Solucao desafio tecnico - Gabriel Cordeiro

.gitignore

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+.env

PYTHON_TEST.py

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+# 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:
+
+# product_code: integer
+# store_code: integer
+# date: list of ISO-like strings
+
+# Date e.g.
+# ['2019-01-01', '2019-01-31']
+# It should look like this my_data = retrieve_data(product_code, store_code, date)
+
+# Extra instructions:
+
+# Retrieve all columns from table data_product_sales;
+# Imagine people from other teams will also utilize this function!
+
+from dotenv import load_dotenv
+import mysql.connector
+import pandas as pd
+import os
+
+load_dotenv()
+
+# Pesquisei na documentação do mysql disponível no repositório do desafio o template da conexão
+def connect_db():
+    cnx = mysql.connector.connect(
+        user=os.getenv("USER"),
+        password=os.getenv("PASSWORD"),
+        host=os.getenv("HOST"),
+        port=os.getenv("PORT"),
+        database=os.getenv("DATABASE")
+    )
+
+    return cnx
+
+def retrieve_data(product_code: int, store_code: int, date: list) -> pd.DataFrame:
+    cnx = connect_db()
+
+    query = '''
+    SELECT *
+    FROM data_product_sales
+    WHERE PRODUCT_CODE = %s
+        AND STORE_CODE = %s
+        AND DATE BETWEEN %s AND %s
+    ORDER BY DATE DESC;
+    '''
+
+    data = pd.read_sql(query, params=(product_code, store_code, date[0], date[1]), con=cnx)
+    cnx.close()
+
+    return data
+
+# 2) A brand new client sent you two ready-to-go queries. Those are listed below:
+
+# Query 1:
+# SELECT
+#       STORE_CODE,
+#       STORE_NAME,
+#       START_DATE,
+#       END_DATE,
+#       BUSINESS_NAME,
+#       BUSINESS_CODE
+# FROM data_store_cad
+
+# Query 2:
+# SELECT
+#         STORE_CODE,
+#         DATE,
+#         SALES_VALUE,
+#         SALES_QTY
+# FROM data_store_sales
+# WHERE DATE BETWEEN '2019-01-01' AND '2019-12-31'
+# In addition, he gave you this set of instructions:
+
+# Use the queries as they are (do not modify them or create a new one);
+
+# Please filter the period between this given range:
+# ['2019-10-01','2019-12-31']
+
+# We are in need of this visualization (click here to see it)! Please, create it with Python
+
+# Loja	Categoria	TM
+# Bahia	Atacado	15.39
+# Bangkok	Posto	13.67
+# Belem	Proximidade	15.37
+# ...
+
+def tm_viz():
+    cnx = connect_db()
+
+    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'
+    '''
+
+    table_store_cad = pd.read_sql(query1, con=cnx)
+    table_store_sales = pd.read_sql(query2, con=cnx)
+    cnx.close()
+
+    data = pd.merge(left=table_store_sales, right=table_store_cad, how='left', on='STORE_CODE').groupby(['STORE_NAME', 'BUSINESS_NAME']).agg({
+        'SALES_VALUE': 'sum',
+        'SALES_QTY': 'sum'
+    }).reset_index()
+
+    data['TM'] = (data['SALES_VALUE'] / data['SALES_QTY']).round(2)
+    data = data[['STORE_NAME', 'BUSINESS_NAME', 'TM']].rename(columns={'STORE_NAME': 'Loja', 'BUSINESS_NAME': 'Categoria'})
+
+    return data
+
+# 3) Building your own visualization
+# 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.
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def imdb_viz():
+    cnx = connect_db()
+
+    query = '''
+    SELECT *
+    FROM IMDB_movies
+    '''
+
+    data = pd.read_sql(query, con=cnx)
+    cnx.close()
+
+    #--------------------------------------------------------
+    # Primeira viz
+    #--------------------------------------------------------
+
+    # Escolhi essa visualização para mostrar a relação das notas com o tempo dos filmes.
+    # É uma forma interessante de mostrar que filmes antigos que tinham maior duração, 
+    # tinham notas mais altas, sugerindo que uma maior duração permite um melhor desenvolvimento
+    # da história e dos personagens, deixando uma percepção de maior qualidade resultando em maiores notas.
+
+    # Vou utilizar a coluna Rating como a nota padrão porque é o público geral quem consome e traz a 
+    # receita do filme, não a crítica (MetaScore)
+
+    rating_mean = data.groupby('Year')['Rating'].mean()
+    runtime_mean = data.groupby('Year')['Runtime'].mean()
+
+    # Pesquisei no Gemini como utilizar dois eixos no mesmo gráfico, foi sugerido a estrutura com subplots (ax1 e ax2)
+    fig, ax1 = plt.subplots(figsize=(12, 6))
+    color = "tab:blue"
+    ax1.set_xlabel("Ano")
+    ax1.set_ylabel("Nota Média (Rating)", color=color)
+    ax1.plot(rating_mean.index, rating_mean.values, color=color, marker="o")
+
+    ax2 = ax1.twinx()
+    color = "tab:red"
+    ax2.set_ylabel("Duração Média (Minutos)", color=color)
+    ax2.plot(runtime_mean.index, runtime_mean.values, color=color, marker="D")
+
+    plt.title("Comparação da Nota Média vs Duração Média dos Filmes ao Longo dos Anos", fontweight="bold")
+    plt.savefig("chart1.png", dpi=300, bbox_inches="tight")
+    plt.show()
+
+    #--------------------------------------------------------
+    # Segunda viz
+    #--------------------------------------------------------
+
+    # Escolhi essa visualização por ajudar a direcionar qual gênero será escolhido para produzir um filme,
+    # ajudando a ver um panorama geral do mercado.
+
+    # Utilizei o Gemini para sugerir formas de separar os gêneros
+    genres = data['Genre'].str.get_dummies(",")
+    df_final = pd.concat([data, genres], axis=1).drop(columns=["Genre"])
+
+    revenue_by_genre = df_final[genres.columns].multiply(df_final["RevenueMillions"], axis=0).sum().sort_values()
+
+    ax = revenue_by_genre.plot(kind="barh", color="skyblue", figsize=(12, 6))
+    ax.bar_label(ax.containers[0], fontsize=10)
+
+    plt.title('Receita por Gênero', fontweight="bold")
+    plt.xlabel('Receita (Milhões $)')
+    plt.ylabel('Gênero')
+    plt.tight_layout()
+    plt.savefig("chart2.png", dpi=300, bbox_inches="tight")
+    plt.show()
+
+if __name__ == "__main__":
+
+    # Case 1
+    data = retrieve_data(21, 9, ['2019-01-01', '2019-01-31'])
+    print(data)
+
+    # Case 2
+    viz = tm_viz()
+    print(viz)
+
+    # Case 3
+    imdb_viz()

SQL_TEST.sql

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+#What are the 10 most expensive products in the company?
+SELECT PRODUCT_NAME, PRODUCT_VAL
+FROM data_product
+ORDER BY PRODUCT_VAL DESC
+LIMIT 10;
+
+#What sections do the 'BEBIDAS' and 'PADARIA' departments have?
+SELECT DISTINCT SECTION_NAME
+FROM data_product
+WHERE DEP_NAME IN ('BEBIDAS', 'PADARIA');
+
+#What was the total sale of products (in $) of each Business Area in the first quarter of 2019?
+SELECT sc.BUSINESS_NAME, SUM(ps.SALES_VALUE * ps.SALES_QTY) AS TOTAL_SALES
+FROM data_product_sales AS ps
+LEFT JOIN data_store_cad AS sc
+ON ps.STORE_CODE = sc.STORE_CODE
+WHERE ps.DATE BETWEEN '2019-01-01' AND '2019-03-31'
+GROUP BY sc.BUSINESS_NAME
+ORDER BY TOTAL_SALES DESC;