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 GingerDJ. 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)¶

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

Usage example:

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Cast
>>> Author.objects.create(age=25, name="Margaret Smith")
>>> author = Author.objects.annotate(
...     age_as_float=Cast("age", output_field=FloatField()),
... ).get()
>>> print(author.age_as_float)
25.0

`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 gingerdj.db.models import Sum
>>> from gingerdj.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
>>> # The aggregate default argument uses Coalesce() under the hood.
>>> aggregated = Author.objects.aggregate(
...     combined_age=Sum("age"),
...     combined_age_default=Sum("age", default=0),
...     combined_age_coalesce=Coalesce(Sum("age"), 0),
... )
>>> print(aggregated["combined_age"])
None
>>> print(aggregated["combined_age_default"])
0
>>> print(aggregated["combined_age_coalesce"])
0

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

`Collate`¶

class Collate(expression, collation)¶

Takes an expression and a collation name to query against.

For example, to filter case-insensitively in SQLite:

>>> Author.objects.filter(name=Collate(Value("john"), "nocase"))
<QuerySet [<Author: John>, <Author: john>]>

It can also be used when ordering, for example with PostgreSQL:

>>> Author.objects.order_by(Collate("name", "et-x-icu"))
<QuerySet [<Author: Ursula>, <Author: Veronika>, <Author: Ülle>]>

`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 gingerdj.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.

`JSONObject`¶

class JSONObject(**fields)¶

Takes a list of key-value pairs and returns a JSON object containing those pairs.

Usage example:

>>> from gingerdj.db.models import F
>>> from gingerdj.db.models.functions import JSONObject, Lower
>>> Author.objects.create(name="Margaret Smith", alias="msmith", age=25)
>>> author = Author.objects.annotate(
...     json_object=JSONObject(
...         name=Lower("name"),
...         alias="alias",
...         age=F("age") * 2,
...     )
... ).get()
>>> author.json_object
{'name': 'margaret smith', 'alias': 'msmith', 'age': 50}

`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.

`NullIf`¶

class NullIf(expression1, expression2)¶

Accepts two expressions and returns None if they are equal, otherwise returns expression1.

Caveats on Oracle

Due to an Oracle convention, this function returns the empty string instead of None when the expressions are of type CharField.

Passing Value(None) to expression1 is prohibited on Oracle since Oracle doesn’t accept NULL as the first argument.

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)¶

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. GingerDJ usually uses the databases’ extract function, so you may use any lookup_name that your database supports. A tzinfo subclass, usually provided by zoneinfo, can be passed to extract a value in a specific timezone.

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

“year”: 2015
“iso_year”: 2015
“quarter”: 2
“month”: 6
“day”: 15
“week”: 25
“week_day”: 2
“iso_week_day”: 1
“hour”: 23
“minute”: 30
“second”: 1

If a different timezone like Australia/Melbourne is active in GingerDJ, 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
“iso_week_day”: 2
“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 of a year is the one that contains the year’s first Thursday, i.e. the first week has the majority (four or more) of its days in the year. 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 gingerdj.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)¶

lookup_name = 'year'

class ExtractIsoYear(expression, tzinfo=None, **extra)¶

Returns the ISO-8601 week-numbering year.

lookup_name = 'iso_year'

class ExtractMonth(expression, tzinfo=None, **extra)¶

lookup_name = 'month'

class ExtractDay(expression, tzinfo=None, **extra)¶

lookup_name = 'day'

class ExtractWeekDay(expression, tzinfo=None, **extra)¶

lookup_name = 'week_day'

class ExtractIsoWeekDay(expression, tzinfo=None, **extra)¶

Returns the ISO-8601 week day with day 1 being Monday and day 7 being Sunday.

lookup_name = 'iso_week_day'

class ExtractWeek(expression, tzinfo=None, **extra)¶

lookup_name = 'week'

class ExtractQuarter(expression, tzinfo=None, **extra)¶

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, timezone
>>> from gingerdj.db.models.functions import (
...     ExtractDay,
...     ExtractMonth,
...     ExtractQuarter,
...     ExtractWeek,
...     ExtractIsoWeekDay,
...     ExtractWeekDay,
...     ExtractIsoYear,
...     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"),
...     isoyear=ExtractIsoYear("start_date"),
...     quarter=ExtractQuarter("start_date"),
...     month=ExtractMonth("start_date"),
...     week=ExtractWeek("start_date"),
...     day=ExtractDay("start_date"),
...     weekday=ExtractWeekDay("start_date"),
...     isoweekday=ExtractIsoWeekDay("start_date"),
... ).values(
...     "year",
...     "isoyear",
...     "quarter",
...     "month",
...     "week",
...     "day",
...     "weekday",
...     "isoweekday",
... ).get(
...     end_date__year=ExtractYear("start_date")
... )
{'year': 2015, 'isoyear': 2015, 'quarter': 2, 'month': 6, 'week': 25,
 'day': 15, 'weekday': 2, 'isoweekday': 1}

`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)¶

lookup_name = 'hour'

class ExtractMinute(expression, tzinfo=None, **extra)¶

lookup_name = 'minute'

class ExtractSecond(expression, tzinfo=None, **extra)¶

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, timezone
>>> from gingerdj.db.models.functions import (
...     ExtractDay,
...     ExtractHour,
...     ExtractMinute,
...     ExtractMonth,
...     ExtractQuarter,
...     ExtractSecond,
...     ExtractWeek,
...     ExtractIsoWeekDay,
...     ExtractWeekDay,
...     ExtractIsoYear,
...     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"),
...     isoyear=ExtractIsoYear("start_datetime"),
...     quarter=ExtractQuarter("start_datetime"),
...     month=ExtractMonth("start_datetime"),
...     week=ExtractWeek("start_datetime"),
...     day=ExtractDay("start_datetime"),
...     weekday=ExtractWeekDay("start_datetime"),
...     isoweekday=ExtractIsoWeekDay("start_datetime"),
...     hour=ExtractHour("start_datetime"),
...     minute=ExtractMinute("start_datetime"),
...     second=ExtractSecond("start_datetime"),
... ).values(
...     "year",
...     "isoyear",
...     "month",
...     "week",
...     "day",
...     "weekday",
...     "isoweekday",
...     "hour",
...     "minute",
...     "second",
... ).get(
...     end_datetime__year=ExtractYear("start_datetime")
... )
{'year': 2015, 'isoyear': 2015, 'quarter': 2, 'month': 6, 'week': 25,
 'day': 15, 'weekday': 2, 'isoweekday': 1, '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 GingerDJ, 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:

>>> from gingerdj.utils import timezone
>>> import zoneinfo
>>> melb = zoneinfo.ZoneInfo("Australia/Melbourne")  # UTC+10:00
>>> with timezone.override(melb):
...     Experiment.objects.annotate(
...         day=ExtractDay("start_datetime"),
...         weekday=ExtractWeekDay("start_datetime"),
...         isoweekday=ExtractIsoWeekDay("start_datetime"),
...         hour=ExtractHour("start_datetime"),
...     ).values("day", "weekday", "isoweekday", "hour").get(
...         end_datetime__year=ExtractYear("start_datetime"),
...     )
...
{'day': 16, 'weekday': 3, 'isoweekday': 2, 'hour': 9}

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

>>> import zoneinfo
>>> melb = zoneinfo.ZoneInfo("Australia/Melbourne")
>>> Experiment.objects.annotate(
...     day=ExtractDay("start_datetime", tzinfo=melb),
...     weekday=ExtractWeekDay("start_datetime", tzinfo=melb),
...     isoweekday=ExtractIsoWeekDay("start_datetime", tzinfo=melb),
...     hour=ExtractHour("start_datetime", tzinfo=melb),
... ).values("day", "weekday", "isoweekday", "hour").get(
...     end_datetime__year=ExtractYear("start_datetime"),
... )
{'day': 16, 'weekday': 3, 'isoweekday': 2, 'hour': 9}

`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 gingerdj.db.models.functions import Now
>>> Article.objects.filter(published__lte=Now())
<QuerySet [<Article: How to GingerDJ>]>

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 gingerdj.contrib.postgres.functions.TransactionNow.

Oracle

On Oracle, the SQL LOCALTIMESTAMP is used to avoid issues with casting CURRENT_TIMESTAMP to DateTimeField.

Changed in GingerDJ 5.0:

In older versions, the SQL CURRENT_TIMESTAMP was used on Oracle instead of LOCALTIMESTAMP.

`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 zoneinfo, 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
“week”: 2015-06-15 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 GingerDJ, 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
“week”: 2015-06-16 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 lookup names are already reserved by the Extract subclasses.

Usage example:

>>> from datetime import datetime
>>> from gingerdj.db.models import Count, DateTimeField
>>> from gingerdj.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)¶

kind = 'year'

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

kind = 'month'

class TruncWeek(expression, output_field=None, tzinfo=None, **extra)¶

Truncates to midnight on the Monday of the week.

kind = 'week'

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

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, timezone
>>> from gingerdj.db.models import Count
>>> from gingerdj.db.models.functions import TruncMonth, TruncYear
>>> 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 zoneinfo
>>> melb = zoneinfo.ZoneInfo("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, tzinfo=None, **extra)¶

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, tzinfo=None, **extra)¶

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)¶

kind = 'day'

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

kind = 'hour'

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

kind = 'minute'

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

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, timezone
>>> from gingerdj.db.models import Count
>>> from gingerdj.db.models.functions import (
...     TruncDate,
...     TruncDay,
...     TruncHour,
...     TruncMinute,
...     TruncSecond,
... )
>>> import zoneinfo
>>> start1 = datetime(2014, 6, 15, 14, 30, 50, 321, tzinfo=timezone.utc)
>>> Experiment.objects.create(start_datetime=start1, start_date=start1.date())
>>> melb = zoneinfo.ZoneInfo("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=zoneinfo.ZoneInfo('Australia/Melbourne')),
 'hour': datetime.datetime(2014, 6, 16, 0, 0, tzinfo=zoneinfo.ZoneInfo('Australia/Melbourne')),
 'minute': 'minute': datetime.datetime(2014, 6, 15, 14, 30, tzinfo=timezone.utc),
 'second': datetime.datetime(2014, 6, 15, 14, 30, 50, tzinfo=timezone.utc)
}

`TimeField` truncation¶

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

kind = 'hour'

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

kind = 'minute'

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

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, timezone
>>> from gingerdj.db.models import Count, TimeField
>>> from gingerdj.db.models.functions import TruncHour
>>> 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 zoneinfo
>>> melb = zoneinfo.ZoneInfo("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

Math Functions¶

We’ll be using the following model in math function examples:

class Vector(models.Model):
    x = models.FloatField()
    y = models.FloatField()

`Abs`¶

class Abs(expression, **extra)¶

Returns the absolute value of a numeric field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Abs
>>> Vector.objects.create(x=-0.5, y=1.1)
>>> vector = Vector.objects.annotate(x_abs=Abs("x"), y_abs=Abs("y")).get()
>>> vector.x_abs, vector.y_abs
(0.5, 1.1)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Abs
>>> FloatField.register_lookup(Abs)
>>> # Get vectors inside the unit cube
>>> vectors = Vector.objects.filter(x__abs__lt=1, y__abs__lt=1)

`ACos`¶

class ACos(expression, **extra)¶

Returns the arccosine of a numeric field or expression. The expression value must be within the range -1 to 1.

Usage example:

>>> from gingerdj.db.models.functions import ACos
>>> Vector.objects.create(x=0.5, y=-0.9)
>>> vector = Vector.objects.annotate(x_acos=ACos("x"), y_acos=ACos("y")).get()
>>> vector.x_acos, vector.y_acos
(1.0471975511965979, 2.6905658417935308)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import ACos
>>> FloatField.register_lookup(ACos)
>>> # Get vectors whose arccosine is less than 1
>>> vectors = Vector.objects.filter(x__acos__lt=1, y__acos__lt=1)

`ASin`¶

class ASin(expression, **extra)¶

Returns the arcsine of a numeric field or expression. The expression value must be in the range -1 to 1.

Usage example:

>>> from gingerdj.db.models.functions import ASin
>>> Vector.objects.create(x=0, y=1)
>>> vector = Vector.objects.annotate(x_asin=ASin("x"), y_asin=ASin("y")).get()
>>> vector.x_asin, vector.y_asin
(0.0, 1.5707963267948966)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import ASin
>>> FloatField.register_lookup(ASin)
>>> # Get vectors whose arcsine is less than 1
>>> vectors = Vector.objects.filter(x__asin__lt=1, y__asin__lt=1)

`ATan`¶

class ATan(expression, **extra)¶

Returns the arctangent of a numeric field or expression.

Usage example:

>>> from gingerdj.db.models.functions import ATan
>>> Vector.objects.create(x=3.12, y=6.987)
>>> vector = Vector.objects.annotate(x_atan=ATan("x"), y_atan=ATan("y")).get()
>>> vector.x_atan, vector.y_atan
(1.2606282660069106, 1.428638798133829)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import ATan
>>> FloatField.register_lookup(ATan)
>>> # Get vectors whose arctangent is less than 2
>>> vectors = Vector.objects.filter(x__atan__lt=2, y__atan__lt=2)

`ATan2`¶

class ATan2(expression1, expression2, **extra)¶

Returns the arctangent of expression1 / expression2.

Usage example:

>>> from gingerdj.db.models.functions import ATan2
>>> Vector.objects.create(x=2.5, y=1.9)
>>> vector = Vector.objects.annotate(atan2=ATan2("x", "y")).get()
>>> vector.atan2
0.9209258773829491

`Ceil`¶

class Ceil(expression, **extra)¶

Returns the smallest integer greater than or equal to a numeric field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Ceil
>>> Vector.objects.create(x=3.12, y=7.0)
>>> vector = Vector.objects.annotate(x_ceil=Ceil("x"), y_ceil=Ceil("y")).get()
>>> vector.x_ceil, vector.y_ceil
(4.0, 7.0)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Ceil
>>> FloatField.register_lookup(Ceil)
>>> # Get vectors whose ceil is less than 10
>>> vectors = Vector.objects.filter(x__ceil__lt=10, y__ceil__lt=10)

`Cos`¶

class Cos(expression, **extra)¶

Returns the cosine of a numeric field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Cos
>>> Vector.objects.create(x=-8.0, y=3.1415926)
>>> vector = Vector.objects.annotate(x_cos=Cos("x"), y_cos=Cos("y")).get()
>>> vector.x_cos, vector.y_cos
(-0.14550003380861354, -0.9999999999999986)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Cos
>>> FloatField.register_lookup(Cos)
>>> # Get vectors whose cosine is less than 0.5
>>> vectors = Vector.objects.filter(x__cos__lt=0.5, y__cos__lt=0.5)

`Cot`¶

class Cot(expression, **extra)¶

Returns the cotangent of a numeric field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Cot
>>> Vector.objects.create(x=12.0, y=1.0)
>>> vector = Vector.objects.annotate(x_cot=Cot("x"), y_cot=Cot("y")).get()
>>> vector.x_cot, vector.y_cot
(-1.5726734063976826, 0.642092615934331)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Cot
>>> FloatField.register_lookup(Cot)
>>> # Get vectors whose cotangent is less than 1
>>> vectors = Vector.objects.filter(x__cot__lt=1, y__cot__lt=1)

`Degrees`¶

class Degrees(expression, **extra)¶

Converts a numeric field or expression from radians to degrees.

Usage example:

>>> from gingerdj.db.models.functions import Degrees
>>> Vector.objects.create(x=-1.57, y=3.14)
>>> vector = Vector.objects.annotate(x_d=Degrees("x"), y_d=Degrees("y")).get()
>>> vector.x_d, vector.y_d
(-89.95437383553924, 179.9087476710785)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Degrees
>>> FloatField.register_lookup(Degrees)
>>> # Get vectors whose degrees are less than 360
>>> vectors = Vector.objects.filter(x__degrees__lt=360, y__degrees__lt=360)

`Exp`¶

class Exp(expression, **extra)¶

Returns the value of e (the natural logarithm base) raised to the power of a numeric field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Exp
>>> Vector.objects.create(x=5.4, y=-2.0)
>>> vector = Vector.objects.annotate(x_exp=Exp("x"), y_exp=Exp("y")).get()
>>> vector.x_exp, vector.y_exp
(221.40641620418717, 0.1353352832366127)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Exp
>>> FloatField.register_lookup(Exp)
>>> # Get vectors whose exp() is greater than 10
>>> vectors = Vector.objects.filter(x__exp__gt=10, y__exp__gt=10)

`Floor`¶

class Floor(expression, **extra)¶

Returns the largest integer value not greater than a numeric field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Floor
>>> Vector.objects.create(x=5.4, y=-2.3)
>>> vector = Vector.objects.annotate(x_floor=Floor("x"), y_floor=Floor("y")).get()
>>> vector.x_floor, vector.y_floor
(5.0, -3.0)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Floor
>>> FloatField.register_lookup(Floor)
>>> # Get vectors whose floor() is greater than 10
>>> vectors = Vector.objects.filter(x__floor__gt=10, y__floor__gt=10)

`Ln`¶

class Ln(expression, **extra)¶

Returns the natural logarithm a numeric field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Ln
>>> Vector.objects.create(x=5.4, y=233.0)
>>> vector = Vector.objects.annotate(x_ln=Ln("x"), y_ln=Ln("y")).get()
>>> vector.x_ln, vector.y_ln
(1.6863989535702288, 5.4510384535657)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Ln
>>> FloatField.register_lookup(Ln)
>>> # Get vectors whose value greater than e
>>> vectors = Vector.objects.filter(x__ln__gt=1, y__ln__gt=1)

`Log`¶

class Log(expression1, expression2, **extra)¶

Accepts two numeric fields or expressions and returns the logarithm of the second to base of the first.

Usage example:

>>> from gingerdj.db.models.functions import Log
>>> Vector.objects.create(x=2.0, y=4.0)
>>> vector = Vector.objects.annotate(log=Log("x", "y")).get()
>>> vector.log
2.0

`Mod`¶

class Mod(expression1, expression2, **extra)¶

Accepts two numeric fields or expressions and returns the remainder of the first divided by the second (modulo operation).

Usage example:

>>> from gingerdj.db.models.functions import Mod
>>> Vector.objects.create(x=5.4, y=2.3)
>>> vector = Vector.objects.annotate(mod=Mod("x", "y")).get()
>>> vector.mod
0.8

`Pi`¶

class Pi(**extra)¶

Returns the value of the mathematical constant π.

`Power`¶

class Power(expression1, expression2, **extra)¶

Accepts two numeric fields or expressions and returns the value of the first raised to the power of the second.

Usage example:

>>> from gingerdj.db.models.functions import Power
>>> Vector.objects.create(x=2, y=-2)
>>> vector = Vector.objects.annotate(power=Power("x", "y")).get()
>>> vector.power
0.25

`Radians`¶

class Radians(expression, **extra)¶

Converts a numeric field or expression from degrees to radians.

Usage example:

>>> from gingerdj.db.models.functions import Radians
>>> Vector.objects.create(x=-90, y=180)
>>> vector = Vector.objects.annotate(x_r=Radians("x"), y_r=Radians("y")).get()
>>> vector.x_r, vector.y_r
(-1.5707963267948966, 3.141592653589793)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Radians
>>> FloatField.register_lookup(Radians)
>>> # Get vectors whose radians are less than 1
>>> vectors = Vector.objects.filter(x__radians__lt=1, y__radians__lt=1)

`Random`¶

class Random(**extra)¶

Returns a random value in the range 0.0 ≤ x < 1.0.

`Round`¶

class Round(expression, precision=0, **extra)¶

Rounds a numeric field or expression to precision (must be an integer) decimal places. By default, it rounds to the nearest integer. Whether half values are rounded up or down depends on the database.

Usage example:

>>> from gingerdj.db.models.functions import Round
>>> Vector.objects.create(x=5.4, y=-2.37)
>>> vector = Vector.objects.annotate(x_r=Round("x"), y_r=Round("y", precision=1)).get()
>>> vector.x_r, vector.y_r
(5.0, -2.4)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Round
>>> FloatField.register_lookup(Round)
>>> # Get vectors whose round() is less than 20
>>> vectors = Vector.objects.filter(x__round__lt=20, y__round__lt=20)

`Sign`¶

class Sign(expression, **extra)¶

Returns the sign (-1, 0, 1) of a numeric field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Sign
>>> Vector.objects.create(x=5.4, y=-2.3)
>>> vector = Vector.objects.annotate(x_sign=Sign("x"), y_sign=Sign("y")).get()
>>> vector.x_sign, vector.y_sign
(1, -1)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Sign
>>> FloatField.register_lookup(Sign)
>>> # Get vectors whose signs of components are less than 0.
>>> vectors = Vector.objects.filter(x__sign__lt=0, y__sign__lt=0)

`Sin`¶

class Sin(expression, **extra)¶

Returns the sine of a numeric field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Sin
>>> Vector.objects.create(x=5.4, y=-2.3)
>>> vector = Vector.objects.annotate(x_sin=Sin("x"), y_sin=Sin("y")).get()
>>> vector.x_sin, vector.y_sin
(-0.7727644875559871, -0.7457052121767203)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Sin
>>> FloatField.register_lookup(Sin)
>>> # Get vectors whose sin() is less than 0
>>> vectors = Vector.objects.filter(x__sin__lt=0, y__sin__lt=0)

`Sqrt`¶

class Sqrt(expression, **extra)¶

Returns the square root of a nonnegative numeric field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Sqrt
>>> Vector.objects.create(x=4.0, y=12.0)
>>> vector = Vector.objects.annotate(x_sqrt=Sqrt("x"), y_sqrt=Sqrt("y")).get()
>>> vector.x_sqrt, vector.y_sqrt
(2.0, 3.46410)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Sqrt
>>> FloatField.register_lookup(Sqrt)
>>> # Get vectors whose sqrt() is less than 5
>>> vectors = Vector.objects.filter(x__sqrt__lt=5, y__sqrt__lt=5)

`Tan`¶

class Tan(expression, **extra)¶

Returns the tangent of a numeric field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Tan
>>> Vector.objects.create(x=0, y=12)
>>> vector = Vector.objects.annotate(x_tan=Tan("x"), y_tan=Tan("y")).get()
>>> vector.x_tan, vector.y_tan
(0.0, -0.6358599286615808)

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

>>> from gingerdj.db.models import FloatField
>>> from gingerdj.db.models.functions import Tan
>>> FloatField.register_lookup(Tan)
>>> # Get vectors whose tangent is less than 0
>>> vectors = Vector.objects.filter(x__tan__lt=0, y__tan__lt=0)

Text functions¶

`Chr`¶

class Chr(expression, **extra)¶

Accepts a numeric field or expression and returns the text representation of the expression as a single character. It works the same as Python’s chr() function.

Like Length, it can be registered as a transform on IntegerField. The default lookup name is chr.

Usage example:

>>> from gingerdj.db.models.functions import Chr
>>> Author.objects.create(name="Margaret Smith")
>>> author = Author.objects.filter(name__startswith=Chr(ord("M"))).get()
>>> print(author.name)
Margaret Smith

`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 GingerDJ 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, GingerDJ will ensure that each null part is converted to an empty string first.

Usage example:

>>> # Get the display name as "name (goes_by)"
>>> from gingerdj.db.models import CharField, Value as V
>>> from gingerdj.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)

`Left`¶

class Left(expression, length, **extra)¶

Returns the first length characters of the given text field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Left
>>> Author.objects.create(name="Margaret Smith")
>>> author = Author.objects.annotate(first_initial=Left("name", 1)).get()
>>> print(author.first_initial)
M

`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 gingerdj.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 gingerdj.db.models import CharField
>>> from gingerdj.db.models.functions import Length
>>> CharField.register_lookup(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 Length.

Usage example:

>>> from gingerdj.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

`LPad`¶

class LPad(expression, length, fill_text=Value(' '), **extra)¶

Returns the value of the given text field or expression padded on the left side with fill_text so that the resulting value is length characters long. The default fill_text is a space.

Usage example:

>>> from gingerdj.db.models import Value
>>> from gingerdj.db.models.functions import LPad
>>> Author.objects.create(name="John", alias="j")
>>> Author.objects.update(name=LPad("name", 8, Value("abc")))
1
>>> print(Author.objects.get(alias="j").name)
abcaJohn

`LTrim`¶

class LTrim(expression, **extra)¶

Similar to Trim, but removes only leading spaces.

`MD5`¶

class MD5(expression, **extra)¶

Accepts a single text field or expression and returns the MD5 hash of the string.

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

Usage example:

>>> from gingerdj.db.models.functions import MD5
>>> Author.objects.create(name="Margaret Smith")
>>> author = Author.objects.annotate(name_md5=MD5("name")).get()
>>> print(author.name_md5)
749fb689816b2db85f5b169c2055b247

`Ord`¶

class Ord(expression, **extra)¶

Accepts a single text field or expression and returns the Unicode code point value for the first character of that expression. It works similar to Python’s ord() function, but an exception isn’t raised if the expression is more than one character long.

It can also be registered as a transform as described in Length. The default lookup name is ord.

Usage example:

>>> from gingerdj.db.models.functions import Ord
>>> Author.objects.create(name="Margaret Smith")
>>> author = Author.objects.annotate(name_code_point=Ord("name")).get()
>>> print(author.name_code_point)
77

`Repeat`¶

class Repeat(expression, number, **extra)¶

Returns the value of the given text field or expression repeated number times.

Usage example:

>>> from gingerdj.db.models.functions import Repeat
>>> Author.objects.create(name="John", alias="j")
>>> Author.objects.update(name=Repeat("name", 3))
1
>>> print(Author.objects.get(alias="j").name)
JohnJohnJohn

`Replace`¶

class Replace(expression, text, replacement=Value(''), **extra)¶

Replaces all occurrences of text with replacement in expression. The default replacement text is the empty string. The arguments to the function are case-sensitive.

Usage example:

>>> from gingerdj.db.models import Value
>>> from gingerdj.db.models.functions import Replace
>>> Author.objects.create(name="Margaret Johnson")
>>> Author.objects.create(name="Margaret Smith")
>>> Author.objects.update(name=Replace("name", Value("Margaret"), Value("Margareth")))
2
>>> Author.objects.values("name")
<QuerySet [{'name': 'Margareth Johnson'}, {'name': 'Margareth Smith'}]>

`Reverse`¶

class Reverse(expression, **extra)¶

Accepts a single text field or expression and returns the characters of that expression in reverse order.

It can also be registered as a transform as described in Length. The default lookup name is reverse.

Usage example:

>>> from gingerdj.db.models.functions import Reverse
>>> Author.objects.create(name="Margaret Smith")
>>> author = Author.objects.annotate(backward=Reverse("name")).get()
>>> print(author.backward)
htimS teragraM

`Right`¶

class Right(expression, length, **extra)¶

Returns the last length characters of the given text field or expression.

Usage example:

>>> from gingerdj.db.models.functions import Right
>>> Author.objects.create(name="Margaret Smith")
>>> author = Author.objects.annotate(last_letter=Right("name", 1)).get()
>>> print(author.last_letter)
h

`RPad`¶

class RPad(expression, length, fill_text=Value(' '), **extra)¶

Similar to LPad, but pads on the right side.

`RTrim`¶

class RTrim(expression, **extra)¶

Similar to Trim, but removes only trailing spaces.

`SHA1`, `SHA224`, `SHA256`, `SHA384`, and `SHA512`¶

class SHA1(expression, **extra)¶

class SHA224(expression, **extra)¶

class SHA256(expression, **extra)¶

class SHA384(expression, **extra)¶

class SHA512(expression, **extra)¶

Accepts a single text field or expression and returns the particular hash of the string.

They can also be registered as transforms as described in Length.

Usage example:

>>> from gingerdj.db.models.functions import SHA1
>>> Author.objects.create(name="Margaret Smith")
>>> author = Author.objects.annotate(name_sha1=SHA1("name")).get()
>>> print(author.name_sha1)
b87efd8a6c991c390be5a68e8a7945a7851c7e5c

PostgreSQL

The pgcrypto extension must be installed. You can use the CryptoExtension migration operation to install it.

Oracle

Oracle doesn’t support the SHA224 function.

`StrIndex`¶

class StrIndex(string, substring, **extra)¶

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 gingerdj.db.models import Value as V
>>> from gingerdj.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)¶

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 gingerdj.db.models.functions import Lower, Substr
>>> 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

`Trim`¶

class Trim(expression, **extra)¶

Returns the value of the given text field or expression with leading and trailing spaces removed.

Usage example:

>>> from gingerdj.db.models.functions import Trim
>>> Author.objects.create(name="  John  ", alias="j")
>>> Author.objects.update(name=Trim("name"))
1
>>> print(Author.objects.get(alias="j").name)
John

`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 Length.

Usage example:

>>> from gingerdj.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)¶

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 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.

MariaDB and default

MariaDB doesn’t support the default parameter.

`LastValue`¶

class LastValue(expression, **extra)¶

Comparable to 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 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.

MariaDB and default

MariaDB doesn’t support the default parameter.

`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. GingerDJ 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 relative 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 relative rank of a row:

Row #	Value	Rank	Calculation	Relative 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 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 window frame.

Database Functions¶

Comparison and conversion functions¶

Cast¶

Coalesce¶

Collate¶

Greatest¶

JSONObject¶

Least¶

NullIf¶

Date functions¶

Extract¶

DateField extracts¶

DateTimeField extracts¶

Now¶

Trunc¶

DateField truncation¶

DateTimeField truncation¶

TimeField truncation¶

Math Functions¶

Abs¶

ACos¶

ASin¶

ATan¶

ATan2¶

Ceil¶

Cos¶

Cot¶

Degrees¶

Exp¶

Floor¶

Ln¶

Log¶

Mod¶

Pi¶

Power¶

Radians¶

Random¶

Round¶

Sign¶

Sin¶

Sqrt¶

Tan¶

Text functions¶

Chr¶

Concat¶

Left¶

Length¶

Lower¶

LPad¶

LTrim¶

MD5¶

Ord¶

Repeat¶

Replace¶

Reverse¶

Right¶

RPad¶

RTrim¶

SHA1, SHA224, SHA256, SHA384, and SHA512¶

StrIndex¶

Substr¶

Trim¶

Upper¶

Window functions¶

CumeDist¶

DenseRank¶

FirstValue¶

Lag¶

LastValue¶

Lead¶

NthValue¶

Ntile¶

PercentRank¶

Rank¶

RowNumber¶

`Cast`¶

`Coalesce`¶

`Collate`¶

`Greatest`¶

`JSONObject`¶

`Least`¶

`NullIf`¶

`Extract`¶

`DateField` extracts¶

`DateTimeField` extracts¶

`Now`¶

`Trunc`¶

`DateField` truncation¶

`DateTimeField` truncation¶

`TimeField` truncation¶

`Abs`¶

`ACos`¶

`ASin`¶

`ATan`¶

`ATan2`¶

`Ceil`¶

`Cos`¶

`Cot`¶

`Degrees`¶

`Exp`¶

`Floor`¶

`Ln`¶

`Log`¶

`Mod`¶

`Pi`¶

`Power`¶

`Radians`¶

`Random`¶

`Round`¶

`Sign`¶

`Sin`¶

`Sqrt`¶

`Tan`¶

`Chr`¶

`Concat`¶

`Left`¶

`Length`¶

`Lower`¶

`LPad`¶

`LTrim`¶

`MD5`¶

`Ord`¶

`Repeat`¶

`Replace`¶

`Reverse`¶

`Right`¶

`RPad`¶

`RTrim`¶

`SHA1`, `SHA224`, `SHA256`, `SHA384`, and `SHA512`¶

`StrIndex`¶

`Substr`¶

`Trim`¶

`Upper`¶

`CumeDist`¶

`DenseRank`¶

`FirstValue`¶

`Lag`¶

`LastValue`¶

`Lead`¶

`NthValue`¶

`Ntile`¶

`PercentRank`¶

`Rank`¶

`RowNumber`¶