BPC.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.core.display import Image, display\n",
    "display(Image('py013_sticker.png'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <font color='blue'>Explorando os dados do BPC</font>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Beneficio de Progressão Continuada: https://www.inss.gov.br/beneficios/beneficio-assistencia-a-pessoa-com-deficiencia-bpc/ \n",
    "\n",
    "O Benefício de Prestação Continuada (BPC) da Lei Orgânica da Assistência Social (LOAS) é a garantia de um salário mínimo mensal à pessoa com deficiência que comprove não possuir meios de prover a própria manutenção, nem de tê-la provida por sua família."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 - Carregando os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import de bibliotecas\n",
    "\n",
    "#Pandas\n",
    "import pandas as pd \n",
    "\n",
    "#Numpy\n",
    "import numpy as np\n",
    "pd.set_option('display.float_format', lambda x: '%.2f' % x)\n",
    "\n",
    "#Matplotlib\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Carrega dados no dataset inicial \n",
    "dados_bpc = pd.read_csv('201901_BPC.csv', sep = ';', encoding = 'latin-1', low_memory=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4631027, 14)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Tamanho do dataset\n",
    "dados_bpc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Verifica o tipo do objeto\n",
    "type(dados_bpc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "#Verifica a tipo das colunas do dataframe\n",
    "dados_bpc.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "source": [
    "dados_bpc.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Renomeia o nomes das colunas do dataset\n",
    "nome_colunas = ['MÊS_COMPETÊNCIA', 'MÊS_REFERÊNCIA','UF', 'COD_MUNICIPIO', 'NM_MUNICIPIO','NIS','CPF','NM_BENEFICIARIO',\n",
    "                'NIS_REPRESENTANTE','CPF_REPRESENTANTE','NM_REPRESENTANTE','NR_BENEFICIO','JUDICIALMENTE','VALOR']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Carrega dados no dataset inicial \n",
    "dados_bpc = pd.read_csv('201901_BPC.csv', names = nome_colunas, sep = ';', encoding = 'latin-1', low_memory=False, header=0, dtype = {'VALOR': float }, decimal=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>MÊS_COMPETÊNCIA</th>\n",
       "      <th>MÊS_REFERÊNCIA</th>\n",
       "      <th>UF</th>\n",
       "      <th>COD_MUNICIPIO</th>\n",
       "      <th>NM_MUNICIPIO</th>\n",
       "      <th>NIS</th>\n",
       "      <th>CPF</th>\n",
       "      <th>NM_BENEFICIARIO</th>\n",
       "      <th>NIS_REPRESENTANTE</th>\n",
       "      <th>CPF_REPRESENTANTE</th>\n",
       "      <th>NM_REPRESENTANTE</th>\n",
       "      <th>NR_BENEFICIO</th>\n",
       "      <th>JUDICIALMENTE</th>\n",
       "      <th>VALOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201901</td>\n",
       "      <td>201901</td>\n",
       "      <td>AC</td>\n",
       "      <td>643</td>\n",
       "      <td>ACRELANDIA</td>\n",
       "      <td>***Titular menor de 16 anos***</td>\n",
       "      <td>***Titular menor de 16 anos***</td>\n",
       "      <td>***Titular menor de 16 anos***</td>\n",
       "      <td>12611806006.00</td>\n",
       "      <td>***.063.782-**</td>\n",
       "      <td>PEDRO DANTAS DA SILVA</td>\n",
       "      <td>7030464630</td>\n",
       "      <td>NÃO</td>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201901</td>\n",
       "      <td>201901</td>\n",
       "      <td>AC</td>\n",
       "      <td>643</td>\n",
       "      <td>ACRELANDIA</td>\n",
       "      <td>***Titular menor de 16 anos***</td>\n",
       "      <td>***Titular menor de 16 anos***</td>\n",
       "      <td>***Titular menor de 16 anos***</td>\n",
       "      <td>26721463073.00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ALBERTINA BEZERRA DE SOUZA</td>\n",
       "      <td>7003011647</td>\n",
       "      <td>NÃO</td>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201901</td>\n",
       "      <td>201901</td>\n",
       "      <td>AC</td>\n",
       "      <td>643</td>\n",
       "      <td>ACRELANDIA</td>\n",
       "      <td>***Titular menor de 16 anos***</td>\n",
       "      <td>***Titular menor de 16 anos***</td>\n",
       "      <td>***Titular menor de 16 anos***</td>\n",
       "      <td>16804173334.00</td>\n",
       "      <td>***.503.382-**</td>\n",
       "      <td>ROSANA LIMA SOARES</td>\n",
       "      <td>5220412180</td>\n",
       "      <td>NÃO</td>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   MÊS_COMPETÊNCIA  MÊS_REFERÊNCIA  UF  COD_MUNICIPIO NM_MUNICIPIO  \\\n",
       "0           201901          201901  AC            643   ACRELANDIA   \n",
       "1           201901          201901  AC            643   ACRELANDIA   \n",
       "2           201901          201901  AC            643   ACRELANDIA   \n",
       "\n",
       "                              NIS                             CPF  \\\n",
       "0  ***Titular menor de 16 anos***  ***Titular menor de 16 anos***   \n",
       "1  ***Titular menor de 16 anos***  ***Titular menor de 16 anos***   \n",
       "2  ***Titular menor de 16 anos***  ***Titular menor de 16 anos***   \n",
       "\n",
       "                  NM_BENEFICIARIO  NIS_REPRESENTANTE CPF_REPRESENTANTE  \\\n",
       "0  ***Titular menor de 16 anos***     12611806006.00    ***.063.782-**   \n",
       "1  ***Titular menor de 16 anos***     26721463073.00               NaN   \n",
       "2  ***Titular menor de 16 anos***     16804173334.00    ***.503.382-**   \n",
       "\n",
       "             NM_REPRESENTANTE  NR_BENEFICIO JUDICIALMENTE  VALOR  \n",
       "0       PEDRO DANTAS DA SILVA    7030464630           NÃO 998.00  \n",
       "1  ALBERTINA BEZERRA DE SOUZA    7003011647           NÃO 998.00  \n",
       "2          ROSANA LIMA SOARES    5220412180           NÃO 998.00  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dados_bpc.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MÊS_COMPETÊNCIA        int64\n",
       "MÊS_REFERÊNCIA         int64\n",
       "UF                    object\n",
       "COD_MUNICIPIO          int64\n",
       "NM_MUNICIPIO          object\n",
       "NIS                   object\n",
       "CPF                   object\n",
       "NM_BENEFICIARIO       object\n",
       "NIS_REPRESENTANTE    float64\n",
       "CPF_REPRESENTANTE     object\n",
       "NM_REPRESENTANTE      object\n",
       "NR_BENEFICIO           int64\n",
       "JUDICIALMENTE         object\n",
       "VALOR                float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verifica a tipo das colunas do dataframe\n",
    "dados_bpc.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2- Conhecendo os dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MÊS_COMPETÊNCIA      4631027\n",
       "MÊS_REFERÊNCIA       4631027\n",
       "UF                   4631027\n",
       "COD_MUNICIPIO        4631027\n",
       "NM_MUNICIPIO         4631027\n",
       "NIS                  4631027\n",
       "CPF                  3042382\n",
       "NM_BENEFICIARIO      4631027\n",
       "NIS_REPRESENTANTE    4619620\n",
       "CPF_REPRESENTANTE    2924529\n",
       "NM_REPRESENTANTE     4619393\n",
       "NR_BENEFICIO         4631027\n",
       "JUDICIALMENTE        4631027\n",
       "VALOR                4631027\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Número de ocorrências por coluna\n",
    "dados_bpc.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verificar se há valores nulos na coluna CPF\n",
    "dados_bpc.CPF.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1588645"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Quantidade de valores nulos\n",
    "dados_bpc.CPF.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Remove as colunas que não serão usadas para análises.\n",
    "df_bpc = dados_bpc.drop([\"MÊS_REFERÊNCIA\", \"COD_MUNICIPIO\",'NIS','CPF','NM_BENEFICIARIO','NIS_REPRESENTANTE','CPF_REPRESENTANTE','NM_REPRESENTANTE','NR_BENEFICIO'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MÊS_COMPETÊNCIA      int64\n",
       "UF                  object\n",
       "NM_MUNICIPIO        object\n",
       "JUDICIALMENTE       object\n",
       "VALOR              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Verifica a tipo das colunas do dataframe\n",
    "df_bpc.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4631027, 5)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bpc.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MÊS_COMPETÊNCIA     object\n",
       "UF                  object\n",
       "NM_MUNICIPIO        object\n",
       "JUDICIALMENTE       object\n",
       "VALOR              float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Converte a coluna para o tipo object\n",
    "df_bpc['MÊS_COMPETÊNCIA'] = df_bpc['MÊS_COMPETÊNCIA'].astype('object')\n",
    "df_bpc.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>MÊS_COMPETÊNCIA</th>\n",
       "      <th>UF</th>\n",
       "      <th>NM_MUNICIPIO</th>\n",
       "      <th>JUDICIALMENTE</th>\n",
       "      <th>VALOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>201901</td>\n",
       "      <td>AC</td>\n",
       "      <td>ACRELANDIA</td>\n",
       "      <td>NÃO</td>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>201901</td>\n",
       "      <td>AC</td>\n",
       "      <td>ACRELANDIA</td>\n",
       "      <td>NÃO</td>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>201901</td>\n",
       "      <td>AC</td>\n",
       "      <td>ACRELANDIA</td>\n",
       "      <td>NÃO</td>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  MÊS_COMPETÊNCIA  UF NM_MUNICIPIO JUDICIALMENTE  VALOR\n",
       "0          201901  AC   ACRELANDIA           NÃO 998.00\n",
       "1          201901  AC   ACRELANDIA           NÃO 998.00\n",
       "2          201901  AC   ACRELANDIA           NÃO 998.00"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_bpc.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 - Análises dos dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UF\n",
       "SP    746620\n",
       "MG    465668\n",
       "BA    449389\n",
       "RJ    338231\n",
       "PE    312926\n",
       "CE    270880\n",
       "PA    211999\n",
       "PR    209807\n",
       "RS    200704\n",
       "MA    192226\n",
       "GO    155480\n",
       "PB    114873\n",
       "AL    114243\n",
       "AM    103543\n",
       "RN     84350\n",
       "MS     83944\n",
       "MT     83593\n",
       "SC     77687\n",
       "PI     77268\n",
       "ES     72442\n",
       "SE     61527\n",
       "DF     55014\n",
       "RO     45032\n",
       "TO     38889\n",
       "AP     25851\n",
       "AC     25752\n",
       "RR     13089\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantidade de Beneficios por estado\n",
    "bpc_uf = df_bpc.groupby(['UF'])\n",
    "total_uf = bpc_uf.size()\n",
    "total_uf.sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>VALOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4631027.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>333.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1325.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           VALOR\n",
       "count 4631027.00\n",
       "mean      998.00\n",
       "std         0.73\n",
       "min       333.33\n",
       "25%       998.00\n",
       "50%       998.00\n",
       "75%       998.00\n",
       "max      1325.34"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Quantidade de estados brasileiros com BPC\n",
    "len(bpc_uf.groups)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UF\n",
       "SP   745128111.70\n",
       "MG   464737084.16\n",
       "BA   448491373.77\n",
       "RJ   337554969.76\n",
       "PE   312300401.47\n",
       "CE   270339412.89\n",
       "PA   211575497.99\n",
       "PR   209386725.14\n",
       "RS   200302510.60\n",
       "MA   191841630.15\n",
       "GO   155169741.09\n",
       "PB   114643805.74\n",
       "AL   114014807.86\n",
       "AM   103335882.37\n",
       "RN    84181420.38\n",
       "MS    83776689.89\n",
       "MT    83428242.43\n",
       "SC    77532482.66\n",
       "PI    77113506.30\n",
       "ES    72297300.57\n",
       "SE    61403985.04\n",
       "DF    54904097.30\n",
       "RO    44941908.56\n",
       "TO    38811273.78\n",
       "AP    25799323.98\n",
       "AC    25700895.06\n",
       "RR    13062837.74\n",
       "Name: VALOR, dtype: float64"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Valor consolidado por UF\n",
    "df_ufValor = df_bpc['VALOR'].groupby(df_bpc['UF'])  \n",
    "df_ufValor.sum().sort_values(ascending = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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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>MÊS_COMPETÊNCIA</th>\n",
       "      <th>VALOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>4631027.00</td>\n",
       "      <td>4631027.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>201901.00</td>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>201901.00</td>\n",
       "      <td>333.33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>201901.00</td>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>201901.00</td>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>201901.00</td>\n",
       "      <td>998.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>201901.00</td>\n",
       "      <td>1325.34</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       MÊS_COMPETÊNCIA      VALOR\n",
       "count       4631027.00 4631027.00\n",
       "mean         201901.00     998.00\n",
       "std               0.00       0.73\n",
       "min          201901.00     333.33\n",
       "25%          201901.00     998.00\n",
       "50%          201901.00     998.00\n",
       "75%          201901.00     998.00\n",
       "max          201901.00    1325.34"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coletando estatísticas das colunas númericas\n",
    "df_bpc.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NÃO    4163401\n",
       "SIM     467626\n",
       "Name: JUDICIALMENTE, dtype: int64"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Número de Beneficios que entram ou não por via judicial\n",
    "df_jud = df_bpc['JUDICIALMENTE'].value_counts()\n",
    "df_jud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "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>MÊS_COMPETÊNCIA</th>\n",
       "      <th>UF</th>\n",
       "      <th>NM_MUNICIPIO</th>\n",
       "      <th>JUDICIALMENTE</th>\n",
       "      <th>VALOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3531700</th>\n",
       "      <td>201901</td>\n",
       "      <td>RS</td>\n",
       "      <td>CANDIDO GODOI</td>\n",
       "      <td>SIM</td>\n",
       "      <td>333.33</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         MÊS_COMPETÊNCIA  UF   NM_MUNICIPIO JUDICIALMENTE  VALOR\n",
       "3531700           201901  RS  CANDIDO GODOI           SIM 333.33"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Menor valor de beneficio \n",
    "df_min = df_bpc[df_bpc['VALOR'] <= 400]\n",
    "df_min.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Beneficios maiores que R$1200,00 \n",
    "df_min = df_bpc[df_bpc['VALOR'] > 1200]\n",
    "df_min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4590802.07"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Valor para a cidade de Santos\n",
    "df_santos = df_bpc[df_bpc['NM_MUNICIPIO'] == 'SANTOS']\n",
    "df_santos.VALOR.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SANTOS    4600\n",
       "Name: NM_MUNICIPIO, dtype: int64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Quantidade de Beneficios\n",
    "df_santos['NM_MUNICIPIO'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_bpc['REGIÃO'] = np.where(df_bpc['NM_MUNICIPIO'].isin(['SANTOS','PRAIA GRANDE','GUARUJA','CUBATÃO']),'BAIXADA SANTISTA', 'BRASIL')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_baixada = df_bpc[df_bpc['REGIÃO'] == 'BAIXADA SANTISTA']\n",
    "#df_baixada\n",
    "\n",
    "#df_baixada_agg = df_baixada['VALOR'].groupby(df_baixada['NM_MUNICIPIO'])  \n",
    "#df_baixada_agg.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4- Gráficos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [],
   "source": [
    "x2 = ['NÃO', 'SIM']\n",
    "y2 = df_jud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plt.barh(x, y, label = 'Barras1', color = 'r')\n",
    "plt.figure(figsize = (3, 4))\n",
    "plt.bar(x2, y2, label = 'Nr. Beneficios', color = 'y', width = 0.3)\n",
    "plt.xlabel('Beneficios Concedidos Judicialmente', fontweight='bold', color = 'green', fontsize='12', horizontalalignment='center')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib: https://matplotlib.org/3.1.1/gallery/index.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Seaborn: https://seaborn.pydata.org/examples/index.html"
   ]
  }
 ],
 "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}