/docs/topics/db/aggregation.txt
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1=========== 2Aggregation 3=========== 4 5.. currentmodule:: django.db.models 6 7The topic guide on :doc:`Django's database-abstraction API </topics/db/queries>` 8described the way that you can use Django queries that create, 9retrieve, update and delete individual objects. However, sometimes you will 10need to retrieve values that are derived by summarizing or *aggregating* a 11collection of objects. This topic guide describes the ways that aggregate values 12can be generated and returned using Django queries. 13 14Throughout this guide, we'll refer to the following models. These models are 15used to track the inventory for a series of online bookstores: 16 17.. _queryset-model-example: 18 19.. code-block:: python 20 21 class Author(models.Model): 22 name = models.CharField(max_length=100) 23 age = models.IntegerField() 24 friends = models.ManyToManyField('self', blank=True) 25 26 class Publisher(models.Model): 27 name = models.CharField(max_length=300) 28 num_awards = models.IntegerField() 29 30 class Book(models.Model): 31 isbn = models.CharField(max_length=9) 32 name = models.CharField(max_length=300) 33 pages = models.IntegerField() 34 price = models.DecimalField(max_digits=10, decimal_places=2) 35 rating = models.FloatField() 36 authors = models.ManyToManyField(Author) 37 publisher = models.ForeignKey(Publisher) 38 pubdate = models.DateField() 39 40 class Store(models.Model): 41 name = models.CharField(max_length=300) 42 books = models.ManyToManyField(Book) 43 44 45Generating aggregates over a QuerySet 46===================================== 47 48Django provides two ways to generate aggregates. The first way is to generate 49summary values over an entire ``QuerySet``. For example, say you wanted to 50calculate the average price of all books available for sale. Django's query 51syntax provides a means for describing the set of all books:: 52 53 >>> Book.objects.all() 54 55What we need is a way to calculate summary values over the objects that 56belong to this ``QuerySet``. This is done by appending an ``aggregate()`` 57clause onto the ``QuerySet``:: 58 59 >>> from django.db.models import Avg 60 >>> Book.objects.all().aggregate(Avg('price')) 61 {'price__avg': 34.35} 62 63The ``all()`` is redundant in this example, so this could be simplified to:: 64 65 >>> Book.objects.aggregate(Avg('price')) 66 {'price__avg': 34.35} 67 68The argument to the ``aggregate()`` clause describes the aggregate value that 69we want to compute - in this case, the average of the ``price`` field on the 70``Book`` model. A list of the aggregate functions that are available can be 71found in the :ref:`QuerySet reference <aggregation-functions>`. 72 73``aggregate()`` is a terminal clause for a ``QuerySet`` that, when invoked, 74returns a dictionary of name-value pairs. The name is an identifier for the 75aggregate value; the value is the computed aggregate. The name is 76automatically generated from the name of the field and the aggregate function. 77If you want to manually specify a name for the aggregate value, you can do so 78by providing that name when you specify the aggregate clause:: 79 80 >>> Book.objects.aggregate(average_price=Avg('price')) 81 {'average_price': 34.35} 82 83If you want to generate more than one aggregate, you just add another 84argument to the ``aggregate()`` clause. So, if we also wanted to know 85the maximum and minimum price of all books, we would issue the query:: 86 87 >>> from django.db.models import Avg, Max, Min, Count 88 >>> Book.objects.aggregate(Avg('price'), Max('price'), Min('price')) 89 {'price__avg': 34.35, 'price__max': Decimal('81.20'), 'price__min': Decimal('12.99')} 90 91Generating aggregates for each item in a QuerySet 92================================================= 93 94The second way to generate summary values is to generate an independent 95summary for each object in a ``QuerySet``. For example, if you are retrieving 96a list of books, you may want to know how many authors contributed to 97each book. Each Book has a many-to-many relationship with the Author; we 98want to summarize this relationship for each book in the ``QuerySet``. 99 100Per-object summaries can be generated using the ``annotate()`` clause. 101When an ``annotate()`` clause is specified, each object in the ``QuerySet`` 102will be annotated with the specified values. 103 104The syntax for these annotations is identical to that used for the 105``aggregate()`` clause. Each argument to ``annotate()`` describes an 106aggregate that is to be calculated. For example, to annotate Books with 107the number of authors:: 108 109 # Build an annotated queryset 110 >>> q = Book.objects.annotate(Count('authors')) 111 # Interrogate the first object in the queryset 112 >>> q[0] 113 <Book: The Definitive Guide to Django> 114 >>> q[0].authors__count 115 2 116 # Interrogate the second object in the queryset 117 >>> q[1] 118 <Book: Practical Django Projects> 119 >>> q[1].authors__count 120 1 121 122As with ``aggregate()``, the name for the annotation is automatically derived 123from the name of the aggregate function and the name of the field being 124aggregated. You can override this default name by providing an alias when you 125specify the annotation:: 126 127 >>> q = Book.objects.annotate(num_authors=Count('authors')) 128 >>> q[0].num_authors 129 2 130 >>> q[1].num_authors 131 1 132 133Unlike ``aggregate()``, ``annotate()`` is *not* a terminal clause. The output 134of the ``annotate()`` clause is a ``QuerySet``; this ``QuerySet`` can be 135modified using any other ``QuerySet`` operation, including ``filter()``, 136``order_by``, or even additional calls to ``annotate()``. 137 138Joins and aggregates 139==================== 140 141So far, we have dealt with aggregates over fields that belong to the 142model being queried. However, sometimes the value you want to aggregate 143will belong to a model that is related to the model you are querying. 144 145When specifying the field to be aggregated in an aggregate function, Django 146will allow you to use the same :ref:`double underscore notation 147<field-lookups-intro>` that is used when referring to related fields in 148filters. Django will then handle any table joins that are required to retrieve 149and aggregate the related value. 150 151For example, to find the price range of books offered in each store, 152you could use the annotation:: 153 154 >>> Store.objects.annotate(min_price=Min('books__price'), max_price=Max('books__price')) 155 156This tells Django to retrieve the Store model, join (through the 157many-to-many relationship) with the Book model, and aggregate on the 158price field of the book model to produce a minimum and maximum value. 159 160The same rules apply to the ``aggregate()`` clause. If you wanted to 161know the lowest and highest price of any book that is available for sale 162in a store, you could use the aggregate:: 163 164 >>> Store.objects.aggregate(min_price=Min('books__price'), max_price=Max('books__price')) 165 166Join chains can be as deep as you require. For example, to extract the 167age of the youngest author of any book available for sale, you could 168issue the query:: 169 170 >>> Store.objects.aggregate(youngest_age=Min('books__authors__age')) 171 172Aggregations and other QuerySet clauses 173======================================= 174 175``filter()`` and ``exclude()`` 176------------------------------ 177 178Aggregates can also participate in filters. Any ``filter()`` (or 179``exclude()``) applied to normal model fields will have the effect of 180constraining the objects that are considered for aggregation. 181 182When used with an ``annotate()`` clause, a filter has the effect of 183constraining the objects for which an annotation is calculated. For example, 184you can generate an annotated list of all books that have a title starting 185with "Django" using the query:: 186 187 >>> Book.objects.filter(name__startswith="Django").annotate(num_authors=Count('authors')) 188 189When used with an ``aggregate()`` clause, a filter has the effect of 190constraining the objects over which the aggregate is calculated. 191For example, you can generate the average price of all books with a 192title that starts with "Django" using the query:: 193 194 >>> Book.objects.filter(name__startswith="Django").aggregate(Avg('price')) 195 196Filtering on annotations 197~~~~~~~~~~~~~~~~~~~~~~~~ 198 199Annotated values can also be filtered. The alias for the annotation can be 200used in ``filter()`` and ``exclude()`` clauses in the same way as any other 201model field. 202 203For example, to generate a list of books that have more than one author, 204you can issue the query:: 205 206 >>> Book.objects.annotate(num_authors=Count('authors')).filter(num_authors__gt=1) 207 208This query generates an annotated result set, and then generates a filter 209based upon that annotation. 210 211Order of ``annotate()`` and ``filter()`` clauses 212~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 213 214When developing a complex query that involves both ``annotate()`` and 215``filter()`` clauses, particular attention should be paid to the order 216in which the clauses are applied to the ``QuerySet``. 217 218When an ``annotate()`` clause is applied to a query, the annotation is 219computed over the state of the query up to the point where the annotation 220is requested. The practical implication of this is that ``filter()`` and 221``annotate()`` are not commutative operations -- that is, there is a 222difference between the query:: 223 224 >>> Publisher.objects.annotate(num_books=Count('book')).filter(book__rating__gt=3.0) 225 226and the query:: 227 228 >>> Publisher.objects.filter(book__rating__gt=3.0).annotate(num_books=Count('book')) 229 230Both queries will return a list of Publishers that have at least one good 231book (i.e., a book with a rating exceeding 3.0). However, the annotation in 232the first query will provide the total number of all books published by the 233publisher; the second query will only include good books in the annotated 234count. In the first query, the annotation precedes the filter, so the 235filter has no effect on the annotation. In the second query, the filter 236precedes the annotation, and as a result, the filter constrains the objects 237considered when calculating the annotation. 238 239``order_by()`` 240-------------- 241 242Annotations can be used as a basis for ordering. When you 243define an ``order_by()`` clause, the aggregates you provide can reference 244any alias defined as part of an ``annotate()`` clause in the query. 245 246For example, to order a ``QuerySet`` of books by the number of authors 247that have contributed to the book, you could use the following query:: 248 249 >>> Book.objects.annotate(num_authors=Count('authors')).order_by('num_authors') 250 251``values()`` 252------------ 253 254Ordinarily, annotations are generated on a per-object basis - an annotated 255``QuerySet`` will return one result for each object in the original 256``QuerySet``. However, when a ``values()`` clause is used to constrain the 257columns that are returned in the result set, the method for evaluating 258annotations is slightly different. Instead of returning an annotated result 259for each result in the original ``QuerySet``, the original results are 260grouped according to the unique combinations of the fields specified in the 261``values()`` clause. An annotation is then provided for each unique group; 262the annotation is computed over all members of the group. 263 264For example, consider an author query that attempts to find out the average 265rating of books written by each author: 266 267 >>> Author.objects.annotate(average_rating=Avg('book__rating')) 268 269This will return one result for each author in the database, annotated with 270their average book rating. 271 272However, the result will be slightly different if you use a ``values()`` clause:: 273 274 >>> Author.objects.values('name').annotate(average_rating=Avg('book__rating')) 275 276In this example, the authors will be grouped by name, so you will only get 277an annotated result for each *unique* author name. This means if you have 278two authors with the same name, their results will be merged into a single 279result in the output of the query; the average will be computed as the 280average over the books written by both authors. 281 282Order of ``annotate()`` and ``values()`` clauses 283~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 284 285As with the ``filter()`` clause, the order in which ``annotate()`` and 286``values()`` clauses are applied to a query is significant. If the 287``values()`` clause precedes the ``annotate()``, the annotation will be 288computed using the grouping described by the ``values()`` clause. 289 290However, if the ``annotate()`` clause precedes the ``values()`` clause, 291the annotations will be generated over the entire query set. In this case, 292the ``values()`` clause only constrains the fields that are generated on 293output. 294 295For example, if we reverse the order of the ``values()`` and ``annotate()`` 296clause from our previous example:: 297 298 >>> Author.objects.annotate(average_rating=Avg('book__rating')).values('name', 'average_rating') 299 300This will now yield one unique result for each author; however, only 301the author's name and the ``average_rating`` annotation will be returned 302in the output data. 303 304You should also note that ``average_rating`` has been explicitly included 305in the list of values to be returned. This is required because of the 306ordering of the ``values()`` and ``annotate()`` clause. 307 308If the ``values()`` clause precedes the ``annotate()`` clause, any annotations 309will be automatically added to the result set. However, if the ``values()`` 310clause is applied after the ``annotate()`` clause, you need to explicitly 311include the aggregate column. 312 313Interaction with default ordering or ``order_by()`` 314~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ 315 316Fields that are mentioned in the ``order_by()`` part of a queryset (or which 317are used in the default ordering on a model) are used when selecting the 318output data, even if they are not otherwise specified in the ``values()`` 319call. These extra fields are used to group "like" results together and they 320can make otherwise identical result rows appear to be separate. This shows up, 321particularly, when counting things. 322 323By way of example, suppose you have a model like this:: 324 325 class Item(models.Model): 326 name = models.CharField(max_length=10) 327 data = models.IntegerField() 328 329 class Meta: 330 ordering = ["name"] 331 332The important part here is the default ordering on the ``name`` field. If you 333want to count how many times each distinct ``data`` value appears, you might 334try this:: 335 336 # Warning: not quite correct! 337 Item.objects.values("data").annotate(Count("id")) 338 339...which will group the ``Item`` objects by their common ``data`` values and 340then count the number of ``id`` values in each group. Except that it won't 341quite work. The default ordering by ``name`` will also play a part in the 342grouping, so this query will group by distinct ``(data, name)`` pairs, which 343isn't what you want. Instead, you should construct this queryset:: 344 345 Item.objects.values("data").annotate(Count("id")).order_by() 346 347...clearing any ordering in the query. You could also order by, say, ``data`` 348without any harmful effects, since that is already playing a role in the 349query. 350 351This behavior is the same as that noted in the queryset documentation for 352:meth:`~django.db.models.QuerySet.distinct` and the general rule is the same: 353normally you won't want extra columns playing a part in the result, so clear 354out the ordering, or at least make sure it's restricted only to those fields 355you also select in a ``values()`` call. 356 357.. note:: 358 You might reasonably ask why Django doesn't remove the extraneous columns 359 for you. The main reason is consistency with ``distinct()`` and other 360 places: Django **never** removes ordering constraints that you have 361 specified (and we can't change those other methods' behavior, as that 362 would violate our :doc:`/misc/api-stability` policy). 363 364Aggregating annotations 365----------------------- 366 367You can also generate an aggregate on the result of an annotation. When you 368define an ``aggregate()`` clause, the aggregates you provide can reference 369any alias defined as part of an ``annotate()`` clause in the query. 370 371For example, if you wanted to calculate the average number of authors per 372book you first annotate the set of books with the author count, then 373aggregate that author count, referencing the annotation field:: 374 375 >>> Book.objects.annotate(num_authors=Count('authors')).aggregate(Avg('num_authors')) 376 {'num_authors__avg': 1.66}