path: root/docs/ref/models/database-functions.txt

==================
Database Functions
==================

.. module:: django.db.models.functions
    :synopsis: Database Functions

The classes documented below provide a way for users to use functions provided
by the underlying database as annotations, aggregations, or filters in Django.
Functions are also :doc:`expressions <expressions>`, so they can be used and
combined with other expressions like :ref:`aggregate functions
<aggregation-functions>`.

We'll be using the following model in examples of each function::

    class Author(models.Model):
        name = models.CharField(max_length=50)
        age = models.PositiveIntegerField(null=True, blank=True)
        alias = models.CharField(max_length=50, null=True, blank=True)
        goes_by = models.CharField(max_length=50, null=True, blank=True)

We don't usually recommend allowing ``null=True`` for ``CharField`` since this
allows the field to have two "empty values", but it's important for the
``Coalesce`` example below.

``Cast``
========

.. class:: Cast(expression, output_field)

Forces the result type of ``expression`` to be the one from ``output_field``.

Usage example::

    >>> from django.db.models import FloatField
    >>> from django.db.models.functions import Cast
    >>> Value.objects.create(integer=4)
    >>> value = Value.objects.annotate(as_float=Cast('integer', FloatField())).get()
    >>> print(value.as_float)
    4.0

``Coalesce``
============

.. class:: Coalesce(*expressions, **extra)

Accepts a list of at least two field names or expressions and returns the
first non-null value (note that an empty string is not considered a null
value). Each argument must be of a similar type, so mixing text and numbers
will result in a database error.

Usage examples::

    >>> # Get a screen name from least to most public
    >>> from django.db.models import Sum, Value as V
    >>> from django.db.models.functions import Coalesce
    >>> Author.objects.create(name='Margaret Smith', goes_by='Maggie')
    >>> author = Author.objects.annotate(
    ...    screen_name=Coalesce('alias', 'goes_by', 'name')).get()
    >>> print(author.screen_name)
    Maggie

    >>> # Prevent an aggregate Sum() from returning None
    >>> aggregated = Author.objects.aggregate(
    ...    combined_age=Coalesce(Sum('age'), V(0)),
    ...    combined_age_default=Sum('age'))
    >>> print(aggregated['combined_age'])
    0
    >>> print(aggregated['combined_age_default'])
    None

.. warning::

    A Python value passed to ``Coalesce`` on MySQL may be converted to an
    incorrect type unless explicitly cast to the correct database type:

    >>> from django.db.models import DateTimeField
    >>> from django.db.models.functions import Cast, Coalesce
    >>> from django.utils import timezone
    >>> now = timezone.now()
    >>> Coalesce('updated', Cast(now, DateTimeField()))

``Concat``
==========

.. class:: Concat(*expressions, **extra)

Accepts a list of at least two text fields or expressions and returns the
concatenated text. Each argument must be of a text or char type. If you want
to concatenate a ``TextField()`` with a ``CharField()``, then be sure to tell
Django that the ``output_field`` should be a ``TextField()``. Specifying an
``output_field`` is also required when concatenating a ``Value`` as in the
example below.

This function will never have a null result. On backends where a null argument
results in the entire expression being null, Django will ensure that each null
part is converted to an empty string first.

Usage example::

    >>> # Get the display name as "name (goes_by)"
    >>> from django.db.models import CharField, Value as V
    >>> from django.db.models.functions import Concat
    >>> Author.objects.create(name='Margaret Smith', goes_by='Maggie')
    >>> author = Author.objects.annotate(
    ...     screen_name=Concat(
    ...         'name', V(' ('), 'goes_by', V(')'),
    ...         output_field=CharField()
    ...     )
    ... ).get()
    >>> print(author.screen_name)
    Margaret Smith (Maggie)

``Greatest``
============

.. class:: Greatest(*expressions, **extra)

Accepts a list of at least two field names or expressions and returns the
greatest value. Each argument must be of a similar type, so mixing text and
numbers will result in a database error.

Usage example::

    class Blog(models.Model):
        body = models.TextField()
        modified = models.DateTimeField(auto_now=True)

    class Comment(models.Model):
        body = models.TextField()
        modified = models.DateTimeField(auto_now=True)
        blog = models.ForeignKey(Blog, on_delete=models.CASCADE)

    >>> from django.db.models.functions import Greatest
    >>> blog = Blog.objects.create(body='Greatest is the best.')
    >>> comment = Comment.objects.create(body='No, Least is better.', blog=blog)
    >>> comments = Comment.objects.annotate(last_updated=Greatest('modified', 'blog__modified'))
    >>> annotated_comment = comments.get()

``annotated_comment.last_updated`` will be the most recent of ``blog.modified``
and ``comment.modified``.

.. warning::

    The behavior of ``Greatest`` when one or more expression may be ``null``
    varies between databases:

    - PostgreSQL: ``Greatest`` will return the largest non-null expression,
      or ``null`` if all expressions are ``null``.
    - SQLite, Oracle, and MySQL: If any expression is ``null``, ``Greatest``
      will return ``null``.

    The PostgreSQL behavior can be emulated using ``Coalesce`` if you know
    a sensible minimum value to provide as a default.

``Least``
=========

.. class:: Least(*expressions, **extra)

Accepts a list of at least two field names or expressions and returns the
least value. Each argument must be of a similar type, so mixing text and numbers
will result in a database error.

.. warning::

    The behavior of ``Least`` when one or more expression may be ``null``
    varies between databases:

    - PostgreSQL: ``Least`` will return the smallest non-null expression,
      or ``null`` if all expressions are ``null``.
    - SQLite, Oracle, and MySQL: If any expression is ``null``, ``Least``
      will return ``null``.

    The PostgreSQL behavior can be emulated using ``Coalesce`` if you know
    a sensible maximum value to provide as a default.

``Length``
==========

.. class:: Length(expression, **extra)

Accepts a single text field or expression and returns the number of characters
the value has. If the expression is null, then the length will also be null.

Usage example::

    >>> # Get the length of the name and goes_by fields
    >>> from django.db.models.functions import Length
    >>> Author.objects.create(name='Margaret Smith')
    >>> author = Author.objects.annotate(
    ...    name_length=Length('name'),
    ...    goes_by_length=Length('goes_by')).get()
    >>> print(author.name_length, author.goes_by_length)
    (14, None)

It can also be registered as a transform. For example::

    >>> from django.db.models import CharField
    >>> from django.db.models.functions import Length
    >>> CharField.register_lookup(Length, 'length')
    >>> # Get authors whose name is longer than 7 characters
    >>> authors = Author.objects.filter(name__length__gt=7)

``Lower``
=========

.. class:: Lower(expression, **extra)

Accepts a single text field or expression and returns the lowercase
representation.

It can also be registered as a transform as described in :class:`Length`.

Usage example::

    >>> from django.db.models.functions import Lower
    >>> Author.objects.create(name='Margaret Smith')
    >>> author = Author.objects.annotate(name_lower=Lower('name')).get()
    >>> print(author.name_lower)
    margaret smith

``Now``
=======

.. class:: Now()

Returns the database server's current date and time when the query is executed,
typically using the SQL ``CURRENT_TIMESTAMP``.

Usage example::

    >>> from django.db.models.functions import Now
    >>> Article.objects.filter(published__lte=Now())
    <QuerySet [<Article: How to Django>]>

.. admonition:: PostgreSQL considerations

    On PostgreSQL, the SQL ``CURRENT_TIMESTAMP`` returns the time that the
    current transaction started. Therefore for cross-database compatibility,
    ``Now()`` uses ``STATEMENT_TIMESTAMP`` instead. If you need the transaction
    timestamp, use :class:`django.contrib.postgres.functions.TransactionNow`.

``StrIndex``
============

.. class:: StrIndex(string, substring, **extra)

.. versionadded:: 2.0

Returns a positive integer corresponding to the 1-indexed position of the first
occurrence of ``substring`` inside ``string``, or 0 if ``substring`` is not
found.

Usage example::

    >>> from django.db.models import Value as V
    >>> from django.db.models.functions import StrIndex
    >>> Author.objects.create(name='Margaret Smith')
    >>> Author.objects.create(name='Smith, Margaret')
    >>> Author.objects.create(name='Margaret Jackson')
    >>> authors = Author.objects.annotate(
    ...     smith_index=StrIndex('name', V('Smith'))
    ... ).order_by('smith_index')
    >>> authors.first().smith_index
    0
    >>> authors = Author.objects.annotate(
    ...    smith_index=StrIndex('name', V('Smith'))
    ... ).filter(smith_index__gt=0)
    <QuerySet [<Author: Margaret Smith>, <Author: Smith, Margaret>]>

.. warning::

    In MySQL, a database table's :ref:`collation<mysql-collation>` determines
    whether string comparisons (such as the ``expression`` and ``substring`` of
    this function) are case-sensitive. Comparisons are case-insensitive by
    default.

``Substr``
==========

.. class:: Substr(expression, pos, length=None, **extra)

Returns a substring of length ``length`` from the field or expression starting
at position ``pos``. The position is 1-indexed, so the position must be greater
than 0. If ``length`` is ``None``, then the rest of the string will be returned.

Usage example::

    >>> # Set the alias to the first 5 characters of the name as lowercase
    >>> from django.db.models.functions import Substr, Lower
    >>> Author.objects.create(name='Margaret Smith')
    >>> Author.objects.update(alias=Lower(Substr('name', 1, 5)))
    1
    >>> print(Author.objects.get(name='Margaret Smith').alias)
    marga

``Upper``
=========

.. class:: Upper(expression, **extra)

Accepts a single text field or expression and returns the uppercase
representation.

It can also be registered as a transform as described in :class:`Length`.

Usage example::

    >>> from django.db.models.functions import Upper
    >>> Author.objects.create(name='Margaret Smith')
    >>> author = Author.objects.annotate(name_upper=Upper('name')).get()
    >>> print(author.name_upper)
    MARGARET SMITH

Date Functions
==============

.. module:: django.db.models.functions.datetime

We'll be using the following model in examples of each function::

    class Experiment(models.Model):
        start_datetime = models.DateTimeField()
        start_date = models.DateField(null=True, blank=True)
        start_time = models.TimeField(null=True, blank=True)
        end_datetime = models.DateTimeField(null=True, blank=True)
        end_date = models.DateField(null=True, blank=True)
        end_time = models.TimeField(null=True, blank=True)

``Extract``
-----------

.. class:: Extract(expression, lookup_name=None, tzinfo=None, **extra)

Extracts a component of a date as a number.

Takes an ``expression`` representing a ``DateField``, ``DateTimeField``,
``TimeField``, or ``DurationField`` and a ``lookup_name``, and returns the part
of the date referenced by ``lookup_name`` as an ``IntegerField``.
Django usually uses the databases' extract function, so you may use any
``lookup_name`` that your database supports. A ``tzinfo`` subclass, usually
provided by ``pytz``, can be passed to extract a value in a specific timezone.

.. versionchanged:: 2.0

    Support for ``DurationField`` was added.

Given the datetime ``2015-06-15 23:30:01.000321+00:00``, the built-in
``lookup_name``\s return:

* "year": 2015
* "quarter": 2
* "month": 6
* "day": 15
* "week": 25
* "week_day": 2
* "hour": 23
* "minute": 30
* "second": 1

If a different timezone like ``Australia/Melbourne`` is active in Django, then
the datetime is converted to the timezone before the value is extracted. The
timezone offset for Melbourne in the example date above is +10:00. The values
returned when this timezone is active will be the same as above except for:

* "day": 16
* "week_day": 3
* "hour": 9

.. admonition:: ``week_day`` values

    The ``week_day`` ``lookup_type`` is calculated differently from most
    databases and from Python's standard functions. This function will return
    ``1`` for Sunday, ``2`` for Monday, through ``7`` for Saturday.

    The equivalent calculation in Python is::

        >>> from datetime import datetime
        >>> dt = datetime(2015, 6, 15)
        >>> (dt.isoweekday() % 7) + 1
        2

.. admonition:: ``week`` values

    The ``week`` ``lookup_type`` is calculated based on `ISO-8601
    <https://en.wikipedia.org/wiki/ISO-8601>`_, i.e.,
    a week starts on a Monday. The first week is the one with the majority
    of the days, i.e., a week that starts on or before Thursday. The value
    returned is in the range 1 to 52 or 53.

Each ``lookup_name`` above has a corresponding ``Extract`` subclass (listed
below) that should typically be used instead of the more verbose equivalent,
e.g. use ``ExtractYear(...)`` rather than ``Extract(..., lookup_name='year')``.

Usage example::

    >>> from datetime import datetime
    >>> from django.db.models.functions import Extract
    >>> start = datetime(2015, 6, 15)
    >>> end = datetime(2015, 7, 2)
    >>> Experiment.objects.create(
    ...    start_datetime=start, start_date=start.date(),
    ...    end_datetime=end, end_date=end.date())
    >>> # Add the experiment start year as a field in the QuerySet.
    >>> experiment = Experiment.objects.annotate(
    ...    start_year=Extract('start_datetime', 'year')).get()
    >>> experiment.start_year
    2015
    >>> # How many experiments completed in the same year in which they started?
    >>> Experiment.objects.filter(
    ...    start_datetime__year=Extract('end_datetime', 'year')).count()
    1

``DateField`` extracts
~~~~~~~~~~~~~~~~~~~~~~

.. class:: ExtractYear(expression, tzinfo=None, **extra)

    .. attribute:: lookup_name = 'year'

.. class:: ExtractMonth(expression, tzinfo=None, **extra)

    .. attribute:: lookup_name = 'month'

.. class:: ExtractDay(expression, tzinfo=None, **extra)

    .. attribute:: lookup_name = 'day'

.. class:: ExtractWeekDay(expression, tzinfo=None, **extra)

    .. attribute:: lookup_name = 'week_day'

.. class:: ExtractWeek(expression, tzinfo=None, **extra)

    .. attribute:: lookup_name = 'week'

.. class:: ExtractQuarter(expression, tzinfo=None, **extra)

    .. versionadded:: 2.0

    .. attribute:: lookup_name = 'quarter'

These are logically equivalent to ``Extract('date_field', lookup_name)``. Each
class is also a ``Transform`` registered on ``DateField`` and ``DateTimeField``
as ``__(lookup_name)``, e.g. ``__year``.

Since ``DateField``\s don't have a time component, only ``Extract`` subclasses
that deal with date-parts can be used with ``DateField``::

    >>> from datetime import datetime
    >>> from django.utils import timezone
    >>> from django.db.models.functions import (
    ...     ExtractDay, ExtractMonth, ExtractQuarter, ExtractWeek,
    ...     ExtractWeekDay, ExtractYear,
    ... )
    >>> start_2015 = datetime(2015, 6, 15, 23, 30, 1, tzinfo=timezone.utc)
    >>> end_2015 = datetime(2015, 6, 16, 13, 11, 27, tzinfo=timezone.utc)
    >>> Experiment.objects.create(
    ...    start_datetime=start_2015, start_date=start_2015.date(),
    ...    end_datetime=end_2015, end_date=end_2015.date())
    >>> Experiment.objects.annotate(
    ...     year=ExtractYear('start_date'),
    ...     quarter=ExtractQuarter('start_date'),
    ...     month=ExtractMonth('start_date'),
    ...     week=ExtractWeek('start_date'),
    ...     day=ExtractDay('start_date'),
    ...     weekday=ExtractWeekDay('start_date'),
    ... ).values('year', 'quarter', 'month', 'week', 'day', 'weekday').get(
    ...     end_date__year=ExtractYear('start_date'),
    ... )
    {'year': 2015, 'quarter': 2, 'month': 6, 'week': 25, 'day': 15, 'weekday': 2}

``DateTimeField`` extracts
~~~~~~~~~~~~~~~~~~~~~~~~~~

In addition to the following, all extracts for ``DateField`` listed above may
also be used on ``DateTimeField``\s .

.. class:: ExtractHour(expression, tzinfo=None, **extra)

    .. attribute:: lookup_name = 'hour'

.. class:: ExtractMinute(expression, tzinfo=None, **extra)

    .. attribute:: lookup_name = 'minute'

.. class:: ExtractSecond(expression, tzinfo=None, **extra)

    .. attribute:: lookup_name = 'second'

These are logically equivalent to ``Extract('datetime_field', lookup_name)``.
Each class is also a ``Transform`` registered on ``DateTimeField`` as
``__(lookup_name)``, e.g. ``__minute``.

``DateTimeField`` examples::

    >>> from datetime import datetime
    >>> from django.utils import timezone
    >>> from django.db.models.functions import (
    ...     ExtractDay, ExtractHour, ExtractMinute, ExtractMonth,
    ...     ExtractQuarter, ExtractSecond, ExtractWeek, ExtractWeekDay,
    ...     ExtractYear,
    ... )
    >>> start_2015 = datetime(2015, 6, 15, 23, 30, 1, tzinfo=timezone.utc)
    >>> end_2015 = datetime(2015, 6, 16, 13, 11, 27, tzinfo=timezone.utc)
    >>> Experiment.objects.create(
    ...    start_datetime=start_2015, start_date=start_2015.date(),
    ...    end_datetime=end_2015, end_date=end_2015.date())
    >>> Experiment.objects.annotate(
    ...     year=ExtractYear('start_datetime'),
    ...     quarter=ExtractQuarter('start_datetime'),
    ...     month=ExtractMonth('start_datetime'),
    ...     week=ExtractWeek('start_datetime'),
    ...     day=ExtractDay('start_datetime'),
    ...     weekday=ExtractWeekDay('start_datetime'),
    ...     hour=ExtractHour('start_datetime'),
    ...     minute=ExtractMinute('start_datetime'),
    ...     second=ExtractSecond('start_datetime'),
    ... ).values(
    ...     'year', 'month', 'week', 'day', 'weekday', 'hour', 'minute', 'second',
    ... ).get(end_datetime__year=ExtractYear('start_datetime'))
    {'year': 2015, 'quarter': 2, 'month': 6, 'week': 25, 'day': 15, 'weekday': 2,
     'hour': 23, 'minute': 30, 'second': 1}

When :setting:`USE_TZ` is ``True`` then datetimes are stored in the database
in UTC. If a different timezone is active in Django, the datetime is converted
to that timezone before the value is extracted. The example below converts to
the Melbourne timezone (UTC +10:00), which changes the day, weekday, and hour
values that are returned::

    >>> import pytz
    >>> melb = pytz.timezone('Australia/Melbourne')  # UTC+10:00
    >>> with timezone.override(melb):
    ...    Experiment.objects.annotate(
    ...        day=ExtractDay('start_datetime'),
    ...        weekday=ExtractWeekDay('start_datetime'),
    ...        hour=ExtractHour('start_datetime'),
    ...    ).values('day', 'weekday', 'hour').get(
    ...        end_datetime__year=ExtractYear('start_datetime'),
    ...    )
    {'day': 16, 'weekday': 3, 'hour': 9}

Explicitly passing the timezone to the ``Extract`` function behaves in the same
way, and takes priority over an active timezone::

    >>> import pytz
    >>> melb = pytz.timezone('Australia/Melbourne')
    >>> Experiment.objects.annotate(
    ...     day=ExtractDay('start_datetime', tzinfo=melb),
    ...     weekday=ExtractWeekDay('start_datetime', tzinfo=melb),
    ...     hour=ExtractHour('start_datetime', tzinfo=melb),
    ... ).values('day', 'weekday', 'hour').get(
    ...     end_datetime__year=ExtractYear('start_datetime'),
    ... )
    {'day': 16, 'weekday': 3, 'hour': 9}


``Trunc``
---------

.. class:: Trunc(expression, kind, output_field=None, tzinfo=None, **extra)

Truncates a date up to a significant component.

When you only care if something happened in a particular year, hour, or day,
but not the exact second, then ``Trunc`` (and its subclasses) can be useful to
filter or aggregate your data. For example, you can use ``Trunc`` to calculate
the number of sales per day.

``Trunc`` takes a single ``expression``, representing a ``DateField``,
``TimeField``, or ``DateTimeField``, a ``kind`` representing a date or time
part, and an ``output_field`` that's either ``DateTimeField()``,
``TimeField()``, or ``DateField()``. It returns a datetime, date, or time
depending on ``output_field``, with fields up to ``kind`` set to their minimum
value. If ``output_field`` is omitted, it will default to the ``output_field``
of ``expression``. A ``tzinfo`` subclass, usually provided by ``pytz``, can be
passed to truncate a value in a specific timezone.

Given the datetime ``2015-06-15 14:30:50.000321+00:00``, the built-in ``kind``\s
return:

* "year": 2015-01-01 00:00:00+00:00
* "quarter": 2015-04-01 00:00:00+00:00
* "month": 2015-06-01 00:00:00+00:00
* "day": 2015-06-15 00:00:00+00:00
* "hour": 2015-06-15 14:00:00+00:00
* "minute": 2015-06-15 14:30:00+00:00
* "second": 2015-06-15 14:30:50+00:00

If a different timezone like ``Australia/Melbourne`` is active in Django, then
the datetime is converted to the new timezone before the value is truncated.
The timezone offset for Melbourne in the example date above is +10:00. The
values returned when this timezone is active will be:

* "year": 2015-01-01 00:00:00+11:00
* "quarter": 2015-04-01 00:00:00+10:00
* "month": 2015-06-01 00:00:00+10:00
* "day": 2015-06-16 00:00:00+10:00
* "hour": 2015-06-16 00:00:00+10:00
* "minute": 2015-06-16 00:30:00+10:00
* "second": 2015-06-16 00:30:50+10:00

The year has an offset of +11:00 because the result transitioned into daylight
saving time.

Each ``kind`` above has a corresponding ``Trunc`` subclass (listed below) that
should typically be used instead of the more verbose equivalent,
e.g. use ``TruncYear(...)`` rather than ``Trunc(..., kind='year')``.

The subclasses are all defined as transforms, but they aren't registered with
any fields, because the obvious lookup names are already reserved by the
``Extract`` subclasses.

Usage example::

    >>> from datetime import datetime
    >>> from django.db.models import Count, DateTimeField
    >>> from django.db.models.functions import Trunc
    >>> Experiment.objects.create(start_datetime=datetime(2015, 6, 15, 14, 30, 50, 321))
    >>> Experiment.objects.create(start_datetime=datetime(2015, 6, 15, 14, 40, 2, 123))
    >>> Experiment.objects.create(start_datetime=datetime(2015, 12, 25, 10, 5, 27, 999))
    >>> experiments_per_day = Experiment.objects.annotate(
    ...    start_day=Trunc('start_datetime', 'day', output_field=DateTimeField())
    ... ).values('start_day').annotate(experiments=Count('id'))
    >>> for exp in experiments_per_day:
    ...     print(exp['start_day'], exp['experiments'])
    ...
    2015-06-15 00:00:00 2
    2015-12-25 00:00:00 1
    >>> experiments = Experiment.objects.annotate(
    ...    start_day=Trunc('start_datetime', 'day', output_field=DateTimeField())
    ... ).filter(start_day=datetime(2015, 6, 15))
    >>> for exp in experiments:
    ...     print(exp.start_datetime)
    ...
    2015-06-15 14:30:50.000321
    2015-06-15 14:40:02.000123

``DateField`` truncation
~~~~~~~~~~~~~~~~~~~~~~~~

.. class:: TruncYear(expression, output_field=None, tzinfo=None, **extra)

    .. attribute:: kind = 'year'

.. class:: TruncMonth(expression, output_field=None, tzinfo=None, **extra)

    .. attribute:: kind = 'month'

.. class:: TruncQuarter(expression, output_field=None, tzinfo=None, **extra)

    .. versionadded:: 2.0

    .. attribute:: kind = 'quarter'

These are logically equivalent to ``Trunc('date_field', kind)``. They truncate
all parts of the date up to ``kind`` which allows grouping or filtering dates
with less precision. ``expression`` can have an ``output_field`` of either
``DateField`` or ``DateTimeField``.

Since ``DateField``\s don't have a time component, only ``Trunc`` subclasses
that deal with date-parts can be used with ``DateField``::

    >>> from datetime import datetime
    >>> from django.db.models import Count
    >>> from django.db.models.functions import TruncMonth, TruncYear
    >>> from django.utils import timezone
    >>> start1 = datetime(2014, 6, 15, 14, 30, 50, 321, tzinfo=timezone.utc)
    >>> start2 = datetime(2015, 6, 15, 14, 40, 2, 123, tzinfo=timezone.utc)
    >>> start3 = datetime(2015, 12, 31, 17, 5, 27, 999, tzinfo=timezone.utc)
    >>> Experiment.objects.create(start_datetime=start1, start_date=start1.date())
    >>> Experiment.objects.create(start_datetime=start2, start_date=start2.date())
    >>> Experiment.objects.create(start_datetime=start3, start_date=start3.date())
    >>> experiments_per_year = Experiment.objects.annotate(
    ...    year=TruncYear('start_date')).values('year').annotate(
    ...    experiments=Count('id'))
    >>> for exp in experiments_per_year:
    ...     print(exp['year'], exp['experiments'])
    ...
    2014-01-01 1
    2015-01-01 2

    >>> import pytz
    >>> melb = pytz.timezone('Australia/Melbourne')
    >>> experiments_per_month = Experiment.objects.annotate(
    ...    month=TruncMonth('start_datetime', tzinfo=melb)).values('month').annotate(
    ...    experiments=Count('id'))
    >>> for exp in experiments_per_month:
    ...     print(exp['month'], exp['experiments'])
    ...
    2015-06-01 00:00:00+10:00 1
    2016-01-01 00:00:00+11:00 1
    2014-06-01 00:00:00+10:00 1

``TimeField`` truncation
~~~~~~~~~~~~~~~~~~~~~~~~

.. class:: TruncHour(expression, output_field=None, tzinfo=None, **extra)

    .. attribute:: kind = 'hour'

.. class:: TruncMinute(expression, output_field=None, tzinfo=None, **extra)

    .. attribute:: kind = 'minute'

.. class:: TruncSecond(expression, output_field=None, tzinfo=None, **extra)

    .. attribute:: kind = 'second'

These are logically equivalent to ``Trunc('time_field', kind)``. They truncate
all parts of the time up to ``kind`` which allows grouping or filtering times
with less precision. ``expression`` can have an ``output_field`` of either
``TimeField`` or ``DateTimeField``.

Since ``TimeField``\s don't have a date component, only ``Trunc`` subclasses
that deal with time-parts can be used with ``TimeField``::

    >>> from datetime import datetime
    >>> from django.db.models import Count, TimeField
    >>> from django.db.models.functions import TruncHour
    >>> from django.utils import timezone
    >>> start1 = datetime(2014, 6, 15, 14, 30, 50, 321, tzinfo=timezone.utc)
    >>> start2 = datetime(2014, 6, 15, 14, 40, 2, 123, tzinfo=timezone.utc)
    >>> start3 = datetime(2015, 12, 31, 17, 5, 27, 999, tzinfo=timezone.utc)
    >>> Experiment.objects.create(start_datetime=start1, start_time=start1.time())
    >>> Experiment.objects.create(start_datetime=start2, start_time=start2.time())
    >>> Experiment.objects.create(start_datetime=start3, start_time=start3.time())
    >>> experiments_per_hour = Experiment.objects.annotate(
    ...    hour=TruncHour('start_datetime', output_field=TimeField()),
    ... ).values('hour').annotate(experiments=Count('id'))
    >>> for exp in experiments_per_hour:
    ...     print(exp['hour'], exp['experiments'])
    ...
    14:00:00 2
    17:00:00 1

    >>> import pytz
    >>> melb = pytz.timezone('Australia/Melbourne')
    >>> experiments_per_hour = Experiment.objects.annotate(
    ...    hour=TruncHour('start_datetime', tzinfo=melb),
    ... ).values('hour').annotate(experiments=Count('id'))
    >>> for exp in experiments_per_hour:
    ...     print(exp['hour'], exp['experiments'])
    ...
    2014-06-16 00:00:00+10:00 2
    2016-01-01 04:00:00+11:00 1

``DateTimeField`` truncation
~~~~~~~~~~~~~~~~~~~~~~~~~~~~

.. class:: TruncDate(expression, **extra)

    .. attribute:: lookup_name = 'date'
    .. attribute:: output_field = DateField()

``TruncDate`` casts ``expression`` to a date rather than using the built-in SQL
truncate function. It's also registered as a transform on  ``DateTimeField`` as
``__date``.

.. class:: TruncTime(expression, **extra)

    .. attribute:: lookup_name = 'time'
    .. attribute:: output_field = TimeField()

``TruncTime`` casts ``expression`` to a time rather than using the built-in SQL
truncate function. It's also registered as a transform on ``DateTimeField`` as
``__time``.

.. class:: TruncDay(expression, output_field=None, tzinfo=None, **extra)

    .. attribute:: kind = 'day'

.. class:: TruncHour(expression, output_field=None, tzinfo=None, **extra)

    .. attribute:: kind = 'hour'

.. class:: TruncMinute(expression, output_field=None, tzinfo=None, **extra)

    .. attribute:: kind = 'minute'

.. class:: TruncSecond(expression, output_field=None, tzinfo=None, **extra)

    .. attribute:: kind = 'second'

These are logically equivalent to ``Trunc('datetime_field', kind)``. They
truncate all parts of the date up to ``kind`` and allow grouping or filtering
datetimes with less precision. ``expression`` must have an ``output_field`` of
``DateTimeField``.

Usage example::

    >>> from datetime import date, datetime
    >>> from django.db.models import Count
    >>> from django.db.models.functions import (
    ...     TruncDate, TruncDay, TruncHour, TruncMinute, TruncSecond,
    ... )
    >>> from django.utils import timezone
    >>> import pytz
    >>> start1 = datetime(2014, 6, 15, 14, 30, 50, 321, tzinfo=timezone.utc)
    >>> Experiment.objects.create(start_datetime=start1, start_date=start1.date())
    >>> melb = pytz.timezone('Australia/Melbourne')
    >>> Experiment.objects.annotate(
    ...     date=TruncDate('start_datetime'),
    ...     day=TruncDay('start_datetime', tzinfo=melb),
    ...     hour=TruncHour('start_datetime', tzinfo=melb),
    ...     minute=TruncMinute('start_datetime'),
    ...     second=TruncSecond('start_datetime'),
    ... ).values('date', 'day', 'hour', 'minute', 'second').get()
    {'date': datetime.date(2014, 6, 15),
     'day': datetime.datetime(2014, 6, 16, 0, 0, tzinfo=<DstTzInfo 'Australia/Melbourne' AEST+10:00:00 STD>),
     'hour': datetime.datetime(2014, 6, 16, 0, 0, tzinfo=<DstTzInfo 'Australia/Melbourne' AEST+10:00:00 STD>),
     'minute': 'minute': datetime.datetime(2014, 6, 15, 14, 30, tzinfo=<UTC>),
     'second': datetime.datetime(2014, 6, 15, 14, 30, 50, tzinfo=<UTC>)
    }

.. _window-functions:

Window functions
================

.. versionadded:: 2.0

There are a number of functions to use in a
:class:`~django.db.models.expressions.Window` expression for computing the rank
of elements or the :class:`Ntile` of some rows.

``CumeDist``
------------

.. class:: CumeDist(*expressions, **extra)

Calculates the cumulative distribution of a value within a window or partition.
The cumulative distribution is defined as the number of rows preceding or
peered with the current row divided by the total number of rows in the frame.

``DenseRank``
-------------

.. class:: DenseRank(*expressions, **extra)

Equivalent to :class:`Rank` but does not have gaps.

``FirstValue``
--------------

.. class:: FirstValue(expression, **extra)

Returns the value evaluated at the row that's the first row of the window
frame, or ``None`` if no such value exists.

``Lag``
-------

.. class:: Lag(expression, offset=1, default=None, **extra)

Calculates the value offset by ``offset``, and if no row exists there, returns
``default``.

``default`` must have the same type as the ``expression``, however, this is
only validated by the database and not in Python.

``LastValue``
-------------

.. class:: LastValue(expression, **extra)

Comparable to :class:`FirstValue`, it calculates the last value in a given
frame clause.

``Lead``
--------

.. class:: Lead(expression, offset=1, default=None, **extra)

Calculates the leading value in a given :ref:`frame <window-frames>`. Both
``offset`` and ``default`` are evaluated with respect to the current row.

``default`` must have the same type as the ``expression``, however, this is
only validated by the database and not in Python.

``NthValue``
------------

.. class:: NthValue(expression, nth=1, **extra)

Computes the row relative to the offset ``nth`` (must be a positive value)
within the window. Returns ``None`` if no row exists.

Some databases may handle a nonexistent nth-value differently. For example,
Oracle returns an empty string rather than ``None`` for character-based
expressions. Django doesn't do any conversions in these cases.

``Ntile``
---------

.. class:: Ntile(num_buckets=1, **extra)

Calculates a partition for each of the rows in the frame clause, distributing
numbers as evenly as possible between 1 and ``num_buckets``. If the rows don't
divide evenly into a number of buckets, one or more buckets will be represented
more frequently.

``PercentRank``
---------------

.. class:: PercentRank(*expressions, **extra)

Computes the percentile rank of the rows in the frame clause. This
computation is equivalent to evaluating::

    (rank - 1) / (total rows - 1)

The following table explains the calculation for the percentile rank of a row:

===== ===== ==== ============ ============
Row # Value Rank Calculation  Percent Rank
===== ===== ==== ============ ============
1     15    1    (1-1)/(7-1)  0.0000
2     20    2    (2-1)/(7-1)  0.1666
3     20    2    (2-1)/(7-1)  0.1666
4     20    2    (2-1)/(7-1)  0.1666
5     30    5    (5-1)/(7-1)  0.6666
6     30    5    (5-1)/(7-1)  0.6666
7     40    7    (7-1)/(7-1)  1.0000
===== ===== ==== ============ ============

``Rank``
--------

.. class:: Rank(*expressions, **extra)

Comparable to ``RowNumber``, this function ranks rows in the window. The
computed rank contains gaps. Use :class:`DenseRank` to compute rank without
gaps.

``RowNumber``
-------------

.. class:: RowNumber(*expressions, **extra)

Computes the row number according to the ordering of either the frame clause
or the ordering of the whole query if there is no partitioning of the
:ref:`window frame <window-frames>`.