Add solution for data challenge

.gitignore

@@ -0,0 +1,2 @@
+.env
+*.html

case1.py

@@ -0,0 +1,39 @@
+from dotenv import load_dotenv
+import os
+import mysql.connector
+import pandas as pd
+from sales import SalesRepository
+
+load_dotenv()
+
+host = os.getenv("DB_HOST")
+user = os.getenv("DB_USER")
+password = os.getenv("DB_PASSWORD")
+database = os.getenv("DB_NAME")
+port = os.getenv("DB_PORT")
+
+
+try:
+    conexao = mysql.connector.connect(
+        host=host,
+        user=user,
+        password=password,
+        database=database,
+        port=port
+    )
+    print("Conexão bem-sucedida!")
+except mysql.connector.Error as err:
+    print(f"Erro ao conectar ao banco de dados: {err}")
+    conexao = None
+
+
+sales_repo = SalesRepository(conexao)
+
+df = sales_repo.retrieve_data(
+    product_code=18,
+    store_code=1,
+    start_date='2019-01-01',
+    end_date='2019-01-31'
+)
+
+print(df)

case2.py

@@ -0,0 +1,109 @@
+from dotenv import load_dotenv
+import os
+import mysql.connector
+import pandas as pd
+
+load_dotenv()
+
+host = os.getenv("DB_HOST")
+user = os.getenv("DB_USER")
+password = os.getenv("DB_PASSWORD")
+database = os.getenv("DB_NAME")
+port = os.getenv("DB_PORT")
+
+
+try:
+    conexao = mysql.connector.connect(
+        host=host,
+        user=user,
+        password=password,
+        database=database,
+        port=port
+    )
+    print("Conexão bem-sucedida!")
+except mysql.connector.Error as err:
+    print(f"Erro ao conectar ao banco de dados: {err}")
+    conexao = None
+
+
+def manual_query(conexao, query):
+    if conexao is None:
+        raise ConnectionError("Sem conexão ativa!")
+
+    cursor = conexao.cursor()
+
+    cursor.execute(query)
+
+    result = cursor.fetchall()
+
+    columns = [desc[0] for desc in cursor.description]
+
+    return pd.DataFrame(result, columns=columns)
+
+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_query_cad = manual_query(conexao, query1)
+df_query_sales = manual_query(conexao, query2)
+
+
+df_query_sales['DATE'] = pd.to_datetime(df_query_sales['DATE'])
+
+df_query_sales = df_query_sales[
+    (df_query_sales['DATE'] >= '2019-10-01') &
+    (df_query_sales['DATE'] <= '2019-12-31')
+]
+
+df_merged = pd.merge(
+    df_query_sales,
+    df_query_cad,
+    on='STORE_CODE'
+)
+df_grouped = (
+    df_merged.groupby(
+        ['STORE_NAME', 'BUSINESS_NAME'],
+        as_index=False
+    )
+    .agg({
+        'SALES_VALUE': 'sum',
+        'SALES_QTY': 'sum'
+    })
+)
+
+df_grouped['TM'] = (
+    df_grouped['SALES_VALUE'] /
+    df_grouped['SALES_QTY']
+).round(2)
+
+df_final = df_grouped[[
+    'STORE_NAME',
+    'BUSINESS_NAME',
+    'TM'
+]].copy()
+
+df_final = (
+    df_grouped[['STORE_NAME', 'BUSINESS_NAME', 'TM']].copy().rename(columns=
+    {
+        'STORE_NAME': 'Loja',
+        'BUSINESS_NAME': 'Categoria'
+    })
+)
+
+print(df_final)

case3.py

@@ -0,0 +1,89 @@
+from dotenv import load_dotenv
+import os
+import mysql.connector
+import pandas as pd
+import plotly.express as px
+load_dotenv()
+
+host = os.getenv("DB_HOST")
+user = os.getenv("DB_USER")
+password = os.getenv("DB_PASSWORD")
+database = os.getenv("DB_NAME")
+port = os.getenv("DB_PORT")
+
+
+try:
+    conexao = mysql.connector.connect(
+        host=host,
+        user=user,
+        password=password,
+        database=database,
+        port=port
+    )
+    print("Conexão bem-sucedida!")
+except mysql.connector.Error as err:
+    print(f"Erro ao conectar ao banco de dados: {err}")
+    conexao = None
+
+
+def manual_query(conexao, query):
+    if conexao is None:
+        raise ConnectionError("Sem conexão ativa!")
+
+    cursor = conexao.cursor()
+
+    cursor.execute(query)
+
+    result = cursor.fetchall()
+
+    columns = [desc[0] for desc in cursor.description]
+
+    return pd.DataFrame(result, columns=columns)
+
+query = """
+select * from IMDB_movies
+"""
+df_query = manual_query(conexao, query)
+#print(f'Colunas: {df_query.columns.tolist()}')
+#print(df_query)
+
+df_sorted = df_query.sort_values("RevenueMillions", ascending=False)
+
+qnt_filmes = 35
+
+top_movies = df_sorted.head(qnt_filmes)
+
+fig = px.bar(
+    top_movies,
+    x='RevenueMillions',
+    y='Title',
+    orientation='h',
+    color_continuous_scale=[
+        [0.0, '#5B8FF9'],
+        [1.0, '#7DAAFF']
+    ],
+    hover_data={
+        'Votes': True,
+        'Director': True,
+        'Year': True,
+        'RevenueMillions': ':.2f',
+        'Metascore': True
+    },
+    labels={
+        'RevenueMillions': 'Receita (Milhões USD)',
+        'Title': 'Filme',
+        'Metascore': 'Metascore',
+        'Votes': 'Votos',
+        'Director': 'Diretor',
+        'Year': 'Ano'
+    },
+    title=f'Top {qnt_filmes} Filmes por Receita'
+)
+
+fig.update_layout(
+    yaxis={'categoryorder': 'total ascending'}
+)
+
+nome_arquivo = 'grafico_filmes.html'
+fig.write_html(nome_arquivo)
+print("Gráfico salvo como:",nome_arquivo)

sales.py

@@ -0,0 +1,50 @@
+import pandas as pd
+
+class SalesRepository:
+
+    def __init__(self, connection):
+        self.connection = connection
+
+    def retrieve_data(
+        self,
+        product_code=None,
+        store_code=None,
+        start_date=None,
+        end_date=None
+    ):
+
+        if self.connection is None:
+            raise ConnectionError("Sem conexão ativa!")
+
+        query = """
+            SELECT *
+            FROM data_product_sales
+            WHERE 1=1
+        """
+
+        params = []
+
+        if product_code is not None:
+            query += " AND PRODUCT_CODE = %s"
+            params.append(product_code)
+
+        if store_code is not None:
+            query += " AND STORE_CODE = %s"
+            params.append(store_code)
+
+        if start_date is not None and end_date is not None:
+            query += " AND DATE BETWEEN %s AND %s"
+            params.extend([start_date, end_date])
+
+        print("Query:", query)
+        print("Params:", params)
+
+        cursor = self.connection.cursor()
+
+        cursor.execute(query, params)
+
+        result = cursor.fetchall()
+
+        columns = [desc[0] for desc in cursor.description]
+
+        return pd.DataFrame(result, columns=columns)