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

• from a pandas DataFrame, pyarrow table, Polars table, or raw python dicts/lists with ibis.memtable(df)
• from an existing table in a data platform with connection.table("name")
• from a file or URL, into a specific backend with connection.read_csv/parquet/json("path/to/file") (only some backends, typically local ones, support this)
• from a file or URL, into the default backend with ibis.read_csv/read_json/read_parquet("path/to/file")

See the user guide for more info.

Attributes

Name	Description
columns	The list of column names in this table.

Methods

Name	Description
aggregate	Aggregate a table with a given set of reductions grouping by by.
alias	Create a table expression with a specific name alias.
as_scalar	Inform ibis that the table expression should be treated as a scalar.
as_table	Promote the expression to a table.
asof_join	Perform an “as-of” join between left and right.
cache	Cache the provided expression.
cast	Cast the columns of a table.
count	Compute the number of rows in the table.
cross_join	Compute the cross join of a sequence of tables.
describe	Return summary information about a table.
difference	Compute the set difference of multiple table expressions.
distinct	Return a Table with duplicate rows removed.
drop	Remove fields from a table.
dropna	Remove rows with null values from the table.
fillna	Fill null values in a table expression.
filter	Select rows from table based on predicates.
get_name	Return the fully qualified name of the table.
group_by	Create a grouped table expression.
head	Select the first n rows of a table.
info	Return summary information about a table.
intersect	Compute the set intersection of multiple table expressions.
join	Perform a join between two tables.
limit	Select n rows from self starting at offset.
mutate	Add columns to a table expression.
nunique	Compute the number of unique rows in the table.
order_by	Sort a table by one or more expressions.
pivot_longer	Transform a table from wider to longer.
pivot_wider	Pivot a table to a wider format.
preview	Return a subset as a Rich Table.
relabel	Deprecated in favor of Table.rename.
relocate	Relocate columns before or after other specified columns.
rename	Rename columns in the table.
rowid	A unique integer per row.
sample	Sample a fraction of rows from a table.
schema	Return the Schema for this table.
select	Compute a new table expression using exprs and named_exprs.
sql	Run a SQL query against a table expression.
to_array	View a single column table as an array.
to_pandas	Convert a table expression to a pandas DataFrame.
try_cast	Cast the columns of a table.
union	Compute the set union of multiple table expressions.
unpack	Project the struct fields of each of columns into self.
view	Create a new table expression distinct from the current one.
window_by	Create a windowing table-valued function (TVF) expression.

aggregate

aggregate(metrics=(), by=(), having=(), **kwargs)

Aggregate a table with a given set of reductions grouping by by.

Parameters

Name	Type	Description	Default
metrics	Sequence[ir.Scalar] | None	Aggregate expressions. These can be any scalar-producing expression, including aggregation functions like sum or literal values like ibis.literal(1).	()
by	Sequence[ir.Value] | None	Grouping expressions.	()
having	Sequence[ir.BooleanValue] | None	Post-aggregation filters. The shape requirements are the same metrics, but the output type for having is boolean. ::: {.callout-warning} ## Expressions like x is None return bool and will not generate a SQL comparison to NULL :::	()
kwargs	ir.Value	Named aggregate expressions	{}

Returns

Type	Description
Table	An aggregate table expression

Examples

>>> import ibis
>>> from ibis import _
>>> ibis.options.interactive = True
>>> t = ibis.memtable(
...     {
...         "fruit": ["apple", "apple", "banana", "orange"],
...         "price": [0.5, 0.5, 0.25, 0.33],
...     }
... )
>>> t

┏━━━━━━━━┳━━━━━━━━━┓
┃ fruit  ┃ price   ┃
┡━━━━━━━━╇━━━━━━━━━┩
│ string │ float64 │
├────────┼─────────┤
│ apple  │    0.50 │
│ apple  │    0.50 │
│ banana │    0.25 │
│ orange │    0.33 │
└────────┴─────────┘

>>> t.aggregate(
...     by=["fruit"],
...     total_cost=_.price.sum(),
...     avg_cost=_.price.mean(),
...     having=_.price.sum() < 0.5,
... )

┏━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━┓
┃ fruit  ┃ total_cost ┃ avg_cost ┃
┡━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━┩
│ string │ float64    │ float64  │
├────────┼────────────┼──────────┤
│ banana │       0.25 │     0.25 │
│ orange │       0.33 │     0.33 │
└────────┴────────────┴──────────┘

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

Type	Description
Table	An table expression

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.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       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │               193 │        3450 │ 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

Type	Description
Scalar	A scalar subquery

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.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 │
└─────────┴───────────────┘

as_table

as_table()

Promote the expression to a table.

This method is a no-op for table expressions.

Returns

Type	Description
Table	A table expression

Examples

>>> t = ibis.table(dict(a="int"), name="t")
>>> s = t.as_table()
>>> t is s

True

asof_join

asof_join(left, right, on, predicates=(), tolerance=None, *, lname='', rname='{name}_right')

Perform an “as-of” join between left and right.

Similar to a left join except that the match is done on nearest key rather than equal keys.

Parameters

Name	Type	Description	Default
left	Table	Table expression	required
right	Table	Table expression	required
on	str | ir.BooleanColumn	Closest match inequality condition	required
predicates	str | ir.Column | Sequence[str | ir.Column]	Additional join predicates	()
tolerance	str | ir.IntervalScalar | None	Amount of time to look behind when joining	None
lname	str	A format string to use to rename overlapping columns in the left table (e.g. "left_{name}").	''
rname	str	A format string to use to rename overlapping columns in the right table (e.g. "right_{name}").	'{name}_right'

Returns

Type	Description
Table	Table expression

cache

cache()

Cache the provided expression.

All subsequent operations on the returned expression will be performed on the cached data. Use the with statement to limit the lifetime of a cached table.

This method is idempotent: calling it multiple times in succession will return the same value as the first call.

This method eagerly evaluates the expression prior to caching

Subsequent evaluations will not recompute the expression so method chaining will not incur the overhead of caching more than once.

Returns

Type	Description
Table	Cached table

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.examples.penguins.fetch()
>>> heavy_computation = ibis.literal("Heavy Computation")
>>> cached_penguins = t.mutate(computation=heavy_computation).cache()
>>> cached_penguins

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃ computation       ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │ string            │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┼───────────────────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │ Heavy Computation │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │ Heavy Computation │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │ Heavy Computation │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │ Heavy Computation │
│ Adelie  │ Torgersen │           36.7 │          19.3 │               193 │        3450 │ female │  2007 │ Heavy Computation │
│ Adelie  │ Torgersen │           39.3 │          20.6 │               190 │        3650 │ male   │  2007 │ Heavy Computation │
│ Adelie  │ Torgersen │           38.9 │          17.8 │               181 │        3625 │ female │  2007 │ Heavy Computation │
│ Adelie  │ Torgersen │           39.2 │          19.6 │               195 │        4675 │ male   │  2007 │ Heavy Computation │
│ Adelie  │ Torgersen │           34.1 │          18.1 │               193 │        3475 │ NULL   │  2007 │ Heavy Computation │
│ Adelie  │ Torgersen │           42.0 │          20.2 │               190 │        4250 │ NULL   │  2007 │ Heavy Computation │
│ …       │ …         │              … │             … │                 … │           … │ …      │     … │ …                 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┴───────────────────┘

Explicit cache cleanup

>>> with t.mutate(computation=heavy_computation).cache() as cached_penguins:
...     cached_penguins

cast

cast(schema)

Cast the columns of a table.

Similar to pandas.DataFrame.astype.

If you need to cast columns to a single type, use selectors.

Parameters

Name	Type	Description	Default
schema	SchemaLike	Mapping, schema or iterable of pairs to use for casting	required

Returns

Type	Description
Table	Casted table

Examples

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

ibis.Schema {
  species            string
  island             string
  bill_length_mm     float64
  bill_depth_mm      float64
  flipper_length_mm  int64
  body_mass_g        int64
  sex                string
  year               int64
}

>>> cols = ["body_mass_g", "bill_length_mm"]
>>> t[cols].head()

┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ body_mass_g ┃ bill_length_mm ┃
┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ int64       │ float64        │
├─────────────┼────────────────┤
│        3750 │           39.1 │
│        3800 │           39.5 │
│        3250 │           40.3 │
│        NULL │           NULL │
│        3450 │           36.7 │
└─────────────┴────────────────┘

Columns not present in the input schema will be passed through unchanged

>>> t.columns

['species',
 'island',
 'bill_length_mm',
 'bill_depth_mm',
 'flipper_length_mm',
 'body_mass_g',
 'sex',
 'year']

>>> expr = t.cast({"body_mass_g": "float64", "bill_length_mm": "int"})
>>> expr.select(*cols).head()

┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ body_mass_g ┃ bill_length_mm ┃
┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ float64     │ int64          │
├─────────────┼────────────────┤
│      3750.0 │             39 │
│      3800.0 │             40 │
│      3250.0 │             40 │
│        NULL │           NULL │
│      3450.0 │             37 │
└─────────────┴────────────────┘

Columns that are in the input schema but not in the table raise an error

>>> t.cast({"foo": "string"})  

IbisError: Cast schema has fields that are not in the table: ['foo']

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

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

cross_join

cross_join(left, right, *rest, lname='', rname='{name}_right')

Compute the cross join of a sequence of tables.

Parameters

Name	Type	Description	Default
left	Table	Left table	required
right	Table	Right table	required
rest	Table	Additional tables to cross join	()
lname	str	A format string to use to rename overlapping columns in the left table (e.g. "left_{name}").	''
rname	str	A format string to use to rename overlapping columns in the right table (e.g. "right_{name}").	'{name}_right'

Returns

Type	Description
Table	Cross join of left, right and rest

Examples

>>> import ibis
>>> import ibis.selectors as s
>>> from ibis import _
>>> ibis.options.interactive = True
>>> t = ibis.examples.penguins.fetch()
>>> t.count()

344

>>> agg = t.drop("year").agg(s.across(s.numeric(), _.mean()))
>>> expr = t.cross_join(agg)
>>> expr

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃ bill_length_mm_right ┃ bill_depth_mm_right ┃ flipper_length_mm_right ┃ body_mass_g_right ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │ float64              │ float64             │ float64                 │ float64           │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┼──────────────────────┼─────────────────────┼─────────────────────────┼───────────────────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │             43.92193 │            17.15117 │              200.915205 │       4201.754386 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │             43.92193 │            17.15117 │              200.915205 │       4201.754386 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │             43.92193 │            17.15117 │              200.915205 │       4201.754386 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │             43.92193 │            17.15117 │              200.915205 │       4201.754386 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │               193 │        3450 │ female │  2007 │             43.92193 │            17.15117 │              200.915205 │       4201.754386 │
│ Adelie  │ Torgersen │           39.3 │          20.6 │               190 │        3650 │ male   │  2007 │             43.92193 │            17.15117 │              200.915205 │       4201.754386 │
│ Adelie  │ Torgersen │           38.9 │          17.8 │               181 │        3625 │ female │  2007 │             43.92193 │            17.15117 │              200.915205 │       4201.754386 │
│ Adelie  │ Torgersen │           39.2 │          19.6 │               195 │        4675 │ male   │  2007 │             43.92193 │            17.15117 │              200.915205 │       4201.754386 │
│ Adelie  │ Torgersen │           34.1 │          18.1 │               193 │        3475 │ NULL   │  2007 │             43.92193 │            17.15117 │              200.915205 │       4201.754386 │
│ Adelie  │ Torgersen │           42.0 │          20.2 │               190 │        4250 │ NULL   │  2007 │             43.92193 │            17.15117 │              200.915205 │       4201.754386 │
│ …       │ …         │              … │             … │                 … │           … │ …      │     … │                    … │                   … │                       … │                 … │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┴──────────────────────┴─────────────────────┴─────────────────────────┴───────────────────┘

>>> expr.columns

['species',
 'island',
 'bill_length_mm',
 'bill_depth_mm',
 'flipper_length_mm',
 'body_mass_g',
 'sex',
 'year',
 'bill_length_mm_right',
 'bill_depth_mm_right',
 'flipper_length_mm_right',
 'body_mass_g_right']

>>> expr.count()

344

describe

describe(quantile=(0.25, 0.5, 0.75))

Return summary information about a table.

Parameters

Name	Type	Description	Default
quantile	Sequence[ir.NumericValue | float]	The quantiles to compute for numerical columns. Defaults to (0.25, 0.5, 0.75).	(0.25, 0.5, 0.75)

Returns

Type	Description
Table	A table containing summary information about the columns of self.

Notes

This function computes summary statistics for each column in the table. For numerical columns, it computes statistics such as minimum, maximum, mean, standard deviation, and quantiles. For string columns, it computes the mode and the number of unique values.

Examples

>>> import ibis
>>> import ibis.selectors as s
>>> ibis.options.interactive = True
>>> p = ibis.examples.penguins.fetch()
>>> p.describe()

┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓
┃ name              ┃ type    ┃ count ┃ nulls ┃ unique ┃ mode   ┃ mean        ┃ std        ┃ min     ┃ p25      ┃ p50     ┃ p75     ┃ max     ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│ string            │ string  │ int64 │ int64 │ int64  │ string │ float64     │ float64    │ float64 │ float64  │ float64 │ float64 │ float64 │
├───────────────────┼─────────┼───────┼───────┼────────┼────────┼─────────────┼────────────┼─────────┼──────────┼─────────┼─────────┼─────────┤
│ species           │ string  │   344 │     0 │      3 │ Adelie │        NULL │       NULL │    NULL │     NULL │    NULL │    NULL │    NULL │
│ island            │ string  │   344 │     0 │      3 │ Biscoe │        NULL │       NULL │    NULL │     NULL │    NULL │    NULL │    NULL │
│ bill_length_mm    │ float64 │   344 │     2 │    164 │ NULL   │   43.921930 │   5.459584 │    32.1 │   39.225 │   44.45 │    48.5 │    59.6 │
│ bill_depth_mm     │ float64 │   344 │     2 │     80 │ NULL   │   17.151170 │   1.974793 │    13.1 │   15.600 │   17.30 │    18.7 │    21.5 │
│ flipper_length_mm │ int64   │   344 │     2 │     55 │ NULL   │  200.915205 │  14.061714 │   172.0 │  190.000 │  197.00 │   213.0 │   231.0 │
│ body_mass_g       │ int64   │   344 │     2 │     94 │ NULL   │ 4201.754386 │ 801.954536 │  2700.0 │ 3550.000 │ 4050.00 │  4750.0 │  6300.0 │
│ sex               │ string  │   344 │    11 │      2 │ male   │        NULL │       NULL │    NULL │     NULL │    NULL │    NULL │    NULL │
│ year              │ int64   │   344 │     0 │      3 │ NULL   │ 2008.029070 │   0.818356 │  2007.0 │ 2007.000 │ 2008.00 │  2009.0 │  2009.0 │
└───────────────────┴─────────┴───────┴───────┴────────┴────────┴─────────────┴────────────┴─────────┴──────────┴─────────┴─────────┴─────────┘

>>> p.select(s.of_type("numeric")).describe()

┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓
┃ name              ┃ type    ┃ count ┃ nulls ┃ unique ┃ mean        ┃ std        ┃ min     ┃ p25      ┃ p50     ┃ p75     ┃ max     ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│ string            │ string  │ int64 │ int64 │ int64  │ float64     │ float64    │ float64 │ float64  │ float64 │ float64 │ float64 │
├───────────────────┼─────────┼───────┼───────┼────────┼─────────────┼────────────┼─────────┼──────────┼─────────┼─────────┼─────────┤
│ bill_length_mm    │ float64 │   344 │     2 │    164 │   43.921930 │   5.459584 │    32.1 │   39.225 │   44.45 │    48.5 │    59.6 │
│ bill_depth_mm     │ float64 │   344 │     2 │     80 │   17.151170 │   1.974793 │    13.1 │   15.600 │   17.30 │    18.7 │    21.5 │
│ flipper_length_mm │ int64   │   344 │     2 │     55 │  200.915205 │  14.061714 │   172.0 │  190.000 │  197.00 │   213.0 │   231.0 │
│ body_mass_g       │ int64   │   344 │     2 │     94 │ 4201.754386 │ 801.954536 │  2700.0 │ 3550.000 │ 4050.00 │  4750.0 │  6300.0 │
│ year              │ int64   │   344 │     0 │      3 │ 2008.029070 │   0.818356 │  2007.0 │ 2007.000 │ 2008.00 │  2009.0 │  2009.0 │
└───────────────────┴─────────┴───────┴───────┴────────┴─────────────┴────────────┴─────────┴──────────┴─────────┴─────────┴─────────┘

>>> p.select(s.of_type("string")).describe()

┏━━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┓
┃ name    ┃ type   ┃ count ┃ nulls ┃ unique ┃ mode   ┃
┡━━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━┩
│ string  │ string │ int64 │ int64 │ int64  │ string │
├─────────┼────────┼───────┼───────┼────────┼────────┤
│ species │ string │   344 │     0 │      3 │ Adelie │
│ island  │ string │   344 │     0 │      3 │ Biscoe │
│ sex     │ string │   344 │    11 │      2 │ male   │
└─────────┴────────┴───────┴───────┴────────┴────────┘

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

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 ibis.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       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │               193 │        3450 │ female │  2007 │
│ Adelie  │ Torgersen │           39.3 │          20.6 │               190 │        3650 │ male   │  2007 │
│ Adelie  │ Torgersen │           38.9 │          17.8 │               181 │        3625 │ female │  2007 │
│ Adelie  │ Torgersen │           39.2 │          19.6 │               195 │        4675 │ male   │  2007 │
│ Adelie  │ Torgersen │           34.1 │          18.1 │               193 │        3475 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           42.0 │          20.2 │               190 │        4250 │ 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       │ int64             │ int64       │ string │ int64 │
├───────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie    │ Biscoe    │           37.8 │          18.3 │               174 │        3400 │ female │  2007 │
│ Adelie    │ Dream     │           39.5 │          16.7 │               178 │        3250 │ female │  2007 │
│ Adelie    │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Chinstrap │ Dream     │           46.5 │          17.9 │               192 │        3500 │ female │  2007 │
│ Gentoo    │ Biscoe    │           46.1 │          13.2 │               211 │        4500 │ 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       │ int64             │ int64       │ string │ int64 │
├───────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie    │ Biscoe    │           42.7 │          18.3 │               196 │        4075 │ male   │  2009 │
│ Adelie    │ Dream     │           41.5 │          18.5 │               201 │        4000 │ male   │  2009 │
│ Adelie    │ Torgersen │           43.1 │          19.2 │               197 │        3500 │ male   │  2009 │
│ Chinstrap │ Dream     │           50.2 │          18.7 │               198 │        3775 │ female │  2009 │
│ Gentoo    │ Biscoe    │           49.9 │          16.1 │               213 │        5400 │ 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       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Biscoe    │           37.8 │          18.3 │               174 │        3400 │ female │  2007 │
│ Gentoo  │ Biscoe    │           46.1 │          13.2 │               211 │        4500 │ female │  2007 │
│ Adelie  │ Biscoe    │           37.7 │          18.7 │               180 │        3600 │ male   │  2007 │
│ Gentoo  │ Biscoe    │           50.0 │          16.3 │               230 │        5700 │ male   │  2007 │
│ Gentoo  │ Biscoe    │           44.5 │          14.3 │               216 │        4100 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Dream     │           37.2 │          18.1 │               178 │        3900 │ male   │  2007 │
│ Adelie  │ Dream     │           37.5 │          18.9 │               179 │        2975 │ NULL   │  2007 │
│ …       │ …         │              … │             … │                 … │           … │ …      │     … │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

The only valid values of keep are "first", "last" and [`None][None]

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

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

drop

drop(*fields)

Remove fields from a table.

Parameters

Name	Type	Description	Default
fields	str | Selector	Fields to drop. Strings and selectors are accepted.	()

Returns

Type	Description
Table	A table with all columns matching fields removed.

Examples

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

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │               193 │        3450 │ female │  2007 │
│ Adelie  │ Torgersen │           39.3 │          20.6 │               190 │        3650 │ male   │  2007 │
│ Adelie  │ Torgersen │           38.9 │          17.8 │               181 │        3625 │ female │  2007 │
│ Adelie  │ Torgersen │           39.2 │          19.6 │               195 │        4675 │ male   │  2007 │
│ Adelie  │ Torgersen │           34.1 │          18.1 │               193 │        3475 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           42.0 │          20.2 │               190 │        4250 │ NULL   │  2007 │
│ …       │ …         │              … │             … │                 … │           … │ …      │     … │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

Drop one or more columns

>>> t.drop("species").head()

┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │
├───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
│ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Torgersen │           36.7 │          19.3 │               193 │        3450 │ female │  2007 │
└───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

>>> t.drop("species", "bill_length_mm").head()

┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ island    ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string    │ float64       │ int64             │ int64       │ string │ int64 │
├───────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Torgersen │          18.7 │               181 │        3750 │ male   │  2007 │
│ Torgersen │          17.4 │               186 │        3800 │ female │  2007 │
│ Torgersen │          18.0 │               195 │        3250 │ female │  2007 │
│ Torgersen │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Torgersen │          19.3 │               193 │        3450 │ female │  2007 │
└───────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

Drop with selectors, mix and match

>>> import ibis.selectors as s
>>> t.drop("species", s.startswith("bill_")).head()

┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ island    ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string    │ int64             │ int64       │ string │ int64 │
├───────────┼───────────────────┼─────────────┼────────┼───────┤
│ Torgersen │               181 │        3750 │ male   │  2007 │
│ Torgersen │               186 │        3800 │ female │  2007 │
│ Torgersen │               195 │        3250 │ female │  2007 │
│ Torgersen │              NULL │        NULL │ NULL   │  2007 │
│ Torgersen │               193 │        3450 │ female │  2007 │
└───────────┴───────────────────┴─────────────┴────────┴───────┘

dropna

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

Remove rows with null values from the table.

Parameters

Name	Type	Description	Default
subset	Sequence[str] | str | None	Columns names to consider when dropping nulls. By default all columns are considered.	None
how	Literal[‘any’, ‘all’]	Determine whether a row is removed if there is at least one null value in the row ('any'), or if all row values are null ('all').	'any'

Returns

Type	Description
Table	Table expression

Examples

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

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │               193 │        3450 │ female │  2007 │
│ Adelie  │ Torgersen │           39.3 │          20.6 │               190 │        3650 │ male   │  2007 │
│ Adelie  │ Torgersen │           38.9 │          17.8 │               181 │        3625 │ female │  2007 │
│ Adelie  │ Torgersen │           39.2 │          19.6 │               195 │        4675 │ male   │  2007 │
│ Adelie  │ Torgersen │           34.1 │          18.1 │               193 │        3475 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           42.0 │          20.2 │               190 │        4250 │ NULL   │  2007 │
│ …       │ …         │              … │             … │                 … │           … │ …      │     … │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

>>> t.count()

344

>>> t.dropna(["bill_length_mm", "body_mass_g"]).count()

342

>>> t.dropna(how="all").count()  # no rows where all columns are null

344

fillna

fillna(replacements)

Fill null values in a table expression.

There is potential lack of type stability with the fillna API

For example, different library versions may impact whether a given backend promotes integer replacement values to floats.

Parameters

Name	Type	Description	Default
replacements	ir.Scalar | Mapping[str, ir.Scalar]	Value with which to fill nulls. If replacements is a mapping, the keys are column names that map to their replacement value. If passed as a scalar all columns are filled with that value.	required

Examples

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

┏━━━━━━━━┓
┃ sex    ┃
┡━━━━━━━━┩
│ string │
├────────┤
│ male   │
│ female │
│ female │
│ NULL   │
│ female │
│ male   │
│ female │
│ male   │
│ NULL   │
│ NULL   │
│ …      │
└────────┘

>>> t.fillna({"sex": "unrecorded"}).sex

┏━━━━━━━━━━━━┓
┃ sex        ┃
┡━━━━━━━━━━━━┩
│ string     │
├────────────┤
│ male       │
│ female     │
│ female     │
│ unrecorded │
│ female     │
│ male       │
│ female     │
│ male       │
│ unrecorded │
│ unrecorded │
│ …          │
└────────────┘

Returns

Type	Description
Table	Table expression

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

Type	Description
Table	Filtered table expression

Examples

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

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │               193 │        3450 │ female │  2007 │
│ Adelie  │ Torgersen │           39.3 │          20.6 │               190 │        3650 │ male   │  2007 │
│ Adelie  │ Torgersen │           38.9 │          17.8 │               181 │        3625 │ female │  2007 │
│ Adelie  │ Torgersen │           39.2 │          19.6 │               195 │        4675 │ male   │  2007 │
│ Adelie  │ Torgersen │           34.1 │          18.1 │               193 │        3475 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           42.0 │          20.2 │               190 │        4250 │ NULL   │  2007 │
│ …       │ …         │              … │             … │                 … │           … │ …      │     … │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

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

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

get_name

get_name()

Return the fully qualified name of the table.

group_by

group_by(*by, **key_exprs)

Create a grouped table expression.

Similar to SQL’s GROUP BY statement, or pandas .groupby() method.

Parameters

Name	Type	Description	Default
by	str | ir.Value | Iterable[str] | Iterable[ir.Value] | None	Grouping expressions	()
key_exprs	str | ir.Value | Iterable[str] | Iterable[ir.Value]	Named grouping expressions	{}

Returns

Type	Description
GroupedTable	A grouped table expression

Examples

>>> import ibis
>>> from ibis import _
>>> ibis.options.interactive = True
>>> t = ibis.memtable(
...     {
...         "fruit": ["apple", "apple", "banana", "orange"],
...         "price": [0.5, 0.5, 0.25, 0.33],
...     }
... )
>>> t

┏━━━━━━━━┳━━━━━━━━━┓
┃ fruit  ┃ price   ┃
┡━━━━━━━━╇━━━━━━━━━┩
│ string │ float64 │
├────────┼─────────┤
│ apple  │    0.50 │
│ apple  │    0.50 │
│ banana │    0.25 │
│ orange │    0.33 │
└────────┴─────────┘

>>> t.group_by("fruit").agg(total_cost=_.price.sum(), avg_cost=_.price.mean()).order_by(
...     "fruit"
... )

┏━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━┓
┃ fruit  ┃ total_cost ┃ avg_cost ┃
┡━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━┩
│ string │ float64    │ float64  │
├────────┼────────────┼──────────┤
│ apple  │       1.00 │     0.50 │
│ banana │       0.25 │     0.25 │
│ orange │       0.33 │     0.33 │
└────────┴────────────┴──────────┘

head

head(n=5)

Select the first n rows of a table.

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

Parameters

Name	Type	Description	Default
n	int	Number of rows to include	5

Returns

Type	Description
Table	self limited to n rows

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.head(2)

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

See Also

Table.limit Table.order_by

info

info()

Return summary information about a table.

Returns

Type	Description
Table	Summary of self

Examples

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

┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━┓
┃ name              ┃ type    ┃ nullable ┃ nulls ┃ non_nulls ┃ null_frac ┃ pos  ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━┩
│ string            │ string  │ boolean  │ int64 │ int64     │ float64   │ int8 │
├───────────────────┼─────────┼──────────┼───────┼───────────┼───────────┼──────┤
│ species           │ string  │ True     │     0 │       344 │  0.000000 │    0 │
│ island            │ string  │ True     │     0 │       344 │  0.000000 │    1 │
│ bill_length_mm    │ float64 │ True     │     2 │       342 │  0.005814 │    2 │
│ bill_depth_mm     │ float64 │ True     │     2 │       342 │  0.005814 │    3 │
│ flipper_length_mm │ int64   │ True     │     2 │       342 │  0.005814 │    4 │
│ body_mass_g       │ int64   │ True     │     2 │       342 │  0.005814 │    5 │
│ sex               │ string  │ True     │    11 │       333 │  0.031977 │    6 │
│ year              │ int64   │ True     │     0 │       344 │  0.000000 │    7 │
└───────────────────┴─────────┴──────────┴───────┴───────────┴───────────┴──────┘

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

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 │
└───────┘

join

join(left, right, predicates=(), how='inner', *, lname='', rname='{name}_right')

Perform a join between two tables.

Parameters

Name	Type	Description	Default
left	Table	Left table to join	required
right	Table	Right table to join	required
predicates	str | Sequence[str | ir.BooleanColumn | Literal[True] | Literal[False] | tuple[str | ir.Column | ir.Deferred, str | ir.Column | ir.Deferred]]	Condition(s) to join on. See examples for details.	()
how	JoinKind	Join method, e.g. "inner" or "left".	'inner'
lname	str	A format string to use to rename overlapping columns in the left table (e.g. "left_{name}").	''
rname	str	A format string to use to rename overlapping columns in the right table (e.g. "right_{name}").	'{name}_right'

Examples

>>> import ibis
>>> from ibis import _
>>> ibis.options.interactive = True
>>> movies = ibis.examples.ml_latest_small_movies.fetch()
>>> movies.head()

┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ movieId ┃ title                              ┃ genres                                      ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ int64   │ string                             │ string                                      │
├─────────┼────────────────────────────────────┼─────────────────────────────────────────────┤
│       1 │ Toy Story (1995)                   │ Adventure|Animation|Children|Comedy|Fantasy │
│       2 │ Jumanji (1995)                     │ Adventure|Children|Fantasy                  │
│       3 │ Grumpier Old Men (1995)            │ Comedy|Romance                              │
│       4 │ Waiting to Exhale (1995)           │ Comedy|Drama|Romance                        │
│       5 │ Father of the Bride Part II (1995) │ Comedy                                      │
└─────────┴────────────────────────────────────┴─────────────────────────────────────────────┘

>>> ratings = ibis.examples.ml_latest_small_ratings.fetch().drop("timestamp")
>>> ratings.head()

┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓
┃ userId ┃ movieId ┃ rating  ┃
┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│ int64  │ int64   │ float64 │
├────────┼─────────┼─────────┤
│      1 │       1 │     4.0 │
│      1 │       3 │     4.0 │
│      1 │       6 │     4.0 │
│      1 │      47 │     5.0 │
│      1 │      50 │     5.0 │
└────────┴─────────┴─────────┘

Equality left join on the shared movieId column. Note the _right suffix added to all overlapping columns from the right table (in this case only the “movieId” column).

>>> ratings.join(movies, "movieId", how="left").head(5)

┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ userId ┃ movieId ┃ rating  ┃ movieId_right ┃ title                       ┃ genres                                      ┃
┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ int64  │ int64   │ float64 │ int64         │ string                      │ string                                      │
├────────┼─────────┼─────────┼───────────────┼─────────────────────────────┼─────────────────────────────────────────────┤
│      1 │       1 │     4.0 │             1 │ Toy Story (1995)            │ Adventure|Animation|Children|Comedy|Fantasy │
│      1 │       3 │     4.0 │             3 │ Grumpier Old Men (1995)     │ Comedy|Romance                              │
│      1 │       6 │     4.0 │             6 │ Heat (1995)                 │ Action|Crime|Thriller                       │
│      1 │      47 │     5.0 │            47 │ Seven (a.k.a. Se7en) (1995) │ Mystery|Thriller                            │
│      1 │      50 │     5.0 │            50 │ Usual Suspects, The (1995)  │ Crime|Mystery|Thriller                      │
└────────┴─────────┴─────────┴───────────────┴─────────────────────────────┴─────────────────────────────────────────────┘

Explicit equality join using the default how value of "inner". Note how there is no _right suffix added to the movieId column since this is an inner join and the movieId column is part of the join condition.

>>> ratings.join(movies, ratings.movieId == movies.movieId).head(5)

┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ userId ┃ movieId ┃ rating  ┃ title                       ┃ genres                                      ┃
┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ int64  │ int64   │ float64 │ string                      │ string                                      │
├────────┼─────────┼─────────┼─────────────────────────────┼─────────────────────────────────────────────┤
│      1 │       1 │     4.0 │ Toy Story (1995)            │ Adventure|Animation|Children|Comedy|Fantasy │
│      1 │       3 │     4.0 │ Grumpier Old Men (1995)     │ Comedy|Romance                              │
│      1 │       6 │     4.0 │ Heat (1995)                 │ Action|Crime|Thriller                       │
│      1 │      47 │     5.0 │ Seven (a.k.a. Se7en) (1995) │ Mystery|Thriller                            │
│      1 │      50 │     5.0 │ Usual Suspects, The (1995)  │ Crime|Mystery|Thriller                      │
└────────┴─────────┴─────────┴─────────────────────────────┴─────────────────────────────────────────────┘

>>> tags = ibis.examples.ml_latest_small_tags.fetch()
>>> tags.head()

┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ userId ┃ movieId ┃ tag             ┃ timestamp  ┃
┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ int64  │ int64   │ string          │ int64      │
├────────┼─────────┼─────────────────┼────────────┤
│      2 │   60756 │ funny           │ 1445714994 │
│      2 │   60756 │ Highly quotable │ 1445714996 │
│      2 │   60756 │ will ferrell    │ 1445714992 │
│      2 │   89774 │ Boxing story    │ 1445715207 │
│      2 │   89774 │ MMA             │ 1445715200 │
└────────┴─────────┴─────────────────┴────────────┘

You can join on multiple columns/conditions by passing in a sequence. Find all instances where a user both tagged and rated a movie:

>>> tags.join(ratings, ["userId", "movieId"]).head(5).order_by("userId")

┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┓
┃ userId ┃ movieId ┃ tag            ┃ timestamp  ┃ rating  ┃
┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━┩
│ int64  │ int64   │ string         │ int64      │ float64 │
├────────┼─────────┼────────────────┼────────────┼─────────┤
│     62 │       2 │ Robin Williams │ 1528843907 │     4.0 │
│     62 │     110 │ sword fight    │ 1528152535 │     4.5 │
│     62 │     410 │ gothic         │ 1525636609 │     4.5 │
│     62 │    2023 │ mafia          │ 1525636733 │     5.0 │
│     62 │    2124 │ quirky         │ 1525636846 │     5.0 │
└────────┴─────────┴────────────────┴────────────┴─────────┘

To self-join a table with itself, you need to call .view() on one of the arguments so the two tables are distinct from each other.

For crafting more complex join conditions, a valid form of a join condition is a 2-tuple like ({left_key}, {right_key}), where each key can be

• a Column
• Deferred expression
• lambda of the form (Table) -> Column

For example, to find all movies pairings that received the same (ignoring case) tags:

>>> movie_tags = tags["movieId", "tag"]
>>> view = movie_tags.view()
>>> movie_tags.join(
...     view,
...     [
...         movie_tags.movieId != view.movieId,
...         (_.tag.lower(), lambda t: t.tag.lower()),
...     ],
... ).head().order_by(("movieId", "movieId_right"))

┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ movieId ┃ tag               ┃ movieId_right ┃ tag_right         ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ int64   │ string            │ int64         │ string            │
├─────────┼───────────────────┼───────────────┼───────────────────┤
│    1732 │ funny             │         60756 │ funny             │
│    1732 │ Highly quotable   │         60756 │ Highly quotable   │
│    1732 │ drugs             │        106782 │ drugs             │
│    5989 │ Leonardo DiCaprio │        106782 │ Leonardo DiCaprio │
│  139385 │ tom hardy         │         89774 │ Tom Hardy         │
└─────────┴───────────────────┴───────────────┴───────────────────┘

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

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

mutate

mutate(*exprs, **mutations)

Add columns to a table expression.

Parameters

Name	Type	Description	Default
exprs	Sequence[ir.Expr] | None	List of named expressions to add as columns	()
mutations	ir.Value	Named expressions using keyword arguments	{}

Returns

Type	Description
Table	Table expression with additional columns

Examples

>>> import ibis
>>> import ibis.selectors as s
>>> from ibis import _
>>> ibis.options.interactive = True
>>> t = ibis.examples.penguins.fetch().select("species", "year", "bill_length_mm")
>>> t

┏━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ species ┃ year  ┃ bill_length_mm ┃
┡━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ string  │ int64 │ float64        │
├─────────┼───────┼────────────────┤
│ Adelie  │  2007 │           39.1 │
│ Adelie  │  2007 │           39.5 │
│ Adelie  │  2007 │           40.3 │
│ Adelie  │  2007 │           NULL │
│ Adelie  │  2007 │           36.7 │
│ Adelie  │  2007 │           39.3 │
│ Adelie  │  2007 │           38.9 │
│ Adelie  │  2007 │           39.2 │
│ Adelie  │  2007 │           34.1 │
│ Adelie  │  2007 │           42.0 │
│ …       │     … │              … │
└─────────┴───────┴────────────────┘

Add a new column from a per-element expression

>>> t.mutate(next_year=_.year + 1).head()

┏━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┓
┃ species ┃ year  ┃ bill_length_mm ┃ next_year ┃
┡━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━┩
│ string  │ int64 │ float64        │ int64     │
├─────────┼───────┼────────────────┼───────────┤
│ Adelie  │  2007 │           39.1 │      2008 │
│ Adelie  │  2007 │           39.5 │      2008 │
│ Adelie  │  2007 │           40.3 │      2008 │
│ Adelie  │  2007 │           NULL │      2008 │
│ Adelie  │  2007 │           36.7 │      2008 │
└─────────┴───────┴────────────────┴───────────┘

Add a new column based on an aggregation. Note the automatic broadcasting.

>>> t.select("species", bill_demean=_.bill_length_mm - _.bill_length_mm.mean()).head()

┏━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ species ┃ bill_demean ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━┩
│ string  │ float64     │
├─────────┼─────────────┤
│ Adelie  │    -4.82193 │
│ Adelie  │    -4.42193 │
│ Adelie  │    -3.62193 │
│ Adelie  │        NULL │
│ Adelie  │    -7.22193 │
└─────────┴─────────────┘

Mutate across multiple columns

>>> t.mutate(s.across(s.numeric() & ~s.c("year"), _ - _.mean())).head()

┏━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ species ┃ year  ┃ bill_length_mm ┃
┡━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ string  │ int64 │ float64        │
├─────────┼───────┼────────────────┤
│ Adelie  │  2007 │       -4.82193 │
│ Adelie  │  2007 │       -4.42193 │
│ Adelie  │  2007 │       -3.62193 │
│ Adelie  │  2007 │           NULL │
│ Adelie  │  2007 │       -7.22193 │
└─────────┴───────┴────────────────┘

nunique

nunique(where=None)

Compute the number of unique rows in the table.

Parameters

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

Returns

Type	Description
IntegerScalar	Number of unique rows in the table

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.memtable({"a": ["foo", "bar", "bar"]})
>>> t

┏━━━━━━━━┓
┃ a      ┃
┡━━━━━━━━┩
│ string │
├────────┤
│ foo    │
│ bar    │
│ bar    │
└────────┘

>>> t.nunique()

2

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

1

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

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 │
├───────┼────────┼───────┤
│     2 │ B      │     6 │
│     3 │ a      │     4 │
│     1 │ c      │     5 │
│     3 │ D      │     7 │
└───────┴────────┴───────┘

pivot_longer

pivot_longer(col, *, names_to='name', names_pattern='(.+)', names_transform=None, values_to='value', values_transform=None)

Transform a table from wider to longer.

Parameters

Name	Type	Description	Default
col	str | s.Selector	String column name or selector.	required
names_to	str | Iterable[str]	A string or iterable of strings indicating how to name the new pivoted columns.	'name'
names_pattern	str | re.Pattern	Pattern to use to extract column names from the input. By default the entire column name is extracted.	'(.+)'
names_transform	Callable[[str], ir.Value] | Mapping[str, Callable[[str], ir.Value]] | None	Function or mapping of a name in names_to to a function to transform a column name to a value.	None
values_to	str	Name of the pivoted value column.	'value'
values_transform	Callable[[ir.Value], ir.Value] | Deferred | None	Apply a function to the value column. This can be a lambda or deferred expression.	None

Returns

Type	Description
Table	Pivoted table

Examples

Basic usage

>>> import ibis
>>> import ibis.selectors as s
>>> from ibis import _
>>> ibis.options.interactive = True
>>> relig_income = ibis.examples.relig_income_raw.fetch()
>>> relig_income

┏━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ religion                ┃ <$10k ┃ $10-20k ┃ $20-30k ┃ $30-40k ┃ $40-50k ┃ $50-75k ┃ $75-100k ┃ $100-150k ┃ >150k ┃ Don't know/refused ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
│ string                  │ int64 │ int64   │ int64   │ int64   │ int64   │ int64   │ int64    │ int64     │ int64 │ int64              │
├─────────────────────────┼───────┼─────────┼─────────┼─────────┼─────────┼─────────┼──────────┼───────────┼───────┼────────────────────┤
│ Agnostic                │    27 │      34 │      60 │      81 │      76 │     137 │      122 │       109 │    84 │                 96 │
│ Atheist                 │    12 │      27 │      37 │      52 │      35 │      70 │       73 │        59 │    74 │                 76 │
│ Buddhist                │    27 │      21 │      30 │      34 │      33 │      58 │       62 │        39 │    53 │                 54 │
│ Catholic                │   418 │     617 │     732 │     670 │     638 │    1116 │      949 │       792 │   633 │               1489 │
│ Don’t know/refused      │    15 │      14 │      15 │      11 │      10 │      35 │       21 │        17 │    18 │                116 │
│ Evangelical Prot        │   575 │     869 │    1064 │     982 │     881 │    1486 │      949 │       723 │   414 │               1529 │
│ Hindu                   │     1 │       9 │       7 │       9 │      11 │      34 │       47 │        48 │    54 │                 37 │
│ Historically Black Prot │   228 │     244 │     236 │     238 │     197 │     223 │      131 │        81 │    78 │                339 │
│ Jehovah's Witness       │    20 │      27 │      24 │      24 │      21 │      30 │       15 │        11 │     6 │                 37 │
│ Jewish                  │    19 │      19 │      25 │      25 │      30 │      95 │       69 │        87 │   151 │                162 │
│ …                       │     … │       … │       … │       … │       … │       … │        … │         … │     … │                  … │
└─────────────────────────┴───────┴─────────┴─────────┴─────────┴─────────┴─────────┴──────────┴───────────┴───────┴────────────────────┘

Here we convert column names not matching the selector for the religion column and convert those names into values

>>> relig_income.pivot_longer(~s.c("religion"), names_to="income", values_to="count")

┏━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┳━━━━━━━┓
┃ religion ┃ income             ┃ count ┃
┡━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━╇━━━━━━━┩
│ string   │ string             │ int64 │
├──────────┼────────────────────┼───────┤
│ Agnostic │ <$10k              │    27 │
│ Agnostic │ $10-20k            │    34 │
│ Agnostic │ $20-30k            │    60 │
│ Agnostic │ $30-40k            │    81 │
│ Agnostic │ $40-50k            │    76 │
│ Agnostic │ $50-75k            │   137 │
│ Agnostic │ $75-100k           │   122 │
│ Agnostic │ $100-150k          │   109 │
│ Agnostic │ >150k              │    84 │
│ Agnostic │ Don't know/refused │    96 │
│ …        │ …                  │     … │
└──────────┴────────────────────┴───────┘

Similarly for a different example dataset, we convert names to values but using a different selector and the default values_to value.

>>> world_bank_pop = ibis.examples.world_bank_pop_raw.fetch()
>>> world_bank_pop.head()

┏━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┓
┃ country ┃ indicator   ┃ 2000         ┃ 2001         ┃ 2002         ┃ 2003         ┃ 2004         ┃ 2005         ┃ 2006         ┃ 2007         ┃ 2008         ┃ 2009         ┃ 2010         ┃ 2011         ┃ 2012         ┃ 2013         ┃ 2014         ┃ 2015         ┃ 2016         ┃ 2017         ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━┩
│ string  │ string      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │ float64      │
├─────────┼─────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┤
│ ABW     │ SP.URB.TOTL │ 4.162500e+04 │ 4.202500e+04 │ 4.219400e+04 │ 4.227700e+04 │ 4.231700e+04 │ 4.239900e+04 │ 4.255500e+04 │ 4.272900e+04 │ 4.290600e+04 │ 4.307900e+04 │ 4.320600e+04 │ 4.349300e+04 │ 4.386400e+04 │ 4.422800e+04 │ 4.458800e+04 │ 4.494300e+04 │ 4.529700e+04 │ 4.564800e+04 │
│ ABW     │ SP.URB.GROW │ 1.664222e+00 │ 9.563731e-01 │ 4.013352e-01 │ 1.965172e-01 │ 9.456936e-02 │ 1.935880e-01 │ 3.672580e-01 │ 4.080490e-01 │ 4.133830e-01 │ 4.023963e-01 │ 2.943735e-01 │ 6.620631e-01 │ 8.493932e-01 │ 8.264135e-01 │ 8.106692e-01 │ 7.930256e-01 │ 7.845785e-01 │ 7.718989e-01 │
│ ABW     │ SP.POP.TOTL │ 8.910100e+04 │ 9.069100e+04 │ 9.178100e+04 │ 9.270100e+04 │ 9.354000e+04 │ 9.448300e+04 │ 9.560600e+04 │ 9.678700e+04 │ 9.799600e+04 │ 9.921200e+04 │ 1.003410e+05 │ 1.012880e+05 │ 1.021120e+05 │ 1.028800e+05 │ 1.035940e+05 │ 1.042570e+05 │ 1.048740e+05 │ 1.054390e+05 │
│ ABW     │ SP.POP.GROW │ 2.539234e+00 │ 1.768757e+00 │ 1.194718e+00 │ 9.973955e-01 │ 9.009892e-01 │ 1.003077e+00 │ 1.181566e+00 │ 1.227711e+00 │ 1.241397e+00 │ 1.233231e+00 │ 1.131541e+00 │ 9.393559e-01 │ 8.102306e-01 │ 7.493010e-01 │ 6.916153e-01 │ 6.379592e-01 │ 5.900625e-01 │ 5.372957e-01 │
│ AFE     │ SP.URB.TOTL │ 1.155517e+08 │ 1.197755e+08 │ 1.242275e+08 │ 1.288340e+08 │ 1.336475e+08 │ 1.387456e+08 │ 1.440267e+08 │ 1.492313e+08 │ 1.553838e+08 │ 1.617762e+08 │ 1.684561e+08 │ 1.754157e+08 │ 1.825587e+08 │ 1.901087e+08 │ 1.980733e+08 │ 2.065563e+08 │ 2.150833e+08 │ 2.237321e+08 │
└─────────┴─────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┘

>>> world_bank_pop.pivot_longer(s.matches(r"\d{4}"), names_to="year").head()

┏━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━━┓
┃ country ┃ indicator   ┃ year   ┃ value   ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━━┩
│ string  │ string      │ string │ float64 │
├─────────┼─────────────┼────────┼─────────┤
│ ABW     │ SP.URB.TOTL │ 2000   │ 41625.0 │
│ ABW     │ SP.URB.TOTL │ 2001   │ 42025.0 │
│ ABW     │ SP.URB.TOTL │ 2002   │ 42194.0 │
│ ABW     │ SP.URB.TOTL │ 2003   │ 42277.0 │
│ ABW     │ SP.URB.TOTL │ 2004   │ 42317.0 │
└─────────┴─────────────┴────────┴─────────┘

pivot_longer has some preprocessing capabiltiies like stripping a prefix and applying a function to column names

>>> billboard = ibis.examples.billboard.fetch()
>>> billboard

┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━┓
┃ artist         ┃ track                   ┃ date_entered ┃ wk1   ┃ wk2   ┃ wk3   ┃ wk4   ┃ wk5   ┃ wk6   ┃ wk7   ┃ wk8   ┃ wk9   ┃ wk10  ┃ wk11  ┃ wk12  ┃ wk13  ┃ wk14  ┃ wk15  ┃ wk16  ┃ wk17  ┃ wk18  ┃ wk19  ┃ wk20  ┃ wk21  ┃ wk22  ┃ wk23  ┃ wk24  ┃ wk25  ┃ wk26  ┃ wk27  ┃ wk28  ┃ wk29  ┃ wk30  ┃ wk31  ┃ wk32  ┃ wk33  ┃ wk34  ┃ wk35  ┃ wk36  ┃ wk37  ┃ wk38  ┃ wk39  ┃ wk40  ┃ wk41  ┃ wk42  ┃ wk43  ┃ wk44  ┃ wk45  ┃ wk46  ┃ wk47  ┃ wk48  ┃ wk49  ┃ wk50  ┃ wk51  ┃ wk52  ┃ wk53  ┃ wk54  ┃ wk55  ┃ wk56  ┃ wk57  ┃ wk58  ┃ wk59  ┃ wk60  ┃ wk61  ┃ wk62  ┃ wk63  ┃ wk64  ┃ wk65  ┃ wk66   ┃ wk67   ┃ wk68   ┃ wk69   ┃ wk70   ┃ wk71   ┃ wk72   ┃ wk73   ┃ wk74   ┃ wk75   ┃ wk76   ┃
┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━┩
│ string         │ string                  │ date         │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │ string │ string │ string │ string │ string │ string │ string │ string │ string │ string │ string │
├────────────────┼─────────────────────────┼──────────────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼───────┼────────┼────────┼────────┼────────┼────────┼────────┼────────┼────────┼────────┼────────┼────────┤
│ 2 Pac          │ Baby Don't Cry (Keep... │ 2000-02-26   │    87 │    82 │    72 │    77 │    87 │    94 │    99 │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │
│ 2Ge+her        │ The Hardest Part Of ... │ 2000-09-02   │    91 │    87 │    92 │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │
│ 3 Doors Down   │ Kryptonite              │ 2000-04-08   │    81 │    70 │    68 │    67 │    66 │    57 │    54 │    53 │    51 │    51 │    51 │    51 │    47 │    44 │    38 │    28 │    22 │    18 │    18 │    14 │    12 │     7 │     6 │     6 │     6 │     5 │     5 │     4 │     4 │     4 │     4 │     3 │     3 │     3 │     4 │     5 │     5 │     9 │     9 │    15 │    14 │    13 │    14 │    16 │    17 │    21 │    22 │    24 │    28 │    33 │    42 │    42 │    49 │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │
│ 3 Doors Down   │ Loser                   │ 2000-10-21   │    76 │    76 │    72 │    69 │    67 │    65 │    55 │    59 │    62 │    61 │    61 │    59 │    61 │    66 │    72 │    76 │    75 │    67 │    73 │    70 │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │
│ 504 Boyz       │ Wobble Wobble           │ 2000-04-15   │    57 │    34 │    25 │    17 │    17 │    31 │    36 │    49 │    53 │    57 │    64 │    70 │    75 │    76 │    78 │    85 │    92 │    96 │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │
│ 98^0           │ Give Me Just One Nig... │ 2000-08-19   │    51 │    39 │    34 │    26 │    26 │    19 │     2 │     2 │     3 │     6 │     7 │    22 │    29 │    36 │    47 │    67 │    66 │    84 │    93 │    94 │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │
│ A*Teens        │ Dancing Queen           │ 2000-07-08   │    97 │    97 │    96 │    95 │   100 │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │
│ Aaliyah        │ I Don't Wanna           │ 2000-01-29   │    84 │    62 │    51 │    41 │    38 │    35 │    35 │    38 │    38 │    36 │    37 │    37 │    38 │    49 │    61 │    63 │    62 │    67 │    83 │    86 │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │
│ Aaliyah        │ Try Again               │ 2000-03-18   │    59 │    53 │    38 │    28 │    21 │    18 │    16 │    14 │    12 │    10 │     9 │     8 │     6 │     1 │     2 │     2 │     2 │     2 │     3 │     4 │     5 │     5 │     6 │     9 │    13 │    14 │    16 │    23 │    22 │    33 │    36 │    43 │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │
│ Adams, Yolanda │ Open My Heart           │ 2000-08-26   │    76 │    76 │    74 │    69 │    68 │    67 │    61 │    58 │    57 │    59 │    66 │    68 │    61 │    67 │    59 │    63 │    67 │    71 │    79 │    89 │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │  NULL │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │ NULL   │
│ …              │ …                       │ …            │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │     … │ …      │ …      │ …      │ …      │ …      │ …      │ …      │ …      │ …      │ …      │ …      │
└────────────────┴─────────────────────────┴──────────────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴────────┴────────┴────────┴────────┴────────┴────────┴────────┴────────┴────────┴────────┴────────┘

>>> billboard.pivot_longer(
...     s.startswith("wk"),
...     names_to="week",
...     names_pattern=r"wk(.+)",
...     names_transform=int,
...     values_to="rank",
...     values_transform=_.cast("int"),
... ).dropna("rank")

┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━┳━━━━━━━┓
┃ artist  ┃ track                   ┃ date_entered ┃ week ┃ rank  ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━╇━━━━━━━┩
│ string  │ string                  │ date         │ int8 │ int64 │
├─────────┼─────────────────────────┼──────────────┼──────┼───────┤
│ 2 Pac   │ Baby Don't Cry (Keep... │ 2000-02-26   │    1 │    87 │
│ 2 Pac   │ Baby Don't Cry (Keep... │ 2000-02-26   │    2 │    82 │
│ 2 Pac   │ Baby Don't Cry (Keep... │ 2000-02-26   │    3 │    72 │
│ 2 Pac   │ Baby Don't Cry (Keep... │ 2000-02-26   │    4 │    77 │
│ 2 Pac   │ Baby Don't Cry (Keep... │ 2000-02-26   │    5 │    87 │
│ 2 Pac   │ Baby Don't Cry (Keep... │ 2000-02-26   │    6 │    94 │
│ 2 Pac   │ Baby Don't Cry (Keep... │ 2000-02-26   │    7 │    99 │
│ 2Ge+her │ The Hardest Part Of ... │ 2000-09-02   │    1 │    91 │
│ 2Ge+her │ The Hardest Part Of ... │ 2000-09-02   │    2 │    87 │
│ 2Ge+her │ The Hardest Part Of ... │ 2000-09-02   │    3 │    92 │
│ …       │ …                       │ …            │    … │     … │
└─────────┴─────────────────────────┴──────────────┴──────┴───────┘

You can use regular expression capture groups to extract multiple variables stored in column names

>>> who = ibis.examples.who.fetch()
>>> who

┏━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ country     ┃ iso2   ┃ iso3   ┃ year  ┃ new_sp_m014 ┃ new_sp_m1524 ┃ new_sp_m2534 ┃ new_sp_m3544 ┃ new_sp_m4554 ┃ new_sp_m5564 ┃ new_sp_m65 ┃ new_sp_f014 ┃ new_sp_f1524 ┃ new_sp_f2534 ┃ new_sp_f3544 ┃ new_sp_f4554 ┃ new_sp_f5564 ┃ new_sp_f65 ┃ new_sn_m014 ┃ new_sn_m1524 ┃ new_sn_m2534 ┃ new_sn_m3544 ┃ new_sn_m4554 ┃ new_sn_m5564 ┃ new_sn_m65 ┃ new_sn_f014 ┃ new_sn_f1524 ┃ new_sn_f2534 ┃ new_sn_f3544 ┃ new_sn_f4554 ┃ new_sn_f5564 ┃ new_sn_f65 ┃ new_ep_m014 ┃ new_ep_m1524 ┃ new_ep_m2534 ┃ new_ep_m3544 ┃ new_ep_m4554 ┃ new_ep_m5564 ┃ new_ep_m65 ┃ new_ep_f014 ┃ new_ep_f1524 ┃ new_ep_f2534 ┃ new_ep_f3544 ┃ new_ep_f4554 ┃ new_ep_f5564 ┃ new_ep_f65 ┃ newrel_m014 ┃ newrel_m1524 ┃ newrel_m2534 ┃ newrel_m3544 ┃ newrel_m4554 ┃ newrel_m5564 ┃ newrel_m65 ┃ newrel_f014 ┃ newrel_f1524 ┃ newrel_f2534 ┃ newrel_f3544 ┃ newrel_f4554 ┃ newrel_f5564 ┃ newrel_f65 ┃
┡━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ string      │ string │ string │ int64 │ int64       │ int64        │ int64        │ int64        │ int64        │ int64        │ int64      │ int64       │ int64        │ int64        │ int64        │ int64        │ int64        │ int64      │ int64       │ int64        │ int64        │ int64        │ int64        │ int64        │ int64      │ int64       │ int64        │ int64        │ int64        │ int64        │ int64        │ int64      │ int64       │ int64        │ int64        │ int64        │ int64        │ int64        │ int64      │ int64       │ int64        │ int64        │ int64        │ int64        │ int64        │ int64      │ int64       │ int64        │ int64        │ int64        │ int64        │ int64        │ int64      │ int64       │ int64        │ int64        │ int64        │ int64        │ int64        │ int64      │
├─────────────┼────────┼────────┼───────┼─────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼────────────┼─────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼────────────┼─────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼────────────┼─────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼────────────┼─────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼────────────┼─────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼────────────┼─────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼────────────┼─────────────┼──────────────┼──────────────┼──────────────┼──────────────┼──────────────┼────────────┤
│ Afghanistan │ AF     │ AFG    │  1980 │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │
│ Afghanistan │ AF     │ AFG    │  1981 │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │
│ Afghanistan │ AF     │ AFG    │  1982 │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │
│ Afghanistan │ AF     │ AFG    │  1983 │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │
│ Afghanistan │ AF     │ AFG    │  1984 │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │
│ Afghanistan │ AF     │ AFG    │  1985 │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │
│ Afghanistan │ AF     │ AFG    │  1986 │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │
│ Afghanistan │ AF     │ AFG    │  1987 │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │
│ Afghanistan │ AF     │ AFG    │  1988 │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │
│ Afghanistan │ AF     │ AFG    │  1989 │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │        NULL │         NULL │         NULL │         NULL │         NULL │         NULL │       NULL │
│ …           │ …      │ …      │     … │           … │            … │            … │            … │            … │            … │          … │           … │            … │            … │            … │            … │            … │          … │           … │            … │            … │            … │            … │            … │          … │           … │            … │            … │            … │            … │            … │          … │           … │            … │            … │            … │            … │            … │          … │           … │            … │            … │            … │            … │            … │          … │           … │            … │            … │            … │            … │            … │          … │           … │            … │            … │            … │            … │            … │          … │
└─────────────┴────────┴────────┴───────┴─────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴────────────┴─────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴────────────┴─────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴────────────┴─────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴────────────┴─────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴────────────┴─────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴────────────┴─────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴────────────┴─────────────┴──────────────┴──────────────┴──────────────┴──────────────┴──────────────┴────────────┘

>>> len(who.columns)

60

>>> who.pivot_longer(
...     s.r["new_sp_m014":"newrel_f65"],
...     names_to=["diagnosis", "gender", "age"],
...     names_pattern="new_?(.*)_(.)(.*)",
...     values_to="count",
... )

┏━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ country     ┃ iso2   ┃ iso3   ┃ year  ┃ diagnosis ┃ gender ┃ age    ┃ count ┃
┡━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string      │ string │ string │ int64 │ string    │ string │ string │ int64 │
├─────────────┼────────┼────────┼───────┼───────────┼────────┼────────┼───────┤
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │ m      │ 014    │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │ m      │ 1524   │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │ m      │ 2534   │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │ m      │ 3544   │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │ m      │ 4554   │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │ m      │ 5564   │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │ m      │ 65     │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │ f      │ 014    │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │ f      │ 1524   │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │ f      │ 2534   │  NULL │
│ …           │ …      │ …      │     … │ …         │ …      │ …      │     … │
└─────────────┴────────┴────────┴───────┴───────────┴────────┴────────┴───────┘

names_transform is flexible, and can be:

1. A mapping of one or more names in `names_to` to callable
2. A callable that will be applied to every name

Let’s recode gender and age to numeric values using a mapping

>>> who.pivot_longer(
...     s.r["new_sp_m014":"newrel_f65"],
...     names_to=["diagnosis", "gender", "age"],
...     names_pattern="new_?(.*)_(.)(.*)",
...     names_transform=dict(
...         gender={"m": 1, "f": 2}.get,
...         age=dict(
...             zip(
...                 ["014", "1524", "2534", "3544", "4554", "5564", "65"],
...                 range(7),
...             )
...         ).get,
...     ),
...     values_to="count",
... )

┏━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┳━━━━━━┳━━━━━━━┓
┃ country     ┃ iso2   ┃ iso3   ┃ year  ┃ diagnosis ┃ gender ┃ age  ┃ count ┃
┡━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━╇━━━━━━╇━━━━━━━┩
│ string      │ string │ string │ int64 │ string    │ int8   │ int8 │ int64 │
├─────────────┼────────┼────────┼───────┼───────────┼────────┼──────┼───────┤
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │      1 │    0 │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │      1 │    1 │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │      1 │    2 │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │      1 │    3 │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │      1 │    4 │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │      1 │    5 │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │      1 │    6 │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │      2 │    0 │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │      2 │    1 │  NULL │
│ Afghanistan │ AF     │ AFG    │  1980 │ sp        │      2 │    2 │  NULL │
│ …           │ …      │ …      │     … │ …         │      … │    … │     … │
└─────────────┴────────┴────────┴───────┴───────────┴────────┴──────┴───────┘

The number of match groups in names_pattern must match the length of names_to

>>> who.pivot_longer(  
...     s.r["new_sp_m014":"newrel_f65"],
...     names_to=["diagnosis", "gender", "age"],
...     names_pattern="new_?(.*)_.(.*)",
... )

IbisInputError: Number of match groups in `names_pattern`'new_?(.*)_.(.*)' (2 groups) doesn't match the length of `names_to` ['diagnosis', 'gender', 'age'] (length 3)

names_transform must be a mapping or callable

>>> who.pivot_longer(
...     s.r["new_sp_m014":"newrel_f65"], names_transform="upper"
... )  # quartodoc: +EXPECTED_FAILURE

IbisTypeError: `names_transform` must be a mapping or callable. Got <class 'str'>

pivot_wider

pivot_wider(id_cols=None, names_from='name', names_prefix='', names_sep='_', names_sort=False, names=None, values_from='value', values_fill=None, values_agg='arbitrary')

Pivot a table to a wider format.

Parameters

Name	Type	Description	Default
id_cols	s.Selector | None	A set of columns that uniquely identify each observation.	None
names_from	str | Iterable[str] | s.Selector	An argument describing which column or columns to use to get the name of the output columns.	'name'
names_prefix	str	String added to the start of every column name.	''
names_sep	str	If names_from or values_from contains multiple columns, this argument will be used to join their values together into a single string to use as a column name.	'_'
names_sort	bool	If True columns are sorted. If False column names are ordered by appearance.	False
names	Iterable[str] | None	An explicit sequence of values to look for in columns matching names_from. * When this value is None, the values will be computed from names_from. * When this value is not None, each element’s length must match the length of names_from. See examples below for more detail.	None
values_from	str | Iterable[str] | s.Selector	An argument describing which column or columns to get the cell values from.	'value'
values_fill	int | float | str | ir.Scalar | None	A scalar value that specifies what each value should be filled with when missing.	None
values_agg	str | Callable[[ir.Value], ir.Scalar] | Deferred	A function applied to the value in each cell in the output.	'arbitrary'

Returns

Type	Description
Table	Wider pivoted table

Examples

>>> import ibis
>>> import ibis.selectors as s
>>> from ibis import _
>>> ibis.options.interactive = True

Basic usage

>>> fish_encounters = ibis.examples.fish_encounters.fetch()
>>> fish_encounters

┏━━━━━━━┳━━━━━━━━━┳━━━━━━━┓
┃ fish  ┃ station ┃ seen  ┃
┡━━━━━━━╇━━━━━━━━━╇━━━━━━━┩
│ int64 │ string  │ int64 │
├───────┼─────────┼───────┤
│  4842 │ Release │     1 │
│  4842 │ I80_1   │     1 │
│  4842 │ Lisbon  │     1 │
│  4842 │ Rstr    │     1 │
│  4842 │ Base_TD │     1 │
│  4842 │ BCE     │     1 │
│  4842 │ BCW     │     1 │
│  4842 │ BCE2    │     1 │
│  4842 │ BCW2    │     1 │
│  4842 │ MAE     │     1 │
│     … │ …       │     … │
└───────┴─────────┴───────┘

>>> fish_encounters.pivot_wider(names_from="station", values_from="seen")

┏━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┓
┃ fish  ┃ MAW   ┃ Lisbon ┃ Base_TD ┃ MAE   ┃ Release ┃ I80_1 ┃ BCE   ┃ BCE2  ┃ Rstr  ┃ BCW   ┃ BCW2  ┃
┡━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━┩
│ int64 │ int64 │ int64  │ int64   │ int64 │ int64   │ int64 │ int64 │ int64 │ int64 │ int64 │ int64 │
├───────┼───────┼────────┼─────────┼───────┼─────────┼───────┼───────┼───────┼───────┼───────┼───────┤
│  4844 │     1 │      1 │       1 │     1 │       1 │     1 │     1 │     1 │     1 │     1 │     1 │
│  4845 │  NULL │      1 │       1 │  NULL │       1 │     1 │  NULL │  NULL │     1 │  NULL │  NULL │
│  4849 │  NULL │   NULL │    NULL │  NULL │       1 │     1 │  NULL │  NULL │  NULL │  NULL │  NULL │
│  4859 │  NULL │      1 │       1 │  NULL │       1 │     1 │  NULL │  NULL │     1 │  NULL │  NULL │
│  4861 │     1 │      1 │       1 │     1 │       1 │     1 │     1 │     1 │     1 │     1 │     1 │
│  4851 │  NULL │   NULL │    NULL │  NULL │       1 │     1 │  NULL │  NULL │  NULL │  NULL │  NULL │
│  4857 │  NULL │      1 │       1 │  NULL │       1 │     1 │     1 │     1 │     1 │     1 │     1 │
│  4858 │     1 │      1 │       1 │     1 │       1 │     1 │     1 │     1 │     1 │     1 │     1 │
│  4862 │  NULL │      1 │       1 │  NULL │       1 │     1 │     1 │     1 │     1 │     1 │     1 │
│  4863 │  NULL │   NULL │    NULL │  NULL │       1 │     1 │  NULL │  NULL │  NULL │  NULL │  NULL │
│     … │     … │      … │       … │     … │       … │     … │     … │     … │     … │     … │     … │
└───────┴───────┴────────┴─────────┴───────┴─────────┴───────┴───────┴───────┴───────┴───────┴───────┘

Fill missing pivoted values using values_fill

>>> fish_encounters.pivot_wider(
...     names_from="station", values_from="seen", values_fill=0
... )

┏━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┓
┃ fish  ┃ Lisbon ┃ Base_TD ┃ MAE   ┃ Rstr  ┃ BCW   ┃ BCW2  ┃ MAW   ┃ Release ┃ I80_1 ┃ BCE   ┃ BCE2  ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━┩
│ int64 │ int64  │ int64   │ int64 │ int64 │ int64 │ int64 │ int64 │ int64   │ int64 │ int64 │ int64 │
├───────┼────────┼─────────┼───────┼───────┼───────┼───────┼───────┼─────────┼───────┼───────┼───────┤
│  4843 │      1 │       1 │     1 │     1 │     1 │     1 │     1 │       1 │     1 │     1 │     1 │
│  4848 │      1 │       0 │     0 │     1 │     0 │     0 │     0 │       1 │     1 │     0 │     0 │
│  4865 │      1 │       0 │     0 │     0 │     0 │     0 │     0 │       1 │     1 │     0 │     0 │
│  4844 │      1 │       1 │     1 │     1 │     1 │     1 │     1 │       1 │     1 │     1 │     1 │
│  4845 │      1 │       1 │     0 │     1 │     0 │     0 │     0 │       1 │     1 │     0 │     0 │
│  4849 │      0 │       0 │     0 │     0 │     0 │     0 │     0 │       1 │     1 │     0 │     0 │
│  4859 │      1 │       1 │     0 │     1 │     0 │     0 │     0 │       1 │     1 │     0 │     0 │
│  4861 │      1 │       1 │     1 │     1 │     1 │     1 │     1 │       1 │     1 │     1 │     1 │
│  4851 │      0 │       0 │     0 │     0 │     0 │     0 │     0 │       1 │     1 │     0 │     0 │
│  4857 │      1 │       1 │     0 │     1 │     1 │     1 │     0 │       1 │     1 │     1 │     1 │
│     … │      … │       … │     … │     … │     … │     … │     … │       … │     … │     … │     … │
└───────┴────────┴─────────┴───────┴───────┴───────┴───────┴───────┴─────────┴───────┴───────┴───────┘

Compute multiple values columns

>>> us_rent_income = ibis.examples.us_rent_income.fetch()
>>> us_rent_income

┏━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━┓
┃ geoid  ┃ name       ┃ variable ┃ estimate ┃ moe   ┃
┡━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━┩
│ string │ string     │ string   │ int64    │ int64 │
├────────┼────────────┼──────────┼──────────┼───────┤
│ 01     │ Alabama    │ income   │    24476 │   136 │
│ 01     │ Alabama    │ rent     │      747 │     3 │
│ 02     │ Alaska     │ income   │    32940 │   508 │
│ 02     │ Alaska     │ rent     │     1200 │    13 │
│ 04     │ Arizona    │ income   │    27517 │   148 │
│ 04     │ Arizona    │ rent     │      972 │     4 │
│ 05     │ Arkansas   │ income   │    23789 │   165 │
│ 05     │ Arkansas   │ rent     │      709 │     5 │
│ 06     │ California │ income   │    29454 │   109 │
│ 06     │ California │ rent     │     1358 │     3 │
│ …      │ …          │ …        │        … │     … │
└────────┴────────────┴──────────┴──────────┴───────┘

>>> us_rent_income.pivot_wider(
...     names_from="variable", values_from=["estimate", "moe"]
... )

┏━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━┓
┃ geoid  ┃ name         ┃ estimate_income ┃ moe_income ┃ estimate_rent ┃ moe_rent ┃
┡━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━┩
│ string │ string       │ int64           │ int64      │ int64         │ int64    │
├────────┼──────────────┼─────────────────┼────────────┼───────────────┼──────────┤
│ 05     │ Arkansas     │           23789 │        165 │           709 │        5 │
│ 06     │ California   │           29454 │        109 │          1358 │        3 │
│ 13     │ Georgia      │           27024 │        106 │           927 │        3 │
│ 15     │ Hawaii       │           32453 │        218 │          1507 │       18 │
│ 16     │ Idaho        │           25298 │        208 │           792 │        7 │
│ 30     │ Montana      │           26249 │        206 │           751 │        9 │
│ 38     │ North Dakota │           32336 │        245 │           775 │        9 │
│ 39     │ Ohio         │           27435 │         94 │           764 │        2 │
│ 40     │ Oklahoma     │           26207 │        101 │           766 │        3 │
│ 47     │ Tennessee    │           25453 │        102 │           808 │        4 │
│ …      │ …            │               … │          … │             … │        … │
└────────┴──────────────┴─────────────────┴────────────┴───────────────┴──────────┘

The column name separator can be changed using the names_sep parameter

>>> us_rent_income.pivot_wider(
...     names_from="variable",
...     names_sep=".",
...     values_from=("estimate", "moe"),
... )

┏━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━┓
┃ geoid  ┃ name         ┃ estimate.income ┃ moe.income ┃ estimate.rent ┃ moe.rent ┃
┡━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━┩
│ string │ string       │ int64           │ int64      │ int64         │ int64    │
├────────┼──────────────┼─────────────────┼────────────┼───────────────┼──────────┤
│ 05     │ Arkansas     │           23789 │        165 │           709 │        5 │
│ 06     │ California   │           29454 │        109 │          1358 │        3 │
│ 13     │ Georgia      │           27024 │        106 │           927 │        3 │
│ 15     │ Hawaii       │           32453 │        218 │          1507 │       18 │
│ 16     │ Idaho        │           25298 │        208 │           792 │        7 │
│ 30     │ Montana      │           26249 │        206 │           751 │        9 │
│ 38     │ North Dakota │           32336 │        245 │           775 │        9 │
│ 39     │ Ohio         │           27435 │         94 │           764 │        2 │
│ 40     │ Oklahoma     │           26207 │        101 │           766 │        3 │
│ 47     │ Tennessee    │           25453 │        102 │           808 │        4 │
│ …      │ …            │               … │          … │             … │        … │
└────────┴──────────────┴─────────────────┴────────────┴───────────────┴──────────┘

Supply an alternative function to summarize values

>>> warpbreaks = ibis.examples.warpbreaks.fetch().select("wool", "tension", "breaks")
>>> warpbreaks

┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━┓
┃ wool   ┃ tension ┃ breaks ┃
┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━┩
│ string │ string  │ int64  │
├────────┼─────────┼────────┤
│ A      │ L       │     26 │
│ A      │ L       │     30 │
│ A      │ L       │     54 │
│ A      │ L       │     25 │
│ A      │ L       │     70 │
│ A      │ L       │     52 │
│ A      │ L       │     51 │
│ A      │ L       │     26 │
│ A      │ L       │     67 │
│ A      │ M       │     18 │
│ …      │ …       │      … │
└────────┴─────────┴────────┘

>>> warpbreaks.pivot_wider(
...     names_from="wool", values_from="breaks", values_agg="mean"
... ).select("tension", "A", "B").order_by("tension")

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┓
┃ tension ┃ A         ┃ B         ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━┩
│ string  │ float64   │ float64   │
├─────────┼───────────┼───────────┤
│ H       │ 24.555556 │ 18.777778 │
│ L       │ 44.555556 │ 28.222222 │
│ M       │ 24.000000 │ 28.777778 │
└─────────┴───────────┴───────────┘

Passing Deferred objects to values_agg is supported

>>> warpbreaks.pivot_wider(
...     names_from="tension",
...     values_from="breaks",
...     values_agg=_.sum(),
... ).select("wool", "H", "L", "M").order_by(s.all())

┏━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┓
┃ wool   ┃ H     ┃ L     ┃ M     ┃
┡━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━┩
│ string │ int64 │ int64 │ int64 │
├────────┼───────┼───────┼───────┤
│ A      │   221 │   401 │   216 │
│ B      │   169 │   254 │   259 │
└────────┴───────┴───────┴───────┘

Use a custom aggregate function

>>> warpbreaks.pivot_wider(
...     names_from="wool",
...     values_from="breaks",
...     values_agg=lambda col: col.std() / col.mean(),
... ).select("tension", "A", "B").order_by("tension")

┏━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓
┃ tension ┃ A        ┃ B        ┃
┡━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩
│ string  │ float64  │ float64  │
├─────────┼──────────┼──────────┤
│ H       │ 0.418344 │ 0.260590 │
│ L       │ 0.406183 │ 0.349325 │
│ M       │ 0.360844 │ 0.327719 │
└─────────┴──────────┴──────────┘

Generate some random data, setting the random seed for reproducibility

>>> import random
>>> random.seed(0)
>>> raw = ibis.memtable(
...     [
...         dict(
...             product=product,
...             country=country,
...             year=year,
...             production=random.random(),
...         )
...         for product in "AB"
...         for country in ["AI", "EI"]
...         for year in range(2000, 2015)
...     ]
... )
>>> production = raw.filter(((_.product == "A") & (_.country == "AI")) | (_.product == "B"))
>>> production.order_by(s.all())

┏━━━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━┓
┃ product ┃ country ┃ year  ┃ production ┃
┡━━━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━┩
│ string  │ string  │ int64 │ float64    │
├─────────┼─────────┼───────┼────────────┤
│ A       │ AI      │  2000 │   0.844422 │
│ A       │ AI      │  2001 │   0.757954 │
│ A       │ AI      │  2002 │   0.420572 │
│ A       │ AI      │  2003 │   0.258917 │
│ A       │ AI      │  2004 │   0.511275 │
│ A       │ AI      │  2005 │   0.404934 │
│ A       │ AI      │  2006 │   0.783799 │
│ A       │ AI      │  2007 │   0.303313 │
│ A       │ AI      │  2008 │   0.476597 │
│ A       │ AI      │  2009 │   0.583382 │
│ …       │ …       │     … │          … │
└─────────┴─────────┴───────┴────────────┘

Pivoting with multiple name columns

>>> production.pivot_wider(
...     names_from=["product", "country"],
...     values_from="production",
... )

┏━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓
┃ year  ┃ B_AI     ┃ B_EI     ┃ A_AI     ┃
┡━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩
│ int64 │ float64  │ float64  │ float64  │
├───────┼──────────┼──────────┼──────────┤
│  2004 │ 0.548699 │ 0.967540 │ 0.511275 │
│  2006 │ 0.719705 │ 0.447970 │ 0.783799 │
│  2007 │ 0.398824 │ 0.080446 │ 0.303313 │
│  2008 │ 0.824845 │ 0.320055 │ 0.476597 │
│  2011 │ 0.493578 │ 0.109058 │ 0.504687 │
│  2001 │ 0.865310 │ 0.191067 │ 0.757954 │
│  2003 │ 0.805028 │ 0.238616 │ 0.258917 │
│  2009 │ 0.668153 │ 0.507941 │ 0.583382 │
│  2012 │ 0.867603 │ 0.551267 │ 0.281838 │
│  2000 │ 0.477010 │ 0.870471 │ 0.844422 │
│     … │        … │        … │        … │
└───────┴──────────┴──────────┴──────────┘

Select a subset of names. This call incurs no computation when constructing the expression.

>>> production.pivot_wider(
...     names_from=["product", "country"],
...     names=[("A", "AI"), ("B", "AI")],
...     values_from="production",
... )

┏━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓
┃ year  ┃ A_AI     ┃ B_AI     ┃
┡━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩
│ int64 │ float64  │ float64  │
├───────┼──────────┼──────────┤
│  2001 │ 0.757954 │ 0.865310 │
│  2003 │ 0.258917 │ 0.805028 │
│  2009 │ 0.583382 │ 0.668153 │
│  2012 │ 0.281838 │ 0.867603 │
│  2002 │ 0.420572 │ 0.260492 │
│  2005 │ 0.404934 │ 0.014042 │
│  2013 │ 0.755804 │ 0.243911 │
│  2004 │ 0.511275 │ 0.548699 │
│  2006 │ 0.783799 │ 0.719705 │
│  2007 │ 0.303313 │ 0.398824 │
│     … │        … │        … │
└───────┴──────────┴──────────┘

Sort the new columns’ names

>>> production.pivot_wider(
...     names_from=["product", "country"],
...     values_from="production",
...     names_sort=True,
... )

┏━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓
┃ year  ┃ A_AI     ┃ B_AI     ┃ B_EI     ┃
┡━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩
│ int64 │ float64  │ float64  │ float64  │
├───────┼──────────┼──────────┼──────────┤
│  2000 │ 0.844422 │ 0.477010 │ 0.870471 │
│  2010 │ 0.908113 │ 0.001143 │ 0.932834 │
│  2014 │ 0.618369 │ 0.325204 │ 0.547441 │
│  2004 │ 0.511275 │ 0.548699 │ 0.967540 │
│  2006 │ 0.783799 │ 0.719705 │ 0.447970 │
│  2007 │ 0.303313 │ 0.398824 │ 0.080446 │
│  2008 │ 0.476597 │ 0.824845 │ 0.320055 │
│  2001 │ 0.757954 │ 0.865310 │ 0.191067 │
│  2003 │ 0.258917 │ 0.805028 │ 0.238616 │
│  2009 │ 0.583382 │ 0.668153 │ 0.507941 │
│     … │        … │        … │        … │
└───────┴──────────┴──────────┴──────────┘

preview

preview(max_rows=None, max_columns=None, max_length=None, max_string=None, max_depth=None, console_width=None)

Return a subset as a Rich Table.

This is an explicit version of what you get when you inspect this object in interactive mode, except with this version you can pass formatting options. The options are the same as those exposed in ibis.options.interactive.

Parameters

Name	Type	Description	Default
max_rows	int | None	Maximum number of rows to display	None
max_columns	int | None	Maximum number of columns to display	None
max_length	int | None	Maximum length for pretty-printed arrays and maps	None
max_string	int | None	Maximum length for pretty-printed strings	None
max_depth	int | None	Maximum depth for nested data types	None
console_width	int | float | None	Width of the console in characters. If not specified, the width will be inferred from the console.	None

Examples

>>> import ibis
>>> t = ibis.examples.penguins.fetch()

Because the console_width is too small, only 2 columns are shown even though we specified up to 3.

>>> t.preview(
...     max_rows=3,
...     max_columns=3,
...     max_string=8,
...     console_width=30,
... )

┏━━━━━━━━━┳━━━━━━━━━━┳━━━┓
┃ species ┃ island   ┃ … ┃
┡━━━━━━━━━╇━━━━━━━━━━╇━━━┩
│ string  │ string   │ … │
├─────────┼──────────┼───┤
│ Adelie  │ Torgers… │ … │
│ Adelie  │ Torgers… │ … │
│ Adelie  │ Torgers… │ … │
│ …       │ …        │ … │
└─────────┴──────────┴───┘

relabel

relabel(substitutions)

Deprecated in favor of Table.rename.

relocate

relocate(*columns, before=None, after=None, **kwargs)

Relocate columns before or after other specified columns.

Parameters

Name	Type	Description	Default
columns	str | s.Selector	Columns to relocate. Selectors are accepted.	()
before	str | s.Selector | None	A column name or selector to insert the new columns before.	None
after	str | s.Selector | None	A column name or selector. Columns in columns are relocated after the last column selected in after.	None
kwargs	str	Additional column names to relocate, renaming argument values to keyword argument names.	{}

Returns

Type	Description
Table	A table with the columns relocated.

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> import ibis.selectors as s
>>> t = ibis.memtable(dict(a=[1], b=[1], c=[1], d=["a"], e=["a"], f=["a"]))
>>> t

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

>>> t.relocate("f")

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

>>> t.relocate("a", after="c")

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

>>> t.relocate("f", before="b")

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

>>> t.relocate("a", after=s.last())

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

Relocate allows renaming

>>> t.relocate(ff="f")

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

You can relocate based on any predicate selector, such as of_type

>>> t.relocate(s.of_type("string"))

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

>>> t.relocate(s.numeric(), after=s.last())

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

>>> t.relocate(s.any_of(s.c(*"ae")))

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

When multiple columns are selected with before or after, those selected columns are moved before and after the selectors input

>>> t = ibis.memtable(dict(a=[1], b=["a"], c=[1], d=["a"]))
>>> t.relocate(s.numeric(), after=s.of_type("string"))

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

>>> t.relocate(s.numeric(), before=s.of_type("string"))

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

When there are duplicate renames in a call to relocate, the last one is preserved

>>> t.relocate(e="d", f="d")

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

However, if there are duplicates that are not part of a rename, the order specified in the relocate call is preserved

>>> t.relocate(
...     "b",
...     s.of_type("string"),  # "b" is a string column, so the selector matches
... )

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

rename

rename(method=None, /, **substitutions)

Rename columns in the table.

Parameters

Name	Type	Description	Default
method	str | Callable[[str], str | None] | Literal[‘snake_case’, ‘ALL_CAPS’] | Mapping[str, str] | None	An optional method for renaming columns. May be one of: - A format string to use to rename all columns, like "prefix_{name}". - A function from old name to new name. If the function returns None the old name is used. - The literal strings "snake_case" or "ALL_CAPS" to rename all columns using a snake_case or "ALL_CAPS" naming convention respectively. - A mapping from new name to old name. Existing columns not present in the mapping will passthrough with their original name.	None
substitutions	str	Columns to be explicitly renamed, expressed as new_name=old_name keyword arguments.	{}

Returns

Type	Description
Table	A renamed table expression

Examples

>>> import ibis
>>> import ibis.selectors as s
>>> ibis.options.interactive = True
>>> first3 = s.r[:3]  # first 3 columns
>>> t = ibis.examples.penguins_raw_raw.fetch().select(first3)
>>> t

┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ studyName ┃ Sample Number ┃ Species                             ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ string    │ int64         │ string                              │
├───────────┼───────────────┼─────────────────────────────────────┤
│ PAL0708   │             1 │ Adelie Penguin (Pygoscelis adeliae) │
│ PAL0708   │             2 │ Adelie Penguin (Pygoscelis adeliae) │
│ PAL0708   │             3 │ Adelie Penguin (Pygoscelis adeliae) │
│ PAL0708   │             4 │ Adelie Penguin (Pygoscelis adeliae) │
│ PAL0708   │             5 │ Adelie Penguin (Pygoscelis adeliae) │
│ PAL0708   │             6 │ Adelie Penguin (Pygoscelis adeliae) │
│ PAL0708   │             7 │ Adelie Penguin (Pygoscelis adeliae) │
│ PAL0708   │             8 │ Adelie Penguin (Pygoscelis adeliae) │
│ PAL0708   │             9 │ Adelie Penguin (Pygoscelis adeliae) │
│ PAL0708   │            10 │ Adelie Penguin (Pygoscelis adeliae) │
│ …         │             … │ …                                   │
└───────────┴───────────────┴─────────────────────────────────────┘

Rename specific columns by passing keyword arguments like new_name="old_name"

>>> t.rename(study_name="studyName").head(1)

┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ study_name ┃ Sample Number ┃ Species                             ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ string     │ int64         │ string                              │
├────────────┼───────────────┼─────────────────────────────────────┤
│ PAL0708    │             1 │ Adelie Penguin (Pygoscelis adeliae) │
└────────────┴───────────────┴─────────────────────────────────────┘

Rename all columns using a format string

>>> t.rename("p_{name}").head(1)

┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ p_studyName ┃ p_Sample Number ┃ p_Species                           ┃
┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ string      │ int64           │ string                              │
├─────────────┼─────────────────┼─────────────────────────────────────┤
│ PAL0708     │               1 │ Adelie Penguin (Pygoscelis adeliae) │
└─────────────┴─────────────────┴─────────────────────────────────────┘

Rename all columns using a snake_case convention

>>> t.rename("snake_case").head(1)

┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ study_name ┃ sample_number ┃ species                             ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ string     │ int64         │ string                              │
├────────────┼───────────────┼─────────────────────────────────────┤
│ PAL0708    │             1 │ Adelie Penguin (Pygoscelis adeliae) │
└────────────┴───────────────┴─────────────────────────────────────┘

Rename all columns using an ALL_CAPS convention

>>> t.rename("ALL_CAPS").head(1)

┏━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ STUDY_NAME ┃ SAMPLE_NUMBER ┃ SPECIES                             ┃
┡━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ string     │ int64         │ string                              │
├────────────┼───────────────┼─────────────────────────────────────┤
│ PAL0708    │             1 │ Adelie Penguin (Pygoscelis adeliae) │
└────────────┴───────────────┴─────────────────────────────────────┘

Rename all columns using a callable

>>> t.rename(str.upper).head(1)

┏━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ STUDYNAME ┃ SAMPLE NUMBER ┃ SPECIES                             ┃
┡━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ string    │ int64         │ string                              │
├───────────┼───────────────┼─────────────────────────────────────┤
│ PAL0708   │             1 │ Adelie Penguin (Pygoscelis adeliae) │
└───────────┴───────────────┴─────────────────────────────────────┘

rowid

rowid()

A unique integer per row.

This operation is only valid on physical tables

Any further meaning behind this expression is backend dependent. Generally this corresponds to some index into the database storage (for example, SQLite and DuckDB’s rowid).

For a monotonically increasing row number, see ibis.row_number.

Returns

Type	Description
IntegerColumn	An integer column

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

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      │
└───────┴────────┘

schema

schema()

Return the Schema for this table.

Returns

Type	Description
Schema	The table’s schema.

Examples

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

ibis.Schema {
  species            string
  island             string
  bill_length_mm     float64
  bill_depth_mm      float64
  flipper_length_mm  int64
  body_mass_g        int64
  sex                string
  year               int64
}

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

Type	Description
Table	Table expression

Examples

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

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │               193 │        3450 │ female │  2007 │
│ Adelie  │ Torgersen │           39.3 │          20.6 │               190 │        3650 │ male   │  2007 │
│ Adelie  │ Torgersen │           38.9 │          17.8 │               181 │        3625 │ female │  2007 │
│ Adelie  │ Torgersen │           39.2 │          19.6 │               195 │        4675 │ male   │  2007 │
│ Adelie  │ Torgersen │           34.1 │          18.1 │               193 │        3475 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           42.0 │          20.2 │               190 │        4250 │ 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  │ int64             │
├─────────┼───────────────────┤
│ Adelie  │               181 │
│ Adelie  │               186 │
│ Adelie  │               195 │
│ Adelie  │              NULL │
│ Adelie  │               193 │
└─────────┴───────────────────┘

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.c("year")).head()

┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃
┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ float64        │ float64       │ int64             │ int64       │
├────────────────┼───────────────┼───────────────────┼─────────────┤
│           39.1 │          18.7 │               181 │        3750 │
│           39.5 │          17.4 │               186 │        3800 │
│           40.3 │          18.0 │               195 │        3250 │
│           NULL │          NULL │              NULL │        NULL │
│           36.7 │          19.3 │               193 │        3450 │
└────────────────┴───────────────┴───────────────────┴─────────────┘

Projection + aggregation across multiple columns

>>> from ibis import _
>>> t.select(s.across(s.numeric() & ~s.c("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

Type	Description
Table	An opaque table expression

Examples

>>> import ibis
>>> from ibis import _
>>> ibis.options.interactive = True
>>> t = ibis.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 = ibis.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

to_array

to_array()

View a single column table as an array.

Returns

Type	Description
Value	A single column view of a table

to_pandas

to_pandas(**kwargs)

Convert a table expression to a pandas DataFrame.

Parameters

Name	Type	Description	Default
kwargs		Same as keyword arguments to execute	{}

try_cast

try_cast(schema)

Cast the columns of a table.

If the cast fails for a row, the value is returned as NULL or NaN depending on backend behavior.

Parameters

Name	Type	Description	Default
schema	SchemaLike	Mapping, schema or iterable of pairs to use for casting	required

Returns

Type	Description
Table	Casted table

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.memtable({"a": ["1", "2", "3"], "b": ["2.2", "3.3", "book"]})
>>> t.try_cast({"a": "int", "b": "float"})

┏━━━━━━━┳━━━━━━━━━┓
┃ a     ┃ b       ┃
┡━━━━━━━╇━━━━━━━━━┩
│ int64 │ float64 │
├───────┼─────────┤
│     1 │     2.2 │
│     2 │     3.3 │
│     3 │    NULL │
└───────┴─────────┘

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

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 │
└───────┘

unpack

unpack(*columns)

Project the struct fields of each of columns into self.

Existing fields are retained in the projection.

Parameters

Name	Type	Description	Default
columns	str	String column names to project into self.	()

Returns

Type	Description
Table	The child table with struct fields of each of columns projected.

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> lines = '''
...     {"name": "a", "pos": {"lat": 10.1, "lon": 30.3}}
...     {"name": "b", "pos": {"lat": 10.2, "lon": 30.2}}
...     {"name": "c", "pos": {"lat": 10.3, "lon": 30.1}}
... '''
>>> with open("/tmp/lines.json", "w") as f:
...     nbytes = f.write(lines)  # nbytes is unused
>>> t = ibis.read_json("/tmp/lines.json")
>>> t

┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ name   ┃ pos                                ┃
┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ string │ struct<lat: float64, lon: float64> │
├────────┼────────────────────────────────────┤
│ a      │ {'lat': 10.1, 'lon': 30.3}         │
│ b      │ {'lat': 10.2, 'lon': 30.2}         │
│ c      │ {'lat': 10.3, 'lon': 30.1}         │
└────────┴────────────────────────────────────┘

>>> t.unpack("pos")

┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓
┃ name   ┃ lat     ┃ lon     ┃
┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│ string │ float64 │ float64 │
├────────┼─────────┼─────────┤
│ a      │    10.1 │    30.3 │
│ b      │    10.2 │    30.2 │
│ c      │    10.3 │    30.1 │
└────────┴─────────┴─────────┘

See Also

StructValue.lift

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

Type	Description
Table	Table expression

window_by

window_by(time_col)

Create a windowing table-valued function (TVF) expression.

Windowing table-valued functions (TVF) assign rows of a table to windows based on a time attribute column in the table.

Parameters

Name	Type	Description	Default
time_col	ir.Value	Column of the table that will be mapped to windows.	required

Returns

Type	Description
WindowedTable	WindowedTable expression.

GroupedTable

GroupedTable(self, groupings, **kwargs)

An intermediate table expression to hold grouping information.

Methods

Name	Description
aggregate	Compute aggregates over a group by.
count	Computing the number of rows per group.
having	Add a post-aggregation result filter expr.
mutate	Return a table projection with window functions applied.
order_by	Sort a grouped table expression by expr.
over	Apply a window over the input expressions.
select	Project new columns out of the grouped table.

aggregate

aggregate(*metrics, **kwds)

Compute aggregates over a group by.

count

count()

Computing the number of rows per group.

Returns

Type	Description
Table	The aggregated table

having

having(*expr)

Add a post-aggregation result filter expr.

Expressions like x is None return bool and will not generate a SQL comparison to NULL

Parameters

Name	Type	Description	Default
expr	ir.BooleanScalar	An expression that filters based on an aggregate value.	()

Returns

Type	Description
GroupedTable	A grouped table expression

mutate

mutate(*exprs, **kwexprs)

Return a table projection with window functions applied.

Any arguments can be functions.

Parameters

Name	Type	Description	Default
exprs	ir.Value | Sequence[ir.Value]	List of expressions	()
kwexprs	ir.Value	Expressions	{}

Examples

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

┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ float64        │ float64       │ int64             │ int64       │ string │ int64 │
├─────────┼───────────┼────────────────┼───────────────┼───────────────────┼─────────────┼────────┼───────┤
│ Adelie  │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie  │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ Adelie  │ Torgersen │           40.3 │          18.0 │               195 │        3250 │ female │  2007 │
│ Adelie  │ Torgersen │           NULL │          NULL │              NULL │        NULL │ NULL   │  2007 │
│ Adelie  │ Torgersen │           36.7 │          19.3 │               193 │        3450 │ female │  2007 │
│ Adelie  │ Torgersen │           39.3 │          20.6 │               190 │        3650 │ male   │  2007 │
│ Adelie  │ Torgersen │           38.9 │          17.8 │               181 │        3625 │ female │  2007 │
│ Adelie  │ Torgersen │           39.2 │          19.6 │               195 │        4675 │ male   │  2007 │
│ Adelie  │ Torgersen │           34.1 │          18.1 │               193 │        3475 │ NULL   │  2007 │
│ Adelie  │ Torgersen │           42.0 │          20.2 │               190 │        4250 │ NULL   │  2007 │
│ …       │ …         │              … │             … │                 … │           … │ …      │     … │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

>>> (
...     t.select("species", "bill_length_mm")
...     .group_by("species")
...     .mutate(centered_bill_len=ibis._.bill_length_mm - ibis._.bill_length_mm.mean())
...     .order_by(s.all())
... )

┏━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ species ┃ bill_length_mm ┃ centered_bill_len ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ string  │ float64        │ float64           │
├─────────┼────────────────┼───────────────────┤
│ Adelie  │           32.1 │         -6.691391 │
│ Adelie  │           33.1 │         -5.691391 │
│ Adelie  │           33.5 │         -5.291391 │
│ Adelie  │           34.0 │         -4.791391 │
│ Adelie  │           34.1 │         -4.691391 │
│ Adelie  │           34.4 │         -4.391391 │
│ Adelie  │           34.5 │         -4.291391 │
│ Adelie  │           34.6 │         -4.191391 │
│ Adelie  │           34.6 │         -4.191391 │
│ Adelie  │           35.0 │         -3.791391 │
│ …       │              … │                 … │
└─────────┴────────────────┴───────────────────┘

Returns

Type	Description
Table	A table expression with window functions applied

order_by

order_by(*expr)

Sort a grouped table expression by expr.

Notes

This API call is ignored in aggregations.

Parameters

Name	Type	Description	Default
expr	ir.Value | Iterable[ir.Value]	Expressions to order the results by	()

Returns

Type	Description
GroupedTable	A sorted grouped GroupedTable

over

over(window=None, *, rows=None, range=None, group_by=None, order_by=None)

Apply a window over the input expressions.

Parameters

Name	Type	Description	Default
window		Window to add to the input	None
rows		Whether to use the ROWS window clause	None
range		Whether to use the RANGE window clause	None
group_by		Grouping key	None
order_by		Ordering key	None

Returns

Type	Description
GroupedTable	A new grouped table expression

select

select(*exprs, **kwexprs)

Project new columns out of the grouped table.

See Also

GroupedTable.mutate

read_csv

ibis.read_csv(sources, table_name=None, **kwargs)

Lazily load a CSV or set of CSVs.

This function delegates to the read_csv method on the current default backend (DuckDB or ibis.config.default_backend).

Parameters

Name	Type	Description	Default
sources	str | Path | Sequence[str | Path]	A filesystem path or URL or list of same. Supports CSV and TSV files.	required
table_name	str | None	A name to refer to the table. If not provided, a name will be generated.	None
kwargs	Any	Backend-specific keyword arguments for the file type. For the DuckDB backend used by default, please refer to: * CSV/TSV: https://duckdb.org/docs/data/csv/overview.html#parameters.	{}

Returns

Type	Description
ir.Table	Table expression representing a file

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> lines = '''a,b
... 1,d
... 2,
... ,f
... '''
>>> with open("/tmp/lines.csv", mode="w") as f:
...     nbytes = f.write(lines)  # nbytes is unused
>>> t = ibis.read_csv("/tmp/lines.csv")
>>> t

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

read_delta

ibis.read_delta(source, table_name=None, **kwargs)

Lazily load a Delta Lake table.

Parameters

Name	Type	Description	Default
source	str | Path	A filesystem path or URL.	required
table_name	str | None	A name to refer to the table. If not provided, a name will be generated.	None
kwargs	Any	Backend-specific keyword arguments for the file type.	{}

Returns

Type	Description
ir.Table	Table expression representing a file

Examples

>>> import ibis
>>> import pandas as pd
>>> ibis.options.interactive = True
>>> df = pd.DataFrame({"a": [1, 2, 3], "b": list("ghi")})
>>> df

	a	b
0	1	g
1	2	h
2	3	i

>>> import deltalake as dl
>>> dl.write_deltalake("/tmp/data.delta", df, mode="overwrite")
>>> t = ibis.read_delta("/tmp/data.delta")
>>> t

┏━━━━━━━┳━━━━━━━━┓
┃ a     ┃ b      ┃
┡━━━━━━━╇━━━━━━━━┩
│ int64 │ string │
├───────┼────────┤
│     1 │ g      │
│     2 │ h      │
│     3 │ i      │
└───────┴────────┘

read_json

ibis.read_json(sources, table_name=None, **kwargs)

Lazily load newline-delimited JSON data.

This API is experimental and subject to change.

This function delegates to the read_json method on the current default backend (DuckDB or ibis.config.default_backend).

Parameters

Name	Type	Description	Default
sources	str | Path | Sequence[str | Path]	A filesystem path or URL or list of same.	required
table_name	str | None	A name to refer to the table. If not provided, a name will be generated.	None
kwargs	Any	Backend-specific keyword arguments for the file type. See https://duckdb.org/docs/extensions/json.html for details.	{}

Returns

Type	Description
ir.Table	Table expression representing a file

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> lines = '''
... {"a": 1, "b": "d"}
... {"a": 2, "b": null}
... {"a": null, "b": "f"}
... '''
>>> with open("/tmp/lines.json", mode="w") as f:
...     nbytes = f.write(lines)  # nbytes is unused
>>> t = ibis.read_json("/tmp/lines.json")
>>> t

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

read_parquet

ibis.read_parquet(sources, table_name=None, **kwargs)

Lazily load a parquet file or set of parquet files.

This function delegates to the read_parquet method on the current default backend (DuckDB or ibis.config.default_backend).

Parameters

Name	Type	Description	Default
sources	str | Path | Sequence[str | Path]	A filesystem path or URL or list of same.	required
table_name	str | None	A name to refer to the table. If not provided, a name will be generated.	None
kwargs	Any	Backend-specific keyword arguments for the file type. For the DuckDB backend used by default, please refer to: * Parquet: https://duckdb.org/docs/data/parquet	{}

Returns

Type	Description
ir.Table	Table expression representing a file

Examples

>>> import ibis
>>> import pandas as pd
>>> ibis.options.interactive = True
>>> df = pd.DataFrame({"a": [1, 2, 3], "b": list("ghi")})
>>> df

	a	b
0	1	g
1	2	h
2	3	i

>>> df.to_parquet("/tmp/data.parquet")
>>> t = ibis.read_parquet("/tmp/data.parquet")
>>> t

┏━━━━━━━┳━━━━━━━━┓
┃ a     ┃ b      ┃
┡━━━━━━━╇━━━━━━━━┩
│ int64 │ string │
├───────┼────────┤
│     1 │ g      │
│     2 │ h      │
│     3 │ i      │
└───────┴────────┘

memtable

ibis.memtable(data, *, columns=None, schema=None, name=None)

Construct an ibis table expression from in-memory data.

Parameters

Name	Type	Description	Default
data		A table-like object (pandas.DataFrame, pyarrow.Table, or polars.DataFrame), or any data accepted by the pandas.DataFrame constructor (e.g. a list of dicts). Note that ibis objects (e.g. MapValue) may not be passed in as part of data and will result in an error. Do not depend on the underlying storage type (e.g., pyarrow.Table), it’s subject to change across non-major releases.	required
columns	Iterable[str] | None	Optional typing.Iterable of str column names. If provided, must match the number of columns in data.	None
schema	SchemaLike | None	Optional Schema. The functions use data to infer a schema if not passed.	None
name	str | None	Optional name of the table.	None

Returns

Type	Description
Table	A table expression backed by in-memory data.

Examples

>>> import ibis
>>> t = ibis.memtable([{"a": 1}, {"a": 2}])
>>> t

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

>>> t = ibis.memtable([{"a": 1, "b": "foo"}, {"a": 2, "b": "baz"}])
>>> t

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

Create a table literal without column names embedded in the data and pass columns

>>> t = ibis.memtable([(1, "foo"), (2, "baz")], columns=["a", "b"])
>>> t

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

Create a table literal without column names embedded in the data. Ibis generates column names if none are provided.

>>> t = ibis.memtable([(1, "foo"), (2, "baz")])
>>> t

┏━━━━━━━┳━━━━━━━━┓
┃ col0  ┃ col1   ┃
┡━━━━━━━╇━━━━━━━━┩
│ int64 │ string │
├───────┼────────┤
│     1 │ foo    │
│     2 │ baz    │
└───────┴────────┘

table

ibis.table(schema=None, name=None, catalog=None, database=None)

Create a table literal or an abstract table without data.

Ibis uses the word database to refer to a collection of tables, and the word catalog to refer to a collection of databases. You can use a combination of catalog and database to specify a hierarchical location for table.

Parameters

Name	Type	Description	Default
schema	SchemaLike | None	A schema for the table	None
name	str | None	Name for the table. One is generated if this value is None.	None
catalog	str | None	A collection of database.	None
database	str | None	A collection of tables. Required if catalog is not None.	None

Returns

Type	Description
Table	A table expression

Examples

Create a table with no data backing it

>>> import ibis
>>> ibis.options.interactive = False
>>> t = ibis.table(schema=dict(a="int", b="string"), name="t")
>>> t

UnboundTable: t
  a int64
  b string

Create a table with no data backing it in a specific location

>>> import ibis
>>> ibis.options.interactive = False
>>> t = ibis.table(schema=dict(a="int"), name="t", catalog="cat", database="db")
>>> t

UnboundTable: cat.db.t
  a int64

difference

ibis.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	ir.Table	A table expression	required
*rest	ir.Table	Additional table expressions	()
distinct	bool	Only diff distinct rows not occurring in the calling table	True

Returns

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 │
└───────┘

>>> ibis.difference(t1, t2)

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

intersect

ibis.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	ir.Table	A table expression	required
*rest	ir.Table	Additional table expressions	()
distinct	bool	Only return distinct rows	True

Returns

Type	Description
Table	A new table containing the intersection of all input 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 │
└───────┘

>>> ibis.intersect(t1, t2)

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

union

ibis.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	ir.Table	A table expression	required
*rest	ir.Table	Additional table expressions	()
distinct	bool	Only return distinct rows	False

Returns

Type	Description
Table	A new table containing the union of all input 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 │
└───────┘

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

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

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

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

join

ibis.join(left, right, predicates=(), how='inner', *, lname='', rname='{name}_right')

Perform a join between two tables.

Parameters

Name	Type	Description	Default
left	Table	Left table to join	required
right	Table	Right table to join	required
predicates	str | Sequence[str | ir.BooleanColumn | Literal[True] | Literal[False] | tuple[str | ir.Column | ir.Deferred, str | ir.Column | ir.Deferred]]	Condition(s) to join on. See examples for details.	()
how	JoinKind	Join method, e.g. "inner" or "left".	'inner'
lname	str	A format string to use to rename overlapping columns in the left table (e.g. "left_{name}").	''
rname	str	A format string to use to rename overlapping columns in the right table (e.g. "right_{name}").	'{name}_right'

Examples

>>> import ibis
>>> from ibis import _
>>> ibis.options.interactive = True
>>> movies = ibis.examples.ml_latest_small_movies.fetch()
>>> movies.head()

┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ movieId ┃ title                              ┃ genres                                      ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ int64   │ string                             │ string                                      │
├─────────┼────────────────────────────────────┼─────────────────────────────────────────────┤
│       1 │ Toy Story (1995)                   │ Adventure|Animation|Children|Comedy|Fantasy │
│       2 │ Jumanji (1995)                     │ Adventure|Children|Fantasy                  │
│       3 │ Grumpier Old Men (1995)            │ Comedy|Romance                              │
│       4 │ Waiting to Exhale (1995)           │ Comedy|Drama|Romance                        │
│       5 │ Father of the Bride Part II (1995) │ Comedy                                      │
└─────────┴────────────────────────────────────┴─────────────────────────────────────────────┘

>>> ratings = ibis.examples.ml_latest_small_ratings.fetch().drop("timestamp")
>>> ratings.head()

┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓
┃ userId ┃ movieId ┃ rating  ┃
┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│ int64  │ int64   │ float64 │
├────────┼─────────┼─────────┤
│      1 │       1 │     4.0 │
│      1 │       3 │     4.0 │
│      1 │       6 │     4.0 │
│      1 │      47 │     5.0 │
│      1 │      50 │     5.0 │
└────────┴─────────┴─────────┘

Equality left join on the shared movieId column. Note the _right suffix added to all overlapping columns from the right table (in this case only the “movieId” column).

>>> ratings.join(movies, "movieId", how="left").head(5)

┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ userId ┃ movieId ┃ rating  ┃ movieId_right ┃ title                       ┃ genres                                      ┃
┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ int64  │ int64   │ float64 │ int64         │ string                      │ string                                      │
├────────┼─────────┼─────────┼───────────────┼─────────────────────────────┼─────────────────────────────────────────────┤
│      1 │       1 │     4.0 │             1 │ Toy Story (1995)            │ Adventure|Animation|Children|Comedy|Fantasy │
│      1 │       3 │     4.0 │             3 │ Grumpier Old Men (1995)     │ Comedy|Romance                              │
│      1 │       6 │     4.0 │             6 │ Heat (1995)                 │ Action|Crime|Thriller                       │
│      1 │      47 │     5.0 │            47 │ Seven (a.k.a. Se7en) (1995) │ Mystery|Thriller                            │
│      1 │      50 │     5.0 │            50 │ Usual Suspects, The (1995)  │ Crime|Mystery|Thriller                      │
└────────┴─────────┴─────────┴───────────────┴─────────────────────────────┴─────────────────────────────────────────────┘

Explicit equality join using the default how value of "inner". Note how there is no _right suffix added to the movieId column since this is an inner join and the movieId column is part of the join condition.

>>> ratings.join(movies, ratings.movieId == movies.movieId).head(5)

┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ userId ┃ movieId ┃ rating  ┃ title                       ┃ genres                                      ┃
┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ int64  │ int64   │ float64 │ string                      │ string                                      │
├────────┼─────────┼─────────┼─────────────────────────────┼─────────────────────────────────────────────┤
│      1 │       1 │     4.0 │ Toy Story (1995)            │ Adventure|Animation|Children|Comedy|Fantasy │
│      1 │       3 │     4.0 │ Grumpier Old Men (1995)     │ Comedy|Romance                              │
│      1 │       6 │     4.0 │ Heat (1995)                 │ Action|Crime|Thriller                       │
│      1 │      47 │     5.0 │ Seven (a.k.a. Se7en) (1995) │ Mystery|Thriller                            │
│      1 │      50 │     5.0 │ Usual Suspects, The (1995)  │ Crime|Mystery|Thriller                      │
└────────┴─────────┴─────────┴─────────────────────────────┴─────────────────────────────────────────────┘

>>> tags = ibis.examples.ml_latest_small_tags.fetch()
>>> tags.head()

┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ userId ┃ movieId ┃ tag             ┃ timestamp  ┃
┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ int64  │ int64   │ string          │ int64      │
├────────┼─────────┼─────────────────┼────────────┤
│      2 │   60756 │ funny           │ 1445714994 │
│      2 │   60756 │ Highly quotable │ 1445714996 │
│      2 │   60756 │ will ferrell    │ 1445714992 │
│      2 │   89774 │ Boxing story    │ 1445715207 │
│      2 │   89774 │ MMA             │ 1445715200 │
└────────┴─────────┴─────────────────┴────────────┘

You can join on multiple columns/conditions by passing in a sequence. Find all instances where a user both tagged and rated a movie:

>>> tags.join(ratings, ["userId", "movieId"]).head(5).order_by("userId")

┏━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┓
┃ userId ┃ movieId ┃ tag            ┃ timestamp  ┃ rating  ┃
┡━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━┩
│ int64  │ int64   │ string         │ int64      │ float64 │
├────────┼─────────┼────────────────┼────────────┼─────────┤
│     62 │       2 │ Robin Williams │ 1528843907 │     4.0 │
│     62 │     110 │ sword fight    │ 1528152535 │     4.5 │
│     62 │     410 │ gothic         │ 1525636609 │     4.5 │
│     62 │    2023 │ mafia          │ 1525636733 │     5.0 │
│     62 │    2124 │ quirky         │ 1525636846 │     5.0 │
└────────┴─────────┴────────────────┴────────────┴─────────┘

To self-join a table with itself, you need to call .view() on one of the arguments so the two tables are distinct from each other.

For crafting more complex join conditions, a valid form of a join condition is a 2-tuple like ({left_key}, {right_key}), where each key can be

• a Column
• Deferred expression
• lambda of the form (Table) -> Column

For example, to find all movies pairings that received the same (ignoring case) tags:

>>> movie_tags = tags["movieId", "tag"]
>>> view = movie_tags.view()
>>> movie_tags.join(
...     view,
...     [
...         movie_tags.movieId != view.movieId,
...         (_.tag.lower(), lambda t: t.tag.lower()),
...     ],
... ).head().order_by(("movieId", "movieId_right"))

┏━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ movieId ┃ tag               ┃ movieId_right ┃ tag_right         ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ int64   │ string            │ int64         │ string            │
├─────────┼───────────────────┼───────────────┼───────────────────┤
│    1732 │ funny             │         60756 │ funny             │
│    1732 │ Highly quotable   │         60756 │ Highly quotable   │
│    1732 │ drugs             │        106782 │ drugs             │
│    5989 │ Leonardo DiCaprio │        106782 │ Leonardo DiCaprio │
│  139385 │ tom hardy         │         89774 │ Tom Hardy         │
└─────────┴───────────────────┴───────────────┴───────────────────┘

row_number

ibis.row_number()

Return an analytic function expression for the current row number.

Note

row_number is normalized across backends to start at 0

Returns

Type	Description
IntegerColumn	A column expression enumerating rows

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]})
>>> t.mutate(rownum=ibis.row_number())

┏━━━━━━━━┳━━━━━━━━┓
┃ values ┃ rownum ┃
┡━━━━━━━━╇━━━━━━━━┩
│ int64  │ int64  │
├────────┼────────┤
│      1 │      0 │
│      2 │      1 │
│      1 │      2 │
│      2 │      3 │
│      3 │      4 │
│      2 │      5 │
└────────┴────────┘

rank

ibis.rank()

Compute position of first element within each equal-value group in sorted order.

Equivalent to SQL’s RANK() window function.

Returns

Type	Description
Int64Column	The min rank

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]})
>>> t.mutate(rank=ibis.rank().over(order_by=t.values))

┏━━━━━━━━┳━━━━━━━┓
┃ values ┃ rank  ┃
┡━━━━━━━━╇━━━━━━━┩
│ int64  │ int64 │
├────────┼───────┤
│      1 │     0 │
│      1 │     0 │
│      2 │     2 │
│      2 │     2 │
│      2 │     2 │
│      3 │     5 │
└────────┴───────┘

dense_rank

ibis.dense_rank()

Position of first element within each group of equal values.

Values are returned in sorted order and duplicate values are ignored.

Equivalent to SQL’s DENSE_RANK().

Returns

Type	Description
IntegerColumn	The rank

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]})
>>> t.mutate(rank=ibis.dense_rank().over(order_by=t.values))

┏━━━━━━━━┳━━━━━━━┓
┃ values ┃ rank  ┃
┡━━━━━━━━╇━━━━━━━┩
│ int64  │ int64 │
├────────┼───────┤
│      1 │     0 │
│      1 │     0 │
│      2 │     1 │
│      2 │     1 │
│      2 │     1 │
│      3 │     2 │
└────────┴───────┘

percent_rank

ibis.percent_rank()

Return the relative rank of the values in the column.

Returns

Type	Description
FloatingColumn	The percent rank

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]})
>>> t.mutate(pct_rank=ibis.percent_rank().over(order_by=t.values))

┏━━━━━━━━┳━━━━━━━━━━┓
┃ values ┃ pct_rank ┃
┡━━━━━━━━╇━━━━━━━━━━┩
│ int64  │ float64  │
├────────┼──────────┤
│      1 │      0.0 │
│      1 │      0.0 │
│      2 │      0.4 │
│      2 │      0.4 │
│      2 │      0.4 │
│      3 │      1.0 │
└────────┴──────────┘

cume_dist

ibis.cume_dist()

Return the cumulative distribution over a window.

Returns

Type	Description
FloatingColumn	The cumulative distribution

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]})
>>> t.mutate(dist=ibis.cume_dist().over(order_by=t.values))

┏━━━━━━━━┳━━━━━━━━━━┓
┃ values ┃ dist     ┃
┡━━━━━━━━╇━━━━━━━━━━┩
│ int64  │ float64  │
├────────┼──────────┤
│      1 │ 0.333333 │
│      1 │ 0.333333 │
│      2 │ 0.833333 │
│      2 │ 0.833333 │
│      2 │ 0.833333 │
│      3 │ 1.000000 │
└────────┴──────────┘

ntile

ibis.ntile(buckets)

Return the integer number of a partitioning of the column values.

Parameters

Name	Type	Description	Default
buckets	int | ir.IntegerValue	Number of buckets to partition into	required

Examples

>>> import ibis
>>> ibis.options.interactive = True
>>> t = ibis.memtable({"values": [1, 2, 1, 2, 3, 2]})
>>> t.mutate(ntile=ibis.ntile(2).over(order_by=t.values))

┏━━━━━━━━┳━━━━━━━┓
┃ values ┃ ntile ┃
┡━━━━━━━━╇━━━━━━━┩
│ int64  │ int64 │
├────────┼───────┤
│      1 │     0 │
│      1 │     0 │
│      2 │     0 │
│      2 │     1 │
│      2 │     1 │
│      3 │     1 │
└────────┴───────┘

window

ibis.window(preceding=None, following=None, order_by=None, group_by=None, *, rows=None, range=None, between=None)

Create a window clause for use with window functions.

The ROWS window clause includes peer rows based on differences in row number whereas RANGE includes rows based on the differences in row value of a single order_by expression.

All window frame bounds are inclusive.

Parameters

Name	Type	Description	Default
preceding		Number of preceding rows in the window	None
following		Number of following rows in the window	None
group_by		Grouping key	None
order_by		Ordering key	None
rows		Whether to use the ROWS window clause	None
range		Whether to use the RANGE window clause	None
between		Automatically infer the window kind based on the boundaries	None

Returns

Type	Description
Window	A window frame

cumulative_window

ibis.cumulative_window(group_by=None, order_by=None)

Create a cumulative window for use with window functions.

All window frames / ranges are inclusive.

Parameters

Name	Type	Description	Default
group_by		Grouping key	None
order_by		Ordering key	None

Returns

Type	Description
Window	A window frame

range_window

ibis.range_window(preceding=None, following=None, group_by=None, order_by=None)

Create a range-based window clause for use with window functions.

This RANGE window clause aggregates rows based upon differences in the value of the order-by expression.

All window frames / ranges are inclusive.

Parameters

Name	Type	Description	Default
preceding		Number of preceding rows in the window	None
following		Number of following rows in the window	None
group_by		Grouping key	None
order_by		Ordering key	None

Returns

Type	Description
Window	A window frame

trailing_range_window

ibis.trailing_range_window(preceding, order_by, group_by=None)

Create a trailing range window for use with window functions.

Parameters

Name	Type	Description	Default
preceding		A value expression	required
order_by		Ordering key	required
group_by		Grouping key	None

Returns

Type	Description
Window	A window frame

trailing_window

ibis.trailing_window(preceding, group_by=None, order_by=None)

Create a trailing window for use with window functions.

Parameters

Name	Type	Description	Default
preceding		The number of preceding rows	required
group_by		Grouping key	None
order_by		Ordering key	None

Returns

Type	Description
Window	A window frame