Table

Table(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

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.
bind Bind column values to a table expression.
cache Cache the results of a computation to improve performance on subsequent executions.
cast Cast the columns of a table.
compile Compile to an execution target.
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.
drop_null Remove rows with null values from the table.
dropna Deprecated - use drop_null instead.
equals Return whether this expression is structurally equivalent to other.
execute Execute an expression against its backend if one exists.
fill_null Fill null values in a table expression.
fillna Deprecated - use fill_null instead.
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.
has_name Check whether this expression has an explicit name.
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.
into_backend Converts the Expr to a table in the given backend con with an optional table name name.
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.
pipe Compose f with self.
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_csv Write the results of executing the given expression to a CSV file.
to_json Write the results of expr to a NDJSON file.
to_pandas Convert a table expression to a pandas DataFrame.
to_parquet Write the results of executing the given expression to a parquet file.
to_pyarrow Execute expression and return results in as a pyarrow table.
to_pyarrow_batches Execute expression and return a RecordBatchReader.
try_cast Cast the columns of a table.
unbind Return an expression built on UnboundTable instead of backend-specific objects.
union Compute the set union of multiple table expressions.
unnest Unnest an array column from a table.
unpack Project the struct fields of each of columns into self.
value_counts Compute a frequency table of this table’s values.
view Create a new table expression distinct from the current one.
visualize Visualize an expression as a GraphViz graph in the browser.

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

Name Type Description
Table An aggregate table expression

Examples

>>> import xorq.api as xo
>>> from xorq.api import _
>>> xo.options.interactive = True
>>> t = xo.memtable(
...     {
...         "fruit": ["apple", "apple", "banana", "orange"],
...         "price": [0.5, 0.5, 0.25, 0.33],
...     }
... )
>>> t
>>> t.aggregate(
...     by=["fruit"],
...     total_cost=_.price.sum(),
...     avg_cost=_.price.mean(),
...     having=_.price.sum() < 0.5,
... )

alias

alias(alias)

Create a table expression with a specific name alias.

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

.alias will create a temporary view

.alias creates a temporary view in the database.

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

Parameters

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

Returns

Name Type Description
Table An table expression

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.examples.penguins.fetch(deferred=False)
>>> expr = t.alias("pingüinos").sql('SELECT * FROM "pingüinos" LIMIT 5')
>>> expr # quartodoc: +SKIP

as_scalar

as_scalar()

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

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

Returns

Name Type Description
Scalar A scalar subquery

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.examples.penguins.fetch(deferred=False)
>>> 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")

as_table

as_table()

Promote the expression to a table.

This method is a no-op for table expressions.

Returns

Name Type Description
Table A table expression

Examples

>>> import xorq.api as xo
>>> t = xo.table(dict(a="int"), name="t")
>>> s = t.as_table()
>>> t is s

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

Name Type Description
Table Table expression

bind

bind(*args, **kwargs)

Bind column values to a table expression.

This method handles the binding of every kind of column-like value that Ibis handles, including strings, integers, deferred expressions and selectors, to a table expression.

Parameters

Name Type Description Default
args Any Column-like values to bind. ()
kwargs Any Column-like values to bind, with names. {}

Returns

Name Type Description
tuple[Value, …] A tuple of bound values

cache

cache(storage=None)

Cache the results of a computation to improve performance on subsequent executions. This method allows you to cache the results of a computation either in memory, on disk using Parquet files, or in a database table. The caching strategy and storage location are determined by the storage parameter.

Parameters

Name Type Description Default
storage CacheStorage The storage strategy to use for caching. Can be one of: - ParquetStorage: Caches results as Parquet files on disk - SourceStorage: Caches results in the source database - ParquetSnapshotStorage: Creates a snapshot of data in Parquet format - SourceSnapshotStorage: Creates a snapshot in the source database If None, uses the default storage configuration. None

Returns

Name Type Description
Expr A new expression that represents the cached computation.

Notes

The cache method supports two main strategies: 1. ModificationTimeStrategy: Tracks changes based on modification time 2. SnapshotStrategy: Creates point-in-time snapshots of the data

Each strategy can be combined with either Parquet or database storage.

Examples

Using ParquetStorage:

>>> import xorq.api as xo
>>> from xorq.caching import ParquetStorage
>>> from pathlib import Path
>>> pg = xo.postgres.connect_examples()
>>> con = xo.connect()
>>> storage = ParquetStorage(source=con, relative_path=Path.cwd())
>>> alltypes = pg.table("functional_alltypes")
>>> cached = (alltypes
...     .select(alltypes.smallint_col, alltypes.int_col, alltypes.float_col)
...     .cache(storage=storage))

Using SourceStorage with PostgreSQL:

>>> from xorq.caching import SourceStorage
>>> from xorq.api import _
>>> ddb = xo.duckdb.connect()
>>> path = xo.config.options.pins.get_path("batting")
>>> right = (ddb.read_parquet(path, table_name="batting")
...          .filter(_.yearID == 2014)
...          .pipe(con.register, table_name="ddb-batting"))
>>> left = (pg.table("batting")
...         .filter(_.yearID == 2015)
...         .pipe(con.register, table_name="pg-batting"))
>>> # Cache the joined result
>>> expr = left.join(right, "playerID").cache(SourceStorage(source=pg))

Using cache with filtering:

>>> cached = alltypes.cache(storage=storage)
>>> expr = cached.filter([
...     cached.float_col > 0,
...     cached.smallint_col > 4,
...     cached.int_col < cached.float_col * 2
... ])

See Also

ParquetStorage : Storage implementation for Parquet files SourceStorage : Storage implementation for database tables ModificationTimeStrategy : Strategy for tracking changes by modification time SnapshotStrategy : Strategy for creating data snapshots

Notes

  • The cache is identified by a unique key based on the computation and strategy
  • Cache invalidation is handled automatically based on the chosen strategy
  • Cross-source caching (e.g., from PostgreSQL to DuckDB) is supported
  • Cache locations can be configured globally through xorq.config.options

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

Name Type Description
Table Cast table

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.examples.penguins.fetch(deferred=False)
>>> t.schema()
>>> cols = ["body_mass_g", "bill_length_mm"]
>>> t[cols].head()

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

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

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

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

compile

compile(limit=None, params=None, pretty=False)

Compile to an execution target.

Parameters

Name Type Description Default
limit int | None An integer to effect a specific row limit. A value of None means “no limit”. The default is in ibis/config.py. None
params Mapping[ir.Value, Any] | None Mapping of scalar parameter expressions to value None
pretty bool In case of SQL backends, return a pretty formatted SQL query. False

count

count(where=None)

Compute the number of rows in the table.

Parameters

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

Returns

Name Type Description
IntegerScalar Number of rows in the table

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.memtable({"a": ["foo", "bar", "baz"]})
>>> t
>>> t.count()
>>> t.count(t.a != "foo")
>>> type(t.count())

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

Name Type Description
Table Cross join of left, right and rest

Examples

>>> import xorq.api as xo
>>> import xorq.expr.selectors as s
>>> from xorq.api import _
>>> xo.options.interactive = True
>>> t = xo.examples.penguins.fetch(deferred=False)
>>> t.count()
>>> agg = t.drop("year").agg(s.across(s.numeric(), _.mean()))
>>> expr = t.cross_join(agg)
>>> expr
>>> expr.columns
>>> expr.count()

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

Name 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 xorq.api as xo
>>> import xorq.expr.selectors as s
>>> xo.options.interactive = True
>>> p = xo.examples.penguins.fetch(deferred=False)
>>> p.describe()
>>> p.select(s.of_type("numeric")).describe()
>>> p.select(s.of_type("string")).describe()

difference

difference(table, *rest, distinct=True)

Compute the set difference of multiple table expressions.

The input tables must have identical schemas.

Parameters

Name Type Description Default
table Table A table expression required
*rest Table Additional table expressions ()
distinct bool Only diff distinct rows not occurring in the calling table True

See Also

ibis.difference

Returns

Name Type Description
Table The rows present in self that are not present in tables.

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t1 = xo.memtable({"a": [1, 2]})
>>> t1
>>> t2 = xo.memtable({"a": [2, 3]})
>>> t2
>>> t1.difference(t2)

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 xorq.api as xo
>>> import xorq.examples as ex
>>> import xorq.expr.selectors as s
>>> xo.options.interactive = True
>>> t = ex.penguins.fetch()
>>> t

Compute the distinct rows of a subset of columns

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

Drop all duplicate rows except the first

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

Drop all duplicate rows except the last

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

Drop all duplicated rows

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

You can pass selectors to on

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

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

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

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

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

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.examples.penguins.fetch(deferred=False)
>>> t

Drop one or more columns

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

Drop with selectors, mix and match

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

drop_null

drop_null(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

Name Type Description
Table Table expression

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.examples.penguins.fetch(deferred=False)
>>> t
>>> t.count()
>>> t.drop_null(["bill_length_mm", "body_mass_g"]).count()
>>> t.drop_null(how="all").count()  # no rows where all columns are null

dropna

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

Deprecated - use drop_null instead.

equals

equals(other)

Return whether this expression is structurally equivalent to other.

If you want to produce an equality expression, use == syntax.

Parameters

Name Type Description Default
other Another expression required

Examples

>>> import xorq.api as xo
>>> t1 = xo.table(dict(a="int"), name="t")
>>> t2 = xo.table(dict(a="int"), name="t")
>>> t1.equals(t2)
>>> v = xo.table(dict(a="string"), name="v")
>>> t1.equals(v)

execute

execute(**kwargs)

Execute an expression against its backend if one exists.

Parameters

Name Type Description Default
kwargs Any Keyword arguments {}

Examples

>>> import xorq.api as xo
>>> t = xo.examples.penguins.fetch()
>>> t.execute()

Scalar parameters can be supplied dynamically during execution.

>>> species = xo.param("string")
>>> expr = t.filter(t.species == species).order_by(t.bill_length_mm)
>>> expr.execute(limit=3, params={species: "Gentoo"})

fill_null

fill_null(replacements)

Fill null values in a table expression.

There is potential lack of type stability with the fill_null 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

Returns

Name Type Description
Table Table expression

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.examples.penguins.fetch(deferred=False)
>>> t.sex
>>> t.fill_null({"sex": "unrecorded"}).sex

fillna

fillna(replacements)

Deprecated - use fill_null instead.

filter

filter(*predicates)

Select rows from table based on predicates.

Parameters

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

Returns

Name Type Description
Table Filtered table expression

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.examples.penguins.fetch(deferred=False)
>>> t
>>> t.filter([t.species == "Adelie", t.body_mass_g > 3500]).sex.value_counts().drop_null(
...     "sex"
... ).order_by("sex")

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

Name Type Description
GroupedTable A grouped table expression

Examples

>>> import xorq.api as xo
>>> from xorq.api import _
>>> xo.options.interactive = True
>>> t = xo.memtable(
...     {
...         "fruit": ["apple", "apple", "banana", "orange"],
...         "price": [0.5, 0.5, 0.25, 0.33],
...     }
... )
>>> t
>>> t.group_by("fruit").agg(total_cost=_.price.sum(), avg_cost=_.price.mean()).order_by(
...     "fruit"
... )

has_name

has_name()

Check whether this expression has an explicit name.

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

Name Type Description
Table self limited to n rows

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.memtable({"a": [1, 1, 2], "b": ["c", "a", "a"]})
>>> t
>>> t.head(2)

See Also

Table.limit Table.order_by

info

info()

Return summary information about a table.

Returns

Name Type Description
Table Summary of self

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.examples.penguins.fetch(deferred=False)
>>> t.info()

intersect

intersect(table, *rest, distinct=True)

Compute the set intersection of multiple table expressions.

The input tables must have identical schemas.

Parameters

Name Type Description Default
table Table A table expression required
*rest Table Additional table expressions ()
distinct bool Only return distinct rows True

Returns

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

See Also

ibis.intersect

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t1 = xo.memtable({"a": [1, 2]})
>>> t1
>>> t2 = xo.memtable({"a": [2, 3]})
>>> t2
>>> t1.intersect(t2)

into_backend

into_backend(con, name=None)

Converts the Expr to a table in the given backend con with an optional table name name.

The table is backed by a PyArrow RecordBatchReader, the RecordBatchReader is teed so it can safely be reaused without spilling to disk.

Parameters

Name Type Description Default
con The backend where the table should be created required
name The name of the table None

Examples

>>> import xorq.api as xo
>>> from xorq.api import _
>>> xo.options.interactive = True
>>> ls_con = xo.connect()
>>> pg_con = xo.postgres.connect_examples()
>>> t = pg_con.table("batting").into_backend(ls_con, "ls_batting")
>>> expr = (
...     t.join(t, "playerID")
...     .order_by("playerID", "yearID")
...     .limit(15)
...     .select(player_id="playerID", year_id="yearID_right")
... )
>>> expr

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 xorq.api as xo
>>> from xorq.api import _
>>> xo.options.interactive = True
>>> movies = xo.examples.ml_latest_small_movies.fetch()
>>> movies.head()
>>> ratings = xo.examples.ml_latest_small_ratings.fetch().drop("timestamp")
>>> ratings.head()

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)

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

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")

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"))

limit

limit(n, offset=0)

Select n rows from self starting at offset.

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

Parameters

Name Type Description Default
n int | None Number of rows to include. If None, the entire table is selected starting from offset. required
offset int Number of rows to skip first 0

Returns

Name Type Description
Table The first n rows of self starting at offset

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.memtable({"a": [1, 1, 2], "b": ["c", "a", "a"]})
>>> t
>>> t.limit(2)

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

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

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

Name Type Description
Table Table expression with additional columns

Examples

>>> import xorq.api as xo
>>> import xorq.expr.selectors as s
>>> from xorq.api import _
>>> xo.options.interactive = True
>>> t = xo.examples.penguins.fetch(deferred=False).select("species", "year", "bill_length_mm")
>>> t

Add a new column from a per-element expression

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

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()

Mutate across multiple columns

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

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

Name Type Description
IntegerScalar Number of unique rows in the table

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.memtable({"a": ["foo", "bar", "bar"]})
>>> t
>>> t.nunique()
>>> t.nunique(t.a != "foo")

order_by

order_by(*by)

Sort a table by one or more expressions.

Similar to pandas.DataFrame.sort_values().

Parameters

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

Returns

Name Type Description
Table Sorted table

Examples

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

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

>>> t.order_by("b")

Sort in descending order

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

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

>>> from xorq.api import _
>>> t.order_by(_.b.desc())

Sort by multiple columns/expressions

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

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())

This means that shuffling a Table is super simple

>>> t.order_by(xo.random())

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

>>> import xorq.expr.selectors as s
>>> penguins = xo.examples.penguins.fetch(deferred=False)
>>> penguins[["year", "island"]].value_counts().order_by(s.startswith("year"))

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

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

pipe

pipe(f, *args, **kwargs)

Compose f with self.

Parameters

Name Type Description Default
f If the expression needs to be passed as anything other than the first argument to the function, pass a tuple with the argument name. For example, (f, ‘data’) if the function f expects a ‘data’ keyword required
args Any Positional arguments to f ()
kwargs Any Keyword arguments to f {}

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = False
>>> t = xo.table([("a", "int64"), ("b", "string")], name="t")
>>> f = lambda a: (a + 1).name("a")
>>> g = lambda a: (a * 2).name("a")
>>> result1 = t.a.pipe(f).pipe(g)
>>> result1
>>> result2 = g(f(t.a))  # equivalent to the above
>>> result1.equals(result2)

Returns

Name Type Description
Expr Result type of passed function

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

Name Type Description
Table Pivoted table

Examples

Basic usage

>>> import xorq.api as xo
>>> import xorq.expr.selectors as s
>>> from xorq.api import _
>>> xo.options.interactive = True
>>> relig_income = xo.examples.relig_income_raw.fetch()
>>> relig_income

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

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

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

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

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

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

>>> who = xo.examples.who.fetch()
>>> who
>>> len(who.columns)
>>> who.pivot_longer(
...     s.index["new_sp_m014":"newrel_f65"],
...     names_to=["diagnosis", "gender", "age"],
...     names_pattern="new_?(.*)_(.)(.*)",
...     values_to="count",
... )

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.index["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",
... )

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

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

names_transform must be a mapping or callable

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

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 columns are sorted. If 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

Name 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_encounters.pivot_wider(names_from="station", values_from="seen")

You can do simple transpose-like operations using pivot_wider

>>> t = ibis.memtable(dict(outcome=["yes", "no"], counted=[3, 4]))
>>> t
>>> t.pivot_wider(names_from="outcome", values_from="counted", names_sort=True)

Fill missing pivoted values using values_fill

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

Compute multiple values columns

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

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"),
... )

Supply an alternative function to summarize values

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

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())

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")

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())

Pivoting with multiple name columns

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

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",
... )

Sort the new columns’ names

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

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 xorq.api as xo
>>> t = xo.examples.penguins.fetch(deferred=False)

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,
... )

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

Name Type Description
Table A table with the columns relocated.

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> import xorq.expr.selectors as s
>>> t = xo.memtable(dict(a=[1], b=[1], c=[1], d=["a"], e=["a"], f=["a"]))
>>> t
>>> t.relocate("f")
>>> t.relocate("a", after="c")
>>> t.relocate("f", before="b")
>>> t.relocate("a", after=s.last())

Relocate allows renaming

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

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

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

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

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

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

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

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
... )

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](str) | Columns to be explicitly renamed, expressed asnew_name=old_name`keyword arguments. |`

Returns

Name Type Description
Table A renamed table expression

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

Name 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

Name Type Description
Table The input table, with fraction of rows selected.

Examples

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

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

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

schema

schema()

Return the Schema for this table.

Returns

Name Type Description
Schema The table’s schema.

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.examples.penguins.fetch(deferred=False)
>>> t.schema()

select

select(*exprs, **named_exprs)

Compute a new table expression using exprs and named_exprs.

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

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

Parameters

Name Type Description Default
exprs ir.Value | str | Iterable[ir.Value | str] Column expression, string, or list of column expressions and strings. ()
named_exprs ir.Value | str Column expressions {}

Returns

Name Type Description
Table Table expression

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t = xo.examples.penguins.fetch(deferred=False)
>>> t

Simple projection

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

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

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

Projection by zero-indexed column position

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

Projection with renaming and compute in one call

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

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

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

Projection with aggregation expressions

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

Projection with a selector

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

Projection + aggregation across multiple columns

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

sql

sql(query, dialect=None)

Run a SQL query against a table expression.

Parameters

Name Type Description Default
query str Query string required
dialect str | None Optional string indicating the dialect of query. Defaults to the backend’s native dialect. None

Returns

Name Type Description
Table An opaque table expression

Examples

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

Mix and match ibis expressions with SQL queries

>>> t = xo.examples.penguins.fetch(table_name="penguins", deferred=False)
>>> 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

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")

See Also

Table.alias

to_array

to_array()

View a single column table as an array.

Returns

Name Type Description
Value A single column view of a table

to_csv

to_csv(path, *, params=None, **kwargs)

Write the results of executing the given expression to a CSV file.

This method is eager and will execute the associated expression immediately.

Parameters

Name Type Description Default
path str | Path The data source. A string or Path to the CSV file. required
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
**kwargs Any Additional keyword arguments passed to pyarrow.csv.CSVWriter {}
https required

to_json

to_json(path, *, params=None, **kwargs)

Write the results of expr to a NDJSON file.

This method is eager and will execute the associated expression immediately.

Parameters

Name Type Description Default
path str | Path The data source. A string or Path to the Delta Lake table. required
**kwargs Any Additional, backend-specific keyword arguments. {}
https required

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 {}

to_parquet

to_parquet(path, params=None, **kwargs)

Write the results of executing the given expression to a parquet file.

This method is eager and will execute the associated expression immediately.

See https://arrow.apache.org/docs/python/generated/pyarrow.parquet.ParquetWriter.html for details.

Parameters

Name Type Description Default
path str | Path A string or Path where the Parquet file will be written. required
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
**kwargs Any Additional keyword arguments passed to pyarrow.parquet.ParquetWriter {}

Examples

Write out an expression to a single parquet file.

>>> import ibis
>>> import tempfile
>>> penguins = ibis.examples.penguins.fetch()
>>> penguins.to_parquet(tempfile.mktemp())

to_pyarrow

to_pyarrow(**kwargs)

Execute expression and return results in as a pyarrow table.

This method is eager and will execute the associated expression immediately.

Parameters

Name Type Description Default
kwargs Any Keyword arguments {}

Returns

Name Type Description
Table A pyarrow table holding the results of the executed expression.

to_pyarrow_batches

to_pyarrow_batches(chunk_size=1000000, **kwargs)

Execute expression and return a RecordBatchReader.

This method is eager and will execute the associated expression immediately.

Parameters

Name Type Description Default
chunk_size int Maximum number of rows in each returned record batch. 1000000
kwargs Any Keyword arguments {}

Returns

Name Type Description
results RecordBatchReader

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

Name Type Description
Table Cast table

Examples

unbind

unbind()

Return an expression built on UnboundTable instead of backend-specific objects.

union

union(table, *rest, distinct=False)

Compute the set union of multiple table expressions.

The input tables must have identical schemas.

Parameters

Name Type Description Default
table Table A table expression required
*rest Table Additional table expressions ()
distinct bool Only return distinct rows False

Returns

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

See Also

ibis.union

Examples

>>> import xorq.api as xo
>>> xo.options.interactive = True
>>> t1 = xo.memtable({"a": [1, 2]})
>>> t1
>>> t2 = xo.memtable({"a": [2, 3]})
>>> t2
>>> t1.union(t2)  # union all by default doctest: +SKIP
>>> t1.union(t2, distinct=True).order_by("a")

unnest

unnest(column, offset=None, keep_empty=False)

Unnest an array column from a table.

When unnesting an existing column the newly unnested column replaces the existing column.

Parameters

Name Type Description Default
column Array column to unnest. required
offset str | None Name of the resulting index column. None
keep_empty bool Keep empty array values as NULL in the output table, as well as existing NULL values. False

Returns

Name Type Description
Table Table with the array column column unnested.

See Also

ArrayValue.unnest

Examples

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

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

See Also

StructValue.lift

value_counts

value_counts()

Compute a frequency table of this table’s values.

Returns

Name Type Description
Table Frequency table of this table’s values.

Examples

>>> import xorq.api as xo
>>> from xorq import examples
>>> xo.options.interactive = True
>>> t = examples.penguins.fetch()
>>> t.head()
>>> t.year.value_counts().order_by("year")
>>> t[["year", "island"]].value_counts().order_by("year", "island")

view

view()

Create a new table expression distinct from the current one.

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

Returns

Name Type Description
Table Table expression

visualize

visualize(
    format='svg',
    *,
    label_edges=False,
    verbose=False,
    node_attr=None,
    node_attr_getter=None,
    edge_attr=None,
    edge_attr_getter=None,
)

Visualize an expression as a GraphViz graph in the browser.

Parameters

Name Type Description Default
format str Image output format. These are specified by the graphviz Python library. 'svg'
label_edges bool Show operation input names as edge labels False
verbose bool Print the graphviz DOT code to stderr if False
node_attr Mapping[str, str] | None Mapping of (attribute, value) pairs set for all nodes. Options are specified by the graphviz Python library. None
node_attr_getter NodeAttributeGetter | None Callback taking a node and returning a mapping of (attribute, value) pairs for that node. Options are specified by the graphviz Python library. None
edge_attr Mapping[str, str] | None Mapping of (attribute, value) pairs set for all edges. Options are specified by the graphviz Python library. None
edge_attr_getter EdgeAttributeGetter | None Callback taking two adjacent nodes and returning a mapping of (attribute, value) pairs for the edge between those nodes. Options are specified by the graphviz Python library. None

Examples

Open the visualization of an expression in default browser:

>>> import xorq.api as xo
>>> import xorq.vendor.ibis.expr.operations as ops
>>> left = ibis.table(dict(a="int64", b="string"), name="left")
>>> right = ibis.table(dict(b="string", c="int64", d="string"), name="right")
>>> expr = left.inner_join(right, "b").select(left.a, b=right.c, c=right.d)
>>> expr.visualize(
...     format="svg",
...     label_edges=True,
...     node_attr={"fontname": "Roboto Mono", "fontsize": "10"},
...     node_attr_getter=lambda node: isinstance(node, ops.Field) and {"shape": "oval"},
...     edge_attr={"fontsize": "8"},
...     edge_attr_getter=lambda u, v: isinstance(u, ops.Field) and {"color": "red"},
... )  # quartodoc: +SKIP

Raises

Name Type Description
ImportError If graphviz is not installed.