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resolucao dos desafios - byGabrielDuarte #31

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22 changes: 22 additions & 0 deletions 01_gd_sql-LooqboxDataChallenge.sql
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#1. What are the 10 most expensive products in the company?
SELECT PRODUCT_NAME, PRODUCT_VAL
FROM `looqbox-challenge`.data_product
ORDER BY PRODUCT_VAL DESC
LIMIT 10;

#2. What sections do the 'BEBIDAS' and 'PADARIA' departments have?
SELECT DISTINCT DEP_NAME , SECTION_NAME
FROM `looqbox-challenge`.data_product
WHERE DEP_NAME IN ('BEBIDAS','PADARIA')
ORDER BY DEP_NAME, SECTION_NAME;

#3. What was the total sale of products (in $) of each Business Area in the first quarter of 2019?
SELECT CAD.BUSINESS_NAME , SUM(SALES.SALES_VALUE) AS SALES_VALUE
FROM `looqbox-challenge`.data_store_sales AS SALES
LEFT JOIN `looqbox-challenge`.data_store_cad as CAD
ON SALES.STORE_CODE = CAD.STORE_CODE
WHERE SALES.DATE BETWEEN '2019-01-01' AND '2019-03-31'
GROUP BY CAD.BUSINESS_NAME
ORDER BY SUM(SALES.SALES_VALUE) DESC;


340 changes: 340 additions & 0 deletions 02-gd-Case1-datachallenge.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"id": "5ade6e06",
"metadata": {},
"source": [
"### Case 1\n",
"#### 1) The Dev Team was tired of developing the same old queries just varying the filters accordingly to their boss demands.\n",
"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:\n",
"\n",
"- product_code: integer\n",
"\n",
"- store_code: integer\n",
"\n",
"- date: list of ISO-like strings\n",
"\n",
"- Date e.g.\n",
" - ['2019-01-01', '2019-01-31']\n",
"\n",
"It should look like this\n",
"my_data = retrieve_data(product_code, store_code, date)\n",
"\n",
"Make your team proud!\n",
"\n",
"Extra instructions:\n",
"- Retrieve all columns from table data_product_sales;\n",
"- Imagine people from other teams will also utilize this function!"
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3e772ed3",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from sqlalchemy import create_engine"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6959a8b8",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>STORE_CODE</th>\n",
" <th>PRODUCT_CODE</th>\n",
" <th>DATE</th>\n",
" <th>SALES_VALUE</th>\n",
" <th>SALES_QTY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-01-01</td>\n",
" <td>708.5</td>\n",
" <td>65.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-01-02</td>\n",
" <td>1297.1</td>\n",
" <td>119.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-01-03</td>\n",
" <td>1144.5</td>\n",
" <td>105.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-01-04</td>\n",
" <td>1090.0</td>\n",
" <td>100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-01-05</td>\n",
" <td>893.8</td>\n",
" <td>82.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" STORE_CODE PRODUCT_CODE DATE SALES_VALUE SALES_QTY\n",
"0 1 18 2019-01-01 708.5 65.0\n",
"1 1 18 2019-01-02 1297.1 119.0\n",
"2 1 18 2019-01-03 1144.5 105.0\n",
"3 1 18 2019-01-04 1090.0 100.0\n",
"4 1 18 2019-01-05 893.8 82.0"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"def retrieve_data(product_code, store_code, date):\n",
" # 1. Credenciais de acesso\n",
" usuario = 'looqbox-challenge'\n",
" senha = 'looq-challenge'\n",
" host = '35.199.115.174'\n",
" porta = '3306' # porta padrão do MySQL\n",
" nome_do_banco = 'looqbox-challenge'\n",
"\n",
" # 2. String de conexão (URL de conexão)\n",
" # Formato: mysql+pymysql://usuario:senha@host:porta/nome_do_banco\n",
" string_conexao = f'mysql+pymysql://{usuario}:{senha}@{host}:{porta}/{nome_do_banco}'\n",
"\n",
" # 3. Motor de conexão (engine)\n",
" engine = create_engine(string_conexao)\n",
"\n",
" # 4. Consulta SQL\n",
" query = \"\"\"SELECT * FROM `looqbox-challenge`.`data_product_sales`\n",
" WHERE PRODUCT_CODE = %s\n",
" AND DATE BETWEEN %s AND %s\n",
" AND STORE_CODE = %s\"\"\"\n",
"\n",
" df = pd.read_sql(query, con=engine, params=(product_code, date[0], date[1], store_code))\n",
"\n",
" # 6. retorna o DataFrame\n",
" return df\n"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "361ee7b7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>STORE_CODE</th>\n",
" <th>PRODUCT_CODE</th>\n",
" <th>DATE</th>\n",
" <th>SALES_VALUE</th>\n",
" <th>SALES_QTY</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-01-01</td>\n",
" <td>708.5</td>\n",
" <td>65.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-01-02</td>\n",
" <td>1297.1</td>\n",
" <td>119.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-01-03</td>\n",
" <td>1144.5</td>\n",
" <td>105.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-01-04</td>\n",
" <td>1090.0</td>\n",
" <td>100.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-01-05</td>\n",
" <td>893.8</td>\n",
" <td>82.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>85</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-03-27</td>\n",
" <td>1308.0</td>\n",
" <td>120.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>86</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-03-28</td>\n",
" <td>1079.1</td>\n",
" <td>99.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>87</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-03-29</td>\n",
" <td>1002.8</td>\n",
" <td>92.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>88</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-03-30</td>\n",
" <td>741.2</td>\n",
" <td>68.0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>89</th>\n",
" <td>1</td>\n",
" <td>18</td>\n",
" <td>2019-03-31</td>\n",
" <td>741.2</td>\n",
" <td>68.0</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>90 rows × 5 columns</p>\n",
"</div>"
],
"text/plain": [
" STORE_CODE PRODUCT_CODE DATE SALES_VALUE SALES_QTY\n",
"0 1 18 2019-01-01 708.5 65.0\n",
"1 1 18 2019-01-02 1297.1 119.0\n",
"2 1 18 2019-01-03 1144.5 105.0\n",
"3 1 18 2019-01-04 1090.0 100.0\n",
"4 1 18 2019-01-05 893.8 82.0\n",
".. ... ... ... ... ...\n",
"85 1 18 2019-03-27 1308.0 120.0\n",
"86 1 18 2019-03-28 1079.1 99.0\n",
"87 1 18 2019-03-29 1002.8 92.0\n",
"88 1 18 2019-03-30 741.2 68.0\n",
"89 1 18 2019-03-31 741.2 68.0\n",
"\n",
"[90 rows x 5 columns]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"my_data = retrieve_data(product_code=18, store_code=1, date=['2019-01-01', '2019-03-31'])\n",
"my_data"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "base",
"language": "python",
"name": "python3"
},
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"version": 3
},
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"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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