From a551570760c052f02da22567fc5adb47e173da7c Mon Sep 17 00:00:00 2001
From: Carlos Augusto Rodrigues Teixeira
 <135044199+carlos1818augusto@users.noreply.github.com>
Date: Fri, 26 Jun 2026 15:05:18 -0300
Subject: [PATCH 01/11] Add requirements for challenge solution

---
 requirements.txt | 6 ++++++
 1 file changed, 6 insertions(+)
 create mode 100644 requirements.txt

diff --git a/requirements.txt b/requirements.txt
new file mode 100644
index 0000000..8566940
--- /dev/null
+++ b/requirements.txt
@@ -0,0 +1,6 @@
+pandas>=3.0.0
+PyMySQL>=1.1.0
+SQLAlchemy>=2.0.0
+matplotlib>=3.8.0
+reportlab>=4.0.0
+python-dotenv>=1.0.0

From 5d4ee070461109133c481bfbf0488f775542bd8d Mon Sep 17 00:00:00 2001
From: Carlos Augusto Rodrigues Teixeira
 <135044199+carlos1818augusto@users.noreply.github.com>
Date: Fri, 26 Jun 2026 15:06:23 -0300
Subject: [PATCH 02/11] Add Python and environment files to .gitignore

---
 .gitignore | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 .gitignore

diff --git a/.gitignore b/.gitignore
new file mode 100644
index 0000000..0b2fb07
--- /dev/null
+++ b/.gitignore
@@ -0,0 +1,5 @@
+__pycache__/
+*.py[cod]
+.env
+.venv/
+venv/

From 9a753141e226a34d2a15469544abd2a8305e3ed0 Mon Sep 17 00:00:00 2001
From: Carlos Augusto Rodrigues Teixeira
 <135044199+carlos1818augusto@users.noreply.github.com>
Date: Fri, 26 Jun 2026 15:06:33 -0300
Subject: [PATCH 03/11] Add example environment variables for configuration

---
 .env.example | 5 +++++
 1 file changed, 5 insertions(+)
 create mode 100644 .env.example

diff --git a/.env.example b/.env.example
new file mode 100644
index 0000000..83fbdd0
--- /dev/null
+++ b/.env.example
@@ -0,0 +1,5 @@
+DB_HOST=35.199.115.174
+DB_PORT=3306
+DB_NAME=looqbox-challenge
+DB_USER=looqbox-challenge
+DB_PASSWORD=

From ba3fe66de592ff9dd899f17a9e94cdd5ae023486 Mon Sep 17 00:00:00 2001
From: Carlos Augusto Rodrigues Teixeira
 <135044199+carlos1818augusto@users.noreply.github.com>
Date: Fri, 26 Jun 2026 15:06:43 -0300
Subject: [PATCH 04/11] Add README for Looqbox Data Challenge solution

---
 README_SOLUTION.md | 28 ++++++++++++++++++++++++++++
 1 file changed, 28 insertions(+)
 create mode 100644 README_SOLUTION.md

diff --git a/README_SOLUTION.md b/README_SOLUTION.md
new file mode 100644
index 0000000..6a589c8
--- /dev/null
+++ b/README_SOLUTION.md
@@ -0,0 +1,28 @@
+# Looqbox Data Challenge - Solução
+
+Esta solução contém as respostas em SQL, código Python reutilizável e artefatos gerados para o desafio técnico da Looqbox.
+
+## Arquivos
+
+- `sql/answers.sql`: consultas SQL das três perguntas.
+- `src/db.py`: configuração do banco e criação da engine SQLAlchemy.
+- `src/data_access.py`: função reutilizável `retrieve_data(product_code, store_code, date)`.
+- `src/generate_outputs.py`: executa as respostas SQL, a transformação do caso 2, o gráfico IMDB e a geração do PDF.
+- `output/`: CSVs gerados, gráfico PNG e PDF final.
+
+## Como executar
+
+Crie um arquivo `.env` a partir de `.env.example` e preencha `DB_PASSWORD`.
+
+```powershell
+py -m pip install -r requirements.txt
+py src\generate_outputs.py
+```
+
+O schema do banco se chama `looqbox-challenge`, com hífen. O código Python conecta diretamente nesse schema; os arquivos SQL usam crase quando necessário.
+
+## Observações
+
+- O caso 1 usa SQL parametrizado e valida as datas antes da consulta.
+- O caso 2 mantem as duas consultas do cliente inalteradas e aplica o filtro de datas solicitado no pandas.
+- O caso 3 expande os gêneros da tabela IMDB separados por vírgula e compara os principais gêneros por receita média.

From c13de17746d154476949380692e7f4f5b0bdfce5 Mon Sep 17 00:00:00 2001
From: Carlos Augusto Rodrigues Teixeira
 <135044199+carlos1818augusto@users.noreply.github.com>
Date: Fri, 26 Jun 2026 15:06:52 -0300
Subject: [PATCH 05/11] Add SQL queries for product analysis and sales

---
 sql/answers.sql | 34 ++++++++++++++++++++++++++++++++++
 1 file changed, 34 insertions(+)
 create mode 100644 sql/answers.sql

diff --git a/sql/answers.sql b/sql/answers.sql
new file mode 100644
index 0000000..f5b0a77
--- /dev/null
+++ b/sql/answers.sql
@@ -0,0 +1,34 @@
+USE `looqbox-challenge`;
+
+-- 1) What are the 10 most expensive products in the company?
+SELECT
+    PRODUCT_COD,
+    PRODUCT_NAME,
+    PRODUCT_VAL,
+    DEP_NAME,
+    DEP_COD,
+    SECTION_NAME,
+    SECTION_COD
+FROM data_product
+ORDER BY PRODUCT_VAL DESC
+LIMIT 10;
+
+-- 2) What sections do the 'BEBIDAS' and 'PADARIA' departments have?
+SELECT DISTINCT
+    DEP_NAME,
+    SECTION_COD,
+    SECTION_NAME
+FROM 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
+    sc.BUSINESS_NAME,
+    ROUND(SUM(ps.SALES_VALUE), 2) AS TOTAL_SALES_VALUE
+FROM data_product_sales AS ps
+INNER JOIN data_store_cad AS sc
+    ON CAST(ps.STORE_CODE AS UNSIGNED) = sc.STORE_CODE
+WHERE ps.DATE BETWEEN '2019-01-01' AND '2019-03-31'
+GROUP BY sc.BUSINESS_NAME
+ORDER BY TOTAL_SALES_VALUE DESC;

From 5147375f0084484065c49b91879be2786c5cd9a3 Mon Sep 17 00:00:00 2001
From: Carlos Augusto Rodrigues Teixeira
 <135044199+carlos1818augusto@users.noreply.github.com>
Date: Fri, 26 Jun 2026 15:07:01 -0300
Subject: [PATCH 06/11] Add src/db.py

---
 src/db.py | 43 +++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 43 insertions(+)
 create mode 100644 src/db.py

diff --git a/src/db.py b/src/db.py
new file mode 100644
index 0000000..048738d
--- /dev/null
+++ b/src/db.py
@@ -0,0 +1,43 @@
+from __future__ import annotations
+
+import os
+from dataclasses import dataclass
+
+from dotenv import load_dotenv
+from sqlalchemy import Engine, create_engine
+from sqlalchemy.engine import URL
+
+
+load_dotenv()
+
+
+@dataclass(frozen=True)
+class DatabaseConfig:
+    host: str = os.getenv("DB_HOST", "35.199.115.174")
+    port: int = int(os.getenv("DB_PORT", "3306"))
+    database: str = os.getenv("DB_NAME", "looqbox-challenge")
+    user: str = os.getenv("DB_USER", "looqbox-challenge")
+    password: str | None = os.getenv("DB_PASSWORD")
+
+    @classmethod
+    def from_env(cls) -> "DatabaseConfig":
+        config = cls()
+        if not config.password:
+            raise RuntimeError(
+                "DB_PASSWORD is required. Copy .env.example to .env and fill the password, "
+                "or export DB_PASSWORD before running the scripts."
+            )
+        return config
+
+
+def get_engine(config: DatabaseConfig | None = None) -> Engine:
+    config = config or DatabaseConfig.from_env()
+    url = URL.create(
+        "mysql+pymysql",
+        username=config.user,
+        password=config.password,
+        host=config.host,
+        port=config.port,
+        database=config.database,
+    )
+    return create_engine(url, pool_pre_ping=True)

From 7fdd69a52e52b15d67e7eda237a54d713cd426fb Mon Sep 17 00:00:00 2001
From: Carlos Augusto Rodrigues Teixeira
 <135044199+carlos1818augusto@users.noreply.github.com>
Date: Fri, 26 Jun 2026 15:07:11 -0300
Subject: [PATCH 07/11] Implement data retrieval function with filters

Added functions to retrieve data from data_product_sales with optional filters for product code, store code, and date range.
---
 src/data_access.py | 65 ++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 65 insertions(+)
 create mode 100644 src/data_access.py

diff --git a/src/data_access.py b/src/data_access.py
new file mode 100644
index 0000000..f0628f2
--- /dev/null
+++ b/src/data_access.py
@@ -0,0 +1,65 @@
+from __future__ import annotations
+
+from datetime import date as Date
+from typing import Sequence
+
+import pandas as pd
+from sqlalchemy import Engine, text
+
+from db import get_engine
+
+
+def _parse_iso_date(value: str) -> Date:
+    try:
+        return Date.fromisoformat(value)
+    except ValueError as exc:
+        raise ValueError(f"Invalid date '{value}'. Expected ISO format YYYY-MM-DD.") from exc
+
+
+def retrieve_data(
+    product_code: int | None = None,
+    store_code: int | str | None = None,
+    date: Sequence[str] | None = None,
+    engine: Engine | None = None,
+) -> pd.DataFrame:
+    """Retrieve rows from data_product_sales using optional, parameterized filters.
+
+    Parameters are optional to keep the function flexible for other teams. When
+    `date` is provided, it must be a two-item interval: [start_date, end_date].
+    """
+    filters: list[str] = []
+    params: dict[str, object] = {}
+
+    if product_code is not None:
+        if not isinstance(product_code, int):
+            raise TypeError("product_code must be an integer.")
+        filters.append("PRODUCT_CODE = :product_code")
+        params["product_code"] = product_code
+
+    if store_code is not None:
+        filters.append("STORE_CODE = :store_code")
+        params["store_code"] = str(store_code)
+
+    if date is not None:
+        if len(date) != 2:
+            raise ValueError("date must contain exactly two values: [start_date, end_date].")
+        start_date = _parse_iso_date(date[0])
+        end_date = _parse_iso_date(date[1])
+        if start_date > end_date:
+            raise ValueError("start_date cannot be greater than end_date.")
+        filters.append("DATE BETWEEN :start_date AND :end_date")
+        params["start_date"] = start_date
+        params["end_date"] = end_date
+
+    query = "SELECT * FROM data_product_sales"
+    if filters:
+        query += " WHERE " + " AND ".join(filters)
+    query += " ORDER BY DATE, STORE_CODE, PRODUCT_CODE"
+
+    owns_engine = engine is None
+    engine = engine or get_engine()
+    try:
+        return pd.read_sql_query(text(query), engine, params=params)
+    finally:
+        if owns_engine:
+            engine.dispose()

From 32a5a7ba93f527396cf2eafe41eb6aa74076eaa9 Mon Sep 17 00:00:00 2001
From: Carlos Augusto Rodrigues Teixeira
 <135044199+carlos1818augusto@users.noreply.github.com>
Date: Fri, 26 Jun 2026 15:07:21 -0300
Subject: [PATCH 08/11] Add src/generate_outputs.py

---
 src/generate_outputs.py | 330 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 330 insertions(+)
 create mode 100644 src/generate_outputs.py

diff --git a/src/generate_outputs.py b/src/generate_outputs.py
new file mode 100644
index 0000000..868578d
--- /dev/null
+++ b/src/generate_outputs.py
@@ -0,0 +1,330 @@
+from __future__ import annotations
+
+from decimal import Decimal
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import pandas as pd
+from reportlab.lib import colors
+from reportlab.lib.enums import TA_CENTER
+from reportlab.lib.pagesizes import A4, landscape
+from reportlab.lib.styles import ParagraphStyle, getSampleStyleSheet
+from reportlab.lib.units import inch
+from reportlab.platypus import Image, PageBreak, Paragraph, Preformatted, SimpleDocTemplate, Spacer, Table, TableStyle
+from sqlalchemy import Engine, text
+
+from data_access import retrieve_data
+from db import get_engine
+
+
+ROOT = Path(__file__).resolve().parents[1]
+OUTPUT = ROOT / "output"
+
+SQL_MOST_EXPENSIVE = """
+SELECT
+    PRODUCT_COD,
+    PRODUCT_NAME,
+    PRODUCT_VAL,
+    DEP_NAME,
+    DEP_COD,
+    SECTION_NAME,
+    SECTION_COD
+FROM data_product
+ORDER BY PRODUCT_VAL DESC
+LIMIT 10;
+""".strip()
+
+SQL_DEPARTMENT_SECTIONS = """
+SELECT DISTINCT
+    DEP_NAME,
+    SECTION_COD,
+    SECTION_NAME
+FROM data_product
+WHERE DEP_NAME IN ('BEBIDAS', 'PADARIA')
+ORDER BY DEP_NAME, SECTION_NAME;
+""".strip()
+
+SQL_Q1_BUSINESS_AREA_SALES = """
+SELECT
+    sc.BUSINESS_NAME,
+    ROUND(SUM(ps.SALES_VALUE), 2) AS TOTAL_SALES_VALUE
+FROM data_product_sales AS ps
+INNER JOIN data_store_cad AS sc
+    ON CAST(ps.STORE_CODE AS UNSIGNED) = sc.STORE_CODE
+WHERE ps.DATE BETWEEN '2019-01-01' AND '2019-03-31'
+GROUP BY sc.BUSINESS_NAME
+ORDER BY TOTAL_SALES_VALUE DESC;
+""".strip()
+
+CLIENT_QUERY_STORE_CAD = """
+SELECT
+      STORE_CODE,
+      STORE_NAME,
+      START_DATE,
+      END_DATE,
+      BUSINESS_NAME,
+      BUSINESS_CODE
+FROM data_store_cad
+""".strip()
+
+CLIENT_QUERY_STORE_SALES = """
+SELECT
+        STORE_CODE,
+        DATE,
+        SALES_VALUE,
+        SALES_QTY
+FROM data_store_sales
+WHERE DATE BETWEEN '2019-01-01' AND '2019-12-31'
+""".strip()
+
+
+def run_sql_tests(engine: Engine) -> dict[str, pd.DataFrame]:
+    return {
+        "sql_1_most_expensive_products": pd.read_sql_query(text(SQL_MOST_EXPENSIVE), engine),
+        "sql_2_department_sections": pd.read_sql_query(text(SQL_DEPARTMENT_SECTIONS), engine),
+        "sql_3_q1_sales_by_business_area": pd.read_sql_query(text(SQL_Q1_BUSINESS_AREA_SALES), engine),
+    }
+
+
+def build_case_2_visualization_table(engine: Engine) -> pd.DataFrame:
+    stores = pd.read_sql_query(text(CLIENT_QUERY_STORE_CAD), engine)
+    sales = pd.read_sql_query(text(CLIENT_QUERY_STORE_SALES), engine)
+
+    sales["DATE"] = pd.to_datetime(sales["DATE"])
+    period_sales = sales[sales["DATE"].between("2019-10-01", "2019-12-31")].copy()
+
+    merged = period_sales.merge(stores, on="STORE_CODE", how="left", validate="many_to_one")
+    result = (
+        merged.groupby(["STORE_NAME", "BUSINESS_NAME"], as_index=False)
+        .agg(SALES_VALUE=("SALES_VALUE", "sum"), SALES_QTY=("SALES_QTY", "sum"))
+        .assign(TM=lambda df: (df["SALES_VALUE"] / df["SALES_QTY"]).round(2))
+        .rename(columns={"STORE_NAME": "Loja", "BUSINESS_NAME": "Categoria"})
+        [["Loja", "Categoria", "TM"]]
+        .sort_values("Loja")
+        .reset_index(drop=True)
+    )
+    return result
+
+
+def build_imdb_chart(engine: Engine) -> tuple[pd.DataFrame, Path]:
+    movies = pd.read_sql_query(
+        text(
+            """
+            SELECT Title, Genre, Rating, Votes, RevenueMillions
+            FROM IMDB_movies
+            WHERE RevenueMillions IS NOT NULL
+            """
+        ),
+        engine,
+    )
+
+    exploded = movies.assign(Genre=movies["Genre"].str.split(",")).explode("Genre")
+    exploded["Genre"] = exploded["Genre"].str.strip()
+
+    genre_summary = (
+        exploded.groupby("Genre", as_index=False)
+        .agg(
+            movies=("Title", "count"),
+            avg_revenue_millions=("RevenueMillions", "mean"),
+            avg_rating=("Rating", "mean"),
+            total_votes=("Votes", "sum"),
+        )
+        .query("movies >= 15")
+        .assign(
+            avg_revenue_millions=lambda df: df["avg_revenue_millions"].round(2),
+            avg_rating=lambda df: df["avg_rating"].round(2),
+        )
+        .sort_values("avg_revenue_millions", ascending=False)
+        .head(10)
+        .reset_index(drop=True)
+    )
+
+    chart_data = genre_summary.sort_values("avg_revenue_millions", ascending=True)
+    chart_path = OUTPUT / "imdb_top_genres_by_avg_revenue.png"
+
+    fig, ax = plt.subplots(figsize=(10, 6))
+    ax.barh(chart_data["Genre"], chart_data["avg_revenue_millions"], color="#2F6B7C")
+    ax.set_title("Filmes IMDB: gêneros com maior receita média")
+    ax.set_xlabel("Receita média (US$ milhões)")
+    ax.set_ylabel("")
+    ax.grid(axis="x", alpha=0.25)
+    for index, value in enumerate(chart_data["avg_revenue_millions"]):
+        ax.text(float(value) + 2, index, f"{value:.2f}", va="center", fontsize=9)
+    fig.tight_layout()
+    fig.savefig(chart_path, dpi=180)
+    plt.close(fig)
+
+    return genre_summary, chart_path
+
+
+def save_outputs(frames: dict[str, pd.DataFrame], case_2: pd.DataFrame, imdb_summary: pd.DataFrame) -> None:
+    OUTPUT.mkdir(exist_ok=True)
+    for name, df in frames.items():
+        df.to_csv(OUTPUT / f"{name}.csv", index=False)
+    case_2.to_csv(OUTPUT / "case_2_store_ticket.csv", index=False)
+    imdb_summary.to_csv(OUTPUT / "imdb_genre_summary.csv", index=False)
+
+
+def _format_value(value: object) -> str:
+    if isinstance(value, Decimal):
+        return f"{float(value):,.2f}"
+    if isinstance(value, float):
+        return f"{value:,.2f}"
+    if hasattr(value, "isoformat"):
+        return value.isoformat()
+    return "" if pd.isna(value) else str(value)
+
+
+def _table_from_df(df: pd.DataFrame, widths: list[float] | None = None, max_rows: int | None = None) -> Table:
+    styles = getSampleStyleSheet()
+    cell_style = ParagraphStyle(
+        "TableCell",
+        parent=styles["BodyText"],
+        fontName="Helvetica",
+        fontSize=7,
+        leading=8,
+    )
+    data_frame = df.head(max_rows) if max_rows else df
+    data = [[Paragraph(str(col), cell_style) for col in data_frame.columns]]
+    for _, row in data_frame.iterrows():
+        data.append([Paragraph(_format_value(value), cell_style) for value in row])
+
+    table = Table(data, colWidths=widths, repeatRows=1)
+    table.setStyle(
+        TableStyle(
+            [
+                ("BACKGROUND", (0, 0), (-1, 0), colors.HexColor("#D9EAF0")),
+                ("TEXTCOLOR", (0, 0), (-1, 0), colors.HexColor("#17313A")),
+                ("GRID", (0, 0), (-1, -1), 0.25, colors.HexColor("#A8B7BD")),
+                ("VALIGN", (0, 0), (-1, -1), "TOP"),
+                ("LEFTPADDING", (0, 0), (-1, -1), 4),
+                ("RIGHTPADDING", (0, 0), (-1, -1), 4),
+                ("TOPPADDING", (0, 0), (-1, -1), 3),
+                ("BOTTOMPADDING", (0, 0), (-1, -1), 3),
+            ]
+        )
+    )
+    return table
+
+
+def _add_code(story: list, code: str, title: str) -> None:
+    styles = getSampleStyleSheet()
+    story.append(Paragraph(title, styles["Heading3"]))
+    story.append(Preformatted(code, ParagraphStyle("Code", fontName="Courier", fontSize=6, leading=7)))
+    story.append(Spacer(1, 0.12 * inch))
+
+
+def generate_pdf(
+    frames: dict[str, pd.DataFrame],
+    case_2: pd.DataFrame,
+    imdb_summary: pd.DataFrame,
+    chart_path: Path,
+    retrieve_sample: pd.DataFrame,
+) -> Path:
+    pdf_path = OUTPUT / "looqbox_data_challenge_carlos.pdf"
+    doc = SimpleDocTemplate(
+        str(pdf_path),
+        pagesize=landscape(A4),
+        rightMargin=28,
+        leftMargin=28,
+        topMargin=26,
+        bottomMargin=26,
+    )
+    styles = getSampleStyleSheet()
+    styles.add(ParagraphStyle("CenteredTitle", parent=styles["Title"], alignment=TA_CENTER))
+
+    story: list = []
+    story.append(Paragraph("Looqbox Data Challenge", styles["CenteredTitle"]))
+    story.append(Paragraph("Candidato: Carlos", styles["Normal"]))
+    story.append(Paragraph("Stack: Python, SQL, MySQL, pandas e matplotlib.", styles["Normal"]))
+    story.append(Spacer(1, 0.15 * inch))
+
+    story.append(Paragraph("Teste SQL", styles["Heading2"]))
+    _add_code(story, SQL_MOST_EXPENSIVE, "1) Dez produtos mais caros")
+    story.append(
+        _table_from_df(
+            frames["sql_1_most_expensive_products"],
+            widths=[48, 250, 54, 72, 42, 100, 46],
+        )
+    )
+    story.append(PageBreak())
+
+    _add_code(story, SQL_DEPARTMENT_SECTIONS, "2) Seções dos departamentos BEBIDAS e PADARIA")
+    story.append(_table_from_df(frames["sql_2_department_sections"], widths=[90, 70, 160]))
+    story.append(Spacer(1, 0.2 * inch))
+
+    _add_code(story, SQL_Q1_BUSINESS_AREA_SALES, "3) Vendas de produtos por área de negócio no 1º trimestre de 2019")
+    story.append(_table_from_df(frames["sql_3_q1_sales_by_business_area"], widths=[130, 130]))
+    story.append(PageBreak())
+
+    story.append(Paragraph("Caso 1 - Recuperação dinâmica de dados", styles["Heading2"]))
+    _add_code(
+        story,
+        """my_data = retrieve_data(
+    product_code=18,
+    store_code=1,
+    date=["2019-01-01", "2019-01-31"],
+    engine=engine,
+)""",
+        "Exemplo de chamada",
+    )
+    story.append(Paragraph("Resultado de exemplo para product_code=18, store_code=1 e janeiro de 2019:", styles["Normal"]))
+    story.append(_table_from_df(retrieve_sample.head(10), widths=[70, 80, 80, 90, 80]))
+    story.append(Spacer(1, 0.2 * inch))
+    story.append(
+        Paragraph(
+            "A função mantém todos os filtros opcionais, valida as entradas e usa parâmetros vinculados para evitar SQL injection.",
+            styles["Normal"],
+        )
+    )
+    story.append(PageBreak())
+
+    story.append(Paragraph("Caso 2 - Consultas do cliente e tabela solicitada", styles["Heading2"]))
+    _add_code(story, CLIENT_QUERY_STORE_CAD, "Consulta 1 do cliente, usada como fornecida")
+    _add_code(story, CLIENT_QUERY_STORE_SALES, "Consulta 2 do cliente, usada como fornecida")
+    story.append(
+        Paragraph(
+            "O intervalo de datas solicitado foi aplicado no pandas depois da carga da consulta 2, mantendo as duas consultas do cliente inalteradas. "
+            "O TM foi calculado como o total de SALES_VALUE dividido pelo total de SALES_QTY para cada loja.",
+            styles["Normal"],
+        )
+    )
+    story.append(_table_from_df(case_2, widths=[150, 110, 60]))
+    story.append(PageBreak())
+
+    story.append(Paragraph("Caso 3 - Visualizacao com IMDB", styles["Heading2"]))
+    story.append(
+        Paragraph(
+            "Escolhi um grafico de barras horizontais porque o objetivo e comparar categorias com clareza. "
+            "A coluna Genre possui múltiplos valores por filme, então cada filme foi expandido para suas tags de gênero. "
+            "Considerei apenas gêneros com pelo menos 15 filmes e receita não nula para reduzir ruído de grupos muito pequenos.",
+            styles["Normal"],
+        )
+    )
+    story.append(Image(str(chart_path), width=7.2 * inch, height=4.0 * inch))
+    story.append(Spacer(1, 0.15 * inch))
+    story.append(_table_from_df(imdb_summary, widths=[90, 60, 120, 80, 90]))
+
+    doc.build(story)
+    return pdf_path
+
+
+def main() -> None:
+    OUTPUT.mkdir(exist_ok=True)
+    engine = get_engine()
+    try:
+        sql_frames = run_sql_tests(engine)
+        retrieve_sample = retrieve_data(product_code=18, store_code=1, date=["2019-01-01", "2019-01-31"], engine=engine)
+        case_2 = build_case_2_visualization_table(engine)
+        imdb_summary, chart_path = build_imdb_chart(engine)
+        save_outputs(sql_frames, case_2, imdb_summary)
+        pdf_path = generate_pdf(sql_frames, case_2, imdb_summary, chart_path, retrieve_sample)
+    finally:
+        engine.dispose()
+
+    print(f"Generated outputs in: {OUTPUT}")
+    print(f"PDF: {pdf_path}")
+
+
+if __name__ == "__main__":
+    main()

From 62f33af939783127c53e00270425e81934de516c Mon Sep 17 00:00:00 2001
From: Carlos Augusto Rodrigues Teixeira
 <135044199+carlos1818augusto@users.noreply.github.com>
Date: Fri, 26 Jun 2026 15:08:26 -0300
Subject: [PATCH 09/11] Fix spelling and accentuation in output generation

Corrected spelling and accentuation in the text for clarity.
---
 src/generate_outputs.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/src/generate_outputs.py b/src/generate_outputs.py
index 868578d..92afefd 100644
--- a/src/generate_outputs.py
+++ b/src/generate_outputs.py
@@ -292,10 +292,10 @@ def generate_pdf(
     story.append(_table_from_df(case_2, widths=[150, 110, 60]))
     story.append(PageBreak())
 
-    story.append(Paragraph("Caso 3 - Visualizacao com IMDB", styles["Heading2"]))
+    story.append(Paragraph("Caso 3 - Visualização com IMDB", styles["Heading2"]))
     story.append(
         Paragraph(
-            "Escolhi um grafico de barras horizontais porque o objetivo e comparar categorias com clareza. "
+            "Escolhi um gráfico de barras horizontais porque o objetivo é comparar categorias com clareza. "
             "A coluna Genre possui múltiplos valores por filme, então cada filme foi expandido para suas tags de gênero. "
             "Considerei apenas gêneros com pelo menos 15 filmes e receita não nula para reduzir ruído de grupos muito pequenos.",
             styles["Normal"],

From e669aa0fd5e58bc5eb20212524abba00a56387b0 Mon Sep 17 00:00:00 2001
From: Carlos Augusto Rodrigues Teixeira
 <135044199+carlos1818augusto@users.noreply.github.com>
Date: Fri, 26 Jun 2026 15:08:36 -0300
Subject: [PATCH 10/11] Fix typo in README_SOLUTION.md

---
 README_SOLUTION.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README_SOLUTION.md b/README_SOLUTION.md
index 6a589c8..5e40fd2 100644
--- a/README_SOLUTION.md
+++ b/README_SOLUTION.md
@@ -24,5 +24,5 @@ O schema do banco se chama `looqbox-challenge`, com hífen. O código Python con
 ## Observações
 
 - O caso 1 usa SQL parametrizado e valida as datas antes da consulta.
-- O caso 2 mantem as duas consultas do cliente inalteradas e aplica o filtro de datas solicitado no pandas.
+- O caso 2 mantém as duas consultas do cliente inalteradas e aplica o filtro de datas solicitado no pandas.
 - O caso 3 expande os gêneros da tabela IMDB separados por vírgula e compara os principais gêneros por receita média.

From a81c54409ee71cd9a9487a230212c1f681448afa Mon Sep 17 00:00:00 2001
From: Carlos Augusto Rodrigues Teixeira
 <135044199+carlos1818augusto@users.noreply.github.com>
Date: Fri, 26 Jun 2026 15:14:25 -0300
Subject: [PATCH 11/11] Add AI assistance disclosure to README

---
 README_SOLUTION.md | 4 ++++
 1 file changed, 4 insertions(+)

diff --git a/README_SOLUTION.md b/README_SOLUTION.md
index 5e40fd2..fa7c1dd 100644
--- a/README_SOLUTION.md
+++ b/README_SOLUTION.md
@@ -26,3 +26,7 @@ O schema do banco se chama `looqbox-challenge`, com hífen. O código Python con
 - O caso 1 usa SQL parametrizado e valida as datas antes da consulta.
 - O caso 2 mantém as duas consultas do cliente inalteradas e aplica o filtro de datas solicitado no pandas.
 - O caso 3 expande os gêneros da tabela IMDB separados por vírgula e compara os principais gêneros por receita média.
+
+Durante a resolução do desafio, utilizei apoio pontual de ferramenta de IA/LLM para revisão de trechos específicos, organização textual da documentação e validação de dúvidas pontuais durante o desenvolvimento.
+
+A construção da lógica, execução dos testes, interpretação dos resultados e estruturação principal da solução foram realizadas por mim, com base no meu conhecimento em Python, SQL e análise de dados.