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 expressions, so they can be used and combined with other expressions like aggregate 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.

Comparison and conversion functions

Cast

class Cast(expression, output_field)[source]

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)[source]

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()))

Greatest

class Greatest(*expressions, **extra)[source]

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)[source]

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.

Date functions

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)[source]

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.

Support for DurationField was added.

Given the datetime 2015-06-15 23:30:01.000321+00:00, the built-in lookup_names 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

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

week values

The week lookup_type is calculated based on 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)[source]
lookup_name = 'year'
class ExtractMonth(expression, tzinfo=None, **extra)[source]
lookup_name = 'month'
class ExtractDay(expression, tzinfo=None, **extra)[source]
lookup_name = 'day'
class ExtractWeekDay(expression, tzinfo=None, **extra)[source]
lookup_name = 'week_day'
class ExtractWeek(expression, tzinfo=None, **extra)[source]
lookup_name = 'week'
class ExtractQuarter(expression, tzinfo=None, **extra)[source]
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 DateFields 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 DateTimeFields .

class ExtractHour(expression, tzinfo=None, **extra)[source]
lookup_name = 'hour'
class ExtractMinute(expression, tzinfo=None, **extra)[source]
lookup_name = 'minute'
class ExtractSecond(expression, tzinfo=None, **extra)[source]
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 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}

Now

class Now[source]

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>]>

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 django.contrib.postgres.functions.TransactionNow.

Trunc

class Trunc(expression, kind, output_field=None, tzinfo=None, **extra)[source]

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 kinds 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)[source]
kind = 'year'
class TruncMonth(expression, output_field=None, tzinfo=None, **extra)[source]
kind = 'month'
class TruncQuarter(expression, output_field=None, tzinfo=None, **extra)[source]
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 DateFields 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

DateTimeField truncation

class TruncDate(expression, **extra)[source]
lookup_name = 'date'
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)[source]
lookup_name = 'time'
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)[source]
kind = 'day'
class TruncHour(expression, output_field=None, tzinfo=None, **extra)[source]
kind = 'hour'
class TruncMinute(expression, output_field=None, tzinfo=None, **extra)[source]
kind = 'minute'
class TruncSecond(expression, output_field=None, tzinfo=None, **extra)[source]
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>)
}

TimeField truncation

class TruncHour(expression, output_field=None, tzinfo=None, **extra)[source]
kind = 'hour'
class TruncMinute(expression, output_field=None, tzinfo=None, **extra)[source]
kind = 'minute'
class TruncSecond(expression, output_field=None, tzinfo=None, **extra)[source]
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 TimeFields 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

Text functions

Concat

class Concat(*expressions, **extra)[source]

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)

Length

class Length(expression, **extra)[source]

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)[source]

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

It can also be registered as a transform as described in 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

StrIndex

class StrIndex(string, substring, **extra)[source]

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')
>>> Author.objects.filter(name='Margaret Jackson').annotate(
...     smith_index=StrIndex('name', V('Smith'))
... ).get().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 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)[source]

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)[source]

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

It can also be registered as a transform as described in 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

Window functions

There are a number of functions to use in a Window expression for computing the rank of elements or the Ntile of some rows.

CumeDist

class CumeDist(*expressions, **extra)[source]

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)[source]

Equivalent to Rank but does not have gaps.

FirstValue

class FirstValue(expression, **extra)[source]

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)[source]

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)[source]

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

Lead

class Lead(expression, offset=1, default=None, **extra)[source]

Calculates the leading value in a given frame. 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)[source]

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)[source]

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)[source]

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)[source]

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

RowNumber

class RowNumber(*expressions, **extra)[source]

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 window frame.