def populate(self):
product = Product(pid=1,
brand='Dr. Martens',
gender='unisex',
type='boots',
price=189,
image='dr_martens.jpg',
in_cart=0,
quantity=0)
self.repository.data.append(product)
product = Product(pid=2,
brand='Christian Louboutin',
gender='women\'s',
type='high heels',
price=699,
image='louboutin.png',
in_cart=0,
quantity=0)
self.repository.data.append(product)
product = Product(pid=3,
brand='Valentino Garavani',
gender='women\'s',
type='high heels',
price=749,
image='valentino.jpg',
in_cart=0,
quantity=0)
self.repository.data.append(product)
def add_list_event(self):
# param : name, yongdo, yang, dec, price, time
pprice = util.check_input(self.lineCost.text())
pyang = util.check_input(self.lineAmount.text())
if (pprice is 0) or (pyang is 0):
QMessageBox.question(self, "입력오류", "입력형식 오류", QMessageBox.Yes)
return
pname = self.comboKind.currentText()
pyongdo = self.comboBuySell.currentText()
pdec = self.textDesc.toPlainText()
# 기존 값 체크가 안 되어 있는 경우
date = datetime.datetime.now()
preDate = date.strftime('%y%m%d')
if self.checkDefault.checkState() == 0:
pTime = self.editTime.dateTime().toString('hhmm')
elif self.checkDefault.checkState() == 2:
pTime = date.strftime('%H%M')
rTime = preDate + pTime
p = Product(name=pname,
yongdo=pyongdo,
yang=pyang,
dec=pdec,
price=pprice,
time=rTime)
global_list_product.append(p)
view = self.listProduct
item = QListWidgetItem(view)
item.setText('(' + str(pTime)[:2] + ':' + str(pTime)[2:] + ') ' +
str(p.name) + '/' + str(p.yongdo))
def find(self, product_name):
query = "SELECT * FROM Products WHERE name='{}'".format( product_name )
row = self.execute_query( query )
if row:
p = Product(row[0][0], row[0][1], row[0][2], row[0][3])
return p
return None
def readAll(self):
products = []
query = "SELECT * FROM Products"
rows = self.execute_query( query )
for row in rows:
p = Product(row[0], row[1], row[2], row[3])
products.append( p )
return products
def index(self):
user_model = User()
category_model = Category()
product_model = Product()
users = user_model.get_total_users()
categories = category_model.get_total_categories()
products = product_model.get_total_products()
last_products = product_model.get_last_products()
return self.render('home_admin.html',
report={
'users': users[0],
'categories': categories[0],
'products': products[0]
},
last_products=last_products)
def index(self):
#return self.render('home_admin.html', data={'username':'******'})
user_model = User()
category_model = Category()
product_model = Product()
users = user_model.get_total_users()
categories = category_model.get_total_categories()
products = product_model.get_total_products()
last_products = product_model.get_last_products()
return self.render('home_admin.html', report={
'users': 0 if not users else users[0],
'categories': 0 if not categories else categories[0],
'products': 0 if not products else products[0]
}, last_products=last_products)
def index(self):
user_model = User()
category_model = Category()
product_model = Product()
users = user_model.get_total_users()
categories = category_model.get_total_categories()
products = product_model.get_total_products()
last_products = product_model.get_last_products()
return self.render(
"home_admin.html",
report={
"users": 0 if not users else users[0],
"categories": 0 if not categories else categories[0],
"products": 0 if not products else products[0]
},
last_products=last_products
)
def __init__(self):
self.product_model = Product()
finFilePath = "s3a://" + app_conf["s3_conf"]["s3_bucket"] + "/finances-small"
financeDf = spark.read.parquet(finFilePath)
financeDf.printSchema()
accNumPrev4WindowSpec = Window.partitionBy("AccountNumber")\
.orderBy("Date")\
.rowsBetween(-4, 0) # takes the first first 5 rows to window aggregation
financeDf\
.withColumn("Date", to_date(from_unixtime(unix_timestamp("Date", "MM/dd/yyyy"))))\
.withColumn("RollingAvg", avg("Amount").over(accNumPrev4WindowSpec))\
.show(5, False)
productList = [
Product("Thin", "Cell phone", 6000),
Product("Normal", "Tablet", 1500),
Product("Mini", "Tablet", 5500),
Product("Ultra Thin", "Cell phone", 5000),
Product("Very Thin", "Cell phone", 6000),
Product("Big", "Tablet", 2500),
Product("Bendable", "Cell phone", 3000),
Product("Foldable", "Cell phone", 3000),
Product("Pro", "Tablet", 4500),
Product("Pro2", "Tablet", 6500)
]
products = spark.createDataFrame(productList)
products.printSchema()
catRevenueWindowSpec = Window.partitionBy("category")\
productTypeSneakers = ProductType(name="Кроссовки",
parent_type=productTypeShoes)
productTypeBoots = ProductType(name="Ботинки",
parent_type=productTypeShoes)
productTypeTShirtMen = ProductType(name="Мужская футболка",
parent_type=productTypeTShirt)
productTypeTShirtWoman = ProductType(name="Женская футболка",
parent_type=productTypeTShirt)
# Продукты
Sneakers = Product(size=42,
color="Red",
price=4500,
quantity=10,
product_type=productTypeSneakers)
Sneakers1 = Product(size=44,
color="Black",
price=4500,
quantity=10,
product_type=productTypeSneakers)
Boots = Product(size=40,
color="Brown",
price=5500,
quantity=2,
product_type=productTypeBoots)
Boots1 = Product(size=45,
color="Green",