from django.db.models import F, Window
from django.db.models.functions import RowNumber, Rank, DenseRank
from products.models import Sale
def get_sales_with_ranking():
"""Add rank to sales by amount."""
sales = Sale.objects.annotate(
rank=Window(
expression=Rank(),
order_by=F('amount').desc()
),
row_number=Window(
expression=RowNumber(),
order_by=F('created_at').desc()
)
)
return sales
def get_salesperson_rankings():
"""Rank salespeople within each region."""
from django.db.models import Sum
sales = Sale.objects.values(
'salesperson__name',
'salesperson__region'
).annotate(
total_sales=Sum('amount')
).annotate(
region_rank=Window(
expression=Rank(),
partition_by=[F('salesperson__region')],
order_by=F('total_sales').desc()
)
).order_by('salesperson__region', 'region_rank')
return sales
Window functions perform calculations across rows related to the current row. I use them for running totals, rankings, and moving averages. Django's Window expression with functions like RowNumber, Rank, DenseRank provide SQL window function support. This is more efficient than Python-side calculations. I partition data with partition_by and order with order_by. Window functions are perfect for leaderboards, time-series analysis, and comparative metrics. They require PostgreSQL or other databases supporting window functions.