diff --git a/01_gd_sql-LooqboxDataChallenge.sql b/01_gd_sql-LooqboxDataChallenge.sql
new file mode 100644
index 0000000..e25a281
--- /dev/null
+++ b/01_gd_sql-LooqboxDataChallenge.sql
@@ -0,0 +1,22 @@
+#1. What are the 10 most expensive products in the company?
+SELECT PRODUCT_NAME, PRODUCT_VAL
+FROM `looqbox-challenge`.data_product
+ORDER BY PRODUCT_VAL DESC
+LIMIT 10;
+
+#2. What sections do the 'BEBIDAS' and 'PADARIA' departments have?
+SELECT DISTINCT DEP_NAME , SECTION_NAME
+FROM `looqbox-challenge`.data_product
+WHERE DEP_NAME IN ('BEBIDAS','PADARIA')
+ORDER BY DEP_NAME, SECTION_NAME;
+
+#3. What was the total sale of products (in $) of each Business Area in the first quarter of 2019?
+SELECT CAD.BUSINESS_NAME , SUM(SALES.SALES_VALUE) AS SALES_VALUE
+FROM `looqbox-challenge`.data_store_sales AS SALES
+LEFT JOIN `looqbox-challenge`.data_store_cad as CAD 
+ON SALES.STORE_CODE = CAD.STORE_CODE
+WHERE SALES.DATE BETWEEN '2019-01-01' AND '2019-03-31'
+GROUP BY CAD.BUSINESS_NAME
+ORDER BY SUM(SALES.SALES_VALUE) DESC;
+
+
diff --git a/02-gd-Case1-datachallenge.ipynb b/02-gd-Case1-datachallenge.ipynb
new file mode 100644
index 0000000..1753c19
--- /dev/null
+++ b/02-gd-Case1-datachallenge.ipynb
@@ -0,0 +1,340 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5ade6e06",
+   "metadata": {},
+   "source": [
+    "### Case 1\n",
+    "#### 1) The Dev Team was tired of developing the same old queries just varying the filters accordingly to their boss demands.\n",
+    "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",
+    "\n",
+    "- store_code: integer\n",
+    "\n",
+    "- date: list of ISO-like strings\n",
+    "\n",
+    "- Date e.g.\n",
+    "  - ['2019-01-01', '2019-01-31']\n",
+    "\n",
+    "It should look like this\n",
+    "my_data = retrieve_data(product_code, store_code, date)\n",
+    "\n",
+    "Make your team proud!\n",
+    "\n",
+    "Extra instructions:\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": 1,
+   "id": "3e772ed3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sqlalchemy import create_engine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6959a8b8",
+   "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>PRODUCT_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>18</td>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>708.5</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-01-02</td>\n",
+       "      <td>1297.1</td>\n",
+       "      <td>119.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-01-03</td>\n",
+       "      <td>1144.5</td>\n",
+       "      <td>105.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-01-04</td>\n",
+       "      <td>1090.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-01-05</td>\n",
+       "      <td>893.8</td>\n",
+       "      <td>82.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  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"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "def retrieve_data(product_code, store_code, date):\n",
+    "    # 1. Credenciais de acesso\n",
+    "    usuario = 'looqbox-challenge'\n",
+    "    senha = 'looq-challenge'\n",
+    "    host = '35.199.115.174'\n",
+    "    porta = '3306'          # porta padrão do MySQL\n",
+    "    nome_do_banco = 'looqbox-challenge'\n",
+    "\n",
+    "    # 2. String de conexão (URL de conexão)\n",
+    "    # Formato: mysql+pymysql://usuario:senha@host:porta/nome_do_banco\n",
+    "    string_conexao = f'mysql+pymysql://{usuario}:{senha}@{host}:{porta}/{nome_do_banco}'\n",
+    "\n",
+    "    # 3. Motor de conexão (engine)\n",
+    "    engine = create_engine(string_conexao)\n",
+    "\n",
+    "    # 4. Consulta SQL\n",
+    "    query = \"\"\"SELECT * FROM `looqbox-challenge`.`data_product_sales`\n",
+    "            WHERE PRODUCT_CODE = %s\n",
+    "            AND DATE BETWEEN %s AND %s\n",
+    "            AND STORE_CODE = %s\"\"\"\n",
+    "\n",
+    "    df = pd.read_sql(query, con=engine, params=(product_code, date[0], date[1], store_code))\n",
+    "\n",
+    "    # 6. retorna o DataFrame\n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "361ee7b7",
+   "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>PRODUCT_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>18</td>\n",
+       "      <td>2019-01-01</td>\n",
+       "      <td>708.5</td>\n",
+       "      <td>65.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-01-02</td>\n",
+       "      <td>1297.1</td>\n",
+       "      <td>119.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-01-03</td>\n",
+       "      <td>1144.5</td>\n",
+       "      <td>105.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-01-04</td>\n",
+       "      <td>1090.0</td>\n",
+       "      <td>100.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-01-05</td>\n",
+       "      <td>893.8</td>\n",
+       "      <td>82.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>85</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-03-27</td>\n",
+       "      <td>1308.0</td>\n",
+       "      <td>120.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>86</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-03-28</td>\n",
+       "      <td>1079.1</td>\n",
+       "      <td>99.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-03-29</td>\n",
+       "      <td>1002.8</td>\n",
+       "      <td>92.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>88</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-03-30</td>\n",
+       "      <td>741.2</td>\n",
+       "      <td>68.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>89</th>\n",
+       "      <td>1</td>\n",
+       "      <td>18</td>\n",
+       "      <td>2019-03-31</td>\n",
+       "      <td>741.2</td>\n",
+       "      <td>68.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>90 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   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",
+       "..        ...           ...         ...          ...        ...\n",
+       "85          1            18  2019-03-27       1308.0      120.0\n",
+       "86          1            18  2019-03-28       1079.1       99.0\n",
+       "87          1            18  2019-03-29       1002.8       92.0\n",
+       "88          1            18  2019-03-30        741.2       68.0\n",
+       "89          1            18  2019-03-31        741.2       68.0\n",
+       "\n",
+       "[90 rows x 5 columns]"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "my_data = retrieve_data(product_code=18, store_code=1, date=['2019-01-01', '2019-03-31'])\n",
+    "my_data"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/03-gd-Case2-datachallenge.ipynb b/03-gd-Case2-datachallenge.ipynb
new file mode 100644
index 0000000..4a506e2
--- /dev/null
+++ b/03-gd-Case2-datachallenge.ipynb
@@ -0,0 +1,555 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5ade6e06",
+   "metadata": {},
+   "source": [
+    "### Case 2\n",
+    "\n",
+    "#### 2) A brand new client sent you two ready-to-go queries. Those are listed below:\n",
+    "\n",
+    "Query 1:\n",
+    "\n",
+    "```\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",
+    "\n",
+    "```\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",
+    "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",
+    "  - ['2019-10-01','2019-12-31']\n",
+    "\n",
+    "\n",
+    "<details>\n",
+    " <summary><b> We are in need of this visualization (click here to see it)! Please, create it with Python</b></summary>\n",
+    "  \n",
+    "| Loja           | Categoria   | TM    | \n",
+    "|----------------|-------------|-------| \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",
+    "  \n",
+    "</details>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "3e772ed3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sqlalchemy import create_engine"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6959a8b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def retrieve_data():\n",
+    "    # 1. Credenciais de acesso\n",
+    "    usuario = 'looqbox-challenge'\n",
+    "    senha = 'looq-challenge'\n",
+    "    host = '35.199.115.174'\n",
+    "    porta = '3306'          # porta padrão do MySQL\n",
+    "    nome_do_banco = 'looqbox-challenge'\n",
+    "\n",
+    "    # 2. String de conexão (URL de conexão)\n",
+    "    # Formato: mysql+pymysql://usuario:senha@host:porta/nome_do_banco\n",
+    "    string_conexao = f'mysql+pymysql://{usuario}:{senha}@{host}:{porta}/{nome_do_banco}'\n",
+    "\n",
+    "    # 3. Motor de conexão (engine)\n",
+    "    engine = create_engine(string_conexao)\n",
+    "\n",
+    "    # 4. Consulta SQL\n",
+    "    query1 = \"\"\"SELECT\n",
+    "        STORE_CODE,\n",
+    "        STORE_NAME,\n",
+    "        START_DATE,\n",
+    "        END_DATE,\n",
+    "        BUSINESS_NAME,\n",
+    "        BUSINESS_CODE\n",
+    "        FROM `looqbox-challenge`.`data_store_cad`;\n",
+    "        \"\"\"\n",
+    "    \n",
+    "    query2 = \"\"\"SELECT\n",
+    "        STORE_CODE,\n",
+    "        DATE,\n",
+    "        SALES_VALUE,\n",
+    "        SALES_QTY\n",
+    "        FROM `looqbox-challenge`.`data_store_sales`\n",
+    "        WHERE DATE BETWEEN '2019-01-01' AND '2019-12-31'\n",
+    "        \"\"\"\n",
+    "\n",
+    "    df1 = pd.read_sql(query1, con=engine)\n",
+    "    df2 = pd.read_sql(query2, con=engine)\n",
+    "\n",
+    "    # 6. retorna o DataFrame\n",
+    "    return df1, df2\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "361ee7b7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df1, df2 = retrieve_data()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "387b82c1",
+   "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": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7ffc99f2",
+   "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": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "90ffb22b",
+   "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>26.33</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>London</td>\n",
+       "      <td>Farma</td>\n",
+       "      <td>28.96</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Madri</td>\n",
+       "      <td>Farma</td>\n",
+       "      <td>29.00</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>29.56</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  26.33\n",
+       "8           London        Farma  28.96\n",
+       "9            Madri        Farma  29.00\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  29.56\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": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = pd.merge(df2, df1, on='STORE_CODE', how='left')\n",
+    "df = df[['STORE_NAME','BUSINESS_NAME','SALES_VALUE','SALES_QTY']]\n",
+    "df = df.groupby(['STORE_NAME','BUSINESS_NAME']).agg({'SALES_VALUE':'sum','SALES_QTY':'sum'}).reset_index()\n",
+    "df['TM'] = df['SALES_VALUE'] / df['SALES_QTY']\n",
+    "df =df.drop(columns=['SALES_VALUE','SALES_QTY'])\n",
+    "df.rename(columns={'STORE_NAME':'Loja','BUSINESS_NAME':'Categoria'}, inplace=True)\n",
+    "df['TM'] = df['TM'].round(2)\n",
+    "df"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/04-gd-Case3-datachallenge.ipynb b/04-gd-Case3-datachallenge.ipynb
new file mode 100644
index 0000000..e32912f
--- /dev/null
+++ b/04-gd-Case3-datachallenge.ipynb
@@ -0,0 +1,629 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "5ade6e06",
+   "metadata": {},
+   "source": [
+    "### Case 3\n",
+    "\n",
+    "#### 3) Building your own visualization\n",
+    "\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": 1,
+   "id": "3e772ed3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from sqlalchemy import create_engine\n",
+    "import matplotlib.pyplot as plt\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6959a8b8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def retrieve_data():\n",
+    "    # 1. Credenciais de acesso\n",
+    "    usuario = 'looqbox-challenge'\n",
+    "    senha = 'looq-challenge'\n",
+    "    host = '35.199.115.174'\n",
+    "    porta = '3306'          # porta padrão do MySQL\n",
+    "    nome_do_banco = 'looqbox-challenge'\n",
+    "\n",
+    "    # 2. String de conexão (URL de conexão)\n",
+    "    # Formato: mysql+pymysql://usuario:senha@host:porta/nome_do_banco\n",
+    "    string_conexao = f'mysql+pymysql://{usuario}:{senha}@{host}:{porta}/{nome_do_banco}'\n",
+    "\n",
+    "    # 3. Motor de conexão (engine)\n",
+    "    engine = create_engine(string_conexao)\n",
+    "\n",
+    "    # 4. Consulta SQL\n",
+    "    query = \"SELECT * FROM `looqbox-challenge`.`IMDB_movies`\"\n",
+    "    df = pd.read_sql(query, con=engine)\n",
+    "\n",
+    "    # 6. retorna o DataFrame\n",
+    "    return df\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "361ee7b7",
+   "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = retrieve_data()\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "cf756318",
+   "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>995</th>\n",
+       "      <td>996</td>\n",
+       "      <td>Secret in Their Eyes</td>\n",
+       "      <td>Crime,Drama,Mystery</td>\n",
+       "      <td>Billy Ray</td>\n",
+       "      <td>Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>111</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>27585</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>45.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>996</th>\n",
+       "      <td>997</td>\n",
+       "      <td>Hostel: Part II</td>\n",
+       "      <td>Horror</td>\n",
+       "      <td>Eli Roth</td>\n",
+       "      <td>Lauren German, Heather Matarazzo, Bijou Philli...</td>\n",
+       "      <td>2007</td>\n",
+       "      <td>94</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>73152</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>46.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>997</th>\n",
+       "      <td>998</td>\n",
+       "      <td>Step Up 2: The Streets</td>\n",
+       "      <td>Drama,Music,Romance</td>\n",
+       "      <td>Jon M. Chu</td>\n",
+       "      <td>Robert Hoffman, Briana Evigan, Cassie Ventura,...</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>98</td>\n",
+       "      <td>6.0</td>\n",
+       "      <td>70699</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>50.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>998</th>\n",
+       "      <td>999</td>\n",
+       "      <td>Search Party</td>\n",
+       "      <td>Adventure,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,Family,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",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Id                   Title                  Genre          Director  \\\n",
+       "995   996    Secret in Their Eyes    Crime,Drama,Mystery         Billy Ray   \n",
+       "996   997         Hostel: Part II                 Horror          Eli Roth   \n",
+       "997   998  Step Up 2: The Streets    Drama,Music,Romance        Jon M. Chu   \n",
+       "998   999            Search Party       Adventure,Comedy    Scot Armstrong   \n",
+       "999  1000              Nine Lives  Comedy,Family,Fantasy  Barry Sonnenfeld   \n",
+       "\n",
+       "                                                Actors  Year  Runtime  Rating  \\\n",
+       "995  Chiwetel Ejiofor, Nicole Kidman, Julia Roberts...  2015      111     6.0   \n",
+       "996  Lauren German, Heather Matarazzo, Bijou Philli...  2007       94     6.0   \n",
+       "997  Robert Hoffman, Briana Evigan, Cassie Ventura,...  2008       98     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",
+       "\n",
+       "     Votes  RevenueMillions  Metascore  \n",
+       "995  27585              NaN       45.0  \n",
+       "996  73152             18.0       46.0  \n",
+       "997  70699             58.0       50.0  \n",
+       "998   4881              NaN       22.0  \n",
+       "999  12435             20.0       11.0  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.tail()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "13776ca6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 11 columns):\n",
+      " #   Column           Non-Null Count  Dtype  \n",
+      "---  ------           --------------  -----  \n",
+      " 0   Id               1000 non-null   int64  \n",
+      " 1   Title            1000 non-null   object \n",
+      " 2   Genre            1000 non-null   object \n",
+      " 3   Director         1000 non-null   object \n",
+      " 4   Actors           1000 non-null   object \n",
+      " 5   Year             1000 non-null   int64  \n",
+      " 6   Runtime          1000 non-null   int64  \n",
+      " 7   Rating           1000 non-null   float64\n",
+      " 8   Votes            1000 non-null   int64  \n",
+      " 9   RevenueMillions  872 non-null    float64\n",
+      " 10  Metascore        936 non-null    float64\n",
+      "dtypes: float64(3), int64(4), object(4)\n",
+      "memory usage: 86.1+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "f7bd369c",
+   "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>Year</th>\n",
+       "      <th>Rating</th>\n",
+       "      <th>Id</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2006</td>\n",
+       "      <td>7.272727</td>\n",
+       "      <td>44</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2007</td>\n",
+       "      <td>7.207547</td>\n",
+       "      <td>53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2008</td>\n",
+       "      <td>6.846154</td>\n",
+       "      <td>52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2009</td>\n",
+       "      <td>7.058824</td>\n",
+       "      <td>51</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2010</td>\n",
+       "      <td>6.883333</td>\n",
+       "      <td>60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2011</td>\n",
+       "      <td>6.888889</td>\n",
+       "      <td>63</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2012</td>\n",
+       "      <td>6.937500</td>\n",
+       "      <td>64</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2013</td>\n",
+       "      <td>6.857143</td>\n",
+       "      <td>91</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>2014</td>\n",
+       "      <td>6.887755</td>\n",
+       "      <td>98</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>2015</td>\n",
+       "      <td>6.629921</td>\n",
+       "      <td>127</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>2016</td>\n",
+       "      <td>6.474747</td>\n",
+       "      <td>297</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Year    Rating   Id\n",
+       "0   2006  7.272727   44\n",
+       "1   2007  7.207547   53\n",
+       "2   2008  6.846154   52\n",
+       "3   2009  7.058824   51\n",
+       "4   2010  6.883333   60\n",
+       "5   2011  6.888889   63\n",
+       "6   2012  6.937500   64\n",
+       "7   2013  6.857143   91\n",
+       "8   2014  6.887755   98\n",
+       "9   2015  6.629921  127\n",
+       "10  2016  6.474747  297"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Média de Avaliação dos filmes por ano de lançamento\n",
+    "df = df.dropna(subset=['Rating', 'Year'])  # Remove linhas com valores nulos em 'Rating' ou 'Year'\n",
+    "df_years = df.groupby('Year').agg({'Rating': 'mean','Id': 'count' }).reset_index()\n",
+    "df_years"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "ace0f01b",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
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+      "text/plain": [
+       "<Figure size 1200x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# tema do seaborn\n",
+    "sns.set_theme(style=\"white\")\n",
+    "fig, ax1 = plt.subplots(figsize=(12, 6))\n",
+    "\n",
+    "# Certifique-se de que os dados estão ordenados por ano\n",
+    "df_years = df_years.sort_values('Year')\n",
+    "\n",
+    "# --- GRÁFICO 1: Quantidade de Filmes (Barras) ---\n",
+    "ax1.bar(df_years['Year'], df_years['Id'], color='royalblue', alpha=0.6, width=0.6, label='Quantidade de Filmes')\n",
+    "ax1.set_xlabel('Year', fontsize=12)\n",
+    "ax1.set_ylabel('Quantidade de Filmes', color='b', fontsize=12)\n",
+    "ax1.tick_params(axis='y', labelcolor='b')\n",
+    "\n",
+    "plt.xticks(df_years['Year'], rotation=45)\n",
+    "\n",
+    "# --- GRÁFICO 2: Média de Avaliação (Linha no Eixo secundário) ---\n",
+    "ax2 = ax1.twinx()  # Cria o segundo eixo Y compartilhando o mesmo eixo X numérico\n",
+    "sns.lineplot(data=df_years, x='Year', y='Rating', ax=ax2, color='red', marker='o', linewidth=2)\n",
+    "ax2.set_ylabel('Média de Avaliação', color='r', fontsize=12)\n",
+    "ax2.tick_params(axis='y', labelcolor='r')\n",
+    "\n",
+    "# Remove as linhas de grade de ambos para não sobrepor o visual\n",
+    "ax1.grid(False)\n",
+    "ax2.grid(False)\n",
+    "\n",
+    "#ajusta o eixo da avaliação de 0 a 10\n",
+    "ax2.set_ylim(6.4, 7.5)\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "43da8f75",
+   "metadata": {},
+   "source": [
+    "Nesse gráifico podemos verificar a evolução da quantidade de filmes lançados anualmente, porém tempos um piora na qualidade dos filmes que pode ser verificada pela linha vermelha no gráfico. A queda nas avaliações entre 2006 e 2016 foi de 7.3 para 6.5, o que representa uma queda percentual de 12%."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "397e10fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.12324492979719182"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "(df_years.Rating.max()-df_years.Rating.min())/df_years.Rating.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "f7474aae",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "base",
+   "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
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