                 NaN   \n",
       "3                                                 NaN   \n",
       "4                                                 NaN   \n",
       "5   National accounts have been revised from 1965 ...   \n",
       "6   In 2010, the Bureau of Statistics introduced a...   \n",
       "7   National accounts data have been revised from ...   \n",
       "8                          The new base year is 2007.   \n",
       "9                                                 NaN   \n",
       "10  In 2011, the Central Bank revised national acc...   \n",
       "11                                                NaN   \n",
       "\n",
       "                       Region  \\\n",
       "0   Latin America & Caribbean   \n",
       "1   Latin America & Caribbean   \n",
       "2   Latin America & Caribbean   \n",
       "3   Latin America & Caribbean   \n",
       "4   Latin America & Caribbean   \n",
       "5   Latin America & Caribbean   \n",
       "6   Latin America & Caribbean   \n",
       "7   Latin America & Caribbean   \n",
       "8   Latin America & Caribbean   \n",
       "9   Latin America & Caribbean   \n",
       "10  Latin America & Caribbean   \n",
       "11  Latin America & Caribbean   \n",
       "\n",
       "                                         Income Group WB-2 code  ...  \\\n",
       "0   Upper middle income; based on FY13 income clas...        AR  ...   \n",
       "1   Lower middle income; based on FY13 income clas...        BO  ...   \n",
       "2   Upper middle income; based on FY13 income clas...        BR  ...   \n",
       "3   Upper middle income; based on FY13 income clas...        CL  ...   \n",
       "4   Upper middle income; based on FY13 income clas...        CO  ...   \n",
       "5   Upper middle income; based on FY13 income clas...        EC  ...   \n",
       "6   Lower middle income; based on FY13 income clas...        GY  ...   \n",
       "7   Lower middle income; based on FY13 income clas...        PY  ...   \n",
       "8   Upper middle income; based on FY13 income clas...        PE  ...   \n",
       "9   Upper middle income; based on FY13 income clas...        SR  ...   \n",
       "10  Upper middle income; based on FY13 income clas...        UY  ...   \n",
       "11  Upper middle income; based on FY13 income clas...        VE  ...   \n",
       "\n",
       "      Government Accounting concept  \\\n",
       "0   Consolidated central government   \n",
       "1   Consolidated central government   \n",
       "2   Consolidated central government   \n",
       "3   Consolidated central government   \n",
       "4   Consolidated central government   \n",
       "5      Budgetary central government   \n",
       "6                               NaN   \n",
       "7   Consolidated central government   \n",
       "8   Consolidated central government   \n",
       "9      Budgetary central government   \n",
       "10  Consolidated central government   \n",
       "11  Consolidated central government   \n",
       "\n",
       "               IMF data dissemination standard Latest population census  \\\n",
       "0   Special Data Dissemination Standard (SDDS)                     2010   \n",
       "1     General Data Dissemination System (GDDS)                     2012   \n",
       "2   Special Data Dissemination Standard (SDDS)                     2010   \n",
       "3   Special Data Dissemination Standard (SDDS)                     2012   \n",
       "4   Special Data Dissemination Standard (SDDS)                     2006   \n",
       "5   Special Data Dissemination Standard (SDDS)                     2010   \n",
       "6     General Data Dissemination System (GDDS)                     2012   \n",
       "7     General Data Dissemination System (GDDS)                     2012   \n",
       "8   Special Data Dissemination Standard (SDDS)                     2007   \n",
       "9     General Data Dissemination System (GDDS)                     2012   \n",
       "10  Special Data Dissemination Standard (SDDS)                     2011   \n",
       "11    General Data Dissemination System (GDDS)                     2011   \n",
       "\n",
       "                              Latest household survey  \\\n",
       "0   Multiple Indicator Cluster Survey (MICS), 2011/12   \n",
       "1           Demographic and Health Survey (DHS), 2008   \n",
       "2                     World Health Survey (WHS), 2003   \n",
       "3                                                 NaN   \n",
       "4           Demographic and Health Survey (DHS), 2010   \n",
       "5              Reproductive Health Survey (RHS), 2004   \n",
       "6      Multiple Indicator Cluster Survey (MICS), 2014   \n",
       "7              Reproductive Health Survey (RHS), 2008   \n",
       "8   Continuous Demographic and Health Survey (DHS)...   \n",
       "9      Multiple Indicator Cluster Survey (MICS), 2010   \n",
       "10  Multiple Indicator Cluster Survey (MICS), 2012/13   \n",
       "11     Multiple Indicator Cluster Survey (MICS), 2000   \n",
       "\n",
       "   Source of most recent Income and expenditure data  \\\n",
       "0            Integrated household survey (IHS), 2012   \n",
       "1            Integrated household survey (IHS), 2012   \n",
       "2            Integrated household survey (IHS), 2012   \n",
       "3            Integrated household survey (IHS), 2011   \n",
       "4            Integrated household survey (IHS), 2012   \n",
       "5            Integrated household survey (IHS), 2013   \n",
       "6            Integrated household survey (IHS), 1998   \n",
       "7            Integrated household survey (IHS), 2013   \n",
       "8            Integrated household survey (IHS), 2013   \n",
       "9     Expenditure survey/budget survey (ES/BS), 1999   \n",
       "10           Integrated household survey (IHS), 2013   \n",
       "11           Integrated household survey (IHS), 2012   \n",
       "\n",
       "   Vital registration complete Latest agricultural census  \\\n",
       "0                          Yes                       2013   \n",
       "1                          NaN                       2013   \n",
       "2                          NaN                       2006   \n",
       "3                          Yes                       2007   \n",
       "4                          NaN                       2013   \n",
       "5                          NaN                    2013/15   \n",
       "6                          NaN                        NaN   \n",
       "7                          NaN                       2008   \n",
       "8                          NaN                       2012   \n",
       "9                          Yes                       2008   \n",
       "10                         Yes                       2011   \n",
       "11                         Yes                       2007   \n",
       "\n",
       "   Latest industrial data Latest trade data Latest water withdrawal data  \n",
       "0                  2002.0            2013.0                       2011.0  \n",
       "1                     NaN            2013.0                       2000.0  \n",
       "2                  2011.0            2013.0                       2010.0  \n",
       "3                     NaN            2013.0                       2006.0  \n",
       "4                  2011.0            2013.0                       2008.0  \n",
       "5                  2008.0            2013.0                       2005.0  \n",
       "6                     NaN            2013.0                       2010.0  \n",
       "7                  2002.0            2013.0                       2012.0  \n",
       "8                  2011.0            2013.0                       2008.0  \n",
       "9                  2004.0            2011.0                       2006.0  \n",
       "10                 2009.0            2013.0                       2000.0  \n",
       "11                    NaN            2011.0                       2000.0  \n",
       "\n",
       "[12 rows x 31 columns]"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_paises = ler_arquivo_csv(\n",
    "    nome_arquivo='dataset_bancomundial_paises.csv', decimal=',')\n",
    "\n",
    "filtro_paises_sul_americanos = ['ARG', 'BRA', 'BOL',  'CHL',\n",
    "                                'COL', 'ECU', 'GUY', 'PER', 'PRY', 'SUR', 'URY', 'VEN']\n",
    "\n",
    "# filtrar ocorrencias de varios termos em uma coluna\n",
    "df_paises_sul_americanos = df_paises.query(\n",
    "    \"`Country Code` in @filtro_paises_sul_americanos\")\n",
    "\n",
    "# apagar colunas do dataframe\n",
    "df_paises_sul_americanos = apagar_colunas(df_paises_sul_americanos, ['index'])\n",
    "\n",
    "df_paises_sul_americanos = df_paises_sul_americanos.reset_index(drop=True)\n",
    "\n",
    "# criando arquivo com dataframe\n",
    "gravar_arquivo_csv(nome_arquivo='dataset_paises_sul_americanos_ex2.csv',\n",
    "                   dataframe=df_paises_sul_americanos)\n",
    "\n",
    "df_paises_sul_americanos\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "169fa0d4",
   "metadata": {},
   "source": [
    "## Atividade 3\n",
    " Crie um único gráfico de controle, considerando a média e uma amplitude de 2 vezes o desvio padrão da para a série com o GDP para todos os países da América do Sul; o propósito é avaliar a média histórica do GDP para os países mencionados, no período 1960-2013; no eixo x do gráfico constará o ano (1960-2013) e o eixo y terá a média do GDP daquele ano, considerando todos os países; o desvio padrão será calculado a partir da média histórica do GDP para todos os países da América do Sul;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "22a4be16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média Geral =  3131.7418649434762\n",
      "Desvio Padrão Geral =  294.8354550437983\n",
      "Limite Superior de Controle =  3721.4127750310727\n",
      "Limite Inferior de Controle =  2542.07095485588\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# separando apenas o nome abreviado dos países\n",
    "filtro_cod_sul_americanos = df_paises_sul_americanos['Country Code']\n",
    "\n",
    "# filtrando apenas as linhas que contém o critério do filtro na coluna Country Code\n",
    "df_registros_paises_sul_americanos = df_gpd.query(\n",
    "    \"`Country Code` in @filtro_cod_sul_americanos\")\n",
    "\n",
    "# extraindo o nome dos países dos dados\n",
    "nome_paises = df_registros_paises_sul_americanos['Country Name']\n",
    "cod_paises_original = df_registros_paises_sul_americanos['Country Code']\n",
    "\n",
    "# apagando colunas para deixar apenas colunas numéricas\n",
    "df_registros_paises_sul_americanos = apagar_colunas(dataframe=df_registros_paises_sul_americanos,\n",
    "                                                    nome_colunas=[\n",
    "                                                        'Country Name', 'Country Code'])\n",
    "\n",
    "gerar_grafico_controle(\n",
    "    dataframe = df_registros_paises_sul_americanos,\n",
    "    dataframe_indices = nome_paises,\n",
    "    rotulo_eixo_x = 'Período em Anos',\n",
    "    rotulo_eixo_y = 'Valor GDP',\n",
    "    titulo_grafico = 'Gráfico de Controle GDP Per Capita da América Latina')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a56c16f9",
   "metadata": {},
   "source": [
    "## Exercício 4\n",
    "Identifique os anos e os países que se encontram fora da faixa de controle;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "3d75ff97",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Limite Superior de Controle =  3721.4127750310727\n",
      "Limite Inferior de Controle =  2542.07095485588\n",
      "Argentina ficou acima da faixa de controle 46 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1964', '1965', '1966', '1967', '1968',\n",
       "       '1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976',\n",
       "       '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984',\n",
       "       '1985', '1986', '1987', '1988', '1989', '1990', '1991', '1992',\n",
       "       '1993', '1994', '1995', '1996', '1997', '1998', '1999', '2000',\n",
       "       '2001', '2002', '2003', '2004', '2005', '2006'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Bolivia não ficou acima da faixa de controle nenhuma vez.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brazil ficou acima da faixa de controle 36 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1977', '1978', '1979', '1980', '1981', '1982', '1984', '1985',\n",
       "       '1986', '1987', '1988', '1989', '1990', '1991', '1992', '1993',\n",
       "       '1994', '1995', '1996', '1997', '1998', '1999', '2000', '2001',\n",
       "       '2002', '2003', '2004', '2005', '2006', '2007', '2008', '2009',\n",
       "       '2010', '2011', '2012', '2013'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chile ficou acima da faixa de controle 26 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1988', '1989', '1990', '1991', '1992', '1993', '1994', '1995',\n",
       "       '1996', '1997', '1998', '1999', '2000', '2001', '2002', '2003',\n",
       "       '2004', '2005', '2006', '2007', '2008', '2009', '2010', '2011',\n",
       "       '2012', '2013'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colombia ficou acima da faixa de controle 7 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['2007', '2008', '2009', '2010', '2011', '2012', '2013'],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Ecuador não ficou acima da faixa de controle nenhuma vez.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Guyana não ficou acima da faixa de controle nenhuma vez.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Paraguay não ficou acima da faixa de controle nenhuma vez.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peru ficou acima da faixa de controle 3 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['2011', '2012', '2013'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suriname ficou acima da faixa de controle 14 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1975', '1977', '1978', '1979', '1980', '1981', '1982', '2007',\n",
       "       '2008', '2009', '2010', '2011', '2012', '2013'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uruguay ficou acima da faixa de controle 32 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1978', '1979', '1980', '1981', '1982', '1987', '1988', '1989',\n",
       "       '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997',\n",
       "       '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005',\n",
       "       '2006', '2007', '2008', '2009', '2010', '2011', '2012', '2013'],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Venezuela, RB ficou acima da faixa de controle 54 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n",
       "       '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999',\n",
       "       '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007',\n",
       "       '2008', '2009', '2010', '2011', '2012', '2013'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------\n",
      "\n",
      "Limite Inferior Agora\n",
      "\n",
      "-----------------------------------------------------\n",
      "Argentina ficou abaixo da faixa de controle 1 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1963'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Bolivia ficou abaixo da faixa de controle 54 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n",
       "       '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999',\n",
       "       '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007',\n",
       "       '2008', '2009', '2010', '2011', '2012', '2013'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brazil ficou abaixo da faixa de controle 18 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1983'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chile ficou abaixo da faixa de controle 28 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Colombia ficou abaixo da faixa de controle 47 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n",
       "       '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999',\n",
       "       '2000', '2001', '2002', '2003', '2004', '2005', '2006'],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ecuador ficou abaixo da faixa de controle 54 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n",
       "       '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999',\n",
       "       '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007',\n",
       "       '2008', '2009', '2010', '2011', '2012', '2013'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Guyana ficou abaixo da faixa de controle 54 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n",
       "       '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999',\n",
       "       '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007',\n",
       "       '2008', '2009', '2010', '2011', '2012', '2013'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paraguay ficou abaixo da faixa de controle 54 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n",
       "       '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999',\n",
       "       '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007',\n",
       "       '2008', '2009', '2010', '2011', '2012', '2013'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peru ficou abaixo da faixa de controle 51 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n",
       "       '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999',\n",
       "       '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007',\n",
       "       '2008', '2009', '2010'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Suriname ficou abaixo da faixa de controle 25 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1976', '1983', '1984', '1985', '1986', '1987', '1988', '1989',\n",
       "       '1990', '1991', '1992', '1993', '1994', '1995', '1996', '1997',\n",
       "       '1998', '1999', '2000', '2001', '2002', '2003', '2004', '2005',\n",
       "       '2006'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uruguay ficou abaixo da faixa de controle 22 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1983', '1984', '1985', '1986'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Venezuela, RB não ficou abaixo da faixa de controle nenhuma vez.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# médias\n",
    "medias = df_registros_paises_sul_americanos.mean(\n",
    "    numeric_only=True, skipna=True, axis=0)\n",
    "media_geral = medias.mean()\n",
    "\n",
    "# desvio padrão anuais\n",
    "desvios_padrao = df_registros_paises_sul_americanos.std(\n",
    "    numeric_only=True, skipna=True, axis=0)\n",
    "desvio_padrao_geral = desvios_padrao.std()\n",
    "\n",
    "# limite superior de controle\n",
    "limite_superior = obter_limite_controle(media_dataframe=media_geral,\n",
    "                                        desvio_padrao_dataframe=desvio_padrao_geral * 2, limite_superior=True)\n",
    "\n",
    "# limite inferior de controle\n",
    "limite_inferior = obter_limite_controle(media_dataframe=media_geral,\n",
    "                                        desvio_padrao_dataframe=desvio_padrao_geral * 2, limite_superior=False)\n",
    "\n",
    "# separando quem saiu da faixa de controle superior e inferior\n",
    "\n",
    "\n",
    "fora_faixa_controle_superior = obter_elementos_saem_faixa_controle(\n",
    "    dataframe=df_registros_paises_sul_americanos,\n",
    "    limite_superior=True,\n",
    "    valor_limite=limite_superior)\n",
    "\n",
    "fora_faixa_controle_inferior = obter_elementos_saem_faixa_controle(\n",
    "    dataframe=df_registros_paises_sul_americanos,\n",
    "    limite_superior=False,\n",
    "    valor_limite=limite_inferior)\n",
    "\n",
    "exibir_saida_faixa_controle(dataframe=fora_faixa_controle_superior,\n",
    "                            dataframe_indices=nome_paises, limite_superior=True)\n",
    "\n",
    "print(\"-----------------------------------------------------\")\n",
    "print(\"\\nLimite Inferior Agora\\n\")\n",
    "print(\"-----------------------------------------------------\")\n",
    "\n",
    "\n",
    "exibir_saida_faixa_controle(dataframe=fora_faixa_controle_superior,\n",
    "                            dataframe_indices=nome_paises, limite_superior=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf62f525",
   "metadata": {},
   "source": [
    "## Exercício 5\n",
    "\n",
    "Considerando os países \"Brasil\", \"Uruguai\", \"Argentina\" e \"Paraguai\", assim denomidados de países do ConeSul, crie um dataset que envolva apenas o indicador GDP e persista em um arquivo."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "3137336a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>index</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Short Name</th>\n",
       "      <th>Table Name</th>\n",
       "      <th>Long Name</th>\n",
       "      <th>2-alpha code</th>\n",
       "      <th>Currency Unit</th>\n",
       "      <th>Special Notes</th>\n",
       "      <th>Region</th>\n",
       "      <th>Income Group</th>\n",
       "      <th>...</th>\n",
       "      <th>Government Accounting concept</th>\n",
       "      <th>IMF data dissemination standard</th>\n",
       "      <th>Latest population census</th>\n",
       "      <th>Latest household survey</th>\n",
       "      <th>Source of most recent Income and expenditure data</th>\n",
       "      <th>Vital registration complete</th>\n",
       "      <th>Latest agricultural census</th>\n",
       "      <th>Latest industrial data</th>\n",
       "      <th>Latest trade data</th>\n",
       "      <th>Latest water withdrawal data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentine Republic</td>\n",
       "      <td>AR</td>\n",
       "      <td>Argentine peso</td>\n",
       "      <td>The base year has changed to 2004.</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2010</td>\n",
       "      <td>Multiple Indicator Cluster Survey (MICS), 2011/12</td>\n",
       "      <td>Integrated household survey (IHS), 2012</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2013</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Federative Republic of Brazil</td>\n",
       "      <td>BR</td>\n",
       "      <td>Brazilian real</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2010</td>\n",
       "      <td>World Health Survey (WHS), 2003</td>\n",
       "      <td>Integrated household survey (IHS), 2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>152</td>\n",
       "      <td>PRY</td>\n",
       "      <td>Paraguay</td>\n",
       "      <td>Paraguay</td>\n",
       "      <td>Republic of Paraguay</td>\n",
       "      <td>PY</td>\n",
       "      <td>Paraguayan guarani</td>\n",
       "      <td>National accounts data have been revised from ...</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Lower middle income; based on FY13 income clas...</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>General Data Dissemination System (GDDS)</td>\n",
       "      <td>2012</td>\n",
       "      <td>Reproductive Health Survey (RHS), 2008</td>\n",
       "      <td>Integrated household survey (IHS), 2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>207</td>\n",
       "      <td>URY</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Oriental Republic of Uruguay</td>\n",
       "      <td>UY</td>\n",
       "      <td>Uruguayan peso</td>\n",
       "      <td>In 2011, the Central Bank revised national acc...</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2011</td>\n",
       "      <td>Multiple Indicator Cluster Survey (MICS), 2012/13</td>\n",
       "      <td>Integrated household survey (IHS), 2013</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index Country Code Short Name Table Name                      Long Name  \\\n",
       "6        6          ARG  Argentina  Argentina             Argentine Republic   \n",
       "24      24          BRA     Brazil     Brazil  Federative Republic of Brazil   \n",
       "152    152          PRY   Paraguay   Paraguay           Republic of Paraguay   \n",
       "207    207          URY    Uruguay    Uruguay   Oriental Republic of Uruguay   \n",
       "\n",
       "    2-alpha code       Currency Unit  \\\n",
       "6             AR      Argentine peso   \n",
       "24            BR      Brazilian real   \n",
       "152           PY  Paraguayan guarani   \n",
       "207           UY      Uruguayan peso   \n",
       "\n",
       "                                         Special Notes  \\\n",
       "6                   The base year has changed to 2004.   \n",
       "24                                                 NaN   \n",
       "152  National accounts data have been revised from ...   \n",
       "207  In 2011, the Central Bank revised national acc...   \n",
       "\n",
       "                        Region  \\\n",
       "6    Latin America & Caribbean   \n",
       "24   Latin America & Caribbean   \n",
       "152  Latin America & Caribbean   \n",
       "207  Latin America & Caribbean   \n",
       "\n",
       "                                          Income Group  ...  \\\n",
       "6    Upper middle income; based on FY13 income clas...  ...   \n",
       "24   Upper middle income; based on FY13 income clas...  ...   \n",
       "152  Lower middle income; based on FY13 income clas...  ...   \n",
       "207  Upper middle income; based on FY13 income clas...  ...   \n",
       "\n",
       "       Government Accounting concept  \\\n",
       "6    Consolidated central government   \n",
       "24   Consolidated central government   \n",
       "152  Consolidated central government   \n",
       "207  Consolidated central government   \n",
       "\n",
       "                IMF data dissemination standard Latest population census  \\\n",
       "6    Special Data Dissemination Standard (SDDS)                     2010   \n",
       "24   Special Data Dissemination Standard (SDDS)                     2010   \n",
       "152    General Data Dissemination System (GDDS)                     2012   \n",
       "207  Special Data Dissemination Standard (SDDS)                     2011   \n",
       "\n",
       "                               Latest household survey  \\\n",
       "6    Multiple Indicator Cluster Survey (MICS), 2011/12   \n",
       "24                     World Health Survey (WHS), 2003   \n",
       "152             Reproductive Health Survey (RHS), 2008   \n",
       "207  Multiple Indicator Cluster Survey (MICS), 2012/13   \n",
       "\n",
       "    Source of most recent Income and expenditure data  \\\n",
       "6             Integrated household survey (IHS), 2012   \n",
       "24            Integrated household survey (IHS), 2012   \n",
       "152           Integrated household survey (IHS), 2013   \n",
       "207           Integrated household survey (IHS), 2013   \n",
       "\n",
       "    Vital registration complete Latest agricultural census  \\\n",
       "6                           Yes                       2013   \n",
       "24                          NaN                       2006   \n",
       "152                         NaN                       2008   \n",
       "207                         Yes                       2011   \n",
       "\n",
       "    Latest industrial data Latest trade data Latest water withdrawal data  \n",
       "6                   2002.0            2013.0                       2011.0  \n",
       "24                  2011.0            2013.0                       2010.0  \n",
       "152                 2002.0            2013.0                       2012.0  \n",
       "207                 2009.0            2013.0                       2000.0  \n",
       "\n",
       "[4 rows x 32 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "    }\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>index</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Short Name</th>\n",
       "      <th>Table Name</th>\n",
       "      <th>Long Name</th>\n",
       "      <th>2-alpha code</th>\n",
       "      <th>Currency Unit</th>\n",
       "      <th>Special Notes</th>\n",
       "      <th>Region</th>\n",
       "      <th>Income Group</th>\n",
       "      <th>...</th>\n",
       "      <th>Government Accounting concept</th>\n",
       "      <th>IMF data dissemination standard</th>\n",
       "      <th>Latest population census</th>\n",
       "      <th>Latest household survey</th>\n",
       "      <th>Source of most recent Income and expenditure data</th>\n",
       "      <th>Vital registration complete</th>\n",
       "      <th>Latest agricultural census</th>\n",
       "      <th>Latest industrial data</th>\n",
       "      <th>Latest trade data</th>\n",
       "      <th>Latest water withdrawal data</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>ARG</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentina</td>\n",
       "      <td>Argentine Republic</td>\n",
       "      <td>AR</td>\n",
       "      <td>Argentine peso</td>\n",
       "      <td>The base year has changed to 2004.</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2010</td>\n",
       "      <td>Multiple Indicator Cluster Survey (MICS), 2011/12</td>\n",
       "      <td>Integrated household survey (IHS), 2012</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2013</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2011.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>BRA</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Brazil</td>\n",
       "      <td>Federative Republic of Brazil</td>\n",
       "      <td>BR</td>\n",
       "      <td>Brazilian real</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2010</td>\n",
       "      <td>World Health Survey (WHS), 2003</td>\n",
       "      <td>Integrated household survey (IHS), 2012</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2006</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2010.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>152</td>\n",
       "      <td>PRY</td>\n",
       "      <td>Paraguay</td>\n",
       "      <td>Paraguay</td>\n",
       "      <td>Republic of Paraguay</td>\n",
       "      <td>PY</td>\n",
       "      <td>Paraguayan guarani</td>\n",
       "      <td>National accounts data have been revised from ...</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Lower middle income; based on FY13 income clas...</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>General Data Dissemination System (GDDS)</td>\n",
       "      <td>2012</td>\n",
       "      <td>Reproductive Health Survey (RHS), 2008</td>\n",
       "      <td>Integrated household survey (IHS), 2013</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2008</td>\n",
       "      <td>2002.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2012.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>207</td>\n",
       "      <td>URY</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Uruguay</td>\n",
       "      <td>Oriental Republic of Uruguay</td>\n",
       "      <td>UY</td>\n",
       "      <td>Uruguayan peso</td>\n",
       "      <td>In 2011, the Central Bank revised national acc...</td>\n",
       "      <td>Latin America &amp; Caribbean</td>\n",
       "      <td>Upper middle income; based on FY13 income clas...</td>\n",
       "      <td>...</td>\n",
       "      <td>Consolidated central government</td>\n",
       "      <td>Special Data Dissemination Standard (SDDS)</td>\n",
       "      <td>2011</td>\n",
       "      <td>Multiple Indicator Cluster Survey (MICS), 2012/13</td>\n",
       "      <td>Integrated household survey (IHS), 2013</td>\n",
       "      <td>Yes</td>\n",
       "      <td>2011</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2013.0</td>\n",
       "      <td>2000.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index Country Code Short Name Table Name                      Long Name  \\\n",
       "6        6          ARG  Argentina  Argentina             Argentine Republic   \n",
       "24      24          BRA     Brazil     Brazil  Federative Republic of Brazil   \n",
       "152    152          PRY   Paraguay   Paraguay           Republic of Paraguay   \n",
       "207    207          URY    Uruguay    Uruguay   Oriental Republic of Uruguay   \n",
       "\n",
       "    2-alpha code       Currency Unit  \\\n",
       "6             AR      Argentine peso   \n",
       "24            BR      Brazilian real   \n",
       "152           PY  Paraguayan guarani   \n",
       "207           UY      Uruguayan peso   \n",
       "\n",
       "                                         Special Notes  \\\n",
       "6                   The base year has changed to 2004.   \n",
       "24                                                 NaN   \n",
       "152  National accounts data have been revised from ...   \n",
       "207  In 2011, the Central Bank revised national acc...   \n",
       "\n",
       "                        Region  \\\n",
       "6    Latin America & Caribbean   \n",
       "24   Latin America & Caribbean   \n",
       "152  Latin America & Caribbean   \n",
       "207  Latin America & Caribbean   \n",
       "\n",
       "                                          Income Group  ...  \\\n",
       "6    Upper middle income; based on FY13 income clas...  ...   \n",
       "24   Upper middle income; based on FY13 income clas...  ...   \n",
       "152  Lower middle income; based on FY13 income clas...  ...   \n",
       "207  Upper middle income; based on FY13 income clas...  ...   \n",
       "\n",
       "       Government Accounting concept  \\\n",
       "6    Consolidated central government   \n",
       "24   Consolidated central government   \n",
       "152  Consolidated central government   \n",
       "207  Consolidated central government   \n",
       "\n",
       "                IMF data dissemination standard Latest population census  \\\n",
       "6    Special Data Dissemination Standard (SDDS)                     2010   \n",
       "24   Special Data Dissemination Standard (SDDS)                     2010   \n",
       "152    General Data Dissemination System (GDDS)                     2012   \n",
       "207  Special Data Dissemination Standard (SDDS)                     2011   \n",
       "\n",
       "                               Latest household survey  \\\n",
       "6    Multiple Indicator Cluster Survey (MICS), 2011/12   \n",
       "24                     World Health Survey (WHS), 2003   \n",
       "152             Reproductive Health Survey (RHS), 2008   \n",
       "207  Multiple Indicator Cluster Survey (MICS), 2012/13   \n",
       "\n",
       "    Source of most recent Income and expenditure data  \\\n",
       "6             Integrated household survey (IHS), 2012   \n",
       "24            Integrated household survey (IHS), 2012   \n",
       "152           Integrated household survey (IHS), 2013   \n",
       "207           Integrated household survey (IHS), 2013   \n",
       "\n",
       "    Vital registration complete Latest agricultural census  \\\n",
       "6                           Yes                       2013   \n",
       "24                          NaN                       2006   \n",
       "152                         NaN                       2008   \n",
       "207                         Yes                       2011   \n",
       "\n",
       "    Latest industrial data Latest trade data Latest water withdrawal data  \n",
       "6                   2002.0            2013.0                       2011.0  \n",
       "24                  2011.0            2013.0                       2010.0  \n",
       "152                 2002.0            2013.0                       2012.0  \n",
       "207                 2009.0            2013.0                       2000.0  \n",
       "\n",
       "[4 rows x 32 columns]"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filtro_paises_conesul = ['ARG', 'BRA', 'PRY', 'URY']\n",
    "\n",
    "# filtrar ocorrencias de varios termos em uma coluna\n",
    "df_paises_conesul = df_paises.query(\"`Country Code` in @filtro_paises_conesul\")\n",
    "\n",
    "display(df_paises_conesul)\n",
    "\n",
    "# criando arquivo com dataframe\n",
    "gravar_arquivo_csv(dataframe=df_paises_conesul,\n",
    "                   nome_arquivo='dataset_paises_conesul_ex5.csv')\n",
    "\n",
    "df_paises_conesul.head(4)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "787cb24f",
   "metadata": {},
   "source": [
    "## Exercício 6\n",
    "\n",
    "Crie um gráfico de controle, considerando a média e uma amplitude de 2 vezes o desvio padrão da série com o GDP para todos os países do ConeSul, com a mesma abordagem descrita no item 3;"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "52a684d4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Média Geral =  3530.0188826652775\n",
      "Desvio Padrão Geral =  405.7032948673396\n",
      "Limite Superior de Controle =  4341.425472399957\n",
      "Limite Inferior de Controle =  2718.6122929305984\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# separando apenas o nome abreviado dos países\n",
    "filtro_cod_conesul = df_paises_conesul['Country Code']\n",
    "\n",
    "# filtrando apenas as linhas que contém o critério do filtro na coluna Country Code\n",
    "df_registros_paises_conesul = df_gpd.query(\n",
    "    \"`Country Code` in @filtro_cod_conesul\")\n",
    "\n",
    "# extraindo o nome dos países dos dados\n",
    "nome_paises = df_registros_paises_conesul['Country Name']\n",
    "cod_paises_original = df_registros_paises_conesul['Country Code']\n",
    "\n",
    "# apagando colunas para deixar apenas colunas numéricas\n",
    "df_registros_paises_conesul = apagar_colunas(dataframe=df_registros_paises_conesul,\n",
    "                                                    nome_colunas=[\n",
    "                                                        'Country Name', 'Country Code'])\n",
    "\n",
    "gerar_grafico_controle(\n",
    "    dataframe = df_registros_paises_conesul,\n",
    "    dataframe_indices = nome_paises,\n",
    "    rotulo_eixo_x = 'Período em Anos',\n",
    "    rotulo_eixo_y = 'Valor GDP',\n",
    "    titulo_grafico = 'Gráfico de Controle GDP Per Capita da América Latina')\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ffbe58e",
   "metadata": {},
   "source": [
    "## Exercício 7\n",
    "Identifique os anos e os países que se encontram foram da faixa de controle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "7b603859",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Limite Superior de Controle =  4341.425472399957\n",
      "Limite Inferior de Controle =  2718.6122929305984\n",
      "Argentina ficou acima da faixa de controle 36 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1969', '1970', '1971', '1972', '1973', '1974', '1975', '1976',\n",
       "       '1977', '1978', '1979', '1980', '1981', '1982', '1983', '1984',\n",
       "       '1985', '1986', '1987', '1988', '1991', '1992', '1993', '1994',\n",
       "       '1995', '1996', '1997', '1998', '1999', '2000', '2001', '2002',\n",
       "       '2003', '2004', '2005', '2006'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brazil ficou acima da faixa de controle 15 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1997', '2000', '2001', '2002', '2003', '2004', '2005', '2006',\n",
       "       '2007', '2008', '2009', '2010', '2011', '2012', '2013'],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "'Paraguay não ficou acima da faixa de controle nenhuma vez.'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uruguay ficou acima da faixa de controle 22 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999',\n",
       "       '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007',\n",
       "       '2008', '2009', '2010', '2011', '2012', '2013'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-----------------------------------------------------\n",
      "\n",
      "Limite Inferior Agora\n",
      "\n",
      "-----------------------------------------------------\n",
      "Argentina ficou abaixo da faixa de controle 11 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1989', '1990'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Brazil ficou abaixo da faixa de controle 39 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n",
       "       '1992', '1993', '1994', '1995', '1996', '1998', '1999'],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Paraguay ficou abaixo da faixa de controle 54 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991',\n",
       "       '1992', '1993', '1994', '1995', '1996', '1997', '1998', '1999',\n",
       "       '2000', '2001', '2002', '2003', '2004', '2005', '2006', '2007',\n",
       "       '2008', '2009', '2010', '2011', '2012', '2013'], dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Uruguay ficou abaixo da faixa de controle 32 vezes, sendo os anos de:\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array(['1960', '1961', '1962', '1963', '1964', '1965', '1966', '1967',\n",
       "       '1968', '1969', '1970', '1971', '1972', '1973', '1974', '1975',\n",
       "       '1976', '1977', '1978', '1979', '1980', '1981', '1982', '1983',\n",
       "       '1984', '1985', '1986', '1987', '1988', '1989', '1990', '1991'],\n",
       "      dtype=object)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# médias\n",
    "medias = df_registros_paises_conesul.mean(\n",
    "    numeric_only=True, skipna=True, axis=0)\n",
    "media_geral = medias.mean()\n",
    "\n",
    "# desvio padrão anuais\n",
    "desvios_padrao = df_registros_paises_conesul.std(\n",
    "    numeric_only=True, skipna=True, axis=0)\n",
    "desvio_padrao_geral = desvios_padrao.std()\n",
    "\n",
    "# limite superior de controle\n",
    "limite_superior = obter_limite_controle(media_dataframe=media_geral,\n",
    "                                        desvio_padrao_dataframe=desvio_padrao_geral * 2, limite_superior=True)\n",
    "\n",
    "# limite inferior de controle\n",
    "limite_inferior = obter_limite_controle(media_dataframe=media_geral,\n",
    "                                        desvio_padrao_dataframe=desvio_padrao_geral * 2, limite_superior=False)\n",
    "\n",
    "# separando quem saiu da faixa de controle superior e inferior\n",
    "\n",
    "fora_faixa_controle_superior = obter_elementos_saem_faixa_controle(\n",
    "    dataframe=df_registros_paises_conesul,\n",
    "    limite_superior=True,\n",
    "    valor_limite=limite_superior)\n",
    "\n",
    "fora_faixa_controle_inferior = obter_elementos_saem_faixa_controle(\n",
    "    dataframe=df_registros_paises_conesul,\n",
    "    limite_superior=False,\n",
    "    valor_limite=limite_inferior)\n",
    "\n",
    "exibir_saida_faixa_controle(dataframe=fora_faixa_controle_superior,\n",
    "                            dataframe_indices=nome_paises, limite_superior=True)\n",
    "\n",
    "print(\"-----------------------------------------------------\")\n",
    "print(\"\\nLimite Inferior Agora\\n\")\n",
    "print(\"-----------------------------------------------------\")\n",
    "\n",
    "\n",
    "exibir_saida_faixa_controle(dataframe=fora_faixa_controle_superior,\n",
    "                            dataframe_indices=nome_paises, limite_superior=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3e8bff1",
   "metadata": {},
   "source": [
    "## Exercício 8\n",
    "Descreva as estatísticas sobre o indicador GDP para os países da América do Sul, com uma tabela que contenha o número de medidas válidas, o número de medidas ausentes, a média, o mínimo, o máximo, a mediana, o desvio padrão e a variância do GDP por país"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "9e6c0559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<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>Países</th>\n",
       "      <th>Médias</th>\n",
       "      <th>Desvios</th>\n",
       "      <th>Mínimos</th>\n",
       "      <th>Máximos</th>\n",
       "      <th>Medianas</th>\n",
       "      <th>Variância</th>\n",
       "      <th>Válidas</th>\n",
       "      <th>Ausentes</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ARG</td>\n",
       "      <td>4790.404661</td>\n",
       "      <td>603.273175</td>\n",
       "      <td>3522.337065</td>\n",
       "      <td>6195.384942</td>\n",
       "      <td>4855.354103</td>\n",
       "      <td>3.639385e+05</td>\n",
       "      <td>47</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BOL</td>\n",
       "      <td>980.230419</td>\n",
       "      <td>117.342266</td>\n",
       "      <td>799.950086</td>\n",
       "      <td>1323.124571</td>\n",
       "      <td>963.695207</td>\n",
       "      <td>1.376921e+04</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BRA</td>\n",
       "      <td>3791.851543</td>\n",
       "      <td>1142.202778</td>\n",
       "      <td>1726.566347</td>\n",
       "      <td>5823.043784</td>\n",
       "      <td>3996.243028</td>\n",
       "      <td>1.304627e+06</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>CHL</td>\n",
       "      <td>4737.001266</td>\n",
       "      <td>2236.816807</td>\n",
       "      <td>2479.185118</td>\n",
       "      <td>9728.481224</td>\n",
       "      <td>3498.735159</td>\n",
       "      <td>5.003349e+06</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>COL</td>\n",
       "      <td>2654.812012</td>\n",
       "      <td>787.063610</td>\n",
       "      <td>1452.070326</td>\n",
       "      <td>4394.066614</td>\n",
       "      <td>2594.982776</td>\n",
       "      <td>6.194691e+05</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ECU</td>\n",
       "      <td>2511.124108</td>\n",
       "      <td>551.126518</td>\n",
       "      <td>1572.431900</td>\n",
       "      <td>3700.526148</td>\n",
       "      <td>2629.246711</td>\n",
       "      <td>3.037404e+05</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GUY</td>\n",
       "      <td>913.797234</td>\n",
       "      <td>168.069157</td>\n",
       "      <td>658.976280</td>\n",
       "      <td>1336.341400</td>\n",
       "      <td>887.478025</td>\n",
       "      <td>2.824724e+04</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>PRY</td>\n",
       "      <td>1229.303304</td>\n",
       "      <td>392.862647</td>\n",
       "      <td>569.512739</td>\n",
       "      <td>1929.094840</td>\n",
       "      <td>1372.977508</td>\n",
       "      <td>1.543411e+05</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>PER</td>\n",
       "      <td>2495.336615</td>\n",
       "      <td>501.221096</td>\n",
       "      <td>1855.983158</td>\n",
       "      <td>4109.596136</td>\n",
       "      <td>2365.303708</td>\n",
       "      <td>2.512226e+05</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SUR</td>\n",
       "      <td>3503.401865</td>\n",
       "      <td>532.028103</td>\n",
       "      <td>2730.513671</td>\n",
       "      <td>4568.685095</td>\n",
       "      <td>3476.253539</td>\n",
       "      <td>2.830539e+05</td>\n",
       "      <td>39</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>URY</td>\n",
       "      <td>4323.779943</td>\n",
       "      <td>1228.118807</td>\n",
       "      <td>3021.774573</td>\n",
       "      <td>7808.626662</td>\n",
       "      <td>3967.121299</td>\n",
       "      <td>1.508276e+06</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>VEN</td>\n",
       "      <td>6008.967337</td>\n",
       "      <td>647.454483</td>\n",
       "      <td>4322.636853</td>\n",
       "      <td>7138.186093</td>\n",
       "      <td>5974.962032</td>\n",
       "      <td>4.191973e+05</td>\n",
       "      <td>54</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Países       Médias      Desvios      Mínimos      Máximos     Medianas  \\\n",
       "0     ARG  4790.404661   603.273175  3522.337065  6195.384942  4855.354103   \n",
       "1     BOL   980.230419   117.342266   799.950086  1323.124571   963.695207   \n",
       "2     BRA  3791.851543  1142.202778  1726.566347  5823.043784  3996.243028   \n",
       "3     CHL  4737.001266  2236.816807  2479.185118  9728.481224  3498.735159   \n",
       "4     COL  2654.812012   787.063610  1452.070326  4394.066614  2594.982776   \n",
       "5     ECU  2511.124108   551.126518  1572.431900  3700.526148  2629.246711   \n",
       "6     GUY   913.797234   168.069157   658.976280  1336.341400   887.478025   \n",
       "7     PRY  1229.303304   392.862647   569.512739  1929.094840  1372.977508   \n",
       "8     PER  2495.336615   501.221096  1855.983158  4109.596136  2365.303708   \n",
       "9     SUR  3503.401865   532.028103  2730.513671  4568.685095  3476.253539   \n",
       "10    URY  4323.779943  1228.118807  3021.774573  7808.626662  3967.121299   \n",
       "11    VEN  6008.967337   647.454483  4322.636853  7138.186093  5974.962032   \n",
       "\n",
       "       Variância  Válidas  Ausentes  \n",
       "0   3.639385e+05       47         7  \n",
       "1   1.376921e+04       54         0  \n",
       "2   1.304627e+06       54         0  \n",
       "3   5.003349e+06       54         0  \n",
       "4   6.194691e+05       54         0  \n",
       "5   3.037404e+05       54         0  \n",
       "6   2.824724e+04       54         0  \n",
       "7   1.543411e+05       54         0  \n",
       "8   2.512226e+05       54         0  \n",
       "9   2.830539e+05       39        15  \n",
       "10  1.508276e+06       54         0  \n",
       "11  4.191973e+05       54         0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "medias = []\n",
    "desvios = []\n",
    "minimos = []\n",
    "maximos = []\n",
    "medianas = []\n",
    "variancia = []\n",
    "validas = ausentes = []\n",
    "ausentes = []\n",
    "for indice, linha in df_registros_paises_sul_americanos.iterrows():\n",
    "    dados_pais: pd.Series = linha.to_frame() \n",
    "    \n",
    "    medias.append(dados_pais.mean(numeric_only=True, skipna=True).values[0])\n",
    "    desvios.append(dados_pais.std(numeric_only=True, skipna=True).values[0])\n",
    "    minimos.append(dados_pais.min(numeric_only=True, skipna=True).values[0])\n",
    "    maximos.append(dados_pais.max(numeric_only=True, skipna=True).values[0])\n",
    "    medianas.append(dados_pais.median(numeric_only=True, skipna=True).values[0])\n",
    "    variancia.append(dados_pais.var(numeric_only=True, skipna=True).values[0])\n",
    "    ausentes.append(dados_pais.isna().sum().values[0])\n",
    "    validas.append(dados_pais.shape[0] - dados_pais.isna().sum(axis=0).values[0])\n",
    "\n",
    "dados = pd.DataFrame({'Países': filtro_cod_sul_americanos ,'Médias': medias,  'Desvios': desvios, 'Mínimos': minimos, 'Máximos' : maximos, 'Medianas': medianas, 'Variância': variancia, 'Válidas': validas, 'Ausentes': ausentes })\n",
    "\n",
    "display(dados)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78e6be22",
   "metadata": {},
   "source": [
    "## Exercício 9\n",
    "Construa um boxplot que relacione o indicador GDP no período 1960-2013, para os países do ConeSul, no período 1960-2013."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "id": "51ff71c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1332x756 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "nomes_colunas: list[str] = df_registros_paises_conesul.columns\n",
    "\n",
    "data = []\n",
    "\n",
    "dataframe =  df_registros_paises_conesul.dropna(axis = 1)\n",
    "\n",
    "for indice, linha in dataframe.iterrows():\n",
    "    dados_pais = linha.values\n",
    "    data.append(dados_pais)\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "fig.set_size_inches(18.5, 10.5, forward=True)\n",
    "plt.title(\"PIB per capita - Paises Cone Sul\", fontsize=15)\n",
    "plt.xlabel(\"Paises\", fontsize=13)\n",
    "plt.ylabel(\"PIB per capita\", fontsize=13)\n",
    "ax.boxplot(data, labels=nome_paises)\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "d4ecc00314b053340c011f30feae2cb0599056a19fa729c140ca6ecfe444bda2"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}