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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "12e6eef3-32fb-4053-be4e-6bcf85aff741",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Collecting sqlalchemy"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "[notice] A new release of pip is available: 25.2 -> 26.1.1\n",
+      "[notice] To update, run: python.exe -m pip install --upgrade pip\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "  Downloading sqlalchemy-2.0.50-cp312-cp312-win_amd64.whl.metadata (9.8 kB)\n",
+      "Collecting pymysql\n",
+      "  Downloading pymysql-1.2.0-py3-none-any.whl.metadata (4.3 kB)\n",
+      "Collecting greenlet>=1 (from sqlalchemy)\n",
+      "  Downloading greenlet-3.5.1-cp312-cp312-win_amd64.whl.metadata (3.9 kB)\n",
+      "Requirement already satisfied: typing-extensions>=4.6.0 in c:\\users\\ferna\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from sqlalchemy) (4.15.0)\n",
+      "Downloading sqlalchemy-2.0.50-cp312-cp312-win_amd64.whl (2.1 MB)\n",
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+      "Downloading pymysql-1.2.0-py3-none-any.whl (45 kB)\n",
+      "Downloading greenlet-3.5.1-cp312-cp312-win_amd64.whl (238 kB)\n",
+      "Installing collected packages: pymysql, greenlet, sqlalchemy\n",
+      "\n",
+      "   ---------------------------------------- 0/3 [pymysql]\n",
+      "   ------------- -------------------------- 1/3 [greenlet]\n",
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+      "   ---------------------------------------- 3/3 [sqlalchemy]\n",
+      "\n",
+      "Successfully installed greenlet-3.5.1 pymysql-1.2.0 sqlalchemy-2.0.50\n"
+     ]
+    }
+   ],
+   "source": [
+    "!pip install sqlalchemy pymysql"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "66609a93-580f-4cc5-a91c-098d9354b327",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('IMDB_movies',)\n",
+      "('data_product',)\n",
+      "('data_product_sales',)\n",
+      "('data_store_cad',)\n",
+      "('data_store_sales',)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from sqlalchemy import create_engine, text\n",
+    "import pandas as pd\n",
+    "\n",
+    "engine = create_engine(\"mysql+pymysql://looqbox-challenge:looq-challenge@35.199.115.174/looqbox-challenge\")\n",
+    "\n",
+    "# Ver todas as tabelas do schema\n",
+    "with engine.connect() as c:\n",
+    "    resultado = c.execute(text(\"SHOW TABLES\"))\n",
+    "    for tabela in resultado:\n",
+    "        print(tabela)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "3d545c6c-f991-4367-8dff-6c700518a595",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   product_cod                                       product_name  product_val\n",
+      "0       301409  Whisky Escoces THE MACALLAN Ruby Garrafa 700ml...       741.99\n",
+      "1       176185  Whisky Escoces JOHNNIE WALKER Blue Label Garra...       735.90\n",
+      "2       315481   Cafeteira Expresso 3 CORACOES Tres Modo Vermelho       499.00\n",
+      "3       100280  Vinho Portugues Tinto Vintage QUINTA DO CRASTO...       445.90\n",
+      "4       320046  Escova Dental Eletrica ORAL B D34 Professional...       399.90\n",
+      "5       190817  Champagne Rose VEUVE CLICQUOT PONSARDIM Garraf...       366.90\n",
+      "6       153795  Champagne Frances Brut Imperial MOET Rose Garr...       359.90\n",
+      "7       311397  Conjunto de Panelas Allegra em Inox TRAMONTINA...       359.00\n",
+      "8       147706  Whisky Escoces CHIVAS REGAL 18 Anos Garrafa 750ml       329.90\n",
+      "9       154431  Champagne Frances Brut Imperial MOET & CHANDON...       315.90\n"
+     ]
+    }
+   ],
+   "source": [
+    "query1 = \"\"\"\n",
+    "SELECT product_cod, product_name, product_val\n",
+    "FROM data_product\n",
+    "ORDER BY product_val DESC\n",
+    "LIMIT 10\n",
+    "\"\"\"\n",
+    "\n",
+    "df1 = pd.read_sql(query1, engine)\n",
+    "print(df1)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "5912ebd7-37da-46d8-9d90-687b8b3067a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  dep_name        section_name\n",
+      "0  BEBIDAS             BEBIDAS\n",
+      "1  BEBIDAS            CERVEJAS\n",
+      "2  BEBIDAS           REFRESCOS\n",
+      "3  BEBIDAS              VINHOS\n",
+      "4  PADARIA  DOCES-E-SOBREMESAS\n",
+      "5  PADARIA            GESTANTE\n",
+      "6  PADARIA             PADARIA\n",
+      "7  PADARIA     QUEIJOS-E-FRIOS\n"
+     ]
+    }
+   ],
+   "source": [
+    "query2 = \"\"\"\n",
+    "SELECT DISTINCT dep_name, section_name\n",
+    "FROM data_product\n",
+    "WHERE dep_name IN ('BEBIDAS', 'PADARIA')\n",
+    "ORDER BY dep_name, section_name\n",
+    "\"\"\"\n",
+    "\n",
+    "df2 = pd.read_sql(query2, engine)\n",
+    "print(df2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "21eb6abe-5a0b-4664-8f6b-11da656a783e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  business_name  total_vendas\n",
+      "0         Farma   81776691.73\n",
+      "1        Varejo   81032347.65\n",
+      "2       Atacado   80384884.60\n",
+      "3   Proximidade   80171122.80\n",
+      "4         Posto   32072326.40\n"
+     ]
+    }
+   ],
+   "source": [
+    "query3 = \"\"\"\n",
+    "SELECT \n",
+    "    sc.business_name,\n",
+    "    SUM(ss.sales_value) AS total_vendas\n",
+    "FROM data_store_sales ss\n",
+    "JOIN data_store_cad sc ON ss.store_code = sc.store_code\n",
+    "WHERE ss.date BETWEEN '2019-01-01' AND '2019-03-31'\n",
+    "GROUP BY sc.business_name\n",
+    "ORDER BY total_vendas DESC\n",
+    "\"\"\"\n",
+    "\n",
+    "df3 = pd.read_sql(query3, engine)\n",
+    "print(df3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "5b08b7db-2f00-4977-9439-cbd350c08153",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def retrieve_data(product_code=None, store_code=None, date=None):\n",
+    "    \n",
+    "    query = \"SELECT * FROM data_product_sales WHERE 1=1\"\n",
+    "    \n",
+    "    if product_code is not None:\n",
+    "        query += f\" AND product_code = {product_code}\"\n",
+    "    \n",
+    "    if store_code is not None:\n",
+    "        query += f\" AND store_code = {store_code}\"\n",
+    "    \n",
+    "    if date is not None:\n",
+    "        query += f\" AND date BETWEEN '{date[0]}' AND '{date[1]}'\"\n",
+    "    \n",
+    "    return pd.read_sql(query, engine)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "b40d0991-244c-4927-880d-b7ef7ded4014",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  STORE_CODE  PRODUCT_CODE        DATE  SALES_VALUE  SALES_QTY\n",
+      "0          1            18  2019-01-01        708.5       65.0\n",
+      "1          1            18  2019-01-02       1297.1      119.0\n",
+      "2          1            18  2019-01-03       1144.5      105.0\n",
+      "3          1            18  2019-01-04       1090.0      100.0\n",
+      "4          1            18  2019-01-05        893.8       82.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Teste\n",
+    "df_teste = retrieve_data(product_code=None, store_code=1, date=['2019-01-01', '2019-01-31'])\n",
+    "print(df_teste.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "8839e1fb-4507-4fb7-9f07-62e806bd49b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "          Loja   Categoria    TM\n",
+      "         Bahia     Atacado 15.39\n",
+      "       Bangkok       Posto 13.67\n",
+      "         Belem Proximidade 15.37\n",
+      "        Berlin Proximidade 15.39\n",
+      "  Buenos Aires     Atacado 15.39\n",
+      "       Chicago      Varejo 15.53\n",
+      "         Dubai     Atacado 15.39\n",
+      "     Hong Kong       Farma 26.35\n",
+      "        London       Farma 28.99\n",
+      "         Madri       Farma 29.03\n",
+      "         Miami       Posto 13.67\n",
+      "      New York Proximidade 15.39\n",
+      "         Paris Proximidade 15.39\n",
+      "Rio de Janeiro       Farma 29.59\n",
+      "          Roma      Varejo 15.39\n",
+      "      Salvador     Atacado 15.39\n",
+      "     Sao Paulo      Varejo 15.39\n",
+      "        Sidney       Posto 13.67\n",
+      "         Tokio      Varejo 15.39\n",
+      "     Vancouver       Posto 13.67\n"
+     ]
+    }
+   ],
+   "source": [
+    "query_lojas = \"\"\"\n",
+    "SELECT\n",
+    "    STORE_CODE,\n",
+    "    STORE_NAME,\n",
+    "    START_DATE,\n",
+    "    END_DATE,\n",
+    "    BUSINESS_NAME,\n",
+    "    BUSINESS_CODE\n",
+    "FROM data_store_cad\n",
+    "\"\"\"\n",
+    "\n",
+    "query_vendas = \"\"\"\n",
+    "SELECT\n",
+    "    STORE_CODE,\n",
+    "    DATE,\n",
+    "    SALES_VALUE,\n",
+    "    SALES_QTY\n",
+    "FROM data_store_sales\n",
+    "WHERE DATE BETWEEN '2019-01-01' AND '2019-12-31'\n",
+    "\"\"\"\n",
+    "\n",
+    "df_lojas = pd.read_sql(query_lojas, engine)\n",
+    "df_vendas = pd.read_sql(query_vendas, engine)\n",
+    "\n",
+    "# Converter DATE para datetime e filtrar\n",
+    "df_vendas['DATE'] = pd.to_datetime(df_vendas['DATE'])\n",
+    "df_vendas = df_vendas[\n",
+    "    (df_vendas['DATE'] >= '2019-10-01') & \n",
+    "    (df_vendas['DATE'] <= '2019-12-31')\n",
+    "]\n",
+    "\n",
+    "# Juntar as duas tabelas\n",
+    "df_merged = df_vendas.merge(df_lojas, on='STORE_CODE')\n",
+    "\n",
+    "# Calcular TM por loja\n",
+    "df_tm = df_merged.groupby(['STORE_NAME', 'BUSINESS_NAME']).agg(\n",
+    "    total_valor=('SALES_VALUE', 'sum'),\n",
+    "    total_qty=('SALES_QTY', 'sum')\n",
+    ").reset_index()\n",
+    "\n",
+    "df_tm['TM'] = (df_tm['total_valor'] / df_tm['total_qty']).round(2)\n",
+    "\n",
+    "# Tabela final\n",
+    "df_resultado = df_tm[['STORE_NAME', 'BUSINESS_NAME', 'TM']].rename(columns={\n",
+    "    'STORE_NAME': 'Loja',\n",
+    "    'BUSINESS_NAME': 'Categoria',\n",
+    "})\n",
+    "\n",
+    "print(df_resultado.sort_values('Loja').to_string(index=False))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "e4d5a1e1-a737-4821-983e-7b5c845a1855",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Id', 'Title', 'Genre', 'Director', 'Actors', 'Year', 'Runtime', 'Rating', 'Votes', 'RevenueMillions', 'Metascore']\n",
+      "   Id                    Title                     Genre  \\\n",
+      "0   1  Guardians of the Galaxy   Action,Adventure,Sci-Fi   \n",
+      "1   2               Prometheus  Adventure,Mystery,Sci-Fi   \n",
+      "2   3                    Split           Horror,Thriller   \n",
+      "3   4                     Sing   Animation,Comedy,Family   \n",
+      "4   5            Suicide Squad  Action,Adventure,Fantasy   \n",
+      "\n",
+      "               Director                                             Actors  \\\n",
+      "0            James Gunn  Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...   \n",
+      "1          Ridley Scott  Noomi Rapace, Logan Marshall-Green, Michael Fa...   \n",
+      "2    M. Night Shyamalan  James McAvoy, Anya Taylor-Joy, Haley Lu Richar...   \n",
+      "3  Christophe Lourdelet  Matthew McConaughey,Reese Witherspoon, Seth Ma...   \n",
+      "4            David Ayer  Will Smith, Jared Leto, Margot Robbie, Viola D...   \n",
+      "\n",
+      "   Year  Runtime  Rating   Votes  RevenueMillions  Metascore  \n",
+      "0  2014      121     8.0  757074            333.0         76  \n",
+      "1  2012      124     7.0  485820            126.0         65  \n",
+      "2  2016      117     7.0  157606            138.0         62  \n",
+      "3  2016      108     7.0   60545            270.0         59  \n",
+      "4  2016      123     6.0  393727            325.0         40  \n"
+     ]
+    }
+   ],
+   "source": [
+    "df_imdb = pd.read_sql(\"SELECT * FROM IMDB_movies LIMIT 5\", engine)\n",
+    "print(df_imdb.columns.tolist())\n",
+    "print(df_imdb.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "36021d9c-28ea-4225-ab77-fc607edfaecb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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P+6RulCmrwnPZZZe5oIqCzLrZ610v60baBRdc4IKnaQ2Gw043hJRpq2Pgb7/9luJY6a0v3dAYNGhQ5JBDDok8/PDDCV3enMRbTwo0aRv0U+ajss0UqBKde1W14WXfhlVaN7f9xz+9R4GpevXqpUjKIFM5ZyEolUspQq5SAA0K3njjDTernndHQnd3lAmgoJWCAbE7qO6E60Sg7Kmw08BLFx0qL/NOmFp/JUqUSDFToYKACvgpKICUdFBXAE/b45NPPulmqhk1apQLECg48Msvv0S2bdvmLv4UJNBAzT+TUtgHEGPGjIl+rpJRXcwpY093tj1c+MbnXy8aXPnvGCoor+1N+y2z/KRvXWpWJA1adfzzSi/8F3b+CzgFpnTsVJla2MRmkck999zjgid+n3/+uSuf1yBWs3iJAs4ERFPbsmWLu55RIMp/J1uBUgVRNHuwzi2x26RaEmg2Jfxr/Pjx7pinkjRvPcVmBGgbVADGu/EWRtoHvUCTroUVmFIpmVfGqIxvBe6OPfbYFF+nQIpKwZXdjNQUIFaWqDceURBZ61TVCLHVHdoulRmum+jat8N8neP97rpeFt08Uwmp+BMIdHxUVr2Ol4i4gKbOAdqW/Ncn3rFPWbQKQm3YsCFayqeKg7Bnl+VUBKVyeWaKBl0qEVCqsT+LRycABaZU6qOBhse7OFGmVNimwFQ9tpc14c+u0AXG0qVL3efeQU2DDmX7qKbbQ5ZU/JOoLmx14aEDvz+QoumnK1eu7O5A+vtVKECFf/tYaB9VJsC4ceNSDG510aGLD+8CBWlvf7oIVqmAtrOrrroqxXsUmFJ2noIFSoGP/dowi3eHUOcHnTuOPvpod0dRPaNigwD+dacsIP8xMgzef/99d1xT7zL/YF/nFwVPYgf6ChJokBbmnj3ppaBUbHaAqORWQQP1mfJn5ulGh7Kcw54p4KfjoLKS4+3n3AzaV36nigJlyXuZd8rq0TZ24403un1Y1yk6r+g4qOvl/v37uwxHyr73T+MN9WLVDVy1EVG5lK6xVdKsGx7Lli1L8X5lTKncnuvrSOSFF15wmco6t9x5553uYy/TzL+dKqgS5oCyn8Z03vHfW0fe/wq+6yZv3759o8dBlYrqpq/Gxl6PW+QcBKVyIf9Fhk6qOmEq5T024DJv3jxXcqaBg/81NcjTyTdsF3Iq51EQyk/rQMEnBfi8dauH7kKo1l2ZK9h/Dx8NFHQRrEfsNvjEE0+4bAr1lApjRsWBqJxCAy5tm8oy8wemlKatO7c0X0ybLmgLFCjgeqWUK1fOZYDGZrHoIvnkk092GXr+hrZh5j+HaB1q21PwxLuTrXIzBUs1uIgXmBJll+q8E7Ymv7rb6g0U/Mc0nYuVWaFePf7tTANYDcpizz1hFi8gquc0ENNdb21//oCfylVUaqbMW//XaoBL1llK2p+1Haqsx58ppSBLx44d3fYZdsrKUasGnRcUVPcyUbzAlHqVaX3pOlAZU8pYUSmfng972wE/7waF/0aFskN1Tlb2k1dxoLIz3SDSNc2MGTNSfA9dY6v/WVgDBN66U3mZJhFRWajovKuxnfZlf4WG+sDpmts7L+NfyszTuvLOzbp5oZtH2v5it1MFQOP1KUTiEZTKRdKqfVX5njKllLKtciA/DdAUFfZf4On7aKaBsIjNilB5nvp/eNF0zV6hUkdlCPjpwE+te9qUXaY7OQMHDnRBJ92RuPzyy1PdjdVJVncbw3rREW82ED+VASg7QBcg/oyp22+/3a232PeHmX9fVvNK7Z/eOtOsKrqTrbuIsYEpZbcwy2NqGuTrwk3NpDURgYJ7Kg33MtD0uR5KfY+lwVrsXe+wBZQVBFUgxaP1qAwMDW613+q8qwxS7cdkAqS+jtEAVcE8BVJ0PtbA7LjjjnM9HJXZqIGXnlPpngL13v6v9Rr2bEfv99e1nHqLegFlHRd1k0NBPAUDRNueV/bjNegOI5Uce0ElZZq0aNHCXfulFZgiGyV9+7FKk9Xw3cuC1/W1yqR0jvAyG/Wa9m31i/O2X72m/o5h72um/feoo45ymWT+SQeUaaZtVME9je/00McK/CF1UErXfiq39cYaGtOF/TyR2xCUyoUnAN1x0MHeXzahizqV8alM5dNPP437PXQhF3u3O5n5D0b+5qgnnHCCC6B4Ne86cKnnhzIDlD6r/jMarKlxPNPFx6fAk1LbFTjxaIYz3eVWCZVXDunxeoGEnfZNDRZiS540UNC+q7KpiRMnRp/nbs6+7E4/bV+6ONOFnL8sTxkWyg495phjIjNnzkzAkuauEhYNUr0bGcpqLFSoUHTb1PFTx0bN2Brmaae9c6//HOLtl7rho7v8w4cPj76mBvDaLitUqOCCAzqPUO4TvxmySnoUgNL5WMdFrScNcBUo0ABDWbYaZOgOuHftwiBjHwX0dGNI2fCahcvrjakyepVCHnnkkW4qeWUEaXsM83aoY5lu3vpL4hVs182gtAJTKoX0Z6mw7aUej2iCFmVFaR3q2s9bv9661P+6/lMZvd4T298srBM+eNuSAk/a3rSPaqIML2jsva5gnrLJVFaqfmZhSijYH2/9aH152dq6ltH5ROdfLzDFDHu5C0GpXMB/ItTdG13k6mLNu9jw0tdVgqZIuu7WqnQvzPxpmh5/o0XVE6v/h1dupjs7mmpVgzId0JQ1EOYLuP1Reqyamms7VK22n/qUKbin6ZP9d7/CdjHnn2nG+9012NLJU5kn2keV4ein9aUBhrIqVErlfX3YffXVVy6bxz/tu55ToET7qwLJfgpMaYZH9fFR9hT+FZvCrgvcbt26uY9V6qMgnwJTogwVb33rrmzYL+x03lDPKAVG1BRZJd/an3Xhq4CU1p1e9yhQqgCBpj+nIXJqY8eOdRNheDfQNOhSyY83Y7C2P03SogGv1mNaDbvD3hBZ1ypalxrYasp4BVLUOFoUCND6VYaZ9u+wldnG42Ud6/zhldOmFZjSOtP6vO2225ihKw3Kktfslyr/Vg9Rr+TbC0ypjFmZpBqnaP16ASj2438pa0wljTru6Zyh/VltQ7z1FKYkgswcAxWUV2ayzh86F+t5jeVUmusPTLEecw+CUjmcf1CqUgkd9HUBogGDIua6i6jsCq+OVjup3uOf8jKslMauE6FKfBQA0AWGv+m7snrU68PfB0klPjqhUmqxf7pg011H3aHV1N1+yvRRf6l+/fqlmDUkbBQs1qDUW1/K3tHJUfuv0rQ1SYE/MKW0bQX7dLeRUrN9NCDwysf8aesqw9AMNbq7GNunQtmOWo8EBFLzLtTUuFcBFQXxihUrFg1IaX0rKPrII4+kGDyEeSChAZcaaqs8VBkpTz/9dIrzjBeYuvvuuxO6nLmFbmbozr8oyFeyZEm3jr2AVLyyqTBvf7E0gNUMt3369EmxXrQOdZ2jAGlYM1Di8Q9KdfzTjR/1mvFK+WIDU9660/W0AlhITdUaunnrXT+rWkPnEV37KVDg9cLU+UWBUu9vEPYAgTem05hNN8+8FiE67+o6RteJCuL5M81ivxb/HgNVPq+ZV2NbgygwpYwpjY+ZYCR3ISiVS2iQoEGsGi7qoO4dnHQnwpsu2T8g487OvwEm3eVSxoQGErr4jZ39TYEprT+dOLmIiy+tE6Eu3hRcUdZPbGBKfWnC3thcd280QFAGnv73z3bpD0xpBikNxFQK1KNHjxSzS4Wdf9vTXW7NfOQ/1ikwr2wfPR8bmGKWx32DB6/Pm0qmlCElyjDTdqmHgqYebYteU2nso31T60rHu9h91AtMqZTPX9KMlLxeUBdeeKHL8NF5wx8Q1esKtuj8wTVM2jSjmbZF3ZBUxp6fAlMFCxZ05xMmdkh9LFQG2ZgxY1zGsnphxmZM6Wabyqm4Htw/9W30zhHK+NGxTwEClc0ri143x3/66acUX0Ng+V+qZNG1n4KgXhsR0dhO/S+VLaVsH/XpCjvvPOA/HyhQpz6suvHt5w94qpRPx0dVxehrCejlDgSlcgFdAGvnUwmLmm/HDrrUzFKBldgTABd1EVcCoIs3pRjrzn+8AasOWjqhxjaJx76ggNaNBgtah/5ePQr0qZeZZqZRoAUpacY3lfp07do1VTmfsn4UZFY/FWXsafY4Glim5K0rTU4wffp0N6hQ2bLuMHp0t1uBKe3j2t+xjwas6i2joF2XLl1ccN7bxrRuFaRSCaTWrTJEtS6VracBRdjvaIv/Qlb9ZbyyUDXnjc1m1N1aPa/X1XOKi+C0r0EeffRRd5dbx0avsb4oQ0rnE/UrxP555XpeqbffAw884AID9CTcR6WMWl9elqMCKGq8HRuYUi8zbYPMbraPv/m2Mnu8mxzK9tG1tG6wefusJiZQwM/LLBWOhanLwdXvTdujlyHqD9wp4KcWLWrHEmbe+UOlxyoRVUN4jwJ6Q4cOjRvs9MpH9X7KlnMXglI5ULwDuLJOVL+tAYR/th959dVX3aCWqeP38Q5S6tOjXls6YapZ6r333hs3MKXMgDDPSnOgum0NatWoUv0ClFqsKbo9yrLQa0qXJaiS8mSquzlqYJ43b95oE1r/62qiql5nauQdG1QOM/8xULPAaQCr8gDdOdRdWZX7+ANTyphSAFClA5rGm4vgfbRdKVCinj2afUq8LAAd81QCpMwKvUd3aJUtQO+PfduggvD+gb8yINUgXgEoNZT2eGUCzJaZOiCl7G0FjLVu9LyCpZ06dXLrUQMH3fnWdqrziLKXCYim3g613mL3R2VMab/WpAWxmFwkkqL1hTLz1F/LT+dkBaZ0DPQCUwqMci5OGZDSeUEZoNdff70LpHhlj6ISed0wf/fdd6OBKt1sU2CFG+Np0zambU+ZebGzBWs/1424MLcf8LYdBUBVDqoMed088+g6RTfcYt+vm0Xaz/0TGiD3ICiVw/gP4jo56uLMuxBRxFd3thVB1wWxSgY0qNCdbU0byglg3wVcbC8j3ZnQBZwCU/6Aiu7SMsNe2jSY0AWH0t1FqcYKCGgw4S9T0UWxThph74XkbX+xgyr1mokNTAl3cfaJF0jSoF/Njr3mvd774gWmvvvuuxQzJYWZ/1ygi98zzjjDBY213/ozpTwKDKhHg/qDeF9LYCASefnll13GiTLx/JmgCkwpiKdSSF0061iowKmyBJCSMsw085t6v2nmW5Ux65pGpXsXXXRRJH/+/C7TWwNf3fAgILqPt4+qpEeZjgraaVvz99zSNaECU8qYR2rqv6pAp3q+jRgxwj3nL4vSOVmZPVq/zGyWmjLGhgwZ4vZhldp6x0Hv/KDXdW5RtpRuHGkmPp1v4pVdhXkf1ral/VjnWu9mhq5Z1ENKGVG6+YaUFPzUDXGN3WKTLtSPVWNhTf7lp5JSnUs0PkbuQ1AqB/EfvB988EFXc6yDu+5OeBe7CqDoIkRp7yo5U1mQ+jN4vQPCfALwDv7qLaMmx7pbows4b90pgKdsMwWmVA6pflO66xPmuxEHooCA1qU3uFVj88svv9ytP5VL+QN88ZrThnH708WF+nkoO0/7q7dPKjCl/VZlA7qLrWa0ulhWyUDYM3u8daQLCV20qVxU+632T81IGHuHW+9XYEqlfG3btk3QUudM/nOAsvCUZavzg7ZFnSs061lsRmPsxA5hPo94NPhSQGrcuHFplqApQ1l9KxS4p/w79SyPyrLQgF/BTp1/lTmqwJSOgV7QSZOPKFNUAzbvOQKiKXs3KgCv2UZ1PlZZlD72Z6soOBrbGw7/0vlV6037qgInHv+NS13HKPOCpsiRuOcB3bzVeEPHOl1T+wPH2s9VraEeSFrHKn30Xg/7ecQ7Furmhm4IaaY9BVI0BvFmBtaEOApMKaPeyzbDv5Us7du3d+W1ftq2lI2nZuaqfFEz89NOOy1y3XXXuTGfjpVUbOReBKVyIEWFNeDX3VjtdDoRKDjl1bgru0Lv0Qwi+t9DY99/Sxl1N0cHKD3UL8rfH0ADNF2gqG+ALo4XL16c6EXOsYEVZUlpMKsBhbYtpRnrYlg0I40uUtSjxp/FEnaa2lcZUbrA0OBB258u6LxBlvZnDR40UCtevDh9uHwXrtqmtI0pG8BrZq7Bq9aXBrOxd770ddrfq1evTuny//MHNzVQVQaKLoi9c4OCBAri6QLZ2/a0btWkG5FUs4jqvKt1522jsdk7alirDDPKfSKpBqEaPOiGjxrEe8+rtFbnEB3/1J8w3jULGVL7LF261O3DXiN4rT/1HtQ5RsdJf2aPboJ4JWhh5e/ZKN52p76sOpcoaOJdw8QGpmIbxodZbDBJAWUFQXWzTdfNurkbGzjWtqmsNDJt/+WtB92s0LWeqg1UUqayPGV469pZ19je+q1WrZrLHNV6xL/bjzLwdPPHoxsYyozSGE/jYp1HtD6V5ahSPt1AZ7bM3I2gVA6jAYSCTd40q+qHpMGt7m6rn48XXFHap5f1o6wq/Ns4WutDDfG84J2aSOuE4F93uhDWxQg9F9Lu4aNSC22LHmWv6G6OejN4259moVI6fNhL0Lx1p0bHOimqd4Voe9OJUpMTqLzRu0hT/wB9HvbZCf3rTtuVMqIUSNGFrX/mI2+ac2Waxc56pq+nZCo13c1WuYW2NWUK+GnfVcaUBrbK1FMfrrDONBU7+PJ/rmObgnfeIMF/jFQwCmlT30sF5NXMV4E9Py8wpVI9/1TxSE3ZFF4DaQ1olWmhQZl6Zeq68IorrnCBq7Dz+sd425ICxaowUEa3Gpx7mdwKhOqGpJf9Ha/VQ9j5j4Eah2j78q7xFLjTjXAFptRCxDsmqmTKHwwIc4aU1pV3naL189RTT7kMPf86UUZex44dXU8pLztPX0fVxj66btH5Qzc1FHzXjTON79Q7VJMuab3qc1W8eLihkfsRlEow/4FKO5RS2FWe59XMKhtFkWLVausiRBd43iBDgzedGGJnlgsrRdG9iw2tG90V0+dad1qPqnVnRpUDX9wpk0ylZeJddCirQqVSXsBFwYMLLrggVdlPWCmVWH0BFIDyl/HoIk4zE2qAphKVsA7+90fbkFL/9ze9r2b8UWBKFyaxQRakLJlSXzdd7L7wwgvuOWWYKSNUfUE01bnOOcqAVCaQZuny1nNYgwPKANDxTNlO/sCTyqYUsFM2npfRo3Wnhy6MvZsfSHkdo55R6t+jkltlKevGkPpK+Y99CkwpMKoBR9hLl/dHxzoFBbR+NZOZ2jVoW9Q60zlFx0SVuIT5vKIbt1oPXtaJrvfU3029VnUzUiXzmiXYW5+6VtbzKvVB2hRoUlaeMnh0o1zX16KbubohriwVZYQrY09jkLCeP/y0HyoApSQC76a3Aijqi+R97h3v1H5A6zbs2Y37o5tq6jmokkcv28ybkErrWtfc/r6inEtyP4JSOYTSYr0yKJWiKHiiUhbvOU3tW79+fXd3W6mKHmVbaFpMmnX/y8vkURqsZvfxDl6aPlQXLqo95sAVn7YlrSPVZMf28FEWkAYQOtnq7oVOstRt76NAgDLJtP5ie9DorpkGYEcddVT04hj76A6rAsjKCoiXueLtrxpMaIChQDyBqX38xzPd9VeQTyU/CoKqnFRNupURpXWsc4g/HT7sdxh1btDgSvtt7dq1IzfddFOKfVTZoFpvCuZpveqh7U89pJRxhpR0I00ZjV6WrYKfGsAqgKIMC//AVc2mvf2dc/K+daBrPe2P/tJGBfF0w8O7KSTKmNKgTT1pwsjbdnQj7eqrr3aB0Pnz57vKAS9grEBAbCN4ZUzp+kY3QpgYYx//PqhJCJSVp4xQBf169uzpzr3av71rGjWIV0BA2XpMTrCPgsg6p+h6TzclddND511tl/7qDO236tFKL8IDX1tre4yd1Vb7vwLyypSKLdtF7kVQKkH8gy/didUdWf/BSdFzlQ5otjPvxKveH5qeOnbgFsY7FN4BSIPT2AGqmuCp+Z13YawLOg3M1NvHP4V32Gm96ECvcj0v/V3rSAM03UWM7eGjoJUuSnQxQhA0Na1DXYwomKyMRz9dnHTo0CH0pY7xaLCgu2H+qc/jbavar5WyrTI/DdyQkjJUdHNDdLGmXlsFCxaM/Oc//3HlLFqvmqVVGVPYR33eNGDQOlLZowLu2ld1LBRlqCjzTH0sdHNDmT/0IkxNJd4K7Ck4oCwAj87PXmBKGWmx1ythLvWJpQCAtjWVR914443R84XO07oeVOa3Ai8K8OnzsGYqe9uMrpN141ZlT7oJqQwpZUF5wRNRwESBKZ1jvOxRBaboIRWfspJ1HPT3ClWJWe/evd1NcW/fjt2PwzgO8fNfvygQpSCyzhcK4ClTVGWjOtfoOkZJB9omdfNIN3yRMbr5pmAUN4eSD0GpBJs+fbobNHjlUt6dBl1s6CCmDAtlEJx11lkuHTmthqthpPIKNcLTHQmVnHkXcLoD26hRI7fulOqpNGQ1xeOuWCTFXRpl3CnrSQ0XlRqrWfWUpacgqQJTuiiJ7eGDfRcfGoQpK0oZPF7fCgU9dVGsFO7YwBR3cuJT6aO2QX8Ps1hKgdcxUMI6ENsfbVvan5UR5dF5Q43N/RSU8s41+JeC8gqkaH8WnSfUx0fZFTrnah9XwO+ll15yQQOVhiM1nSu0n6okJXZGTAUBlGGmzACvaTdSZ1ioRH7kyJEuAKB2A8rs9mbZ08xcOk5qHVatWjW0gVHvGlh9tXSdovUlGtxrpi49pyxR/3sVMPFmKNR+jPgU/FR2qNaTN+uZd92iwFSfPn3cjY5XXnklxdeF+drGn9HoL6PVuE7rURl5OjYqkKyb5dqHFaxSaWRY9+GsUD9WtXpQz0zWX/IhKJXAaLoyUVQOpQPXlVdemeI9Cjpp59NBTFlUmq6WaVb30cFIByXdgVXJhUrOtA69QZjuiKkJntav6pGZ5WwfrSOtl169ekWeeeYZd9GruzbazrTOlC7rZUzRwyc+BVC0DrVfqmeU1pVXMuAFphREUdAZB84wU08K9Sjzz2Lmv9DVBZ7uNvrL+cLMfw7wl7HomKhMxtiAgPZx9f9QiWnY72jHo3OIMi28AYaykhWw13MKDihAFa/sMazSahCvDABtf+pB428mLTqPqCcIN9T28R/LdGND2VEeZfuoZ4oawnuBKZ2bv/zyS5dtEfaZWpUVpYweP60XZcVrohbd7PCvY10/q9UFPXz2iXcuVVsGleVp/XrVG977tH5181KBFvx7zlVWsipY/FQeqj62yuxu2LChC0KpdE/v1w0O3fhlxtaMU8Nz3VjT7Mzsx8mJoFSCTgDejFHKWNEAVpk8uhPmp4s3b0AR9mlWY2uGdVdbGVAerTsFn5Qt4KVz6gSqmngypFIGpHTBFm9KX/VSURBUpQPKNtMAQoMxlQQRmEq5DhVE0foRZegpKKU7sd6AS4MHBfkUaGGK3wPTnddChQq5/hT+WXy07rStat8Oa++U9NK6Ut83HQN1rPTOGQrQK3NUwRV6f8Sn7AmVW2idKZii4J7Xn1AXwipp8T4PO39ASj2O+vbt6waq2of1ms4do0ePdgFQ9fqJh+1v3/WgpjRX+Wj//v1TBfJUKqXAlDKmwj7LnrfdeRllypCPl6Gim70KqmhioNjAFNI386jWsSZz0HWOlwXurUNlKnNj/F8qG9V5Qzd8vBlZNWtrmTJlopnyCp7oulrnYLK8s04ZkVRwJC+CUgHxH8SVWqwmld5UoAo6KSilA5s3g0js18T7PEy8E+LcuXPdjFG6ePMHpUSzg6iPiqabpgl3agqU6GJOd3b869UfnNIgQxdzXkNVNa1VjxV6+OzbBt9+++1ImzZton22VEqhrDOPV96jjCm9jgPTsU1BPvX9UIaK9mGVsCiopwtj0rT3Bd+9viga+KucQkETb5CvQawCyf7ziKjhufeesN7YOBAN/NUzRX0qVBqEA2eXqQRF/QdV8qN1p7IKDRrU4FyZZRqIaXCL+FQOqowUXf+p7FH98mLPGdp3lWmhUlIFX8IYYPGX7OmmmrIltJ/6Z2z1H9dUhqbAlNangn6IpDmW0HlXGaFaXzqneJSRp2tFBei90ua0vkeY6Sa4ZiBUybJuCumYGJtgoDGeblJqPybbG0gbQakA+A/eOtBrmmQdoJRK7DW5U3aAUt412FXgBalpwKWMFKWz63+Vmmn2GT+dDNSQVoM13bHFPsroUSNuDfRjtzH/SVKDM/Xj8oS9IWjsBYTKatWMVvuyMniuueaa6D6uu2Oatjus5RVZpXIBNZZWyrv6xamslEaW/9KdWB33lFmrEikNJjQznC50FRTQHVmVn+mGhwYYKqWKHTiQoZL2/q1S2zp16rhehf7nkZoG+ipf9rIovExbZQgos1F0N1vl3zoeMoBNTfunspDHjx/v1o96wJ1xxhlu1rPYiUTUhD/svcwUGFHAXf3edBxTubxusu0vMKVAgRrCK0iKf/krB3R+VXBP/aPU80j9ovwlkbrGUSmzzjteCSlSUxa32jUowHz//fdHn/cf9/QeblIC+0dQKkAaLKgW+6KLLnKp7bqA00WJlzGlwJSe1yCDO7UpB1C6IFNfGa9vz4wZM1yZme7kxN4JU6CKgez+7+rorqs/MOUfgOkuZMeOHeO+FlaaBVOZO97MKuolpbuwKpXyryNtowr6+af+RcYQOEk7q0KDAwWNddzT59ruFETR/qwAqQJ6GtgqoOedVwgIpI8CyZoNSbP6ICVdj2h780pUlJWsgKgGuNpfvePfs88+6wIH3vWLggH7m1UzrDTFuc4fmpHQf/2i57UvKzDFADYlBe38ASgFPQ8UmFKGtyZvwb80EYGqCbRfqm+o9uEFCxa413QeyZcvnzvHqCTXo36sOiZyXt4/BZJVatu6desU19Yc94D0IygVEDW2UxmUylC8+nedSOvXr++m6FYdvOhiTqm0YT6QTZ48OcXnWie6C6FSAH8Zj+4e6qJO5QGx5SpIX2DKG2SItjmVnOmkqgboQkDq3/WiO/7K3tHdbdFdRZWVaSYzNa/UBYnuOirQHPbeH1nl3+bY/lJSzy0FpXRjQzc4/DNJvfjiiy77VoNdDSyULYqMURakype9Br/4t92Ajn0KtntZUMoIVamtd6zzspIVjFdwNHYmTfbjlHSTTc16tZ/GToahwJRaOWhGSG9GYUTibk/qdRkvMOXvMYV/KbNWfRu962uV16oVhleFoPOGyvfUukHbpcYlsQhMZe7aGkD6EJQKiGZc0F1YpRT7L9DUCFTlZjoBeHe2w3xnUcElNQ5U/yOPsqJU8qjBggYNfro4VlaAglZeU0tkPmNKgZUGDRq44BQiKe64qleA/0JNmVMKlCozQGWR6oVELzMcDN6gXwEC9a3Q3W0FpnTs8wLI/uarapp8+umnR292IH0UYFamKMe/SDTzSSUp6mPmz/7U4FSlUTpXaHvzqB1B7dq13SAX+6drHM3cqoBKbGmU9m/dbFuxYkXCli+38AemdNxDago0qTTPK02WHTt2uElbdKNNFRr33Xefe17PeTc2vOeQsWtr7de6Ya4MewDpR1DqIPMCSwqmqJGlNw2oTgii1GKdTJs0aeKmEQ177btOkArcefXsnvnz57t+WyptfP3111M1AlXaLAOJzAemlIGm7U8B0jCXjmqwFRsM9oLEuouo7c+/XWpApgGY1hl9pJCdNM20ppT2U7mUeqQ8/fTT7iaGAlMKosQG67VdKoivPj/IGPXlQsTNNqgG3OPGjUtz1jhl1ao/pkqBFLhSho8C9WRUpF5f6vmmEjTdZPNmX9a1js6/uuERG5iiJ2bGAlPaThVIURYz9pk1a5ZbL8qi9bvyyivdTHG6ttYNNa9nma4LNZGQ2mCwH2eO9mWV0oe9DxyQUQSlslla2U2qc1f9toIn/tRipb+rXE+zTR1++OEpMoTCxp9BpuCdShs7dOgQfU4XwUp51yDsjTfeSDUdOjJ/V0elaMr4UelAGCnbRHcI/Q3zVZrnD3Tq7rWyHZU9ABzsgJQGEnpo0PrEE09ES6U0+FdjcwXw1YdQGRUtW7aMzpjpnYP03COPPJLQ3wO5l46BCjipQW9a5Xc6ZipjVGXLKvHTjSPv+oYB7b5rGpUz6hyraxrt0+r75mU4KjClm0NqOK1AIDJHPaa0TrW9IuU1njJqVX7rzaKnmxkKROnGhmZv1TZ57733uo8VaNZ7vW2X2Vozx0s8AJB+eQ3ZZu/evZY377+r9LnnnrObbrrJBg8ebC+//LLlz5/f/f/NN9/YGWecYa+99prNmjXL/vvf/1rhwoXtqaeesrVr19rrr79uYZUnT57ox2XKlLGuXbvaihUr7Morr3TPNW/e3Pr162elSpWyhx9+2F599dXo+4sUKZKQZc7tateubffff7+ddNJJ9vnnn1vjxo0tbH744QcbO3as286+/vpr95z+v+uuu+yKK66w3r1724YNG+zEE0+0Hj162MCBA+2PP/5I9GIjiVWrVs1OPfVUO/30023Hjh1ue2zRooU77uk8sWXLFvviiy+sXr16duedd9qePXvsyy+/dF+rc9CkSZNs5syZ1qpVq0T/KsilFi1aZJs2bbI6deq4c7NuYvqvdaRAgQJ27bXX2urVq2327Nn2xhtvuOd2795t+fLls7DTelu4cKE7b9x99902Y8YMty/r+mbChAn2/PPP26GHHuqu/w477DBr166d7dq1K9GLnSuVLFnSunTp4rZXpLzGGz9+vO3cudPuuOMOd1756aef7O2337aKFSvaEUccYSNHjnTXNW3atLE///zTjVW8fV5jF2RcwYIFE70IQK6TR5GpRC9EslGgaeLEiW4Qq4u6Dz/80G6++WZ34P/xxx+tU6dO9vvvv7uLjxo1ath7773nDv4nnHCC3XvvvXbuueda2Oj394JSXnBv/fr1bj3qhKpgydNPP+1enzNnjg0dOtSKFi3qBl/FihVL8NLnftoWNZgIK12gjR492v7++283WDjqqKPc4H/KlCkuwKwLtcsvv9zq169vL774ol100UXWvXv3RC82kth3333nBgraNxWMV+BJwdNt27bZO++8Y23btnWDBw3+NcioXr169KaIjp3aZjUgATLjpZdecjeGpk2bZmeffXbc99xyyy32zz//2BNPPBENQvlvzsFszJgxbr/9+OOP3UBV1zm6EXLjjTe6gPP06dPd+lqzZo3bxxWQBrKbbvD26dPHPvvsMxs3bpy1b98+xfX3999/b3/99Zcbt2h7VGCZgBSAIBGUyma6O92xY0eXxXPyySfb9u3bXVaULu4GDBjg7lSILkpEd8d0kaKMKt01UwBLg4swBqR0F/HNN990n3fu3NmOP/5427hxoz377LPuTqI/MPXRRx+5gF7VqlUTvfjIxTQI8AZT2k+feeYZlwWl7U2BKe/1ESNG2OLFi90AQvv0JZdcYi+88ALZADiovv32W7vhhhvcQF9ZUgoy6bkHH3zQ+vbtaw0aNEgV0NfnbJfIKl2jNGrUyM4880y3vXnXJd72pnOzskuVwaxtEeYyxnSTUfvhkUce6bJSdM1yzz33uKCUMqS8wb6XmTx//nyXqQwcbCtXrnSZjQo6DRo0yE455ZS4gWQCywASIgOlfoihmfNimxur30e9evVS1ROr30fp0qVT9exRT5DOnTu7ZudhnrlLzaI1y4/6pzRu3DiSL1++aJNeNbFUbxQ9r+aBQHbx+iaof8oVV1zhGpmrv4JmgIzt76GeFa+99prrC+f19wGC6AmibU4PzU7qF8YZWhEcXc9oGvmOHTu6yTA8mqBFvWeaNWtGzxlff60aNWpETjjhhEjZsmVdD1Fd12gGPZ1TRo0alWq/ViN5fy9DIKgJbvSYN29eohcHAKIIhWeSsilUHqE7X34VKlRwJXpLlixxn3uJaEqJVer25s2bU7xfJWhNmza1uXPnWsOGDS2MdMdVpSqjRo1yZSnKmFI/LmVLqTyvRIkS1q1bN7v00kvtt99+c2VVQHbQHX/1djvnnHNcZt4DDzzgSmjlqquucj3gvP1Y2+EFF1zgsvlUxgcEQdlRKi3Vnevhw4fbvHnzoq9xNxsHk0p8tO2plE/HvtatW7s+ZSpfVqmPjp3K+lFGaZipn5uu41TirXUyefJkV2b72GOPuZ496tuoaxr9r2wqXfMoK3fr1q1Wvnz5RC8+QnY+eeSRR1w2rbJwvV6EAJBolO9lgr9cQpSe3bJlS1dOpr4ACqCUK1fO9ZFSeYUokKI0eAVezjrrrP1+vzDRCVG9tJTqrsbS5513nntefVRU0qgggXr6dOjQwfXnUup76dKlE73YSALeoU9ltSqJUu8UjyYc0PaoPlsqH9XAwivlC/P+isT2BOnfv7/rR6g+e8cee2yiFwkhoab6us7RcVI9j4477jjr1auXOx6GvfeMgkwqc9SkBOo36NF1jXq7qYePJmLRa7rRoZYDWl+aPEPl4PpaIGi64aY2Bffddx83NwDkCOG9ksgCBUx0IaYLDQVK1OxT/RY0kK1SpYrrs6A7ZApKafYuzXChA7/er5n3YoVhgBtbo+4N8JVZpsbvai6tizTvvQoGKCig96hHly7i1McHyIz99UzQNqb+Kbpr7c3iqKyApUuXusCoPn7llVdcj6mw7K/ImXe4dR7RQIJMPQRJWdzKroil83iYA1LeOlBvUDUtV6/LZs2auR6EmnWvSZMmbkY4ZdRrYgLNTqjrHGXd1q1b193IBBJB1zO66Sv0kAKQE4T7aiITNDhVSZlK9DR9rwataoCstPYLL7zQBaaU1VO4cGGbOnWquzOmAYQuStTQUgNgf3PlsNAJb/ny5S7r6ZprronOMKOglIJPCvLpeV3cqUG8NxWtGsMXKlTIjj766ET/Ckii7c8/mYAyTpQl9cEHH7hZprS9ie5gqyRDgwftz0CiMZBAosTLEA3bdUw8NWvWtP/9739uhkyVfqscT5NmKDNK2VKLFi2yZcuWWY8ePax48eIuUOXPqAISjfMIgJyA8r0MePLJJ132kwJNWm26c6iMKKWx//LLL64s75BDDnEp2ZUqVXJfo+cVXFHwRRd0YU11V3a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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "df_imdb = pd.read_sql(\"SELECT * FROM IMDB_movies\", engine)\n",
+    "\n",
+    "# Pegar só o primeiro gênero de cada filme\n",
+    "df_imdb['GenrePrimary'] = df_imdb['Genre'].str.split(',').str[0]\n",
+    "\n",
+    "# Rating médio por gênero\n",
+    "df_genre = df_imdb.groupby('GenrePrimary')['Rating'].mean().round(2).sort_values(ascending=False)\n",
+    "\n",
+    "# Gráfico\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "bars = plt.bar(df_genre.index, df_genre.values, color='steelblue')\n",
+    "plt.title('Rating Médio por Gênero (IMDB)', fontsize=14)\n",
+    "plt.xlabel('Gênero')\n",
+    "plt.ylabel('Rating Médio')\n",
+    "plt.xticks(rotation=45, ha='right')\n",
+    "\n",
+    "# Mostrar valor em cima de cada barra\n",
+    "for bar, val in zip(bars, df_genre.values):\n",
+    "    plt.text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.05,\n",
+    "             str(val), ha='center', fontsize=9)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.savefig('grafico_imdb.png', dpi=150)\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/isaque_looqbox_challenge.pdf b/isaque_looqbox_challenge.pdf
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