Solução do desafio

main.py

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+import pandas as pd
+from sqlalchemy import create_engine, text
+
+import matplotlib.pyplot as plt
+from sqlalchemy import create_engine
+
+### CONEXÃO
+USER = "looqbox-challenge"
+PASSWORD = "looq-challenge"
+HOST = "35.199.115.174"
+PORT = 3306
+
+engine = create_engine(
+    f"mysql+pymysql://{USER}:{PASSWORD}@{HOST}:{PORT}/looqbox-challenge"
+)
+
+''' SQL TEST - query questão 1
+
+query = """
+SELECT
+    PRODUCT_COD,
+    PRODUCT_NAME,
+    PRODUCT_VAL
+FROM `looqbox-challenge`.data_product
+ORDER BY PRODUCT_VAL DESC
+LIMIT 10
+"""
+
+df = pd.read_sql(query, engine)
+
+print(df)
+'''
+
+''' SQL TEST - query questão 2
+query = """
+SELECT DISTINCT
+    DEP_NAME,
+    SECTION_COD,
+    SECTION_NAME
+FROM `looqbox-challenge`.data_product
+WHERE DEP_NAME IN ('BEBIDAS','PADARIA')
+ORDER BY DEP_NAME, SECTION_NAME;
+"""
+
+df = pd.read_sql(query, engine)
+
+print(df)
+'''
+
+''' SQL TEST - query questão 3
+query = """
+SELECT
+    c.BUSINESS_NAME,
+    c.BUSINESS_CODE,
+    ROUND(SUM(s.SALES_VALUE),2) AS TOTAL_SALES
+FROM `looqbox-challenge`.data_store_sales s
+
+JOIN `looqbox-challenge`.data_store_cad c
+ON s.STORE_CODE = c.STORE_CODE
+
+WHERE s.DATE BETWEEN '2019-01-01'
+AND '2019-03-31'
+
+GROUP BY
+    c.BUSINESS_NAME,
+    c.BUSINESS_CODE
+
+ORDER BY TOTAL_SALES DESC;
+"""
+
+df = pd.read_sql(query, engine)
+
+print(df)
+'''
+
+
+
+''' CASE 1 
+### FUNÇÃO DINÂMICA
+
+def retrieve_data(engine, product_code=None, store_code=None, date=None):
+
+    # Condições para filtrar a consulta
+    conditions=[]
+
+    if not product_code and not store_code and not date:
+        raise ValueError(
+            "Informe pelo menos um filtro: product_code, store_code ou date."
+        )
+
+    if product_code:
+        conditions.append(
+            f"PRODUCT_CODE={product_code}"
+        )
+
+    if store_code:
+        conditions.append(
+            f"STORE_CODE='{store_code}'"
+        )
+
+    if date:
+        conditions.append(
+            f"DATE BETWEEN '{date[0]}' AND '{date[1]}'"
+        )
+
+    where=""
+
+    if conditions:
+        where="WHERE " + " AND ".join(conditions)
+
+    # Monta a query
+    query=f"""
+        SELECT *
+        FROM `looqbox-challenge`.data_product_sales
+        {where}
+    """
+
+    df = pd.read_sql(query, engine)
+
+    # Coloca com inteiro pois quantidade de produto nesse caso faz mais sentido ser inteiro
+    df["SALES_QTY"] = df["SALES_QTY"].astype(int)
+
+    return df
+
+### EXECUÇÃO
+df = retrieve_data(
+    engine,
+    product_code=18,
+    store_code=1,
+    date=[
+        "2019-01-01",
+        "2019-01-31"
+    ]
+)
+
+# Para visualizar duas casas decimais no valor financeiro
+pd.set_option('display.float_format','{:.2f}'.format)
+
+print(df)
+'''
+
+
+
+''' CASE 2 - PARTE 1 
+df1 = pd.read_sql(query1, engine)
+df2 = pd.read_sql(query2, engine)
+
+df2["DATE"] = pd.to_datetime(df2["DATE"])
+
+df2_filtered = df2[
+    df2["DATE"].between("2019-10-01", "2019-12-31")
+]
+
+
+print(df1)
+print(df2_filtered)
+'''
+
+''' CASE 2 - PARTE 2 
+query1 = """
+SELECT STORE_CODE, STORE_NAME, START_DATE, END_DATE, BUSINESS_NAME, BUSINESS_CODE
+FROM data_store_cad
+"""
+
+query2 = """
+SELECT STORE_CODE, DATE, SALES_VALUE, SALES_QTY
+FROM data_store_sales
+WHERE DATE BETWEEN '2019-01-01' AND '2019-12-31'
+"""
+
+df_store = pd.read_sql(query1, engine)
+df_sales = pd.read_sql(query2, engine)
+
+# muda campo DATE para datetime
+df_sales["DATE"] = pd.to_datetime(df_sales["DATE"])
+
+# Filtra data para um intervalo
+df_sales = df_sales[
+    df_sales["DATE"].between("2019-10-01", "2019-12-31")
+]
+
+# Agrupa por loja
+sales_grouped = (
+    df_sales
+    .groupby("STORE_CODE")
+    .agg({ # define o que fazer com cada coluna, aqui é soma
+        "SALES_VALUE": "sum",
+        "SALES_QTY": "sum"
+    })
+    .reset_index() # transforma em coluna
+)
+
+# Calcula o Ticket Médio
+sales_grouped["TM"] = (
+    sales_grouped["SALES_VALUE"]
+    / sales_grouped["SALES_QTY"]
+)
+
+# Junta com cadastro das lojas
+result = sales_grouped.merge(
+    df_store,
+    on="STORE_CODE",
+    how="left"
+)
+
+# Seleciona colunas
+result = result[
+    ["STORE_NAME", "BUSINESS_NAME", "TM"]
+]
+
+# Renomeia colunas
+result.columns = [
+    "Loja",
+    "Categoria",
+    "TM"
+]
+
+# Para visualizar duas casas decimais em TM
+pd.set_option(
+    'display.float_format',
+    '{:.2f}'.format
+)
+
+print(result)
+
+### GERA TABELA NO MATPLOTLIB
+
+result_display = result.copy() # Copia dataframe
+result_display["TM"] = result_display["TM"].map(
+    lambda x: f"{x:.2f}" # Formata para duas casas decimais em TM
+)
+
+fig, ax = plt.subplots(figsize=(8, 6)) # Cria figura 8x6 (polegadas)
+ax.axis('off') # Remove eixos
+
+# Cria tabela
+table = ax.table(
+    cellText=result_display.values,   # Valores em cada celula
+    colLabels=result_display.columns, # Labels das colunas
+    loc='center' # Centraliza a tabela 
+)
+
+# Configura font, escala...
+table.auto_set_font_size(False)
+table.set_fontsize(10)
+table.scale(1, 1.5)
+
+plt.show()
+'''
+
+
+
+''' CASE 3
+query = """
+SELECT
+    Genre,
+    SUM(RevenueMillions) AS total_revenue
+FROM IMDB_movies
+WHERE Genre IS NOT NULL
+AND RevenueMillions IS NOT NULL
+GROUP BY Genre
+ORDER BY total_revenue DESC
+LIMIT 10;
+"""
+
+df = pd.read_sql(query, engine)
+
+### GERA GRAFICO NO MATPLOTLIB
+
+# Tamanho da figura gerada em polegadas
+plt.figure(figsize=(12, 8))
+
+# Define eixos X e Y de um gráfico de barras
+plt.barh(df["Genre"], df["total_revenue"])
+
+# Títulos e labels
+plt.title("Top 10 Genres by Total Revenue")
+plt.xlabel("Revenue (Millions USD)")
+plt.ylabel("Genre")
+
+# Coloca o maior valor no topo
+plt.gca().invert_yaxis()
+
+plt.tight_layout() # Ajusta espaços
+plt.savefig("receita_por_genero.png", dpi=300) # Salva aquivo, dpi é qualidade da imagem
+plt.show()
+'''
+