Mod05 - Tarefa01.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Módulo 5 Tarefa 1\n",
    "## Base de nascidos vivos do DataSUS\n",
    "O DataSUS disponibiliza diversos arquivos de dados com relação a seus segurados, conforme a [lei da transparência de informações públicas](https://www.sisgov.com/transparencia-acesso-informacao/#:~:text=A%20Lei%20da%20Transpar%C3%AAncia%20(LC,em%20um%20site%20na%20internet.). \n",
    "\n",
    "Essas informações podem ser obtidas pela internet [aqui](http://www2.datasus.gov.br/DATASUS/index.php?area=0901&item=1). Como o processo de obtenção desses arquivos foge um pouco do nosso escopo, deixamos o arquivo ```SINASC_RO_2019.csv``` já como vai ser encontrado no DataSUS. O dicionário de dados está no arquivo ```estrutura_sinasc_para_CD.pdf``` (o nome do arquivo tal qual no portal do DataSUS).\n",
    "\n",
    "### Nosso objetivo\n",
    "Queremos deixar uma base organizada para podermos estudar a relação entre partos com risco para o bebê e algumas condições como tempo de parto, consultas de pré-natal etc.\n",
    "\n",
    "#### Preparação da base\n",
    "1. Carregue a base 'SINASC_RO_2019.csv'. Conte o número de registros e o número de registros não duplicados da base. Dica: você aprendeu um método que remove duplicados, encadeie este método com um outro método que conta o número de linhas. **Há linhas duplicadas?**  \n",
    "\n",
    "2. Conte o número de valores *missing* por variável.  \n",
    "\n",
    "3. Ok, no item anterior você deve ter achado pouco prático ler a informação de tantas variáveis, muitas delas nem devem ser interesantes. Então crie uma seleção dessa base somente com as colunas que interessam. São elas:\n",
    "``` \n",
    "['LOCNASC', 'IDADEMAE', 'ESTCIVMAE', 'ESCMAE', 'QTDFILVIVO', \n",
    "    'GESTACAO', 'GRAVIDEZ', 'CONSULTAS', 'APGAR5'] \n",
    "```\n",
    "Refaça a contagem de valores *missings*.  \n",
    "\n",
    "4. Apgar é uma *nota* que o pediatra dá ao bebê quando nasce de acordo com algumas características associadas principalmente à respiração. Apgar 1 e Apgar 5 são as notas 1 e 5 minutos do nascimento. Apgar5 será a nossa variável de interesse principal. Então remova todos os registros com Apgar5 não preenchido. Para esta seleção, conte novamente o número de linhas e o número de *missings*.  \n",
    "\n",
    "5. observe que as variáveis ```['ESTCIVMAE', 'CONSULTAS']``` possuem o código ```9```, que significa *ignorado*. Vamos assumir que o não preenchido é o mesmo que o código ```9```.<br>\n",
    "6. Substitua os valores faltantes da quantitativa (```QTDFILVIVO```) por zero.  \n",
    "7. Das restantes, decida que valore te parece mais adequado (um 'não preenchido' ou um valor 'mais provável' como no item anterior) e preencha. Justifique. Lembre-se de que tratamento de dados é trabalho do cientista, e que estamos tomando decisões a todo o momento - não há necessariamente certo e errado aqui.  \n",
    "8. O Apgar possui uma classificação indicando se o bebê passou por asfixia:\n",
    "- Entre 8 e 10 está em uma faixa 'normal'. \n",
    "- Entre 6 e 7, significa que o recém-nascido passou por 'asfixia leve'. \n",
    "- Entre 4 e 5 significa 'asfixia moderada'.\n",
    "- Entre 0 e 3 significa 'asfixia severa'.  \n",
    "\n",
    "Crie uma categorização dessa variável com essa codificação e calcule as frequências dessa categorização.  \n",
    "<br>\n",
    "9. Renomeie as variáveis para que fiquem no *snake case*, ou seja, em letras minúsculas, com um *underscore* entre as palávras. Dica: repare que se você não quiser criar um *dataframe* novo, você vai precisar usar a opção ```inplace = True```."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import requests"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 1) Carregue a base 'SINASC_RO_2019.csv'. Conte o número de registros e o número de registros não duplicados da base. \n",
    "# Dica: você aprendeu um método que remove duplicados, encadeie este método com um outro método que conta o número de \n",
    "# linhas. Há linhas duplicadas?\n",
    "\n",
    "sinasc = pd.read_csv('SINASC_RO_2019.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ORIGEM</th>\n",
       "      <th>CODESTAB</th>\n",
       "      <th>CODMUNNASC</th>\n",
       "      <th>LOCNASC</th>\n",
       "      <th>IDADEMAE</th>\n",
       "      <th>ESTCIVMAE</th>\n",
       "      <th>ESCMAE</th>\n",
       "      <th>CODOCUPMAE</th>\n",
       "      <th>QTDFILVIVO</th>\n",
       "      <th>QTDFILMORT</th>\n",
       "      <th>...</th>\n",
       "      <th>KOTELCHUCK</th>\n",
       "      <th>CONTADOR</th>\n",
       "      <th>munResStatus</th>\n",
       "      <th>munResTipo</th>\n",
       "      <th>munResNome</th>\n",
       "      <th>munResUf</th>\n",
       "      <th>munResLat</th>\n",
       "      <th>munResLon</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2679477.0</td>\n",
       "      <td>110001</td>\n",
       "      <td>1</td>\n",
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       "      <td>5.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>ATIVO</td>\n",
       "      <td>MUNIC</td>\n",
       "      <td>Alta Floresta D'Oeste</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>-11.93554</td>\n",
       "      <td>-61.99982</td>\n",
       "      <td>338.0</td>\n",
       "      <td>7067.025</td>\n",
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       "      <td>1</td>\n",
       "      <td>2679477.0</td>\n",
       "      <td>110001</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>999992.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>ATIVO</td>\n",
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       "      <td>Alta Floresta D'Oeste</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>-11.93554</td>\n",
       "      <td>-61.99982</td>\n",
       "      <td>338.0</td>\n",
       "      <td>7067.025</td>\n",
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       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2679477.0</td>\n",
       "      <td>110001</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>513205.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>ATIVO</td>\n",
       "      <td>MUNIC</td>\n",
       "      <td>Alta Floresta D'Oeste</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>-11.93554</td>\n",
       "      <td>-61.99982</td>\n",
       "      <td>338.0</td>\n",
       "      <td>7067.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>2516500.0</td>\n",
       "      <td>110001</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12 anos ou mais</td>\n",
       "      <td>231205.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>ATIVO</td>\n",
       "      <td>MUNIC</td>\n",
       "      <td>Alto Alegre dos Parecis</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>-12.13178</td>\n",
       "      <td>-61.85308</td>\n",
       "      <td>397.0</td>\n",
       "      <td>3958.273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>2516500.0</td>\n",
       "      <td>110001</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>999992.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>ATIVO</td>\n",
       "      <td>MUNIC</td>\n",
       "      <td>Alta Floresta D'Oeste</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>-11.93554</td>\n",
       "      <td>-61.99982</td>\n",
       "      <td>338.0</td>\n",
       "      <td>7067.025</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>2516500.0</td>\n",
       "      <td>110001</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1 a 3 anos</td>\n",
       "      <td>621005.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>ATIVO</td>\n",
       "      <td>MUNIC</td>\n",
       "      <td>Alta Floresta D'Oeste</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>-11.93554</td>\n",
       "      <td>-61.99982</td>\n",
       "      <td>338.0</td>\n",
       "      <td>7067.025</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>2516500.0</td>\n",
       "      <td>110001</td>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>999992.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>ATIVO</td>\n",
       "      <td>MUNIC</td>\n",
       "      <td>Novo Horizonte do Oeste</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>-11.70974</td>\n",
       "      <td>-61.99439</td>\n",
       "      <td>230.0</td>\n",
       "      <td>843.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>2516500.0</td>\n",
       "      <td>110001</td>\n",
       "      <td>1</td>\n",
       "      <td>27</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12 anos ou mais</td>\n",
       "      <td>514120.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>ATIVO</td>\n",
       "      <td>MUNIC</td>\n",
       "      <td>Alta Floresta D'Oeste</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>-11.93554</td>\n",
       "      <td>-61.99982</td>\n",
       "      <td>338.0</td>\n",
       "      <td>7067.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>2679477.0</td>\n",
       "      <td>110001</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>621005.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>ATIVO</td>\n",
       "      <td>MUNIC</td>\n",
       "      <td>Alta Floresta D'Oeste</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>-11.93554</td>\n",
       "      <td>-61.99982</td>\n",
       "      <td>338.0</td>\n",
       "      <td>7067.025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1</td>\n",
       "      <td>2679477.0</td>\n",
       "      <td>110001</td>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1 a 3 anos</td>\n",
       "      <td>999992.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>ATIVO</td>\n",
       "      <td>MUNIC</td>\n",
       "      <td>Alta Floresta D'Oeste</td>\n",
       "      <td>Rondônia</td>\n",
       "      <td>-11.93554</td>\n",
       "      <td>-61.99982</td>\n",
       "      <td>338.0</td>\n",
       "      <td>7067.025</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>10 rows × 69 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ORIGEM   CODESTAB  CODMUNNASC  LOCNASC  IDADEMAE  ESTCIVMAE  \\\n",
       "0       1  2679477.0      110001        1        19        5.0   \n",
       "1       1  2679477.0      110001        1        29        2.0   \n",
       "2       1  2679477.0      110001        1        37        9.0   \n",
       "3       1  2516500.0      110001        1        30        5.0   \n",
       "4       1  2516500.0      110001        1        30        2.0   \n",
       "5       1  2516500.0      110001        1        33        5.0   \n",
       "6       1  2516500.0      110001        1        37        1.0   \n",
       "7       1  2516500.0      110001        1        27        2.0   \n",
       "8       1  2679477.0      110001        1        20        5.0   \n",
       "9       1  2679477.0      110001        1        30        2.0   \n",
       "\n",
       "            ESCMAE  CODOCUPMAE  QTDFILVIVO  QTDFILMORT  ...  KOTELCHUCK  \\\n",
       "0      8 a 11 anos         NaN         0.0         0.0  ...           5   \n",
       "1      8 a 11 anos    999992.0         1.0         0.0  ...           5   \n",
       "2      8 a 11 anos    513205.0         2.0         0.0  ...           5   \n",
       "3  12 anos ou mais    231205.0         0.0         0.0  ...           4   \n",
       "4      8 a 11 anos    999992.0         1.0         0.0  ...           5   \n",
       "5       1 a 3 anos    621005.0         2.0         1.0  ...           5   \n",
       "6      8 a 11 anos    999992.0         1.0         0.0  ...           5   \n",
       "7  12 anos ou mais    514120.0         0.0         0.0  ...           5   \n",
       "8      8 a 11 anos    621005.0         0.0         1.0  ...           2   \n",
       "9       1 a 3 anos    999992.0         1.0         1.0  ...           5   \n",
       "\n",
       "  CONTADOR munResStatus munResTipo               munResNome  munResUf  \\\n",
       "0        1        ATIVO      MUNIC    Alta Floresta D'Oeste  Rondônia   \n",
       "1        2        ATIVO      MUNIC    Alta Floresta D'Oeste  Rondônia   \n",
       "2        3        ATIVO      MUNIC    Alta Floresta D'Oeste  Rondônia   \n",
       "3        4        ATIVO      MUNIC  Alto Alegre dos Parecis  Rondônia   \n",
       "4        5        ATIVO      MUNIC    Alta Floresta D'Oeste  Rondônia   \n",
       "5        6        ATIVO      MUNIC    Alta Floresta D'Oeste  Rondônia   \n",
       "6        7        ATIVO      MUNIC  Novo Horizonte do Oeste  Rondônia   \n",
       "7        8        ATIVO      MUNIC    Alta Floresta D'Oeste  Rondônia   \n",
       "8        9        ATIVO      MUNIC    Alta Floresta D'Oeste  Rondônia   \n",
       "9       10        ATIVO      MUNIC    Alta Floresta D'Oeste  Rondônia   \n",
       "\n",
       "   munResLat munResLon  munResAlt  munResArea  \n",
       "0  -11.93554 -61.99982      338.0    7067.025  \n",
       "1  -11.93554 -61.99982      338.0    7067.025  \n",
       "2  -11.93554 -61.99982      338.0    7067.025  \n",
       "3  -12.13178 -61.85308      397.0    3958.273  \n",
       "4  -11.93554 -61.99982      338.0    7067.025  \n",
       "5  -11.93554 -61.99982      338.0    7067.025  \n",
       "6  -11.70974 -61.99439      230.0     843.446  \n",
       "7  -11.93554 -61.99982      338.0    7067.025  \n",
       "8  -11.93554 -61.99982      338.0    7067.025  \n",
       "9  -11.93554 -61.99982      338.0    7067.025  \n",
       "\n",
       "[10 rows x 69 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinasc.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(27028, 69)\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "# Conte o número de registros e o número de registros não duplicados da base. \n",
    "\n",
    "print(sinasc.shape) # número de registros\n",
    "print(sinasc.duplicated().sum()) # número de registros não duplicados da base\n",
    "# -> Não tem registros duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        False\n",
       "1        False\n",
       "2        False\n",
       "3        False\n",
       "4        False\n",
       "         ...  \n",
       "27023    False\n",
       "27024    False\n",
       "27025    False\n",
       "27026    False\n",
       "27027    False\n",
       "Length: 27028, dtype: bool"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinasc.duplicated() # Não tem registros duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(27028, 69)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinasc.drop_duplicates().shape # Não tem registros duplicados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['CODESTAB', 'ESTCIVMAE', 'ESCMAE', 'CODOCUPMAE', 'QTDFILVIVO',\n",
       "       'QTDFILMORT', 'GESTACAO', 'GRAVIDEZ', 'PARTO', 'HORANASC', 'SEXO',\n",
       "       'APGAR1', 'APGAR5', 'RACACOR', 'IDANOMAL', 'CODANOMAL', 'DTRECORIGA',\n",
       "       'NATURALMAE', 'CODMUNNATU', 'CODUFNATU', 'ESCMAE2010', 'SERIESCMAE',\n",
       "       'DTNASCMAE', 'RACACORMAE', 'QTDGESTANT', 'QTDPARTNOR', 'QTDPARTCES',\n",
       "       'IDADEPAI', 'DTULTMENST', 'SEMAGESTAC', 'TPMETESTIM', 'CONSPRENAT',\n",
       "       'MESPRENAT', 'TPAPRESENT', 'STTRABPART', 'STCESPARTO', 'TPNASCASSI',\n",
       "       'TPFUNCRESP', 'TPDOCRESP', 'DTDECLARAC', 'ESCMAEAGR1', 'munResLat',\n",
       "       'munResLon', 'munResAlt', 'munResArea'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2) Conte o número de valores missing por variável\n",
    "\n",
    "colunas_com_NaN = sinasc.loc[:, sinasc.isna().sum() > 0] # filtrando apenas as colunas que possuem dados faltantes\n",
    "colunas_com_NaN.columns # exibindo as colunas com dados faltantes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "munResArea        1\n",
       "munResLon         1\n",
       "munResLat         1\n",
       "munResAlt         1\n",
       "SEXO              4\n",
       "TPDOCRESP        14\n",
       "HORANASC         21\n",
       "DTNASCMAE        40\n",
       "PARTO            49\n",
       "DTDECLARAC       52\n",
       "TPNASCASSI       61\n",
       "TPFUNCRESP       67\n",
       "GRAVIDEZ         79\n",
       "APGAR1           96\n",
       "APGAR5          103\n",
       "CODESTAB        115\n",
       "ESCMAE2010      249\n",
       "ESCMAEAGR1      249\n",
       "TPAPRESENT      265\n",
       "NATURALMAE      298\n",
       "CODMUNNATU      298\n",
       "CODUFNATU       298\n",
       "ESCMAE          312\n",
       "ESTCIVMAE       317\n",
       "IDANOMAL        591\n",
       "RACACOR         647\n",
       "RACACORMAE      661\n",
       "STCESPARTO      747\n",
       "CONSPRENAT      930\n",
       "STTRABPART      947\n",
       "QTDGESTANT     1212\n",
       "GESTACAO       1232\n",
       "SEMAGESTAC     1238\n",
       "TPMETESTIM     1238\n",
       "QTDFILVIVO     1573\n",
       "QTDPARTCES     1791\n",
       "QTDPARTNOR     1879\n",
       "QTDFILMORT     2098\n",
       "MESPRENAT      2867\n",
       "CODOCUPMAE     2907\n",
       "DTULTMENST    10072\n",
       "SERIESCMAE    12710\n",
       "IDADEPAI      19421\n",
       "CODANOMAL     26814\n",
       "DTRECORIGA    27028\n",
       "dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# mostrando apenas as colunas que possuem dados faltantes e a quantidade de dados faltantes\n",
    "\n",
    "colunas_com_NaN.isna().sum().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>LOCNASC</th>\n",
       "      <th>IDADEMAE</th>\n",
       "      <th>ESTCIVMAE</th>\n",
       "      <th>ESCMAE</th>\n",
       "      <th>QTDFILVIVO</th>\n",
       "      <th>GESTACAO</th>\n",
       "      <th>GRAVIDEZ</th>\n",
       "      <th>CONSULTAS</th>\n",
       "      <th>APGAR5</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37 a 41 semanas</td>\n",
       "      <td>Única</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37 a 41 semanas</td>\n",
       "      <td>Única</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>37</td>\n",
       "      <td>9.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>2.0</td>\n",
       "      <td>37 a 41 semanas</td>\n",
       "      <td>Única</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>5.0</td>\n",
       "      <td>12 anos ou mais</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37 a 41 semanas</td>\n",
       "      <td>Única</td>\n",
       "      <td>3</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37 a 41 semanas</td>\n",
       "      <td>Única</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27023</th>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>2.0</td>\n",
       "      <td>12 anos ou mais</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32 a 36 semanas</td>\n",
       "      <td>Única</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27024</th>\n",
       "      <td>1</td>\n",
       "      <td>19</td>\n",
       "      <td>1.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37 a 41 semanas</td>\n",
       "      <td>Única</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27025</th>\n",
       "      <td>1</td>\n",
       "      <td>24</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>0.0</td>\n",
       "      <td>37 a 41 semanas</td>\n",
       "      <td>Única</td>\n",
       "      <td>4</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27026</th>\n",
       "      <td>1</td>\n",
       "      <td>21</td>\n",
       "      <td>2.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>1.0</td>\n",
       "      <td>32 a 36 semanas</td>\n",
       "      <td>Única</td>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27027</th>\n",
       "      <td>1</td>\n",
       "      <td>18</td>\n",
       "      <td>5.0</td>\n",
       "      <td>8 a 11 anos</td>\n",
       "      <td>1.0</td>\n",
       "      <td>37 a 41 semanas</td>\n",
       "      <td>Única</td>\n",
       "      <td>4</td>\n",
       "      <td>8.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>27028 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       LOCNASC  IDADEMAE  ESTCIVMAE           ESCMAE  QTDFILVIVO  \\\n",
       "0            1        19        5.0      8 a 11 anos         0.0   \n",
       "1            1        29        2.0      8 a 11 anos         1.0   \n",
       "2            1        37        9.0      8 a 11 anos         2.0   \n",
       "3            1        30        5.0  12 anos ou mais         0.0   \n",
       "4            1        30        2.0      8 a 11 anos         1.0   \n",
       "...        ...       ...        ...              ...         ...   \n",
       "27023        1        32        2.0  12 anos ou mais         1.0   \n",
       "27024        1        19        1.0      8 a 11 anos         0.0   \n",
       "27025        1        24        2.0      8 a 11 anos         0.0   \n",
       "27026        1        21        2.0      8 a 11 anos         1.0   \n",
       "27027        1        18        5.0      8 a 11 anos         1.0   \n",
       "\n",
       "              GESTACAO GRAVIDEZ  CONSULTAS  APGAR5  \n",
       "0      37 a 41 semanas    Única          4    10.0  \n",
       "1      37 a 41 semanas    Única          4     9.0  \n",
       "2      37 a 41 semanas    Única          4    10.0  \n",
       "3      37 a 41 semanas    Única          3    10.0  \n",
       "4      37 a 41 semanas    Única          4    10.0  \n",
       "...                ...      ...        ...     ...  \n",
       "27023  32 a 36 semanas    Única          4     9.0  \n",
       "27024  37 a 41 semanas    Única          4     9.0  \n",
       "27025  37 a 41 semanas    Única          4    10.0  \n",
       "27026  32 a 36 semanas    Única          4     9.0  \n",
       "27027  37 a 41 semanas    Única          4     8.0  \n",
       "\n",
       "[27028 rows x 9 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3) Ok, no item anterior você deve ter achado pouco prático ler a informação de tantas variáveis,\n",
    "# muitas delas nem devem ser interesantes. Então crie uma seleção dessa base somente com as colunas que interessam. \n",
    "# São elas:\n",
    "\n",
    "# ['LOCNASC', 'IDADEMAE', 'ESTCIVMAE', 'ESCMAE', 'QTDFILVIVO', 'GESTACAO', 'GRAVIDEZ', 'CONSULTAS', 'APGAR5'] \n",
    "\n",
    "lista = sinasc[['LOCNASC', 'IDADEMAE', 'ESTCIVMAE', 'ESCMAE', 'QTDFILVIVO', 'GESTACAO', 'GRAVIDEZ', 'CONSULTAS', 'APGAR5']]\n",
    "lista"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4) Apgar é uma nota que o pediatra dá ao bebê quando nasce de acordo com algumas características associadas \n",
    "# principalmente à respiração. Apgar 1 e Apgar 5 são as notas 1 e 5 minutos do nascimento. \n",
    "# Apgar5 será a nossa variável de interesse principal. Então remova todos os registros com Apgar5 não preenchido. \n",
    "# Para esta seleção, conte novamente o número de linhas e o número de missings.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "103"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# contando a quantidade de linhas missing\n",
    "\n",
    "sinasc['APGAR5'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# removendo os registros não preenchidos\n",
    "\n",
    "sinasc.dropna(subset=['APGAR5'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# conferindo se foi removido os dados faltantes\n",
    "\n",
    "sinasc['APGAR5'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 5) observe que as variáveis ['ESTCIVMAE', 'CONSULTAS'] possuem o código 9, que significa ignorado. \n",
    "# Vamos assumir que o não preenchido é o mesmo que o código 9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Tem 315 faltantes\n",
    "\n",
    "sinasc['ESTCIVMAE'].isnull().sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Substituindo faltantes por 9\n",
    "sinasc['ESTCIVMAE'].fillna(9, inplace=True)\n",
    "## Exibe mensagem de erro quando executo pela lista criada, mas substitui por 9. Se fizer no df não aparece a msg de erro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinasc['ESTCIVMAE'].isnull().sum() # Conferindo a substituição de faltantes por 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinasc['CONSULTAS'].isnull().sum() # Não tem nenhum NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 6) Substitua os valores faltantes da quantitativa (QTDFILVIVO) por zero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1566"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinasc['QTDFILVIVO'].isnull().sum()  # contando a quantidade de linhas missing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "sinasc['QTDFILVIVO'].fillna(0, inplace=True) # sustituindo NaN por 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinasc['QTDFILVIVO'].isnull().sum() # conferindo a substituição"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ORIGEM            0\n",
       "DTRECEBIM         0\n",
       "VERSAOSIST        0\n",
       "NUMEROLOTE        0\n",
       "STDNEPIDEM        0\n",
       "              ...  \n",
       "DTULTMENST    10016\n",
       "SERIESCMAE    12664\n",
       "IDADEPAI      19332\n",
       "CODANOMAL     26714\n",
       "DTRECORIGA    26925\n",
       "Length: 69, dtype: int64"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 7) Das restantes, decida que valore te parece mais adequado (um 'não preenchido' ou um valor 'mais provável' \n",
    "# como no item anterior) e preencha. Justifique. Lembre-se de que tratamento de dados é trabalho do cientista, \n",
    "# e que estamos tomando decisões a todo o momento - não há necessariamente certo e errado aqui.\n",
    "\n",
    "sinasc.isna().sum().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26714"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Verifico a quantidade de registros vazios na váriável escolhida\n",
    "sinasc['CODANOMAL'].isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinasc['CODANOMAL'].fillna(9, inplace=True) # Altero o valor vazio para 9 e confirmo a gravação do dado.\n",
    "sinasc['CODANOMAL'].isnull().sum() #Na linha seguinte verifico se a quantidade de registros vazios ficou zerada."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 8) O Apgar possui uma classificação indicando se o bebê passou por asfixia:\n",
    "# Entre 8 e 10 está em uma faixa 'normal'.\n",
    "# Entre 6 e 7, significa que o recém-nascido passou por 'asfixia leve'.\n",
    "# Entre 4 e 5 significa 'asfixia moderada'.\n",
    "# Entre 0 e 3 significa 'asfixia severa'.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Crie uma categorização dessa variável com essa codificação\n",
    "\n",
    "sinasc.loc[(sinasc['APGAR1'] >= 0) & (sinasc['APGAR1'] <= 3),\n",
    "           'APGAR_CAT'] = 'ASFIXIA SEVERA'\n",
    "sinasc.loc[(sinasc['APGAR1'] >= 4) & (sinasc['APGAR1'] <= 5),\n",
    "           'APGAR_CAT'] = 'ASFIXIA MODERADA'\n",
    "sinasc.loc[(sinasc['APGAR1'] >= 6) & (sinasc['APGAR1'] <= 7),\n",
    "           'APGAR_CAT'] = 'ASFIXIA LEVE'\n",
    "sinasc.loc[(sinasc['APGAR1'] >= 8) & (sinasc['APGAR1'] <= 10),\n",
    "           'APGAR_CAT'] = 'NORMAL'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APGAR1</th>\n",
       "      <th>APGAR_CAT</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <td>9.0</td>\n",
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       "      <th>4</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NORMAL</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>27023</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NORMAL</td>\n",
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       "    <tr>\n",
       "      <th>27024</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NORMAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27025</th>\n",
       "      <td>9.0</td>\n",
       "      <td>NORMAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27026</th>\n",
       "      <td>8.0</td>\n",
       "      <td>NORMAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27027</th>\n",
       "      <td>7.0</td>\n",
       "      <td>ASFIXIA LEVE</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>26925 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       APGAR1     APGAR_CAT\n",
       "0         9.0        NORMAL\n",
       "1         8.0        NORMAL\n",
       "2         8.0        NORMAL\n",
       "3         9.0        NORMAL\n",
       "4         8.0        NORMAL\n",
       "...       ...           ...\n",
       "27023     8.0        NORMAL\n",
       "27024     8.0        NORMAL\n",
       "27025     9.0        NORMAL\n",
       "27026     8.0        NORMAL\n",
       "27027     7.0  ASFIXIA LEVE\n",
       "\n",
       "[26925 rows x 2 columns]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sinasc[['APGAR1', 'APGAR_CAT']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>27024</th>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>27025</th>\n",
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       "      <th>27026</th>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>26925 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ASFIXIA LEVE  ASFIXIA MODERADA  ASFIXIA SEVERA  NORMAL\n",
       "0                 0                 0               0       1\n",
       "1                 0                 0               0       1\n",
       "2                 0                 0               0       1\n",
       "3                 0                 0               0       1\n",
       "4                 0                 0               0       1\n",
       "...             ...               ...             ...     ...\n",
       "27023             0                 0               0       1\n",
       "27024             0                 0               0       1\n",
       "27025             0                 0               0       1\n",
       "27026             0                 0               0       1\n",
       "27027             1                 0               0       0\n",
       "\n",
       "[26925 rows x 4 columns]"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# E calcule as frequências dessa categorização\n",
    "\n",
    "pd.get_dummies(sinasc['APGAR_CAT'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 9) Renomeie as variáveis para que fiquem no snake case, ou seja, em letras minúsculas, com um underscore entre as \n",
    "# palávras. Dica: repare que se você não quiser criar um dataframe novo, você vai precisar usar a opção inplace = True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [],
   "source": [
    "# renomendo para letras minúsculas e incluindo underscore\n",
    "\n",
    "sinasc['APGAR_CAT'] = sinasc['APGAR_CAT'].str.replace(' ', '_').str.lower() # transformando os valores em snake case"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
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       "<p>26925 rows × 2 columns</p>\n",
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      ],
      "text/plain": [
       "       APGAR1     APGAR_CAT\n",
       "0         9.0        normal\n",
       "1         8.0        normal\n",
       "2         8.0        normal\n",
       "3         9.0        normal\n",
       "4         8.0        normal\n",
       "...       ...           ...\n",
       "27023     8.0        normal\n",
       "27024     8.0        normal\n",
       "27025     9.0        normal\n",
       "27026     8.0        normal\n",
       "27027     7.0  asfixia_leve\n",
       "\n",
       "[26925 rows x 2 columns]"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# exibindo a alteração\n",
    "\n",
    "sinasc[['APGAR1', 'APGAR_CAT']]"
   ]
  }
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