OpendataSUS SRAG 2020.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Os dados, até a data do presente estudo, estão no https://opendatasus.saude.gov.br/dataset/bd-srag-2020\n",
    "#### O presente arquivo é de 10 de maio. Atenção: a última barra no gráfico por semana é menor, pois a semana é incompleta! Note também que o crescimento é diferente por cidades.\n",
    "#### Disclaimer/Aviso Legal: Este notebook tem um fim única e exclusivamente didático, para alunos da FATEC, como uma introdução à Análise de Dados Públicos. Desse modo, está excluída a garantia ou responsabilidade de qualquer tipo, por exemplo, de precisão, confiabilidade, completude e atualidade das informações."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "low_memory=False\n",
    "%matplotlib inline\n",
    "pd.options.display.max_columns = 80\n",
    "pd.options.display.max_rows = 90"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\users\\fmasa\\appdata\\local\\programs\\python\\python39\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3165: DtypeWarning: Columns (115) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "filename = r'C:\\Users\\fmasa\\Downloads\\INFLUD-10-05-2021.csv'\n",
    "df = pd.read_csv(filename, delimiter=';', \n",
    "                 usecols='CS_RACA CS_ESCOL_N SEM_NOT DT_NOTIFIC SG_UF_NOT HISTO_VGM PAIS_VGM CARDIOPATI DIABETES OBESIDADE NU_IDADE_N ID_MUNICIP'.split(),\n",
    "                 encoding='ISO-8859-1',\n",
    "                 error_bad_lines=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1193095, 12)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1193095 entries, 0 to 1193094\n",
      "Data columns (total 12 columns):\n",
      " #   Column      Non-Null Count    Dtype  \n",
      "---  ------      --------------    -----  \n",
      " 0   DT_NOTIFIC  1193095 non-null  object \n",
      " 1   SEM_NOT     1193095 non-null  int64  \n",
      " 2   SG_UF_NOT   1193095 non-null  object \n",
      " 3   ID_MUNICIP  1193095 non-null  object \n",
      " 4   NU_IDADE_N  1193095 non-null  int64  \n",
      " 5   CS_RACA     1140319 non-null  float64\n",
      " 6   CS_ESCOL_N  832725 non-null   float64\n",
      " 7   CARDIOPATI  617347 non-null   float64\n",
      " 8   DIABETES    576827 non-null   float64\n",
      " 9   OBESIDADE   494523 non-null   float64\n",
      " 10  HISTO_VGM   1193095 non-null  int64  \n",
      " 11  PAIS_VGM    1294 non-null     object \n",
      "dtypes: float64(5), int64(3), object(4)\n",
      "memory usage: 109.2+ MB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0, 'DT_NOTIFIC')\n",
      "(1, 'SEM_NOT')\n",
      "(2, 'SG_UF_NOT')\n",
      "(3, 'ID_MUNICIP')\n",
      "(4, 'NU_IDADE_N')\n",
      "(5, 'CS_RACA')\n",
      "(6, 'CS_ESCOL_N')\n",
      "(7, 'CARDIOPATI')\n",
      "(8, 'DIABETES')\n",
      "(9, 'OBESIDADE')\n",
      "(10, 'HISTO_VGM')\n",
      "(11, 'PAIS_VGM')\n"
     ]
    }
   ],
   "source": [
    "for par in enumerate(df.columns): print (par)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tabelas de microdados usam números, faço a correspondência desses números com seus significados\n",
    "troca_raça = {1:'Branca', 2:'Preta', 3:'Amarela',\n",
    "              4:'Parda', 5:'Indígena', 9:'Ignorado'}\n",
    "troca_escolaridade = {0:'Sem', 1:'Fund1', \n",
    "                      2:'Fund2', 3:'Médio',\n",
    "                      4:'Superior', \n",
    "                      5:'Não se aplica',\n",
    "                      9:'Ignorado'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[(df.CS_RACA.isnull()), 'CS_RACA'] = 9\n",
    "df['CS_RACA'] = df['CS_RACA'].apply(lambda x: troca_raça[x])\n",
    "df.loc[(df.CS_ESCOL_N.isnull()), 'CS_ESCOL_N'] = 9\n",
    "df['CS_ESCOL_N'] = df['CS_ESCOL_N'].apply(lambda x: troca_escolaridade[x])\n",
    "df.SEM_NOT = df.SEM_NOT.apply(pd.to_numeric, errors='coerce')\n",
    "df['DT_NOTIFIC'].dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DT_NOTIFIC</th>\n",
       "      <th>SEM_NOT</th>\n",
       "      <th>SG_UF_NOT</th>\n",
       "      <th>ID_MUNICIP</th>\n",
       "      <th>NU_IDADE_N</th>\n",
       "      <th>CS_RACA</th>\n",
       "      <th>CS_ESCOL_N</th>\n",
       "      <th>CARDIOPATI</th>\n",
       "      <th>DIABETES</th>\n",
       "      <th>OBESIDADE</th>\n",
       "      <th>HISTO_VGM</th>\n",
       "      <th>PAIS_VGM</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>221629</th>\n",
       "      <td>29/11/2020</td>\n",
       "      <td>49</td>\n",
       "      <td>BA</td>\n",
       "      <td>VITORIA DA CONQUISTA</td>\n",
       "      <td>75</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Ignorado</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1063215</th>\n",
       "      <td>18/12/2020</td>\n",
       "      <td>51</td>\n",
       "      <td>RS</td>\n",
       "      <td>PORTO ALEGRE</td>\n",
       "      <td>56</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Ignorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1106958</th>\n",
       "      <td>26/07/2020</td>\n",
       "      <td>31</td>\n",
       "      <td>SP</td>\n",
       "      <td>SAO JOSE DOS CAMPOS</td>\n",
       "      <td>39</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Ignorado</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967110</th>\n",
       "      <td>07/10/2020</td>\n",
       "      <td>41</td>\n",
       "      <td>CE</td>\n",
       "      <td>FORTALEZA</td>\n",
       "      <td>17</td>\n",
       "      <td>Parda</td>\n",
       "      <td>Ignorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>713587</th>\n",
       "      <td>16/06/2020</td>\n",
       "      <td>25</td>\n",
       "      <td>SP</td>\n",
       "      <td>CERQUILHO</td>\n",
       "      <td>42</td>\n",
       "      <td>Ignorado</td>\n",
       "      <td>Ignorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>680607</th>\n",
       "      <td>27/12/2020</td>\n",
       "      <td>53</td>\n",
       "      <td>SP</td>\n",
       "      <td>SALTO</td>\n",
       "      <td>67</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Médio</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>859755</th>\n",
       "      <td>30/03/2020</td>\n",
       "      <td>14</td>\n",
       "      <td>SP</td>\n",
       "      <td>SOROCABA</td>\n",
       "      <td>73</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Ignorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>787252</th>\n",
       "      <td>30/07/2020</td>\n",
       "      <td>31</td>\n",
       "      <td>SP</td>\n",
       "      <td>SAO PAULO</td>\n",
       "      <td>30</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Ignorado</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1106597</th>\n",
       "      <td>22/06/2020</td>\n",
       "      <td>26</td>\n",
       "      <td>MG</td>\n",
       "      <td>UBA</td>\n",
       "      <td>65</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Ignorado</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>551645</th>\n",
       "      <td>29/04/2020</td>\n",
       "      <td>18</td>\n",
       "      <td>RJ</td>\n",
       "      <td>RIO DE JANEIRO</td>\n",
       "      <td>87</td>\n",
       "      <td>Branca</td>\n",
       "      <td>Fund2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "         DT_NOTIFIC  SEM_NOT SG_UF_NOT            ID_MUNICIP  NU_IDADE_N  \\\n",
       "221629   29/11/2020       49        BA  VITORIA DA CONQUISTA          75   \n",
       "1063215  18/12/2020       51        RS          PORTO ALEGRE          56   \n",
       "1106958  26/07/2020       31        SP   SAO JOSE DOS CAMPOS          39   \n",
       "967110   07/10/2020       41        CE             FORTALEZA          17   \n",
       "713587   16/06/2020       25        SP             CERQUILHO          42   \n",
       "680607   27/12/2020       53        SP                 SALTO          67   \n",
       "859755   30/03/2020       14        SP              SOROCABA          73   \n",
       "787252   30/07/2020       31        SP             SAO PAULO          30   \n",
       "1106597  22/06/2020       26        MG                   UBA          65   \n",
       "551645   29/04/2020       18        RJ        RIO DE JANEIRO          87   \n",
       "\n",
       "          CS_RACA CS_ESCOL_N  CARDIOPATI  DIABETES  OBESIDADE  HISTO_VGM  \\\n",
       "221629      Parda   Ignorado         1.0       2.0        2.0          2   \n",
       "1063215    Branca   Ignorado         NaN       NaN        NaN          0   \n",
       "1106958    Branca   Ignorado         2.0       2.0        2.0          2   \n",
       "967110      Parda   Ignorado         NaN       NaN        NaN          0   \n",
       "713587   Ignorado   Ignorado         NaN       NaN        NaN          2   \n",
       "680607     Branca      Médio         NaN       1.0        NaN          2   \n",
       "859755     Branca   Ignorado         NaN       NaN        NaN          0   \n",
       "787252     Branca   Ignorado         NaN       NaN        NaN          2   \n",
       "1106597    Branca   Ignorado         1.0       1.0        2.0          2   \n",
       "551645     Branca      Fund2         1.0       NaN        NaN          0   \n",
       "\n",
       "        PAIS_VGM  \n",
       "221629       NaN  \n",
       "1063215      NaN  \n",
       "1106958      NaN  \n",
       "967110       NaN  \n",
       "713587       NaN  \n",
       "680607       NaN  \n",
       "859755       NaN  \n",
       "787252       NaN  \n",
       "1106597      NaN  \n",
       "551645       NaN  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sample(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='SG_UF_NOT'>"
      ]
     },
     "execution_count": 9,
     "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": [
    "df.groupby('SG_UF_NOT').size().sort_values().tail(5).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='CS_RACA'>"
      ]
     },
     "execution_count": 10,
     "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": [
    "df.groupby('CS_RACA').size().sort_values().plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CS_RACA\n",
       "Indígena      3229\n",
       "Amarela      12502\n",
       "Preta        57817\n",
       "Ignorado    255846\n",
       "Parda       411189\n",
       "Branca      452512\n",
       "dtype: int64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('CS_RACA').size().sort_values()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CS_ESCOL_N\n",
       "Ignorado         749246\n",
       "Médio            120155\n",
       "Fund1            119505\n",
       "Fund2             74651\n",
       "Superior          57232\n",
       "Sem               37500\n",
       "Não se aplica     34806\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('CS_ESCOL_N').size().sort_values(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    1222\n",
       "Name: HISTO_VGM, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('HISTO_VGM == 1')['HISTO_VGM'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='PAIS_VGM'>"
      ]
     },
     "execution_count": 14,
     "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": [
    "df.query('HISTO_VGM == 1 and PAIS_VGM != \"BRASIL\"').groupby('PAIS_VGM').size().sort_values(ascending=False).head(10).plot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    379163\n",
       "Name: CARDIOPATI, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('CARDIOPATI == 1')['CARDIOPATI'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    267021\n",
       "Name: DIABETES, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('DIABETES == 1')['DIABETES'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0    57274\n",
       "Name: OBESIDADE, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('OBESIDADE == 1')['OBESIDADE'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "609672"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.query('NU_IDADE_N >= 60')['NU_IDADE_N'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SEM_NOT'>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEKCAYAAAD9xUlFAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAedUlEQVR4nO3de7QdZZnn8e+PcBGlIQEiRhIMA1EnMGOECHHEkYY2BJwxOIMusJdEGok9grdBm2BPd5BLD9qtdDMKa0WJBLwEvDURQscMwqjdAgkSEkKAHAOYxACBcLNBhPQzf7zPqZSbvc/Z535Ozu+zVq1T9dRbb721T+166vLuvRURmJmZAewy1A0wM7Phw0nBzMwqTgpmZlZxUjAzs4qTgpmZVZwUzMyssutQN6C39t9//5g8efJQN8PMbES56667noiI8a3mj9ikMHnyZFauXDnUzTAzG1EkPdLVfN8+MjOzipOCmZlVnBTMzKzipGBmZhUnBTMzqzgpmJlZxUnBzMwqTgpmZlYZsR9eM9uZTZ530x9MP3zpe4aoJTba+ErBzMwqvlIwGwT1M3+f9dtw5isFMzOrOCmYmVnFScHMzCpOCmZmVvGDZrMh5AfQNtz4SsHMzCq+UjBrwWfxNhp1e6Ug6VWS7pR0j6S1kj6f8aslPSRpVQ7TMi5Jl0vqkLRa0hG1uuZIWp/DnFr8SElrcpnLJWkAttXMzLrRzpXCi8BxEfFbSbsBP5d0c877bER8r6H8icCUHI4GrgSOlrQvMB+YDgRwl6QlEfFUljkLuANYCswCbsbMzAZVt0khIgL4bU7ulkN0schs4Jpc7nZJYyVNAI4FlkfENgBJy4FZkm4D9o6I2zN+DXAyTgo2AvmWk410bT1oljRG0irgccqB/Y6cdUneIrpM0h4ZOxDYWFt8U8a6im9qEjczs0HW1oPmiNgOTJM0FvihpMOB84FHgd2BBcB5wIUD1E4AJM0F5gIcdNBBA7kqs5Z8NWA7sx51SY2Ip4FbgVkRsSWKF4FvAEdlsc3ApNpiEzPWVXxik3iz9S+IiOkRMX38+PE9abqZmbWhnd5H4/MKAUl7Au8G7s/nBGRPoZOBe3ORJcDp2QtpBvBMRGwBlgEzJY2TNA6YCSzLec9KmpF1nQ7c0J8babYzmTzvpmow62/t3D6aACySNIaSRK6PiBsl/UTSeEDAKuDPs/xS4CSgA3geOAMgIrZJughYkeUu7HzoDHwMuBrYk/KA2Q+ZrUu+hWM2MNrpfbQaeGuT+HEtygdwdot5C4GFTeIrgcO7a4uZmQ0sf82FmZlVnBTMzKzipGBmZhUnBTMzqzgpmJlZxV+dbcOWu52aDT4nBRv1nHzMdnBSsJ2KD/BmfeNnCmZmVnFSMDOzipOCmZlV/EzBRoXR/Kyh8dtUR9v2W884KZjtJEZz4rP+49tHZmZWcVIwM7OKk4KZmVWcFMzMrOIHzTbk/IDUbIdW74fBep/4SsHMzCrdJgVJr5J0p6R7JK2V9PmMHyzpDkkdkq6TtHvG98jpjpw/uVbX+Rl/QNIJtfisjHVImjcA22lmZm1o50rhReC4iHgLMA2YJWkG8AXgsog4FHgKODPLnwk8lfHLshySpgKnAocBs4ArJI2RNAb4KnAiMBU4Lcuamdkg6/aZQkQE8Nuc3C2HAI4DPpjxRcAFwJXA7BwH+B7wFUnK+OKIeBF4SFIHcFSW64iIDQCSFmfZ+/qyYWbWPT/PsUZtPWjOs/m7gEMpZ/W/Ap6OiJezyCbgwBw/ENgIEBEvS3oG2C/jt9eqrS+zsSF+dIt2zAXmAhx00EHtNN3MbNgZzl890taD5ojYHhHTgImUs/s3D2SjumjHgoiYHhHTx48fPxRNMDPbqfWo91FEPA3cCrwdGCup80pjIrA5xzcDkwBy/j7Ak/V4wzKt4mZmNsja6X00XtLYHN8TeDewjpIcTslic4AbcnxJTpPzf5LPJZYAp2bvpIOBKcCdwApgSvZm2p3yMHpJP2ybmZn1UDvPFCYAi/K5wi7A9RFxo6T7gMWSLgbuBq7K8lcB1+aD5G2UgzwRsVbS9ZQHyC8DZ0fEdgBJ5wDLgDHAwohY229baGY95gfQo1c7vY9WA29tEt/Ajt5D9fjvgPe3qOsS4JIm8aXA0jbaa2ZmA8ifaDYzs4qTgpmZVfyFeGZmO6nePBvylYKZmVV8pWBmbRvOn8QdjkZiLy5fKZiZWcVXCjZoRuJZk9lo4ysFMzOrOCmYmVnFScHMzCpOCmZmVnFSMDOzipOCmZlVRnSXVHdxNLPRrr8/UOgrBTMzqzgpmJlZxUnBzMwqI/qZgpkNH37Gt3Po9kpB0iRJt0q6T9JaSZ/M+AWSNktalcNJtWXOl9Qh6QFJJ9TiszLWIWleLX6wpDsyfp2k3ft7Q83MrHvt3D56GTg3IqYCM4CzJU3NeZdFxLQclgLkvFOBw4BZwBWSxkgaA3wVOBGYCpxWq+cLWdehwFPAmf20fWZm1gPd3j6KiC3Alhx/TtI64MAuFpkNLI6IF4GHJHUAR+W8jojYACBpMTA76zsO+GCWWQRcAFzZ880xs+HGt5VGlh49aJY0GXgrcEeGzpG0WtJCSeMydiCwsbbYpoy1iu8HPB0RLzfEzcxskLWdFCTtBXwf+FREPEs5kz8EmEa5kvjSQDSwoQ1zJa2UtHLr1q0DvTozs7ZMnndTNYx0bSUFSbtREsK3IuIHABHxWERsj4h/A77GjltEm4FJtcUnZqxV/ElgrKRdG+KvEBELImJ6REwfP358O003M7MeaKf3kYCrgHUR8eVafEKt2PuAe3N8CXCqpD0kHQxMAe4EVgBTsqfR7pSH0UsiIoBbgVNy+TnADX3bLDMz6412PqfwDuBDwBpJqzL2OUrvoWlAAA8DHwWIiLWSrgfuo/RcOjsitgNIOgdYBowBFkbE2qzvPGCxpIuBuylJyMzMBlk7vY9+DqjJrKVdLHMJcEmT+NJmy2WPpKMa42ZmNrj8NRdmZlZxUjAzs4qTgpmZVZwUzMys4m9JNbMh4a+/GJ6cFKxf9fdPA9ro5IQxdHz7yMzMKk4KZmZWcVIwM7OKnymY2YjhZw0Dz1cKZmZWcVIwM7OKk4KZmVX8TMHMrE2j4ZmGrxTMzKzipGBmZhUnBTMzqzgpmJlZxUnBzMwq3SYFSZMk3SrpPklrJX0y4/tKWi5pff4dl3FJulxSh6TVko6o1TUny6+XNKcWP1LSmlzmcknNfhPazMwGWDtXCi8D50bEVGAGcLakqcA84JaImALcktMAJwJTcpgLXAkliQDzgaOBo4D5nYkky5xVW25W3zfNzMx6qtukEBFbIuKXOf4csA44EJgNLMpii4CTc3w2cE0UtwNjJU0ATgCWR8S2iHgKWA7Mynl7R8TtERHANbW6zMxsEPXow2uSJgNvBe4ADoiILTnrUeCAHD8Q2FhbbFPGuopvahI3M+uz0fCBs/7U9oNmSXsB3wc+FRHP1uflGX70c9uatWGupJWSVm7dunWgV2dmNuq0daUgaTdKQvhWRPwgw49JmhARW/IW0OMZ3wxMqi0+MWObgWMb4rdlfGKT8q8QEQuABQDTp0+PJ9ppvJnt9Hw10H/a6X0k4CpgXUR8uTZrCdDZg2gOcEMtfnr2QpoBPJO3mZYBMyWNywfMM4FlOe9ZSTNyXafX6jIzGxCT591UDbZDO1cK7wA+BKyRtCpjnwMuBa6XdCbwCPCBnLcUOAnoAJ4HzgCIiG2SLgJWZLkLI2Jbjn8MuBrYE7g5BxvGfGZmtnPqNilExM+BVp8bOL5J+QDOblHXQmBhk/hK4PDu2mJmZgPLn2g2M7OKf0/BzKxmtN8a9ZWCmZlVnBTMzKzipGBmZhUnBTMzqzgpmJlZxUnBzMwqTgpmZlZxUjAzs4qTgpmZVZwUzMys4qRgZmYVJwUzM6s4KZiZWcVJwczMKk4KZmZWcVIwM7OKk4KZmVW6TQqSFkp6XNK9tdgFkjZLWpXDSbV550vqkPSApBNq8VkZ65A0rxY/WNIdGb9O0u79uYFmZta+dq4UrgZmNYlfFhHTclgKIGkqcCpwWC5zhaQxksYAXwVOBKYCp2VZgC9kXYcCTwFn9mWDzMys97r9jeaI+KmkyW3WNxtYHBEvAg9J6gCOynkdEbEBQNJiYLakdcBxwAezzCLgAuDKtrfABtRo/71as9GmL88UzpG0Om8vjcvYgcDGWplNGWsV3w94OiJebog3JWmupJWSVm7durUPTTczs2a6vVJo4UrgIiDy75eAP+uvRrUSEQuABQDTp0+PJwZ6haNE/WoAfEVgNpr1KilExGOd45K+BtyYk5uBSbWiEzNGi/iTwFhJu+bVQr28mZkNsl7dPpI0oTb5PqCzZ9IS4FRJe0g6GJgC3AmsAKZkT6PdKQ+jl0REALcCp+Tyc4AbetMmMzPru26vFCR9BzgW2F/SJmA+cKykaZTbRw8DHwWIiLWSrgfuA14Gzo6I7VnPOcAyYAywMCLW5irOAxZLuhi4G7iqvzbOzMx6pp3eR6c1Cbc8cEfEJcAlTeJLgaVN4hvY0UPJzMyGkD/RbGZmFScFMzOrOCmYmVnFScHMzCpOCmZmVnFSMDOzSm+/5sJGIH+5nZl1x1cKZmZWcVIwM7OKk4KZmVWcFMzMrOKkYGZmFScFMzOrOCmYmVnFScHMzCpOCmZmVtlpP9HsT++amfWcrxTMzKzSbVKQtFDS45LurcX2lbRc0vr8Oy7jknS5pA5JqyUdUVtmTpZfL2lOLX6kpDW5zOWS1N8baWZm7WnnSuFqYFZDbB5wS0RMAW7JaYATgSk5zAWuhJJEgPnA0ZTfY57fmUiyzFm15RrXZWZmg6TbpBARPwW2NYRnA4tyfBFwci1+TRS3A2MlTQBOAJZHxLaIeApYDszKeXtHxO0REcA1tbrMzGyQ9faZwgERsSXHHwUOyPEDgY21cpsy1lV8U5O4mZkNgT4/aM4z/OiHtnRL0lxJKyWt3Lp162Cs0sxsVOltUngsb/2Qfx/P+GZgUq3cxIx1FZ/YJN5URCyIiOkRMX38+PG9bLqZmbXS26SwBOjsQTQHuKEWPz17Ic0AnsnbTMuAmZLG5QPmmcCynPespBnZ6+j0Wl1mZjbIuv3wmqTvAMcC+0vaROlFdClwvaQzgUeAD2TxpcBJQAfwPHAGQERsk3QRsCLLXRgRnQ+vP0bp4bQncHMOZmY2BLpNChFxWotZxzcpG8DZLepZCCxsEl8JHN5dO8zMbOD5E81mZlZxUjAzs4qTgpmZVZwUzMys4qRgZmYVJwUzM6s4KZiZWcVJwczMKk4KZmZWcVIwM7OKk4KZmVWcFMzMrOKkYGZmFScFMzOrOCmYmVnFScHMzCrd/siOjTyT591UjT986XuGsCVmNtL4SsHMzCpOCmZmVulTUpD0sKQ1klZJWpmxfSUtl7Q+/47LuCRdLqlD0mpJR9TqmZPl10ua07dNMjOz3uqPK4U/johpETE9p+cBt0TEFOCWnAY4EZiSw1zgSihJBJgPHA0cBczvTCRmZja4BuL20WxgUY4vAk6uxa+J4nZgrKQJwAnA8ojYFhFPAcuBWQPQLjMz60Zfk0IAP5Z0l6S5GTsgIrbk+KPAATl+ILCxtuymjLWKm5nZIOtrl9RjImKzpNcCyyXdX58ZESEp+riOSiaeuQAHHXQQ6q+KzcwM6OOVQkRszr+PAz+kPBN4LG8LkX8fz+KbgUm1xSdmrFW82foWRMT0iJg+fvz4vjTdzMya6HVSkPQaSX/UOQ7MBO4FlgCdPYjmADfk+BLg9OyFNAN4Jm8zLQNmShqXD5hnZszMzAZZX24fHQD8UFJnPd+OiH+StAK4XtKZwCPAB7L8UuAkoAN4HjgDICK2SboIWJHlLoyIbX1ol5mZ9VKvk0JEbADe0iT+JHB8k3gAZ7eoayGwsLdtMTOz/uFPNJuZWcVJwczMKqPuW1L9DaJmZq35SsHMzCpOCmZmVnFSMDOzipOCmZlVnBTMzKzipGBmZhUnBTMzqzgpmJlZxUnBzMwqo+4Tza2MtE8619sLI6PNZjb8OSm0YaQlDDOz3nJSGEQ+uzez4c5JoQ/68wqiVV2+SjGzweSkMAB8IDezkcq9j8zMrOKkYGZmlWGTFCTNkvSApA5J84a6PWZmo9GwSAqSxgBfBU4EpgKnSZo6tK0yMxt9hkVSAI4COiJiQ0T8HlgMzB7iNpmZjTqKiKFuA5JOAWZFxEdy+kPA0RFxTkO5ucDcnHwT8ECO7w880aTqnsZdl+sainW4Lv9/B7OuN0TE+BZ1Q0QM+QCcAny9Nv0h4Cs9WH5lf8Rdl+sa6e11XcNvHcO5rmbDcLl9tBmYVJuemDEzMxtEwyUprACmSDpY0u7AqcCSIW6TmdmoMyw+0RwRL0s6B1gGjAEWRsTaHlSxoJ/irst1DcU6XNfQ1TXS2tvfdb3CsHjQbGZmw8NwuX1kZmbDgJOCmZlVnBTMzKwyLB409xdJbwYOBO6IiN/W4p8AfhERK/LrM2YB90fE0oblr4mI05vUewzlU9cvAYsi4llJewLzgCOA+4Ange9ExMaGZTt7U/0mIv6vpA8C/wlYBywH3kvpjrsdeBD4dkQ82w8vh/WApNdGxOM9XGa/iHhyoNpkNiR68qGG4TwAnwC2AP8IPAzMzvh84F+BlcD/Bn4C/BXlIL6O0vV1CfAj4Lc5/lSt3rOAVVnP88DnMr4A+HvgmJz3EvAb4GfAx4DxWe5bwHVZ/7XADykfzrsjy/8v4F8o3/10CSXBHDtEr+Fre7HMfv3chn2AS4H7gW21/9OlwNgm5fcGfpWv7Qcb5n0DuDJf2/2AC4A1uY/8e2DfHPbLfeYUYN9aO64CVgPfBi4H9s9504ENQAfwIvB14JCGdU8HbgW+SUn6y4FngLty31mb01uB24EPU07SPgr8U653NXAz8OfAbk22fUzubxcB72iY93ngL4DPAq/K+pcAXwT2aij7IPAfa9O75X65BPgb4NO1bT8U+CnwdP5/5jWp798BC4GLgb2ArwH3At8FDgb+DLgJuAf4JeVrbY4fxdv+Hnqwz3e13wOvy/U17vPXAxPaeg8OxcFnIIbc8I05PpmSBD6Z8buBVwPPAntnmbuBp4BjgXfl3y05vr5W7wp2HODvB9bk+C8b1v885XbcTMrBZGvu4BuBP6K84R8DxtTauzrHXw3cluMH5Q6zMx8YV1Culi7klQfHNcB5wOsadvR/AH5BuTKrD7dQkvnJlDfy94E9crlngI9T3ryrs95JwL9RThQeqg0v5XZsyGW/Tnljv4FyYHim1p5bgbfl+EbgUeDXwJ1Z9vU5fiJwWpY5Jcv/c75mE4H/STlBmQIsytfiSmBGzp+Y4wspJxP7NgzXUva7T1GSzZdrbXwK+BJwRb5GXwHeCfw+h2eB53LYnsOzueyXgKsp74XLgKdr9d4EvC/Ht1K+PmEb5aDzPmB3yoHzf+Trfi9wbr7uZ1JOhC6gnEz9PWUfeHe+hj8bpdv+JHADr9znz6P5Pt9yv2fHMadxn/84cMNOmRTYcRbROPwOeLFWbq98gR4HVnUmgtr8XSifml4OTMtY5wHhHmAc5UC5srbMd4FHagfb6Tn+RuBfG9q5G+XW0NO5A43LnbDzgHsv5RYWOa++nufYuQ+Mx1PeTB/mlQfHZ4C/afJ/357tvbVheA54oVbuLykH3v2A52vxX9fGz6UcGP5DLfYQtURP7jO16d8Bu+b47bX4L9lxovBOyoHo0WzX3Cbrvoc/3A9X1PbH37fY57dTDmb1/9WGjP0+y+xKuQL5AeXg8HzGle3p7H5+OeWgeUDDttfbtIo8O8/lf9fY3s73E2Wf2pty9buUsq8/Ccxs3PacfqFh+vb8ux5YN0q3/cFm257zgnJ3o939fjW5HzdZ/6pm63jFOtspNJwGytn2NMqBqj78C/B4Q9ld8x+1vfONV5u3D+UNPZFysP9K54tIOWPeUNsBJ2T89ZSD2a8ot39eyvn/r4t/6qeznkcot7huoVxS/iZ32K9RrgjOyPLjqR3MmrxBRvyBscUbpPPg+GNKIq+/cQ/I+v65yWuyjrxCrMU+TDnr/n0tdnGT5b4LfJlyJbcB2ERJUOfmtGrlN2fbjqOc7f0D5WxyC3BtQ91jsv5lwPvzf39yzqufDLwXWFZ/TbJ8fT/dJddxd5Ntv7/Jts/P/339BGlhQ5kHKQeaT2T9G3L4b8B/p2FfprznrqbcGvkc5ez8DZT3yY0NZffL7f0F5TncE+w4eTqUcnZ/SE4fAfy0c3+inDgM1ba/r5+3/W092PYf53oa9/nzKCd7U3qw3/+OHSeujfv86mbHlVfU3U6h4TRQbmcc0yQ+EfhBk/geNNxzzPj+/OEB8T00OUNtWObVlPuCewNvAY7s/EcCb+xiudcDr8/xsZRbNEcBh+X4mxvK/5hyT3RnPTC+K3f2Y3K6OjhSrpqepLzpn6Ik4XWU211HNdn2LwJ/3SQ+K+vZq8m8Q4Hv1dZ9e7628xuGztuGrwOuodxivI5ylriGcna4gub3vN+S234z8OZ8rZ6m3Dq6L7ft58Cbsvz4fE2voyTFB3N4nHJ75MQm6/gmcEWT+EcoV4LNtv2QXO8ulAPjzygnKN9oGA6obfstuT/dQTnQPZfb8GtgnybrOJ7yDcbrKLdKvk+5Eniccr/+1zn9EOXbkKEcJNdRTuIerJUfjG2/upttP6Oftv2vcrmO3PYZtf3xDl65z38hX/c39WC//xblZwha7vPdDUN+kPfQ5J9SDoxfYMczhfqB8W092EEG88C4a5N1tDowrqVcct/JjoPjG3OZ8ZRbVn/S2O58wx/fg/iJud4ulwH2BA7vpq5Z7dTVED+ri3iz7ZsFHE05YdgPeAfwGeCkjHXerptKSdwt4zndbN57GuLvBP466zq6zboOo5w0dNWuoxvKf6ZW19tbrSdj++XwzRbvj2t6Eq/P4w9PciYAT/airmt72K4bySsgyi2p/btaJv8n55K3oRrmHZOv18y+xLsa/DUXI4ykMyLiG32NN87LLraHRMS9fa2rj/FPAOdTzpymAZ+MiBsy/kXKc6J6/OPA3zbGs66NlEv2dQNYV9NlulhH03iu4zeUM8ldKc+6jgJuozyk3IVytrmccsC9tYv4uyn33Peq1dVqmVbr6Iw3q6un7Wqnrk9l+fXscBzlihLKCQSUg+ofdxH/SdbZGe9qmVbr6Iw3q6un7WqnrpeAn0XEeyV9BDibcgI4EzgoIiYBSDor5/2QkmQviYhL24zPBH4UEZfSnXazh4fhMdBwb7638eFaF133Irun3XhOv0CejQ+XutpYxxhe2VPuXsoDxHbje47Auu6meW/A9ZRblu3G35Xzvtlk3oO9qOvBFnX1Z7vWA+/K16He2/E1/OGzwvq8e9jxHK+d+Gs6490eY4b6IOeh6YGxVQ+rFyj3S9uNr2kRH851tepF9gS1B99txL/MKx9mD4e6ulpHvVPA3fVxmvegaxrP6ZFWV9PegBn/dLvxWl1tLzMM6mra2zHnv9BsXi6zut14s/9Fy+PPYB3oPLQ/0LqH1VbKJXa78cmUHksjqa5Wvci2kL3I2oxfQ+nON22Y1dXdOl6dsXovnJWdb+g24/tQeqmNpLr2oUVvwJzfo3hvlhmqumjd23Evyu22ZvMeoXQfbze+Fztrl9TRMNC6h9VVwPJ24znvVyOsrla9yCYC/7XdeM47mdpnPYZDXd2s49gW8ddT6ynXRnx/4IgRVldbvQF7Gh+JddXmvxo4uCfzehpvNvhBs5mZVXYZ6gaYmdnw4aRgZmYVJwUzM6s4KdioJekvJa2VtFrSKklHS7pN0gM5vUrS97LsBZJC0qG15T+VseldrONhSd+vTZ8i6era9Mm5/nWS1kg6OeNfzfXfJ+mFWntOGYjXwqzTTvUjO2btkvR24L9QesO8KGl/ylcfA/xpRKxsstgayg8mXZzT76d8ZUd3jpQ0NSLua2jDW4C/A94dEQ9JOhhYLmlDRJydZSZTvnhtWs+20Kx3fKVgo9UE4ImIeBEgIp6IiN90s8w/ArMBJB1C+ZrvJ9pY15co31zb6DOULokPZRseovwQ1Gfb2QCzgeCkYKPVj4FJkh6UdIWkd9Xmfat2u+Zva/FngY2SDqdcMVzX5rquB46o33pKh1G+BbRuZcbNhoSTgo1KUX7D+0hgLuWT19dJ+nDO/tOImJZD41n7YkpCOJnyRWPt2E750rzz+9pus4HmpGCjVkRsj4jbImI+cA7lR1a6cyPla79/HRHP9mB11wL/mfKrd53uoySmuiNp7zmF2YBwUrBRSdKbJE2phaZRvjemSxHxPOUXsS7pyfoi4iXKb/5+uhb+O+D8fJjc+VD5c5RnEGZDwr2PbLTaC/g/ksYCL1N+DWsu8D3KM4UXstwTEfEn9QUjYnEv13kV5ZfHOutZJek84EeSdqN8r/5fRMSqXtZv1mf+7iMzM6v49pGZmVV8+8isH0i6A9ijIfyhiFgzFO0x6y3fPjIzs4pvH5mZWcVJwczMKk4KZmZWcVIwM7OKk4KZmVX+P2s/6FXBUh0BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby('SEM_NOT').size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SEM_NOT'>"
      ]
     },
     "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": [
    "df.query('SG_UF_NOT == \"SP\"').groupby('SEM_NOT').size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SEM_NOT'>"
      ]
     },
     "execution_count": 21,
     "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": [
    "df.query('ID_MUNICIP == \"SAO JOSE DOS CAMPOS\"').groupby('SEM_NOT').size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='SEM_NOT'>"
      ]
     },
     "execution_count": 22,
     "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": [
    "df.query('ID_MUNICIP == \"SAO PAULO\"').groupby('SEM_NOT').size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.9.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}