Table expressions

Tables are one of the core data structures in Ibis.

Table

Table(self, arg)

An immutable and lazy dataframe.

Analogous to a SQL table or a pandas DataFrame. A table expression contains an ordered set of named columns, each with a single known type. Unless explicitly ordered with an .order_by(), the order of rows is undefined.

Table immutability means that the data underlying an Ibis Table cannot be modified: every method on a Table returns a new Table with those changes. Laziness means that an Ibis Table expression does not run your computation every time you call one of its methods. Instead, it is a symbolic expression that represents a set of operations to be performed, which typically is translated into a SQL query. That SQL query is then executed on a backend, where the data actually lives. The result (now small enough to be manageable) can then be materialized back into python as a pandas/pyarrow/python DataFrame/Column/scalar.

You will not create Table objects directly. Instead, you will create one

See the user guide for more info.

Methods

Name Description
alias Create a table expression with a specific name alias.
as_scalar Inform ibis that the table expression should be treated as a scalar.
count Compute the number of rows in the table.
difference Compute the set difference of multiple table expressions.
distinct Return a Table with duplicate rows removed.
dropna Deprecated - use drop_null instead.
fillna Deprecated - use fill_null instead.
filter Select rows from table based on predicates.
intersect Compute the set intersection of multiple table expressions.
limit Select n rows from self starting at offset.
order_by Sort a table by one or more expressions.
sample Sample a fraction of rows from a table.
select Compute a new table expression using exprs and named_exprs.
sql Run a SQL query against a table expression.
union Compute the set union of multiple table expressions.
view Create a new table expression distinct from the current one.

alias

alias(alias)

Create a table expression with a specific name alias.

This method is useful for exposing an ibis expression to the underlying backend for use in the Table.sql method.

.alias will create a temporary view

.alias creates a temporary view in the database.

This side effect will be removed in a future version of ibis and is not part of the public API.

Parameters

Name Type Description Default
alias str Name of the child expression required

Returns

Name Type Description
Table An table expression

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = letsql.examples.penguins.fetch()
>>> expr = t.alias("pingüinos").sql('SELECT * FROM "pingüinos" LIMIT 5')
>>> expr
┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ float64           │ float64     │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │             181.0 │      3750.0 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │             186.0 │      3800.0 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │             195.0 │      3250.0 │ female │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │             193.0 │      3450.0 │ female │  2007 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

as_scalar

as_scalar()

Inform ibis that the table expression should be treated as a scalar.

Note that the table must have exactly one column and one row for this to work. If the table has more than one column an error will be raised in expression construction time. If the table has more than one row an error will be raised by the backend when the expression is executed.

Returns

Name Type Description
Scalar A scalar subquery

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = letsql.examples.penguins.fetch()
>>> heavy_gentoo = t.filter(t.species == "Gentoo", t.body_mass_g > 6200)
>>> from_that_island = t.filter(t.island == heavy_gentoo.select("island").as_scalar())
>>> from_that_island.species.value_counts().order_by("species")
┏━━━━━━━━━┳━━━━━━━━━━━━━━━┓
┃ species ┃ species_count ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━┩
│ string  │ int64         │
├─────────┼───────────────┤
│ Adelie  │            44 │
│ Gentoo  │           124 │
└─────────┴───────────────┘

count

count(where=None)

Compute the number of rows in the table.

Parameters

Name Type Description Default
where ir.BooleanValue | None Optional boolean expression to filter rows when counting. None

Returns

Name Type Description
IntegerScalar Number of rows in the table

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.memtable({"a": ["foo", "bar", "baz"]})
>>> t
┏━━━━━━━━┓
┃ a      ┃
┡━━━━━━━━┩
│ string │
├────────┤
│ foo    │
│ bar    │
│ baz    │
└────────┘

>>> t.count()








┌───┐
│ 3 │
└───┘










>>> t.count(t.a != "foo")












┌───┐
│ 2 │
└───┘










>>> type(t.count())




ibis.expr.types.numeric.IntegerScalar












difference


difference(table, *rest, distinct=True)


Compute the set difference of multiple table expressions.


The input tables must have identical schemas.




Parameters






Name
Type
Description
Default






table
Table
A table expression
required




*rest
Table
Additional table expressions
()




distinct
bool
Only diff distinct rows not occurring in the calling table
True










See Also


ibis.difference






Returns






Name
Type
Description







Table
The rows present in self that are not present in tables.










Examples




>>> import ibis
>>> ibis.options.interactive = True
>>> t1 = ibis.memtable({"a": [1, 2]})
>>> t1




┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
│     2 │
└───────┘









>>> t2 = ibis.memtable({"a": [2, 3]})
>>> t2




┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     2 │
│     3 │
└───────┘









>>> t1.difference(t2)




┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
└───────┘













distinct


distinct(on=None, keep='first')


Return a Table with duplicate rows removed.


Similar to pandas.DataFrame.drop_duplicates().












Some backends do not support keep='last'














Parameters






Name
Type
Description
Default






on
str | Iterable[str] | s.Selector | None
Only consider certain columns for identifying duplicates. By default deduplicate all of the columns.
None




keep
Literal['first', 'last'] | None
Determines which duplicates to keep. - "first": Drop duplicates except for the first occurrence. - "last": Drop duplicates except for the last occurrence. - None: Drop all duplicates
'first'










Examples




>>> import ibis
>>> import letsql.examples as ex
>>> import ibis.selectors as s
>>> ibis.options.interactive = True
>>> t = ex.penguins.fetch()
>>> t




┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ float64           │ float64     │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │             181.0 │      3750.0 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │             186.0 │      3800.0 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │             195.0 │      3250.0 │ female │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │             193.0 │      3450.0 │ female │  2007 │
│ Adelie  │ Torgersen │           39.3 │          20.6 │             190.0 │      3650.0 │ male   │  2007 │
│ Adelie  │ Torgersen │           38.9 │          17.8 │             181.0 │      3625.0 │ female │  2007 │
│ Adelie  │ Torgersen │           39.2 │          19.6 │             195.0 │      4675.0 │ male   │  2007 │
│ Adelie  │ Torgersen │           34.1 │          18.1 │             193.0 │      3475.0 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           42.0 │          20.2 │             190.0 │      4250.0 │ NULL   │  2007 │
│ …       │ …         │              … │             … │                 … │           … │ …      │     … │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘







Compute the distinct rows of a subset of columns




>>> t[["species", "island"]].distinct().order_by(s.all())




┏━━━━━━━━━━━┳━━━━━━━━━━━┓
┃ species   ┃ island    ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━┩
│ string    │ string    │
├───────────┼───────────┤
│ Adelie    │ Biscoe    │
│ Adelie    │ Dream     │
│ Adelie    │ Torgersen │
│ Chinstrap │ Dream     │
│ Gentoo    │ Biscoe    │
└───────────┴───────────┘







Drop all duplicate rows except the first




>>> t.distinct(on=["species", "island"], keep="first").order_by(s.all())




┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ float64        │ float64       │ float64           │ float64     │ string │ int64 │
├───────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie    │ Biscoe    │           37.8 │          18.3 │             174.0 │      3400.0 │ female │  2007 │
│ Adelie    │ Dream     │           39.5 │          16.7 │             178.0 │      3250.0 │ female │  2007 │
│ Adelie    │ Torgersen │           39.1 │          18.7 │             181.0 │      3750.0 │ male   │  2007 │
│ Chinstrap │ Dream     │           46.5 │          17.9 │             192.0 │      3500.0 │ female │  2007 │
│ Gentoo    │ Biscoe    │           46.1 │          13.2 │             211.0 │      4500.0 │ female │  2007 │
└───────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘







Drop all duplicate rows except the last




>>> t.distinct(on=["species", "island"], keep="last").order_by(s.all())




┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ float64        │ float64       │ float64           │ float64     │ string │ int64 │
├───────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie    │ Biscoe    │           42.7 │          18.3 │             196.0 │      4075.0 │ male   │  2009 │
│ Adelie    │ Dream     │           41.5 │          18.5 │             201.0 │      4000.0 │ male   │  2009 │
│ Adelie    │ Torgersen │           43.1 │          19.2 │             197.0 │      3500.0 │ male   │  2009 │
│ Chinstrap │ Dream     │           50.2 │          18.7 │             198.0 │      3775.0 │ female │  2009 │
│ Gentoo    │ Biscoe    │           49.9 │          16.1 │             213.0 │      5400.0 │ male   │  2009 │
└───────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘







Drop all duplicated rows




>>> expr = t.distinct(on=["species", "island", "year", "bill_length_mm"], keep=None)
>>> expr.count()












┌─────┐
│ 273 │
└─────┘










>>> t.count()












┌─────┐
│ 344 │
└─────┘








You can pass selectors to on




>>> t.distinct(on=~s.numeric())




┏━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species   ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string    │ string    │ float64        │ float64       │ float64           │ float64     │ string │ int64 │
├───────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Chinstrap │ Dream     │           46.5 │          17.9 │             192.0 │      3500.0 │ female │  2007 │
│ Chinstrap │ Dream     │           50.0 │          19.5 │             196.0 │      3900.0 │ male   │  2007 │
│ Adelie    │ Biscoe    │           37.8 │          18.3 │             174.0 │      3400.0 │ female │  2007 │
│ Adelie    │ Biscoe    │           37.7 │          18.7 │             180.0 │      3600.0 │ male   │  2007 │
│ Adelie    │ Dream     │           39.5 │          16.7 │             178.0 │      3250.0 │ female │  2007 │
│ Adelie    │ Dream     │           37.2 │          18.1 │             178.0 │      3900.0 │ male   │  2007 │
│ Adelie    │ Dream     │           37.5 │          18.9 │             179.0 │      2975.0 │ NULL   │  2007 │
│ Adelie    │ Torgersen │           39.1 │          18.7 │             181.0 │      3750.0 │ male   │  2007 │
│ Adelie    │ Torgersen │           39.5 │          17.4 │             186.0 │      3800.0 │ female │  2007 │
│ Adelie    │ Torgersen │           34.1 │          18.1 │             193.0 │      3475.0 │ NULL   │  2007 │
│ …         │ …         │              … │             … │                 … │           … │ …      │     … │
└───────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘







The only valid values of keep are "first", "last" and .




>>> t.distinct(on="species", keep="second")  




IbisError: Invalid value for `keep`: 'second', must be 'first', 'last' or None












dropna


dropna(subset=None, how='any')


Deprecated - use drop_null instead.






fillna


fillna(replacements)


Deprecated - use fill_null instead.






filter


filter(*predicates)


Select rows from table based on predicates.




Parameters






Name
Type
Description
Default






predicates
ir.BooleanValue | Sequence[ir.BooleanValue] | IfAnyAll
Boolean value expressions used to select rows in table.
()










Returns






Name
Type
Description







Table
Filtered table expression










Examples




>>> import ibis
>>> ibis.options.interactive = True
>>> t = letsql.examples.penguins.fetch()
>>> t




┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ float64           │ float64     │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │             181.0 │      3750.0 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │             186.0 │      3800.0 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │             195.0 │      3250.0 │ female │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │             193.0 │      3450.0 │ female │  2007 │
│ Adelie  │ Torgersen │           39.3 │          20.6 │             190.0 │      3650.0 │ male   │  2007 │
│ Adelie  │ Torgersen │           38.9 │          17.8 │             181.0 │      3625.0 │ female │  2007 │
│ Adelie  │ Torgersen │           39.2 │          19.6 │             195.0 │      4675.0 │ male   │  2007 │
│ Adelie  │ Torgersen │           34.1 │          18.1 │             193.0 │      3475.0 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           42.0 │          20.2 │             190.0 │      4250.0 │ NULL   │  2007 │
│ …       │ …         │              … │             … │                 … │           … │ …      │     … │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘









>>> t.filter([t.species == "Adelie", t.body_mass_g > 3500]).sex.value_counts().drop_null(
...     "sex"
... ).order_by("sex")




┏━━━━━━━━┳━━━━━━━━━━━┓
┃ sex    ┃ sex_count ┃
┡━━━━━━━━╇━━━━━━━━━━━┩
│ string │ int64     │
├────────┼───────────┤
│ female │        22 │
│ male   │        68 │
└────────┴───────────┘













intersect


intersect(table, *rest, distinct=True)


Compute the set intersection of multiple table expressions.


The input tables must have identical schemas.




Parameters






Name
Type
Description
Default






table
Table
A table expression
required




*rest
Table
Additional table expressions
()




distinct
bool
Only return distinct rows
True










Returns






Name
Type
Description







Table
A new table containing the intersection of all input tables.










See Also


ibis.intersect






Examples




>>> import ibis
>>> ibis.options.interactive = True
>>> t1 = ibis.memtable({"a": [1, 2]})
>>> t1




┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
│     2 │
└───────┘









>>> t2 = ibis.memtable({"a": [2, 3]})
>>> t2




┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     2 │
│     3 │
└───────┘









>>> t1.intersect(t2)




┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     2 │
└───────┘













limit


limit(n, offset=0)


Select n rows from self starting at offset.












The result set is not deterministic without a call to order_by.














Parameters






Name
Type
Description
Default






n
int | None
Number of rows to include. If None, the entire table is selected starting from offset.
required




offset
int
Number of rows to skip first
0










Returns






Name
Type
Description







Table
The first n rows of self starting at offset










Examples




>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.memtable({"a": [1, 1, 2], "b": ["c", "a", "a"]})
>>> t




┏━━━━━━━┳━━━━━━━━┓
┃ a     ┃ b      ┃
┡━━━━━━━╇━━━━━━━━┩
│ int64 │ string │
├───────┼────────┤
│     1 │ c      │
│     1 │ a      │
│     2 │ a      │
└───────┴────────┘









>>> t.limit(2)




┏━━━━━━━┳━━━━━━━━┓
┃ a     ┃ b      ┃
┡━━━━━━━╇━━━━━━━━┩
│ int64 │ string │
├───────┼────────┤
│     1 │ c      │
│     1 │ a      │
└───────┴────────┘







You can use None with offset to slice starting from a particular row




>>> t.limit(None, offset=1)




┏━━━━━━━┳━━━━━━━━┓
┃ a     ┃ b      ┃
┡━━━━━━━╇━━━━━━━━┩
│ int64 │ string │
├───────┼────────┤
│     1 │ a      │
│     2 │ a      │
└───────┴────────┘











See Also


Table.order_by








order_by


order_by(*by)


Sort a table by one or more expressions.


Similar to pandas.DataFrame.sort_values().




Parameters






Name
Type
Description
Default






by
str | ir.Column | s.Selector | Sequence[str] | Sequence[ir.Column] | Sequence[s.Selector] | None
Expressions to sort the table by.
()










Returns






Name
Type
Description







Table
Sorted table










Examples




>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.memtable(
...     {
...         "a": [3, 2, 1, 3],
...         "b": ["a", "B", "c", "D"],
...         "c": [4, 6, 5, 7],
...     }
... )
>>> t




┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ a     ┃ b      ┃ c     ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ int64 │ string │ int64 │
├───────┼────────┼───────┤
│     3 │ a      │     4 │
│     2 │ B      │     6 │
│     1 │ c      │     5 │
│     3 │ D      │     7 │
└───────┴────────┴───────┘







Sort by b. Default is ascending. Note how capital letters come before lowercase




>>> t.order_by("b")




┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ a     ┃ b      ┃ c     ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ int64 │ string │ int64 │
├───────┼────────┼───────┤
│     2 │ B      │     6 │
│     3 │ D      │     7 │
│     3 │ a      │     4 │
│     1 │ c      │     5 │
└───────┴────────┴───────┘







Sort in descending order




>>> t.order_by(ibis.desc("b"))




┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ a     ┃ b      ┃ c     ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ int64 │ string │ int64 │
├───────┼────────┼───────┤
│     1 │ c      │     5 │
│     3 │ a      │     4 │
│     3 │ D      │     7 │
│     2 │ B      │     6 │
└───────┴────────┴───────┘







You can also use the deferred API to get the same result




>>> from ibis import _
>>> t.order_by(_.b.desc())




┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ a     ┃ b      ┃ c     ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ int64 │ string │ int64 │
├───────┼────────┼───────┤
│     1 │ c      │     5 │
│     3 │ a      │     4 │
│     3 │ D      │     7 │
│     2 │ B      │     6 │
└───────┴────────┴───────┘







Sort by multiple columns/expressions




>>> t.order_by(["a", _.c.desc()])




┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ a     ┃ b      ┃ c     ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ int64 │ string │ int64 │
├───────┼────────┼───────┤
│     1 │ c      │     5 │
│     2 │ B      │     6 │
│     3 │ D      │     7 │
│     3 │ a      │     4 │
└───────┴────────┴───────┘







You can actually pass arbitrary expressions to use as sort keys. For example, to ignore the case of the strings in column b




>>> t.order_by(_.b.lower())




┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ a     ┃ b      ┃ c     ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ int64 │ string │ int64 │
├───────┼────────┼───────┤
│     3 │ a      │     4 │
│     2 │ B      │     6 │
│     1 │ c      │     5 │
│     3 │ D      │     7 │
└───────┴────────┴───────┘







This means that shuffling a Table is super simple




>>> t.order_by(ibis.random())




┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ a     ┃ b      ┃ c     ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ int64 │ string │ int64 │
├───────┼────────┼───────┤
│     1 │ c      │     5 │
│     2 │ B      │     6 │
│     3 │ D      │     7 │
│     3 │ a      │     4 │
└───────┴────────┴───────┘







Selectors are allowed as sort keys and are a concise way to sort by multiple columns matching some criteria




>>> import ibis.selectors as s
>>> penguins = letsql.examples.penguins.fetch()
>>> penguins[["year", "island"]].value_counts().order_by(s.startswith("year"))




┏━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ year  ┃ island    ┃ year_island_count ┃
┡━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ int64 │ string    │ int64             │
├───────┼───────────┼───────────────────┤
│  2007 │ Torgersen │                20 │
│  2007 │ Biscoe    │                44 │
│  2007 │ Dream     │                46 │
│  2008 │ Torgersen │                16 │
│  2008 │ Dream     │                34 │
│  2008 │ Biscoe    │                64 │
│  2009 │ Torgersen │                16 │
│  2009 │ Dream     │                44 │
│  2009 │ Biscoe    │                60 │
└───────┴───────────┴───────────────────┘







Use the across selector to apply a specific order to multiple columns




>>> penguins[["year", "island"]].value_counts().order_by(
...     s.across(s.startswith("year"), _.desc())
... )




┏━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ year  ┃ island    ┃ year_island_count ┃
┡━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ int64 │ string    │ int64             │
├───────┼───────────┼───────────────────┤
│  2009 │ Biscoe    │                60 │
│  2009 │ Dream     │                44 │
│  2009 │ Torgersen │                16 │
│  2008 │ Biscoe    │                64 │
│  2008 │ Dream     │                34 │
│  2008 │ Torgersen │                16 │
│  2007 │ Dream     │                46 │
│  2007 │ Biscoe    │                44 │
│  2007 │ Torgersen │                20 │
└───────┴───────────┴───────────────────┘













sample


sample(fraction, *, method='row', seed=None)


Sample a fraction of rows from a table.












Results may be non-repeatable







Sampling is by definition a random operation. Some backends support specifying a seed for repeatable results, but not all backends support that option. And some backends (duckdb, for example) do support specifying a seed but may still not have repeatable results in all cases.


In all cases, results are backend-specific. An execution against one backend is unlikely to sample the same rows when executed against a different backend, even with the same seed set.








Parameters






Name
Type
Description
Default






fraction
float
The percentage of rows to include in the sample, expressed as a float between 0 and 1.
required




method
Literal['row', 'block']
The sampling method to use. The default is “row”, which includes each row with a probability of fraction. If method is “block”, some backends may instead perform sampling a fraction of blocks of rows (where “block” is a backend dependent definition). This is identical to “row” for backends lacking a blockwise sampling implementation. For those coming from SQL, “row” and “block” correspond to “bernoulli” and “system” respectively in a TABLESAMPLE clause.
'row'




seed
int | None
An optional random seed to use, for repeatable sampling. The range of possible seed values is backend specific (most support at least [0, 2**31 - 1]). Backends that never support specifying a seed for repeatable sampling will error appropriately. Note that some backends (like DuckDB) do support specifying a seed, but may still not have repeatable results in all cases.
None










Returns






Name
Type
Description







Table
The input table, with fraction of rows selected.










Examples




>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.memtable({"x": [1, 2, 3, 4], "y": ["a", "b", "c", "d"]})
>>> t




┏━━━━━━━┳━━━━━━━━┓
┃ x     ┃ y      ┃
┡━━━━━━━╇━━━━━━━━┩
│ int64 │ string │
├───────┼────────┤
│     1 │ a      │
│     2 │ b      │
│     3 │ c      │
│     4 │ d      │
└───────┴────────┘







Sample approximately half the rows, with a seed specified for reproducibility.




>>> t.sample(0.5, seed=1234)




┏━━━━━━━┳━━━━━━━━┓
┃ x     ┃ y      ┃
┡━━━━━━━╇━━━━━━━━┩
│ int64 │ string │
├───────┼────────┤
│     2 │ b      │
│     3 │ c      │
└───────┴────────┘













select


select(*exprs, **named_exprs)


Compute a new table expression using exprs and named_exprs.


Passing an aggregate function to this method will broadcast the aggregate’s value over the number of rows in the table and automatically constructs a window function expression. See the examples section for more details.


For backwards compatibility the keyword argument exprs is reserved and cannot be used to name an expression. This behavior will be removed in v4.




Parameters






Name
Type
Description
Default






exprs
ir.Value | str | Iterable[ir.Value | str]
Column expression, string, or list of column expressions and strings.
()




named_exprs
ir.Value | str
Column expressions
{}










Returns






Name
Type
Description







Table
Table expression










Examples




>>> import ibis
>>> ibis.options.interactive = True
>>> t = letsql.examples.penguins.fetch()
>>> t




┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ float64           │ float64     │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │             181.0 │      3750.0 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │             186.0 │      3800.0 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │             195.0 │      3250.0 │ female │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │             193.0 │      3450.0 │ female │  2007 │
│ Adelie  │ Torgersen │           39.3 │          20.6 │             190.0 │      3650.0 │ male   │  2007 │
│ Adelie  │ Torgersen │           38.9 │          17.8 │             181.0 │      3625.0 │ female │  2007 │
│ Adelie  │ Torgersen │           39.2 │          19.6 │             195.0 │      4675.0 │ male   │  2007 │
│ Adelie  │ Torgersen │           34.1 │          18.1 │             193.0 │      3475.0 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           42.0 │          20.2 │             190.0 │      4250.0 │ NULL   │  2007 │
│ …       │ …         │              … │             … │                 … │           … │ …      │     … │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘







Simple projection




>>> t.select("island", "bill_length_mm").head()




┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ island    ┃ bill_length_mm ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ string    │ float64        │
├───────────┼────────────────┤
│ Torgersen │           39.1 │
│ Torgersen │           39.5 │
│ Torgersen │           40.3 │
│ Torgersen │           NULL │
│ Torgersen │           36.7 │
└───────────┴────────────────┘







In that simple case, you could also just use python’s indexing syntax




>>> t[["island", "bill_length_mm"]].head()




┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ island    ┃ bill_length_mm ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ string    │ float64        │
├───────────┼────────────────┤
│ Torgersen │           39.1 │
│ Torgersen │           39.5 │
│ Torgersen │           40.3 │
│ Torgersen │           NULL │
│ Torgersen │           36.7 │
└───────────┴────────────────┘







Projection by zero-indexed column position




>>> t.select(t[0], t[4]).head()




┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ species ┃ flipper_length_mm ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ string  │ float64           │
├─────────┼───────────────────┤
│ Adelie  │             181.0 │
│ Adelie  │             186.0 │
│ Adelie  │             195.0 │
│ Adelie  │              NULL │
│ Adelie  │             193.0 │
└─────────┴───────────────────┘







Projection with renaming and compute in one call




>>> t.select(next_year=t.year + 1).head()




┏━━━━━━━━━━━┓
┃ next_year ┃
┡━━━━━━━━━━━┩
│ int64     │
├───────────┤
│      2008 │
│      2008 │
│      2008 │
│      2008 │
│      2008 │
└───────────┘







You can do the same thing with a named expression, and using the deferred API




>>> from ibis import _
>>> t.select((_.year + 1).name("next_year")).head()




┏━━━━━━━━━━━┓
┃ next_year ┃
┡━━━━━━━━━━━┩
│ int64     │
├───────────┤
│      2008 │
│      2008 │
│      2008 │
│      2008 │
│      2008 │
└───────────┘







Projection with aggregation expressions




>>> t.select("island", bill_mean=t.bill_length_mm.mean()).head()




┏━━━━━━━━━━━┳━━━━━━━━━━━┓
┃ island    ┃ bill_mean ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━┩
│ string    │ float64   │
├───────────┼───────────┤
│ Torgersen │  43.92193 │
│ Torgersen │  43.92193 │
│ Torgersen │  43.92193 │
│ Torgersen │  43.92193 │
│ Torgersen │  43.92193 │
└───────────┴───────────┘







Projection with a selector




>>> import ibis.selectors as s
>>> t.select(s.numeric() & ~s.cols("year")).head()




┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃
┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ float64        │ float64       │ float64           │ float64     │
├────────────────┼───────────────┼───────────────────┼─────────────┤
│           39.1 │          18.7 │             181.0 │      3750.0 │
│           39.5 │          17.4 │             186.0 │      3800.0 │
│           40.3 │          18.0 │             195.0 │      3250.0 │
│           NULL │          NULL │              NULL │        NULL │
│           36.7 │          19.3 │             193.0 │      3450.0 │
└────────────────┴───────────────┴───────────────────┴─────────────┘







Projection + aggregation across multiple columns




>>> from ibis import _
>>> t.select(s.across(s.numeric() & ~s.cols("year"), _.mean())).head()




┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃
┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ float64        │ float64       │ float64           │ float64     │
├────────────────┼───────────────┼───────────────────┼─────────────┤
│       43.92193 │      17.15117 │        200.915205 │ 4201.754386 │
│       43.92193 │      17.15117 │        200.915205 │ 4201.754386 │
│       43.92193 │      17.15117 │        200.915205 │ 4201.754386 │
│       43.92193 │      17.15117 │        200.915205 │ 4201.754386 │
│       43.92193 │      17.15117 │        200.915205 │ 4201.754386 │
└────────────────┴───────────────┴───────────────────┴─────────────┘













sql


sql(query, dialect=None)


Run a SQL query against a table expression.




Parameters






Name
Type
Description
Default






query
str
Query string
required




dialect
str | None
Optional string indicating the dialect of query. Defaults to the backend’s native dialect.
None










Returns






Name
Type
Description







Table
An opaque table expression










Examples




>>> import ibis
>>> from ibis import _
>>> ibis.options.interactive = True
>>> t = letsql.examples.penguins.fetch(table_name="penguins")
>>> expr = t.sql(
...     """
...     SELECT island, mean(bill_length_mm) AS avg_bill_length
...     FROM penguins
...     GROUP BY 1
...     ORDER BY 2 DESC
...     """
... )
>>> expr




┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ island    ┃ avg_bill_length ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ string    │ float64         │
├───────────┼─────────────────┤
│ Biscoe    │       45.257485 │
│ Dream     │       44.167742 │
│ Torgersen │       38.950980 │
└───────────┴─────────────────┘







Mix and match ibis expressions with SQL queries




>>> t = letsql.examples.penguins.fetch(table_name="penguins")
>>> expr = t.sql(
...     """
...     SELECT island, mean(bill_length_mm) AS avg_bill_length
...     FROM penguins
...     GROUP BY 1
...     ORDER BY 2 DESC
...     """
... )
>>> expr = expr.mutate(
...     island=_.island.lower(),
...     avg_bill_length=_.avg_bill_length.round(1),
... )
>>> expr




┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ island    ┃ avg_bill_length ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ string    │ float64         │
├───────────┼─────────────────┤
│ biscoe    │            45.3 │
│ dream     │            44.2 │
│ torgersen │            39.0 │
└───────────┴─────────────────┘







Because ibis expressions aren’t named, they aren’t visible to subsequent .sql calls. Use the alias method to assign a name to an expression.




>>> expr.alias("b").sql("SELECT * FROM b WHERE avg_bill_length > 40")




┏━━━━━━━━┳━━━━━━━━━━━━━━━━━┓
┃ island ┃ avg_bill_length ┃
┡━━━━━━━━╇━━━━━━━━━━━━━━━━━┩
│ string │ float64         │
├────────┼─────────────────┤
│ biscoe │            45.3 │
│ dream  │            44.2 │
└────────┴─────────────────┘











See Also


Table.alias








union


union(table, *rest, distinct=False)


Compute the set union of multiple table expressions.


The input tables must have identical schemas.




Parameters






Name
Type
Description
Default






table
Table
A table expression
required




*rest
Table
Additional table expressions
()




distinct
bool
Only return distinct rows
False










Returns






Name
Type
Description







Table
A new table containing the union of all input tables.










See Also


ibis.union






Examples




>>> import ibis
>>> ibis.options.interactive = True
>>> t1 = ibis.memtable({"a": [1, 2]})
>>> t1




┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
│     2 │
└───────┘









>>> t2 = ibis.memtable({"a": [2, 3]})
>>> t2




┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     2 │
│     3 │
└───────┘









>>> t1.union(t2)  # union all by default




┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
│     2 │
│     2 │
│     3 │
└───────┘









>>> t1.union(t2, distinct=True).order_by("a")




┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
│     2 │
│     3 │
└───────┘













view


view()


Create a new table expression distinct from the current one.


Use this API for any self-referencing operations like a self-join.




Returns






Name
Type
Description







Table
Table expression