dashboard.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type='text/css'>\n",
       ".datatable table.frame { margin-bottom: 0; }\n",
       ".datatable table.frame thead { border-bottom: none; }\n",
       ".datatable table.frame tr.coltypes td {  color: #FFFFFF;  line-height: 6px;  padding: 0 0.5em;}\n",
       ".datatable .bool    { background: #DDDD99; }\n",
       ".datatable .object  { background: #565656; }\n",
       ".datatable .int     { background: #5D9E5D; }\n",
       ".datatable .float   { background: #4040CC; }\n",
       ".datatable .str     { background: #CC4040; }\n",
       ".datatable .time    { background: #40CC40; }\n",
       ".datatable .row_index {  background: var(--jp-border-color3);  border-right: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  font-size: 9px;}\n",
       ".datatable .frame tbody td { text-align: left; }\n",
       ".datatable .frame tr.coltypes .row_index {  background: var(--jp-border-color0);}\n",
       ".datatable th:nth-child(2) { padding-left: 12px; }\n",
       ".datatable .hellipsis {  color: var(--jp-cell-editor-border-color);}\n",
       ".datatable .vellipsis {  background: var(--jp-layout-color0);  color: var(--jp-cell-editor-border-color);}\n",
       ".datatable .na {  color: var(--jp-cell-editor-border-color);  font-size: 80%;}\n",
       ".datatable .sp {  opacity: 0.25;}\n",
       ".datatable .footer { font-size: 9px; }\n",
       ".datatable .frame_dimensions {  background: var(--jp-border-color3);  border-top: 1px solid var(--jp-border-color0);  color: var(--jp-ui-font-color3);  display: inline-block;  opacity: 0.6;  padding: 1px 10px 1px 5px;}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import config, data_load, data_info, data_plot\n",
    "import plotly.express as px\n",
    "import seaborn as sns\n",
    "import sqlite3\n",
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_zip = config.PATH_DATABASE_ZIP\n",
    "files = data_load.get_namelist_in_zip(path_zip)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>def_procedimento_realizado</th>\n",
       "      <th>records</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>PARTO NORMAL</td>\n",
       "      <td>10410614</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>PARTO CESARIANO</td>\n",
       "      <td>6405986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>PARTO CESARIANO EM GESTACAO DE ALTO RISCO</td>\n",
       "      <td>1219953</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>PARTO NORMAL EM GESTACAO DE ALTO RISCO</td>\n",
       "      <td>791773</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>PARTO CESARIANO C/ LAQUEADURA TUBARIA</td>\n",
       "      <td>284565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>PARTO NORMAL EM CENTRO DE PARTO NORMAL (CPN)</td>\n",
       "      <td>73667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     def_procedimento_realizado   records\n",
       "0                                  PARTO NORMAL  10410614\n",
       "1                               PARTO CESARIANO   6405986\n",
       "2     PARTO CESARIANO EM GESTACAO DE ALTO RISCO   1219953\n",
       "3        PARTO NORMAL EM GESTACAO DE ALTO RISCO    791773\n",
       "4         PARTO CESARIANO C/ LAQUEADURA TUBARIA    284565\n",
       "5  PARTO NORMAL EM CENTRO DE PARTO NORMAL (CPN)     73667"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_query = 'queries/check/partos.sql'\n",
    "with open(path_query) as f:\n",
    "\tquery = f.read()\n",
    "df_partos = data_load.df_query(query)\n",
    "df_partos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "99.12352117169333"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "olhando = df_partos['records'][:2].sum()\n",
    "descarte = df_partos['records'][2:].sum()\n",
    "100 - (olhando / (olhando + descarte))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "16816760\n",
      "2369967\n"
     ]
    }
   ],
   "source": [
    "print(olhando)\n",
    "print(descarte)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "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>CODOCUPMAE</th>\n",
       "      <th>records</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>999992</td>\n",
       "      <td>17399629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00800</td>\n",
       "      <td>9093489</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>999991</td>\n",
       "      <td>1943456</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>621005</td>\n",
       "      <td>1584098</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>622020</td>\n",
       "      <td>1244137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>765005</td>\n",
       "      <td>293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>17000</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>87300</td>\n",
       "      <td>291</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>223102</td>\n",
       "      <td>290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>010205</td>\n",
       "      <td>288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    CODOCUPMAE   records\n",
       "0       999992  17399629\n",
       "1        00800   9093489\n",
       "2       999991   1943456\n",
       "3       621005   1584098\n",
       "4       622020   1244137\n",
       "..         ...       ...\n",
       "995     765005       293\n",
       "996      17000       291\n",
       "997      87300       291\n",
       "998     223102       290\n",
       "999     010205       288\n",
       "\n",
       "[1000 rows x 2 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_query = 'queries/check/sinasc.sql'\n",
    "with open(path_query) as f:\n",
    "\tquery = f.read()\n",
    "df_nasc = data_load.df_query(query)\n",
    "df_nasc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "racacor = {0: 'None', 1: 'Branca', 2: 'Preta', 3: 'Amarela', 4: 'Parda', 5: 'Indígena'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>RACACORMAE</th>\n",
       "      <th>records</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Parda</td>\n",
       "      <td>15126864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Branca</td>\n",
       "      <td>9587813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Preta</td>\n",
       "      <td>1538078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Indígena</td>\n",
       "      <td>228426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Amarela</td>\n",
       "      <td>110413</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>19313</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>None</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  RACACORMAE   records\n",
       "0      Parda  15126864\n",
       "1     Branca   9587813\n",
       "2      Preta   1538078\n",
       "3   Indígena    228426\n",
       "4    Amarela    110413\n",
       "5        NaN     19313\n",
       "6       None         0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nasc['RACACORMAE'] = df_nasc['RACACORMAE'].fillna(0).astype(int)\n",
    "df_nasc['RACACORMAE'] = df_nasc['RACACORMAE'].map(racacor)\n",
    "df_nasc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    587\n",
       "7    413\n",
       "Name: CODMUNRES, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['CODMUNRES'].apply(lambda x: len(str(x))).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6    577\n",
       "7    423\n",
       "Name: CODMUNNASC, dtype: int64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_nasc['CODMUNNASC'].apply(lambda x: len(str(x))).value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "74398079-74398079"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "cnx = sqlite3.connect(config.PATH_DB)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_filters = 'queries/info/check.sql'\n",
    "with open(path_filters) as f:\n",
    "\tquery_filters = f.read()\n",
    "df = data_load.df_query(query_filters)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        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>ano</th>\n",
       "      <th>parto</th>\n",
       "      <th>regiao</th>\n",
       "      <th>uf</th>\n",
       "      <th>grupo</th>\n",
       "      <th>mov_municipio</th>\n",
       "      <th>mov_regiao_saude</th>\n",
       "      <th>records</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Sudeste</td>\n",
       "      <td>SP</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>CESARIANO</td>\n",
       "      <td>Sudeste</td>\n",
       "      <td>SP</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35302</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Sudeste</td>\n",
       "      <td>SP</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Nordeste</td>\n",
       "      <td>MA</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Sudeste</td>\n",
       "      <td>RJ</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31756</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7458</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Norte</td>\n",
       "      <td>AC</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7459</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Norte</td>\n",
       "      <td>RO</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7460</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Norte</td>\n",
       "      <td>RR</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7461</th>\n",
       "      <td>2019</td>\n",
       "      <td>CESARIANO</td>\n",
       "      <td>Norte</td>\n",
       "      <td>RO</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7462</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Norte</td>\n",
       "      <td>RR</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7463 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ano      parto    regiao  uf  grupo  mov_municipio  mov_regiao_saude  \\\n",
       "0     2010     NORMAL   Sudeste  SP      2              0                 0   \n",
       "1     2010  CESARIANO   Sudeste  SP      2              0                 0   \n",
       "2     2010     NORMAL   Sudeste  SP      1              0                 0   \n",
       "3     2010     NORMAL  Nordeste  MA      1              0                 0   \n",
       "4     2010     NORMAL   Sudeste  RJ      3              0                 0   \n",
       "...    ...        ...       ...  ..    ...            ...               ...   \n",
       "7458  2019     NORMAL     Norte  AC      5              1                 1   \n",
       "7459  2019     NORMAL     Norte  RO      4              1                 1   \n",
       "7460  2019     NORMAL     Norte  RR      2              1                 1   \n",
       "7461  2019  CESARIANO     Norte  RO      4              1                 1   \n",
       "7462  2019     NORMAL     Norte  RR      4              1                 1   \n",
       "\n",
       "      records  \n",
       "0       81325  \n",
       "1       35302  \n",
       "2       34039  \n",
       "3       32523  \n",
       "4       31756  \n",
       "...       ...  \n",
       "7458        2  \n",
       "7459        2  \n",
       "7460        2  \n",
       "7461        1  \n",
       "7462        1  \n",
       "\n",
       "[7463 rows x 8 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path_year_percent = 'queries/info/grouped.sql'\n",
    "with open(path_year_percent) as f:\n",
    "\tquery = f.read()\n",
    "df = pd.read_sql_query(query, cnx)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_all_but(df, but='records'):\n",
    "\treturn df.groupby(\n",
    "\t\tby=[col for col in df.columns if col != but],\n",
    "\t\tas_index=False\n",
    "\t).sum().sort_values(\n",
    "\t\tby=['ano', 'records'],\n",
    "\t\tascending=[True, False],\n",
    "\t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_mov_between(df, mov_between):\n",
    "\tcols = [col\n",
    "\t\tfor col in df.columns if col[:3] != 'mov']\n",
    "\tdff = df[cols].copy()\n",
    "\tdff['mov_between'] = mov_between\n",
    "\tdff = group_all_but(dff)\n",
    "\treturn dff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_mov_between(df):\n",
    "\treturn pd.concat([\n",
    "\t\tget_mov_between(df, 'all'),\n",
    "\t\tget_mov_between(df[df['mov_municipio'] == 1], 'municipal'),\n",
    "\t\tget_mov_between(df[df['mov_regiao_saude'] == 1], 'regional')\n",
    "\t]).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def separate_movs_in_dict(df):\n",
    "\td = dict()\n",
    "\tmovs = df['mov_between'].unique()\n",
    "\tcols = [col for col in df.columns if col[:3] != 'mov']\n",
    "\tfor mov in movs:\n",
    "\t\tdff = df[\n",
    "\t\t\tdf['mov_between'] == mov\n",
    "\t\t][cols].set_index(['ano', 'parto'])\n",
    "\t\td[mov] = dff\n",
    "\treturn d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def join_dfs(d, left, right):\n",
    "\tdf = d[left].join(\n",
    "\t\td[right],\n",
    "\t\tlsuffix='_num',\n",
    "\t\trsuffix='_den',\n",
    "\t)\n",
    "\tdf['mov_between'] = left\n",
    "\tdf['percent'] = 100. * df['records_num'] / df['records_den']\n",
    "\tdf = df.reset_index()\n",
    "\treturn df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_frac(df):\n",
    "\td = separate_movs_in_dict(df)\n",
    "\treturn pd.concat([\n",
    "\t\tjoin_dfs(d, 'municipal', 'all'),\n",
    "\t\tjoin_dfs(d, 'regional', 'municipal')\n",
    "\t])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def group_col(df, column=None, value=None):\n",
    "\tdel_cols = ['regiao', 'uf', 'grupo']\n",
    "\tif column:\n",
    "\t\tdel_cols = [col\n",
    "\t\t\tfor col in del_cols\n",
    "\t\t\t\tif col != column]\n",
    "\tcols_filter = [col for col in df.columns\n",
    "\t\t\tif col not in del_cols]\n",
    "\tdff = df[cols_filter].copy()\n",
    "\tif value:\n",
    "\t\tdff = dff[dff[column] == value]\n",
    "\tdff = group_all_but(dff)\n",
    "\treturn get_frac(dff)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>ano</th>\n",
       "      <th>parto</th>\n",
       "      <th>regiao</th>\n",
       "      <th>uf</th>\n",
       "      <th>grupo</th>\n",
       "      <th>mov_between</th>\n",
       "      <th>records</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2010</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Sudeste</td>\n",
       "      <td>SP</td>\n",
       "      <td>2</td>\n",
       "      <td>all</td>\n",
       "      <td>86899</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Sudeste</td>\n",
       "      <td>SP</td>\n",
       "      <td>1</td>\n",
       "      <td>all</td>\n",
       "      <td>42266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2010</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Nordeste</td>\n",
       "      <td>BA</td>\n",
       "      <td>1</td>\n",
       "      <td>all</td>\n",
       "      <td>40926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2010</td>\n",
       "      <td>CESARIANO</td>\n",
       "      <td>Sudeste</td>\n",
       "      <td>SP</td>\n",
       "      <td>2</td>\n",
       "      <td>all</td>\n",
       "      <td>38797</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Nordeste</td>\n",
       "      <td>MA</td>\n",
       "      <td>1</td>\n",
       "      <td>all</td>\n",
       "      <td>36604</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7610</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Norte</td>\n",
       "      <td>AC</td>\n",
       "      <td>5</td>\n",
       "      <td>regional</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7611</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Norte</td>\n",
       "      <td>RO</td>\n",
       "      <td>4</td>\n",
       "      <td>regional</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7612</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Norte</td>\n",
       "      <td>RR</td>\n",
       "      <td>2</td>\n",
       "      <td>regional</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7613</th>\n",
       "      <td>2019</td>\n",
       "      <td>CESARIANO</td>\n",
       "      <td>Norte</td>\n",
       "      <td>RO</td>\n",
       "      <td>4</td>\n",
       "      <td>regional</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7614</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Norte</td>\n",
       "      <td>RR</td>\n",
       "      <td>4</td>\n",
       "      <td>regional</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7615 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ano      parto    regiao  uf  grupo mov_between  records\n",
       "0     2010     NORMAL   Sudeste  SP      2         all    86899\n",
       "1     2010     NORMAL   Sudeste  SP      1         all    42266\n",
       "2     2010     NORMAL  Nordeste  BA      1         all    40926\n",
       "3     2010  CESARIANO   Sudeste  SP      2         all    38797\n",
       "4     2010     NORMAL  Nordeste  MA      1         all    36604\n",
       "...    ...        ...       ...  ..    ...         ...      ...\n",
       "7610  2019     NORMAL     Norte  AC      5    regional        2\n",
       "7611  2019     NORMAL     Norte  RO      4    regional        2\n",
       "7612  2019     NORMAL     Norte  RR      2    regional        2\n",
       "7613  2019  CESARIANO     Norte  RO      4    regional        1\n",
       "7614  2019     NORMAL     Norte  RR      4    regional        1\n",
       "\n",
       "[7615 rows x 7 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_s = group_mov_between(df)\n",
    "df_s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot(df):\n",
    "\treturn px.scatter(df,\n",
    "\t\tx='ano',\n",
    "\t\ty='percent',\n",
    "\t\tcolor='parto',\n",
    "\t\tsymbol='mov_between',\n",
    "\t\ttrendline=\"ols\",\n",
    "\t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "config": {
        "plotlyServerURL": "https://plot.ly"
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   "cell_type": "code",
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   "metadata": {},
   "outputs": [
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     "text": [
      "parto\n",
      "mov_between\n",
      "px_fit_results\n"
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    }
   ],
   "source": [
    "for r in results:\n",
    "\tprint(r)"
   ]
  },
  {
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  {
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       "      <td>69379</td>\n",
       "      <td>municipal</td>\n",
       "      <td>116.409865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2010</td>\n",
       "      <td>CESARIANO</td>\n",
       "      <td>Nordeste</td>\n",
       "      <td>80764</td>\n",
       "      <td>Centro-Oeste</td>\n",
       "      <td>52453</td>\n",
       "      <td>municipal</td>\n",
       "      <td>153.974034</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>495</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Sul</td>\n",
       "      <td>3859</td>\n",
       "      <td>Nordeste</td>\n",
       "      <td>106610</td>\n",
       "      <td>regional</td>\n",
       "      <td>3.619735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>496</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Sul</td>\n",
       "      <td>3859</td>\n",
       "      <td>Sudeste</td>\n",
       "      <td>71948</td>\n",
       "      <td>regional</td>\n",
       "      <td>5.363596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>497</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Sul</td>\n",
       "      <td>3859</td>\n",
       "      <td>Centro-Oeste</td>\n",
       "      <td>50447</td>\n",
       "      <td>regional</td>\n",
       "      <td>7.649612</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>498</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Sul</td>\n",
       "      <td>3859</td>\n",
       "      <td>Sul</td>\n",
       "      <td>34509</td>\n",
       "      <td>regional</td>\n",
       "      <td>11.182590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>499</th>\n",
       "      <td>2019</td>\n",
       "      <td>NORMAL</td>\n",
       "      <td>Sul</td>\n",
       "      <td>3859</td>\n",
       "      <td>Norte</td>\n",
       "      <td>19181</td>\n",
       "      <td>regional</td>\n",
       "      <td>20.118868</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ano      parto regiao_num  records_num    regiao_den  records_den  \\\n",
       "0    2010  CESARIANO   Nordeste        80764       Sudeste       209153   \n",
       "1    2010  CESARIANO   Nordeste        80764      Nordeste       202687   \n",
       "2    2010  CESARIANO   Nordeste        80764           Sul        79104   \n",
       "3    2010  CESARIANO   Nordeste        80764         Norte        69379   \n",
       "4    2010  CESARIANO   Nordeste        80764  Centro-Oeste        52453   \n",
       "..    ...        ...        ...          ...           ...          ...   \n",
       "495  2019     NORMAL        Sul         3859      Nordeste       106610   \n",
       "496  2019     NORMAL        Sul         3859       Sudeste        71948   \n",
       "497  2019     NORMAL        Sul         3859  Centro-Oeste        50447   \n",
       "498  2019     NORMAL        Sul         3859           Sul        34509   \n",
       "499  2019     NORMAL        Sul         3859         Norte        19181   \n",
       "\n",
       "    mov_between     percent  \n",
       "0     municipal   38.614794  \n",
       "1     municipal   39.846660  \n",
       "2     municipal  102.098503  \n",
       "3     municipal  116.409865  \n",
       "4     municipal  153.974034  \n",
       "..          ...         ...  \n",
       "495    regional    3.619735  \n",
       "496    regional    5.363596  \n",
       "497    regional    7.649612  \n",
       "498    regional   11.182590  \n",
       "499    regional   20.118868  \n",
       "\n",
       "[1000 rows x 8 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = group_col(df_s,\n",
    "\tcolumn = 'regiao',\n",
    "\tvalue  = None,\n",
    ")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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