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Diffstat (limited to 'tests/modeltests/aggregation/models.py')
| -rw-r--r-- | tests/modeltests/aggregation/models.py | 321 |
1 files changed, 1 insertions, 320 deletions
diff --git a/tests/modeltests/aggregation/models.py b/tests/modeltests/aggregation/models.py index f50abe651b..ccc12898b7 100644 --- a/tests/modeltests/aggregation/models.py +++ b/tests/modeltests/aggregation/models.py @@ -1,6 +1,7 @@ # coding: utf-8 from django.db import models + class Author(models.Model): name = models.CharField(max_length=100) age = models.IntegerField() @@ -39,323 +40,3 @@ class Store(models.Model): def __unicode__(self): return self.name -# Tests on 'aggregate' -# Different backends and numbers. -__test__ = {'API_TESTS': """ ->>> from django.core import management ->>> from decimal import Decimal ->>> from datetime import date - -# Reset the database representation of this app. -# This will return the database to a clean initial state. ->>> management.call_command('flush', verbosity=0, interactive=False) - -# Empty Call - request nothing, get nothing. ->>> Author.objects.all().aggregate() -{} - ->>> from django.db.models import Avg, Sum, Count, Max, Min - -# Single model aggregation -# - -# Single aggregate -# Average age of Authors ->>> Author.objects.all().aggregate(Avg('age')) -{'age__avg': 37.4...} - -# Multiple aggregates -# Average and Sum of Author ages ->>> Author.objects.all().aggregate(Sum('age'), Avg('age')) -{'age__sum': 337, 'age__avg': 37.4...} - -# Aggreates interact with filters, and only -# generate aggregate values for the filtered values -# Sum of the age of those older than 29 years old ->>> Author.objects.all().filter(age__gt=29).aggregate(Sum('age')) -{'age__sum': 254} - -# Depth-1 Joins -# - -# On Relationships with self -# Average age of the friends of each author ->>> Author.objects.all().aggregate(Avg('friends__age')) -{'friends__age__avg': 34.07...} - -# On ManyToMany Relationships -# - -# Forward -# Average age of the Authors of Books with a rating of less than 4.5 ->>> Book.objects.all().filter(rating__lt=4.5).aggregate(Avg('authors__age')) -{'authors__age__avg': 38.2...} - -# Backward -# Average rating of the Books whose Author's name contains the letter 'a' ->>> Author.objects.all().filter(name__contains='a').aggregate(Avg('book__rating')) -{'book__rating__avg': 4.0} - -# On OneToMany Relationships -# - -# Forward -# Sum of the number of awards of each Book's Publisher ->>> Book.objects.all().aggregate(Sum('publisher__num_awards')) -{'publisher__num_awards__sum': 30} - -# Backward -# Sum of the price of every Book that has a Publisher ->>> Publisher.objects.all().aggregate(Sum('book__price')) -{'book__price__sum': Decimal("270.27")} - -# Multiple Joins -# - -# Forward ->>> Store.objects.all().aggregate(Max('books__authors__age')) -{'books__authors__age__max': 57} - -# Backward -# Note that the very long default alias may be truncated ->>> Author.objects.all().aggregate(Min('book__publisher__num_awards')) -{'book__publisher__num_award...': 1} - -# Aggregate outputs can also be aliased. - -# Average amazon.com Book rating ->>> Store.objects.filter(name='Amazon.com').aggregate(amazon_mean=Avg('books__rating')) -{'amazon_mean': 4.08...} - -# Tests on annotate() - -# An empty annotate call does nothing but return the same QuerySet ->>> Book.objects.all().annotate().order_by('pk') -[<Book: The Definitive Guide to Django: Web Development Done Right>, <Book: Sams Teach Yourself Django in 24 Hours>, <Book: Practical Django Projects>, <Book: Python Web Development with Django>, <Book: Artificial Intelligence: A Modern Approach>, <Book: Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp>] - -# Annotate inserts the alias into the model object with the aggregated result ->>> books = Book.objects.all().annotate(mean_age=Avg('authors__age')) ->>> books.get(pk=1).name -u'The Definitive Guide to Django: Web Development Done Right' - ->>> books.get(pk=1).mean_age -34.5 - -# On ManyToMany Relationships - -# Forward -# Average age of the Authors of each book with a rating less than 4.5 ->>> books = Book.objects.all().filter(rating__lt=4.5).annotate(Avg('authors__age')) ->>> sorted([(b.name, b.authors__age__avg) for b in books]) -[(u'Artificial Intelligence: A Modern Approach', 51.5), (u'Practical Django Projects', 29.0), (u'Python Web Development with Django', 30.3...), (u'Sams Teach Yourself Django in 24 Hours', 45.0)] - -# Count the number of authors of each book ->>> books = Book.objects.annotate(num_authors=Count('authors')) ->>> sorted([(b.name, b.num_authors) for b in books]) -[(u'Artificial Intelligence: A Modern Approach', 2), (u'Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp', 1), (u'Practical Django Projects', 1), (u'Python Web Development with Django', 3), (u'Sams Teach Yourself Django in 24 Hours', 1), (u'The Definitive Guide to Django: Web Development Done Right', 2)] - -# Backward -# Average rating of the Books whose Author's names contains the letter 'a' ->>> authors = Author.objects.all().filter(name__contains='a').annotate(Avg('book__rating')) ->>> sorted([(a.name, a.book__rating__avg) for a in authors]) -[(u'Adrian Holovaty', 4.5), (u'Brad Dayley', 3.0), (u'Jacob Kaplan-Moss', 4.5), (u'James Bennett', 4.0), (u'Paul Bissex', 4.0), (u'Stuart Russell', 4.0)] - -# Count the number of books written by each author ->>> authors = Author.objects.annotate(num_books=Count('book')) ->>> sorted([(a.name, a.num_books) for a in authors]) -[(u'Adrian Holovaty', 1), (u'Brad Dayley', 1), (u'Jacob Kaplan-Moss', 1), (u'James Bennett', 1), (u'Jeffrey Forcier', 1), (u'Paul Bissex', 1), (u'Peter Norvig', 2), (u'Stuart Russell', 1), (u'Wesley J. Chun', 1)] - -# On OneToMany Relationships - -# Forward -# Annotate each book with the number of awards of each Book's Publisher ->>> books = Book.objects.all().annotate(Sum('publisher__num_awards')) ->>> sorted([(b.name, b.publisher__num_awards__sum) for b in books]) -[(u'Artificial Intelligence: A Modern Approach', 7), (u'Paradigms of Artificial Intelligence Programming: Case Studies in Common Lisp', 9), (u'Practical Django Projects', 3), (u'Python Web Development with Django', 7), (u'Sams Teach Yourself Django in 24 Hours', 1), (u'The Definitive Guide to Django: Web Development Done Right', 3)] - -# Backward -# Annotate each publisher with the sum of the price of all books sold ->>> publishers = Publisher.objects.all().annotate(Sum('book__price')) ->>> sorted([(p.name, p.book__price__sum) for p in publishers]) -[(u'Apress', Decimal("59.69")), (u"Jonno's House of Books", None), (u'Morgan Kaufmann', Decimal("75.00")), (u'Prentice Hall', Decimal("112.49")), (u'Sams', Decimal("23.09"))] - -# Calls to values() are not commutative over annotate(). - -# Calling values on a queryset that has annotations returns the output -# as a dictionary ->>> [sorted(o.iteritems()) for o in Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values()] -[[('contact_id', 1), ('id', 1), ('isbn', u'159059725'), ('mean_age', 34.5), ('name', u'The Definitive Guide to Django: Web Development Done Right'), ('pages', 447), ('price', Decimal("30...")), ('pubdate', datetime.date(2007, 12, 6)), ('publisher_id', 1), ('rating', 4.5)]] - ->>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values('pk', 'isbn', 'mean_age') -[{'pk': 1, 'isbn': u'159059725', 'mean_age': 34.5}] - -# Calling values() with parameters reduces the output ->>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values('name') -[{'name': u'The Definitive Guide to Django: Web Development Done Right'}] - -# An empty values() call before annotating has the same effect as an -# empty values() call after annotating ->>> [sorted(o.iteritems()) for o in Book.objects.filter(pk=1).values().annotate(mean_age=Avg('authors__age'))] -[[('contact_id', 1), ('id', 1), ('isbn', u'159059725'), ('mean_age', 34.5), ('name', u'The Definitive Guide to Django: Web Development Done Right'), ('pages', 447), ('price', Decimal("30...")), ('pubdate', datetime.date(2007, 12, 6)), ('publisher_id', 1), ('rating', 4.5)]] - -# Calling annotate() on a ValuesQuerySet annotates over the groups of -# fields to be selected by the ValuesQuerySet. - -# Note that an extra parameter is added to each dictionary. This -# parameter is a queryset representing the objects that have been -# grouped to generate the annotation - ->>> Book.objects.all().values('rating').annotate(n_authors=Count('authors__id'), mean_age=Avg('authors__age')).order_by('rating') -[{'rating': 3.0, 'n_authors': 1, 'mean_age': 45.0}, {'rating': 4.0, 'n_authors': 6, 'mean_age': 37.1...}, {'rating': 4.5, 'n_authors': 2, 'mean_age': 34.5}, {'rating': 5.0, 'n_authors': 1, 'mean_age': 57.0}] - -# If a join doesn't match any objects, an aggregate returns None ->>> authors = Author.objects.all().annotate(Avg('friends__age')).order_by('id') ->>> len(authors) -9 ->>> sorted([(a.name, a.friends__age__avg) for a in authors]) -[(u'Adrian Holovaty', 32.0), (u'Brad Dayley', None), (u'Jacob Kaplan-Moss', 29.5), (u'James Bennett', 34.0), (u'Jeffrey Forcier', 27.0), (u'Paul Bissex', 31.0), (u'Peter Norvig', 46.0), (u'Stuart Russell', 57.0), (u'Wesley J. Chun', 33.6...)] - - -# The Count aggregation function allows an extra parameter: distinct. -# This restricts the count results to unique items ->>> Book.objects.all().aggregate(Count('rating')) -{'rating__count': 6} - ->>> Book.objects.all().aggregate(Count('rating', distinct=True)) -{'rating__count': 4} - -# Retreiving the grouped objects - -# When using Count you can also omit the primary key and refer only to -# the related field name if you want to count all the related objects -# and not a specific column ->>> explicit = list(Author.objects.annotate(Count('book__id'))) ->>> implicit = list(Author.objects.annotate(Count('book'))) ->>> explicit == implicit -True - -# Ordering is allowed on aggregates ->>> Book.objects.values('rating').annotate(oldest=Max('authors__age')).order_by('oldest', 'rating') -[{'rating': 4.5, 'oldest': 35}, {'rating': 3.0, 'oldest': 45}, {'rating': 4.0, 'oldest': 57}, {'rating': 5.0, 'oldest': 57}] - ->>> Book.objects.values('rating').annotate(oldest=Max('authors__age')).order_by('-oldest', '-rating') -[{'rating': 5.0, 'oldest': 57}, {'rating': 4.0, 'oldest': 57}, {'rating': 3.0, 'oldest': 45}, {'rating': 4.5, 'oldest': 35}] - -# It is possible to aggregate over anotated values ->>> Book.objects.all().annotate(num_authors=Count('authors__id')).aggregate(Avg('num_authors')) -{'num_authors__avg': 1.66...} - -# You can filter the results based on the aggregation alias. - -# Lets add a publisher to test the different possibilities for filtering ->>> p = Publisher(name='Expensive Publisher', num_awards=0) ->>> p.save() ->>> Book(name='ExpensiveBook1', pages=1, isbn='111', rating=3.5, price=Decimal("1000"), publisher=p, contact_id=1, pubdate=date(2008,12,1)).save() ->>> Book(name='ExpensiveBook2', pages=1, isbn='222', rating=4.0, price=Decimal("1000"), publisher=p, contact_id=1, pubdate=date(2008,12,2)).save() ->>> Book(name='ExpensiveBook3', pages=1, isbn='333', rating=4.5, price=Decimal("35"), publisher=p, contact_id=1, pubdate=date(2008,12,3)).save() - -# Publishers that have: - -# (i) more than one book ->>> Publisher.objects.annotate(num_books=Count('book__id')).filter(num_books__gt=1).order_by('pk') -[<Publisher: Apress>, <Publisher: Prentice Hall>, <Publisher: Expensive Publisher>] - -# (ii) a book that cost less than 40 ->>> Publisher.objects.filter(book__price__lt=Decimal("40.0")).order_by('pk') -[<Publisher: Apress>, <Publisher: Apress>, <Publisher: Sams>, <Publisher: Prentice Hall>, <Publisher: Expensive Publisher>] - -# (iii) more than one book and (at least) a book that cost less than 40 ->>> Publisher.objects.annotate(num_books=Count('book__id')).filter(num_books__gt=1, book__price__lt=Decimal("40.0")).order_by('pk') -[<Publisher: Apress>, <Publisher: Prentice Hall>, <Publisher: Expensive Publisher>] - -# (iv) more than one book that costs less than $40 ->>> Publisher.objects.filter(book__price__lt=Decimal("40.0")).annotate(num_books=Count('book__id')).filter(num_books__gt=1).order_by('pk') -[<Publisher: Apress>] - -# Now a bit of testing on the different lookup types -# - ->>> Publisher.objects.annotate(num_books=Count('book')).filter(num_books__range=[1, 3]).order_by('pk') -[<Publisher: Apress>, <Publisher: Sams>, <Publisher: Prentice Hall>, <Publisher: Morgan Kaufmann>, <Publisher: Expensive Publisher>] - ->>> Publisher.objects.annotate(num_books=Count('book')).filter(num_books__range=[1, 2]).order_by('pk') -[<Publisher: Apress>, <Publisher: Sams>, <Publisher: Prentice Hall>, <Publisher: Morgan Kaufmann>] - ->>> Publisher.objects.annotate(num_books=Count('book')).filter(num_books__in=[1, 3]).order_by('pk') -[<Publisher: Sams>, <Publisher: Morgan Kaufmann>, <Publisher: Expensive Publisher>] - ->>> Publisher.objects.annotate(num_books=Count('book')).filter(num_books__isnull=True) -[] - ->>> p.delete() - -# Does Author X have any friends? (or better, how many friends does author X have) ->> Author.objects.filter(pk=1).aggregate(Count('friends__id')) -{'friends__id__count': 2.0} - -# Give me a list of all Books with more than 1 authors ->>> Book.objects.all().annotate(num_authors=Count('authors__name')).filter(num_authors__ge=2).order_by('pk') -[<Book: The Definitive Guide to Django: Web Development Done Right>, <Book: Artificial Intelligence: A Modern Approach>] - -# Give me a list of all Authors that have no friends ->>> Author.objects.all().annotate(num_friends=Count('friends__id', distinct=True)).filter(num_friends=0).order_by('pk') -[<Author: Brad Dayley>] - -# Give me a list of all publishers that have published more than 1 books ->>> Publisher.objects.all().annotate(num_books=Count('book__id')).filter(num_books__gt=1).order_by('pk') -[<Publisher: Apress>, <Publisher: Prentice Hall>] - -# Give me a list of all publishers that have published more than 1 books that cost less than 40 ->>> Publisher.objects.all().filter(book__price__lt=Decimal("40.0")).annotate(num_books=Count('book__id')).filter(num_books__gt=1) -[<Publisher: Apress>] - -# Give me a list of all Books that were written by X and one other author. ->>> Book.objects.all().annotate(num_authors=Count('authors__id')).filter(authors__name__contains='Norvig', num_authors__gt=1) -[<Book: Artificial Intelligence: A Modern Approach>] - -# Give me the average rating of all Books that were written by X and one other author. -#(Aggregate over objects discovered using membership of the m2m set) - -# Adding an existing author to another book to test it the right way ->>> a = Author.objects.get(name__contains='Norvig') ->>> b = Book.objects.get(name__contains='Done Right') ->>> b.authors.add(a) ->>> b.save() - -# This should do it ->>> Book.objects.all().annotate(num_authors=Count('authors__id')).filter(authors__name__contains='Norvig', num_authors__gt=1).aggregate(Avg('rating')) -{'rating__avg': 4.25} ->>> b.authors.remove(a) - -# Give me a list of all Authors that have published a book with at least one other person -# (Filters over a count generated on a related object) -# -# Cheating: [a for a in Author.objects.all().annotate(num_coleagues=Count('book__authors__id'), num_books=Count('book__id', distinct=True)) if a.num_coleagues - a.num_books > 0] -# F-Syntax is required. Will be fixed after F objects are available - -# Aggregates also work on dates, times and datetimes ->>> Publisher.objects.annotate(earliest_book=Min('book__pubdate')).exclude(earliest_book=None).order_by('earliest_book').values() -[{'earliest_book': datetime.date(1991, 10, 15), 'num_awards': 9, 'id': 4, 'name': u'Morgan Kaufmann'}, {'earliest_book': datetime.date(1995, 1, 15), 'num_awards': 7, 'id': 3, 'name': u'Prentice Hall'}, {'earliest_book': datetime.date(2007, 12, 6), 'num_awards': 3, 'id': 1, 'name': u'Apress'}, {'earliest_book': datetime.date(2008, 3, 3), 'num_awards': 1, 'id': 2, 'name': u'Sams'}] - ->>> Store.objects.aggregate(Max('friday_night_closing'), Min("original_opening")) -{'friday_night_closing__max': datetime.time(23, 59, 59), 'original_opening__min': datetime.datetime(1945, 4, 25, 16, 24, 14)} - -# values_list() can also be used - ->>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values_list('pk', 'isbn', 'mean_age') -[(1, u'159059725', 34.5)] - ->>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values_list('isbn') -[(u'159059725',)] - ->>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values_list('mean_age') -[(34.5,)] - ->>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values_list('mean_age', flat=True) -[34.5] - ->>> qs = Book.objects.values_list('price').annotate(count=Count('price')).order_by('-count', 'price') ->>> list(qs) == [(Decimal('29.69'), 2), (Decimal('23.09'), 1), (Decimal('30'), 1), (Decimal('75'), 1), (Decimal('82.8'), 1)] -True - -"""} |