366 lines
17 KiB
Python
366 lines
17 KiB
Python
# coding: utf-8
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from django.db import models
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try:
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sorted
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except NameError:
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from django.utils.itercompat import sorted # For Python 2.3
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class Author(models.Model):
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name = models.CharField(max_length=100)
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age = models.IntegerField()
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friends = models.ManyToManyField('self', blank=True)
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def __unicode__(self):
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return self.name
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class Publisher(models.Model):
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name = models.CharField(max_length=300)
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num_awards = models.IntegerField()
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def __unicode__(self):
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return self.name
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class Book(models.Model):
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isbn = models.CharField(max_length=9)
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name = models.CharField(max_length=300)
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pages = models.IntegerField()
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rating = models.FloatField()
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price = models.DecimalField(decimal_places=2, max_digits=6)
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authors = models.ManyToManyField(Author)
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contact = models.ForeignKey(Author, related_name='book_contact_set')
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publisher = models.ForeignKey(Publisher)
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pubdate = models.DateField()
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def __unicode__(self):
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return self.name
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class Store(models.Model):
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name = models.CharField(max_length=300)
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books = models.ManyToManyField(Book)
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original_opening = models.DateTimeField()
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friday_night_closing = models.TimeField()
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def __unicode__(self):
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return self.name
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# Tests on 'aggregate'
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# Different backends and numbers.
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__test__ = {'API_TESTS': """
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>>> from django.core import management
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>>> try:
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... from decimal import Decimal
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... except:
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... from django.utils._decimal import Decimal
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>>> from datetime import date
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# Reset the database representation of this app.
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# This will return the database to a clean initial state.
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>>> management.call_command('flush', verbosity=0, interactive=False)
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# Empty Call - request nothing, get nothing.
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>>> Author.objects.all().aggregate()
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{}
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>>> from django.db.models import Avg, Sum, Count, Max, Min
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# Single model aggregation
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#
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# Single aggregate
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# Average age of Authors
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>>> Author.objects.all().aggregate(Avg('age'))
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{'age__avg': 37.4...}
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# Multiple aggregates
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# Average and Sum of Author ages
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>>> Author.objects.all().aggregate(Sum('age'), Avg('age'))
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{'age__sum': 337, 'age__avg': 37.4...}
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# Aggreates interact with filters, and only
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# generate aggregate values for the filtered values
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# Sum of the age of those older than 29 years old
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>>> Author.objects.all().filter(age__gt=29).aggregate(Sum('age'))
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{'age__sum': 254}
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# Depth-1 Joins
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#
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# On Relationships with self
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# Average age of the friends of each author
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>>> Author.objects.all().aggregate(Avg('friends__age'))
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{'friends__age__avg': 34.07...}
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# On ManyToMany Relationships
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#
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# Forward
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# Average age of the Authors of Books with a rating of less than 4.5
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>>> Book.objects.all().filter(rating__lt=4.5).aggregate(Avg('authors__age'))
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{'authors__age__avg': 38.2...}
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# Backward
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# Average rating of the Books whose Author's name contains the letter 'a'
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>>> Author.objects.all().filter(name__contains='a').aggregate(Avg('book__rating'))
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{'book__rating__avg': 4.0}
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# On OneToMany Relationships
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#
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# Forward
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# Sum of the number of awards of each Book's Publisher
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>>> Book.objects.all().aggregate(Sum('publisher__num_awards'))
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{'publisher__num_awards__sum': 30}
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# Backward
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# Sum of the price of every Book that has a Publisher
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>>> Publisher.objects.all().aggregate(Sum('book__price'))
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{'book__price__sum': Decimal("270.27")}
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# Multiple Joins
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#
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# Forward
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>>> Store.objects.all().aggregate(Max('books__authors__age'))
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{'books__authors__age__max': 57}
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# Backward
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# Note that the very long default alias may be truncated
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>>> Author.objects.all().aggregate(Min('book__publisher__num_awards'))
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{'book__publisher__num_award...': 1}
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# Aggregate outputs can also be aliased.
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# Average amazon.com Book rating
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>>> Store.objects.filter(name='Amazon.com').aggregate(amazon_mean=Avg('books__rating'))
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{'amazon_mean': 4.08...}
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# Tests on annotate()
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# An empty annotate call does nothing but return the same QuerySet
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>>> Book.objects.all().annotate().order_by('pk')
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[<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>]
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# Annotate inserts the alias into the model object with the aggregated result
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>>> books = Book.objects.all().annotate(mean_age=Avg('authors__age'))
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>>> books.get(pk=1).name
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u'The Definitive Guide to Django: Web Development Done Right'
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>>> books.get(pk=1).mean_age
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34.5
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# On ManyToMany Relationships
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# Forward
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# Average age of the Authors of each book with a rating less than 4.5
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>>> books = Book.objects.all().filter(rating__lt=4.5).annotate(Avg('authors__age'))
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>>> sorted([(b.name, b.authors__age__avg) for b in books])
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[(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)]
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# Count the number of authors of each book
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>>> books = Book.objects.annotate(num_authors=Count('authors'))
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>>> sorted([(b.name, b.num_authors) for b in books])
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[(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)]
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# Backward
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# Average rating of the Books whose Author's names contains the letter 'a'
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>>> authors = Author.objects.all().filter(name__contains='a').annotate(Avg('book__rating'))
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>>> sorted([(a.name, a.book__rating__avg) for a in authors])
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[(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)]
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# Count the number of books written by each author
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>>> authors = Author.objects.annotate(num_books=Count('book'))
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>>> sorted([(a.name, a.num_books) for a in authors])
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[(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)]
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# On OneToMany Relationships
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# Forward
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# Annotate each book with the number of awards of each Book's Publisher
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>>> books = Book.objects.all().annotate(Sum('publisher__num_awards'))
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>>> sorted([(b.name, b.publisher__num_awards__sum) for b in books])
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[(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)]
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# Backward
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# Annotate each publisher with the sum of the price of all books sold
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>>> publishers = Publisher.objects.all().annotate(Sum('book__price'))
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>>> sorted([(p.name, p.book__price__sum) for p in publishers])
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[(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"))]
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# Calls to values() are not commutative over annotate().
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# Calling values on a queryset that has annotations returns the output
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# as a dictionary
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>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values()
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[{'rating': 4.5, 'isbn': u'159059725', 'name': u'The Definitive Guide to Django: Web Development Done Right', 'pubdate': datetime.date(2007, 12, 6), 'price': Decimal("30..."), 'contact_id': 1, 'id': 1, 'publisher_id': 1, 'pages': 447, 'mean_age': 34.5}]
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>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values('pk', 'isbn', 'mean_age')
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[{'pk': 1, 'isbn': u'159059725', 'mean_age': 34.5}]
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# Calling values() with parameters reduces the output
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>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values('name')
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[{'name': u'The Definitive Guide to Django: Web Development Done Right'}]
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# An empty values() call before annotating has the same effect as an
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# empty values() call after annotating
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>>> Book.objects.filter(pk=1).values().annotate(mean_age=Avg('authors__age'))
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[{'rating': 4.5, 'isbn': u'159059725', 'name': u'The Definitive Guide to Django: Web Development Done Right', 'pubdate': datetime.date(2007, 12, 6), 'price': Decimal("30..."), 'contact_id': 1, 'id': 1, 'publisher_id': 1, 'pages': 447, 'mean_age': 34.5}]
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# Calling annotate() on a ValuesQuerySet annotates over the groups of
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# fields to be selected by the ValuesQuerySet.
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# Note that an extra parameter is added to each dictionary. This
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# parameter is a queryset representing the objects that have been
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# grouped to generate the annotation
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>>> Book.objects.all().values('rating').annotate(n_authors=Count('authors__id'), mean_age=Avg('authors__age')).order_by('rating')
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[{'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}]
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# If a join doesn't match any objects, an aggregate returns None
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>>> authors = Author.objects.all().annotate(Avg('friends__age')).order_by('id')
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>>> len(authors)
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9
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>>> sorted([(a.name, a.friends__age__avg) for a in authors])
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[(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...)]
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# The Count aggregation function allows an extra parameter: distinct.
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# This restricts the count results to unique items
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>>> Book.objects.all().aggregate(Count('rating'))
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{'rating__count': 6}
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>>> Book.objects.all().aggregate(Count('rating', distinct=True))
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{'rating__count': 4}
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# Retreiving the grouped objects
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# When using Count you can also omit the primary key and refer only to
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# the related field name if you want to count all the related objects
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# and not a specific column
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>>> explicit = list(Author.objects.annotate(Count('book__id')))
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>>> implicit = list(Author.objects.annotate(Count('book')))
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>>> explicit == implicit
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True
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# Ordering is allowed on aggregates
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>>> Book.objects.values('rating').annotate(oldest=Max('authors__age')).order_by('oldest', 'rating')
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[{'rating': 4.5, 'oldest': 35}, {'rating': 3.0, 'oldest': 45}, {'rating': 4.0, 'oldest': 57}, {'rating': 5.0, 'oldest': 57}]
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>>> Book.objects.values('rating').annotate(oldest=Max('authors__age')).order_by('-oldest', '-rating')
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[{'rating': 5.0, 'oldest': 57}, {'rating': 4.0, 'oldest': 57}, {'rating': 3.0, 'oldest': 45}, {'rating': 4.5, 'oldest': 35}]
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# It is possible to aggregate over anotated values
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>>> Book.objects.all().annotate(num_authors=Count('authors__id')).aggregate(Avg('num_authors'))
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{'num_authors__avg': 1.66...}
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# You can filter the results based on the aggregation alias.
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# Lets add a publisher to test the different possibilities for filtering
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>>> p = Publisher(name='Expensive Publisher', num_awards=0)
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>>> p.save()
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>>> Book(name='ExpensiveBook1', pages=1, isbn='111', rating=3.5, price=Decimal("1000"), publisher=p, contact_id=1, pubdate=date(2008,12,1)).save()
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>>> Book(name='ExpensiveBook2', pages=1, isbn='222', rating=4.0, price=Decimal("1000"), publisher=p, contact_id=1, pubdate=date(2008,12,2)).save()
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>>> Book(name='ExpensiveBook3', pages=1, isbn='333', rating=4.5, price=Decimal("35"), publisher=p, contact_id=1, pubdate=date(2008,12,3)).save()
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# Publishers that have:
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# (i) more than one book
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>>> Publisher.objects.annotate(num_books=Count('book__id')).filter(num_books__gt=1).order_by('pk')
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[<Publisher: Apress>, <Publisher: Prentice Hall>, <Publisher: Expensive Publisher>]
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# (ii) a book that cost less than 40
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>>> Publisher.objects.filter(book__price__lt=Decimal("40.0")).order_by('pk')
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[<Publisher: Apress>, <Publisher: Apress>, <Publisher: Sams>, <Publisher: Prentice Hall>, <Publisher: Expensive Publisher>]
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# (iii) more than one book and (at least) a book that cost less than 40
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>>> Publisher.objects.annotate(num_books=Count('book__id')).filter(num_books__gt=1, book__price__lt=Decimal("40.0")).order_by('pk')
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[<Publisher: Apress>, <Publisher: Prentice Hall>, <Publisher: Expensive Publisher>]
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# (iv) more than one book that costs less than $40
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>>> Publisher.objects.filter(book__price__lt=Decimal("40.0")).annotate(num_books=Count('book__id')).filter(num_books__gt=1).order_by('pk')
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[<Publisher: Apress>]
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# Now a bit of testing on the different lookup types
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#
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>>> Publisher.objects.annotate(num_books=Count('book')).filter(num_books__range=[1, 3]).order_by('pk')
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[<Publisher: Apress>, <Publisher: Sams>, <Publisher: Prentice Hall>, <Publisher: Morgan Kaufmann>, <Publisher: Expensive Publisher>]
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>>> Publisher.objects.annotate(num_books=Count('book')).filter(num_books__range=[1, 2]).order_by('pk')
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[<Publisher: Apress>, <Publisher: Sams>, <Publisher: Prentice Hall>, <Publisher: Morgan Kaufmann>]
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>>> Publisher.objects.annotate(num_books=Count('book')).filter(num_books__in=[1, 3]).order_by('pk')
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[<Publisher: Sams>, <Publisher: Morgan Kaufmann>, <Publisher: Expensive Publisher>]
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>>> Publisher.objects.annotate(num_books=Count('book')).filter(num_books__isnull=True)
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[]
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>>> p.delete()
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# Does Author X have any friends? (or better, how many friends does author X have)
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>> Author.objects.filter(pk=1).aggregate(Count('friends__id'))
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{'friends__id__count': 2.0}
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# Give me a list of all Books with more than 1 authors
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>>> Book.objects.all().annotate(num_authors=Count('authors__name')).filter(num_authors__ge=2).order_by('pk')
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[<Book: The Definitive Guide to Django: Web Development Done Right>, <Book: Artificial Intelligence: A Modern Approach>]
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# Give me a list of all Authors that have no friends
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>>> Author.objects.all().annotate(num_friends=Count('friends__id', distinct=True)).filter(num_friends=0).order_by('pk')
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[<Author: Brad Dayley>]
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# Give me a list of all publishers that have published more than 1 books
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>>> Publisher.objects.all().annotate(num_books=Count('book__id')).filter(num_books__gt=1).order_by('pk')
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[<Publisher: Apress>, <Publisher: Prentice Hall>]
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# Give me a list of all publishers that have published more than 1 books that cost less than 40
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>>> Publisher.objects.all().filter(book__price__lt=Decimal("40.0")).annotate(num_books=Count('book__id')).filter(num_books__gt=1)
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[<Publisher: Apress>]
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# Give me a list of all Books that were written by X and one other author.
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>>> Book.objects.all().annotate(num_authors=Count('authors__id')).filter(authors__name__contains='Norvig', num_authors__gt=1)
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[<Book: Artificial Intelligence: A Modern Approach>]
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# Give me the average rating of all Books that were written by X and one other author.
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#(Aggregate over objects discovered using membership of the m2m set)
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# Adding an existing author to another book to test it the right way
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>>> a = Author.objects.get(name__contains='Norvig')
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>>> b = Book.objects.get(name__contains='Done Right')
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>>> b.authors.add(a)
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>>> b.save()
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# This should do it
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>>> Book.objects.all().annotate(num_authors=Count('authors__id')).filter(authors__name__contains='Norvig', num_authors__gt=1).aggregate(Avg('rating'))
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{'rating__avg': 4.25}
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>>> b.authors.remove(a)
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# Give me a list of all Authors that have published a book with at least one other person
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# (Filters over a count generated on a related object)
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#
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# 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]
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# F-Syntax is required. Will be fixed after F objects are available
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# Aggregates also work on dates, times and datetimes
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>>> Publisher.objects.annotate(earliest_book=Min('book__pubdate')).exclude(earliest_book=None).order_by('earliest_book').values()
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[{'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'}]
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>>> Store.objects.aggregate(Max('friday_night_closing'), Min("original_opening"))
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{'friday_night_closing__max': datetime.time(23, 59, 59), 'original_opening__min': datetime.datetime(1945, 4, 25, 16, 24, 14)}
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# values_list() can also be used
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>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values_list('pk', 'isbn', 'mean_age')
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[(1, u'159059725', 34.5)]
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>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values_list('isbn')
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[(u'159059725',)]
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>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values_list('mean_age')
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[(34.5,)]
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>>> Book.objects.filter(pk=1).annotate(mean_age=Avg('authors__age')).values_list('mean_age', flat=True)
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[34.5]
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"""}
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