DW.get_products_sale (eng) - datawizio/pythonAPI GitHub Wiki
Returns data about sales of chosen products during a given period. Shows the turnover of products during the given period, quantity of sold products, stock, self-cost. The results are grouped by days, weeks, months, and years.
Parameters:
-
products: int,list
id of the product or a list of ids that will be used for selection.
-
categories: int,list
id of the category or a list of ids that will be used for selection.
-
shops: int,list
id of the shop or a list of ids that will be used for selection.
-
weekday: int {monday - 0, sunday - 6}
day of week that will be used for selection.
-
date_from: datetime, str {%Y-%m-%d}
starting date of selection.
-
date_to: datetime, str {%Y-%m-%d}
ending date of selection.
Passing the parameter "sum" as the last element of the list, you'll get an additional column with a sum of the corresponding indicator.
If the interval [date_from, date_to]
has not been set, last 30 client's days will be used for selection.
If only one parameter has been set, then 30 client's days from selected parameter will be used for selection.
interval: str,{"days","months","weeks","years", default: "days" }
depending on the parameter, the results will be grouped by days, weeks, months or years.
by: str, list
characteristics that will be used for receiving selection results.{"turnover":
turnover,"qty":
quantity of sold products,"receipts_qty":
quantity of receipts,"profit":
profit,"sold_product_value":
self cost of sold products,"self_price_per_product":
price without extra charge per one unit of product,"price":
average price per one unit of product,default:"turnover"}
show: str,
{"name": <category_name> for names of columns,
"id": <category_id> for names of columns,
"both": <category_id>_<category_name> for names of columns,
default: "name".
view_type: raw, represent
Returns:
if view_type: raw
, than formed table:
date | product | qty | ...N | |
1 | by | by | by | by |
2 | by | by | by | by |
...N | by | by | by | by |
if view_type: represent
, than formed table:
product 1 | product 2 | ...product N | |
date 1 | by | by | by |
date 2 | by | by | by |
...date N | by | by | by |
Examples:
dw = datawiz.DW()
dw.get_products_sale(products = [2833024, 2286946, 'sum'],by='turnover',
shops = [305, 306, 318, 321],
date_from = datetime.date(2015, 8, 9),
date_to = datetime.date(2015, 9, 9),
interval = datawiz.WEEKS)
Returns turnover data for categories with id [2833024, 2286946], from 9.8.2015 till 9.9.2015 for shops [305, 306, 318, 321], grouped by weeks.