From 6e1cf16d95addd13be702f41fe633e1ed3f9286e Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Lu=C3=A3=20Bida=20Vacaro?= <luabidaa@gmail.com>
Date: Tue, 14 May 2024 03:16:54 -0300
Subject: [PATCH] Include total cases by 100k hab on SINAN example

---
 .../tutorials/Preprocessing SINAN.ipynb       | 663 +++++++++++++++++-
 pysus/utilities/brasil.py                     |  17 +-
 2 files changed, 640 insertions(+), 40 deletions(-)

diff --git a/docs/source/tutorials/Preprocessing SINAN.ipynb b/docs/source/tutorials/Preprocessing SINAN.ipynb
index 2efd89d..d2337f7 100644
--- a/docs/source/tutorials/Preprocessing SINAN.ipynb	
+++ b/docs/source/tutorials/Preprocessing SINAN.ipynb	
@@ -553,7 +553,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|███████████████████████████████████████████████████████████████████| 286k/286k [00:00<00:00, 313MB/s]\n"
+      "100%|███████████████████████████████████████████████████████████████████| 286k/286k [00:00<00:00, 266MB/s]\n"
      ]
     },
     {
@@ -970,7 +970,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████████████████████████| 73.8M/73.8M [00:00<00:00, 68.5GB/s]\n"
+      "100%|████████████████████████████████████████████████████████████████| 73.8M/73.8M [00:00<00:00, 71.6GB/s]\n"
      ]
     }
    ],
@@ -1081,7 +1081,7 @@
       "text/plain": [
        "\u001b[0;31mSignature:\u001b[0m       \u001b[0mdecodifica_idade_SINAN\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
        "\u001b[0;31mType:\u001b[0m            vectorize\n",
-       "\u001b[0;31mString form:\u001b[0m     <numpy.vectorize object at 0x7e36e0551610>\n",
+       "\u001b[0;31mString form:\u001b[0m     <numpy.vectorize object at 0x78a3c4530fd0>\n",
        "\u001b[0;31mFile:\u001b[0m            ~/micromamba/envs/pysus/lib/python3.11/site-packages/numpy/__init__.py\n",
        "\u001b[0;31mDocstring:\u001b[0m      \n",
        "Em tabelas do SINAN frequentemente a idade é representada como um inteiro que precisa ser parseado\n",
@@ -1630,14 +1630,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|████████████████████████████████████████████████████████████████| 30.5M/30.5M [00:00<00:00, 37.7GB/s]\n"
+      "100%|████████████████████████████████████████████████████████████████| 30.5M/30.5M [00:00<00:00, 31.1GB/s]\n"
      ]
     }
    ],
@@ -1647,7 +1647,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -1815,51 +1815,652 @@
        "      <td></td>\n",
        "      <td>1</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",
+       "      <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",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1381249</th>\n",
+       "      <td>2</td>\n",
+       "      <td>A90</td>\n",
+       "      <td>2010-01-12</td>\n",
+       "      <td>201002</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>29</td>\n",
+       "      <td>291770</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>2533375</td>\n",
+       "      <td>2009-12-13</td>\n",
+       "      <td>...</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>0.000000000</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1381250</th>\n",
+       "      <td>2</td>\n",
+       "      <td>A90</td>\n",
+       "      <td>2010-01-07</td>\n",
+       "      <td>201001</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>29</td>\n",
+       "      <td>291770</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>2533375</td>\n",
+       "      <td>2010-01-07</td>\n",
+       "      <td>...</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>0.000000000</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1381251</th>\n",
+       "      <td>2</td>\n",
+       "      <td>A90</td>\n",
+       "      <td>2010-01-07</td>\n",
+       "      <td>201001</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>29</td>\n",
+       "      <td>291770</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>2533375</td>\n",
+       "      <td>2010-01-04</td>\n",
+       "      <td>...</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>0.000000000</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1381252</th>\n",
+       "      <td>2</td>\n",
+       "      <td>A90</td>\n",
+       "      <td>2010-01-17</td>\n",
+       "      <td>201003</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>29</td>\n",
+       "      <td>291770</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>2533375</td>\n",
+       "      <td>2010-01-16</td>\n",
+       "      <td>...</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>0.000000000</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1381253</th>\n",
+       "      <td>2</td>\n",
+       "      <td>A90</td>\n",
+       "      <td>2010-01-14</td>\n",
+       "      <td>201002</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>29</td>\n",
+       "      <td>291770</td>\n",
+       "      <td>1406</td>\n",
+       "      <td>2533375</td>\n",
+       "      <td>1982-04-05</td>\n",
+       "      <td>...</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>0.000000000</td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td></td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>5 rows × 66 columns</p>\n",
+       "<p>1381254 rows × 66 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
-       "  TP_NOT ID_AGRAVO  DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP ID_REGIONA  \\\n",
-       "0      2      A90   2010-03-19  201011   2010        51     510760   1579       \n",
-       "1      2      A90   2010-03-26  201012   2010        51     510760   1579       \n",
-       "2      2      A90   2010-03-12  201010   2010        51     510760   1579       \n",
-       "3      2      A90   2010-03-23  201012   2010        51     510760   1579       \n",
-       "4      2      A90   2010-03-12  201010   2010        51     510760   1579       \n",
+       "        TP_NOT ID_AGRAVO  DT_NOTIFIC SEM_NOT NU_ANO SG_UF_NOT ID_MUNICIP  \\\n",
+       "0            2      A90   2010-03-19  201011   2010        51     510760   \n",
+       "1            2      A90   2010-03-26  201012   2010        51     510760   \n",
+       "2            2      A90   2010-03-12  201010   2010        51     510760   \n",
+       "3            2      A90   2010-03-23  201012   2010        51     510760   \n",
+       "4            2      A90   2010-03-12  201010   2010        51     510760   \n",
+       "...        ...       ...         ...     ...    ...       ...        ...   \n",
+       "1381249      2      A90   2010-01-12  201002   2010        29     291770   \n",
+       "1381250      2      A90   2010-01-07  201001   2010        29     291770   \n",
+       "1381251      2      A90   2010-01-07  201001   2010        29     291770   \n",
+       "1381252      2      A90   2010-01-17  201003   2010        29     291770   \n",
+       "1381253      2      A90   2010-01-14  201002   2010        29     291770   \n",
        "\n",
-       "  ID_UNIDADE  DT_SIN_PRI  ... PLASMATICO EVIDENCIA PLAQ_MENOR CON_FHD  \\\n",
-       "0    2701626  2010-03-14  ...                                           \n",
-       "1    2701626  2010-03-21  ...                                           \n",
-       "2    2701626  2010-03-07  ...                                           \n",
-       "3    2701626  2010-03-18  ...                                           \n",
-       "4    2701626  2010-03-07  ...                                           \n",
+       "        ID_REGIONA ID_UNIDADE  DT_SIN_PRI  ... PLASMATICO EVIDENCIA  \\\n",
+       "0         1579        2701626  2010-03-14  ...                        \n",
+       "1         1579        2701626  2010-03-21  ...                        \n",
+       "2         1579        2701626  2010-03-07  ...                        \n",
+       "3         1579        2701626  2010-03-18  ...                        \n",
+       "4         1579        2701626  2010-03-07  ...                        \n",
+       "...            ...        ...         ...  ...        ...       ...   \n",
+       "1381249   1406        2533375  2009-12-13  ...                        \n",
+       "1381250   1406        2533375  2010-01-07  ...                        \n",
+       "1381251   1406        2533375  2010-01-04  ...                        \n",
+       "1381252   1406        2533375  2010-01-16  ...                        \n",
+       "1381253   1406        2533375  1982-04-05  ...                        \n",
        "\n",
-       "  COMPLICA HOSPITALIZ DT_INTERNA UF MUNICIPIO TP_SISTEMA  \n",
-       "0                                                      1  \n",
-       "1                                                      1  \n",
-       "2                                                      1  \n",
-       "3                                                      1  \n",
-       "4                                                      1  \n",
+       "                 PLAQ_MENOR CON_FHD COMPLICA HOSPITALIZ DT_INTERNA UF  \\\n",
+       "0                                                                       \n",
+       "1                                                                       \n",
+       "2                                                                       \n",
+       "3                                                                       \n",
+       "4                                                                       \n",
+       "...                     ...     ...      ...        ...        ... ..   \n",
+       "1381249         0.000000000                                             \n",
+       "1381250         0.000000000                                             \n",
+       "1381251         0.000000000                                             \n",
+       "1381252         0.000000000                                             \n",
+       "1381253         0.000000000                                             \n",
+       "\n",
+       "        MUNICIPIO TP_SISTEMA  \n",
+       "0                          1  \n",
+       "1                          1  \n",
+       "2                          1  \n",
+       "3                          1  \n",
+       "4                          1  \n",
+       "...           ...        ...  \n",
+       "1381249                    1  \n",
+       "1381250                    1  \n",
+       "1381251                    1  \n",
+       "1381252                    1  \n",
+       "1381253                    1  \n",
        "\n",
-       "[5 rows x 66 columns]"
+       "[1381254 rows x 66 columns]"
       ]
      },
-     "execution_count": 18,
+     "execution_count": 25,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "df.head()"
+    "df"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 26,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "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>ID_MUNICIP</th>\n",
+       "      <th>TOTAL_CASES</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1914</th>\n",
+       "      <td>310620</td>\n",
+       "      <td>71056</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4158</th>\n",
+       "      <td>520870</td>\n",
+       "      <td>48397</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3867</th>\n",
+       "      <td>500270</td>\n",
+       "      <td>42761</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>61</th>\n",
+       "      <td>120040</td>\n",
+       "      <td>38333</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3147</th>\n",
+       "      <td>354340</td>\n",
+       "      <td>36107</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3639</th>\n",
+       "      <td>420810</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3972</th>\n",
+       "      <td>510395</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2421</th>\n",
+       "      <td>316020</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1533</th>\n",
+       "      <td>290515</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3789</th>\n",
+       "      <td>431480</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4307 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     ID_MUNICIP  TOTAL_CASES\n",
+       "1914     310620        71056\n",
+       "4158     520870        48397\n",
+       "3867     500270        42761\n",
+       "61       120040        38333\n",
+       "3147     354340        36107\n",
+       "...         ...          ...\n",
+       "3639     420810            1\n",
+       "3972     510395            1\n",
+       "2421     316020            1\n",
+       "1533     290515            1\n",
+       "3789     431480            1\n",
+       "\n",
+       "[4307 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Grouping all the cases by geocode\n",
+    "tot_cases = df[['ID_MUNICIP']].groupby('ID_MUNICIP').size().reset_index(name='TOTAL_CASES').sort_values(by='TOTAL_CASES', ascending=False)\n",
+    "tot_cases"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
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+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
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+       "\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>ID_MUNICIP</th>\n",
+       "      <th>TOTAL_CASES</th>\n",
+       "      <th>POPULACAO</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>310620</td>\n",
+       "      <td>71056</td>\n",
+       "      <td>2375444</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>520870</td>\n",
+       "      <td>48397</td>\n",
+       "      <td>1301892</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>500270</td>\n",
+       "      <td>42761</td>\n",
+       "      <td>787204</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>120040</td>\n",
+       "      <td>38333</td>\n",
+       "      <td>335796</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>354340</td>\n",
+       "      <td>36107</td>\n",
+       "      <td>605114</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4302</th>\n",
+       "      <td>420810</td>\n",
+       "      <td>1</td>\n",
+       "      <td>20315</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4303</th>\n",
+       "      <td>510395</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4304</th>\n",
+       "      <td>316020</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4135</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4305</th>\n",
+       "      <td>290515</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13666</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4306</th>\n",
+       "      <td>431480</td>\n",
+       "      <td>1</td>\n",
+       "      <td>30881</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4307 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     ID_MUNICIP  TOTAL_CASES  POPULACAO\n",
+       "0        310620        71056    2375444\n",
+       "1        520870        48397    1301892\n",
+       "2        500270        42761     787204\n",
+       "3        120040        38333     335796\n",
+       "4        354340        36107     605114\n",
+       "...         ...          ...        ...\n",
+       "4302     420810            1      20315\n",
+       "4303     510395            1       3125\n",
+       "4304     316020            1       4135\n",
+       "4305     290515            1      13666\n",
+       "4306     431480            1      30881\n",
+       "\n",
+       "[4307 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.merge(tot_cases, pop, left_on='ID_MUNICIP', right_on='MUNIC_RES')\n",
+    "df = df[['ID_MUNICIP', 'TOTAL_CASES', 'POPULACAO']]\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "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>ID_MUNICIP</th>\n",
+       "      <th>TOTAL_CASES</th>\n",
+       "      <th>POPULACAO</th>\n",
+       "      <th>CASES_PER_100K</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>340</th>\n",
+       "      <td>411380</td>\n",
+       "      <td>630</td>\n",
+       "      <td>4592</td>\n",
+       "      <td>13719.512195</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>708</th>\n",
+       "      <td>510719</td>\n",
+       "      <td>255</td>\n",
+       "      <td>2199</td>\n",
+       "      <td>11596.180082</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>120040</td>\n",
+       "      <td>38333</td>\n",
+       "      <td>335796</td>\n",
+       "      <td>11415.561829</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>54</th>\n",
+       "      <td>311330</td>\n",
+       "      <td>3617</td>\n",
+       "      <td>32321</td>\n",
+       "      <td>11190.866619</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>27</th>\n",
+       "      <td>270630</td>\n",
+       "      <td>7272</td>\n",
+       "      <td>70434</td>\n",
+       "      <td>10324.559162</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3884</th>\n",
+       "      <td>411915</td>\n",
+       "      <td>2</td>\n",
+       "      <td>117166</td>\n",
+       "      <td>1.706980</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4167</th>\n",
+       "      <td>431240</td>\n",
+       "      <td>1</td>\n",
+       "      <td>59436</td>\n",
+       "      <td>1.682482</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4179</th>\n",
+       "      <td>432250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>61345</td>\n",
+       "      <td>1.630125</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3963</th>\n",
+       "      <td>411950</td>\n",
+       "      <td>1</td>\n",
+       "      <td>93279</td>\n",
+       "      <td>1.072053</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4180</th>\n",
+       "      <td>432240</td>\n",
+       "      <td>1</td>\n",
+       "      <td>125507</td>\n",
+       "      <td>0.796768</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>4307 rows × 4 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     ID_MUNICIP  TOTAL_CASES  POPULACAO  CASES_PER_100K\n",
+       "340      411380          630       4592    13719.512195\n",
+       "708      510719          255       2199    11596.180082\n",
+       "3        120040        38333     335796    11415.561829\n",
+       "54       311330         3617      32321    11190.866619\n",
+       "27       270630         7272      70434    10324.559162\n",
+       "...         ...          ...        ...             ...\n",
+       "3884     411915            2     117166        1.706980\n",
+       "4167     431240            1      59436        1.682482\n",
+       "4179     432250            1      61345        1.630125\n",
+       "3963     411950            1      93279        1.072053\n",
+       "4180     432240            1     125507        0.796768\n",
+       "\n",
+       "[4307 rows x 4 columns]"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df['CASES_PER_100K'] = (df['TOTAL_CASES'].astype(int) / df['POPULACAO'].astype(int)) * 100000\n",
+    "df = df.sort_values(by='CASES_PER_100K', ascending=False)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "from pysus.utilities.brasil import get_city_name_by_geocode\n",
+    "from pysus.preprocessing.decoders import calculate_digit\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df = df.sort_values(by='CASES_PER_100K', ascending=True).head(20)\n",
+    "# Get city name\n",
+    "df['MUNICIP_NAME'] = df['ID_MUNICIP'].apply(lambda x: get_city_name_by_geocode(int(str(x) + str(calculate_digit(x)))))\n",
+    "\n",
+    "plt.figure(figsize=(10, 12))\n",
+    "bars = plt.barh(df['MUNICIP_NAME'], df['CASES_PER_100K'], color='skyblue')\n",
+    "plt.ylabel('City')\n",
+    "plt.xlabel('Cases per 100k inhabitants')\n",
+    "plt.title('Cases per 100k inhabitants by city')\n",
+    "\n",
+    "# Adding labels with CASES_PER_100K values\n",
+    "for bar in bars:\n",
+    "    plt.text(bar.get_width(), bar.get_y() + bar.get_height()/2, f'{int(bar.get_width())}', \n",
+    "             va='center', ha='left', color='black')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {
diff --git a/pysus/utilities/brasil.py b/pysus/utilities/brasil.py
index 6fbf1cc..e2655c9 100644
--- a/pysus/utilities/brasil.py
+++ b/pysus/utilities/brasil.py
@@ -2,6 +2,13 @@
 from pathlib import Path
 from typing import Union
 
+with open(
+    f"{Path(__file__).parent}/municipios.json", 'r', encoding='utf-8-sig'
+) as muns:
+    MUNICIPALITIES = json.loads(muns.read())
+
+MUN_BY_GEOCODE = {mun["geocodigo"]: mun["municipio"] for mun in MUNICIPALITIES}
+
 
 UFs = {
     "BR": "Brasil",
@@ -57,12 +64,4 @@ def get_city_name_by_geocode(geocode: Union[str, int]):
     :return: City name
     """
 
-    with open(f"{Path(__file__).parent}/municipios.json") as muns:
-        _mun_decoded = muns.read().encode().decode("utf-8-sig")
-        municipalities = json.loads(_mun_decoded)
-
-    mun_by_geocode = {
-        mun["geocodigo"]: mun["municipio"] for mun in municipalities
-    }
-
-    return mun_by_geocode[int(geocode)]
+    return MUN_BY_GEOCODE[int(geocode)]