+
+
+
+
+
+
+
+
+
+Looqbox Challenge Test¶
+
+
+
+
+
+
+
+
+
+
+SQL Test¶
+
+
+
+
+
+
+
+
+
+Conection¶
+
+
+
+
+
+
+
+In [6]:
+
+
+
+
+
+# Conection
+
+from sqlalchemy import create_engine
+import pandas as pd
+
+HOST = "35.199.115.174"
+USER = "looqbox-challenge"
+PASSWORD = "looq-challenge"
+DB = "looqbox-challenge"
+
+engine = create_engine(
+ f"mysql+pymysql://{USER}:{PASSWORD}@{HOST}/{DB}"
+)
+
+pd.read_sql("SHOW TABLES;", engine)
+
+
+
+
+
+
+
+
+Out[6]:
+
+
+
+
+
+
+
+
+| + | Tables_in_looqbox-challenge | +
|---|---|
| 0 | +IMDB_movies | +
| 1 | +data_product | +
| 2 | +data_product_sales | +
| 3 | +data_store_cad | +
| 4 | +data_store_sales | +
+
+
+
+
+
+
+
+In [20]:
+
+
+
+
+
+query = """
+SELECT * FROM data_product
+LIMIT 5;
+"""
+
+test = pd.read_sql(query, engine)
+test
+
+
+
+
+
+
+
+
+Out[20]:
+
+
+
+
+
+
+
+
+| + | PRODUCT_COD | +PRODUCT_NAME | +PRODUCT_VAL | +DEP_NAME | +DEP_COD | +SECTION_NAME | +SECTION_COD | +
|---|---|---|---|---|---|---|---|
| 0 | +10 | +Acido Tranexamico 250mg Generico EMS 12 Compri... | +36.71 | +MEDICAMENTOS GENÉRICOS | +10 | +CIRCULAÇÃO | +40 | +
| 1 | +11 | +Bissulfato de Clopidogrel 75mg Generico Teuto ... | +66.51 | +MEDICAMENTOS GENÉRICOS | +10 | +CIRCULAÇÃO | +40 | +
| 2 | +12 | +Cloridrato de Amiodarona 200mg Generico Biosin... | +31.17 | +MEDICAMENTOS GENÉRICOS | +10 | +CIRCULAÇÃO | +40 | +
| 3 | +13 | +Acido Tranexâmico 250mg Generico Legrand 12 Co... | +36.71 | +MEDICAMENTOS GENÉRICOS | +10 | +CIRCULAÇÃO | +40 | +
| 4 | +14 | +Cloridrato Oximetazolina Adulto 5mg/ml Genéric... | +12.08 | +MEDICAMENTOS GENÉRICOS | +10 | +GRIPES E RESFRIADOS | +41 | +
+
+
+
+
+
+
+
+
+1. Top 10 most expensive products¶
+
+
+
+
+
+
+
+
+In [21]:
+
+
+
+
+
+query = """
+SELECT
+ PRODUCT_COD,
+ PRODUCT_NAME,
+ PRODUCT_VAL
+FROM data_product
+ORDER BY PRODUCT_VAL DESC
+LIMIT 10;
+"""
+
+top_10_products = pd.read_sql(query, engine)
+display(top_10_products)
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+| + | PRODUCT_COD | +PRODUCT_NAME | +PRODUCT_VAL | +
|---|---|---|---|
| 0 | +301409 | +Whisky Escoces THE MACALLAN Ruby Garrafa 700ml... | +741.99 | +
| 1 | +176185 | +Whisky Escoces JOHNNIE WALKER Blue Label Garra... | +735.90 | +
| 2 | +315481 | +Cafeteira Expresso 3 CORACOES Tres Modo Vermelho | +499.00 | +
| 3 | +100280 | +Vinho Portugues Tinto Vintage QUINTA DO CRASTO... | +445.90 | +
| 4 | +320046 | +Escova Dental Eletrica ORAL B D34 Professional... | +399.90 | +
| 5 | +190817 | +Champagne Rose VEUVE CLICQUOT PONSARDIM Garraf... | +366.90 | +
| 6 | +153795 | +Champagne Frances Brut Imperial MOET Rose Garr... | +359.90 | +
| 7 | +311397 | +Conjunto de Panelas Allegra em Inox TRAMONTINA... | +359.00 | +
| 8 | +147706 | +Whisky Escoces CHIVAS REGAL 18 Anos Garrafa 750ml | +329.90 | +
| 9 | +154431 | +Champagne Frances Brut Imperial MOET & CHANDON... | +315.90 | +
+
+
+
+
+
+
+
+
+2. Sections of 'BEBIDAS' and 'PADARIA'¶
+
+
+
+
+
+
+
+
+In [22]:
+
+
+
+
+
+query = """
+SELECT DISTINCT
+ DEP_NAME,
+ SECTION_NAME
+FROM data_product
+WHERE DEP_NAME IN ('BEBIDAS', 'PADARIA')
+ORDER BY DEP_NAME, SECTION_NAME;
+"""
+
+sections = pd.read_sql(query, engine)
+sections
+
+
+
+
+
+
+
+
+Out[22]:
+
+
+
+
+
+
+
+
+| + | DEP_NAME | +SECTION_NAME | +
|---|---|---|
| 0 | +BEBIDAS | +BEBIDAS | +
| 1 | +BEBIDAS | +CERVEJAS | +
| 2 | +BEBIDAS | +REFRESCOS | +
| 3 | +BEBIDAS | +VINHOS | +
| 4 | +PADARIA | +DOCES-E-SOBREMESAS | +
| 5 | +PADARIA | +GESTANTE | +
| 6 | +PADARIA | +PADARIA | +
| 7 | +PADARIA | +QUEIJOS-E-FRIOS | +
+
+
+
+
+
+
+
+
+3. Total sale of products (in $) of each Business Area in the first quarter of 2019¶
+
+
+
+
+
+
+
+
+In [23]:
+
+
+
+
+
+query = """
+SELECT
+ c.BUSINESS_NAME,
+ c.BUSINESS_CODE,
+ SUM(s.SALES_VALUE) AS TOTAL_SALES_VALUE
+FROM data_store_sales s
+LEFT JOIN data_store_cad c
+ ON s.STORE_CODE = c.STORE_CODE
+WHERE s.DATE BETWEEN '2019-01-01' AND '2019-03-31'
+GROUP BY
+ c.BUSINESS_NAME,
+ c.BUSINESS_CODE
+ORDER BY TOTAL_SALES_VALUE DESC;
+"""
+
+sales_by_business = pd.read_sql(query, engine)
+sales_by_business
+
+
+
+
+
+
+
+
+Out[23]:
+
+
+
+
+
+
+
+
+| + | BUSINESS_NAME | +BUSINESS_CODE | +TOTAL_SALES_VALUE | +
|---|---|---|---|
| 0 | +Farma | +4 | +81776691.73 | +
| 1 | +Varejo | +1 | +81032347.65 | +
| 2 | +Atacado | +5 | +80384884.60 | +
| 3 | +Proximidade | +2 | +80171122.80 | +
| 4 | +Posto | +3 | +32072326.40 | +
+
+
+
+
+
+
+
+
+Case 1¶
+
+
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+# dynamic function that uses a combination of product, store and date period --- user only inform the params
+
+def retrieve_data(product_code=None, store_code=None, date=None):
+ """
+ Retrieves all columns from data_product_sales using optional filters.
+
+ Parameters
+ ----------
+ product_code : int, optional
+ store_code : int, optional
+ date : list[str], optional
+ Example: ['2019-01-01', '2019-01-31']
+
+ Returns
+ -------
+ pandas.DataFrame
+ """
+
+ query = "SELECT * FROM data_product_sales WHERE 1=1"
+ params = {}
+
+ # I used AI to help me setup the params code
+
+ if product_code is not None:
+ query += " AND PRODUCT_CODE = :product_code"
+ params["product_code"] = product_code
+
+ if store_code is not None:
+ query += " AND STORE_CODE = :store_code"
+ params["store_code"] = store_code
+
+ if date is not None:
+ if len(date) != 2:
+ raise ValueError("date must be a list with two values: [start_date, end_date]")
+
+ query += " AND DATE BETWEEN :start_date AND :end_date"
+ params["start_date"] = date[0]
+ params["end_date"] = date[1]
+
+ return pd.read_sql(text(query), engine, params=params)
+
+
+
+
+
+
+
+
+In [68]:
+
+
+
+
+
+# Test
+
+my_data = retrieve_data(
+ product_code=10,
+ store_code=10,
+ date=['2019-01-01', '2019-01-31']
+)
+
+my_data.head()
+
+
+
+
+
+
+
+
+Out[68]:
+
+
+
+
+
+
+
+
+| + | STORE_CODE | +PRODUCT_CODE | +DATE | +SALES_VALUE | +SALES_QTY | +
|---|---|---|---|---|---|
| 0 | +10 | +10 | +2019-01-01 | +2386.15 | +65.0 | +
| 1 | +10 | +10 | +2019-01-02 | +4368.49 | +119.0 | +
| 2 | +10 | +10 | +2019-01-03 | +3854.55 | +105.0 | +
| 3 | +10 | +10 | +2019-01-04 | +3671.00 | +100.0 | +
| 4 | +10 | +10 | +2019-01-05 | +3010.22 | +82.0 | +
+
+
+
+
+
+
+
+
+Case 2¶
+
+
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+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'
+"""
+
+stores = pd.read_sql(query1, engine)
+sales = pd.read_sql(query2, engine)
+
+
+
+
+
+
+
+In [27]:
+
+
+
+
+
+sales["DATE"] = pd.to_datetime(sales["DATE"])
+
+sales_filtered = sales[
+ (sales["DATE"] >= "2019-10-01") &
+ (sales["DATE"] <= "2019-12-31")
+]
+
+
+
+
+
+
+
+In [28]:
+
+
+
+
+
+df_case2 = sales_filtered.merge(
+ stores,
+ on="STORE_CODE",
+ how="left"
+)
+
+
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+summary_case2 = (
+ df_case2
+ .groupby(["STORE_NAME", "BUSINESS_NAME"], as_index=False)
+ .agg(
+ SALES_VALUE=("SALES_VALUE", "sum"),
+ SALES_QTY=("SALES_QTY", "sum")
+ )
+)
+
+# I used AI to understand what "TM" means!
+
+summary_case2["TM"] = summary_case2["SALES_VALUE"] / summary_case2["SALES_QTY"]
+
+summary_case2.sort_values("STORE_NAME")
+
+
+
+
+
+
+
+
+Out[Â ]:
+
+
+
+
+
+
+
+
+| + | STORE_NAME | +BUSINESS_NAME | +SALES_VALUE | +SALES_QTY | +TM | +
|---|---|---|---|---|---|
| 0 | +Bahia | +Atacado | +21213088.57 | +1378476 | +15.388798 | +
| 1 | +Bangkok | +Posto | +8376271.00 | +612968 | +13.665103 | +
| 2 | +Belem | +Proximidade | +20989553.37 | +1365988 | +15.365840 | +
| 3 | +Berlin | +Proximidade | +21213088.57 | +1378476 | +15.388798 | +
| 4 | +Buenos Aires | +Atacado | +21213088.57 | +1378476 | +15.388798 | +
| 5 | +Chicago | +Varejo | +21928421.28 | +1412372 | +15.525953 | +
| 6 | +Dubai | +Atacado | +21213088.57 | +1378476 | +15.388798 | +
| 7 | +Hong Kong | +Farma | +15039911.54 | +570745 | +26.351368 | +
| 8 | +London | +Farma | +19471788.15 | +671638 | +28.991493 | +
| 9 | +Madri | +Farma | +24129399.10 | +831168 | +29.030712 | +
| 10 | +Miami | +Posto | +8376271.00 | +612968 | +13.665103 | +
| 11 | +New York | +Proximidade | +21213088.57 | +1378476 | +15.388798 | +
| 12 | +Paris | +Proximidade | +21213088.57 | +1378476 | +15.388798 | +
| 13 | +Rio de Janeiro | +Farma | +27172082.37 | +918336 | +29.588389 | +
| 14 | +Roma | +Varejo | +21213088.57 | +1378476 | +15.388798 | +
| 15 | +Salvador | +Atacado | +21213088.57 | +1378476 | +15.388798 | +
| 16 | +Sao Paulo | +Varejo | +21213088.57 | +1378476 | +15.388798 | +
| 17 | +Sidney | +Posto | +8376271.00 | +612968 | +13.665103 | +
| 18 | +Tokio | +Varejo | +21213088.57 | +1378476 | +15.388798 | +
| 19 | +Vancouver | +Posto | +8376271.00 | +612968 | +13.665103 | +
+
+
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+# Chart ---
+
+import matplotlib.pyplot as plt
+import seaborn as sns
+
+plt.figure(figsize=(12, 6))
+
+aux = sns.barplot(
+ data=summary_case2.sort_values("TM", ascending=False),
+ x="TM",
+ y="STORE_NAME",
+ hue="BUSINESS_NAME"
+)
+
+# I used AI to create the label format code!
+for container in aux.containers:
+ aux.bar_label(
+ container,
+ fmt='%.2f',
+ padding=3
+ )
+
+plt.title("Average Ticket by Store and Business Area - Q4 2019")
+plt.xlabel("Average Ticket")
+plt.ylabel("Store")
+plt.legend(title="Business Area")
+plt.tight_layout()
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+Case 3¶
+
+
+
+
+
+
+
+In [33]:
+
+
+
+
+
+pd.read_sql("DESCRIBE IMDB_movies;", engine)
+
+
+
+
+
+
+
+
+Out[33]:
+
+
+
+
+
+
+
+
+| + | Field | +Type | +Null | +Key | +Default | +Extra | +
|---|---|---|---|---|---|---|
| 0 | +Id | +int | +NO | +PRI | +None | ++ |
| 1 | +Title | +varchar(255) | +YES | ++ | None | ++ |
| 2 | +Genre | +varchar(255) | +YES | ++ | None | ++ |
| 3 | +Director | +varchar(255) | +YES | ++ | None | ++ |
| 4 | +Actors | +varchar(255) | +YES | ++ | None | ++ |
| 5 | +Year | +int | +YES | ++ | None | ++ |
| 6 | +Runtime | +int | +YES | ++ | None | ++ |
| 7 | +Rating | +decimal(10,0) | +YES | ++ | None | ++ |
| 8 | +Votes | +int | +YES | ++ | None | ++ |
| 9 | +RevenueMillions | +decimal(10,0) | +YES | ++ | None | ++ |
| 10 | +Metascore | +int | +YES | ++ | None | ++ |
+
+
+
+
+
+
+In [34]:
+
+
+
+
+
+imdb = pd.read_sql("SELECT * FROM IMDB_movies;", engine)
+
+imdb.head()
+
+
+
+
+
+
+
+
+Out[34]:
+
+
+
+
+
+
+
+
+| + | Id | +Title | +Genre | +Director | +Actors | +Year | +Runtime | +Rating | +Votes | +RevenueMillions | +Metascore | +
|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | +1 | +Guardians of the Galaxy | +Action,Adventure,Sci-Fi | +James Gunn | +Chris Pratt, Vin Diesel, Bradley Cooper, Zoe S... | +2014 | +121 | +8.0 | +757074 | +333.0 | +76.0 | +
| 1 | +2 | +Prometheus | +Adventure,Mystery,Sci-Fi | +Ridley Scott | +Noomi Rapace, Logan Marshall-Green, Michael Fa... | +2012 | +124 | +7.0 | +485820 | +126.0 | +65.0 | +
| 2 | +3 | +Split | +Horror,Thriller | +M. Night Shyamalan | +James McAvoy, Anya Taylor-Joy, Haley Lu Richar... | +2016 | +117 | +7.0 | +157606 | +138.0 | +62.0 | +
| 3 | +4 | +Sing | +Animation,Comedy,Family | +Christophe Lourdelet | +Matthew McConaughey,Reese Witherspoon, Seth Ma... | +2016 | +108 | +7.0 | +60545 | +270.0 | +59.0 | +
| 4 | +5 | +Suicide Squad | +Action,Adventure,Fantasy | +David Ayer | +Will Smith, Jared Leto, Margot Robbie, Viola D... | +2016 | +123 | +6.0 | +393727 | +325.0 | +40.0 | +
+
+
+
+
+
+
+In [41]:
+
+
+
+
+
+# Chart --- Top 10 Highest Grossing Movies
+
+top10_revenue = (
+ imdb[['Title', 'RevenueMillions']]
+ .dropna()
+ .sort_values('RevenueMillions', ascending=False)
+ .head(10)
+)
+
+
+
+
+
+
+
+
+In [ ]:
+
+
+
+
+
+plt.figure(figsize=(12, 6))
+
+aux = sns.barplot(
+ data=top10_revenue,
+ x='RevenueMillions',
+ y='Title'
+)
+
+# I used AI to create the label format code!
+for container in aux.containers:
+ labels = [
+ f'{v:,.0f}M'
+ for v in container.datavalues
+ ]
+
+ aux.bar_label(
+ container,
+ labels=labels,
+ padding=3
+ )
+
+plt.title('Top 10 Highest Grossing Movies')
+plt.xlabel('Revenue (Million USD)')
+plt.ylabel('Movie')
+plt.tight_layout()
+plt.show()
+
+
+
+
+
+
+
+
+
+
+
+
+
+