Challenge - Filipe Eismann

.gitignore

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+.env

SQL_test.sql

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+SELECT dp.PRODUCT_COD,
+	   dp.PRODUCT_NAME,
+	   dp.PRODUCT_VAL 
+FROM data_product dp
+ORDER BY dp.PRODUCT_VAL DESC
+LIMIT 10
+/* 
+Whisky Escoces THE MACALLAN Ruby Garrafa 700ml com Caixa
+Whisky Escoces JOHNNIE WALKER Blue Label Garrafa 750ml
+Cafeteira Expresso 3 CORACOES Tres Modo Vermelho
+Vinho Portugues Tinto Vintage QUINTA DO CRASTO Garrafa 750ml
+Escova Dental Eletrica ORAL B D34 Professional Care 5000 110v
+Champagne Rose VEUVE CLICQUOT PONSARDIM Garrafa 750ml
+Champagne Frances Brut Imperial MOET Rose Garrafa 750ml
+Conjunto de Panelas Allegra em Inox TRAMONTINA 5 Pecas Gratis Utensilios 5 Pecas
+Whisky Escoces CHIVAS REGAL 18 Anos Garrafa 750ml
+Champagne Frances Brut Imperial MOET & CHANDON Garrafa 750ml 
+*/
+;
+
+SELECT DISTINCT dp.DEP_NAME,
+	   			dp.SECTION_NAME
+FROM data_product dp 
+WHERE dp.DEP_NAME in ('BEBIDAS', 'PADARIA')
+ORDER BY 1 ASC
+/* Bebidas: Bebidas, Cervejas, Refrescos e Vinhos */
+/* Padaria: Doces e Sobremesas, Gestante, Padaria, Queijos e Frios */
+;
+
+SELECT dsc.BUSINESS_CODE,
+	   dsc.BUSINESS_NAME,
+	   sum(dss.SALES_VALUE) TOTAL_VALUE,
+	   sum(dss.SALES_QTY) TOTAL_QTY
+FROM data_store_sales dss
+INNER JOIN data_store_cad dsc
+ON dsc.STORE_CODE = dss.STORE_CODE
+WHERE YEAR(dss.DATE) = 2019 
+AND QUARTER(dss.DATE) = 1
+GROUP BY 1,2
+ORDER BY TOTAL_VALUE DESC
+/*
+Farma		$ 81.776.691,73
+Varejo		$ 81.032.347,65
+Atacado		$ 80.384.884,60
+Proximidade	$ 80.171.122,80
+Posto		$ 32.072.326,40
+*/
+;

case_1_1.py

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+import os
+from dotenv import load_dotenv
+
+import mysql.connector
+import pandas as pd
+
+def connect_to_db():
+
+    host = os.getenv("HOST")
+    username = os.getenv("USER")
+    password = os.getenv("PASSWORD")
+    schema = os.getenv("SCHEMA")
+
+    connection = mysql.connector.connect(
+        host=host,
+        user=username,
+        password=password,
+        database=schema
+    )
+
+    if connection.is_connected():
+        print("Connection to the database was successful!")
+        return connection
+    else:
+        print("Failed to connect to the database.")
+        return None
+
+def retrieve_data(product_code: int, store_code: int, date: list):
+    load_dotenv()
+    try:
+        connection = connect_to_db()
+    except: 
+        print("Error connecting to the database.")
+        return None
+
+    cursor = connection.cursor(dictionary=True)
+
+    # Abordagem por intervalo
+    query = f'''
+        SELECT * 
+        FROM data_product_sales dps 
+        WHERE DATE(dps.DATE) >= DATE('{date[0]}') 
+        AND DATE(dps.DATE) <= DATE('{date[-1]}')
+        AND dps.store_code = {store_code}
+        AND dps.product_code = {product_code}
+        '''
+    try:
+        cursor.execute(query)
+        dataframe = cursor.fetchall()
+    except:
+        print('Error executing query!')
+        cursor.close()
+        connection.close()
+        raise
+
+    cursor.close()
+    connection.close()
+    return pd.DataFrame(dataframe)
+
+
+
+
+if __name__ == "__main__":
+    my_data = retrieve_data(product_code=18, store_code=1, date=['2019-01-01', '2019-01-31']) # Example
+    print(my_data)

case_1_2.py

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+import os
+from dotenv import load_dotenv
+
+import mysql.connector
+import pandas as pd
+
+def connect_to_db():
+
+    host = os.getenv("HOST")
+    username = os.getenv("USER")
+    password = os.getenv("PASSWORD")
+    schema = os.getenv("SCHEMA")
+
+    connection = mysql.connector.connect(
+        host=host,
+        user=username,
+        password=password,
+        database=schema
+    )
+
+    if connection.is_connected():
+        print("Connection to the database was successful!")
+        return connection
+    else:
+        print("Failed to connect to the database.")
+        return None
+
+def retrieve_data(product_code: int, store_code: int, date: list):
+    load_dotenv()
+    try:
+        connection = connect_to_db()
+    except: 
+        print("Error connecting to the database.")
+        return None
+
+    cursor = connection.cursor(dictionary=True)
+
+    # Abordagem por datas individuais
+    query = f'''
+        SELECT * 
+        FROM data_product_sales dps 
+        WHERE CAST(dps.DATE AS CHAR(10)) IN {date}
+        AND dps.store_code = {store_code}
+        AND dps.product_code = {product_code}
+        '''.replace('[', '(').replace(']', ')')
+    try:
+        cursor.execute(query)
+        dataframe = cursor.fetchall()
+    except:
+        print('Error executing query!')
+        cursor.close()
+        connection.close()
+        raise
+
+    cursor.close()
+    connection.close()
+    return pd.DataFrame(dataframe)
+
+
+
+
+if __name__ == "__main__":
+    my_data = retrieve_data(product_code=18, store_code=1, date=['2019-01-01', '2019-01-31']) # Example
+    print(my_data)

case_2.py

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+import os
+import datetime
+
+import mysql.connector
+import pandas as pd
+from dotenv import load_dotenv
+
+
+def connect_to_db():
+
+    host = os.getenv("HOST")
+    username = os.getenv("USER")
+    password = os.getenv("PASSWORD")
+    schema = os.getenv("SCHEMA")
+
+    connection = mysql.connector.connect(
+        host=host,
+        user=username,
+        password=password,
+        database=schema
+    )
+
+    if connection.is_connected():
+        print("Connection to the database was successful!")
+        return connection
+    else:
+        print("Failed to connect to the database.")
+        return None
+
+if __name__ == "__main__":
+    load_dotenv()
+    try:
+        connection = connect_to_db()
+    except: 
+        print("Error connecting to the database.")
+        connection = None
+
+    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'
+    '''
+
+    cursor = connection.cursor(dictionary=True)
+    cursor.execute(query1)
+    store_cad_df = pd.DataFrame(cursor.fetchall())
+    cursor.execute(query2)
+    store_sales_df = pd.DataFrame(cursor.fetchall())
+
+    cursor.close()
+    connection.close()
+
+    store_info_full = pd.merge(store_cad_df, store_sales_df, how='left')
+
+    # Nesta parte, foi utilizada a resposta de IA do Google apenas para corrigir a sintaxe do método "between", que eu não utilizo frequentemente
+    data_view = store_info_full[store_info_full['DATE'].between(datetime.date(2019,10,1), datetime.date(2019,12,31))] \
+        .groupby(['STORE_NAME', 'BUSINESS_NAME']) \
+        .sum(['SALES_VALUE', 'SALES_QTY']) \
+        .eval('TM = SALES_VALUE / SALES_QTY')['TM'] \
+        .round(2)
+    print(data_view)
+

case_3.py

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+import os
+from dotenv import load_dotenv
+
+import mysql.connector
+import pandas as pd
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+def connect_to_db():
+
+    host = os.getenv("HOST")
+    username = os.getenv("USER")
+    password = os.getenv("PASSWORD")
+    schema = os.getenv("SCHEMA")
+
+    connection = mysql.connector.connect(
+        host=host,
+        user=username,
+        password=password,
+        database=schema
+    )
+
+    if connection.is_connected():
+        print("Connection to the database was successful!")
+        return connection
+    else:
+        print("Failed to connect to the database.")
+        return None
+
+
+if __name__ == '__main__':
+    load_dotenv()
+
+    connection = connect_to_db()
+
+    cursor = connection.cursor(dictionary=True)
+
+    query = '''
+        SELECT *
+        FROM IMDB_movies
+    '''
+
+    cursor.execute(query)
+
+    df = pd.DataFrame(cursor.fetchall())
+
+    # Top 10 filmes por receita em milhões
+    revenue = df.groupby('Title').sum().sort_values(by='RevenueMillions', ascending=False)['RevenueMillions'].head(10)
+
+    # Aqui, foi utilizado apoio do Copilot para a formatação dos plots
+    plt.figure(figsize=(12, 6))
+    ax = sns.barplot(x=revenue.values, y=revenue.index, color='skyblue')
+    ax.bar_label(ax.containers[0], fmt='%.2f', label_type='edge', fontsize=10)
+    plt.title('Top Movies by Revenue')
+    plt.xlabel('Revenue (Millions $)')
+    plt.ylabel('Movie Title')
+    plt.tight_layout()
+    plt.show()
+
+    # Média ponderada de avaliação por diretor - a função lambda foi feita com apoio do Copilot
+    weighted_avg_rating_by_director = df.groupby('Director').apply(lambda x: (x['Rating'] * x['Votes']).sum() / x['Votes'].sum()).sort_values(ascending=False)
+
+    # Aqui, foi utilizado apoio do Copilot para a formatação dos plots
+    plt.figure(figsize=(12, 6))
+    plt.subplot(2, 1, 1)
+    ax = sns.barplot(x=weighted_avg_rating_by_director.head(10).values, y=weighted_avg_rating_by_director.head(10).index, color='lightgreen')
+    ax.bar_label(ax.containers[0], fmt='%.2f', label_type='edge', fontsize=10)
+    plt.title('Weighted Average Rating by Director - Top 10')
+    plt.xlabel('Weighted Average Rating')
+    plt.ylabel('Director')
+    plt.subplot(2, 1, 2)
+    ax = sns.barplot(x=weighted_avg_rating_by_director.tail(10).values, y=weighted_avg_rating_by_director.tail(10).index, color='salmon')
+    ax.bar_label(ax.containers[0], fmt='%.2f', label_type='edge', fontsize=10)
+    plt.title('Weighted Average Rating by Director - Bottom 10')
+    plt.xlabel('Weighted Average Rating')
+    plt.ylabel('Director')
+    plt.tight_layout()
+    plt.show()
+
+    # Receita por gênero
+    genres = df['Genre'].str.split(',').explode().unique()
+
+    # Essa parte do código foi feita com apoio do Copilot, que sugeriu a criação de um dicionário para armazenar a receita por gênero
+    revenue_by_genre = {}
+    for genre in genres:
+        revenue_by_genre[genre] = df[df['Genre'].str.contains(genre)]['RevenueMillions'].sum()
+
+    plt.figure(figsize=(12, 6))
+    ax = sns.barplot(x=sorted(list(revenue_by_genre.values()), reverse=True), y=list(revenue_by_genre.keys()), color='lightcoral')
+    ax.bar_label(ax.containers[0], fmt='%.2f', label_type='edge', fontsize=10)
+    plt.title('Revenue by Genre')
+    plt.xlabel('Revenue (Millions $)')
+    plt.ylabel('Genre')
+    plt.tight_layout()
+    plt.show()
+
+    # Metascore médio por ano de lançamento
+    avg_metascore_by_year = df.groupby('Year').mean('Metascore')['Metascore']
+
+    # Novamente, utilizei apoio do Copilot com os plots, principalmente para os rótulos dos valores.
+    plt.figure(figsize=(12, 6))
+    ax = sns.lineplot(x=avg_metascore_by_year.index, y=avg_metascore_by_year.values, marker='o', color='purple')
+    for i, v in enumerate(avg_metascore_by_year.values):
+        ax.text(avg_metascore_by_year.index[i], v + 0.5, f"{v:.2f}", ha='center', fontsize=9)
+    plt.title('Average Metascore by Year of Release')
+    plt.xlabel('Year of Release')
+    plt.ylabel('Average Metascore')
+    plt.xticks(avg_metascore_by_year.index, rotation=45)
+    plt.grid()
+    plt.tight_layout()
+    plt.show()