Visualização de dados_covidBR.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualização de dados | Covid-19 Brasil"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Os dados usados aqui foram extraidos em maio de 2020 no portal do [ministério da saúde](https://covid.saude.gov.br/).*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_covidBr = pd.read_excel('HIST_PAINEL_COVIDBR_21mai2020.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vou visualizar o geral pra saber o que contém de dados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_covidBr.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Posso perceber que tem informações de covid sobre estado, município, região e que as informações liberadas são sempre relacionadas ao dia anterior. Além disso tem muitos dados nulos nesse dataframe. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Tratamento de dados**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vou começar o tratamento olhando se existe alguma linha não nula. Vou chamar o `tail()` para olhar as últimas linhas."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiao</th>\n",
       "      <th>estado</th>\n",
       "      <th>municipio</th>\n",
       "      <th>coduf</th>\n",
       "      <th>codmun</th>\n",
       "      <th>codRegiaoSaude</th>\n",
       "      <th>nomeRegiaoSaude</th>\n",
       "      <th>data</th>\n",
       "      <th>semanaEpi</th>\n",
       "      <th>populacaoTCU2019</th>\n",
       "      <th>casosAcumulado</th>\n",
       "      <th>obitosAcumulado</th>\n",
       "      <th>Recuperadosnovos</th>\n",
       "      <th>emAcompanhamentoNovos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99517</th>\n",
       "      <td>Centro-Oeste</td>\n",
       "      <td>DF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53</td>\n",
       "      <td>5300108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-15</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3787</td>\n",
       "      <td>55</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99518</th>\n",
       "      <td>Centro-Oeste</td>\n",
       "      <td>DF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53</td>\n",
       "      <td>5300108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-16</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4140</td>\n",
       "      <td>56</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99519</th>\n",
       "      <td>Centro-Oeste</td>\n",
       "      <td>DF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53</td>\n",
       "      <td>5300108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-17</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4368</td>\n",
       "      <td>59</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99520</th>\n",
       "      <td>Centro-Oeste</td>\n",
       "      <td>DF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53</td>\n",
       "      <td>5300108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-18</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4619</td>\n",
       "      <td>66</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99521</th>\n",
       "      <td>Centro-Oeste</td>\n",
       "      <td>DF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>53</td>\n",
       "      <td>5300108.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2020-05-19</td>\n",
       "      <td>21</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4853</td>\n",
       "      <td>72</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             regiao estado municipio  coduf     codmun  codRegiaoSaude  \\\n",
       "99517  Centro-Oeste     DF       NaN     53  5300108.0             NaN   \n",
       "99518  Centro-Oeste     DF       NaN     53  5300108.0             NaN   \n",
       "99519  Centro-Oeste     DF       NaN     53  5300108.0             NaN   \n",
       "99520  Centro-Oeste     DF       NaN     53  5300108.0             NaN   \n",
       "99521  Centro-Oeste     DF       NaN     53  5300108.0             NaN   \n",
       "\n",
       "      nomeRegiaoSaude        data  semanaEpi  populacaoTCU2019  \\\n",
       "99517             NaN  2020-05-15         20               NaN   \n",
       "99518             NaN  2020-05-16         20               NaN   \n",
       "99519             NaN  2020-05-17         21               NaN   \n",
       "99520             NaN  2020-05-18         21               NaN   \n",
       "99521             NaN  2020-05-19         21               NaN   \n",
       "\n",
       "       casosAcumulado  obitosAcumulado  Recuperadosnovos  \\\n",
       "99517            3787               55               NaN   \n",
       "99518            4140               56               NaN   \n",
       "99519            4368               59               NaN   \n",
       "99520            4619               66               NaN   \n",
       "99521            4853               72               NaN   \n",
       "\n",
       "       emAcompanhamentoNovos  \n",
       "99517                    NaN  \n",
       "99518                    NaN  \n",
       "99519                    NaN  \n",
       "99520                    NaN  \n",
       "99521                    NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covidBr.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tem algumas linhas preenchidas sim. Vou filtrar os dados do Distrito Federal para olhar melhor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>regiao</th>\n",
       "      <th>estado</th>\n",
       "      <th>municipio</th>\n",
       "      <th>coduf</th>\n",
       "      <th>codmun</th>\n",
       "      <th>codRegiaoSaude</th>\n",
       "      <th>nomeRegiaoSaude</th>\n",
       "      <th>data</th>\n",
       "      <th>semanaEpi</th>\n",
       "      <th>populacaoTCU2019</th>\n",
       "      <th>casosAcumulado</th>\n",
       "      <th>obitosAcumulado</th>\n",
       "      <th>Recuperadosnovos</th>\n",
       "      <th>emAcompanhamentoNovos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>99467</th>\n",
       "      <td>Centro-Oeste</td>\n",
       "      <td>DF</td>\n",
       "      <td>Brasília</td>\n",
       "      <td>53</td>\n",
       "      <td>530010.0</td>\n",
       "      <td>53001.0</td>\n",
       "      <td>Brasília</td>\n",
       "      <td>2020-05-20</td>\n",
       "      <td>21</td>\n",
       "      <td>3015268.0</td>\n",
       "      <td>5161</td>\n",
       "      <td>77</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99468</th>\n",
       "      <td>Centro-Oeste</td>\n",
       "      <td>DF</td>\n",
       "      <td>Brasília</td>\n",
       "      <td>53</td>\n",
       "      <td>530010.0</td>\n",
       "      <td>53001.0</td>\n",
       "      <td>Brasília</td>\n",
       "      <td>2020-05-21</td>\n",
       "      <td>21</td>\n",
       "      <td>3015268.0</td>\n",
       "      <td>5542</td>\n",
       "      <td>84</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             regiao estado municipio  coduf    codmun  codRegiaoSaude  \\\n",
       "99467  Centro-Oeste     DF  Brasília     53  530010.0         53001.0   \n",
       "99468  Centro-Oeste     DF  Brasília     53  530010.0         53001.0   \n",
       "\n",
       "      nomeRegiaoSaude        data  semanaEpi  populacaoTCU2019  \\\n",
       "99467        Brasília  2020-05-20         21         3015268.0   \n",
       "99468        Brasília  2020-05-21         21         3015268.0   \n",
       "\n",
       "       casosAcumulado  obitosAcumulado  Recuperadosnovos  \\\n",
       "99467            5161               77               NaN   \n",
       "99468            5542               84               NaN   \n",
       "\n",
       "       emAcompanhamentoNovos  \n",
       "99467                    NaN  \n",
       "99468                    NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_covidBr[df_covidBr['municipio']== 'Brasília']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para ajustar o preechimento vou criar 3 dataframes diferentes: brasil, estados e municipios, começando pelo Brasil"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#pegando todas as linhas que tenha dados nulo na coluna estado\n",
    "brasil = df_covidBr[df_covidBr['estado'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pedi para trazer todas as linhas da coluna municipio onde o estado estivesse preenchido\n",
    "estado = df_covidBr[df_covidBr['municipio'].isnull() & df_covidBr['estado'].notnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Trazer as linhas de municipio que não tem o conteudo nulo\n",
    "municipio = df_covidBr[df_covidBr['municipio'].notnull()]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "vou criar um novo dataframe chamado brasil, com as informações tratadas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Os dois pontos (:) é para trazer todas as linhas das duas colunas que eu indiquei\n",
    "\n",
    "brasil_tratado = brasil.loc[:,['data','casosAcumulado']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vou agora criar uma visualização com esse dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x198205ba610>]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = brasil_tratado['data']\n",
    "y = brasil_tratado['casosAcumulado']\n",
    "\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tem muitos dias, fica bem inlegivel. Vou colocar pra visualizar de quatro em quatro dias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([<matplotlib.axis.XTick at 0x19820c119d0>,\n",
       "  <matplotlib.axis.XTick at 0x19820c119a0>,\n",
       "  <matplotlib.axis.XTick at 0x19820cd5cd0>,\n",
       "  <matplotlib.axis.XTick at 0x198204d7790>,\n",
       "  <matplotlib.axis.XTick at 0x198204d7c10>,\n",
       "  <matplotlib.axis.XTick at 0x198204e5160>,\n",
       "  <matplotlib.axis.XTick at 0x198204e5670>,\n",
       "  <matplotlib.axis.XTick at 0x198204e5b80>,\n",
       "  <matplotlib.axis.XTick at 0x198204d7460>,\n",
       "  <matplotlib.axis.XTick at 0x198204e5280>,\n",
       "  <matplotlib.axis.XTick at 0x198209931f0>,\n",
       "  <matplotlib.axis.XTick at 0x19820993700>,\n",
       "  <matplotlib.axis.XTick at 0x19820993c10>,\n",
       "  <matplotlib.axis.XTick at 0x198209a3160>,\n",
       "  <matplotlib.axis.XTick at 0x198209a3670>,\n",
       "  <matplotlib.axis.XTick at 0x198209a3b80>,\n",
       "  <matplotlib.axis.XTick at 0x198209820d0>,\n",
       "  <matplotlib.axis.XTick at 0x198209a36d0>,\n",
       "  <matplotlib.axis.XTick at 0x19820993760>,\n",
       "  <matplotlib.axis.XTick at 0x198204d74c0>,\n",
       "  <matplotlib.axis.XTick at 0x19820982790>,\n",
       "  <matplotlib.axis.XTick at 0x19820982ca0>],\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = brasil_tratado['data']\n",
    "y = brasil_tratado['casosAcumulado']\n",
    "\n",
    "#usei iloc pra pedir que percorrece cada linha e trouxesse os resulados de quatro em quatro. Salvei dentro da variavel ticks\n",
    "ticks = brasil_tratado['data'].iloc[::4]\n",
    "plt.plot(x,y)\n",
    "plt.xticks(ticks, rotation = 'vertical')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "vou mudar para data aparecer só como dia e mês. Primeiro vou mudar o tipo de dado da coluna para 'datetime'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "brasil_tratado['data'] = pd.to_datetime(brasil_tratado['data'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora vou dia e mês"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Uso o strtime para pegar só dia e mês\n",
    "brasil_tratado['data'] = brasil_tratado['data'].dt.strftime('%d/%m')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vou plotar o gráfico novamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-50000.,      0.,  50000., 100000., 150000., 200000., 250000.,\n",
       "        300000., 350000.]),\n",
       " [Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, ''),\n",
       "  Text(0, 0, '')])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "dark"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = brasil_tratado['data']\n",
    "y = brasil_tratado['casosAcumulado']\n",
    "\n",
    "ticks = brasil_tratado['data'].iloc[::4]\n",
    "\n",
    "#\"bo-\" é pra fazer a linha em formato de bolinhas\n",
    "plt.plot(x,y, 'bo-', color = '#1A9073')\n",
    "\n",
    "#colocar titulo no gráfico\n",
    "plt.xlabel('Data de notificação', color = 'k')\n",
    "plt.ylabel('Casos acumulados', color = 'k')\n",
    "plt.title('Casos acumulados de COVID-19 por data de notificação', color = 'k')\n",
    "\n",
    "plt.xticks(ticks, rotation = 'vertical', color = '#8498AE')\n",
    "plt.yticks(color = '#8498AE')"
   ]
  }
 ],
 "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
}