Table expressions

Tables are one of the core data structures in Ibis.

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

See the user guide for more info.

Attributes

Name Description
columns Return a tuple 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 Tell ibis to treat this value as 1 row, 1 column table, referred to 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 provided expression.
cast Cast the columns of a table.
compile Compile expr to a SQL string (for SQL backends) or a LazyFrame (for the polars backend).
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 (or multiset) 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_backend Get the current Ibis backend of the expression.
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 (or multiset) 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.
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.
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 Deprecated - use as_scalar instead.
to_csv Write the results of executing the given expression to a CSV file.
to_delta Write the results of executing the given expression to a Delta Lake table.
to_json Write the results of expr to a json file of [{column -> value}, …] objects.
to_pandas Convert a table expression to a pandas DataFrame.
to_pandas_batches Execute expression and return an iterator of pandas DataFrames.
to_parquet Write the results of executing the given expression to a parquet file.
to_parquet_dir Write the results of executing the given expression to a parquet file in a directory.
to_polars Execute expression and return results as a polars dataframe.
to_pyarrow_batches Execute expression and return a RecordBatchReader.
to_sql Compile to a formatted SQL string.
to_torch Execute an expression and return results as a dictionary of torch tensors.
to_xlsx Write a table to an Excel file.
topk Get the most frequent values of this table.
try_cast Cast the columns of a table, returning NULL/NaN on failure.
unbind Return an expression built on UnboundTable instead of backend-specific objects.
union Compute the multiset (or 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 ir.Scalar | Deferred | Sequence[ir.Scalar | Deferred] | 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 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,
).order_by("fruit")
┏━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━┓
┃ 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.

Note.alias will create a temporary view

.alias creates a temporary view in the database.

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

Parameters

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

Returns

Name Type Description
Table An table expression

Examples

import ibis
ibis.options.interactive = True
t = 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()

Tell ibis to treat this value as 1 row, 1 column table, referred to as a scalar.

Ibis cannot know until execution time whether a table expression contains only one row or many rows.

This method is a way to explicitly tell ibis to trust you that this table expression will only contain one row at execution time. This allows you to use this table expression with other tables.

Note that execution will fail if the table DOES contain more than one row.

Returns

Name 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)

We know from domain knowledge that there is only one Gentoo penguin with body mass greater than 6200g, so heavy_gentoo is a table with exactly one row.

Say we want to get all penguins from the same island as that heavy Gentoo penguin.

If we try to use t.island == heavy_gentoo.select("island") directly in a filter, we won’t get the result we expect because that will evaluate to a literal False (since of course an ibis.Column is never equal to an ibis.Table).

Instead, we should use as_scalar() to tell ibis that we know heavy_gentoo.select("island") contains exactly one row:

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

Name 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(
    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
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

Examples

from datetime import datetime, timedelta
import ibis
ibis.options.interactive = True
sensors = ibis.memtable(
    {
        "site": ["a", "b", "a", "b", "a"],
        "humidity": [0.3, 0.4, 0.5, 0.6, 0.7],
        "event_time": [
            datetime(2024, 11, 16, 12, 0, 15, 500000),
            datetime(2024, 11, 16, 12, 0, 15, 700000),
            datetime(2024, 11, 17, 18, 12, 14, 950000),
            datetime(2024, 11, 17, 18, 12, 15, 120000),
            datetime(2024, 11, 18, 18, 12, 15, 100000),
        ],
    }
)
events = ibis.memtable(
    {
        "site": ["a", "b", "a"],
        "event_type": [
            "cloud coverage",
            "rain start",
            "rain stop",
        ],
        "event_time": [
            datetime(2024, 11, 16, 12, 0, 15, 400000),
            datetime(2024, 11, 17, 18, 12, 15, 100000),
            datetime(2024, 11, 18, 18, 12, 15, 100000),
        ],
    }
)

This setup simulates time-series data by pairing irregularly collected sensor readings with weather events, enabling analysis of environmental conditions before each event. We will use the asof_join method to match each event with the most recent prior sensor reading from the sensors table at the same site.

sensors
┏━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ site   ┃ humidity ┃ event_time              ┃
┡━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ string │ float64  │ timestamp               │
├────────┼──────────┼─────────────────────────┤
│ a      │      0.3 │ 2024-11-16 12:00:15.500 │
│ b      │      0.4 │ 2024-11-16 12:00:15.700 │
│ a      │      0.5 │ 2024-11-17 18:12:14.950 │
│ b      │      0.6 │ 2024-11-17 18:12:15.120 │
│ a      │      0.7 │ 2024-11-18 18:12:15.100 │
└────────┴──────────┴─────────────────────────┘

events
┏━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ site   ┃ event_type     ┃ event_time              ┃
┡━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ string │ string         │ timestamp               │
├────────┼────────────────┼─────────────────────────┤
│ a      │ cloud coverage │ 2024-11-16 12:00:15.400 │
│ b      │ rain start     │ 2024-11-17 18:12:15.100 │
│ a      │ rain stop      │ 2024-11-18 18:12:15.100 │
└────────┴────────────────┴─────────────────────────┘

We can find the closest event to each sensor reading with a 1 second tolerance. Using the “site” column as a join predicate ensures we only match events that occurred at or near the same site as the sensor reading.

tolerance = timedelta(seconds=1)
sensors.asof_join(events, on="event_time", predicates="site", tolerance=tolerance).drop(
    "event_time_right"
).order_by("event_time")
┏━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┓
┃ site   ┃ humidity ┃ event_time              ┃ site_right ┃ event_type     ┃
┡━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━┩
│ string │ float64  │ timestamp               │ string     │ string         │
├────────┼──────────┼─────────────────────────┼────────────┼────────────────┤
│ a      │      0.3 │ 2024-11-16 12:00:15.500 │ a          │ cloud coverage │
│ b      │      0.4 │ 2024-11-16 12:00:15.700 │ NULL       │ NULL           │
│ a      │      0.5 │ 2024-11-17 18:12:14.950 │ NULL       │ NULL           │
│ b      │      0.6 │ 2024-11-17 18:12:15.120 │ b          │ rain start     │
│ a      │      0.7 │ 2024-11-18 18:12:15.100 │ a          │ rain stop      │
└────────┴──────────┴─────────────────────────┴────────────┴────────────────┘

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()

Cache the provided expression.

All subsequent operations on the returned expression will be performed on the cached data. The lifetime of the cached table is tied to its python references (ie. it is released once the last reference to it is garbage collected). Alternatively, use the with statement or call the .release() method for more control.

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

NoteThis 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

Name 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=None, /, **overrides)

Cast the columns of a table.

Similar to pandas.DataFrame.astype.

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

Parameters

Name Type Description Default
schema IntoSchema | None Mapping, schema or iterable of pairs to use for casting. Anything that ibis.schema() can consume. None
overrides IntoDtype Named types to use for casting. Will override types from schema. {}

Returns

Name Type Description
Table Casted table

Examples

import ibis
ibis.options.interactive = True
t = ibis.examples.penguins.fetch().select("species", "bill_length_mm", "year").limit(5)
t
┏━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━┓
┃ species ┃ bill_length_mm ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━┩
│ string  │ float64        │ int64 │
├─────────┼────────────────┼───────┤
│ Adelie  │           39.1 │  2007 │
│ Adelie  │           39.5 │  2007 │
│ Adelie  │           40.3 │  2007 │
│ Adelie  │           NULL │  2007 │
│ Adelie  │           36.7 │  2007 │
└─────────┴────────────────┴───────┘

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

t.cast({"year": "uint16", "bill_length_mm": "int"})
┏━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ species ┃ bill_length_mm ┃ year   ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ string  │ int64          │ uint16 │
├─────────┼────────────────┼────────┤
│ Adelie  │             39 │   2007 │
│ Adelie  │             40 │   2007 │
│ Adelie  │             40 │   2007 │
│ Adelie  │           NULL │   2007 │
│ Adelie  │             37 │   2007 │
└─────────┴────────────────┴────────┘

In addition to passing a schema-like as the first argument, you can also pass column_name= pairs as keyword arguments. These will override any types specified in the schema argument. See below where bill_length_mm is overridden to int8:

t.cast({"year": "uint16", "bill_length_mm": "float32"}, bill_length_mm="int8")
┏━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━┓
┃ species ┃ bill_length_mm ┃ year   ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━┩
│ string  │ int8           │ uint16 │
├─────────┼────────────────┼────────┤
│ Adelie  │             39 │   2007 │
│ Adelie  │             40 │   2007 │
│ Adelie  │             40 │   2007 │
│ Adelie  │           NULL │   2007 │
│ Adelie  │             37 │   2007 │
└─────────┴────────────────┴────────┘

If a value is not castable to the target type, since ibis doesn’t know about the data at this time, we can’t know whether the cast will succeed or fail until execution time, so we still create an expression:

expr = t.cast(species="int")
expr.schema()
ibis.Schema {
  species         int64
  bill_length_mm  float64
  year            int64
}

But executing the expression will raise an error:

expr  
---------------------------------------------------------------------------
ConversionException                       Traceback (most recent call last)
File /nix/store/gwljvyq1f79wwhymp57ki2zawy9riqa8-ibis-3.12/lib/python3.12/site-packages/IPython/core/formatters.py:770, in PlainTextFormatter.__call__(self, obj)
    763 stream = StringIO()
    764 printer = pretty.RepresentationPrinter(stream, self.verbose,
    765     self.max_width, self.newline,
    766     max_seq_length=self.max_seq_length,
    767     singleton_pprinters=self.singleton_printers,
    768     type_pprinters=self.type_printers,
    769     deferred_pprinters=self.deferred_printers)
--> 770 printer.pretty(obj)
    771 printer.flush()
    772 return stream.getvalue()

File /nix/store/gwljvyq1f79wwhymp57ki2zawy9riqa8-ibis-3.12/lib/python3.12/site-packages/IPython/lib/pretty.py:420, in RepresentationPrinter.pretty(self, obj)
    409                         return meth(obj, self, cycle)
    410                 if (
    411                     cls is not object
    412                     # check if cls defines __repr__
   (...)    418                     and callable(_safe_getattr(cls, "__repr__", None))
    419                 ):
--> 420                     return _repr_pprint(obj, self, cycle)
    422     return _default_pprint(obj, self, cycle)
    423 finally:

File /nix/store/gwljvyq1f79wwhymp57ki2zawy9riqa8-ibis-3.12/lib/python3.12/site-packages/IPython/lib/pretty.py:795, in _repr_pprint(obj, p, cycle)
    793 """A pprint that just redirects to the normal repr function."""
    794 # Find newlines and replace them with p.break_()
--> 795 output = repr(obj)
    796 lines = output.splitlines()
    797 with p.group():

File ~/work/ibis/ibis/ibis/expr/types/core.py:54, in Expr.__repr__(self)
     52 def __repr__(self) -> str:
     53     if ibis.options.interactive:
---> 54         return capture_rich_renderable(self)
     55     else:
     56         return self._noninteractive_repr()

File ~/work/ibis/ibis/ibis/expr/types/rich.py:48, in capture_rich_renderable(renderable)
     46 console = Console(force_terminal=False)
     47 with _with_rich_display_disabled(), console.capture() as capture:
---> 48     console.print(renderable)
     49 return capture.get().rstrip()

File /nix/store/gwljvyq1f79wwhymp57ki2zawy9riqa8-ibis-3.12/lib/python3.12/site-packages/rich/console.py:1724, in Console.print(self, sep, end, style, justify, overflow, no_wrap, emoji, markup, highlight, width, height, crop, soft_wrap, new_line_start, *objects)
   1722 if style is None:
   1723     for renderable in renderables:
-> 1724         extend(render(renderable, render_options))
   1725 else:
   1726     render_style = self.get_style(style)

File /nix/store/gwljvyq1f79wwhymp57ki2zawy9riqa8-ibis-3.12/lib/python3.12/site-packages/rich/console.py:1325, in Console.render(self, renderable, options)
   1323 renderable = rich_cast(renderable)
   1324 if hasattr(renderable, "__rich_console__") and not isclass(renderable):
-> 1325     render_iterable = renderable.__rich_console__(self, _options)
   1326 elif isinstance(renderable, str):
   1327     text_renderable = self.render_str(
   1328         renderable, highlight=_options.highlight, markup=_options.markup
   1329     )

File ~/work/ibis/ibis/ibis/expr/types/core.py:75, in Expr.__rich_console__(self, console, options)
     73 try:
     74     if opts.interactive:
---> 75         rich_object = to_rich(self, console_width=console_width)
     76     else:
     77         rich_object = Text(self._noninteractive_repr())

File ~/work/ibis/ibis/ibis/expr/types/rich.py:70, in to_rich(expr, max_rows, max_columns, max_length, max_string, max_depth, console_width)
     66     return to_rich_scalar(
     67         expr, max_length=max_length, max_string=max_string, max_depth=max_depth
     68     )
     69 else:
---> 70     return to_rich_table(
     71         expr,
     72         max_rows=max_rows,
     73         max_columns=max_columns,
     74         max_length=max_length,
     75         max_string=max_string,
     76         max_depth=max_depth,
     77         console_width=console_width,
     78     )

File ~/work/ibis/ibis/ibis/expr/types/_rich.py:331, in to_rich_table(tablish, max_rows, max_columns, max_length, max_string, max_depth, console_width)
    328     if orig_ncols > len(computed_cols):
    329         table = table.select(*computed_cols)
--> 331 result = table.limit(max_rows + 1).to_pyarrow()
    332 # Now format the columns in order, stopping if the console width would
    333 # be exceeded.
    334 col_info = []

File ~/work/ibis/ibis/ibis/expr/types/relations.py:628, in Table.to_pyarrow(self, params, limit, **kwargs)
    620 @experimental
    621 def to_pyarrow(
    622     self,
   (...)    626     **kwargs: Any,
    627 ) -> pa.Table:
--> 628     return super().to_pyarrow(params=params, limit=limit, **kwargs)

File ~/work/ibis/ibis/ibis/expr/types/core.py:605, in Expr.to_pyarrow(self, params, limit, **kwargs)
    575 @experimental
    576 def to_pyarrow(
    577     self,
   (...)    581     **kwargs: Any,
    582 ) -> pa.Table | pa.Array | pa.Scalar:
    583     """Execute expression to a pyarrow object.
    584 
    585     This method is eager and will execute the associated expression
   (...)    603         If the passed expression is a Scalar, a pyarrow scalar is returned.
    604     """
--> 605     return self._find_backend(use_default=True).to_pyarrow(
    606         self, params=params, limit=limit, **kwargs
    607     )

File ~/work/ibis/ibis/ibis/backends/duckdb/__init__.py:1410, in Backend.to_pyarrow(self, expr, params, limit, **kwargs)
   1397 def to_pyarrow(
   1398     self,
   1399     expr: ir.Expr,
   (...)   1404     **kwargs: Any,
   1405 ) -> pa.Table:
   1406     from ibis.backends.duckdb.converter import DuckDBPyArrowData
   1408     table = self._to_duckdb_relation(
   1409         expr, params=params, limit=limit, **kwargs
-> 1410     ).to_arrow_table()
   1411     return expr.__pyarrow_result__(table, data_mapper=DuckDBPyArrowData)

ConversionException: Conversion Error: Could not convert string 'Adelie' to INT64 when casting from source column species

LINE 1: SELECT CAST(t2.species AS BIGINT) AS species, t2.bill_length_mm...
               ^

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

t.cast({"foo": "string"})  
---------------------------------------------------------------------------
IbisError                                 Traceback (most recent call last)
Cell In[53], line 1
----> 1 t.cast({"foo": "string"})  

File ~/work/ibis/ibis/ibis/expr/types/relations.py:880, in Table.cast(self, schema, **overrides)
    779 def cast(
    780     self, schema: IntoSchema | None = None, /, **overrides: IntoDtype
    781 ) -> Table:
    782     """Cast the columns of a table.
    783 
    784     Similar to `pandas.DataFrame.astype`.
   (...)    878     ibis.common.exceptions.IbisError: Cast schema has fields that are not in the table: ['foo']
    879     """
--> 880     return self._cast(schema, overrides, cast_method="cast")

File ~/work/ibis/ibis/ibis/expr/types/relations.py:940, in Table._cast(self, schema, overrides, cast_method)
    938 columns = self.columns
    939 if missing_fields := frozenset(schema.names).difference(columns):
--> 940     raise com.IbisError(
    941         f"Cast schema has fields that are not in the table: {sorted(missing_fields)}"
    942     )
    944 for col in columns:
    945     if (new_type := schema.get(col)) is not None:

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

compile

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

Compile expr to a SQL string (for SQL backends) or a LazyFrame (for the polars backend).

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

Returns

Name Type Description
str | pl.LazyFrame A SQL string or a LazyFrame object, depending on the backend of self.

Examples

import ibis
d = {"a": [1, 2, 3], "b": [4, 5, 6]}
con = ibis.duckdb.connect()
t = con.create_table("t", d)
expr = t.mutate(c=t.a + t.b)
expr.compile()
'SELECT "t0"."a", "t0"."b", "t0"."a" + "t0"."b" AS "c" FROM "memory"."main"."t" AS "t0"'

If you want to see the pretty formatted SQL query, set pretty to True.

expr.compile(pretty=True)
'SELECT\n  "t0"."a",\n  "t0"."b",\n  "t0"."a" + "t0"."b" AS "c"\nFROM "memory"."main"."t" AS "t0"'

If the expression does not have a backend, an error will be raised.

t = ibis.memtable(d)
expr = t.mutate(c=t.a + t.b)
expr.compile()  # quartodoc: +EXPECTED_FAILURE
---------------------------------------------------------------------------
IbisError                                 Traceback (most recent call last)
Cell In[62], line 3
      1 t = ibis.memtable(d)
      2 expr = t.mutate(c=t.a + t.b)
----> 3 expr.compile()  # quartodoc: +EXPECTED_FAILURE

File ~/work/ibis/ibis/ibis/expr/types/core.py:532, in Expr.compile(self, limit, params, pretty)
    479 def compile(
    480     self,
    481     *,
   (...)    484     pretty: bool = False,
    485 ) -> str | pl.LazyFrame:
    486     r"""Compile `expr` to a SQL string (for SQL backends) or a LazyFrame (for the polars backend).
    487 
    488     Parameters
   (...)    530     [`Table.to_sql()`](./expression-tables.qmd#ibis.expr.types.relations.Table.to_sql)
    531     """
--> 532     return self._find_backend().compile(
    533         self, limit=limit, params=params, pretty=pretty
    534     )

File ~/work/ibis/ibis/ibis/expr/types/core.py:337, in Expr._find_backend(self, use_default)
    335     default = _default_backend() if use_default else None
    336     if default is None:
--> 337         raise IbisError(
    338             "Expression depends on no backends, and found no default"
    339         )
    340     return default
    342 if len(backends) > 1:

IbisError: Expression depends on no backends, and found no default

See Also

Value.compile() Table.compile() Value.to_sql() Table.to_sql()

count

count(where=None)

Compute the number of rows in the table.

Parameters

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

Returns

Name Type Description
IntegerScalar Number of rows in the table

Examples

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

t.count()
┌───┐
│ 3 │
└───┘
t.count(where=t.a != "foo")
┌───┐
│ 2 │
└───┘
type(t.count())
ibis.expr.types.numeric.IntegerScalar

cross_join

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

Compute the cross join of a sequence of tables.

Parameters

Name Type Description Default
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 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

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 ibis
import ibis.selectors as s
ibis.options.interactive = True
p = ibis.examples.penguins.fetch()
p.describe()
┏━━━━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┳━━━━━━━━━┓
┃ name              ┃ pos   ┃ type    ┃ count ┃ nulls ┃ unique ┃ mode   ┃ mean        ┃ std        ┃ min     ┃ p25      ┃ p50     ┃ p75     ┃ max     ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│ string            │ int16 │ string  │ int64 │ int64 │ int64  │ string │ float64     │ float64    │ float64 │ float64  │ float64 │ float64 │ float64 │
├───────────────────┼───────┼─────────┼───────┼───────┼────────┼────────┼─────────────┼────────────┼─────────┼──────────┼─────────┼─────────┼─────────┤
│ species           │     0 │ string  │   344 │     0 │      3 │ Adelie │        NULL │       NULL │    NULL │     NULL │    NULL │    NULL │    NULL │
│ island            │     1 │ string  │   344 │     0 │      3 │ Biscoe │        NULL │       NULL │    NULL │     NULL │    NULL │    NULL │    NULL │
│ bill_length_mm    │     2 │ float64 │   344 │     2 │    164 │ NULL   │   43.921930 │   5.459584 │    32.1 │   39.225 │   44.45 │    48.5 │    59.6 │
│ bill_depth_mm     │     3 │ float64 │   344 │     2 │     80 │ NULL   │   17.151170 │   1.974793 │    13.1 │   15.600 │   17.30 │    18.7 │    21.5 │
│ flipper_length_mm │     4 │ int64   │   344 │     2 │     55 │ NULL   │  200.915205 │  14.061714 │   172.0 │  190.000 │  197.00 │   213.0 │   231.0 │
│ body_mass_g       │     5 │ int64   │   344 │     2 │     94 │ NULL   │ 4201.754386 │ 801.954536 │  2700.0 │ 3550.000 │ 4050.00 │  4750.0 │  6300.0 │
│ sex               │     6 │ string  │   344 │    11 │      2 │ male   │        NULL │       NULL │    NULL │     NULL │    NULL │    NULL │    NULL │
│ year              │     7 │ 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              ┃ pos   ┃ type    ┃ count ┃ nulls ┃ unique ┃ mean        ┃ std        ┃ min     ┃ p25      ┃ p50     ┃ p75     ┃ max     ┃
┡━━━━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━━━━━━╇━━━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━╇━━━━━━━━━┩
│ string            │ int16 │ string  │ int64 │ int64 │ int64  │ float64     │ float64    │ float64 │ float64  │ float64 │ float64 │ float64 │
├───────────────────┼───────┼─────────┼───────┼───────┼────────┼─────────────┼────────────┼─────────┼──────────┼─────────┼─────────┼─────────┤
│ flipper_length_mm │     2 │ int64   │   344 │     2 │     55 │  200.915205 │  14.061714 │   172.0 │  190.000 │  197.00 │   213.0 │   231.0 │
│ body_mass_g       │     3 │ int64   │   344 │     2 │     94 │ 4201.754386 │ 801.954536 │  2700.0 │ 3550.000 │ 4050.00 │  4750.0 │  6300.0 │
│ year              │     4 │ int64   │   344 │     0 │      3 │ 2008.029070 │   0.818356 │  2007.0 │ 2007.000 │ 2008.00 │  2009.0 │  2009.0 │
│ bill_length_mm    │     0 │ float64 │   344 │     2 │    164 │   43.921930 │   5.459584 │    32.1 │   39.225 │   44.45 │    48.5 │    59.6 │
│ bill_depth_mm     │     1 │ float64 │   344 │     2 │     80 │   17.151170 │   1.974793 │    13.1 │   15.600 │   17.30 │    18.7 │    21.5 │
└───────────────────┴───────┴─────────┴───────┴───────┴────────┴─────────────┴────────────┴─────────┴──────────┴─────────┴─────────┴─────────┘

p.select(s.of_type("string")).describe()
┏━━━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━┓
┃ name    ┃ pos   ┃ type   ┃ count ┃ nulls ┃ unique ┃ mode   ┃
┡━━━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━┩
│ string  │ int16 │ string │ int64 │ int64 │ int64  │ string │
├─────────┼───────┼────────┼───────┼───────┼────────┼────────┤
│ sex     │     2 │ string │   344 │    11 │      2 │ male   │
│ species │     0 │ string │   344 │     0 │      3 │ Adelie │
│ island  │     1 │ string │   344 │     0 │      3 │ Biscoe │
└─────────┴───────┴────────┴───────┴───────┴────────┴────────┘

difference

difference(table, /, *rest, distinct=True)

Compute the set (or multiset) 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 Use set difference (True) or multiset difference (False). See examples. True

See Also

ibis.difference

Returns

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

Examples

import ibis
ibis.options.interactive = True
t1 = ibis.memtable({"a": [7, 8, 8, 9, 9, 9]})
t2 = ibis.memtable({"a": [8, 9]})

With distinct=True, if a row ever appears in any of *rest, it will not appear in the result. So here, all appearances of 8 and 9 are removed:

t1.difference(t2)
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     7 │
└───────┘

With distinct=False, the algorithm is a multiset difference. This means, that since 8 and 9 each appear once in t2, the result will be the input with a single instance of each removed:

t1.difference(t2, distinct=False).order_by("a")
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     7 │
│     8 │
│     9 │
│     9 │
└───────┘

With multiple tables in *rest, we apply the operation consecutively. Here, we remove two eights and two nines:

t1.difference(t2, t2, distinct=False).order_by("a")
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     7 │
│     9 │
└───────┘

distinct

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

Return a Table with duplicate rows removed.

Similar to pandas.DataFrame.drop_duplicates().

NoteSome 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 │
│ Gentoo    │ Biscoe    │           44.5 │          14.3 │               216 │        4100 │ NULL   │  2007 │
│ Adelie    │ Torgersen │           39.1 │          18.7 │               181 │        3750 │ male   │  2007 │
│ Adelie    │ Dream     │           39.5 │          16.7 │               178 │        3250 │ female │  2007 │
│ Adelie    │ Dream     │           37.5 │          18.9 │               179 │        2975 │ NULL   │  2007 │
│ Chinstrap │ Dream     │           46.5 │          17.9 │               192 │        3500 │ female │  2007 │
│ Adelie    │ Biscoe    │           37.7 │          18.7 │               180 │        3600 │ male   │  2007 │
│ Gentoo    │ Biscoe    │           50.0 │          16.3 │               230 │        5700 │ male   │  2007 │
│ Adelie    │ Torgersen │           39.5 │          17.4 │               186 │        3800 │ female │  2007 │
│ …         │ …         │              … │             … │                 … │           … │ …      │     … │
└───────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

The only valid values of keep are "first", "last" and None.

t.distinct(on="species", keep="second")  
---------------------------------------------------------------------------
IbisError                                 Traceback (most recent call last)
Cell In[131], line 1
----> 1 t.distinct(on="species", keep="second")  

File ~/work/ibis/ibis/ibis/expr/types/relations.py:1657, in Table.distinct(self, on, keep)
   1655     method = keep
   1656 else:
-> 1657     raise com.IbisError(
   1658         f"Invalid value for `keep`: {keep!r}, must be 'first', 'last' or None"
   1659     )
   1661 aggs = {col.get_name(): getattr(col, method)() for col in (~on).expand(self)}
   1662 res = self.aggregate(aggs, by=on, having=having)

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

Name 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 │
└───────────┴───────────────────┴─────────────┴────────┴───────┘

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 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.drop_null(["bill_length_mm", "body_mass_g"]).count()
┌─────┐
│ 342 │
└─────┘
t.drop_null(how="all").count()  # no rows where all columns are null
┌─────┐
│ 344 │
└─────┘

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 ibis
t1 = ibis.table(dict(a="int"), name="t")
t2 = ibis.table(dict(a="int"), name="t")
t1.equals(t2)
True
v = ibis.table(dict(a="string"), name="v")
t1.equals(v)
False

execute

execute(limit='default', params=None, **kwargs)

Execute an expression against its backend if one exists.

Parameters

Name Type Description Default
limit int | str | None An integer to effect a specific row limit. A value of None means “no limit”. The default is in ibis/config.py. 'default'
params Mapping[ir.Value, Any] | None Mapping of scalar parameter expressions to value None
kwargs Any Keyword arguments {}

Examples

import ibis
t = ibis.examples.penguins.fetch()
t.execute()
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
0 Adelie Torgersen 39.1 18.7 181.0 3750.0 male 2007
1 Adelie Torgersen 39.5 17.4 186.0 3800.0 female 2007
2 Adelie Torgersen 40.3 18.0 195.0 3250.0 female 2007
3 Adelie Torgersen NaN NaN NaN NaN None 2007
4 Adelie Torgersen 36.7 19.3 193.0 3450.0 female 2007
... ... ... ... ... ... ... ... ...
339 Chinstrap Dream 55.8 19.8 207.0 4000.0 male 2009
340 Chinstrap Dream 43.5 18.1 202.0 3400.0 female 2009
341 Chinstrap Dream 49.6 18.2 193.0 3775.0 male 2009
342 Chinstrap Dream 50.8 19.0 210.0 4100.0 male 2009
343 Chinstrap Dream 50.2 18.7 198.0 3775.0 female 2009

344 rows × 8 columns

Scalar parameters can be supplied dynamically during execution.

species = ibis.param("string")
expr = t.filter(t.species == species).order_by(t.bill_length_mm)
expr.execute(limit=3, params={species: "Gentoo"})
species island bill_length_mm bill_depth_mm flipper_length_mm body_mass_g sex year
0 Gentoo Biscoe 40.9 13.7 214 4650 female 2007
1 Gentoo Biscoe 41.7 14.7 210 4700 female 2009
2 Gentoo Biscoe 42.0 13.5 210 4150 female 2007

See Also

Table.to_pandas() Value.to_pandas()

fill_null

fill_null(replacements, /)

Fill null values in a table expression.

NoteThere 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 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.fill_null({"sex": "unrecorded"}).sex
┏━━━━━━━━━━━━┓
┃ sex        ┃
┡━━━━━━━━━━━━┩
│ string     │
├────────────┤
│ male       │
│ female     │
│ female     │
│ unrecorded │
│ female     │
│ male       │
│ female     │
│ male       │
│ unrecorded │
│ unrecorded │
│ …          │
└────────────┘

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

Returns

Name Type Description
Table Filtered table expression

Examples

import ibis
ibis.options.interactive = True
t = 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().drop_null(
    "sex"
).order_by("sex")
┏━━━━━━━━┳━━━━━━━━━━━┓
┃ sex    ┃ sex_count ┃
┡━━━━━━━━╇━━━━━━━━━━━┩
│ string │ int64     │
├────────┼───────────┤
│ female │        22 │
│ male   │        68 │
└────────┴───────────┘

get_backend

get_backend()

Get the current Ibis backend of the expression.

Returns

Name Type Description
BaseBackend The Ibis backend.

Examples

import ibis
con = ibis.duckdb.connect()
t = con.create_table("t", {"id": [1, 2, 3]})
t.get_backend()
<ibis.backends.duckdb.Backend at 0x7fff245c7b60>

See Also

ibis.get_backend()

get_name

get_name()

Return the fully qualified name of the table.

Examples

import ibis
con = ibis.duckdb.connect()
t = con.create_table("t", {"id": [1, 2, 3]})
t.get_name()
'memory.main.t'

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 | Deferred | Iterable[str] | Iterable[ir.Value] | Iterable[Deferred] 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 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.

NoteThe 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 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

Name 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   │ int16 │
├───────────────────┼─────────┼──────────┼───────┼───────────┼───────────┼───────┤
│ 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 (or multiset) 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 Use set intersect (True) or multiset intersect (False). See examples. True

Returns

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

See Also

ibis.intersect

Examples

import ibis
ibis.options.interactive = True
two_a = ibis.memtable({"x": ["a", "a", "b"]})
three_a = ibis.memtable({"x": ["a", "a", "a", "b"]})
four_a = ibis.memtable({"x": ["a", "a", "a", "a", "c"]})

With distinct=True, the intersection will return one row for each row that appears in all input tables. This is equivalent to a set intersection. So even though the source tables have multiple "a" values, the result will only have one:

two_a.intersect(three_a).order_by("x")
┏━━━━━━━━┓
┃ x      ┃
┡━━━━━━━━┩
│ string │
├────────┤
│ a      │
│ b      │
└────────┘

With distinct=False, the intersection will return all rows that appear in all input tables. This is equivalent to a multiset intersection. Since the smallest number of appearances of "a" is 2, the result will have two "a" values:

two_a.intersect(three_a, distinct=False).order_by("x")
┏━━━━━━━━┓
┃ x      ┃
┡━━━━━━━━┩
│ string │
├────────┤
│ a      │
│ a      │
│ b      │
└────────┘

More than two table expressions can be intersected at once. - Since "a" appears at minimum one time, it appears once in the result. - Since "b" doesn’t appear in two_a or three_a, it is not included. - Since "c" does not appear in one_a, it is not included.

two_a.intersect(three_a, four_a)
┏━━━━━━━━┓
┃ x      ┃
┡━━━━━━━━┩
│ string │
├────────┤
│ a      │
└────────┘

two_a.intersect(three_a, four_a, distinct=False)
┏━━━━━━━━┓
┃ x      ┃
┡━━━━━━━━┩
│ string │
├────────┤
│ a      │
│ a      │
└────────┘

join

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

Perform a join between two tables.

Parameters

Name Type Description Default
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] | ir.BooleanValue] 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. Show the top 5 users by the number of unique movies that they both rated and tagged:

(
    tags.join(ratings, ["userId", "movieId"])
    .group_by(_.userId)
    .agg(n_rated_and_tagged=_.movieId.nunique())
    .order_by(_.n_rated_and_tagged.desc())
    .head(5)
)
┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ userId ┃ n_rated_and_tagged ┃
┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
│ int64  │ int64              │
├────────┼────────────────────┤
│    474 │               1149 │
│    567 │                109 │
│     62 │                 69 │
│    477 │                 66 │
│    424 │                 58 │
└────────┴────────────────────┘

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            │
├─────────┼───────────────────┼───────────────┼───────────────────┤
│   60756 │ funny             │          1732 │ funny             │
│   60756 │ Highly quotable   │          1732 │ Highly quotable   │
│   89774 │ Tom Hardy         │        139385 │ tom hardy         │
│  106782 │ drugs             │          1732 │ drugs             │
│  106782 │ Leonardo DiCaprio │          5989 │ Leonardo DiCaprio │
└─────────┴───────────────────┴───────────────┴───────────────────┘

limit

limit(n, /, *, offset=0)

Select n rows from self starting at offset.

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

Parameters

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

Returns

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

Examples

import ibis
ibis.options.interactive = True
t = ibis.memtable({"a": [1, 1, 2], "b": ["c", "a", "a"]})
t
┏━━━━━━━┳━━━━━━━━┓
┃ a     ┃ b      ┃
┡━━━━━━━╇━━━━━━━━┩
│ int64 │ string │
├───────┼────────┤
│     1 │ c      │
│     1 │ a      │
│     2 │ a      │
└───────┴────────┘

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

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

t.limit(None, offset=1)
┏━━━━━━━┳━━━━━━━━┓
┃ a     ┃ b      ┃
┡━━━━━━━╇━━━━━━━━┩
│ int64 │ string │
├───────┼────────┤
│     1 │ a      │
│     2 │ a      │
└───────┴────────┘

See Also

Table.order_by

mutate

mutate(*exprs, **mutations)

Add columns to a table expression.

Parameters

Name Type Description Default
exprs ir.Value | Deferred List of named expressions to add as columns ()
mutations ir.Value | Deferred | str Named expressions using keyword arguments {}

Returns

Name 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.cols("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 | Deferred | None Optional boolean expression to filter rows when counting. None

Returns

Name 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(where=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 | Deferred | Sequence[str | ir.Column | s.Selector | Deferred] Expressions to sort the table by. ()

Returns

Name Type Description
Table Sorted table

Examples

import ibis
ibis.options.interactive = True
t = ibis.memtable(
    {
        "a": [3, 2, 1, 3],
        "b": ["a", "B", "c", "D"],
        "c": [4, 6, 5, 7],
    }
)
t
┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ a     ┃ b      ┃ c     ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ int64 │ string │ int64 │
├───────┼────────┼───────┤
│     3 │ a      │     4 │
│     2 │ B      │     6 │
│     1 │ c      │     5 │
│     3 │ D      │     7 │
└───────┴────────┴───────┘

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

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

Sort in descending order

t.order_by(ibis.desc("b"))
┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ a     ┃ b      ┃ c     ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ int64 │ string │ int64 │
├───────┼────────┼───────┤
│     1 │ c      │     5 │
│     3 │ a      │     4 │
│     3 │ D      │     7 │
│     2 │ B      │     6 │
└───────┴────────┴───────┘

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

from ibis import _
t.order_by(_.b.desc())
┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ a     ┃ b      ┃ c     ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ int64 │ string │ int64 │
├───────┼────────┼───────┤
│     1 │ c      │     5 │
│     3 │ a      │     4 │
│     3 │ D      │     7 │
│     2 │ B      │     6 │
└───────┴────────┴───────┘

Sort by multiple columns/expressions

t.order_by(["a", _.c.desc()])
┏━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ a     ┃ b      ┃ c     ┃
┡━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ int64 │ string │ int64 │
├───────┼────────┼───────┤
│     1 │ c      │     5 │
│     2 │ B      │     6 │
│     3 │ D      │     7 │
│     3 │ a      │     4 │
└───────┴────────┴───────┘

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

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

This means that shuffling a Table is super simple

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

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

import ibis.selectors as s
penguins = ibis.examples.penguins.fetch()
penguins[["year", "island"]].value_counts().order_by(s.startswith("year"))
┏━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ year  ┃ island    ┃ year_island_count ┃
┡━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ int64 │ string    │ int64             │
├───────┼───────────┼───────────────────┤
│  2007 │ Torgersen │                20 │
│  2007 │ Biscoe    │                44 │
│  2007 │ Dream     │                46 │
│  2008 │ Torgersen │                16 │
│  2008 │ Dream     │                34 │
│  2008 │ Biscoe    │                64 │
│  2009 │ Torgersen │                16 │
│  2009 │ Dream     │                44 │
│  2009 │ Biscoe    │                60 │
└───────┴───────────┴───────────────────┘

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

penguins[["year", "island"]].value_counts().order_by(
    s.across(s.startswith("year"), _.desc())
)
┏━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ year  ┃ island    ┃ year_island_count ┃
┡━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ int64 │ string    │ int64             │
├───────┼───────────┼───────────────────┤
│  2009 │ Biscoe    │                60 │
│  2009 │ Dream     │                44 │
│  2009 │ Torgersen │                16 │
│  2008 │ Biscoe    │                64 │
│  2008 │ Dream     │                34 │
│  2008 │ Torgersen │                16 │
│  2007 │ Dream     │                46 │
│  2007 │ Biscoe    │                44 │
│  2007 │ Torgersen │                20 │
└───────┴───────────┴───────────────────┘

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 ibis
t = ibis.memtable(
    {
        "a": [5, 10, 15],
        "b": ["a", "b", "c"],
    }
)
f = lambda a: (a + 1).name("a")
g = lambda a: (a * 2).name("a")
result1 = t.a.pipe(f).pipe(g)
result1
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│    12 │
│    22 │
│    32 │
└───────┘

result2 = g(f(t.a))  # equivalent to the above
result1.equals(result2)
True

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 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.cols("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 capabilities 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"),
).drop_null("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.index["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.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",
)
┏━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┳━━━━━━┳━━━━━━━┓
┃ 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.index["new_sp_m014":"newrel_f65"],
    names_to=["diagnosis", "gender", "age"],
    names_pattern="new_?(.*)_.(.*)",
)
---------------------------------------------------------------------------
IbisInputError                            Traceback (most recent call last)
Cell In[332], line 1
----> 1 who.pivot_longer(  
      2     s.index["new_sp_m014":"newrel_f65"],
      3     names_to=["diagnosis", "gender", "age"],
      4     names_pattern="new_?(.*)_.(.*)",
      5 )

File ~/work/ibis/ibis/ibis/expr/types/relations.py:4496, in Table.pivot_longer(self, col, names_to, names_pattern, names_transform, values_to, values_transform)
   4494 names_pattern = re.compile(names_pattern)
   4495 if (ngroups := names_pattern.groups) != (nnames := len(names_to)):
-> 4496     raise com.IbisInputError(
   4497         f"Number of match groups in `names_pattern`"
   4498         f"{names_pattern.pattern!r} ({ngroups:d} groups) doesn't "
   4499         f"match the length of `names_to` {names_to} (length {nnames:d})"
   4500     )
   4502 if names_transform is None:
   4503     names_transform = dict.fromkeys(names_to, toolz.identity)

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.index["new_sp_m014":"newrel_f65"], names_transform="upper"
)  # quartodoc: +EXPECTED_FAILURE
---------------------------------------------------------------------------
IbisTypeError                             Traceback (most recent call last)
Cell In[335], line 1
----> 1 who.pivot_longer(
      2     s.index["new_sp_m014":"newrel_f65"], names_transform="upper"
      3 )  # quartodoc: +EXPECTED_FAILURE

File ~/work/ibis/ibis/ibis/expr/types/relations.py:4508, in Table.pivot_longer(self, col, names_to, names_pattern, names_transform, values_to, values_transform)
   4506         names_transform = dict.fromkeys(names_to, names_transform)
   4507     else:
-> 4508         raise com.IbisTypeError(
   4509             f"`names_transform` must be a mapping or callable. Got {type(names_transform)}"
   4510         )
   4512 for name in names_to:
   4513     names_transform.setdefault(name, toolz.identity)

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 by executing a query against the backend to discover unique values from names_from. This breaks lazy evaluation and may be slow on large or remote datasets. * 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  ┃ 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  ┃ Release ┃ Rstr  ┃ BCW   ┃ BCE2  ┃ BCE   ┃ MAE   ┃ I80_1 ┃ MAW   ┃ Lisbon ┃ Base_TD ┃ BCW2  ┃
┡━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━┩
│ 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 │     1 │  NULL │  NULL │  NULL │  NULL │     1 │  NULL │      1 │    NULL │  NULL │
│  4865 │       1 │  NULL │  NULL │  NULL │  NULL │  NULL │     1 │  NULL │      1 │    NULL │  NULL │
│  4842 │       1 │     1 │     1 │     1 │     1 │     1 │     1 │     1 │      1 │       1 │     1 │
│  4847 │       1 │  NULL │  NULL │  NULL │  NULL │  NULL │     1 │  NULL │      1 │    NULL │  NULL │
│  4850 │       1 │     1 │     1 │  NULL │     1 │  NULL │     1 │  NULL │   NULL │       1 │  NULL │
│  4844 │       1 │     1 │     1 │     1 │     1 │     1 │     1 │     1 │      1 │       1 │     1 │
│  4845 │       1 │     1 │  NULL │  NULL │  NULL │  NULL │     1 │  NULL │      1 │       1 │  NULL │
│  4849 │       1 │  NULL │  NULL │  NULL │  NULL │  NULL │     1 │  NULL │   NULL │    NULL │  NULL │
│  4859 │       1 │     1 │  NULL │  NULL │  NULL │  NULL │     1 │  NULL │      1 │       1 │  NULL │
│     … │       … │     … │     … │     … │     … │     … │     … │     … │      … │       … │     … │
└───────┴─────────┴───────┴───────┴───────┴───────┴───────┴───────┴───────┴────────┴─────────┴───────┘

You can do simple transpose-like operations using pivot_wider

t = ibis.memtable(dict(outcome=["yes", "no"], counted=[3, 4]))
t
┏━━━━━━━━━┳━━━━━━━━━┓
┃ outcome ┃ counted ┃
┡━━━━━━━━━╇━━━━━━━━━┩
│ string  │ int64   │
├─────────┼─────────┤
│ yes     │       3 │
│ no      │       4 │
└─────────┴─────────┘

t.pivot_wider(names_from="outcome", values_from="counted", names_sort=True)
┏━━━━━━━┳━━━━━━━┓
┃ no    ┃ yes   ┃
┡━━━━━━━╇━━━━━━━┩
│ int64 │ int64 │
├───────┼───────┤
│     4 │     3 │
└───────┴───────┘

Fill missing pivoted values using values_fill

fish_encounters.pivot_wider(
    names_from="station", values_from="seen", values_fill=0
)
┏━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━━┳━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┓
┃ fish  ┃ Release ┃ Rstr  ┃ BCW   ┃ BCE2  ┃ Lisbon ┃ Base_TD ┃ BCW2  ┃ BCE   ┃ MAE   ┃ I80_1 ┃ MAW   ┃
┡━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━━╇━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━┩
│ 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 │     1 │     0 │     0 │      1 │       0 │     0 │     0 │     0 │     1 │     0 │
│  4865 │       1 │     0 │     0 │     0 │      1 │       0 │     0 │     0 │     0 │     1 │     0 │
│  4844 │       1 │     1 │     1 │     1 │      1 │       1 │     1 │     1 │     1 │     1 │     1 │
│  4845 │       1 │     1 │     0 │     0 │      1 │       1 │     0 │     0 │     0 │     1 │     0 │
│  4849 │       1 │     0 │     0 │     0 │      0 │       0 │     0 │     0 │     0 │     1 │     0 │
│  4859 │       1 │     1 │     0 │     0 │      1 │       1 │     0 │     0 │     0 │     1 │     0 │
│  4861 │       1 │     1 │     1 │     1 │      1 │       1 │     1 │     1 │     1 │     1 │     1 │
│  4851 │       1 │     0 │     0 │     0 │      0 │       0 │     0 │     0 │     0 │     1 │     0 │
│  4857 │       1 │     1 │     1 │     1 │      1 │       1 │     1 │     1 │     0 │     1 │     0 │
│     … │       … │     … │     … │     … │      … │       … │     … │     … │     … │     … │     … │
└───────┴─────────┴───────┴───────┴───────┴────────┴─────────┴───────┴───────┴───────┴───────┴───────┘

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 │
│ 12     │ Florida  │           25952 │         70 │          1077 │        3 │
│ 13     │ Georgia  │           27024 │        106 │           927 │        3 │
│ 19     │ Iowa     │           30002 │        143 │           740 │        4 │
│ 21     │ Kentucky │           24702 │        159 │           713 │        4 │
│ 23     │ Maine    │           26841 │        187 │           808 │        7 │
│ 24     │ Maryland │           37147 │        152 │          1311 │        5 │
│ 26     │ Michigan │           26987 │         82 │           824 │        3 │
│ 29     │ Missouri │           26999 │        113 │           784 │        4 │
│ 31     │ Nebraska │           30020 │        146 │           773 │        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    │
├────────┼──────────────────────┼─────────────────┼────────────┼───────────────┼──────────┤
│ 06     │ California           │           29454 │        109 │          1358 │        3 │
│ 11     │ District of Columbia │           43198 │        681 │          1424 │       17 │
│ 17     │ Illinois             │           30684 │         83 │           952 │        3 │
│ 18     │ Indiana              │           27247 │        117 │           782 │        3 │
│ 25     │ Massachusetts        │           34498 │        199 │          1173 │        5 │
│ 33     │ New Hampshire        │           33172 │        387 │          1052 │        9 │
│ 39     │ Ohio                 │           27435 │         94 │           764 │        2 │
│ 55     │ Wisconsin            │           29868 │        135 │           813 │        3 │
│ 05     │ Arkansas             │           23789 │        165 │           709 │        5 │
│ 12     │ Florida              │           25952 │         70 │          1077 │        3 │
│ …      │ …                    │               … │          … │             … │        … │
└────────┴──────────────────────┴─────────────────┴────────────┴───────────────┴──────────┘

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  ┃ A_AI     ┃ B_AI     ┃ B_EI     ┃
┡━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩
│ int64 │ float64  │ float64  │ float64  │
├───────┼──────────┼──────────┼──────────┤
│  2002 │ 0.420572 │ 0.260492 │ 0.567511 │
│  2005 │ 0.404934 │ 0.014042 │ 0.803179 │
│  2013 │ 0.755804 │ 0.243911 │ 0.706561 │
│  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 │
│  2011 │ 0.504687 │ 0.493578 │ 0.109058 │
│  2001 │ 0.757954 │ 0.865310 │ 0.191067 │
│  2003 │ 0.258917 │ 0.805028 │ 0.238616 │
│     … │        … │        … │        … │
└───────┴──────────┴──────────┴──────────┘

Select a subset of names. Unlike the previous call, providing names explicitly avoids executing a query to discover unique values, keeping expression construction lazy.

production.pivot_wider(
    names_from=["product", "country"],
    names=[("A", "AI"), ("B", "AI")],
    values_from="production",
)
┏━━━━━━━┳━━━━━━━━━━┳━━━━━━━━━━┓
┃ year  ┃ A_AI     ┃ B_AI     ┃
┡━━━━━━━╇━━━━━━━━━━╇━━━━━━━━━━┩
│ int64 │ float64  │ float64  │
├───────┼──────────┼──────────┤
│  2002 │ 0.420572 │ 0.260492 │
│  2005 │ 0.404934 │ 0.014042 │
│  2013 │ 0.755804 │ 0.243911 │
│  2001 │ 0.757954 │ 0.865310 │
│  2003 │ 0.258917 │ 0.805028 │
│  2009 │ 0.583382 │ 0.668153 │
│  2012 │ 0.281838 │ 0.867603 │
│  2000 │ 0.844422 │ 0.477010 │
│  2010 │ 0.908113 │ 0.001143 │
│  2014 │ 0.618369 │ 0.325204 │
│     … │        … │        … │
└───────┴──────────┴──────────┘

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  │
├───────┼──────────┼──────────┼──────────┤
│  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 │
│  2011 │ 0.504687 │ 0.493578 │ 0.109058 │
│  2001 │ 0.757954 │ 0.865310 │ 0.191067 │
│  2003 │ 0.258917 │ 0.805028 │ 0.238616 │
│  2009 │ 0.583382 │ 0.668153 │ 0.507941 │
│  2012 │ 0.281838 │ 0.867603 │ 0.551267 │
│  2002 │ 0.420572 │ 0.260492 │ 0.567511 │
│     … │        … │        … │        … │
└───────┴──────────┴──────────┴──────────┘

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… │ … │
│ …       │ …        │ … │
└─────────┴──────────┴───┘

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 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 │
└────────┴────────┴────────┴───────┴───────┴───────┘

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

Name Type Description
Table A renamed table expression

Examples

import ibis
import ibis.selectors as s
ibis.options.interactive = True
first3 = s.index[: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.

NoteThis 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.

NoteResults 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 sample a fraction of blocks of rows (where “block” is a backend dependent definition), which may be significantly more efficient (at the cost of a less statistically random sample). This is identical to “row” for backends lacking a blockwise sampling implementation. For those coming from SQL, “row” and “block” correspond to “bernoulli” and “system” respectively in a TABLESAMPLE clause. 'row'
seed int | None An optional random seed to use, for repeatable sampling. The range of possible seed values is backend specific (most support at least [0, 2**31 - 1]). Backends that never support specifying a seed for repeatable sampling will error appropriately. Note that some backends (like DuckDB) do support specifying a seed, but may still not have repeatable results in all cases. None

Returns

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

Examples

import ibis
ibis.options.interactive = True
t = ibis.memtable({"x": [1, 2, 3, 4], "y": ["a", "b", "c", "d"]})
t
┏━━━━━━━┳━━━━━━━━┓
┃ x     ┃ y      ┃
┡━━━━━━━╇━━━━━━━━┩
│ int64 │ string │
├───────┼────────┤
│     1 │ a      │
│     2 │ b      │
│     3 │ c      │
│     4 │ d      │
└───────┴────────┘

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

t.sample(0.5, seed=1234)
┏━━━━━━━┳━━━━━━━━┓
┃ x     ┃ y      ┃
┡━━━━━━━╇━━━━━━━━┩
│ int64 │ string │
├───────┼────────┤
│     2 │ b      │
│     3 │ c      │
└───────┴────────┘

schema

schema()

Return the Schema for this table.

Returns

Name 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] | Deferred Column expression, string, or list of column expressions and strings. ()
named_exprs ir.Value | str | Deferred Column expressions {}

Returns

Name 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.cols("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.cols("year"), _.mean())).head()
┏━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┓
┃ bill_length_mm ┃ bill_depth_mm ┃ flipper_length_mm ┃ body_mass_g ┃
┡━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━┩
│ float64        │ float64       │ float64           │ float64     │
├────────────────┼───────────────┼───────────────────┼─────────────┤
│       43.92193 │      17.15117 │        200.915205 │ 4201.754386 │
│       43.92193 │      17.15117 │        200.915205 │ 4201.754386 │
│       43.92193 │      17.15117 │        200.915205 │ 4201.754386 │
│       43.92193 │      17.15117 │        200.915205 │ 4201.754386 │
│       43.92193 │      17.15117 │        200.915205 │ 4201.754386 │
└────────────────┴───────────────┴───────────────────┴─────────────┘

sql

sql(query, /, *, dialect=None)

Run a SQL query against a table expression.

Parameters

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

Returns

Name Type Description
Table An opaque table expression

Examples

import ibis
from ibis import _
ibis.options.interactive = True
t = 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()

Deprecated - use as_scalar instead.

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.

See https://arrow.apache.org/docs/python/generated/pyarrow.csv.CSVWriter.html for details.

Parameters

Name Type Description Default
path str | Path The data target. A string or Path where the CSV 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.csv.CSVWriter {}

to_delta

to_delta(path, /, *, params=None, **kwargs)

Write the results of executing the given expression to a Delta Lake table.

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

Parameters

Name Type Description Default
path str | Path The data target. A string or Path to the Delta Lake table directory. required
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
**kwargs Any Additional keyword arguments passed to deltalake.writer.write_deltalake method {}

to_json

to_json(path, /, **kwargs)

Write the results of expr to a json file of [{column -> value}, …] objects.

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

Parameters

Name Type Description Default
path str | Path The data target. A string or Path where the JSON file will be written. required
kwargs Any Additional, backend-specific keyword arguments. {}

to_pandas

to_pandas(params=None, limit=None, **kwargs)

Convert a table expression to a pandas DataFrame.

Parameters

Name Type Description Default
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
limit int | str | None An integer to effect a specific row limit. A value of None means no limit. The default is in ibis/config.py. None
kwargs Any Keyword arguments {}

Returns

Name Type Description
DataFrame The result of executing the expression as a pandas DataFrame

to_pandas_batches

to_pandas_batches(limit=None, params=None, chunk_size=1000000, **kwargs)

Execute expression and return an iterator of pandas DataFrames.

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

Parameters

Name Type Description Default
limit int | str | 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
chunk_size int Maximum number of rows in each returned DataFrame. 1000000
kwargs Any Keyword arguments {}

Returns

Name Type Description
Iterator[pd.DataFrame]

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())

Partition on a single column.

penguins.to_parquet(tempfile.mkdtemp(), partition_by="year")

Partition on multiple columns.

penguins.to_parquet(tempfile.mkdtemp(), partition_by=("year", "island"))
NoteHive-partitioned output is currently only supported when using DuckDB

to_parquet_dir

to_parquet_dir(directory, /, *, params=None, **kwargs)

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

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

See https://arrow.apache.org/docs/python/generated/pyarrow.dataset.write_dataset.html for details.

Parameters

Name Type Description Default
directory str | Path The data target. A string or Path to the directory 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.dataset.write_dataset {}

to_polars

to_polars(params=None, limit=None, **kwargs)

Execute expression and return results as a polars dataframe.

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

Parameters

Name Type Description Default
params Mapping[ir.Scalar, Any] | None Mapping of scalar parameter expressions to value. None
limit int | str | None An integer to effect a specific row limit. A value of None means “no limit”. The default is in ibis/config.py. None
kwargs Any Keyword arguments {}

Returns

Name Type Description
DataFrame A polars dataframe holding the results of the executed expression.

to_pyarrow_batches

to_pyarrow_batches(limit=None, params=None, 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
limit int | str | 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
chunk_size int Maximum number of rows in each returned record batch. 1000000
kwargs Any Keyword arguments {}

Returns

Name Type Description
results RecordBatchReader

to_sql

to_sql(dialect=None, pretty=True, **kwargs)

Compile to a formatted SQL string.

Parameters

Name Type Description Default
dialect str | None SQL dialect to use for compilation. Uses the dialect bound to self if not specified, or the default dialect if no dialect is bound. None
pretty bool Whether to use pretty formatting. True
kwargs Scalar parameters {}

Returns

Name Type Description
str Formatted SQL string

Examples

import ibis
t = ibis.table({"a": "int", "b": "int"}, name="t")
expr = t.mutate(c=t.a + t.b)
expr.to_sql()
SELECT
  "t0"."a",
  "t0"."b",
  "t0"."a" + "t0"."b" AS "c"
FROM "t" AS "t0"

You can also specify the SQL dialect to use for compilation:

expr.to_sql(dialect="mysql")
SELECT
  `t0`.`a`,
  `t0`.`b`,
  `t0`.`a` + `t0`.`b` AS `c`
FROM `t` AS `t0`

See Also

Value.to_sql() Table.to_sql() ibis.to_sql() Value.compile() Table.compile()

to_torch

to_torch(params=None, limit=None, **kwargs)

Execute an expression and return results as a dictionary of torch tensors.

Parameters

Name Type Description Default
params Mapping[ir.Scalar, Any] | None Parameters to substitute into the expression. None
limit int | str | None An integer to effect a specific row limit. A value of None means no limit. None
kwargs Any Keyword arguments passed into the backend’s to_torch implementation. {}

Returns

Name Type Description
dict[str, torch.Tensor] A dictionary of torch tensors, keyed by column name.

to_xlsx

to_xlsx(path, /, *, sheet='Sheet1', header=False, params=None, **kwargs)

Write a table to an Excel file.

Parameters

Name Type Description Default
path str | Path Excel output path. required
sheet str The name of the sheet to write to, eg ‘Sheet3’. 'Sheet1'
header bool Whether to include the column names as the first row. False
params Mapping[ir.Scalar, Any] | None Additional Ibis expression parameters to pass to the backend’s write function. None
kwargs Any Additional arguments passed to the backend’s write function. {}

Notes

Requires DuckDB >= 1.2.0.

See Also

DuckDB’s excel extension docs for writing

Examples

import os
import ibis
con = ibis.duckdb.connect()
t = con.create_table(
    "t",
    ibis.memtable({"a": [1, 2, 3], "b": ["a", "b", "c"]}),
    temp=True,
)
t.to_xlsx("/tmp/test.xlsx")
os.path.exists("/tmp/test.xlsx")
True

topk

topk(k=None, *, name=None)

Get the most frequent values of this table.

Parameters

Name Type Description Default
k int | None Number of top values to return. If None, all values are returned in descending order. None
name str | None The name to use for the frequency column. If not provided, a suitable name will be generated. None

Returns

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

See Also

Table.value_counts Column.topk Column.value_counts

Examples

from ibis import examples, selectors as s
ibis.options.interactive = True
t = examples.penguins.fetch().select("species", "island", "sex", "year")
t.head()
┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ species ┃ island    ┃ sex    ┃ year  ┃
┡━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━┩
│ string  │ string    │ string │ int64 │
├─────────┼───────────┼────────┼───────┤
│ Adelie  │ Torgersen │ male   │  2007 │
│ Adelie  │ Torgersen │ female │  2007 │
│ Adelie  │ Torgersen │ female │  2007 │
│ Adelie  │ Torgersen │ NULL   │  2007 │
│ Adelie  │ Torgersen │ female │  2007 │
└─────────┴───────────┴────────┴───────┘

t.topk().order_by(ibis.desc("species_island_sex_year_count"), s.all() & ~s.index[-1])
┏━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┓
┃ species   ┃ island ┃ sex    ┃ year  ┃ species_island_sex_year_count ┃
┡━━━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┩
│ string    │ string │ string │ int64 │ int64                         │
├───────────┼────────┼────────┼───────┼───────────────────────────────┤
│ Gentoo    │ Biscoe │ male   │  2008 │                            23 │
│ Gentoo    │ Biscoe │ female │  2008 │                            22 │
│ Gentoo    │ Biscoe │ male   │  2009 │                            21 │
│ Gentoo    │ Biscoe │ female │  2009 │                            20 │
│ Gentoo    │ Biscoe │ male   │  2007 │                            17 │
│ Gentoo    │ Biscoe │ female │  2007 │                            16 │
│ Chinstrap │ Dream  │ female │  2007 │                            13 │
│ Chinstrap │ Dream  │ male   │  2007 │                            13 │
│ Chinstrap │ Dream  │ female │  2009 │                            12 │
│ Chinstrap │ Dream  │ male   │  2009 │                            12 │
│ …         │ …      │ …      │     … │                             … │
└───────────┴────────┴────────┴───────┴───────────────────────────────┘

t.topk(3, name="n")
┏━━━━━━━━━┳━━━━━━━━┳━━━━━━━━┳━━━━━━━┳━━━━━━━┓
┃ species ┃ island ┃ sex    ┃ year  ┃ n     ┃
┡━━━━━━━━━╇━━━━━━━━╇━━━━━━━━╇━━━━━━━╇━━━━━━━┩
│ string  │ string │ string │ int64 │ int64 │
├─────────┼────────┼────────┼───────┼───────┤
│ Gentoo  │ Biscoe │ male   │  2008 │    23 │
│ Gentoo  │ Biscoe │ female │  2008 │    22 │
│ Gentoo  │ Biscoe │ male   │  2009 │    21 │
└─────────┴────────┴────────┴───────┴───────┘

try_cast

try_cast(schema=None, /, **overrides)

Cast the columns of a table, returning NULL/NaN on failure.

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 IntoSchema | None Mapping, schema or iterable of pairs to use for casting. Anything that ibis.schema() can consume. None
overrides IntoDtype column_name=<type> overrides. Will override types from schema. {}

Returns

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

See Also

Table.cast for more details and examples.

unbind

unbind()

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

Examples

import ibis
import pandas as pd
duckdb_con = ibis.duckdb.connect()
polars_con = ibis.polars.connect()
for backend in (duckdb_con, polars_con):
    t = backend.create_table("t", pd.DataFrame({"a": [1, 2, 3]}))
bound_table = duckdb_con.table("t")
bound_table.get_backend().name
'duckdb'
unbound_table = bound_table.unbind()
polars_con.execute(unbound_table)
a
0 1
1 2
2 3

union

union(table, /, *rest, distinct=False)

Compute the multiset (or 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 Use multiset union (False) or set union (True). See examples. False

Returns

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

See Also

ibis.union

Examples

import ibis
ibis.options.interactive = True
t1 = ibis.memtable({"a": [1, 2]})
t1
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
│     2 │
└───────┘

t2 = ibis.memtable({"a": [2, 3]})
t2
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     2 │
│     3 │
└───────┘

t1.union(t2)  # union all by default
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
│     2 │
│     2 │
│     3 │
└───────┘

t1.union(t2, distinct=True).order_by("a")
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
│     2 │
│     3 │
└───────┘

You can union more than two tables at once.

t1.union(t1, t1).order_by("a")
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
│     1 │
│     1 │
│     2 │
│     2 │
│     2 │
└───────┘

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

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

Construct a table expression with an array column.

t = ibis.memtable({"x": [[1, 2], [], None, [3, 4, 5]], "y": [1, 2, 3, 4]})
t
┏━━━━━━━━━━━━━━━━┳━━━━━━━┓
┃ x              ┃ y     ┃
┡━━━━━━━━━━━━━━━━╇━━━━━━━┩
│ array<int64>   │ int64 │
├────────────────┼───────┤
│ [1, 2]         │     1 │
│ []             │     2 │
│ NULL           │     3 │
│ [3, 4, ... +1] │     4 │
└────────────────┴───────┘

Unnest the array column x, replacing the existing x column.

t.unnest("x").order_by(_.x)
┏━━━━━━━┳━━━━━━━┓
┃ x     ┃ y     ┃
┡━━━━━━━╇━━━━━━━┩
│ int64 │ int64 │
├───────┼───────┤
│     1 │     1 │
│     2 │     1 │
│     3 │     4 │
│     4 │     4 │
│     5 │     4 │
└───────┴───────┘

Unnest the array column x with an offset. The offset parameter is the name of the resulting index column.

t.unnest(t.x, offset="idx").order_by(_.x)
┏━━━━━━━┳━━━━━━━┳━━━━━━━┓
┃ x     ┃ y     ┃ idx   ┃
┡━━━━━━━╇━━━━━━━╇━━━━━━━┩
│ int64 │ int64 │ int64 │
├───────┼───────┼───────┤
│     1 │     1 │     0 │
│     2 │     1 │     1 │
│     3 │     4 │     0 │
│     4 │     4 │     1 │
│     5 │     4 │     2 │
└───────┴───────┴───────┘

Unnest the array column x keep empty array values as NULL in the output table.

t.unnest(_.x, offset="idx", keep_empty=True).order_by(_.x, _.y)
┏━━━━━━━┳━━━━━━━┳━━━━━━━┓
┃ x     ┃ y     ┃ idx   ┃
┡━━━━━━━╇━━━━━━━╇━━━━━━━┩
│ int64 │ int64 │ int64 │
├───────┼───────┼───────┤
│     1 │     1 │     0 │
│     2 │     1 │     1 │
│     3 │     4 │     0 │
│     4 │     4 │     1 │
│     5 │     4 │     2 │
│  NULL │     2 │  NULL │
│  NULL │     3 │  NULL │
└───────┴───────┴───────┘

If you need to preserve the row order of the preserved empty arrays or null values use row_number to create an index column before calling unnest.

(
    t.mutate(original_row=ibis.row_number())
    .unnest("x", offset="idx", keep_empty=True)
    .relocate("original_row")
    .order_by("original_row", "idx")
)
┏━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━┳━━━━━━━┓
┃ original_row ┃ x     ┃ y     ┃ idx   ┃
┡━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━╇━━━━━━━┩
│ int64        │ int64 │ int64 │ int64 │
├──────────────┼───────┼───────┼───────┤
│            0 │     1 │     1 │     0 │
│            0 │     2 │     1 │     1 │
│            1 │  NULL │     2 │  NULL │
│            2 │  NULL │     3 │  NULL │
│            3 │     3 │     4 │     0 │
│            3 │     4 │     4 │     1 │
│            3 │     5 │     4 │     2 │
└──────────────┴───────┴───────┴───────┘

You can also unnest more complex expressions, and the resulting column will be projected as the last expression in the result.

t.unnest(_.x.map(lambda v: v + 1).name("plus_one")).order_by(_.plus_one)
┏━━━━━━━━━━━━━━━━┳━━━━━━━┳━━━━━━━━━━┓
┃ x              ┃ y     ┃ plus_one ┃
┡━━━━━━━━━━━━━━━━╇━━━━━━━╇━━━━━━━━━━┩
│ array<int64>   │ int64 │ int64    │
├────────────────┼───────┼──────────┤
│ [1, 2]         │     1 │        2 │
│ [1, 2]         │     1 │        3 │
│ [3, 4, ... +1] │     4 │        4 │
│ [3, 4, ... +1] │     4 │        5 │
│ [3, 4, ... +1] │     4 │        6 │
└────────────────┴───────┴──────────┘

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.

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 │
└────────┴─────────┴─────────┘

Existing columns are overwritten by unpacking, regardless of existing column ordering.

Here, x follows a:

t = ibis.memtable(
    {"a": [{"x": 1}, {"x": 2}], "x": ["abc", "def"]},
    schema={"a": "struct<x: int>", "x": "string"},
)
t.unpack("a")
┏━━━━━━━┓
┃ x     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
│     2 │
└───────┘

And here, x precedes a:

t = ibis.memtable(
    {"x": ["abc", "def"], "a": [{"x": 1}, {"x": 2}]},
    schema={"x": "string", "a": "struct<x: int>"},
)
t.unpack("a")
┏━━━━━━━┓
┃ x     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
│     2 │
└───────┘

See Also

StructValue.lift

value_counts

value_counts(name=None)

Compute a frequency table of this table’s values.

NoteChanged in version 10.0.0

Added name parameter.

Parameters

Name Type Description Default
name str | None The name to use for the frequency column. If not provided, a suitable name will be generated. None

Returns

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

See Also

Table.topk Column.value_counts Column.topk

Examples

from ibis import examples
ibis.options.interactive = True
t = examples.penguins.fetch()
t.head()
┏━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━┳━━━━━━━━┳━━━━━━━┓
┃ 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 │
└─────────┴───────────┴────────────────┴───────────────┴───────────────────┴─────────────┴────────┴───────┘

t.year.value_counts(name="n").order_by("year")
┏━━━━━━━┳━━━━━━━┓
┃ year  ┃ n     ┃
┡━━━━━━━╇━━━━━━━┩
│ int64 │ int64 │
├───────┼───────┤
│  2007 │   110 │
│  2008 │   114 │
│  2009 │   120 │
└───────┴───────┘

t[["year", "island"]].value_counts().order_by("year", "island")
┏━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┓
┃ year  ┃ island    ┃ year_island_count ┃
┡━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━┩
│ int64 │ string    │ int64             │
├───────┼───────────┼───────────────────┤
│  2007 │ Biscoe    │                44 │
│  2007 │ Dream     │                46 │
│  2007 │ Torgersen │                20 │
│  2008 │ Biscoe    │                64 │
│  2008 │ Dream     │                34 │
│  2008 │ Torgersen │                16 │
│  2009 │ Biscoe    │                60 │
│  2009 │ Dream     │                44 │
│  2009 │ Torgersen │                16 │
└───────┴───────────┴───────────────────┘

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 True 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 ibis
import 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.

GroupedTable

GroupedTable(**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

Name Type Description
Table The aggregated table

having

having(*predicates)

Add a post-aggregation result filter expr.

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

Parameters

Name Type Description Default
predicates ir.BooleanScalar Expressions that filters based on an aggregate value. ()

Returns

Name Type Description
GroupedTable A grouped table expression

Examples

import ibis
ibis.options.interactive = True
t = ibis.memtable(
    {"grouper": ["a", "a", "a", "b", "b", "c"], "values": [1, 2, 3, 1, 2, 1]}
)
expr = (
    t.group_by(t.grouper)
    .having(t.count() < 3)
    .aggregate(values_count=t.count(), values_sum=t.values.sum())
    .order_by(t.grouper)
)
expr
┏━━━━━━━━━┳━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓
┃ grouper ┃ values_count ┃ values_sum ┃
┡━━━━━━━━━╇━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩
│ string  │ int64        │ int64      │
├─────────┼──────────────┼────────────┤
│ b       │            2 │          3 │
│ c       │            1 │          1 │
└─────────┴──────────────┴────────────┘

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

Name Type Description
Table A table expression with window functions applied

order_by

order_by(*by)

Sort a grouped table expression by expr.

Notes

This API call is ignored in aggregations.

Parameters

Name Type Description Default
by ir.Value Expressions to order the results by ()

Returns

Name 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

Name 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(paths, /, *, 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
paths 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

Name 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(path, /, *, table_name=None, **kwargs)

Lazily load a Delta Lake table.

Parameters

Name Type Description Default
path 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

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

read_json

ibis.read_json(paths, /, *, table_name=None, **kwargs)

Lazily load newline-delimited JSON data.

WarningThis 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
paths 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

Name 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(paths, /, *, 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
paths 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

Name 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)

Construct an ibis table expression from in-memory data.

Parameters

Name Type Description Default
data IntoMemtable 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 IntoSchema | None Optional Schema. The functions use data to infer a schema if not passed. None

Returns

Name 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 IntoSchema | 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

Name 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 (or multiset) 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 Use set difference (True) or multiset difference (False). See examples. True

See Also

ibis.difference

Returns

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

Examples

import ibis
ibis.options.interactive = True
t1 = ibis.memtable({"a": [7, 8, 8, 9, 9, 9]})
t2 = ibis.memtable({"a": [8, 9]})

With distinct=True, if a row ever appears in any of *rest, it will not appear in the result. So here, all appearances of 8 and 9 are removed:

t1.difference(t2)
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     7 │
└───────┘

With distinct=False, the algorithm is a multiset difference. This means, that since 8 and 9 each appear once in t2, the result will be the input with a single instance of each removed:

t1.difference(t2, distinct=False).order_by("a")
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     7 │
│     8 │
│     9 │
│     9 │
└───────┘

With multiple tables in *rest, we apply the operation consecutively. Here, we remove two eights and two nines:

t1.difference(t2, t2, distinct=False).order_by("a")
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     7 │
│     9 │
└───────┘

intersect

ibis.intersect(table, /, *rest, distinct=True)

Compute the set (or multiset) 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 Use set intersect (True) or multiset intersect (False). See examples. True

Returns

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

See Also

Table.intersect

Examples

import ibis
ibis.options.interactive = True
two_a = ibis.memtable({"x": ["a", "a", "b"]})
three_a = ibis.memtable({"x": ["a", "a", "a", "b"]})
four_a = ibis.memtable({"x": ["a", "a", "a", "a", "c"]})

With distinct=True, the intersection will return one row for each row that appears in all input tables. This is equivalent to a set intersection. So even though the source tables have multiple "a" values, the result will only have one:

ibis.intersect(two_a, three_a).order_by("x")
┏━━━━━━━━┓
┃ x      ┃
┡━━━━━━━━┩
│ string │
├────────┤
│ a      │
│ b      │
└────────┘

With distinct=False, the intersection will return all rows that appear in all input tables. This is equivalent to a multiset intersection. Since the smallest number of appearances of "a" is 2, the result will have two "a" values:

ibis.intersect(two_a, three_a, distinct=False).order_by("x")
┏━━━━━━━━┓
┃ x      ┃
┡━━━━━━━━┩
│ string │
├────────┤
│ a      │
│ a      │
│ b      │
└────────┘

More than two table expressions can be intersected at once. - Since "a" appears at minimum one time, it appears once in the result. - Since "b" doesn’t appear in two_a or three_a, it is not included. - Since "c" does not appear in one_a, it is not included.

ibis.intersect(two_a, three_a, four_a)
┏━━━━━━━━┓
┃ x      ┃
┡━━━━━━━━┩
│ string │
├────────┤
│ a      │
└────────┘

ibis.intersect(two_a, three_a, four_a, distinct=False)
┏━━━━━━━━┓
┃ x      ┃
┡━━━━━━━━┩
│ string │
├────────┤
│ a      │
│ a      │
└────────┘

union

ibis.union(table, /, *rest, distinct=False)

Compute the multiset (or 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 Use multiset union (False) or set union (True). See examples. False

Returns

Name 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 │
└───────┘

You can union more than two tables at once.

ibis.union(t1, t1, t1).order_by("a")
┏━━━━━━━┓
┃ a     ┃
┡━━━━━━━┩
│ int64 │
├───────┤
│     1 │
│     1 │
│     1 │
│     2 │
│     2 │
│     2 │
└───────┘

join

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

Perform a join between two tables.

Parameters

Name Type Description Default
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] | ir.BooleanValue] 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. Show the top 5 users by the number of unique movies that they both rated and tagged:

(
    tags.join(ratings, ["userId", "movieId"])
    .group_by(_.userId)
    .agg(n_rated_and_tagged=_.movieId.nunique())
    .order_by(_.n_rated_and_tagged.desc())
    .head(5)
)
┏━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━┓
┃ userId ┃ n_rated_and_tagged ┃
┡━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━┩
│ int64  │ int64              │
├────────┼────────────────────┤
│    474 │               1149 │
│    567 │                109 │
│     62 │                 69 │
│    477 │                 66 │
│    424 │                 58 │
└────────┴────────────────────┘

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            │
├─────────┼───────────────────┼───────────────┼───────────────────┤
│   60756 │ funny             │          1732 │ funny             │
│   60756 │ Highly quotable   │          1732 │ Highly quotable   │
│   89774 │ Tom Hardy         │        139385 │ tom hardy         │
│  106782 │ drugs             │          1732 │ drugs             │
│  106782 │ Leonardo DiCaprio │          5989 │ Leonardo DiCaprio │
└─────────┴───────────────────┴───────────────┴───────────────────┘

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

Name 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

Name 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

Name 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

Name 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

Name 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

Name 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

Name 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

Name 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

Name 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

Name Type Description
Window A window frame
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