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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 366,
+   "id": "496a680e",
+   "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>Tables_in_looqbox-challenge</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>IMDB_movies</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>data_product</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>data_product_sales</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>data_store_cad</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>data_store_sales</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  Tables_in_looqbox-challenge\n",
+       "0                 IMDB_movies\n",
+       "1                data_product\n",
+       "2          data_product_sales\n",
+       "3              data_store_cad\n",
+       "4            data_store_sales"
+      ]
+     },
+     "execution_count": 366,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import sqlalchemy\n",
+    "import pymysql\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sqlalchemy import create_engine\n",
+    "\n",
+    "host = \"35.199.115.174\"\n",
+    "user = \"looqbox-challenge\"\n",
+    "password = \"looq-challenge\"\n",
+    "database = \"looqbox-challenge\"\n",
+    "\n",
+    "engine = create_engine(\n",
+    "    f\"mysql+pymysql://{user}:{password}@{host}:3306/{database}\"\n",
+    ")\n",
+    "\n",
+    "df = pd.read_sql(\n",
+    "    \"SHOW TABLES\",\n",
+    "    engine\n",
+    ")\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "f047cecd",
+   "metadata": {},
+   "source": [
+    "### SQL TEST\n",
+    "#### After accessing our database, create queries using the schema looqbox_challenge to answer the following questions:"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63e89af7",
+   "metadata": {},
+   "source": [
+    "\n",
+    "#### 1. What are the 10 most expensive products in the company?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 367,
+   "id": "0cecd56d",
+   "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>PRODUCT_NAME</th>\n",
+       "      <th>PRODUCT_VAL</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Whisky Escoces THE MACALLAN Ruby Garrafa 700ml...</td>\n",
+       "      <td>741.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Whisky Escoces JOHNNIE WALKER Blue Label Garra...</td>\n",
+       "      <td>735.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Cafeteira Expresso 3 CORACOES Tres Modo Vermelho</td>\n",
+       "      <td>499.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Vinho Portugues Tinto Vintage QUINTA DO CRASTO...</td>\n",
+       "      <td>445.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Escova Dental Eletrica ORAL B D34 Professional...</td>\n",
+       "      <td>399.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Champagne Rose VEUVE CLICQUOT PONSARDIM Garraf...</td>\n",
+       "      <td>366.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Champagne Frances Brut Imperial MOET Rose Garr...</td>\n",
+       "      <td>359.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Conjunto de Panelas Allegra em Inox TRAMONTINA...</td>\n",
+       "      <td>359.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Whisky Escoces CHIVAS REGAL 18 Anos Garrafa 750ml</td>\n",
+       "      <td>329.90</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Champagne Frances Brut Imperial MOET &amp; CHANDON...</td>\n",
+       "      <td>315.90</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                        PRODUCT_NAME  PRODUCT_VAL\n",
+       "0  Whisky Escoces THE MACALLAN Ruby Garrafa 700ml...       741.99\n",
+       "1  Whisky Escoces JOHNNIE WALKER Blue Label Garra...       735.90\n",
+       "2   Cafeteira Expresso 3 CORACOES Tres Modo Vermelho       499.00\n",
+       "3  Vinho Portugues Tinto Vintage QUINTA DO CRASTO...       445.90\n",
+       "4  Escova Dental Eletrica ORAL B D34 Professional...       399.90\n",
+       "5  Champagne Rose VEUVE CLICQUOT PONSARDIM Garraf...       366.90\n",
+       "6  Champagne Frances Brut Imperial MOET Rose Garr...       359.90\n",
+       "7  Conjunto de Panelas Allegra em Inox TRAMONTINA...       359.00\n",
+       "8  Whisky Escoces CHIVAS REGAL 18 Anos Garrafa 750ml       329.90\n",
+       "9  Champagne Frances Brut Imperial MOET & CHANDON...       315.90"
+      ]
+     },
+     "execution_count": 367,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT\n",
+    "      PRODUCT_NAME,\n",
+    "      PRODUCT_VAL\n",
+    "FROM data_product\n",
+    "ORDER BY PRODUCT_VAL desc\n",
+    "LIMIT 10\n",
+    "\"\"\"\n",
+    "\n",
+    "df = pd.read_sql(query, engine)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "145daed0",
+   "metadata": {},
+   "source": [
+    "#### 2. What sections do the 'BEBIDAS' and 'PADARIA' departments have?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 368,
+   "id": "c3161b74",
+   "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>DEP_NAME</th>\n",
+       "      <th>SECTION_NAME</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>BEBIDAS</td>\n",
+       "      <td>BEBIDAS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>BEBIDAS</td>\n",
+       "      <td>CERVEJAS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>BEBIDAS</td>\n",
+       "      <td>REFRESCOS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>BEBIDAS</td>\n",
+       "      <td>VINHOS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>PADARIA</td>\n",
+       "      <td>DOCES-E-SOBREMESAS</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>PADARIA</td>\n",
+       "      <td>GESTANTE</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>PADARIA</td>\n",
+       "      <td>PADARIA</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>PADARIA</td>\n",
+       "      <td>QUEIJOS-E-FRIOS</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  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"
+      ]
+     },
+     "execution_count": 368,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query = \"\"\"\n",
+    "SELECT DISTINCT\n",
+    "      DEP_NAME,\n",
+    "      SECTION_NAME\n",
+    "FROM data_product\n",
+    "WHERE DEP_NAME IN ('BEBIDAS', 'PADARIA')\n",
+    "ORDER BY DEP_NAME\n",
+    "\"\"\"\n",
+    "\n",
+    "df = pd.read_sql(query, engine)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "89575ab1",
+   "metadata": {},
+   "source": [
+    "#### 3. What was the total sale of products (in $) of each Business Area in the first quarter of 2019?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 369,
+   "id": "57d50623",
+   "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>BUSINESS_NAME</th>\n",
+       "      <th>SALES_VALUE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Atacado</td>\n",
+       "      <td>106,961,594.56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Farma</td>\n",
+       "      <td>108,311,473.65</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Posto</td>\n",
+       "      <td>42,425,463.16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Proximidade</td>\n",
+       "      <td>106,678,792.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Varejo</td>\n",
+       "      <td>107,845,926.10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  BUSINESS_NAME     SALES_VALUE\n",
+       "0       Atacado  106,961,594.56\n",
+       "1         Farma  108,311,473.65\n",
+       "2         Posto   42,425,463.16\n",
+       "3   Proximidade  106,678,792.46\n",
+       "4        Varejo  107,845,926.10"
+      ]
+     },
+     "execution_count": 369,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "query = \"\"\"\n",
+    "SELECT\n",
+    "  BUSINESS_NAME,\n",
+    "  SUM(SALES_VALUE) AS SALES_VALUE\n",
+    "FROM data_store_cad dsc\n",
+    "LEFT JOIN data_store_sales dss ON dsc.STORE_CODE = dss.STORE_CODE\n",
+    "WHERE DATE BETWEEN '2019-01-01' AND '2019-04-30'\n",
+    "GROUP BY BUSINESS_NAME\n",
+    "ORDER BY BUSINESS_NAME\n",
+    "\"\"\"\n",
+    "\n",
+    "df = pd.read_sql(query, engine)\n",
+    "df['SALES_VALUE'] = df['SALES_VALUE'].map('{:,.2f}'.format)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "63d4ab66",
+   "metadata": {},
+   "source": [
+    "### CASES"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c13b1ee",
+   "metadata": {},
+   "source": [
+    "#### 1) The Dev Team was tired of developing the same old queries just varying the filters accordingly to their boss demands. As a new member of the crew, your mission now is to create a dynamic function in Python, on the most flexible of ways, to produce queries and retrieve a dataframe based on three parameters:\n",
+    "\n",
+    "- `product_code`: integer\n",
+    "- `store_code`: integer\n",
+    "- `date`: list of ISO-like strings\n",
+    "\n",
+    "#### Date e.g.\n",
+    "\n",
+    "['2019-01-01', '2019-01-31']\n",
+    "It should look like this my_data = retrieve_data(product_code, store_code, date)\n",
+    "\n",
+    "Make your team proud!\n",
+    "\n",
+    "Extra instructions:\n",
+    "\n",
+    "Retrieve all columns from table data_product_sales;\n",
+    "Imagine people from other teams will also utilize this function!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 370,
+   "id": "80927822",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from sqlalchemy import text\n",
+    "import pandas as pd\n",
+    "\n",
+    "def retrieve_data(\n",
+    "    product_code=None,\n",
+    "    store_code=None,\n",
+    "    date=None\n",
+    "):\n",
+    "\n",
+    "    conditions = []\n",
+    "    params = {}\n",
+    "\n",
+    "    if product_code is not None:\n",
+    "        conditions.append(\n",
+    "            \"product_code = :product_code\"\n",
+    "        )\n",
+    "        params[\"product_code\"] = product_code\n",
+    "\n",
+    "    if store_code is not None:\n",
+    "        conditions.append(\n",
+    "            \"store_code = :store_code\"\n",
+    "        )\n",
+    "        params[\"store_code\"] = store_code\n",
+    "\n",
+    "    if date is not None:\n",
+    "        conditions.append(\n",
+    "            \"date BETWEEN :start_date AND :end_date\"\n",
+    "        )\n",
+    "\n",
+    "        params[\"start_date\"] = date[0]\n",
+    "        params[\"end_date\"] = date[1]\n",
+    "\n",
+    "    where_clause = \"\"\n",
+    "\n",
+    "    if conditions:\n",
+    "        where_clause = \"WHERE \" + \" AND \".join(conditions)\n",
+    "\n",
+    "    query = text(f\"\"\"\n",
+    "        SELECT *\n",
+    "        FROM data_product_sales\n",
+    "        {where_clause}\n",
+    "    \"\"\")\n",
+    "\n",
+    "    return pd.read_sql(\n",
+    "        query,\n",
+    "        engine,\n",
+    "        params=params\n",
+    "    )"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8b572dec",
+   "metadata": {},
+   "source": [
+    "#### Documentação da função `retrieve_data`\n",
+    "\n",
+    "A função `retrieve_data` permite consultar dados de vendas de maneira rápida e flexível. Ao informar um produto, uma loja e/ou um período, a função retorna apenas os registros que atendem aos critérios selecionados.\n",
+    "\n",
+    "#### Como utilizar\n",
+    "\n",
+    "Execute o comando da célula abaixo, substituindo os valores pelos filtros desejados.\n",
+    "\n",
+    "```python\n",
+    "df = retrieve_data(\n",
+    "    product_code=18,\n",
+    "    store_code=18,\n",
+    "    date=['2019-01-01', '2019-01-05']\n",
+    ")\n",
+    "```\n",
+    "\n",
+    "#### Parâmetros\n",
+    "\n",
+    "| Parâmetro | Descrição |\n",
+    "|------------|------------|\n",
+    "| `product_code` | Código do produto a ser filtrado (número inteiro) |\n",
+    "| `store_code` | Código da loja a ser filtrada (número inteiro) |\n",
+    "| `date` | Período da consulta no formato `['YYYY-MM-DD', 'YYYY-MM-DD']` |\n",
+    "\n",
+    "#### Observações\n",
+    "\n",
+    "- Todos os parâmetros são opcionais.\n",
+    "- Caso nenhum filtro seja informado, a função retornará todos os registros da tabela.\n",
+    "- As datas devem ser informadas no formato `YYYY-MM-DD`.\n",
+    "\n",
+    "#### Retorno da consulta\n",
+    "\n",
+    "A função retorna uma tabela contendo as seguintes colunas:\n",
+    "\n",
+    "| Coluna | Descrição |\n",
+    "|---------|------------|\n",
+    "| `STORE_CODE` | Código da loja |\n",
+    "| `PRODUCT_CODE` | Código do produto |\n",
+    "| `DATE` | Data da venda |\n",
+    "| `SALES_VALUE` | Valor total das vendas |\n",
+    "| `SALES_QTY` | Quantidade vendida |"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 371,
+   "id": "a098a644",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  STORE_CODE  PRODUCT_CODE        DATE  SALES_VALUE  SALES_QTY\n",
+      "0         18            18  2019-01-01        708.5       65.0\n",
+      "1         18            18  2019-01-02       1297.1      119.0\n",
+      "2         18            18  2019-01-03       1144.5      105.0\n",
+      "3         18            18  2019-01-04       1090.0      100.0\n",
+      "4         18            18  2019-01-05        893.8       82.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "my_data = retrieve_data(\n",
+    "  product_code=18,\n",
+    "  store_code=18,\n",
+    "  date=['2019-01-01', '2019-01-05']\n",
+    ")\n",
+    "\n",
+    "print(my_data)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c1b229b",
+   "metadata": {},
+   "source": [
+    "2) A brand new client sent you two ready-to-go queries. Those are listed below:\n",
+    "\n",
+    "Query 1:\n",
+    "```sql\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",
+    "Query 2:\n",
+    "```sql\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",
+    "In addition, he gave you this set of instructions:\n",
+    "\n",
+    "Use the queries as they are (do not modify them or create a new one);\n",
+    "\n",
+    "Please filter the period between this given range:\n",
+    "\n",
+    "```python\n",
+    "['2019-10-01', '2019-12-31']\n",
+    "```\n",
+    "We are in need of this visualization (click here to see it)! Please, create it with Python"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 372,
+   "id": "1690efce",
+   "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>STORE_CODE</th>\n",
+       "      <th>STORE_NAME</th>\n",
+       "      <th>START_DATE</th>\n",
+       "      <th>END_DATE</th>\n",
+       "      <th>BUSINESS_NAME</th>\n",
+       "      <th>BUSINESS_CODE</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Sao Paulo</td>\n",
+       "      <td>2006-10-01</td>\n",
+       "      <td></td>\n",
+       "      <td>Varejo</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Chicago</td>\n",
+       "      <td>2007-10-01</td>\n",
+       "      <td></td>\n",
+       "      <td>Varejo</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Roma</td>\n",
+       "      <td>2008-10-01</td>\n",
+       "      <td></td>\n",
+       "      <td>Varejo</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Tokio</td>\n",
+       "      <td>2009-10-01</td>\n",
+       "      <td></td>\n",
+       "      <td>Varejo</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Paris</td>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td></td>\n",
+       "      <td>Proximidade</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   STORE_CODE STORE_NAME  START_DATE END_DATE BUSINESS_NAME  BUSINESS_CODE\n",
+       "0           1  Sao Paulo  2006-10-01                 Varejo              1\n",
+       "1           2    Chicago  2007-10-01                 Varejo              1\n",
+       "2           3       Roma  2008-10-01                 Varejo              1\n",
+       "3           4      Tokio  2009-10-01                 Varejo              1\n",
+       "4           5      Paris  2019-01-01            Proximidade              2"
+      ]
+     },
+     "execution_count": 372,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_store = \"\"\"\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",
+    "df_store = pd.read_sql(query_store, engine)\n",
+    "df_store.head()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 373,
+   "id": "86496cc4",
+   "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>STORE_CODE</th>\n",
+       "      <th>DATE</th>\n",
+       "      <th>SALES_VALUE</th>\n",
+       "      <th>SALES_QTY</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>196623.22</td>\n",
+       "      <td>12838</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>10</td>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>126795.44</td>\n",
+       "      <td>4933</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>11</td>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>223937.00</td>\n",
+       "      <td>7724</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>12</td>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>200251.80</td>\n",
+       "      <td>7043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>13</td>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>196623.22</td>\n",
+       "      <td>12838</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   STORE_CODE        DATE  SALES_VALUE  SALES_QTY\n",
+       "0           1  2019-01-01    196623.22      12838\n",
+       "1          10  2019-01-01    126795.44       4933\n",
+       "2          11  2019-01-01    223937.00       7724\n",
+       "3          12  2019-01-01    200251.80       7043\n",
+       "4          13  2019-01-01    196623.22      12838"
+      ]
+     },
+     "execution_count": 373,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_sales = \"\"\"\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_sales = pd.read_sql(query_sales, engine)\n",
+    "df_sales.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 374,
+   "id": "ce49a8df",
+   "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>Loja</th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>TM</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Bahia</td>\n",
+       "      <td>Atacado</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Bangkok</td>\n",
+       "      <td>Posto</td>\n",
+       "      <td>13.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Belem</td>\n",
+       "      <td>Proximidade</td>\n",
+       "      <td>15.37</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Berlin</td>\n",
+       "      <td>Proximidade</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Buenos Aires</td>\n",
+       "      <td>Atacado</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Chicago</td>\n",
+       "      <td>Varejo</td>\n",
+       "      <td>15.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Dubai</td>\n",
+       "      <td>Atacado</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>Hong Kong</td>\n",
+       "      <td>Farma</td>\n",
+       "      <td>26.35</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>London</td>\n",
+       "      <td>Farma</td>\n",
+       "      <td>28.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Madri</td>\n",
+       "      <td>Farma</td>\n",
+       "      <td>29.03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>Miami</td>\n",
+       "      <td>Posto</td>\n",
+       "      <td>13.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>New York</td>\n",
+       "      <td>Proximidade</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>Paris</td>\n",
+       "      <td>Proximidade</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>Rio de Janeiro</td>\n",
+       "      <td>Farma</td>\n",
+       "      <td>29.59</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>Roma</td>\n",
+       "      <td>Varejo</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Salvador</td>\n",
+       "      <td>Atacado</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>Sao Paulo</td>\n",
+       "      <td>Varejo</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Sidney</td>\n",
+       "      <td>Posto</td>\n",
+       "      <td>13.67</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Tokio</td>\n",
+       "      <td>Varejo</td>\n",
+       "      <td>15.39</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>Vancouver</td>\n",
+       "      <td>Posto</td>\n",
+       "      <td>13.67</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              Loja    Categoria     TM\n",
+       "0            Bahia      Atacado  15.39\n",
+       "1          Bangkok        Posto  13.67\n",
+       "2            Belem  Proximidade  15.37\n",
+       "3           Berlin  Proximidade  15.39\n",
+       "4     Buenos Aires      Atacado  15.39\n",
+       "5          Chicago       Varejo  15.53\n",
+       "6            Dubai      Atacado  15.39\n",
+       "7        Hong Kong        Farma  26.35\n",
+       "8           London        Farma  28.99\n",
+       "9            Madri        Farma  29.03\n",
+       "10           Miami        Posto  13.67\n",
+       "11        New York  Proximidade  15.39\n",
+       "12           Paris  Proximidade  15.39\n",
+       "13  Rio de Janeiro        Farma  29.59\n",
+       "14            Roma       Varejo  15.39\n",
+       "15        Salvador      Atacado  15.39\n",
+       "16       Sao Paulo       Varejo  15.39\n",
+       "17          Sidney        Posto  13.67\n",
+       "18           Tokio       Varejo  15.39\n",
+       "19       Vancouver        Posto  13.67"
+      ]
+     },
+     "execution_count": 374,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_sales['DATE'] = pd.to_datetime(df_sales['DATE'])\n",
+    "\n",
+    "df_sales = df_sales[(df_sales['DATE'] >= '2019-10-01') & (df_sales['DATE'] <= '2019-12-31')]\n",
+    "\n",
+    "df = pd.merge(df_store, df_sales, on='STORE_CODE', how='inner')\n",
+    "\n",
+    "tm = (df.groupby(['STORE_NAME', 'BUSINESS_NAME'], as_index=False).agg({'SALES_VALUE': 'sum', 'SALES_QTY': 'sum'}))\n",
+    "\n",
+    "tm['TM'] = (tm['SALES_VALUE'] / tm['SALES_QTY']).round(2)\n",
+    "\n",
+    "tm = tm.rename(columns={'STORE_NAME': 'Loja', 'BUSINESS_NAME': 'Categoria'})\n",
+    "\n",
+    "tm_final = tm[['Loja', 'Categoria', 'TM']].sort_values('Loja')\n",
+    "tm_final\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "452691d9",
+   "metadata": {},
+   "source": [
+    "#### 3) Building your own visualization\n",
+    "Create at least one chart using the table IMDB_movies. The code must be in Python, and you are free to use any libraries, data in the table and graphic format. Explain why you chose the visualization (or visualizations) you are submitting."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 375,
+   "id": "f977e246",
+   "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</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Actors</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Votes</th>\n",
+       "      <th>RevenueMillions</th>\n",
+       "      <th>Metascore</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Guardians of the Galaxy</td>\n",
+       "      <td>Action,Adventure,Sci-Fi</td>\n",
+       "      <td>James Gunn</td>\n",
+       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>757074</td>\n",
+       "      <td>333.0</td>\n",
+       "      <td>76.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Prometheus</td>\n",
+       "      <td>Adventure,Mystery,Sci-Fi</td>\n",
+       "      <td>Ridley Scott</td>\n",
+       "      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>124</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>485820</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Split</td>\n",
+       "      <td>Horror,Thriller</td>\n",
+       "      <td>M. Night Shyamalan</td>\n",
+       "      <td>James McAvoy, Anya Taylor-Joy, Haley Lu Richar...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>117</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>157606</td>\n",
+       "      <td>138.0</td>\n",
+       "      <td>62.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Sing</td>\n",
+       "      <td>Animation,Comedy,Family</td>\n",
+       "      <td>Christophe Lourdelet</td>\n",
+       "      <td>Matthew McConaughey,Reese Witherspoon, Seth Ma...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>108</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>60545</td>\n",
+       "      <td>270.0</td>\n",
+       "      <td>59.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Suicide Squad</td>\n",
+       "      <td>Action,Adventure,Fantasy</td>\n",
+       "      <td>David Ayer</td>\n",
+       "      <td>Will Smith, Jared Leto, Margot Robbie, Viola D...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>123</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>393727</td>\n",
+       "      <td>325.0</td>\n",
+       "      <td>40.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   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       76.0  \n",
+       "1  2012      124     7.0  485820            126.0       65.0  \n",
+       "2  2016      117     7.0  157606            138.0       62.0  \n",
+       "3  2016      108     7.0   60545            270.0       59.0  \n",
+       "4  2016      123     6.0  393727            325.0       40.0  "
+      ]
+     },
+     "execution_count": 375,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "query_movies = \"\"\"\n",
+    "SELECT\n",
+    "      *\n",
+    "FROM IMDB_movies\n",
+    "\"\"\"\n",
+    "\n",
+    "df = pd.read_sql(query_movies, engine)\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 376,
+   "id": "e9c55e2b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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SWwAA4Hra7ku7IlTX8EuP67jGIbaR3AIAANfTPrba7ktVTnD9j3Wcfrexj+QWAAB4gvaxnTK8p6SlViw90Md6nD637sAmDgAAwDM0gR3UNY0dylyM5BYAAHiKJrIZnZpF+zQQIZQlAAAAwDVIbgEAAOAaJLcAAABwDZJbAAAAuAbJLQAAAFyD5BYAAACuQXILAAAA1yC5BQAAgGuQ3AIAAMA1SG4BAADgGiS3AAAAcA2SWwAAALgGyS0AAABcg+QWAAAArkFyCwAAANcguQUAAIBrkNwCAADANUhuAQAA4BoktwAAAHANklsAAAC4BsktAAAAXIPkFgAAAK5BcgsAAADXILkFAACAa5DcAgAAwDVIbgEAAOAaJLcAAABwDZJbAAAAuAbJLQAAAFyD5BYAAACuQXILAAAA1yC5BQAAgGuQ3AIAAMA1SG4BAADgGiS3AAAAcA2SWwAAALgGyS0AAABcg+QWAAAArkFyCwAAANcguQUAAIBrkNwCAADANUhuAQAA4BoktwAAAHANklsAAAC4BsktAAAAXIPkFgAAAK5BcgsAAADXILkFAACAa5DcAgAAwDVIbgEAAOAaUU1uS0pK5P7775f09HSpW7eudOrUSSZOnCg+n68sRj9+4IEHpFWrViZm4MCB8vPPP1d4nj179sjVV18tjRo1ksaNG8uoUaNk//79UfiOAAAA4Nnk9rHHHpMpU6bIs88+Kz/88IN5/Pjjj8vf//73shh9/Le//U2mTp0q33zzjdSvX18GDx4shYWFZTGa2H7//fcyd+5cmTVrlixYsEBuuOGGKH1XAAAAiJY4X/lp0lp2/vnnS8uWLeXll18uO3bJJZeYGdoZM2aYWdvWrVvLnXfeKXfddZcZz8vLM58zffp0ueKKK0xS3LVrV1m2bJn07t3bxMyePVvOPfdc2bZtm/n8muTn50tqaqp5bp39BQAAgLNYzdeiOnN78skny7x58+Snn34yj1etWiULFy6Uc845xzzeuHGjZGdnm1IEP/2m+vXrJ0uWLDGP9b2WIvgTW6Xx8fHxZqY3kKKiInOByr8BAAAg9tWJ5hcfO3asSSw7d+4sCQkJpgb34YcfNmUGShNbpTO15elj/5i+b9GiRYXxOnXqSNOmTctiKps8ebJMmDAhQt8VAAAAoiWqM7fvvPOOvP766/LGG29IZmamvPLKK/KXv/zFvI+kcePGmSlt/9vWrVsj+vUAAADggZnbu+++28zeau2s6t69u2zevNnMrI4YMULS0tLM8Z07d5puCX76uEePHuZjjcnJyanwvIcPHzYdFPyfX1lycrJ5AwAAgLtEdea2oKDA1MaWp+UJpaWl5mNtEaYJqtbl+mkZg9bSZmRkmMf6Pjc3V5YvX14WM3/+fPMcWpsLAAAA74jqzO0FF1xgamyPPvpoOf7442XFihXy5JNPynXXXWfG4+Li5LbbbpNJkybJsccea5Jd7YurHRCGDRtmYrp06SJDhgyR0aNHm3ZhxcXFcvPNN5vZYCudEgAAAOAeUU1utZ+tJqt/+MMfTGmBJqM33nij2bTB75577pEDBw6YvrU6Q3vKKaeYVl8pKSllMVq3qwntWWedZWaCtZ2Y9sYFAACAt0S1z61T0OcWAADA2WKizy0AAABgJ5JbAAAAuAbJLQAAAFyD5BYAAACuQXILAAAA1yC5BQAAgGtEtc8tAABAbSsp9cnSjXskZ1+htGiYIn3Tm0pCfFy0Tws2IbkFAACeMXtNlkyYuVay8grLjrVKTZHxF3SVId1aRfXcYA/KEgAAgGcS2zEzMisktio7r9Ac13HEPpJbAADgiVIEnbENtC2r/5iOaxxiG8ktAABwPa2xrTxjW56mtDqucYhtJLcAAMD1dPGYnXFwLpJbAADgetoVwc44OBfJLQAAcD1t96VdEapr+KXHdVzjENtIbgEAgOtpH1tt96UqJ7j+xzpOv9vYR3ILAAA8QfvYThneU9JSK5Ye6GM9Tp9bd2ATBwAA4BmawA7qmsYOZS5GcgsAADxFE9mMTs2ifRqIEMoSAAAA4BoktwAAAHANklsAAAC4BsktAAAAXIPkFgAAAK5BcgsAAADXILkFAACAa5DcAgAAwDVIbgEAAOAaJLcAAABwDZJbAAAAuAbJLQAAAFyD5BYAAACuQXILAAAA1yC5BQAAgGuQ3AIAAMA1SG4BAADgGiS3AAAAcA2SWwAAALgGyS0AAABcg+QWAAAArkFyCwAAANcguQUAAIBrkNwCAADANUhuAQAA4BoktwAAAHANklsAAAC4BsktAAAAXIPkFgAAAK5BcgsAAADXILkFAACAa5DcAgAAwDVIbgEAAOAaJLcAAABwDZJbAAAAuAbJLQAAAFyjTrRPAAAAWFdS6pOlG/dIzr5CadEwRfqmN5WE+LhonxbgGCS3AADEiNlrsmTCzLWSlVdYdqxVaoqMv6CrDOnWKqrnBjgFZQkAAMRIYjtmRmaFxFZl5xWa4zoOgOQWAICYKEXQGVtfgDH/MR3XOMDrSG4BAHA4rbGtPGNbnqa0Oq5xgNeR3AIA4HC6eMzOOMDNSG4BAHA47YpgZxzgZiS3AAA4nLb70q4I1TX80uM6rnGA15HcAgDgcNrHVtt9qcoJrv+xjtPvFiC5BQAgJmgf2ynDe0paasXSA32sx+lzC/wXmzgAABAjNIEd1DWNHcqAIEhuAQCIIZrIZnRqFu3TAByLsgQAAAC4BjO3AADAU3QnN0o73IvkFgAAeMbsNVlmq+LyO75pGzXtNsGiPHegLAEAAHgmsR0zI7PKVsbZeYXmuI4j9pHcAgAAT5Qi6IytL8CY/5iOaxxiG8ktAABwPa2xrTxjW56mtDqucYhtJLcAAMD1dPGYnXFwLpJbAADgetoVwc44OBfJLQAAcD1t96VdEapr+KXHdVzjENtIbgEAgOtpH1tt96UqJ7j+xzpOv9vYR3ILAAA8QfvYThneU9JSK5Ye6GM9Tp9bd2ATBwAA4BmawA7qmsYOZS4WcnK7ceNG+eqrr2Tz5s1SUFAgRx11lJx00kmSkZEhKSkUYQMAAGfTRDajU7NonwaiXZbw+uuvS9++faVTp05y7733ygcffGCS3JdeekmGDBkiLVu2lD/84Q8m6Q3F9u3bZfjw4dKsWTOpW7eudO/eXb799tuycZ/PJw888IC0atXKjA8cOFB+/vnnCs+xZ88eufrqq6VRo0bSuHFjGTVqlOzfvz+k8wAAAIBHkludmf3b3/4mI0eONMlrVlaWLF++XBYuXChr166V/Px8+fDDD6W0tFR69+4t7777rqUvvnfvXhkwYIAkJibKp59+ap7rr3/9qzRp0qQs5vHHHzdfe+rUqfLNN99I/fr1ZfDgwVJY+L8+dJrYfv/99zJ37lyZNWuWLFiwQG644YYjuR4AAACIYXE+nRqtwZw5c0xCacXu3btl06ZN0qtXrxpjx44dK4sWLTIzwIHoqbVu3VruvPNOueuuu8yxvLw8M0s8ffp0ueKKK+SHH36Qrl27yrJly0xirWbPni3nnnuubNu2zXx+TTQ5T01NNc+ts78AAABwFqv5mqWZW6uJrdLyAiuJrfroo49MQnrppZdKixYtzAzxiy++WKG+Nzs725Qi+Ok31a9fP1myZIl5rO+1FMGf2CqNj4+PNzO9gRQVFZkLVP4NAAAAsS/sVmBaiqBJaElJScifu2HDBpkyZYoce+yxZnZ4zJgxcsstt8grr7xixjWxVTpTW54+9o/pe02My6tTp440bdq0LKayyZMnmyTZ/9auXbuQzx0AAAAxntz++9//lnfeeadCctqxY0ezyExv/5dfCGY1Me7Zs6c88sgjZtZW62RHjx5t6msjady4cWZK2/+2devWiH49AAAAODC5feKJJ6S4uLjs8f333y9dunSR7777ToYOHSp33HFHSF9cOyBovWx5+nxbtmwxH6elpZn3O3furBCjj/1j+j4nJ6fC+OHDh00HBX9MZcnJyaZWo/wbAAAAPJLcarKpXRJ++eUX08nA/1gXbl1//fUmOdRZ11WrVpkxf3JaE+2UsG7dugrHfvrpJ2nfvr35OD093SSo8+bNKxvX+litpdW+ukrf5+bmmu4NfvPnzzezwlqbCwAAAO+wtInDtGnTzHvdtEFbdmlnAu1EoAuztAXXmjVrTDJ58OBB08VAaW/amtx+++1y8sknm7KEyy67TJYuXSovvPCCeVNxcXFy2223yaRJk0xdria7OlusJRDDhg0rm+nVPrv+cgadWb755ptNJwUrnRIAAADgsVZgftqR4Pzzz5cHH3xQ7r77bpPYfvLJJ2ZMF5WddtppIdeval9arYHVjRk0edXSBk1U/fT0xo8fbxJenaE95ZRT5B//+Iccd9xxZTFagqAJ7cyZM02XhEsuucT0xm3QoIGlc6AVGAAAgLNZzddCSm51c4arrrpKEhISTBKpmyZoaYF6+umnTbnAm2++KbGG5BYAAMAd+ZqlsgQ/7Ufbo0cPs4BMe9l26NChbEzLA84555zwzhoAAAAIQ0gzt27FzC0AAICHdigDAAAAYgHJLQAAAFyD5BYAAACuQXILAAAA7ya3ulGDbubgpzuVaRuwzz77zO5zAwAAACKb3F544YXy6quvmo91UwXd4vavf/2rOT5lypRQnw4AAACIXnKbmZkpp556qvn4X//6l7Rs2dLM3mrCq7uCAQAAADGT3GpJQsOGDc3HWopw8cUXm93K+vfvb5JcAAAAIGaS22OOOUY++OAD2bp1q8yZM0fOPvtsczwnJ4cNEAAAABBbye0DDzwgd911l9l6t2/fvpKRkVE2i3vSSSdF4hwBAACAyG2/m52dLVlZWXLiiSeakgS1dOlSM3PbuXNniTVsvwsAAODh7XfT0tJM3e3cuXNNazDVp0+fmExsAQAA4B4hJ7e7d++Ws846S4477jg599xzzQyuGjVqlNx5552ROEcAAAAgMsnt7bffLomJibJlyxapV69e2fHLL79cZs+eHerTAQAAALapE+on6MIx7ZLQtm3bCsePPfZYWoEBAAAgtmZuDxw4UGHG1m/Pnj2SnJxs13kBAAAAkU9udXcy//a7Ki4uTkpLS+Xxxx+XM888M/QzAAAAqEUlpT5Zsn63fLhyu3mvj+Ge6xdyWYImsbqg7Ntvv5VDhw7JPffcI99//72ZuV20aFFkzhIAAMAGs9dkyYSZayUrr7DsWKvUFBl/QVcZ0q1VVM8tFsyOgesX8sxtt27d5KeffpJTTjlFLrzwQlOmoFvwrlixQjp16hSZswQAALAhMRszI7NCYqay8wrNcR1H7F+/I9rEwW3YxAEAAHfTW+enPDa/SmLmF6d9/FNTZOG9v5WEeH0Ep10/q/layGUJKjc3V15++WX54YcfzOPjjz9errvuOvMFAQAAnGbpxj3VJmZKZ/p0XOMyOjWr1XOLBUtj6PpZKkt45ZVXzOYNSmtttfzgqaeeMnW2+vbkk0+aY5mZmZE+XwAAgJDl7Cu0Nc5rcmLo+llKbrOzs2Xw4MFlmzgMHTpUNm3aJO+//75527hxo5x//vly2223Rfp8AQAAQtaiYYqtcV7TIoaun6XkNj09vazkQGdu7733XqlT538VDfqxdk3QMQAAAKfpm97UrOqvrhpUj+u4xiG2r5+l5Pbmm2+Wxx57zHysBby69W5lW7dulYYNG9p/hgAAAGHSRU7arkpVTtD8j3WcxWSxf/0sJbfXXnutjB492nx8+eWXy6hRo+Ttt982Ca2+vfXWW3L99dfLlVdeGenzBQAAOCLah3XK8J5mVX95+liPO6VPq1MNiZHrF3IrMN244e6775apU6fK4cOHzbHExEQZM2aMPProozG5BS+twAAA8FZbK13Vr4uftEZUb6U7YcYxVpRE6fpZzdeOuM9tQUGBrF+/3nysnRLq1asnsYrkFgAAwNki2udWaTLbvXv3I/10AAAAwHYhJ7e63a6WH8ybN09ycnKktLS0wviGDRvsPD8AAAAgcsmtLhz78ssv5ZprrpFWrVpJXBw1KgAAAIjR5PbTTz+Vjz/+WAYMGBCZMwIAAAAi2QqsvCZNmkjTptFv0AsAAACEndxOnDhRHnjgAdMtAQAAAIi5soSTTjqpQm3tL7/8Ii1btpQOHTqYHrflZWZm2n+WAAAAgF3J7bBhw6yEAQAAAFF1xJs4uAmbOAAAAHh8EwcAAFD72DoWCK6O1Q4JVvvZ7tmzx1IcAAAIzew1WTJh5lrJyissO9YqNUXGX9BVhnRrFdVzA2IquX366acjfyYAACBoYjtmRqZUriXMzis0x6cM70mCC1hNbkeMGBH5MwEAANWWIuiMbaBFMnpM763q+KCuaZQowPPqWC3g9Rfu6sfBsCALAAB7aY1t+VKEQAmujmtcRqdmtXpuQMzW3GZlZUmLFi2kcePGAetvtemCHi8pKYnEeQIA4Fm6eMzOOEC8ntzOnz+/bMvdL774ItLnBAAAytGuCHbGAeL15Pb0008P+DEAAIg8bfelXRF08Vigulu9n5qW+t+2YIDXWe5zu2XLFktxRx99dDjnAwAAKtFFYtruS7siaCJbPsH1FwrqOIvJgBCS2/T09LKP/Zuala+9peYWAIDI0TZf2u6rcp9bnbGlzy1wBMmtJq5t27aVkSNHygUXXCB16rC5GQAAtUkTWG33xQ5lQPXifP5p2BpkZ2fLK6+8ItOmTZPc3FwZPny4jBo1Srp06SJe2asYAAAAzs7X4q0+YVpamtx7773y448/yr/+9S/Zu3ev9OvXT/r37y8vvviilJaW2nXuAAAgyIYOS9bvlg9Xbjfv9TGAI5i5DWTnzp1y5ZVXypdffim//vprWbuwWMPMLQAgVrbgrVxzq10UqLmFF+TbPXNb3uLFi+X666+X4447Tvbv3y/PPfec2dwBAABELrHVbgmVdyrT9mB6XMcBhLCgTHcoe/XVV03NrZYkXH311bJo0SLp1q1bZM8QAACP09IDnbENdKtVj+lyMh3XxWYsLoPXWU5utX9tmzZtZMSIETJ06FBJTEw0dbbfffddhbgTTjghEucJAIBnaXeEyjO2lRNcHde4jE7NavXcgJhNbrV/rW7kMHHiRJk0aZI5Vrlclz63AADYT9t+2RkHhHsnwcnt6Cwntxs3bozsmQAAgIA0gbAzDnDzokbLyW379u0jeyYAACAgnRnTBEIXjwWqu437/zuVaRwQ6UWNlX8G/YsadQc9JyS4lrolaDlCKLZv336k5wMAACrRW746M6Yq3/z1P9ZxJ90ahrvUtKhR6bgT+i5bSm779OkjN954oyxbtqzaGO05pps5aPeE9957z85zBADA83RGTGfGdIa2PH3slBkzuNfSEBY1xkRZwtq1a+Xhhx+WQYMGSUpKivTq1Utat25tPta2YDr+/fffS8+ePeXxxx+Xc889N/JnDgCAx2gCq+2+nLyYB+6UE0OLGkPaoezgwYPy8ccfy8KFC2Xz5s3mcfPmzeWkk06SwYMHx2zPW3YoAwAAqJ5u9Xzli19LTd4c3T9i7eis5muWF5SpunXryv/93/+ZNwAAAHhD3xha1HhE2+8CAADAOxJiaFEjyS0AAABcs6gxpLIEAACAWOf0HbacbEgMLGokuQUAIIaQmLl/hy2nS4iPi9iiMTuQ3AIAECNIzLyxwxailNxqb1vduezQoUMVjg8dOjTMUwIAAJWRmEV2hy2d+9ZxveXOTLjHktsNGzbIRRddJKtXr5a4uDjxt8nVj1VJSYn9ZwkAgIeRmNXuDltOvuWOCHRLuPXWWyU9PV1ycnKkXr16ZmeyBQsWSO/eveU///lPqE8HAABctPWpU8XSDluo5ZnbJUuWyPz5883OZPHx8ebtlFNOkcmTJ8stt9wiK1asCPOUAABAeSRm4dPFd3bGwUUzt1p20LBhQ/OxJrg7duwwH7dv317WrVtn/xkCAOBxJGbh064SjeslBo3RcSfssIVanrnt1q2brFq1ypQm9OvXTx5//HFJSkqSF154QTp27Bjm6QAAgOq2Pg1WmqDjJGbhoVrZozO39913n5SWlpqPH3roIdm4caOceuqp8sknn8gzzzwTiXMEAMDTdJHY0BODd0LQcRaTVU/rkXMLioPG7C0opm7ZizO3gwcPLvv4mGOOkR9//FH27NkjTZo0KeuYAAAA7O2W8Pa324LGvPPtNrlnSBcS3GpQt+wdIc/cXnfddbJv374Kx5o2bSoFBQVmDACAmhK1Jet3y4crt5v3+hjBfb1ht6VZR41DYM0bJNsaBxclt6+88oocPHiwynE99uqrr9p1XgAAl25EcMpj8+XKF7+WW99aad7rYz2O6umLADvjPMnqayhea3knuc3Pz5e8vDyzaYPO3Opj/9vevXtNzW2LFi2O+EQeffRRU9Zw2223lR0rLCyUm266SZo1ayYNGjSQSy65RHbu3Fnh83SXtPPOO8/03NWvf/fdd8vhw4eP+DwAAJHdYavyoij/DlskuMGQmYVr14EiW+Pggprbxo0bm+RT34477rgq43p8woQJR3QSy5Ytk+eff15OOOGECsdvv/12+fjjj+Xdd9+V1NRUufnmm+Xiiy+WRYsWlbUl08Q2LS1NFi9eLFlZWXLttddKYmKiPPLII0d0LgAA+7HDVngyOjaXZ79YbykOgTWvn2xrHFyQ3H7xxRdm1va3v/2tvPfee6bO1k9bgWmf29atW4d8Avv375err75aXnzxRZk0aVLZcZ0lfvnll+WNN94wX1NNmzZNunTpIl9//bX0799fPvvsM1m7dq18/vnn0rJlS+nRo4dMnDhR7r33XnnwwQfNeQVSVFRk3vx09hkAEDlsfRqe/p2amR6swepudVzjUA2rr5l4beWdsoTTTz9dzjjjDNP6a9iwYeax/y0jI+OIElulZQc6+zpw4MAKx5cvXy7FxcUVjnfu3FmOPvpos0ua0vfdu3c3iW35bg6arOq2wNXR3dR0Jtj/1q5duyM6dwCANaxUD4/OZj96cfegMTrOrHf1du0vsjUOMT5z+91335nNG3SrXZ1RXb16dbWxlUsLgnnrrbckMzPTlCVUlp2dbWZetRyiPE1kdcwfUz6x9Y/7x6ozbtw4ueOOO8oeazJMggsAkcMOW+Eb0q2VTB3eUx786HvJzv9fApbWKFkeHHq8GUf1+Bn0DkvJrd7u12RRF2zpx1pfqyUKlelxrYO1YuvWrXLrrbfK3LlzJSWldn+QkpOTzRsAoHZ32NLFY4HqbnW+MY0dtmqkCazWJWv5hs5yayKm14wZ25rxM+gdlpJbLUU46qijyj62g5Yd5OTkSM+ePcuOaWK8YMECefbZZ2XOnDly6NAhyc3NrTB7q90SdAGZ0vdLly6t8Lz+bgr+GABA9GnyNf6CrqYrgiYR5ZMLf1qm4yRpNdNrRF1y6PgZ9A5LNbe6WMy/+5h+HOzNqrPOOsuUN6xcubLsrXfv3mZxmf9j7Xowb968ss9Zt26daf2lNb5K3+tzaJLspzPBjRo1kq5du4ZyHQAAtTDrOGV4TzM7Vp4+1uPcVkdt/Qy2bMTPoJuFvP2ueu2112Tq1KlmFlcXdWlS+/TTT0t6erpceOGFlp6jYcOGpo63vPr165uetv7jo0aNMrWx2plBE9Y//vGPJqHVTgnq7LPPNknsNddcI48//rgpnbjvvvvMIjXKDgDAebitDmeoWJgQqNQSHtqhbMqUKSbhPPfcc03JgL/GVksHNMG101NPPSXnn3++2bzhtNNOM6UG77//ftl4QkKCzJo1y7zXpHf48OGmz+1DDz1k63kAAOy/rX5hjzbmPYktansjkfIL8tTO/CI2EnGROF+IL1d0plQ3SNB2YDr7umrVKunYsaOsWbPGtArbtWuXxBrtlqAtwbQThM4QAwAA920kols9V9dv2b+gbOG9v+UFV4znayHP3GopwkknnVTluJYBHDhwIPQzBQAAcNBGIohtISe3WlerC74qmz17ttk9DAAAwGnYSMQ7Ql5QpvW2umCrsLDQFGBrK64333zT7Pr10ksvReYsAQCAcehwqby2ZJNs3lMg7ZvWk2syOkhSnZDnqjyHTRy8I+Tk9vrrr5e6deuargQFBQVy1VVXma13n3nmGbniiisic5YAAEAmf7JWXvxqo5SWWy3z8Cc/yOhT02XcubTADIZNHLwj5AVl5Wlyu3//frNzWSxjQRkAIBYS2+cXVL+R0o2nkeBa7ZZQXXJLr1sPLyj7+eefzcf16tUrS2z12KZNm8I5ZwAAUE0pgs7YBqPjGofqaeI6sGvgCTk9TmLrDiEntyNHjpTFixdXOf7NN9+YMQAAYC+tsS1fihCIjmscgs9+z137v11Ny9PjOg4PJrcrVqyQAQMGVDmuu4YF6qIAAADCo4vH7IzzIma/vSPk5DYuLk727dtX5bjWP/h3KwMAAPbRrgh2xnkRs9/eEXJyq9vgatuv8omsfqzHTjnlFLvPDwDgwp2ilqzfLR+u3G7e62MEp+2+ato0S8c1DoEx++0dIbcCe+yxx0yC+5vf/EZOPfVUc+yrr74yK9jmz58fiXMEALhotfqEmWsr7BSl7ZnGX9CVxTxBaB9bbfcVrFuCjtPvtnrtmtS1NQ7OFfK/gq5du8p3330nl112meTk5JgShWuvvVZ+/PFH6datW2TOEgDgmjZMlbdA1b6jelzHUT1t83VC28Dtj/Q4bcCC65zWyNY4uGjmVummDY888oj9ZwMAcCUtPdAZ20AFCHpM77jr+KCuaZJQ0/13j9KV/N9tyw84psd1nAS3ensKDtkaB+c6ovsXWoYwfPhwOfnkk2X79u3m2GuvvSYLFy60+/wAAC6wdOOeKjO2lRNcHdc4VMVK//Cx/a53hJzcvvfeezJ48GCzBW9mZqYUFRWVdUtgNhcAEEjOvkJb47yGlf72bb9b3X0BPa7jbL/rweR20qRJMnXqVHnxxRclMTGx7Lj2vtVkFwCAypg1Cw8r/cOn5S66cLG61wh6XMcpi/Fgcrtu3TrTLaEy3es3NzfXrvMCALgIs2bhoc8tEMHkNi0tTX755Zcqx7XetmPHjqE+HQDAA5g1Cw99bu1b1Fgd/6JG+i57MLkdPXq03HrrrfLNN9+Y3cp27Nghr7/+utx1110yZsyYyJwlACDmrdiyN6xxL9P+tWd1aRE0Rsfpc1s9FjV6R8itwMaOHSulpaVy1llnSUFBgSlRSE5ONsntH//4x8icJQDAE6v97zy7MwlaADqbuGxT8OT/2017TRyz34GxqNE7Qv4NorO1f/7zn2XPnj2yZs0a+frrr+XXX3+ViRMnysGDByNzlgCAmMZq//B8vWG35BYUB43ZW1Bs4hBY8wbJtsbBuY745XFSUpLZraxv376ma8KTTz4p6enp9p4dAMAVWO0fniXrd9sa50lWS2kpufVOcqv9bMeNGye9e/c2mzd88MEH5vi0adNMUvvUU0/J7bffHslzBQDEKFb7h8dnMeOyGudFuw4U2RoHFyS3DzzwgEyZMkU6dOggmzZtkksvvVRuuOEGk9TqrK0eu/feeyN7tgCAmKSr+ONqKAXVcVb7B9a4bqKtcV5Er2XvsLyg7N1335VXX31Vhg4damptTzjhBDl8+LCsWrXK1OECAFAdXeRUNzFBCg6VVBuj4yyGCox6Uft6LWfnFQac39afvDR6LXtr5nbbtm3Sq1cv83G3bt1MhwQtQyCxBQDURNsrBUtslY7ThimwtNS6tsZ5Eb2WvcNycltSUmIWkfnVqVNHGjRoEKnzAgC4CG2YwqOziY3rBS850HFmHYEQyhJ8Pp+MHDnSzNiqwsJC+f3vfy/169evEPf+++/bf5YAgJhGvWPkMd9ozw5lg7qmMXvrleR2xIgRFR4PHz48EucDAHChXu2bmO1hg/W61XGNQ1VarmGlz63GZXRqVmvn5dYdyriGHkluteUXAABHYvnmvZY2cdA4EouqKOsIH9fQO9jjEAAQcSQW4WleP9nWOC+iNMY7SG4BABFHYhGeUp/P1jgvtwILRsdZlBf7SG4BIMRFKbrF6Ycrt5v3+hjWE4vqlunocRKL6n1jsUWa1Tgv0kViQ09sFTRGx1lM5qGaWwDwutlrssxq6vKLUjQh096YQ7oF/6Ppdf4eo2NmZJpEtvxLAn8qQY/RYKy+iOLFVnX0hehrX28JGqPj9wzpws9hjGPmFgAsJraamFVeba27HelxHUdw+gJgyvCeZheo8vSxHucFQvUyOja3Nc6LFv+8y9JGIhqH2MbMLQBY7I8ZaE5Mj9Ef0zpNYPU6abslXTymNbZaisB1C65/p2Zmk4Zg7cB0XOMQ2HsrtlmOO/U3R0X8fGL9d+JSB/8bJrkFgBrQHxPRponDoxd3l9/PyKw2RsedlGA4TU2ztqHGedXsGCjPIrkFgBrQxspbfxidSq/PjaelywsLNlapWb7htHSuXw16t28in63daSkOwcuzKt/F8pdnOaW8iJpbAKgBbazsQd1yePT6VE5s/fQ41y+437RsaGuc15TUUJ6ldNwJHWRIbgGgBrSx8tYfRifi+oVv6eY9tsZ5zdIQyrOijeQWACy2sVKVE1zaWLnvD6MTcf3Ct2PvQVvjvCYnhsqzSG4BIIQ2Vi0b0cbK7X8YnYjrF77WjevaGuc1LWKoPIvkFgBCUvG2r4/tTl33h9GJuH7hO7lTc1vjvKZvDJVnkdwCQAiLobLziyoc35lfxGIol/1hdKJe7ZtITVUvOq5xCN4rOBh6BbujPIvkFgBqwGIeb/1hdKLlm/dKTT9eOq5xCN4rOBh6Bbtjl0H63AJADdjEwd4/jJX73OofRvrcBkfNLZxiSAzsMkhyCwA1ILHw1h9GJ6LmNnx6Z2Xs+6uDxug422jXTK+Pk1/Ik9wCQA1ILLz1h9HJNcvB7iBQsxzc1+t3S25BcdAYHde4AceyqCyWUXMLADVgMRSc8IJg6InByzZ0nBnH6i3ZsMvWODgXyS0A1IDFUHDCLfW3v90WNEbHWdQYjNV/n/w7jnUktwDgolXCcKdQbqkjMKulMJTMxD5qbgHAIhZDIRZuqVMvGlj/jv/tcxvsRUIT7XPbkeQ21pHcAkAIWAyFaLC6ER4b5gX/t3t577by/IKN1cZc1rstL1ZdgLIEAAAcrnG9JFvjvEjrkact3hw0ZvrizdQtuwDJLQAADte8QZKtcV608Kdf5dDh0qAxRYdLTRxiG8ktAAAO17xBsq1xXvTCVxtsjYNzkdwCAOBwP2bvszXOi3bkHbQ1Ds7FgjIAQK3SmkY6ToRm694CW+O8qHWjFNm4q8BSHGIbyS0AoNbMXpMlE2aurbCNrO7upptg0Cu4eu2b1rM1zosGHNNcFm3YYykOsY2yBABArSW2Y2ZkVkhsVXZeoTmu4wjsmowOElfD5LaOaxwCO3CoxNY4OBfJLQCgVkoRdMY2UJMl/zEdpw1TYFq2UTcxIWiMjlPeUb2aXhyEGgfnIrkFAESc1thWnrEtT1NaHdc4VKXXpaCGGUUd5/pVL6Njc1vj4FwktwCAiNPFY3bGec2O3IO2xnlR/07/3X43GB3XOMQ2klsAQMRpVwQ747xm+eY9tsZ5kZZsPHpx96AxOk5pR+wjuQUARFyv9k2kppxBxzUOVa2z2L/WapxXvZe5LaxxxAaSWwBAxC3fvFdqWium4xqHQKwutGNBXnUOHiqRuWtzgsbouMYhtpHcAgAijprb8HROa2RrnBc98slaW+PgXCS3AICIo+Y2PL3aN7U1zos27jpgaxyci+QWABBx1NyGp1XjurbGeVFynThb4+BcJLcAgIij5jY8fdObmm2Kg9FxjUNgdRPr2BoH5yK5BQBEHDW34dH2VOMv6Bo0RsdpY1W9rLyDtsbBuUhuAQARR81t+FZs2RvWuNelWJyRtRoH5yK5BQDU2m316uYV9Ti31at36HCpvPjVxqAxOq5xCKx7m1Rb4+BcJLcAgFq9rV45wfU/5rZ69V5bsslSzbLGIbAm9ZNsjYNzkdwCAGrFkG6tZMrwnpJWaWGUPtbjOo7ANu8psDXOi/ILi22Ng3NRWAIAqDWawA7qmiZLN+4xi8e0xlZLEZixDa5903q2xgFuRnILAKhVmshmdGoW7dOIKZf3OVomfvyDpTgE1jCpjq1xcC7KEgAAtUoXPb381QZ54MM15j2LoGr25tIttsZ50dqsPFvj4FxRTW4nT54sffr0kYYNG0qLFi1k2LBhsm7dugoxhYWFctNNN0mzZs2kQYMGcskll8jOnTsrxGzZskXOO+88qVevnnmeu+++Ww4fPlzL3w0AoCaTP1krne//1MxCvrpks3mvj/U4qrds0x5b47xoR16RrXFwrqgmt19++aVJXL/++muZO3euFBcXy9lnny0HDvxvX+fbb79dZs6cKe+++66J37Fjh1x88cVl4yUlJSaxPXTokCxevFheeeUVmT59ujzwwANR+q4AAIFoAvv8go1VVv3rYz1Oglu9lMR4W+O8qG2TurbGwbmi+q9g9uzZMnLkSDn++OPlxBNPNEmpzsIuX77cjOfl5cnLL78sTz75pPz2t7+VXr16ybRp00wSqwmx+uyzz2Tt2rUyY8YM6dGjh5xzzjkyceJEee6550zCCwCIPvq0hqduYoKtcV500UltbI2DcznqJZ4ms6pp0/828dYkV2dzBw4cWBbTuXNnOfroo2XJkiXmsb7v3r27tGzZsixm8ODBkp+fL99//33Ar1NUVGTGy78BgBUlpT5Zsn63fLhyu3mvj1Ez+rSG55ecfbbGeVGd+Hhb4+BcjlkSWFpaKrfddpsMGDBAunXrZo5lZ2dLUlKSNG7cuEKsJrI65o8pn9j6x/1j1dX6TpgwIULfCQC3mr0mSybMXCtZeYVlx3RXLd18gB6twdGnNTz5hYdtjfOiXQeKbI3zspJSn6Pb+TkmudXa2zVr1sjChQsj/rXGjRsnd9xxR9ljnblt165dxL8ugNhObMfMyJTKk4/ZeYXmOJsQBEef1vA0b5gsP+ccsBSHwDQJszPOq2bHwIt8R8y933zzzTJr1iz54osvpG3btmXH09LSTN1sbm5uhXjtlqBj/pjK3RP8j/0xlSUnJ0ujRo0qvAFAsFkK/WUe6K66/5iOU6IQfv9V+rQGdmKbxrbGeZHOLjaulxg0Rsc1DsFf5JdPbMu/yNdx8Xpy6/P5TGL773//W+bPny/p6ekVxnUBWWJiosybN6/smLYK00VnGRkZ5rG+X716teTk5JTFaOcFTVi7dv3vPuYAEA69/Vb5l3l5mtLquMYhsLeXbbE1zmuaNUiyNc6rimtYsFhcwoJGN7zIj492KYJ2OXjjjTdMr1utkdW3gwcPmvHU1FQZNWqUKSHQWV1dYPa73/3OJLT9+/c3Mdo6TJPYa665RlatWiVz5syR++67zzy3ztACQLi0rszOOC+i5jY8zS3eKrca50Vfb9gtBw6VBI05UFRi4hDbL/KjmtxOmTLFdEg444wzpFWrVmVvb7/9dlnMU089Jeeff77ZvOG0004zpQbvv/9+2XhCQoIpadD3mvQOHz5crr32WnnooYei9F0BcBtq9cJHzW140hql2BrnRYvX77I1zmtyYuhFfp1olyXUJCUlxfSs1bfqtG/fXj755BObzw4A/ktr8HTBhNaVBfqtpWuE01L/u2IYgV2T0UEe/uSHoO3AdLG1xqGqHu0a2xrnRdv3HrQ1zmtaxNCLfEcsKAMAJ9MWN7oSWFVuduN/rONOaoXjNEl14uWsLi2Cxui4xqGqaYs22BrnRa0bp9ga59UX+XHVjOvxVg55kc9vEQCwQFvcaLsvnaEtTx/TBqxmushkzfbgG+bouBMWozjR619vtjXOi/q1b2ZrnNckxNCLfMf0uQUAp9MEdlDXNEc3L4/VxShSbjFKRieSi8pyDxbbGudFP/2633Lc6TXcZfD6i/wJlfrcpjmszy3JLeAxTt9Zxun0WpF8uXsxihM1qZsoBw4VWYpDYFv3Ftga51VDYuBFPskt4CGxsLMM3CmWFqM40ZV92ssTn/9kKQ6BtWtSz9Y4L0tw+It8am4Bj4iVnWXg3d2hmrA7VLU6pzW0Nc6L0pvVtzUOzkVyC3hALO0sA+/ip696/1yyydY4L5q2eIOtcXAuklvAA2JpZxm4k/5s5RYEX+yk4/wMBpZncaGY1Tgv2lHDgsZQ4+BcJLeAB7CYB9G2w+IiHatxXtOyUbKtcV6U1jDJ1jg4F8kt4AEs5kG0rdyWa2uc16Q3r29rnBe1b97A1jg4F8kt4AGxtLMM3MpqmyDntBNykh+y9tka50W79x+yNQ7ORXILeEAs7SwDd2rXpK6tcV6Tkhhva5wX1U+uY2scnIt/BYBHsH0soqnEV2prnNcw7x2+i3q0sTUOzsXLE8BDYmFnGbjT0k17LMeNifjZxJ7DpaW2xnlRvMXfc1bj4Fwkt4DHOH1nGbjTpl8LbI3zmvU5+22N86JF63dZjjv1uKMifj6IHMoSAAARR71jePYfKrE1zotWW+zEYTUOzkVyCwCIuBPaptoa5zVJCfG2xnlRYXGprXFwLv4VAAAibnCXNFvjvKZhSqKtcV5Exwnv4P4PAISgpNTHgrwjkFtUbGuc17RomCS//HrAUhwCa1ovydY4OBfJLQBYNHtNljz40feSnV9UdiytUbI8OPR4WqnVgF3ywpPevIEs3rDXUhwC21NQbGucl5U4/EU+yS0AWExsfz8js8pxTXT1+FR6BVvaJS8rr7DaGHbJq16L1GRb47zoYHGJrXFe/l04YebaCv+W9d+ubgTklN+BFJYAgIVZirHvrw4ao+Mah8B0VmfoicH/8Om4k2Z/nGTWyh22xnlRXYu1tFbjvJrYjpmRWeVFanZeoTmu407A/0EAqMHX63dLbg23KnVc4xCYJv5vf7staMw7327jBUI1dh8otjXOi45vlWprnNeUlPrMjG2gf6H+YzruhH/DJLcAUIPFG3bZGudFX2+o+QXCXn2BsIEXCIE0a5Bka5wX7Ss6bGuc1yzduCdoWZGmtDqucdFGcgsANdix96CtcV701U+/2hrnNT3bNbY1zot+ys63Nc5rcvYV2hoXSSS3AFCD1o3r2hrnRQt/2WVrnNcUFJXYGudFeQeLbY3zmhYx1PGE5BYAanByp+a2xnmRz1dqa5zXrPt1n61xXpRfWGxrnFc7nsRVMx7noI4nJLcAUIP+nZpJ43rBd37ScY1DYGmpdW2N85r6SXVsjfOiwhKfrXFekxAfZ9p9qcoJrv+xjjuh4wnJLQDUQH9ZX967bdAYHXfCL3Wn6mhxcwGrcV7TKDnB1jgvapiUYGucFw3p1kqmDO8paakVSw/0sR53Sp9bXuIBQA20tc1Hq4L3b9Txe4Z0IcGtxtqsfFvjvObnnAO2xnlRxjHN5F/Ld1iKQ/U0gR3UNY0dygDAzS1wpFwLnAxKEwJKrhNva5zXHLC4a5bVOC86qkFdW+O8LCE+ztG/6/gtAgAuaoHjVLkFh2yN85rGKXVsjfMiqxOLDpqAxBEiuQUAF7XAcapCizOKVuO8Rleh2xnnRal1E22Ng3OR3AKAxRY4wTilBY5TFRwqsTXOaxrWTbI1zot2HyiyNQ7ORXILABbqy7q1aRQ0RsedtKDCaZrUT7I1zmt6tm9sa5wXrdmeb2scnIvkFvDgyv8l63fLhyu3m/f6GMEdOlwq837ICRqj4xqHwNo2qWtrnNcctLjzmNU4L0pJTLA1Ds5F5TngIbPXZMmDH62V7Pz/LXxKa5QiDw7t6pj+hE702pJNUtNrAB3XuFGndqyt04op9ZPr2BrnNS8v3GA57o7BnSN+PrEozuezNQ7Oxcwt4KHE9vczMisktkof63EdR2Cb9xTYGudFu/YfsjXOaw4U+2yN8yKflNoaB+ciuQU8QEsPxr6/OmjMuPdXU6JQjfZN69ka50V0S6idP9b8Ua/eQYuJv9U4OBf/DgAP+HrDbsktKA4as7eg2MShqmsyOtTY+1LHNQ6BdWvdyNY4r2nVKMnWOC9qanGxotU4OBfJLeABunDMzjivSaoTL6NPTQ8ao+Mah8Ca1E+2Nc5rrN4o54Z69fZa3CDEahyci9/EgCdYvc3G7bjqnHR0k7DGvW7vgUO2xnlNXg13XkKN86KDFnsoW42Dc5HcAh6Q0bG5rXFeQ81y+L7blmtrnNcUW/zZshrnRbQC8w6SW8AD+ndqJo3rBd9SUsc1DlVRsxy+gkPFtsZ5TWmpvXFe1Lxhsq1xcC6SW8ADdOesRy/uHjRGx9lhKzBqlsO390CxrXFeQ81t+NpY3CDEahyci+QW8AjdpGHq8J6S1qjirIQ+1uNs4hAMNcvhyisstjXOa/gJDF/Tesm2xsG52AoG8BBNYAd1TZOlG/dIzr5CadEwRfqmN2XG1kIt8rNfrLcUh8D2FZbYGuc1cRYTV/4lV4+yBO8guQU8RhPZDGprj6hmOVjdLTXLwenrpxIL2RmvswJLShApKrEWh8Ca1rDuINQ4OBdlCQBQA2qWw9eyYZKtcZ5DXULYPlm9w9Y4OBfJLQCEULNcOflq2ZCaZSs6NK9ra5zXHCq1N86L/rPuV1vj4FwktwBg0XuZ22TnvoqbDOzcV2SOI7ifcwpsjQNCVWBxcwarcXAuklsAsGD0q8tk7tqcgGN6XMdRvQKLU4pW47wmNSXe1jgvalmpU0y4cXAu/hUAgIXtOKtLbP10nG07q+fzldoa5zVtm9S3Nc6LendoamscnIvkFvAY3SJWNxv4cOV2854tY2s26ePvbY3zov2HfLbGec36nH22xnlRr6Ob2BoH56IVGOAhs9dkyYSZayUrr7DsWKvUFBl/QVcWRAXx3bY8W+OAUB0ssTfOi/YUHLI1zstKSn2O7pdOcgt4KLEdMyOzSqeg7LxCc3wKK/6rlVo30dY4IFRs4hC+1RZffFqN86rZMTBJQlkC4JFX2frLKNAfR/8xHadEIbDRp3a0NQ4IldVJMQdNnjnOyi25tsZ5eZIkq1xiW36SRMedgOQW8AC9fVT5l1F5mtLquMahqlOOPUqS6wT/danjGgdEgpXd3UKJ86LiklJb47ymJIYmSUhuAQ/Quig747xGa8meuaJH0Bgdd1LNGYCKfBa3b7Ma5zVLY2iShOQW8AAt+LczDgDgLTkxNElCcgt4gK5k1YL/6uYV9biOaxyq0ttsY99fHTRGx51wO86p6ifG2xoHhOpgcYmtcV7TIoYmSfgtAniA3i7XlayqcoLrf6zj3FYP7Ov1uyW3oDhojI5rHAJLruOzNQ4IldXfb/wejP1JEpJbwCO0RYu2+0pLrfiqWh/TBiy4hb/ssjXOiwqKfLbGAaGqWyfB1jivSYihSRL63AIeognsoK5pjm6+7USrtu6xNc6LDvvsjQNC1bheomTtO2QpDsEnSSr3uU1zWJ9bklvAYzSRzejULNqnEVN27T9ka5wXWX35xMssRMohi33SrMZ51ZAYmCQhuQWAGtRPqmNrnBeZv3sWcgYH/X2Ey+zMO2hrnJclOHyShJpbAKhBXJy9cV5UVGpvHBCq/cU+W+PgXCS3AFCDlKQEW+MAAJFDcgt4jPZiXbJ+t3y4crt5T2/WmqU3rW9rHADnJjwkRrGPAjHAQ2avyaqyyrWVw1a5OlHbZvVsjQNQ+9IaJMiO/SWW4hDbeIECeCixHTMjs8re4Nl5hea4jiOw7bkHbY3zoqQ4e+OAUBUeLrU1Ds5FclvLuCWMaNCfM52xDfTT5j+m4/w8BlZSWmprnBdZvTJcQURKbqHP1jg4F2UJtYhbwogW7UdYeca2PP1VruMa5+T2LtFyoLDE1jgvIrkF3OPQ4VJ5bckm2bynQNo3rSfXZHSQpDrOmS8lua3lW8KVXw/6bwmz/SkiSRtt2xnnNdst9r20GudFiXEiVnbW1TggEpIT4+SghTZfGofqTf5krbywYGOFfGbSxz/IDaely7hz/7s9b7Q5J812MW4J24eyjiOjO8jYGec1SQnxtsZ5ETuUhYfrF75SizuPWY3zamL7fKXEVuljPa7jTsDMbS3glrA9KOs4cro1ol6rYD+HOq5xqCreytZaIcR50WGfvXFeY/WycPmqV1xqb5wXSxGeX7AxaIyO33l256iXKDDNUAu4JRw+VvqHv1Xi0BODvwDQcSftDe4k9ZITbY3zIquT2kx+I1Ks7rHCXiyBTVu00da4SOLXSC3glnB4KOsIn16bj1YFfwGg41zDwKzOaDPzXT2rr5t4fYVIaVyvjq1xXjN37U5b4yKJ5LYWbwlX9ztbj3NL2J6yDhzZNVRcw+pd0rOdrXFeFOezNw4IVUJ8vK1x3uOzOS5yXPN/8LnnnpMOHTpISkqK9OvXT5YuXSpOobd6tS5UVU5w/Y91nFvCgVHWET6uYXjueW+lrXFeZLVLGt3UECnb8w7ZGuc1x7VsaGtcJLkiuX377bfljjvukPHjx0tmZqaceOKJMnjwYMnJyRGn0AVP2u4rLbVi6YE+pg1YcJR1hK95g2Rb47zmx6x9tsZ5EX1ugdh2UrvGtsZFkisKS5588kkZPXq0/O53vzOPp06dKh9//LH885//lLFjx4pTaAI7qGuaufWrM2SajGkpAjO21so6dPFYoJsdcf//RQJlHdWjBU549hcV2xoHALEmv/CwrXGRFPMzt4cOHZLly5fLwIEDy47Fx8ebx0uWLAn4OUVFRZKfn1/hrbZoIqvtvi7s0ca8J7GtGWUd4ftm025b47zm4KESW+MAINY0tXhnz2pcJMV8crtr1y4pKSmRli1bVjiuj7OzswN+zuTJkyU1NbXsrV07FoE4HWUd4aIFfDji4uJsjQOAWJPWKMXWuEhyRVlCqMaNG2dqdP105pYE1/ko6zhyepfg2S9+sRSHqrqkNZQV2/ItxQFwps7N68qPuw5aikNsbwYU8zO3zZs3l4SEBNm5s2JfNX2clpYW8HOSk5OlUaNGFd4QGyjrODL9OzaTxvWCbzDQpF6iiUNV06/rb2ucF02/qretcV7z0mU9bY3zorf/cKqtcV4tEYyrpkQwzkElgjGf3CYlJUmvXr1k3rx5ZcdKS0vN44yMjKieG+AU+svm0Yu7B42ZfHF3R/xScqLUeonSvlnw2Rwd1zgEdsYJLW2N85qBPVvZGudF/Dv2TolgnM/n87mhFdiIESPk+eefl759+8rTTz8t77zzjvz4449VanED0bIErb3Ny8tjFheuptsUP/jRWsnOL6xwG0lfbTvll5KTnf7EfNm8+2DAP4hf3v3bqJxTrOkw9uNqxzY9el6tnkss4vqFj3/H4dPdLKNRImg1X3NFcqueffZZeeKJJ8wish49esjf/vY3s5mDFSS38JJo/VJyi7yCYrlu+lLZkVcorVNT5J8j+zLTE6L/fLdTRr7xbYVSBGZsrfs8M0uufyezQikCM7ah4d9xbPJcchsOklsAAAB35GsxX3MLAAAA+JHcAgAAwDVIbgEAAOAaJLcAAABwDZJbAAAAuAbJLQAAAFyD5BYAAACuQXILAAAA1yC5BQAAgGuQ3AIAAMA1SG4BAADgGiS3AAAAcA2SWwAAALhGnWifgBP4fD7zPj8/P9qnAgAAgAD8eZo/b6sOya2I7Nu3z7xv165dtE8FAAAANeRtqamp1Y7H+WpKfz2gtLRUduzYIQ0bNpS4uLhaeeWhifTWrVulUaNGEf96bsP1Cx/XMDxcv/BxDcPD9Qsf1zD2rp+mrJrYtm7dWuLjq6+sZeZWC4/j46Vt27a1/nX1h4F/UEeO6xc+rmF4uH7h4xqGh+sXPq5hbF2/YDO2fiwoAwAAgGuQ3AIAAMA1SG6jIDk5WcaPH2/eI3Rcv/BxDcPD9Qsf1zA8XL/wcQ3de/1YUAYAAADXYOYWAAAArkFyCwAAANcguQUAAIBrkNwCAADANUhua8nkyZOlT58+Zhe0Fi1ayLBhw2TdunXRPq2YMmXKFDnhhBPKGkZnZGTIp59+Gu3TilmPPvqo2ZHvtttui/apxIwHH3zQXLPyb507d472acWU7du3y/Dhw6VZs2ZSt25d6d69u3z77bfRPq2Y0aFDhyo/g/p20003RfvUYkZJSYncf//9kp6ebn4GO3XqJBMnTjS7X8Ea3SVM/3a0b9/eXMOTTz5Zli1bJk7BDmW15MsvvzS/fDTBPXz4sPzpT3+Ss88+W9auXSv169eP9unFBN1FThOyY4891vwSeuWVV+TCCy+UFStWyPHHHx/t04sp+kvo+eefNy8WEBr9Wfv888/LHtepw69Rq/bu3SsDBgyQM88807wwPeqoo+Tnn3+WJk2aRPvUYurfriZnfmvWrJFBgwbJpZdeGtXziiWPPfaYmSzRvyH671lfXP3ud78zO1/dcsst0T69mHD99debn73XXnvNbIU7Y8YMGThwoMlp2rRpE+3ToxVYtPz6669mBleT3tNOOy3apxOzmjZtKk888YSMGjUq2qcSM/bv3y89e/aUf/zjHzJp0iTp0aOHPP3009E+rZiZuf3ggw9k5cqV0T6VmDR27FhZtGiRfPXVV9E+FdfQ2bNZs2aZFwk6g4uanX/++dKyZUt5+eWXy45dcsklZgZSkzQEd/DgQXMX+sMPP5Tzzjuv7HivXr3knHPOMX9Xoo2yhCjJy8srS84QOp25eOutt+TAgQOmPAHW6R0E/YWkr7IROk0idKaiY8eOcvXVV8uWLVuifUox46OPPpLevXubWUZ9cX/SSSfJiy++GO3TilmHDh0yydh1111HYhsCvYU+b948+emnn8zjVatWycKFC01ihprp3Wf9G5ySklLhuL440OvoBNxPi4LS0lLzaltvz3Xr1i3apxNTVq9ebZLZwsJCadCggfz73/+Wrl27Rvu0Yoa+IMjMzHRUbVQs6devn0yfPl1+85vfSFZWlkyYMEFOPfVUc3tOZzIQ3IYNG8zt4DvuuMOUZunPod4GTkpKkhEjRkT79GKO3kXIzc2VkSNHRvtUYu4OQn5+vqmXT0hIMInaww8/bF6somb6u07/DmudcpcuXcws+JtvvilLliyRY445RhxByxJQu37/+9/72rdv79u6dWu0TyXmFBUV+X7++Wfft99+6xs7dqyvefPmvu+//z7apxUTtmzZ4mvRooVv1apVZcdOP/1036233hrV84ple/fu9TVq1Mj30ksvRftUYkJiYqIvIyOjwrE//vGPvv79+0ftnGLZ2Wef7Tv//POjfRox58033/S1bdvWvP/uu+98r776qq9p06a+6dOnR/vUYsYvv/ziO+2007Ss1ZeQkODr06eP7+qrr/Z17tzZ5wTM3Naym2++2dRHLViwwCyQQmh0hsf/ylDre3Tm55lnnjGLoxDc8uXLJScnx9Tb+umMhf4sPvvss1JUVGRmMWBd48aN5bjjjpNffvkl2qcSE1q1alXlTovO/Lz33ntRO6dYtXnzZrOw8f3334/2qcScu+++28zeXnHFFeaxduzQ66ldjbiDYI12mNA1Q1oaqLPg+m/78ssvN+VaTkDNbS3RdXua2Opt9Pnz55sWJLCnxEOTMtTsrLPOMmUduhjK/6b1j3orTj8msT2yxXnr1683v9hRMy3FqtwCUesetZ0QQjNt2jRTt1x+QQ+sKSgokPj4iumP/v7TvycIjXZ70t9/2gllzpw5poOREzBzW4uLeN544w2zulDrVbKzs81xbT2iRdio2bhx40zB/9FHH2167On1/M9//mP+QaFm+nNXucZbfzFpv1Fqv62566675IILLjDJ2I4dO2T8+PHmj+KVV14Z7VOLCbfffrtZzPPII4/IZZddJkuXLpUXXnjBvME6TcI0udVZRlrRhU7/DWuNrf4t0VZg2k7yySefNAvzYI3+3dVJO11/oHeudDZca5i1pZojRLsuwiv0Ugd6mzZtWrRPLWZcd911plY5KSnJd9RRR/nOOuss32effRbt04pp1NyG5vLLL/e1atXK/Ay2adPGPNbaM1g3c+ZMX7du3XzJycmmPu+FF16I9inFnDlz5pi/H+vWrYv2qcSk/Px883vv6KOP9qWkpPg6duzo+/Of/2zWdMCat99+21w3/V2Ylpbmu+mmm3y5ubk+p6DPLQAAAFyDmlsAAAC4BsktAAAAXIPkFgAAAK5BcgsAAADXILkFAACAa5DcAgAAwDVIbgEAAOAaJLcAAABwDZJbAHAx3aI6Li5OcnNzo30qAFArSG4BwAFGjhxpklB9S0xMlPT0dLnnnnuksLDQ8nOcccYZctttt1U4dvLJJ0tWVpakpqZG4KwBwHnqRPsEAAD/NWTIEJk2bZoUFxfL8uXLZcSIESbZfeyxx474OZOSkiQtLc3W8wQAJ2PmFgAcIjk52SSi7dq1k2HDhsnAgQNl7ty5Zmz37t1y5ZVXSps2baRevXrSvXt3efPNNyvM/H755ZfyzDPPlM0Ab9q0qUpZwvTp06Vx48YyZ84c6dKlizRo0MAk1Tq763f48GG55ZZbTFyzZs3k3nvvNYm2nhMAOB3JLQA40Jo1a2Tx4sVm5lVpeUKvXr3k448/NmM33HCDXHPNNbJ06VIzrkltRkaGjB492iSq+qZJciAFBQXyl7/8RV577TVZsGCBbNmyRe66666ycZ0pfv31180s8qJFiyQ/P18++OCDWvrOASA8lCUAgEPMmjXLzKTqzGlRUZHEx8fLs88+a8Z0xrZ8AvrHP/7RzL6+88470rdvX1NTq4mwzurWVIagZQ9Tp06VTp06mcc333yzPPTQQ2Xjf//732XcuHFy0UUXmcd6Dp988kmEvmsAsBfJLQA4xJlnnilTpkyRAwcOyFNPPSV16tSRSy65xIyVlJTII488YpLZ7du3y6FDh0wCrMlsqPRz/ImtatWqleTk5JiP8/LyZOfOnSZh9ktISDCzxqWlpbZ8nwAQSZQlAIBD1K9fX4455hg58cQT5Z///Kd888038vLLL5uxJ554wpQeaP3rF198IStXrpTBgwebJDdU2o2hPK3J9fl8tn0fABBNJLcA4EBakvCnP/1J7rvvPjl48KCpfb3wwgtl+PDhJvnt2LGj/PTTTxU+R8sSdIY3HFre0LJlS1m2bFnZMX3OzMzMsJ4XAGoLyS0AONSll15qSgKee+45OfbYY03nBF1k9sMPP8iNN95oygfK69Chg5nt1S4Ju3btOuIyAq3nnTx5snz44Yeybt06ufXWW2Xv3r1mhhcAnI7kFgAcSmtudbHX448/Lnfeeaf07NnTlCLoZg26aKxyay5dcKbJcNeuXeWoo44yXRCOhJY+aNuxa6+91nRg0EVu+nVTUlJs+s4AIHLifBRaAQCC0Blg7Yl72WWXycSJE6N9OgAQFN0SAAAVbN68WT777DM5/fTTTUcGbQW2ceNGueqqq6J9agBQI8oSAABVFrPpTmZ9+vSRAQMGyOrVq+Xzzz83s7cA4HSUJQAAAMA1mLkFAACAa5DcAgAAwDVIbgEAAOAaJLcAAABwDZJbAAAAuAbJLQAAAFyD5BYAAACuQXILAAAAcYv/B8b2tFq9AhpwAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 800x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(8,6))\n",
+    "\n",
+    "plt.scatter(\n",
+    "    df['Rating'],\n",
+    "    df['RevenueMillions']\n",
+    ")\n",
+    "\n",
+    "plt.title('Avaliação x Receita')\n",
+    "plt.xlabel('Rating')\n",
+    "plt.ylabel('Receita (Milhões $)')\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2feb2c84",
+   "metadata": {},
+   "source": [
+    "##### O gráfico de dispersão foi utilizado para analisar a relação entre avaliação dos filmes e a receita. Observa-se uma tendência positiva indicando, de maneira geral,  que os filmes com notas mais altas tendem a apresentar receitas maiores. Porém, há casos de filmes com notas mais altas e receitas modestas, indicando outros fatores além da avaliação que podem influenciar no desempenho financeiro."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 377,
+   "id": "14cb556b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "revenue_by_year = (\n",
+    "    df\n",
+    "    .groupby('Year')['RevenueMillions']\n",
+    "    .mean()\n",
+    ")\n",
+    "\n",
+    "plt.figure(figsize=(8,5))\n",
+    "\n",
+    "plt.plot(\n",
+    "    revenue_by_year.index,\n",
+    "    revenue_by_year.values,\n",
+    "    marker='o'\n",
+    ")\n",
+    "\n",
+    "for ano, receita in zip(revenue_by_year.index, revenue_by_year.values):\n",
+    "    plt.annotate(\n",
+    "        f'{receita:.1f}',\n",
+    "        (ano, receita),\n",
+    "        textcoords=\"offset points\",\n",
+    "        xytext=(0, 8),\n",
+    "        ha='center'\n",
+    "    )\n",
+    "\n",
+    "plt.title('Receita Média dos Filmes por Ano')\n",
+    "plt.xlabel('Ano')\n",
+    "plt.ylabel('Receita Média (Milhões USD)')\n",
+    "plt.grid(True)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03fd4ee9",
+   "metadata": {},
+   "source": [
+    "##### O gráfico de linha foi utilizado para analisar a evolução da receita média dos filmes ao longo dos anos. Observa-se um crescimento gradual entre 2006 e 2009, atingindo o pico de 112,6 milhões de dólares. Após isso, ocorreram oscilações e uma tendência de redução, com destaque para o ano de 2016."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 378,
+   "id": "c968d0ca",
+   "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</th>\n",
+       "      <th>Title</th>\n",
+       "      <th>Genre</th>\n",
+       "      <th>Director</th>\n",
+       "      <th>Actors</th>\n",
+       "      <th>Year</th>\n",
+       "      <th>Runtime</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Votes</th>\n",
+       "      <th>RevenueMillions</th>\n",
+       "      <th>Metascore</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Guardians of the Galaxy</td>\n",
+       "      <td>Action</td>\n",
+       "      <td>James Gunn</td>\n",
+       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>757074</td>\n",
+       "      <td>333.0</td>\n",
+       "      <td>76.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Guardians of the Galaxy</td>\n",
+       "      <td>Adventure</td>\n",
+       "      <td>James Gunn</td>\n",
+       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>757074</td>\n",
+       "      <td>333.0</td>\n",
+       "      <td>76.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>Guardians of the Galaxy</td>\n",
+       "      <td>Sci-Fi</td>\n",
+       "      <td>James Gunn</td>\n",
+       "      <td>Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>121</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>757074</td>\n",
+       "      <td>333.0</td>\n",
+       "      <td>76.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Prometheus</td>\n",
+       "      <td>Adventure</td>\n",
+       "      <td>Ridley Scott</td>\n",
+       "      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>124</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>485820</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Prometheus</td>\n",
+       "      <td>Mystery</td>\n",
+       "      <td>Ridley Scott</td>\n",
+       "      <td>Noomi Rapace, Logan Marshall-Green, Michael Fa...</td>\n",
+       "      <td>2012</td>\n",
+       "      <td>124</td>\n",
+       "      <td>7.0</td>\n",
+       "      <td>485820</td>\n",
+       "      <td>126.0</td>\n",
+       "      <td>65.0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>999</td>\n",
+       "      <td>Search Party</td>\n",
+       "      <td>Adventure</td>\n",
+       "      <td>Scot Armstrong</td>\n",
+       "      <td>Adam Pally, T.J. Miller, Thomas Middleditch,Sh...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>93</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4881</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>999</td>\n",
+       "      <td>Search Party</td>\n",
+       "      <td>Comedy</td>\n",
+       "      <td>Scot Armstrong</td>\n",
+       "      <td>Adam Pally, T.J. Miller, Thomas Middleditch,Sh...</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>93</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>4881</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>22.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>1000</td>\n",
+       "      <td>Nine Lives</td>\n",
+       "      <td>Comedy</td>\n",
+       "      <td>Barry Sonnenfeld</td>\n",
+       "      <td>Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>87</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12435</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>1000</td>\n",
+       "      <td>Nine Lives</td>\n",
+       "      <td>Family</td>\n",
+       "      <td>Barry Sonnenfeld</td>\n",
+       "      <td>Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>87</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12435</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>999</th>\n",
+       "      <td>1000</td>\n",
+       "      <td>Nine Lives</td>\n",
+       "      <td>Fantasy</td>\n",
+       "      <td>Barry Sonnenfeld</td>\n",
+       "      <td>Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...</td>\n",
+       "      <td>2016</td>\n",
+       "      <td>87</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>12435</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>11.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>2555 rows × 11 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Id                    Title      Genre          Director  \\\n",
+       "0       1  Guardians of the Galaxy     Action        James Gunn   \n",
+       "0       1  Guardians of the Galaxy  Adventure        James Gunn   \n",
+       "0       1  Guardians of the Galaxy     Sci-Fi        James Gunn   \n",
+       "1       2               Prometheus  Adventure      Ridley Scott   \n",
+       "1       2               Prometheus    Mystery      Ridley Scott   \n",
+       "..    ...                      ...        ...               ...   \n",
+       "998   999             Search Party  Adventure    Scot Armstrong   \n",
+       "998   999             Search Party     Comedy    Scot Armstrong   \n",
+       "999  1000               Nine Lives     Comedy  Barry Sonnenfeld   \n",
+       "999  1000               Nine Lives     Family  Barry Sonnenfeld   \n",
+       "999  1000               Nine Lives    Fantasy  Barry Sonnenfeld   \n",
+       "\n",
+       "                                                Actors  Year  Runtime  Rating  \\\n",
+       "0    Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.0   \n",
+       "0    Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.0   \n",
+       "0    Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S...  2014      121     8.0   \n",
+       "1    Noomi Rapace, Logan Marshall-Green, Michael Fa...  2012      124     7.0   \n",
+       "1    Noomi Rapace, Logan Marshall-Green, Michael Fa...  2012      124     7.0   \n",
+       "..                                                 ...   ...      ...     ...   \n",
+       "998  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...  2014       93     6.0   \n",
+       "998  Adam Pally, T.J. Miller, Thomas Middleditch,Sh...  2014       93     6.0   \n",
+       "999  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...  2016       87     5.0   \n",
+       "999  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...  2016       87     5.0   \n",
+       "999  Kevin Spacey, Jennifer Garner, Robbie Amell,Ch...  2016       87     5.0   \n",
+       "\n",
+       "      Votes  RevenueMillions  Metascore  \n",
+       "0    757074            333.0       76.0  \n",
+       "0    757074            333.0       76.0  \n",
+       "0    757074            333.0       76.0  \n",
+       "1    485820            126.0       65.0  \n",
+       "1    485820            126.0       65.0  \n",
+       "..      ...              ...        ...  \n",
+       "998    4881              NaN       22.0  \n",
+       "998    4881              NaN       22.0  \n",
+       "999   12435             20.0       11.0  \n",
+       "999   12435             20.0       11.0  \n",
+       "999   12435             20.0       11.0  \n",
+       "\n",
+       "[2555 rows x 11 columns]"
+      ]
+     },
+     "execution_count": 378,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_genre = df.copy()\n",
+    "df_genre['Genre'] = df_genre['Genre'].str.split(',')\n",
+    "df_genre = df_genre.explode('Genre')\n",
+    "df_genre"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 379,
+   "id": "ecf0a6fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "genre_revenue = (\n",
+    "    df_genre\n",
+    "    .groupby('Genre')['RevenueMillions']\n",
+    "    .mean()\n",
+    "    .sort_values(ascending=False)\n",
+    ")\n",
+    "\n",
+    "plt.figure(figsize=(10,6))\n",
+    "\n",
+    "ax = genre_revenue.plot(kind='bar')\n",
+    "\n",
+    "# Adiciona os valores acima das barras\n",
+    "for barra in ax.patches:\n",
+    "    altura = barra.get_height()\n",
+    "\n",
+    "    ax.annotate(\n",
+    "        f'{altura:.1f}',\n",
+    "        (barra.get_x() + barra.get_width() / 2, altura),\n",
+    "        ha='center',\n",
+    "        va='bottom',\n",
+    "        xytext=(0, 3),\n",
+    "        textcoords='offset points'\n",
+    "    )\n",
+    "\n",
+    "plt.title('Receita Média por Gênero')\n",
+    "plt.xlabel('Gênero')\n",
+    "plt.ylabel('Receita Média (Milhões $)')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5deb15d5",
+   "metadata": {},
+   "source": [
+    "#### O gráfico de barras foi utilizado para comparar a receita média dos filmes entre os diferentes gêneros. Essa visualização permite identificar quais gêneros apresentam maior potencial de arrecadação.\n",
+    "\n",
+    "#### Como muitos filmes pertencem a mais de um gênero, os registros foram previamente desmembrados para que cada gênero fosse analisado individualmente. Com isso. observa-se que os gêneros de Animação, Aventura e Ficção Científica apresentam as maiores receitas médias enquanto Horror, Romance e Música aparecem entre os gêneros com menor receita média."
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv (3.9.12)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/looqbox_challenge.pdf b/looqbox_challenge.pdf
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