From 3b156e8eed1cc2bd083b9a5825346df198d1d6d7 Mon Sep 17 00:00:00 2001 From: Matheus Mendes <98965078+matheuspmendes@users.noreply.github.com> Date: Sat, 27 Jun 2026 23:31:53 -0300 Subject: [PATCH] Carregamento dos arquivos para o Desafio. MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Adicionando os arquivos .ipynb e .sql para o desafio técnico da Looqbox. --- looqbox-challenge.ipynb | 518 ++++++++++++++++++++++++++++++++++++++++ looqbox.sql | 30 +++ 2 files changed, 548 insertions(+) create mode 100644 looqbox-challenge.ipynb create mode 100644 looqbox.sql diff --git a/looqbox-challenge.ipynb b/looqbox-challenge.ipynb new file mode 100644 index 0000000..ef78c4f --- /dev/null +++ b/looqbox-challenge.ipynb @@ -0,0 +1,518 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "id": "cebf95b5", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "from sqlalchemy import create_engine\n", + "\n", + "# CONEXÃO COM O BANCO DE DADOS\n", + "# Utilizado IA para auxiliar na conexão com o banco, entregando a sintaxe correta para criação da engine.\n", + "user = \"looqbox-challenge\"\n", + "password = \"looq-challenge\"\n", + "host = \"35.199.115.174\"\n", + "port = \"3306\"\n", + "db = \"looqbox-challenge\"\n", + "\n", + "engine = create_engine(f\"mysql+pymysql://{user}:{password}@{host}:{port}/{db}\")" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "99600c8f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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STORE_CODEPRODUCT_CODEDATESALES_VALUESALES_QTY
01182019-01-01708.5065.0
11182019-01-021297.10119.0
21182019-01-031144.50105.0
31182019-01-041090.00100.0
41182019-01-05893.8082.0
..................
217312892414042019-12-277671.75193.0
217312992414042019-12-286201.00156.0
217313092414042019-12-294889.25123.0
217313192414042019-12-304730.25119.0
217313292414042019-12-315127.75129.0
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2173133 rows × 5 columns

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" + ], + "text/plain": [ + " STORE_CODE PRODUCT_CODE DATE SALES_VALUE SALES_QTY\n", + "0 1 18 2019-01-01 708.50 65.0\n", + "1 1 18 2019-01-02 1297.10 119.0\n", + "2 1 18 2019-01-03 1144.50 105.0\n", + "3 1 18 2019-01-04 1090.00 100.0\n", + "4 1 18 2019-01-05 893.80 82.0\n", + "... ... ... ... ... ...\n", + "2173128 9 241404 2019-12-27 7671.75 193.0\n", + "2173129 9 241404 2019-12-28 6201.00 156.0\n", + "2173130 9 241404 2019-12-29 4889.25 123.0\n", + "2173131 9 241404 2019-12-30 4730.25 119.0\n", + "2173132 9 241404 2019-12-31 5127.75 129.0\n", + "\n", + "[2173133 rows x 5 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 1ª QUESTÃO: Criar uma função dinamica que permita filtros ou não na tabela data_product_sales:\n", + "\n", + "def retrieve_data(product_code = None, store_code = None, date = None):\n", + " query = \"SELECT * FROM data_product_sales WHERE 1=1\"\n", + "\n", + " if product_code is not None:\n", + " query += f\" AND PRODUCT_CODE = {product_code}\"\n", + " \n", + " if store_code is not None:\n", + " query += f\" AND STORE_CODE = {store_code}\"\n", + " \n", + " if date is not None and len(date) == 2:\n", + " query += f\" AND DATE BETWEEN '{date[0]}' AND '{date[1]}'\"\n", + " \n", + " df = pd.read_sql(query, con=engine)\n", + " return df\n", + "\n", + "my_data = retrieve_data(product_code=None, store_code=None, date=None)\n", + "my_data" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "da1692b8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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LojaCategoriaTM
0BahiaAtacado15.39
1BangkokPosto13.67
2BelemProximidade15.37
3BerlinProximidade15.39
4Buenos AiresAtacado15.39
5ChicagoVarejo15.53
6DubaiAtacado15.39
7Hong KongFarma26.35
8LondonFarma28.99
9MadriFarma29.03
10MiamiPosto13.67
11New YorkProximidade15.39
12ParisProximidade15.39
13Rio de JaneiroFarma29.59
14RomaVarejo15.39
15SalvadorAtacado15.39
16Sao PauloVarejo15.39
17SidneyPosto13.67
18TokioVarejo15.39
19VancouverPosto13.67
\n", + "
" + ], + "text/plain": [ + " Loja Categoria TM\n", + "0 Bahia Atacado 15.39\n", + "1 Bangkok Posto 13.67\n", + "2 Belem Proximidade 15.37\n", + "3 Berlin Proximidade 15.39\n", + "4 Buenos Aires Atacado 15.39\n", + "5 Chicago Varejo 15.53\n", + "6 Dubai Atacado 15.39\n", + "7 Hong Kong Farma 26.35\n", + "8 London Farma 28.99\n", + "9 Madri Farma 29.03\n", + "10 Miami Posto 13.67\n", + "11 New York Proximidade 15.39\n", + "12 Paris Proximidade 15.39\n", + "13 Rio de Janeiro Farma 29.59\n", + "14 Roma Varejo 15.39\n", + "15 Salvador Atacado 15.39\n", + "16 Sao Paulo Varejo 15.39\n", + "17 Sidney Posto 13.67\n", + "18 Tokio Varejo 15.39\n", + "19 Vancouver Posto 13.67" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#2ª QUESTÃO: Utilizar queries preestabelecidas para dar merge e retornar df com Loja, Categoria e Ticket Médio:\n", + "\n", + "query1 = \"\"\"\n", + "SELECT\n", + " STORE_CODE,\n", + " STORE_NAME,\n", + " START_DATE,\n", + " END_DATE,\n", + " BUSINESS_NAME,\n", + " BUSINESS_CODE\n", + "FROM data_store_cad\n", + "\"\"\"\n", + "query2 = \"\"\"\n", + "SELECT\n", + " STORE_CODE,\n", + " DATE,\n", + " SALES_VALUE,\n", + " SALES_QTY\n", + "FROM data_store_sales\n", + "WHERE DATE BETWEEN '2019-01-01' AND '2019-12-31'\n", + "\"\"\"\n", + "df_store_cad = pd.read_sql(query1, con=engine)\n", + "df_store_sales = pd.read_sql(query2, con=engine)\n", + "\n", + "# Join nas tabelas, PK = STORE_CODE\n", + "\n", + "df_merged = pd.merge(df_store_sales, df_store_cad, on ='STORE_CODE', how = 'inner')\n", + "df_merged['DATE'] = pd.to_datetime(df_merged['DATE'])\n", + "initial_date = \"2019-10-01\"\n", + "end_date = \"2019-12-31\"\n", + "\n", + "df_filtered = df_merged[(df_merged['DATE'] >= initial_date) & (df_merged['DATE'] <= end_date)]\n", + "\n", + "# Realizar group by e agregação para formatar o resultado final\n", + "\n", + "df_grouped = df_filtered.groupby(['STORE_NAME', 'BUSINESS_NAME']).agg(\n", + " TOTAL_VAL = ('SALES_VALUE', 'sum'),\n", + " TOTAL_QTY = ('SALES_QTY', 'sum')\n", + ").reset_index()\n", + "\n", + "df_grouped['TM'] = (df_grouped['TOTAL_VAL'] / df_grouped['TOTAL_QTY']).round(2)\n", + "\n", + "# Renomear colunas para formatação\n", + "\n", + "df_final = df_grouped[['STORE_NAME', 'BUSINESS_NAME', 'TM']].rename(\n", + " columns = {\n", + " 'STORE_NAME': \"Loja\",\n", + " 'BUSINESS_NAME': \"Categoria\"\n", + " }\n", + ")\n", + "df_final\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7e579544", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "query = \"SELECT * FROM IMDB_movies\"\n", + "df_imdb = pd.read_sql(query, con=engine)\n", + "\n", + "# Resolvi plotar o faturamento total por ser um gráfico simples capaz de verificar a evolução temporal do faturamento dos cinemas,\n", + "# servindo como visão geral do mercado, além de servir como aleta para possíveis explorações do que houve por exemplo na queda em 2011.\n", + "# Foi utilizado IA para a sintaxe do sns/plt, facilitando o processo de criação do gráfico.\n", + "\n", + "df_fat_ano = df_imdb.groupby('Year')['RevenueMillions'].sum().reset_index()\n", + "\n", + "sns.set_theme(style=\"darkgrid\")\n", + "plt.figure(figsize=(12, 5))\n", + "sns.lineplot(\n", + " data=df_fat_ano,\n", + " x='Year',\n", + " y='RevenueMillions',\n", + " marker='o',\n", + " markersize=8,\n", + " color='#00B4D8',\n", + " linewidth=3\n", + ")\n", + "\n", + "plt.title(\"Evolução do Faturamento Total do Cinema por Ano de Lançamento\", fontsize=14, fontweight=\"bold\", pad=15)\n", + "plt.xlabel(\"Ano de Lançamento\", fontsize=11, labelpad=10)\n", + "plt.ylabel(\"Faturamento Total (Em Milhões de Dólares)\", fontsize=11, labelpad=10)\n", + "\n", + "anos_unicos = df_fat_ano['Year'].unique()\n", + "plt.xticks(anos_unicos, rotation=45)\n", + "plt.tight_layout()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7c24e168", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.14.2" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/looqbox.sql b/looqbox.sql new file mode 100644 index 0000000..906cf18 --- /dev/null +++ b/looqbox.sql @@ -0,0 +1,30 @@ +-- 1: 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; + + +-- 2: What sections do the 'BEBIDAS' and 'PADARIA' departments have? + +SELECT + DEP_NAME, + SECTION_NAME +FROM data_product +WHERE DEP_NAME IN ('BEBIDAS', 'PADARIA') +GROUP BY DEP_NAME, SECTION_NAME +ORDER BY DEP_NAME ASC; + +-- 3: What was the total sale of products (in $) of +-- each Business Area in the first quarter of 2019? + +SELECT + sc.BUSINESS_NAME as BUSINESS_AREA, + ROUND(SUM(ps.SALES_VALUE), 2) AS TOTAL_SALES +FROM data_product_sales ps +JOIN data_store_cad 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; \ No newline at end of file