SidraFinal.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "closing parenthesis ')' does not match opening parenthesis '[' (4176653631.py, line 22)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;36m  Cell \u001b[1;32mIn[2], line 22\u001b[1;36m\u001b[0m\n\u001b[1;33m    if 'SU' == item['id'] : df_lista = pd.DataFrame(data_list, columns=['id_lista')\u001b[0m\n\u001b[1;37m                                                                                  ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m closing parenthesis ')' does not match opening parenthesis '['\n"
     ]
    }
   ],
   "source": [
    "#valores para montar a base de tabelas/classificacoes/categorias/variaveis\n",
    "\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "response = requests.get('https://servicodados.ibge.gov.br/api/v3/agregados')\n",
    "\n",
    "if response.status_code == 200:\n",
    "    data = response.json()\n",
    "\n",
    "# lista de tabelas\n",
    "\n",
    "    data_list = []\n",
    "\n",
    "    for item in data:\n",
    "        id = item['id']\n",
    "        pesquisa = item['nome']\n",
    "        for agregado in item['agregados']:\n",
    "            id_df = agregado['id']\n",
    "            nome_df = agregado['nome']\n",
    "            data_list.append([id, pesquisa, id_df, nome_df])\n",
    "            if 'SU' == item['id'] : df_lista = pd.DataFrame(data_list, columns=['id_lista')\n",
    "\n",
    "\n",
    "#base de tabelas/classificacoes/categorias/variaveis\n",
    "\n",
    "for tab in df_lista\n",
    "\n",
    "tabela = '2093'\n",
    "\n",
    "endereco = 'https://servicodados.ibge.gov.br/api/v3/agregados/'+tabela+'/metadados'\n",
    "\n",
    "response = requests.get(endereco)\n",
    "\n",
    "if response.status_code == 200:\n",
    "    data = response.json()\n",
    "\n",
    "#Catgoria x classificacao\n",
    "\n",
    "df_categ = pd.json_normalize(data,\n",
    "    record_path=['classificacoes','categorias'],\n",
    "    meta=[\"id\"],#[\"classificacoes\",\"id\"],[\"classificacoes\",\"nome\"]],\n",
    "    meta_prefix='tabela_',\n",
    "    record_prefix='categ_'\n",
    "    )\n",
    "\n",
    "#Variaveis x classificacao\n",
    "\n",
    "df_var = pd.json_normalize(data,\n",
    "    record_path=['variaveis'],\n",
    "    meta=[\"id\",\"nome\",\"URL\",\"pesquisa\",\"assunto\",[\"periodicidade\",\"frequencia\"],[\"periodicidade\",\"inicio\"],[\"periodicidade\",\"fim\"],[\"nivelTerritorial\",\"Administrativo\"]],\n",
    "    meta_prefix='Tabela_',\n",
    "    record_prefix='var_'\n",
    "    )\n",
    "\n",
    "if df_categ.empty:\n",
    "    df = df_var\n",
    "else:\n",
    "    df = df_categ.merge(df_var, left_on='tabela_id',right_on='Tabela_id')\n",
    "\n",
    "#df[\"N1\"]=(df[\"Tabela_nivelTerritorial.Administrativo\"])\n",
    "         \n",
    "#if 'N1' in df[\"Tabela_nivelTerritorial.Administrativo\"]:\n",
    "#    df[\"N1\"]=\"1\"\n",
    "#else:\n",
    "#    df[\"N1\"]=\"0\"\n",
    "# [N15, N1, N2, N8, N9, N6, N14, N7, N13, N3]\n",
    "print(df)\n",
    "\n",
    "#2093 tem + de 1 classificações e categorias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'tabela_id'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp\\ipykernel_2016\\3077420793.py\u001b[0m in \u001b[0;36m?\u001b[1;34m()\u001b[0m\n\u001b[0;32m     31\u001b[0m     \u001b[0mrecord_path\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'variaveis'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     32\u001b[0m     \u001b[0mmeta\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"id\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"nome\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"URL\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"pesquisa\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"assunto\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"periodicidade\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"frequencia\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"periodicidade\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"inicio\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"periodicidade\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"fim\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"nivelTerritorial\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Administrativo\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     33\u001b[0m     meta_prefix='Tabela_')\n\u001b[0;32m     34\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 35\u001b[1;33m \u001b[0mdf\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdf_categ\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_var\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mleft_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tabela_id'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mright_on\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'Tabela_id'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msuffixes\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'_categ'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'_var'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf_var\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Alexandre Rebello\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[0;32m  10815\u001b[0m         \u001b[0mvalidate\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mMergeValidate\u001b[0m \u001b[1;33m|\u001b[0m \u001b[1;32mNone\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m  10816\u001b[0m     ) -> DataFrame:\n\u001b[0;32m  10817\u001b[0m         \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmerge\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m  10818\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m> 10819\u001b[1;33m         return merge(\n\u001b[0m\u001b[0;32m  10820\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m  10821\u001b[0m             \u001b[0mright\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m  10822\u001b[0m             \u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Alexandre Rebello\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate)\u001b[0m\n\u001b[0;32m    166\u001b[0m             \u001b[0mvalidate\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mvalidate\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    167\u001b[0m             \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    168\u001b[0m         )\n\u001b[0;32m    169\u001b[0m     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 170\u001b[1;33m         op = _MergeOperation(\n\u001b[0m\u001b[0;32m    171\u001b[0m             \u001b[0mleft_df\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    172\u001b[0m             \u001b[0mright_df\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    173\u001b[0m             \u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mhow\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Alexandre Rebello\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, indicator, validate)\u001b[0m\n\u001b[0;32m    790\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mright_join_keys\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    791\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin_names\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    792\u001b[0m             \u001b[0mleft_drop\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    793\u001b[0m             \u001b[0mright_drop\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 794\u001b[1;33m         ) = self._get_merge_keys()\n\u001b[0m\u001b[0;32m    795\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    796\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mleft_drop\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    797\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mleft\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_drop_labels_or_levels\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft_drop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Alexandre Rebello\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\reshape\\merge.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   1306\u001b[0m                     \u001b[1;32mif\u001b[0m \u001b[0mlk\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1307\u001b[0m                         \u001b[1;31m# Then we're either Hashable or a wrong-length arraylike,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1308\u001b[0m                         \u001b[1;31m#  the latter of which will raise\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1309\u001b[0m                         \u001b[0mlk\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcast\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mHashable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1310\u001b[1;33m                         \u001b[0mleft_keys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mleft\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label_or_level_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1311\u001b[0m                         \u001b[0mjoin_names\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlk\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1312\u001b[0m                     \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1313\u001b[0m                         \u001b[1;31m# work-around for merge_asof(left_index=True)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\Users\\Alexandre Rebello\\AppData\\Local\\Programs\\Python\\Python311\\Lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m?\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1906\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mother_axes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1907\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_is_level_reference\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1908\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_level_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1909\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1910\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1911\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1912\u001b[0m         \u001b[1;31m# Check for duplicates\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1913\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'tabela_id'"
     ]
    }
   ],
   "source": [
    "#valores para montar a base de tabelas/classificacoes/categorias/variaveis\n",
    "\n",
    "#https://apisidra.ibge.gov.br/values/t/1621/n1/all/v/all/p/last%201/c11255/90687/d/v584%202\n",
    "\n",
    "#https://apisidra.ibge.gov.br/values/t/1621/n1/all/v/all/p/last%201/c11255/90687/d/v584%202\n",
    "\n",
    "#https://servicodados.ibge.gov.br/api/v3/agregados/2093/metadados\n",
    "\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "tabela = '6784'\n",
    "endereco = 'https://servicodados.ibge.gov.br/api/v3/agregados/'+tabela+'/metadados'\n",
    "\n",
    "response = requests.get(endereco)\n",
    "\n",
    "if response.status_code == 200:\n",
    "    data = response.json()\n",
    "\n",
    "#Catgoria x classificacao\n",
    "\n",
    "df_categ = pd.json_normalize(data,\n",
    "    record_path=['classificacoes','categorias'],\n",
    "    meta=[\"id\",[\"classificacoes\",\"id\"],[\"classificacoes\",\"nome\"]],\n",
    "    meta_prefix='tabela_'\n",
    "    )\n",
    "\n",
    "#Variaveis x classificacao\n",
    "\n",
    "df_var = pd.json_normalize(data,\n",
    "    record_path=['variaveis'],\n",
    "    meta=[\"id\",\"nome\",\"URL\",\"pesquisa\",\"assunto\",[\"periodicidade\",\"frequencia\"],[\"periodicidade\",\"inicio\"],[\"periodicidade\",\"fim\"],[\"nivelTerritorial\",\"Administrativo\"]],\n",
    "    meta_prefix='Tabela_')\n",
    "\n",
    "df = df_categ.merge(df_var, left_on='tabela_id',right_on='Tabela_id',suffixes=('_categ', '_var'))\n",
    "\n",
    "print(df_var)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "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>NC</th>\n",
       "      <th>NN</th>\n",
       "      <th>MC</th>\n",
       "      <th>MN</th>\n",
       "      <th>V</th>\n",
       "      <th>D1C</th>\n",
       "      <th>D1N</th>\n",
       "      <th>D2C</th>\n",
       "      <th>D2N</th>\n",
       "      <th>D3C</th>\n",
       "      <th>D3N</th>\n",
       "      <th>D4C</th>\n",
       "      <th>D4N</th>\n",
       "      <th>D5C</th>\n",
       "      <th>D5N</th>\n",
       "      <th>D6C</th>\n",
       "      <th>D6N</th>\n",
       "      <th>D7C</th>\n",
       "      <th>D7N</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Brasil</td>\n",
       "      <td>45</td>\n",
       "      <td>Pessoas</td>\n",
       "      <td>91298042</td>\n",
       "      <td>1</td>\n",
       "      <td>Brasil</td>\n",
       "      <td>93</td>\n",
       "      <td>População residente</td>\n",
       "      <td>2000</td>\n",
       "      <td>2000</td>\n",
       "      <td>2776</td>\n",
       "      <td>Branca</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Brasil</td>\n",
       "      <td>45</td>\n",
       "      <td>Pessoas</td>\n",
       "      <td>90621281</td>\n",
       "      <td>1</td>\n",
       "      <td>Brasil</td>\n",
       "      <td>93</td>\n",
       "      <td>População residente</td>\n",
       "      <td>2010</td>\n",
       "      <td>2010</td>\n",
       "      <td>2776</td>\n",
       "      <td>Branca</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "      <td>0</td>\n",
       "      <td>Total</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NC      NN  MC       MN         V D1C     D1N D2C                  D2N  \\\n",
       "0  1  Brasil  45  Pessoas  91298042   1  Brasil  93  População residente   \n",
       "1  1  Brasil  45  Pessoas  90621281   1  Brasil  93  População residente   \n",
       "\n",
       "    D3C   D3N   D4C     D4N D5C    D5N D6C    D6N D7C    D7N  \n",
       "0  2000  2000  2776  Branca   0  Total   0  Total   0  Total  \n",
       "1  2010  2010  2776  Branca   0  Total   0  Total   0  Total  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#valores para a API\n",
    "\n",
    "#https://apisidra.ibge.gov.br/values/t/1621/n1/all/v/all/p/last%201/c11255/90687/d/v584%202\n",
    "\n",
    "#https://apisidra.ibge.gov.br/values/t/1621/n1/all/v/all/p/last%201/c11255/90687/d/v584%202\n",
    "\n",
    "#https://servicodados.ibge.gov.br/api/v3/agregados/2093/metadados\n",
    "\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "tabela = '2093'\n",
    "variavel = '93'\n",
    "classificacao = '86'\n",
    "categoria ='2776'\n",
    "nivel = 'n1/all'\n",
    "\n",
    "#if N6 == 1:\n",
    "#    nivel = 'n1/all/n6/in n3 33'\n",
    "#else:\n",
    "#    if N3 ==1: \n",
    "#        nivel = 'n1/all/n3/all'\n",
    "#   else: nivel = 'n1/all'\n",
    "\n",
    "endereco = 'https://apisidra.ibge.gov.br/values/t/'+tabela+'/'+nivel+'/v/'+variavel+'/p/all/c'+classificacao+'/'+categoria+'/h/n'\n",
    "\n",
    "response = requests.get(endereco)\n",
    "\n",
    "if response.status_code == 200:\n",
    "    data = response.json()\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  }
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