Analise_1_TCE-RS.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise Exploratória em Conjunto de Dados do TCE-RS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Análise 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Business Understanding"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    - Quais são os principais bens materiais comprados pelos órgãos públicos do Rio Grande do Sul?\n",
    "    - É possível identificar algum padrão nas compras? Seja por época do ano ou por tipo de órgão comprador? \n",
    "    - O que mais é possível descobrir sobre as licitações do estado?\n",
    "    - Manual disponível em: https://portalnovo.tce.rs.gov.br/repo/cex/licitacon/eValidador-licitacon-manual-layout-1.4.pdf"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Understanding (licitacoes.csv)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib as mat\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import figure\n",
    "from IPython.display import display\n",
    "plt.style.use('ggplot')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Gerar output consolidado na pasta raiz como série temporal (2016-2019; reutilizado na Analise_3_TCE-RS)\n",
    "year = [2016, 2017, 2018, 2019]\n",
    "cols = ['NR_LICITACAO', 'ANO_LICITACAO', 'DT_HOMOLOGACAO', 'CD_TIPO_MODALIDADE', 'VL_LICITACAO', 'DS_OBJETO', 'TP_OBJETO', 'NM_ORGAO']\n",
    "consolidated = pd.DataFrame()\n",
    "\n",
    "if not os.path.exists('licitacoes.csv'):\n",
    "    for y in year:\n",
    "        path = r'C:\\Users\\casoto\\Desktop\\TCE-RS\\\\' + str(y) + '\\\\licitacao.csv'\n",
    "        df = pd.read_csv(path, usecols = cols)\n",
    "        consolidated = pd.concat([df, consolidated])\n",
    "    consolidated.to_csv('licitacoes.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['.ipynb_checkpoints',\n",
       " '2016',\n",
       " '2016.csv.zip',\n",
       " '2017',\n",
       " '2017.csv.zip',\n",
       " '2018',\n",
       " '2018.csv.zip',\n",
       " '2019',\n",
       " '2019.csv.zip',\n",
       " 'Analise_1_TCE-RS.ipynb',\n",
       " 'Analise_2_TCE-RS.ipynb',\n",
       " 'Analise_3_TCE-RS.ipynb',\n",
       " 'Descrição do Teste - Data Scientist_1.pdf',\n",
       " 'itens.csv',\n",
       " 'licitacoes.csv']"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando bases disponíveis\n",
    "path = r'C:\\Users\\casoto\\Desktop\\TCE-RS'\n",
    "os.listdir(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NM_ORGAO</th>\n",
       "      <th>NR_LICITACAO</th>\n",
       "      <th>ANO_LICITACAO</th>\n",
       "      <th>CD_TIPO_MODALIDADE</th>\n",
       "      <th>TP_OBJETO</th>\n",
       "      <th>DS_OBJETO</th>\n",
       "      <th>VL_LICITACAO</th>\n",
       "      <th>DT_HOMOLOGACAO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PM DE NOVA PALMA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>TMP</td>\n",
       "      <td>CSE</td>\n",
       "      <td>CONTRATAÇÃO GLOBAL DE EMPRESA PARA CONSTRUÇÃO ...</td>\n",
       "      <td>276405.12</td>\n",
       "      <td>2019-10-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PM DE NOVA PALMA</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>TMP</td>\n",
       "      <td>OSE</td>\n",
       "      <td>Reforma da UBS do Distrito da Vila Cruz, pintu...</td>\n",
       "      <td>75396.00</td>\n",
       "      <td>2019-04-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CIA. ÁGUAS TERMAIS MARCELINO RAMOS - TERMASA</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>EST</td>\n",
       "      <td>PER</td>\n",
       "      <td>Constitui objeto da presente Licitação a explo...</td>\n",
       "      <td>105612.48</td>\n",
       "      <td>2019-08-14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PM DE ESPERANÇA DO SUL</td>\n",
       "      <td>34.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>CNV</td>\n",
       "      <td>OUS</td>\n",
       "      <td>Contratação de serviços de arbitragem para atu...</td>\n",
       "      <td>10600.00</td>\n",
       "      <td>2019-05-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PM DE BOA VISTA DO SUL</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>PRP</td>\n",
       "      <td>OUS</td>\n",
       "      <td>Contratação de empresa para prestação de servi...</td>\n",
       "      <td>430770.00</td>\n",
       "      <td>2019-01-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                       NM_ORGAO  NR_LICITACAO  ANO_LICITACAO  \\\n",
       "0                              PM DE NOVA PALMA           3.0           2019   \n",
       "1                              PM DE NOVA PALMA           1.0           2019   \n",
       "2  CIA. ÁGUAS TERMAIS MARCELINO RAMOS - TERMASA           3.0           2019   \n",
       "3                        PM DE ESPERANÇA DO SUL          34.0           2019   \n",
       "4                        PM DE BOA VISTA DO SUL           3.0           2019   \n",
       "\n",
       "  CD_TIPO_MODALIDADE TP_OBJETO  \\\n",
       "0                TMP       CSE   \n",
       "1                TMP       OSE   \n",
       "2                EST       PER   \n",
       "3                CNV       OUS   \n",
       "4                PRP       OUS   \n",
       "\n",
       "                                           DS_OBJETO  VL_LICITACAO  \\\n",
       "0  CONTRATAÇÃO GLOBAL DE EMPRESA PARA CONSTRUÇÃO ...     276405.12   \n",
       "1  Reforma da UBS do Distrito da Vila Cruz, pintu...      75396.00   \n",
       "2  Constitui objeto da presente Licitação a explo...     105612.48   \n",
       "3  Contratação de serviços de arbitragem para atu...      10600.00   \n",
       "4  Contratação de empresa para prestação de servi...     430770.00   \n",
       "\n",
       "  DT_HOMOLOGACAO  \n",
       "0     2019-10-11  \n",
       "1     2019-04-29  \n",
       "2     2019-08-14  \n",
       "3     2019-05-07  \n",
       "4     2019-01-28  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Analisando arquivo licitacoes.csv\n",
    "licitacao = pd.read_csv(path + '\\\\licitacoes.csv')\n",
    "pd.options.display.max_columns = None # Mostrar todas as colunas\n",
    "licitacao.drop(columns = 'Unnamed: 0', inplace = True)\n",
    "licitacao.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>NR_LICITACAO</th>\n",
       "      <th>ANO_LICITACAO</th>\n",
       "      <th>CD_TIPO_MODALIDADE</th>\n",
       "      <th>VL_LICITACAO</th>\n",
       "      <th>DS_OBJETO</th>\n",
       "      <th>TP_OBJETO</th>\n",
       "      <th>NM_ORGAO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>TMP</td>\n",
       "      <td>276405.12</td>\n",
       "      <td>CONTRATAÇÃO GLOBAL DE EMPRESA PARA CONSTRUÇÃO ...</td>\n",
       "      <td>CSE</td>\n",
       "      <td>PM DE NOVA PALMA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>TMP</td>\n",
       "      <td>75396.00</td>\n",
       "      <td>Reforma da UBS do Distrito da Vila Cruz, pintu...</td>\n",
       "      <td>OSE</td>\n",
       "      <td>PM DE NOVA PALMA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>EST</td>\n",
       "      <td>105612.48</td>\n",
       "      <td>Constitui objeto da presente Licitação a explo...</td>\n",
       "      <td>PER</td>\n",
       "      <td>CIA. ÁGUAS TERMAIS MARCELINO RAMOS - TERMASA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>34.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>CNV</td>\n",
       "      <td>10600.00</td>\n",
       "      <td>Contratação de serviços de arbitragem para atu...</td>\n",
       "      <td>OUS</td>\n",
       "      <td>PM DE ESPERANÇA DO SUL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>PRP</td>\n",
       "      <td>430770.00</td>\n",
       "      <td>Contratação de empresa para prestação de servi...</td>\n",
       "      <td>OUS</td>\n",
       "      <td>PM DE BOA VISTA DO SUL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NR_LICITACAO  ANO_LICITACAO CD_TIPO_MODALIDADE  VL_LICITACAO  \\\n",
       "0           3.0           2019                TMP     276405.12   \n",
       "1           1.0           2019                TMP      75396.00   \n",
       "2           3.0           2019                EST     105612.48   \n",
       "3          34.0           2019                CNV      10600.00   \n",
       "4           3.0           2019                PRP     430770.00   \n",
       "\n",
       "                                           DS_OBJETO TP_OBJETO  \\\n",
       "0  CONTRATAÇÃO GLOBAL DE EMPRESA PARA CONSTRUÇÃO ...       CSE   \n",
       "1  Reforma da UBS do Distrito da Vila Cruz, pintu...       OSE   \n",
       "2  Constitui objeto da presente Licitação a explo...       PER   \n",
       "3  Contratação de serviços de arbitragem para atu...       OUS   \n",
       "4  Contratação de empresa para prestação de servi...       OUS   \n",
       "\n",
       "                                       NM_ORGAO  \n",
       "0                              PM DE NOVA PALMA  \n",
       "1                              PM DE NOVA PALMA  \n",
       "2  CIA. ÁGUAS TERMAIS MARCELINO RAMOS - TERMASA  \n",
       "3                        PM DE ESPERANÇA DO SUL  \n",
       "4                        PM DE BOA VISTA DO SUL  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Avaliação das colunas relevantes (chaves vide manual)\n",
    "\"\"\"\n",
    "    NM_ORGAO: Nome do orgão\n",
    "    ANO_LICITACAO: Ano da licitação (outras referencias de data nas colunas DT_ABERTURA, DT_HOMOLOGACAO, DT_ADJUDICACAO)\n",
    "    VL_LICITACAO: Valor da licitação\n",
    "    DS_OBJETO: Descrição do objeto\n",
    "    TP_OBJETO: Tipo de objeto\n",
    "\"\"\"\n",
    "licitacao.loc[:,['NR_LICITACAO', 'ANO_LICITACAO', 'CD_TIPO_MODALIDADE', 'VL_LICITACAO', 'DS_OBJETO', 'TP_OBJETO', 'NM_ORGAO']].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(237011, 8)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tamanho da base licitacao.csv (linhas, colunas)\n",
    "licitacao.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NR_LICITACAO             0\n",
       "ANO_LICITACAO            0\n",
       "CD_TIPO_MODALIDADE       0\n",
       "VL_LICITACAO          9189\n",
       "DS_OBJETO                0\n",
       "TP_OBJETO                0\n",
       "NM_ORGAO                 0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Identificando registros em branco/nulos nas colunas-chave definidas\n",
    "licitacao.loc[:,['NR_LICITACAO', 'ANO_LICITACAO', 'CD_TIPO_MODALIDADE', 'VL_LICITACAO', 'DS_OBJETO', 'TP_OBJETO', 'NM_ORGAO']].isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    No manual são identificadas três chaves para o arquivo em questão (pag. 21), sendo elas:\n",
    "        - NR_LICITACAO\n",
    "        - ANO_LICITACAO\n",
    "        - CD_TIPO_MODALIDADE\n",
    "    No entanto, existem campos obrigatórios (ex.: VL_LICITACAO) que estão categorizados como obrigatórios (pag. 25) mas não foram preenchidos.\n",
    "    Nestes casos, os valores em branco serão desconsiderados das análises quantitativas.\n",
    "    A mesma tratativa foi aplicada aos dados com valor zerado (coluna VL_LICITACAO)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(227822, 8)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Remoção de linhas NaN\n",
    "licitacao.dropna(subset = ['VL_LICITACAO'], inplace = True)\n",
    "licitacao.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NM_ORGAO</th>\n",
       "      <th>VL_LICITACAO</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DS_OBJETO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Material</th>\n",
       "      <td>153</td>\n",
       "      <td>153</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Contratacao de Servico</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQUISIÇÃO DE MEDICAMENTOS</th>\n",
       "      <td>19</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQUISIÇÃO DE MERENDA ESCOLAR</th>\n",
       "      <td>17</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQUISICAO</th>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AQUISIÇÃO DE TUBOS DE CONCRETO</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Material permanente</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Aquisição de gêneros alimentícios para a merenda escolar.</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONTRATAÇÃO DE PALESTRANTE</th>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    NM_ORGAO  VL_LICITACAO\n",
       "DS_OBJETO                                                                 \n",
       "Material                                                 153           153\n",
       "...                                                       64            64\n",
       "Contratacao de Servico                                    28            28\n",
       "AQUISIÇÃO DE MEDICAMENTOS                                 19            19\n",
       "AQUISIÇÃO DE MERENDA ESCOLAR                              17            17\n",
       "AQUISICAO                                                 13            13\n",
       "AQUISIÇÃO DE TUBOS DE CONCRETO                            11            11\n",
       "Material permanente                                       11            11\n",
       "Aquisição de gêneros alimentícios para a merend...         9             9\n",
       "CONTRATAÇÃO DE PALESTRANTE                                 9             9"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verificando registros de licitações com valor zero (VL_LICITACAO)\n",
    "gpby_zeros = licitacao.loc[licitacao['VL_LICITACAO'] == 0][['NM_ORGAO', 'DS_OBJETO', 'VL_LICITACAO']]\n",
    "gpby_zeros = gpby_zeros.groupby('DS_OBJETO').count().sort_values(by = 'VL_LICITACAO', ascending = False)\n",
    "gpby_zeros.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "        De acordo com a documentação fornecida, o campo 'VL_LICITACAO' é obrigatório, no entanto existem + 12 mil registros cujo valor atribuído foi igual a zero (majoritariamente contratações de empresas, aquisição de serviços/materiais ou descrições incompletas)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(215620, 8)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Removendo registros de licitacoes com valor zero (VL_LICITACAO)\n",
    "licitacao.drop(licitacao.loc[licitacao['VL_LICITACAO'] == 0].index, inplace = True)\n",
    "licitacao.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Quantidade de Registros por Tipo de Objeto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Identificando tipo de objeto com maior representatividade\n",
    "\n",
    "figure(figsize=(12, 8), dpi=80) # Alterar escala\n",
    "labels = licitacao['TP_OBJETO'].value_counts().index\n",
    "\n",
    "plt.bar(labels, licitacao['TP_OBJETO'].value_counts())\n",
    "plt.title('Quantidade de Registros por Tipo de Objeto', fontsize = 18)\n",
    "plt.xlabel('Tipo de objeto', fontsize = 16)\n",
    "plt.ylabel('Registros', fontsize = 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "        No gráfico acima, fica evidente que a distribuição dos tipos de objetos licitatórios entre 2016-2019 têm maior representatividade entre os tipos OUS (Obras e Serviços de Engenharia), COM (Compras de materiais) e CSE (Compras e outros serviços), vide manual (pág. 28)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Identificando tipos de contratação com maior representatividade (Compras/COM)\n",
    "\n",
    "figure(figsize=(12, 8), dpi=80) # Alterar escala\n",
    "labels = licitacao['CD_TIPO_MODALIDADE'].value_counts().index\n",
    "\n",
    "plt.bar(labels, licitacao['CD_TIPO_MODALIDADE'].value_counts())\n",
    "plt.title('Quantidade de Registros por Forma de Contratação', fontsize = 18)\n",
    "plt.xlabel('Forma de contratação', fontsize = 16)\n",
    "plt.ylabel('Registros', fontsize = 16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "        Avaliando as formas de contratação com maior quantidade de licitações registradas no sistema. Esta informação será relevante na análise três, pois usaremos a forma de contratação PRD (processo de dispensa) como exemplo para análise no acompanhamento mensal em Analise_3_TCE-RS."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Valor Total em Licitações por Categoria"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Avaliando maiores gastos por categoria (TP_OBJETO)\n",
    "\"\"\"\n",
    "    Valores (pag. 28): ALB(Alienação de Bens), COM(Compras), CON(Concessão de Uso),\n",
    "    CSE(Compras e Outros Serviços), LOC(Locações), OSE(Obras e Serviços de\n",
    "    Engenharia), OUS(Outros Serviços), PER(Permissão), COL(Concessão Lei 8.987),\n",
    "    PPP(Parceria Público-Privada) e PRI(Privatização).\n",
    "\"\"\"\n",
    "\n",
    "figure(figsize=(12, 8), dpi=80) # Alterar escala\n",
    "labels_dict = {'ALB':'Alienação de Bens', 'COM':'Compras', 'CON':'Concessão de Uso', \\\n",
    "               'CSE':'Compras e Outros Serviços', 'LOC':'Locações', 'OSE':'Obras e Serviços de Engenharia', \\\n",
    "               'OUS':'Outros Serviços', 'PER':'Permissão', 'COL':'Concessão Lei 8.987', \\\n",
    "               'PPP':'Parceria Público-Privada', 'PRI':'Privatização'}\n",
    "\n",
    "# Preparando base para plot\n",
    "gpby_tp_objeto = licitacao.loc[:, ['TP_OBJETO', 'VL_LICITACAO']].groupby(['TP_OBJETO']).sum()\n",
    "gpby_tp_objeto.sort_values(by = ['VL_LICITACAO'], ascending = True, inplace = True)\n",
    "gpby_tp_objeto['TP_OBJETO_DESC'] = 0\n",
    "gpby_tp_objeto['TP_OBJETO_DESC'] = gpby_tp_objeto.index.map(labels_dict) # Mapear descrição a partir de um dicionario\n",
    "total = gpby_tp_objeto['VL_LICITACAO'].sum() # Calculando valor total\n",
    "gpby_tp_objeto['SHAREOF'] = round((gpby_tp_objeto['VL_LICITACAO'] / total) * 100, 2) # Shareof (fatia / total * 100)\n",
    "gpby_tp_objeto\n",
    "\n",
    "# Criando grafico de barras horizontais\n",
    "labels = gpby_tp_objeto.index\n",
    "plt.barh(labels, gpby_tp_objeto['VL_LICITACAO'])\n",
    "plt.title('Valor Total em Licitações por Categoria', fontsize = 18)\n",
    "for i, (p, pr) in enumerate(zip(gpby_tp_objeto['TP_OBJETO_DESC'], gpby_tp_objeto['SHAREOF'])): # Legendas com shareof\n",
    "    plt.text(s=p, x=1, y=i, color=\"k\", verticalalignment=\"center\",horizontalalignment=\"left\", size=12)\n",
    "    plt.text(s=str(pr)+\"%\", x=pr-5, y=i, color=\"k\",\n",
    "             verticalalignment=\"center\", horizontalalignment=\"right\", size=11)\n",
    "plt.axis(\"off\")\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "        Neste gráfico, trouxe uma visão complementar ao gráfico 'Quantidade de Registros por Tipo de Objeto', porém avaliando a representatividade dos valores totais das licitações por categoria, sendo que por mais que a distribuição no primeiro gráfico seja mais bem distribúida entre Obras e Compras, o valor acumulado das licitações é majoritariamente concentrado em Obras e Serviços de Engenharia (93.2%)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Distribuição de Valores de Licitação"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Distribuição de Valores de Licitação')"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x640 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criando histograma com a coluna VL_LICITACAO\n",
    "\n",
    "figure(figsize=(12, 8), dpi=80) # Alterar escala\n",
    "\n",
    "plt.hist(licitacao['VL_LICITACAO'].loc[(licitacao['VL_LICITACAO']  < 100000)], bins = 100)\n",
    "plt.title('Distribuição de Valores de Licitação', fontsize = 18)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "        No gráfico acima, podemos concluir que a de distribuição dos pedidos de licitação está em valores menores (principalmente com dois picos nas faixas entre 0-1000R$ e 7000-8000R$), distribuído como um modelo despenhadeiro (a frequência é menor conforme os valores dos pedidos aumentam)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Top 10 Orgãos com Maiores Despesas em Licitações  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>NM_ORGAO</th>\n",
       "      <th>NR_LICITACAO</th>\n",
       "      <th>ANO_LICITACAO</th>\n",
       "      <th>CD_TIPO_MODALIDADE</th>\n",
       "      <th>TP_OBJETO</th>\n",
       "      <th>DS_OBJETO</th>\n",
       "      <th>VL_LICITACAO</th>\n",
       "      <th>DT_HOMOLOGACAO</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SERVIÇO AUTÔN. MUN. DE ÁGUA E ESGOTO - CX.SUL</td>\n",
       "      <td>79.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>PRP</td>\n",
       "      <td>COM</td>\n",
       "      <td>Aquisição de 78.000 (setenta e oito mil) quilo...</td>\n",
       "      <td>406120.0</td>\n",
       "      <td>2019-11-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SERVIÇO AUTÔN. MUN. DE ÁGUA E ESGOTO - CX.SUL</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>PRP</td>\n",
       "      <td>COM</td>\n",
       "      <td>Aquisição de 3.700 kg de polímero não iônico, ...</td>\n",
       "      <td>92056.0</td>\n",
       "      <td>2019-02-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>SERVIÇO AUTÔN. MUN. DE ÁGUA E ESGOTO - CX.SUL</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>PRP</td>\n",
       "      <td>COM</td>\n",
       "      <td>Aquisição de 65 t (sessenta e cindo toneladas)...</td>\n",
       "      <td>533000.0</td>\n",
       "      <td>2019-02-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SERVIÇO AUTÔN. MUN. DE ÁGUA E ESGOTO - CX.SUL</td>\n",
       "      <td>38.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>PRP</td>\n",
       "      <td>COM</td>\n",
       "      <td>Aquisição de 40.500 (quarenta mil e quinhentos...</td>\n",
       "      <td>359032.5</td>\n",
       "      <td>2019-06-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>SERVIÇO AUTÔN. MUN. DE ÁGUA E ESGOTO - CX.SUL</td>\n",
       "      <td>68.0</td>\n",
       "      <td>2019</td>\n",
       "      <td>PRP</td>\n",
       "      <td>COM</td>\n",
       "      <td>Aquisição de Larvicida biológico BTI (Bacillus...</td>\n",
       "      <td>443133.2</td>\n",
       "      <td>2019-10-09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                        NM_ORGAO  NR_LICITACAO  ANO_LICITACAO  \\\n",
       "5  SERVIÇO AUTÔN. MUN. DE ÁGUA E ESGOTO - CX.SUL          79.0           2019   \n",
       "6  SERVIÇO AUTÔN. MUN. DE ÁGUA E ESGOTO - CX.SUL          10.0           2019   \n",
       "7  SERVIÇO AUTÔN. MUN. DE ÁGUA E ESGOTO - CX.SUL           7.0           2019   \n",
       "8  SERVIÇO AUTÔN. MUN. DE ÁGUA E ESGOTO - CX.SUL          38.0           2019   \n",
       "9  SERVIÇO AUTÔN. MUN. DE ÁGUA E ESGOTO - CX.SUL          68.0           2019   \n",
       "\n",
       "  CD_TIPO_MODALIDADE TP_OBJETO  \\\n",
       "5                PRP       COM   \n",
       "6                PRP       COM   \n",
       "7                PRP       COM   \n",
       "8                PRP       COM   \n",
       "9                PRP       COM   \n",
       "\n",
       "                                           DS_OBJETO  VL_LICITACAO  \\\n",
       "5  Aquisição de 78.000 (setenta e oito mil) quilo...      406120.0   \n",
       "6  Aquisição de 3.700 kg de polímero não iônico, ...       92056.0   \n",
       "7  Aquisição de 65 t (sessenta e cindo toneladas)...      533000.0   \n",
       "8  Aquisição de 40.500 (quarenta mil e quinhentos...      359032.5   \n",
       "9  Aquisição de Larvicida biológico BTI (Bacillus...      443133.2   \n",
       "\n",
       "  DT_HOMOLOGACAO  \n",
       "5     2019-11-05  \n",
       "6     2019-02-26  \n",
       "7     2019-02-11  \n",
       "8     2019-06-06  \n",
       "9     2019-10-09  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Selecão de amostragem com apenas licitações categorizadas como Compras/COM\n",
    "licitacao_sample = licitacao.loc[licitacao['TP_OBJETO'] == 'COM']\n",
    "licitacao_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Os dez orgãos com maior representatividade em licitacões compõem  77.74% do valor total das licitacões registradas.\n"
     ]
    },
    {
     "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>VL_LICITACAO</th>\n",
       "      <th>QUANTIDADE</th>\n",
       "      <th>SHAREOF</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NM_ORGAO</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>PM DE PASSO DO SOBRADO</th>\n",
       "      <td>46022067187.01</td>\n",
       "      <td>31</td>\n",
       "      <td>48.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM DE SANTA MARGARIDA DO SUL</th>\n",
       "      <td>15806172103.88</td>\n",
       "      <td>161</td>\n",
       "      <td>16.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONS. PÚBL. DA ASSOC. DOS MUNIC. DA REGIÃO METR. DE POA - CP GRANPAL</th>\n",
       "      <td>7024258640.87</td>\n",
       "      <td>13</td>\n",
       "      <td>7.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CONS. PÚBL. DO EXTREMO SUL</th>\n",
       "      <td>3499853024.92</td>\n",
       "      <td>25</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PM DE ITATI</th>\n",
       "      <td>1841024793.29</td>\n",
       "      <td>273</td>\n",
       "      <td>1.93</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                     VL_LICITACAO  QUANTIDADE  \\\n",
       "NM_ORGAO                                                                        \n",
       "PM DE PASSO DO SOBRADO                             46022067187.01          31   \n",
       "PM DE SANTA MARGARIDA DO SUL                       15806172103.88         161   \n",
       "CONS. PÚBL. DA ASSOC. DOS MUNIC. DA REGIÃO METR...  7024258640.87          13   \n",
       "CONS. PÚBL. DO EXTREMO SUL                          3499853024.92          25   \n",
       "PM DE ITATI                                         1841024793.29         273   \n",
       "\n",
       "                                                    SHAREOF  \n",
       "NM_ORGAO                                                     \n",
       "PM DE PASSO DO SOBRADO                                48.22  \n",
       "PM DE SANTA MARGARIDA DO SUL                          16.56  \n",
       "CONS. PÚBL. DA ASSOC. DOS MUNIC. DA REGIÃO METR...     7.36  \n",
       "CONS. PÚBL. DO EXTREMO SUL                             3.67  \n",
       "PM DE ITATI                                            1.93  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Avaliando top 5 orgãos com maiores despesas em licitações\n",
    "\"\"\"\n",
    "    Avaliar quantidade de licitações:\n",
    "        1. Agrupado por orgão;\n",
    "        2. Ordenado por valor total acumulado e quantidade de licitações.\n",
    "\"\"\"\n",
    "gpby_nm_orgao = licitacao_sample.loc[:, ['NM_ORGAO', 'VL_LICITACAO']].groupby(['NM_ORGAO']).sum()\n",
    "gpby_nm_orgao['QUANTIDADE'] = licitacao_sample.loc[:, ['NM_ORGAO', 'VL_LICITACAO']].groupby(['NM_ORGAO']).count()\n",
    "gpby_nm_orgao.sort_values(by = ['VL_LICITACAO', 'QUANTIDADE'], inplace = True, ascending = False)\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x) # Remover notação científica do DataFrame\n",
    "total = gpby_nm_orgao['VL_LICITACAO'].sum() \n",
    "gpby_nm_orgao['SHAREOF'] = round((gpby_nm_orgao['VL_LICITACAO'] / total) * 100, 2) # Shareof (fatia / total * 100)\n",
    "\n",
    "gpby_nm_orgao = gpby_nm_orgao[:5]\n",
    "print('Os dez orgãos com maior representatividade em licitacões compõem ', str(round(gpby_nm_orgao['SHAREOF'].sum(),2)) + '%', 'do valor total das licitacões registradas.')\n",
    "gpby_nm_orgao"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Scatter Plot + Histogramas de Distribuição Consolidados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x640 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criando scatter plot com volumetria (outliers definidos com valores acima de 70 mil)\n",
    "\n",
    "figure(figsize=(8, 8), dpi=80) # Alterar escala\n",
    "\n",
    "# Eixos\n",
    "left, width = 0.1, 0.65\n",
    "bottom, height = 0.1, 0.65\n",
    "binwidth = 0.25\n",
    "spacing = 0.005\n",
    "binwidth = 0.25\n",
    "\n",
    "x = gpby_nm_orgao['VL_LICITACAO']\n",
    "y = gpby_nm_orgao['QUANTIDADE']\n",
    "\n",
    "rect_scatter = [left, bottom, width, height]\n",
    "rect_histx = [left, bottom + height + spacing, width, 0.2]\n",
    "rect_histy = [left + width + spacing, bottom, 0.2, height]\n",
    "\n",
    "ax_scatter = plt.axes(rect_scatter)\n",
    "ax_scatter.tick_params(direction='in', top=True, right=True)\n",
    "ax_histx = plt.axes(rect_histx)\n",
    "ax_histx.tick_params(direction='in', labelbottom=False)\n",
    "ax_histy = plt.axes(rect_histy)\n",
    "ax_histy.tick_params(direction='in', labelleft=False)\n",
    "\n",
    "ax_scatter.scatter(gpby_nm_orgao['VL_LICITACAO'], gpby_nm_orgao['QUANTIDADE'], s = (gpby_nm_orgao['VL_LICITACAO']) * 0.0000001)\n",
    "\n",
    "\n",
    "lim = np.ceil(np.abs([x, y]).max() / binwidth) * binwidth\n",
    "\n",
    "bins = 10\n",
    "ax_histx.hist(x, bins = bins)\n",
    "ax_histy.hist(y, bins = bins, orientation = 'horizontal')\n",
    "\n",
    "ax_histx.set_xlim(ax_scatter.get_xlim())\n",
    "ax_histy.set_ylim(ax_scatter.get_ylim())\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "        No gráfico em questão, fica evidente que existem poucos pedidos com maior concentração de valor (outliers, canto inferior direito) e muitas licitações com valores baixos (canto superior esquerdo). Os histogramas conjugados são apoio para a análise em questão, mostrando comportamentos isolados."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Análise Multivariável"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<AxesSubplot:xlabel='VL_LICITACAO', ylabel='VL_LICITACAO'>,\n",
       "        <AxesSubplot:xlabel='QUANTIDADE', ylabel='VL_LICITACAO'>,\n",
       "        <AxesSubplot:xlabel='SHAREOF', ylabel='VL_LICITACAO'>],\n",
       "       [<AxesSubplot:xlabel='VL_LICITACAO', ylabel='QUANTIDADE'>,\n",
       "        <AxesSubplot:xlabel='QUANTIDADE', ylabel='QUANTIDADE'>,\n",
       "        <AxesSubplot:xlabel='SHAREOF', ylabel='QUANTIDADE'>],\n",
       "       [<AxesSubplot:xlabel='VL_LICITACAO', ylabel='SHAREOF'>,\n",
       "        <AxesSubplot:xlabel='QUANTIDADE', ylabel='SHAREOF'>,\n",
       "        <AxesSubplot:xlabel='SHAREOF', ylabel='SHAREOF'>]], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x864 with 9 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualizacao alternativa para analise de multi-variaveis (valores totais de licitacao, quantidade de pedidos e shareof)\n",
    "# Selecão de amostragem com apenas licitações categorizadas como Compras/COM\n",
    "# licitacao_sample = licitacao_sample.loc[licitacao_sample['VL_LICITACAO'] <= 100000]\n",
    "\n",
    "gpby_modalidade = licitacao_sample.loc[:, ['CD_TIPO_MODALIDADE', 'VL_LICITACAO']].groupby(['CD_TIPO_MODALIDADE']).sum()\n",
    "gpby_modalidade['QUANTIDADE'] = licitacao_sample.loc[:, ['CD_TIPO_MODALIDADE', 'VL_LICITACAO']].groupby(['CD_TIPO_MODALIDADE']).count()\n",
    "gpby_modalidade.sort_values(by = ['VL_LICITACAO', 'QUANTIDADE'], inplace = True, ascending = False)\n",
    "total = gpby_modalidade['VL_LICITACAO'].sum() \n",
    "gpby_modalidade['SHAREOF'] = round((gpby_modalidade['VL_LICITACAO'] / total) * 100, 2) # Shareof (fatia / total * 100)\n",
    "gpby_modalidade\n",
    "\n",
    "pd.plotting.scatter_matrix(gpby_modalidade[gpby_modalidade.columns.tolist()], figsize=(16,12))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "        Por fim, foi feita uma análise apêndice entre multi-variáveis para identificar possíveis outliers entre as variáveis de quantidade, valor de licitação e Shareof, mostrando que todas possuem um comportamento similar nos histogramas com evidências de outliers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fim"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}