Selectors#

Selectors allow for more intuitive selection of columns from DataFrame or LazyFrame objects based on their name, dtype or other properties. They unify and build on the related functionality that is available through the col() expression and can also broadcast expressions over the selected columns.

Importing#

Selectors are available as functions imported from polars.selectors

Typical/recommended usage is to import the module as cs and employ selectors from there.

import polars.selectors as cs
import polars as pl

df = pl.DataFrame(
    {
        "w": ["xx", "yy", "xx", "yy", "xx"],
        "x": [1, 2, 1, 4, -2],
        "y": [3.0, 4.5, 1.0, 2.5, -2.0],
        "z": ["a", "b", "a", "b", "b"],
    },
)
df.group_by(by=cs.string()).agg(cs.numeric().sum())

Set operations#

Selectors support set operations such as:

UNION: A | B
INTERSECTION: A & B
DIFFERENCE: A - B
COMPLEMENT: ~A

Examples#

import polars.selectors as cs
import polars as pl

# set up an empty dataframe with plenty of columns of various dtypes
df = pl.DataFrame(
    schema={
        "abc": pl.UInt16,
        "bbb": pl.UInt32,
        "cde": pl.Float64,
        "def": pl.Float32,
        "eee": pl.Boolean,
        "fgg": pl.Boolean,
        "ghi": pl.Time,
        "JJK": pl.Date,
        "Lmn": pl.Duration,
        "opp": pl.Datetime("ms"),
        "qqR": pl.String,
    },
)

# Select the UNION of temporal, strings and columns that start with "e"
assert df.select(cs.temporal() | cs.string() | cs.starts_with("e")).schema == {
    "eee": pl.Boolean,
    "ghi": pl.Time,
    "JJK": pl.Date,
    "Lmn": pl.Duration,
    "opp": pl.Datetime("ms"),
    "qqR": pl.String,
}

# Select the INTERSECTION of temporal and column names that match "opp" OR "JJK"
assert df.select(cs.temporal() & cs.matches("opp|JJK")).schema == {
    "JJK": pl.Date,
    "opp": pl.Datetime("ms"),
}

# Select the DIFFERENCE of temporal columns and columns that contain the name "opp" OR "JJK"
assert df.select(cs.temporal() - cs.matches("opp|JJK")).schema == {
    "ghi": pl.Time,
    "Lmn": pl.Duration,
}

# Select the COMPLEMENT of all columns of dtypes Duration and Time
assert df.select(~cs.by_dtype([pl.Duration, pl.Time])).schema == {
    "abc": pl.UInt16,
    "bbb": pl.UInt32,
    "cde": pl.Float64,
    "def": pl.Float32,
    "eee": pl.Boolean,
    "fgg": pl.Boolean,
    "JJK": pl.Date,
    "opp": pl.Datetime("ms"),
    "qqR": pl.String,
}

Note

If you don’t want to use the set operations on the selectors, you can materialize them as expressions by calling as_expr. This ensures the operations OR, AND, etc are dispatched to the underlying expressions instead.

Functions#

Available selector functions:

`all`()	Select all columns.
`by_dtype`(*dtypes)	Select all columns matching the given dtypes.
`by_name`(*names)	Select all columns matching the given names.
`categorical`()	Select all categorical columns.
`contains`(substring)	Select columns whose names contain the given literal substring(s).
`date`()	Select all date columns.
`datetime`([time_unit, time_zone])	Select all datetime columns, optionally filtering by time unit/zone.
`duration`([time_unit])	Select all duration columns, optionally filtering by time unit.
`ends_with`(*suffix)	Select columns that end with the given substring(s).
`expand_selector`(target, selector)	Expand a selector to column names with respect to a specific frame or schema target.
`first`()	Select the first column in the current scope.
`float`()	Select all float columns.
`integer`()	Select all integer columns.
`is_selector`(obj)	Indicate whether the given object/expression is a selector.
`last`()	Select the last column in the current scope.
`matches`(pattern)	Select all columns that match the given regex pattern.
`numeric`()	Select all numeric columns.
`starts_with`(*prefix)	Select columns that start with the given substring(s).
`string`(*[, include_categorical])	Select all String (and, optionally, Categorical) string columns .
`temporal`()	Select all temporal columns.
`time`()	Select all time columns.

polars.selectors.all() → SelectorType[source]#

Select all columns.

See also

first: Select the first column in the current scope.
last: Select the last column in the current scope.

Examples

>>> from datetime import date
>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "dt": [date(1999, 12, 31), date(2024, 1, 1)],
...         "value": [1_234_500, 5_000_555],
...     },
...     schema_overrides={"value": pl.Int32},
... )

Select all columns, casting them to string:

>>> df.select(cs.all().cast(pl.String))
shape: (2, 2)
┌────────────┬─────────┐
│ dt         ┆ value   │
│ ---        ┆ ---     │
│ str        ┆ str     │
╞════════════╪═════════╡
│ 1999-12-31 ┆ 1234500 │
│ 2024-01-01 ┆ 5000555 │
└────────────┴─────────┘

Select all columns except for those matching the given dtypes:

>>> df.select(cs.all() - cs.numeric())
shape: (2, 1)
┌────────────┐
│ dt         │
│ ---        │
│ date       │
╞════════════╡
│ 1999-12-31 │
│ 2024-01-01 │
└────────────┘

polars.selectors.by_dtype(*dtypes: PolarsDataType | Collection[PolarsDataType]) → SelectorType[source]#

Select all columns matching the given dtypes.

See also

by_name: Select all columns matching the given names.

Examples

>>> from datetime import date
>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "dt": [date(1999, 12, 31), date(2024, 1, 1), date(2010, 7, 5)],
...         "value": [1_234_500, 5_000_555, -4_500_000],
...         "other": ["foo", "bar", "foo"],
...     }
... )

Select all columns with date or integer dtypes:

>>> df.select(cs.by_dtype(pl.Date, pl.INTEGER_DTYPES))
shape: (3, 2)
┌────────────┬──────────┐
│ dt         ┆ value    │
│ ---        ┆ ---      │
│ date       ┆ i64      │
╞════════════╪══════════╡
│ 1999-12-31 ┆ 1234500  │
│ 2024-01-01 ┆ 5000555  │
│ 2010-07-05 ┆ -4500000 │
└────────────┴──────────┘

Select all columns that are not of date or integer dtype:

>>> df.select(~cs.by_dtype(pl.Date, pl.INTEGER_DTYPES))
shape: (3, 1)
┌───────┐
│ other │
│ ---   │
│ str   │
╞═══════╡
│ foo   │
│ bar   │
│ foo   │
└───────┘

Group by string columns and sum the numeric columns:

>>> df.group_by(cs.string()).agg(cs.numeric().sum()).sort(by="other")
shape: (2, 2)
┌───────┬──────────┐
│ other ┆ value    │
│ ---   ┆ ---      │
│ str   ┆ i64      │
╞═══════╪══════════╡
│ bar   ┆ 5000555  │
│ foo   ┆ -3265500 │
└───────┴──────────┘

polars.selectors.by_name(*names: str | Collection[str]) → SelectorType[source]#

Select all columns matching the given names.

Parameters:

*names: One or more names of columns to select.

See also

by_dtype: Select all columns matching the given dtypes.

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "foo": ["x", "y"],
...         "bar": [123, 456],
...         "baz": [2.0, 5.5],
...         "zap": [False, True],
...     }
... )

Select columns by name:

>>> df.select(cs.by_name("foo", "bar"))
shape: (2, 2)
┌─────┬─────┐
│ foo ┆ bar │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════╡
│ x   ┆ 123 │
│ y   ┆ 456 │
└─────┴─────┘

Match all columns except for those given:

>>> df.select(~cs.by_name("foo", "bar"))
shape: (2, 2)
┌─────┬───────┐
│ baz ┆ zap   │
│ --- ┆ ---   │
│ f64 ┆ bool  │
╞═════╪═══════╡
│ 2.0 ┆ false │
│ 5.5 ┆ true  │
└─────┴───────┘

polars.selectors.categorical() → SelectorType[source]#

Select all categorical columns.

See also

by_dtype: Select all columns matching the given dtype(s).
string: Select all string columns (optionally including categoricals).

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "foo": ["xx", "yy"],
...         "bar": [123, 456],
...         "baz": [2.0, 5.5],
...     },
...     schema_overrides={"foo": pl.Categorical},
... )

Select all categorical columns:

>>> df.select(cs.categorical())
shape: (2, 1)
┌─────┐
│ foo │
│ --- │
│ cat │
╞═════╡
│ xx  │
│ yy  │
└─────┘

Select all columns except for those that are categorical:

>>> df.select(~cs.categorical())
shape: (2, 2)
┌─────┬─────┐
│ bar ┆ baz │
│ --- ┆ --- │
│ i64 ┆ f64 │
╞═════╪═════╡
│ 123 ┆ 2.0 │
│ 456 ┆ 5.5 │
└─────┴─────┘

polars.selectors.contains(substring: str | Collection[str]) → SelectorType[source]#

Select columns whose names contain the given literal substring(s).

Parameters:

substring: Substring(s) that matching column names should contain.

See also

matches: Select all columns that match the given regex pattern.
ends_with: Select columns that end with the given substring(s).
starts_with: Select columns that start with the given substring(s).

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "foo": ["x", "y"],
...         "bar": [123, 456],
...         "baz": [2.0, 5.5],
...         "zap": [False, True],
...     }
... )

Select columns that contain the substring ‘ba’:

>>> df.select(cs.contains("ba"))
shape: (2, 2)
┌─────┬─────┐
│ bar ┆ baz │
│ --- ┆ --- │
│ i64 ┆ f64 │
╞═════╪═════╡
│ 123 ┆ 2.0 │
│ 456 ┆ 5.5 │
└─────┴─────┘

Select columns that contain the substring ‘ba’ or the letter ‘z’:

>>> df.select(cs.contains(("ba", "z")))
shape: (2, 3)
┌─────┬─────┬───────┐
│ bar ┆ baz ┆ zap   │
│ --- ┆ --- ┆ ---   │
│ i64 ┆ f64 ┆ bool  │
╞═════╪═════╪═══════╡
│ 123 ┆ 2.0 ┆ false │
│ 456 ┆ 5.5 ┆ true  │
└─────┴─────┴───────┘

Select all columns except for those that contain the substring ‘ba’:

>>> df.select(~cs.contains("ba"))
shape: (2, 2)
┌─────┬───────┐
│ foo ┆ zap   │
│ --- ┆ ---   │
│ str ┆ bool  │
╞═════╪═══════╡
│ x   ┆ false │
│ y   ┆ true  │
└─────┴───────┘

polars.selectors.date() → SelectorType[source]#

Select all date columns.

See also

datetime: Select all datetime columns, optionally filtering by time unit/zone.
duration: Select all duration columns, optionally filtering by time unit.
temporal: Select all temporal columns.
time: Select all time columns.

Examples

>>> from datetime import date, datetime, time
>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "dtm": [datetime(2001, 5, 7, 10, 25), datetime(2031, 12, 31, 0, 30)],
...         "dt": [date(1999, 12, 31), date(2024, 8, 9)],
...         "tm": [time(0, 0, 0), time(23, 59, 59)],
...     },
... )

Select all date columns:

>>> df.select(cs.date())
shape: (2, 1)
┌────────────┐
│ dt         │
│ ---        │
│ date       │
╞════════════╡
│ 1999-12-31 │
│ 2024-08-09 │
└────────────┘

Select all columns except for those that are dates:

>>> df.select(~cs.date())
shape: (2, 2)
┌─────────────────────┬──────────┐
│ dtm                 ┆ tm       │
│ ---                 ┆ ---      │
│ datetime[μs]        ┆ time     │
╞═════════════════════╪══════════╡
│ 2001-05-07 10:25:00 ┆ 00:00:00 │
│ 2031-12-31 00:30:00 ┆ 23:59:59 │
└─────────────────────┴──────────┘

Select all datetime columns, optionally filtering by time unit/zone.

Parameters:

time_unit

One (or more) of the allowed timeunit precision strings, “ms”, “us”, and “ns”. Omit to select columns with any valid timeunit.

time_zone

One or more timezone strings, as defined in zoneinfo (to see valid options run import zoneinfo; zoneinfo.available_timezones() for a full list).
Set None to select Datetime columns that do not have a timezone.
Set “*” to select Datetime columns that have any timezone.

See also

date: Select all date columns.
duration: Select all duration columns, optionally filtering by time unit.
temporal: Select all temporal columns.
time: Select all time columns.

Examples

>>> from datetime import datetime, date
>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "tstamp_tokyo": [
...             datetime(1999, 7, 21, 5, 20, 16, 987654),
...             datetime(2000, 5, 16, 6, 21, 21, 123465),
...         ],
...         "tstamp_utc": [
...             datetime(2023, 4, 10, 12, 14, 16, 999000),
...             datetime(2025, 8, 25, 14, 18, 22, 666000),
...         ],
...         "tstamp": [
...             datetime(2000, 11, 20, 18, 12, 16, 600000),
...             datetime(2020, 10, 30, 10, 20, 25, 123000),
...         ],
...         "dt": [date(1999, 12, 31), date(2010, 7, 5)],
...     },
...     schema_overrides={
...         "tstamp_tokyo": pl.Datetime("ns", "Asia/Tokyo"),
...         "tstamp_utc": pl.Datetime("us", "UTC"),
...     },
... )

Select all datetime columns:

>>> df.select(cs.datetime())
shape: (2, 3)
┌────────────────────────────────┬─────────────────────────────┬─────────────────────────┐
│ tstamp_tokyo                   ┆ tstamp_utc                  ┆ tstamp                  │
│ ---                            ┆ ---                         ┆ ---                     │
│ datetime[ns, Asia/Tokyo]       ┆ datetime[μs, UTC]           ┆ datetime[μs]            │
╞════════════════════════════════╪═════════════════════════════╪═════════════════════════╡
│ 1999-07-21 05:20:16.987654 JST ┆ 2023-04-10 12:14:16.999 UTC ┆ 2000-11-20 18:12:16.600 │
│ 2000-05-16 06:21:21.123465 JST ┆ 2025-08-25 14:18:22.666 UTC ┆ 2020-10-30 10:20:25.123 │
└────────────────────────────────┴─────────────────────────────┴─────────────────────────┘

Select all datetime columns that have ‘us’ precision:

>>> df.select(cs.datetime("us"))
shape: (2, 2)
┌─────────────────────────────┬─────────────────────────┐
│ tstamp_utc                  ┆ tstamp                  │
│ ---                         ┆ ---                     │
│ datetime[μs, UTC]           ┆ datetime[μs]            │
╞═════════════════════════════╪═════════════════════════╡
│ 2023-04-10 12:14:16.999 UTC ┆ 2000-11-20 18:12:16.600 │
│ 2025-08-25 14:18:22.666 UTC ┆ 2020-10-30 10:20:25.123 │
└─────────────────────────────┴─────────────────────────┘

Select all datetime columns that have any timezone:

>>> df.select(cs.datetime(time_zone="*"))
shape: (2, 2)
┌────────────────────────────────┬─────────────────────────────┐
│ tstamp_tokyo                   ┆ tstamp_utc                  │
│ ---                            ┆ ---                         │
│ datetime[ns, Asia/Tokyo]       ┆ datetime[μs, UTC]           │
╞════════════════════════════════╪═════════════════════════════╡
│ 1999-07-21 05:20:16.987654 JST ┆ 2023-04-10 12:14:16.999 UTC │
│ 2000-05-16 06:21:21.123465 JST ┆ 2025-08-25 14:18:22.666 UTC │
└────────────────────────────────┴─────────────────────────────┘

Select all datetime columns that have a specific timezone:

>>> df.select(cs.datetime(time_zone="UTC"))
shape: (2, 1)
┌─────────────────────────────┐
│ tstamp_utc                  │
│ ---                         │
│ datetime[μs, UTC]           │
╞═════════════════════════════╡
│ 2023-04-10 12:14:16.999 UTC │
│ 2025-08-25 14:18:22.666 UTC │
└─────────────────────────────┘

Select all datetime columns that have NO timezone:

>>> df.select(cs.datetime(time_zone=None))
shape: (2, 1)
┌─────────────────────────┐
│ tstamp                  │
│ ---                     │
│ datetime[μs]            │
╞═════════════════════════╡
│ 2000-11-20 18:12:16.600 │
│ 2020-10-30 10:20:25.123 │
└─────────────────────────┘

Select all columns except for datetime columns:

>>> df.select(~cs.datetime())
shape: (2, 1)
┌────────────┐
│ dt         │
│ ---        │
│ date       │
╞════════════╡
│ 1999-12-31 │
│ 2010-07-05 │
└────────────┘

polars.selectors.duration(time_unit: TimeUnit | Collection[TimeUnit] | None = None) → SelectorType[source]#

Select all duration columns, optionally filtering by time unit.

Parameters:

time_unit: One (or more) of the allowed timeunit precision strings, “ms”, “us”, and “ns”. Omit to select columns with any valid timeunit.

See also

date: Select all date columns.
datetime: Select all datetime columns, optionally filtering by time unit/zone.
temporal: Select all temporal columns.
time: Select all time columns.

Examples

>>> from datetime import date, timedelta
>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "dt": [date(2022, 1, 31), date(2025, 7, 5)],
...         "td1": [
...             timedelta(days=1, milliseconds=123456),
...             timedelta(days=1, hours=23, microseconds=987000),
...         ],
...         "td2": [
...             timedelta(days=7, microseconds=456789),
...             timedelta(days=14, minutes=999, seconds=59),
...         ],
...         "td3": [
...             timedelta(weeks=4, days=-10, microseconds=999999),
...             timedelta(weeks=3, milliseconds=123456, microseconds=1),
...         ],
...     },
...     schema_overrides={
...         "td1": pl.Duration("ms"),
...         "td2": pl.Duration("us"),
...         "td3": pl.Duration("ns"),
...     },
... )

Select all duration columns:

>>> df.select(cs.duration())
shape: (2, 3)
┌────────────────┬─────────────────┬────────────────────┐
│ td1            ┆ td2             ┆ td3                │
│ ---            ┆ ---             ┆ ---                │
│ duration[ms]   ┆ duration[μs]    ┆ duration[ns]       │
╞════════════════╪═════════════════╪════════════════════╡
│ 1d 2m 3s 456ms ┆ 7d 456789µs     ┆ 18d 999999µs       │
│ 1d 23h 987ms   ┆ 14d 16h 39m 59s ┆ 21d 2m 3s 456001µs │
└────────────────┴─────────────────┴────────────────────┘

Select all duration columns that have ‘ms’ precision:

>>> df.select(cs.duration("ms"))
shape: (2, 1)
┌────────────────┐
│ td1            │
│ ---            │
│ duration[ms]   │
╞════════════════╡
│ 1d 2m 3s 456ms │
│ 1d 23h 987ms   │
└────────────────┘

Select all duration columns that have ‘ms’ OR ‘ns’ precision:

>>> df.select(cs.duration(["ms", "ns"]))
shape: (2, 2)
┌────────────────┬────────────────────┐
│ td1            ┆ td3                │
│ ---            ┆ ---                │
│ duration[ms]   ┆ duration[ns]       │
╞════════════════╪════════════════════╡
│ 1d 2m 3s 456ms ┆ 18d 999999µs       │
│ 1d 23h 987ms   ┆ 21d 2m 3s 456001µs │
└────────────────┴────────────────────┘

Select all columns except for duration columns:

>>> df.select(~cs.duration())
shape: (2, 1)
┌────────────┐
│ dt         │
│ ---        │
│ date       │
╞════════════╡
│ 2022-01-31 │
│ 2025-07-05 │
└────────────┘

polars.selectors.ends_with(*suffix: str) → SelectorType[source]#

Select columns that end with the given substring(s).

Parameters:

suffix: Substring(s) that matching column names should end with.

See also

contains: Select columns that contain the given literal substring(s).
matches: Select all columns that match the given regex pattern.
starts_with: Select columns that start with the given substring(s).

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "foo": ["x", "y"],
...         "bar": [123, 456],
...         "baz": [2.0, 5.5],
...         "zap": [False, True],
...     }
... )

Select columns that end with the substring ‘z’:

>>> df.select(cs.ends_with("z"))
shape: (2, 1)
┌─────┐
│ baz │
│ --- │
│ f64 │
╞═════╡
│ 2.0 │
│ 5.5 │
└─────┘

Select columns that end with either the letter ‘z’ or ‘r’:

>>> df.select(cs.ends_with("z", "r"))
shape: (2, 2)
┌─────┬─────┐
│ bar ┆ baz │
│ --- ┆ --- │
│ i64 ┆ f64 │
╞═════╪═════╡
│ 123 ┆ 2.0 │
│ 456 ┆ 5.5 │
└─────┴─────┘

Select all columns except for those that end with the substring ‘z’:

>>> df.select(~cs.ends_with("z"))
shape: (2, 3)
┌─────┬─────┬───────┐
│ foo ┆ bar ┆ zap   │
│ --- ┆ --- ┆ ---   │
│ str ┆ i64 ┆ bool  │
╞═════╪═════╪═══════╡
│ x   ┆ 123 ┆ false │
│ y   ┆ 456 ┆ true  │
└─────┴─────┴───────┘

polars.selectors.expand_selector( target: DataFrame | LazyFrame | Mapping[str, PolarsDataType], selector: SelectorType, ) → tuple[str, ...][source]#

Expand a selector to column names with respect to a specific frame or schema target.

Parameters:

target: A polars DataFrame, LazyFrame or schema.
selector: An arbitrary polars selector (or compound selector).

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "colx": ["a", "b", "c"],
...         "coly": [123, 456, 789],
...         "colz": [2.0, 5.5, 8.0],
...     }
... )

Expand selector with respect to an existing DataFrame:

>>> cs.expand_selector(df, cs.numeric())
('coly', 'colz')
>>> cs.expand_selector(df, cs.first() | cs.last())
('colx', 'colz')

This also works with LazyFrame:

>>> cs.expand_selector(df.lazy(), ~(cs.first() | cs.last()))
('coly',)

Expand selector with respect to a standalone schema:

>>> schema = {
...     "colx": pl.Float32,
...     "coly": pl.Float64,
...     "colz": pl.Date,
... }
>>> cs.expand_selector(schema, cs.float())
('colx', 'coly')

polars.selectors.first() → SelectorType[source]#

Select the first column in the current scope.

See also

all: Select all columns.
last: Select the last column in the current scope.

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "foo": ["x", "y"],
...         "bar": [123, 456],
...         "baz": [2.0, 5.5],
...         "zap": [0, 1],
...     }
... )

Select the first column:

>>> df.select(cs.first())
shape: (2, 1)
┌─────┐
│ foo │
│ --- │
│ str │
╞═════╡
│ x   │
│ y   │
└─────┘

Select everything except for the first column:

>>> df.select(~cs.first())
shape: (2, 3)
┌─────┬─────┬─────┐
│ bar ┆ baz ┆ zap │
│ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ i64 │
╞═════╪═════╪═════╡
│ 123 ┆ 2.0 ┆ 0   │
│ 456 ┆ 5.5 ┆ 1   │
└─────┴─────┴─────┘

polars.selectors.float() → SelectorType[source]#

Select all float columns.

See also

integer: Select all integer columns.
numeric: Select all numeric columns.
signed_integer: Select all signed integer columns.
unsigned_integer: Select all unsigned integer columns.

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "foo": ["x", "y"],
...         "bar": [123, 456],
...         "baz": [2.0, 5.5],
...         "zap": [0.0, 1.0],
...     },
...     schema_overrides={"baz": pl.Float32, "zap": pl.Float64},
... )

Select all float columns:

>>> df.select(cs.float())
shape: (2, 2)
┌─────┬─────┐
│ baz ┆ zap │
│ --- ┆ --- │
│ f32 ┆ f64 │
╞═════╪═════╡
│ 2.0 ┆ 0.0 │
│ 5.5 ┆ 1.0 │
└─────┴─────┘

Select all columns except for those that are float:

>>> df.select(~cs.float())
shape: (2, 2)
┌─────┬─────┐
│ foo ┆ bar │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════╡
│ x   ┆ 123 │
│ y   ┆ 456 │
└─────┴─────┘

polars.selectors.integer() → SelectorType[source]#

Select all integer columns.

See also

by_dtype: Select columns by dtype.
float: Select all float columns.
numeric: Select all numeric columns.
signed_integer: Select all signed integer columns.
unsigned_integer: Select all unsigned integer columns.

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "foo": ["x", "y"],
...         "bar": [123, 456],
...         "baz": [2.0, 5.5],
...         "zap": [0, 1],
...     }
... )

Select all integer columns:

>>> df.select(cs.integer())
shape: (2, 2)
┌─────┬─────┐
│ bar ┆ zap │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 123 ┆ 0   │
│ 456 ┆ 1   │
└─────┴─────┘

Select all columns except for those that are integer :

>>> df.select(~cs.integer())
shape: (2, 2)
┌─────┬─────┐
│ foo ┆ baz │
│ --- ┆ --- │
│ str ┆ f64 │
╞═════╪═════╡
│ x   ┆ 2.0 │
│ y   ┆ 5.5 │
└─────┴─────┘

polars.selectors.is_selector(obj: Any) → bool[source]#

Indicate whether the given object/expression is a selector.

Examples

>>> from polars.selectors import is_selector
>>> import polars.selectors as cs
>>> is_selector(pl.col("colx"))
False
>>> is_selector(cs.first() | cs.last())
True

polars.selectors.last() → SelectorType[source]#

Select the last column in the current scope.

See also

all: Select all columns.
first: Select the first column in the current scope.

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "foo": ["x", "y"],
...         "bar": [123, 456],
...         "baz": [2.0, 5.5],
...         "zap": [0, 1],
...     }
... )

Select the last column:

>>> df.select(cs.last())
shape: (2, 1)
┌─────┐
│ zap │
│ --- │
│ i64 │
╞═════╡
│ 0   │
│ 1   │
└─────┘

Select everything except for the last column:

>>> df.select(~cs.last())
shape: (2, 3)
┌─────┬─────┬─────┐
│ foo ┆ bar ┆ baz │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ f64 │
╞═════╪═════╪═════╡
│ x   ┆ 123 ┆ 2.0 │
│ y   ┆ 456 ┆ 5.5 │
└─────┴─────┴─────┘

polars.selectors.matches(pattern: str) → SelectorType[source]#

Select all columns that match the given regex pattern.

Parameters:

pattern: A valid regular expression pattern, compatible with the regex crate.

See also

contains: Select all columns that contain the given substring.
ends_with: Select all columns that end with the given substring(s).
starts_with: Select all columns that start with the given substring(s).

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "foo": ["x", "y"],
...         "bar": [123, 456],
...         "baz": [2.0, 5.5],
...         "zap": [0, 1],
...     }
... )

Match column names containing an ‘a’, preceded by a character that is not ‘z’:

>>> df.select(cs.matches("[^z]a"))
shape: (2, 2)
┌─────┬─────┐
│ bar ┆ baz │
│ --- ┆ --- │
│ i64 ┆ f64 │
╞═════╪═════╡
│ 123 ┆ 2.0 │
│ 456 ┆ 5.5 │
└─────┴─────┘

Do not match column names ending in ‘R’ or ‘z’ (case-insensitively):

>>> df.select(~cs.matches(r"(?i)R|z$"))
shape: (2, 2)
┌─────┬─────┐
│ foo ┆ zap │
│ --- ┆ --- │
│ str ┆ i64 │
╞═════╪═════╡
│ x   ┆ 0   │
│ y   ┆ 1   │
└─────┴─────┘

polars.selectors.numeric() → SelectorType[source]#

Select all numeric columns.

See also

by_dtype: Select columns by dtype.
float: Select all float columns.
integer: Select all integer columns.
signed_integer: Select all signed integer columns.
unsigned_integer: Select all unsigned integer columns.

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "foo": ["x", "y"],
...         "bar": [123, 456],
...         "baz": [2.0, 5.5],
...         "zap": [0, 0],
...     },
...     schema_overrides={"bar": pl.Int16, "baz": pl.Float32, "zap": pl.UInt8},
... )

Match all numeric columns:

>>> df.select(cs.numeric())
shape: (2, 3)
┌─────┬─────┬─────┐
│ bar ┆ baz ┆ zap │
│ --- ┆ --- ┆ --- │
│ i16 ┆ f32 ┆ u8  │
╞═════╪═════╪═════╡
│ 123 ┆ 2.0 ┆ 0   │
│ 456 ┆ 5.5 ┆ 0   │
└─────┴─────┴─────┘

Match all columns except for those that are numeric:

>>> df.select(~cs.numeric())
shape: (2, 1)
┌─────┐
│ foo │
│ --- │
│ str │
╞═════╡
│ x   │
│ y   │
└─────┘

polars.selectors.starts_with(*prefix: str) → SelectorType[source]#

Select columns that start with the given substring(s).

Parameters:

prefix: Substring(s) that matching column names should start with.

See also

contains: Select all columns that contain the given substring.
ends_with: Select all columns that end with the given substring(s).
matches: Select all columns that match the given regex pattern.

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "foo": [1.0, 2.0],
...         "bar": [3.0, 4.0],
...         "baz": [5, 6],
...         "zap": [7, 8],
...     }
... )

Match columns starting with a ‘b’:

>>> df.select(cs.starts_with("b"))
shape: (2, 2)
┌─────┬─────┐
│ bar ┆ baz │
│ --- ┆ --- │
│ f64 ┆ i64 │
╞═════╪═════╡
│ 3.0 ┆ 5   │
│ 4.0 ┆ 6   │
└─────┴─────┘

Match columns starting with either the letter ‘b’ or ‘z’:

>>> df.select(cs.starts_with("b", "z"))
shape: (2, 3)
┌─────┬─────┬─────┐
│ bar ┆ baz ┆ zap │
│ --- ┆ --- ┆ --- │
│ f64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 3.0 ┆ 5   ┆ 7   │
│ 4.0 ┆ 6   ┆ 8   │
└─────┴─────┴─────┘

Match all columns except for those starting with ‘b’:

>>> df.select(~cs.starts_with("b"))
shape: (2, 2)
┌─────┬─────┐
│ foo ┆ zap │
│ --- ┆ --- │
│ f64 ┆ i64 │
╞═════╪═════╡
│ 1.0 ┆ 7   │
│ 2.0 ┆ 8   │
└─────┴─────┘

polars.selectors.string(*, include_categorical: bool = False) → SelectorType[source]#

Select all String (and, optionally, Categorical) string columns .

See also

binary: Select all binary columns.
by_dtype: Select all columns matching the given dtype(s).
categorical: Select all categorical columns.

Examples

>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "w": ["xx", "yy", "xx", "yy", "xx"],
...         "x": [1, 2, 1, 4, -2],
...         "y": [3.0, 4.5, 1.0, 2.5, -2.0],
...         "z": ["a", "b", "a", "b", "b"],
...     },
... ).with_columns(
...     z=pl.col("z").cast(pl.Categorical("lexical")),
... )

Group by all string columns, sum the numeric columns, then sort by the string cols:

>>> df.group_by(cs.string()).agg(cs.numeric().sum()).sort(by=cs.string())
shape: (2, 3)
┌─────┬─────┬─────┐
│ w   ┆ x   ┆ y   │
│ --- ┆ --- ┆ --- │
│ str ┆ i64 ┆ f64 │
╞═════╪═════╪═════╡
│ xx  ┆ 0   ┆ 2.0 │
│ yy  ┆ 6   ┆ 7.0 │
└─────┴─────┴─────┘

Group by all string and categorical columns:

>>> df.group_by(cs.string(include_categorical=True)).agg(cs.numeric().sum()).sort(
...     by=cs.string(include_categorical=True)
... )
shape: (3, 4)
┌─────┬─────┬─────┬──────┐
│ w   ┆ z   ┆ x   ┆ y    │
│ --- ┆ --- ┆ --- ┆ ---  │
│ str ┆ cat ┆ i64 ┆ f64  │
╞═════╪═════╪═════╪══════╡
│ xx  ┆ a   ┆ 2   ┆ 4.0  │
│ xx  ┆ b   ┆ -2  ┆ -2.0 │
│ yy  ┆ b   ┆ 6   ┆ 7.0  │
└─────┴─────┴─────┴──────┘

polars.selectors.temporal() → SelectorType[source]#

Select all temporal columns.

See also

by_dtype: Select all columns matching the given dtype(s).
date: Select all date columns.
datetime: Select all datetime columns, optionally filtering by time unit/zone.
duration: Select all duration columns, optionally filtering by time unit.
time: Select all time columns.

Examples

>>> from datetime import date, time
>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "dt": [date(2021, 1, 1), date(2021, 1, 2)],
...         "tm": [time(12, 0, 0), time(20, 30, 45)],
...         "value": [1.2345, 2.3456],
...     }
... )

Match all temporal columns:

>>> df.select(cs.temporal())
shape: (2, 2)
┌────────────┬──────────┐
│ dt         ┆ tm       │
│ ---        ┆ ---      │
│ date       ┆ time     │
╞════════════╪══════════╡
│ 2021-01-01 ┆ 12:00:00 │
│ 2021-01-02 ┆ 20:30:45 │
└────────────┴──────────┘

Match all temporal columns except for time columns:

>>> df.select(cs.temporal() - cs.time())
shape: (2, 1)
┌────────────┐
│ dt         │
│ ---        │
│ date       │
╞════════════╡
│ 2021-01-01 │
│ 2021-01-02 │
└────────────┘

Match all columns except for temporal columns:

>>> df.select(~cs.temporal())
shape: (2, 1)
┌────────┐
│ value  │
│ ---    │
│ f64    │
╞════════╡
│ 1.2345 │
│ 2.3456 │
└────────┘

polars.selectors.time() → SelectorType[source]#

Select all time columns.

See also

date: Select all date columns.
datetime: Select all datetime columns, optionally filtering by time unit/zone.
duration: Select all duration columns, optionally filtering by time unit.
temporal: Select all temporal columns.

Examples

>>> from datetime import date, datetime, time
>>> import polars.selectors as cs
>>> df = pl.DataFrame(
...     {
...         "dtm": [datetime(2001, 5, 7, 10, 25), datetime(2031, 12, 31, 0, 30)],
...         "dt": [date(1999, 12, 31), date(2024, 8, 9)],
...         "tm": [time(0, 0, 0), time(23, 59, 59)],
...     },
... )

Select all time columns:

>>> df.select(cs.time())
shape: (2, 1)
┌──────────┐
│ tm       │
│ ---      │
│ time     │
╞══════════╡
│ 00:00:00 │
│ 23:59:59 │
└──────────┘

Select all columns except for those that are times:

>>> df.select(~cs.time())
shape: (2, 2)
┌─────────────────────┬────────────┐
│ dtm                 ┆ dt         │
│ ---                 ┆ ---        │
│ datetime[μs]        ┆ date       │
╞═════════════════════╪════════════╡
│ 2001-05-07 10:25:00 ┆ 1999-12-31 │
│ 2031-12-31 00:30:00 ┆ 2024-08-09 │
└─────────────────────┴────────────┘