from __future__ import annotations
from typing import TYPE_CHECKING
from typing import Any
from typing import Callable
from typing import Generic
from typing import Iterable
from typing import Literal
from typing import Mapping
from typing import Sequence
from typing import TypeVar
from narwhals.dependencies import is_numpy_array
from narwhals.dtypes import _validate_dtype
from narwhals.utils import _validate_rolling_arguments
from narwhals.utils import flatten
if TYPE_CHECKING:
from typing_extensions import Self
from narwhals.dtypes import DType
from narwhals.typing import CompliantExpr
from narwhals.typing import CompliantNamespace
from narwhals.typing import CompliantSeriesT_co
from narwhals.typing import IntoExpr
def extract_compliant(
plx: CompliantNamespace[CompliantSeriesT_co], other: Any
) -> CompliantExpr[CompliantSeriesT_co] | CompliantSeriesT_co | Any:
from narwhals.series import Series
if isinstance(other, Expr):
return other._to_compliant_expr(plx)
if isinstance(other, Series):
return other._compliant_series
return other
class Expr:
def __init__(self, to_compliant_expr: Callable[[Any], Any]) -> None:
# callable from CompliantNamespace to CompliantExpr
self._to_compliant_expr = to_compliant_expr
def _taxicab_norm(self) -> Self:
# This is just used to test out the stable api feature in a realistic-ish way.
# It's not intended to be used.
return self.__class__(lambda plx: self._to_compliant_expr(plx).abs().sum())
# --- convert ---
def alias(self, name: str) -> Self:
"""Rename the expression.
Arguments:
name: The new name.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2], "b": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_alias(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select((nw.col("b") + 10).alias("c")).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_alias`:
>>> agnostic_alias(df_pd)
c
0 14
1 15
>>> agnostic_alias(df_pl)
shape: (2, 1)
┌─────┐
│ c │
│ --- │
│ i64 │
╞═════╡
│ 14 │
│ 15 │
└─────┘
>>> agnostic_alias(df_pa)
pyarrow.Table
c: int64
----
c: [[14,15]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).alias(name))
def pipe(self, function: Callable[[Any], Self], *args: Any, **kwargs: Any) -> Self:
"""Pipe function call.
Arguments:
function: Function to apply.
args: Positional arguments to pass to function.
kwargs: Keyword arguments to pass to function.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3, 4]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Lets define a library-agnostic function:
>>> def agnostic_pipe(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").pipe(lambda x: x + 1)).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_pipe`:
>>> agnostic_pipe(df_pd)
a
0 2
1 3
2 4
3 5
>>> agnostic_pipe(df_pl)
shape: (4, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 2 │
│ 3 │
│ 4 │
│ 5 │
└─────┘
>>> agnostic_pipe(df_pa)
pyarrow.Table
a: int64
----
a: [[2,3,4,5]]
"""
return function(self, *args, **kwargs)
def cast(self: Self, dtype: DType | type[DType]) -> Self:
"""Redefine an object's data type.
Arguments:
dtype: Data type that the object will be cast into.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"foo": [1, 2, 3], "bar": [6.0, 7.0, 8.0]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_cast(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("foo").cast(nw.Float32), nw.col("bar").cast(nw.UInt8)
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_cast`:
>>> agnostic_cast(df_pd)
foo bar
0 1.0 6
1 2.0 7
2 3.0 8
>>> agnostic_cast(df_pl)
shape: (3, 2)
┌─────┬─────┐
│ foo ┆ bar │
│ --- ┆ --- │
│ f32 ┆ u8 │
╞═════╪═════╡
│ 1.0 ┆ 6 │
│ 2.0 ┆ 7 │
│ 3.0 ┆ 8 │
└─────┴─────┘
>>> agnostic_cast(df_pa)
pyarrow.Table
foo: float
bar: uint8
----
foo: [[1,2,3]]
bar: [[6,7,8]]
"""
_validate_dtype(dtype)
return self.__class__(
lambda plx: self._to_compliant_expr(plx).cast(dtype),
)
# --- binary ---
def __eq__(self, other: object) -> Self: # type: ignore[override]
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__eq__(extract_compliant(plx, other))
)
def __ne__(self, other: object) -> Self: # type: ignore[override]
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__ne__(extract_compliant(plx, other))
)
def __and__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__and__(
extract_compliant(plx, other)
)
)
def __rand__(self, other: Any) -> Self:
def func(plx: CompliantNamespace[Any]) -> CompliantExpr[Any]:
return plx.lit(extract_compliant(plx, other), dtype=None).__and__(
extract_compliant(plx, self)
)
return self.__class__(func)
def __or__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__or__(extract_compliant(plx, other))
)
def __ror__(self, other: Any) -> Self:
def func(plx: CompliantNamespace[Any]) -> CompliantExpr[Any]:
return plx.lit(extract_compliant(plx, other), dtype=None).__or__(
extract_compliant(plx, self)
)
return self.__class__(func)
def __add__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__add__(
extract_compliant(plx, other)
)
)
def __radd__(self, other: Any) -> Self:
def func(plx: CompliantNamespace[Any]) -> CompliantExpr[Any]:
return plx.lit(extract_compliant(plx, other), dtype=None).__add__(
extract_compliant(plx, self)
)
return self.__class__(func)
def __sub__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__sub__(
extract_compliant(plx, other)
)
)
def __rsub__(self, other: Any) -> Self:
def func(plx: CompliantNamespace[Any]) -> CompliantExpr[Any]:
return plx.lit(extract_compliant(plx, other), dtype=None).__sub__(
extract_compliant(plx, self)
)
return self.__class__(func)
def __truediv__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__truediv__(
extract_compliant(plx, other)
)
)
def __rtruediv__(self, other: Any) -> Self:
def func(plx: CompliantNamespace[Any]) -> CompliantExpr[Any]:
return plx.lit(extract_compliant(plx, other), dtype=None).__truediv__(
extract_compliant(plx, self)
)
return self.__class__(func)
def __mul__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__mul__(
extract_compliant(plx, other)
)
)
def __rmul__(self, other: Any) -> Self:
def func(plx: CompliantNamespace[Any]) -> CompliantExpr[Any]:
return plx.lit(extract_compliant(plx, other), dtype=None).__mul__(
extract_compliant(plx, self)
)
return self.__class__(func)
def __le__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__le__(extract_compliant(plx, other))
)
def __lt__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__lt__(extract_compliant(plx, other))
)
def __gt__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__gt__(extract_compliant(plx, other))
)
def __ge__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__ge__(extract_compliant(plx, other))
)
def __pow__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__pow__(
extract_compliant(plx, other)
)
)
def __rpow__(self, other: Any) -> Self:
def func(plx: CompliantNamespace[Any]) -> CompliantExpr[Any]:
return plx.lit(extract_compliant(plx, other), dtype=None).__pow__(
extract_compliant(plx, self)
)
return self.__class__(func)
def __floordiv__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__floordiv__(
extract_compliant(plx, other)
)
)
def __rfloordiv__(self, other: Any) -> Self:
def func(plx: CompliantNamespace[Any]) -> CompliantExpr[Any]:
return plx.lit(extract_compliant(plx, other), dtype=None).__floordiv__(
extract_compliant(plx, self)
)
return self.__class__(func)
def __mod__(self, other: Any) -> Self:
return self.__class__(
lambda plx: self._to_compliant_expr(plx).__mod__(
extract_compliant(plx, other)
)
)
def __rmod__(self, other: Any) -> Self:
def func(plx: CompliantNamespace[Any]) -> CompliantExpr[Any]:
return plx.lit(extract_compliant(plx, other), dtype=None).__mod__(
extract_compliant(plx, self)
)
return self.__class__(func)
# --- unary ---
def __invert__(self) -> Self:
return self.__class__(lambda plx: self._to_compliant_expr(plx).__invert__())
def any(self) -> Self:
"""Return whether any of the values in the column are `True`.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [True, False], "b": [True, True]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_any(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a", "b").any()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_any`:
>>> agnostic_any(df_pd)
a b
0 True True
>>> agnostic_any(df_pl)
shape: (1, 2)
┌──────┬──────┐
│ a ┆ b │
│ --- ┆ --- │
│ bool ┆ bool │
╞══════╪══════╡
│ true ┆ true │
└──────┴──────┘
>>> agnostic_any(df_pa)
pyarrow.Table
a: bool
b: bool
----
a: [[true]]
b: [[true]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).any())
def all(self) -> Self:
"""Return whether all values in the column are `True`.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [True, False], "b": [True, True]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_all(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a", "b").all()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_all`:
>>> agnostic_all(df_pd)
a b
0 False True
>>> agnostic_all(df_pl)
shape: (1, 2)
┌───────┬──────┐
│ a ┆ b │
│ --- ┆ --- │
│ bool ┆ bool │
╞═══════╪══════╡
│ false ┆ true │
└───────┴──────┘
>>> agnostic_all(df_pa)
pyarrow.Table
a: bool
b: bool
----
a: [[false]]
b: [[true]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).all())
def ewm_mean(
self: Self,
*,
com: float | None = None,
span: float | None = None,
half_life: float | None = None,
alpha: float | None = None,
adjust: bool = True,
min_periods: int = 1,
ignore_nulls: bool = False,
) -> Self:
r"""Compute exponentially-weighted moving average.
!!! warning
This functionality is considered **unstable**. It may be changed at any point
without it being considered a breaking change.
Arguments:
com: Specify decay in terms of center of mass, $\gamma$, with
$\alpha = \frac{1}{1+\gamma}\forall\gamma\geq0$
span: Specify decay in terms of span, $\theta$, with
$\alpha = \frac{2}{\theta + 1} \forall \theta \geq 1$
half_life: Specify decay in terms of half-life, $\tau$, with
$\alpha = 1 - \exp \left\{ \frac{ -\ln(2) }{ \tau } \right\} \forall \tau > 0$
alpha: Specify smoothing factor alpha directly, $0 < \alpha \leq 1$.
adjust: Divide by decaying adjustment factor in beginning periods to account for imbalance in relative weightings
- When `adjust=True` (the default) the EW function is calculated
using weights $w_i = (1 - \alpha)^i$
- When `adjust=False` the EW function is calculated recursively by
$$
y_0=x_0
$$
$$
y_t = (1 - \alpha)y_{t - 1} + \alpha x_t
$$
min_periods: Minimum number of observations in window required to have a value, (otherwise result is null).
ignore_nulls: Ignore missing values when calculating weights.
- When `ignore_nulls=False` (default), weights are based on absolute
positions.
For example, the weights of $x_0$ and $x_2$ used in
calculating the final weighted average of $[x_0, None, x_2]$ are
$(1-\alpha)^2$ and $1$ if `adjust=True`, and
$(1-\alpha)^2$ and $\alpha$ if `adjust=False`.
- When `ignore_nulls=True`, weights are based
on relative positions. For example, the weights of
$x_0$ and $x_2$ used in calculating the final weighted
average of $[x_0, None, x_2]$ are
$1-\alpha$ and $1$ if `adjust=True`,
and $1-\alpha$ and $\alpha$ if `adjust=False`.
Returns:
Expr
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
We define a library agnostic function:
>>> def agnostic_ewm_mean(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("a").ewm_mean(com=1, ignore_nulls=False)
... ).to_native()
We can then pass either pandas or Polars to `agnostic_ewm_mean`:
>>> agnostic_ewm_mean(df_pd)
a
0 1.000000
1 1.666667
2 2.428571
>>> agnostic_ewm_mean(df_pl) # doctest: +NORMALIZE_WHITESPACE
shape: (3, 1)
┌──────────┐
│ a │
│ --- │
│ f64 │
╞══════════╡
│ 1.0 │
│ 1.666667 │
│ 2.428571 │
└──────────┘
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).ewm_mean(
com=com,
span=span,
half_life=half_life,
alpha=alpha,
adjust=adjust,
min_periods=min_periods,
ignore_nulls=ignore_nulls,
)
)
def mean(self) -> Self:
"""Get mean value.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [-1, 0, 1], "b": [2, 4, 6]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_mean(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a", "b").mean()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_mean`:
>>> agnostic_mean(df_pd)
a b
0 0.0 4.0
>>> agnostic_mean(df_pl)
shape: (1, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ f64 ┆ f64 │
╞═════╪═════╡
│ 0.0 ┆ 4.0 │
└─────┴─────┘
>>> agnostic_mean(df_pa)
pyarrow.Table
a: double
b: double
----
a: [[0]]
b: [[4]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).mean())
def median(self) -> Self:
"""Get median value.
Returns:
A new expression.
Notes:
Results might slightly differ across backends due to differences in the underlying algorithms used to compute the median.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 8, 3], "b": [4, 5, 2]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_median(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a", "b").median()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_median`:
>>> agnostic_median(df_pd)
a b
0 3.0 4.0
>>> agnostic_median(df_pl)
shape: (1, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ f64 ┆ f64 │
╞═════╪═════╡
│ 3.0 ┆ 4.0 │
└─────┴─────┘
>>> agnostic_median(df_pa)
pyarrow.Table
a: double
b: double
----
a: [[3]]
b: [[4]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).median())
def std(self, *, ddof: int = 1) -> Self:
"""Get standard deviation.
Arguments:
ddof: "Delta Degrees of Freedom": the divisor used in the calculation is N - ddof,
where N represents the number of elements. By default ddof is 1.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [20, 25, 60], "b": [1.5, 1, -1.4]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_std(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a", "b").std(ddof=0)).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_std`:
>>> agnostic_std(df_pd)
a b
0 17.79513 1.265789
>>> agnostic_std(df_pl)
shape: (1, 2)
┌──────────┬──────────┐
│ a ┆ b │
│ --- ┆ --- │
│ f64 ┆ f64 │
╞══════════╪══════════╡
│ 17.79513 ┆ 1.265789 │
└──────────┴──────────┘
>>> agnostic_std(df_pa)
pyarrow.Table
a: double
b: double
----
a: [[17.795130420052185]]
b: [[1.2657891697365016]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).std(ddof=ddof))
def var(self, *, ddof: int = 1) -> Self:
"""Get variance.
Arguments:
ddof: "Delta Degrees of Freedom": the divisor used in the calculation is N - ddof,
where N represents the number of elements. By default ddof is 1.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [20, 25, 60], "b": [1.5, 1, -1.4]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_var(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a", "b").var(ddof=0)).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_var`:
>>> agnostic_var(df_pd)
a b
0 316.666667 1.602222
>>> agnostic_var(df_pl)
shape: (1, 2)
┌────────────┬──────────┐
│ a ┆ b │
│ --- ┆ --- │
│ f64 ┆ f64 │
╞════════════╪══════════╡
│ 316.666667 ┆ 1.602222 │
└────────────┴──────────┘
>>> agnostic_var(df_pa)
pyarrow.Table
a: double
b: double
----
a: [[316.6666666666667]]
b: [[1.6022222222222222]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).var(ddof=ddof))
def map_batches(
self,
function: Callable[[Any], Self],
return_dtype: DType | None = None,
) -> Self:
"""Apply a custom python function to a whole Series or sequence of Series.
The output of this custom function is presumed to be either a Series,
or a NumPy array (in which case it will be automatically converted into
a Series).
Arguments:
function: Function to apply to Series.
return_dtype: Dtype of the output Series.
If not set, the dtype will be inferred based on the first non-null value
that is returned by the function.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_map_batches(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("a", "b").map_batches(
... lambda s: s.to_numpy() + 1, return_dtype=nw.Float64
... )
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_map_batches`:
>>> agnostic_map_batches(df_pd)
a b
0 2.0 5.0
1 3.0 6.0
2 4.0 7.0
>>> agnostic_map_batches(df_pl)
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ f64 ┆ f64 │
╞═════╪═════╡
│ 2.0 ┆ 5.0 │
│ 3.0 ┆ 6.0 │
│ 4.0 ┆ 7.0 │
└─────┴─────┘
>>> agnostic_map_batches(df_pa)
pyarrow.Table
a: double
b: double
----
a: [[2,3,4]]
b: [[5,6,7]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).map_batches(
function=function, return_dtype=return_dtype
)
)
def skew(self: Self) -> Self:
"""Calculate the sample skewness of a column.
Returns:
An expression representing the sample skewness of the column.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3, 4, 5], "b": [1, 1, 2, 10, 100]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_skew(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a", "b").skew()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_skew`:
>>> agnostic_skew(df_pd)
a b
0 0.0 1.472427
>>> agnostic_skew(df_pl)
shape: (1, 2)
┌─────┬──────────┐
│ a ┆ b │
│ --- ┆ --- │
│ f64 ┆ f64 │
╞═════╪══════════╡
│ 0.0 ┆ 1.472427 │
└─────┴──────────┘
>>> agnostic_skew(df_pa)
pyarrow.Table
a: double
b: double
----
a: [[0]]
b: [[1.4724267269058975]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).skew())
def sum(self) -> Expr:
"""Return the sum value.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [5, 10], "b": [50, 100]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_sum(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a", "b").sum()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_sum`:
>>> agnostic_sum(df_pd)
a b
0 15 150
>>> agnostic_sum(df_pl)
shape: (1, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 15 ┆ 150 │
└─────┴─────┘
>>> agnostic_sum(df_pa)
pyarrow.Table
a: int64
b: int64
----
a: [[15]]
b: [[150]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).sum())
def min(self) -> Self:
"""Returns the minimum value(s) from a column(s).
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2], "b": [4, 3]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_min(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.min("a", "b")).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_min`:
>>> agnostic_min(df_pd)
a b
0 1 3
>>> agnostic_min(df_pl)
shape: (1, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1 ┆ 3 │
└─────┴─────┘
>>> agnostic_min(df_pa)
pyarrow.Table
a: int64
b: int64
----
a: [[1]]
b: [[3]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).min())
def max(self) -> Self:
"""Returns the maximum value(s) from a column(s).
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [10, 20], "b": [50, 100]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_max(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.max("a", "b")).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_max`:
>>> agnostic_max(df_pd)
a b
0 20 100
>>> agnostic_max(df_pl)
shape: (1, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 20 ┆ 100 │
└─────┴─────┘
>>> agnostic_max(df_pa)
pyarrow.Table
a: int64
b: int64
----
a: [[20]]
b: [[100]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).max())
def arg_min(self) -> Self:
"""Returns the index of the minimum value.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [10, 20], "b": [150, 100]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_arg_min(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("a", "b").arg_min().name.suffix("_arg_min")
... ).to_native()
We can then pass any supported library such as Pandas, Polars, or
PyArrow to `agnostic_arg_min`:
>>> agnostic_arg_min(df_pd)
a_arg_min b_arg_min
0 0 1
>>> agnostic_arg_min(df_pl)
shape: (1, 2)
┌───────────┬───────────┐
│ a_arg_min ┆ b_arg_min │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═══════════╪═══════════╡
│ 0 ┆ 1 │
└───────────┴───────────┘
>>> agnostic_arg_min(df_pa)
pyarrow.Table
a_arg_min: int64
b_arg_min: int64
----
a_arg_min: [[0]]
b_arg_min: [[1]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).arg_min())
def arg_max(self) -> Self:
"""Returns the index of the maximum value.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [10, 20], "b": [150, 100]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_arg_max(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("a", "b").arg_max().name.suffix("_arg_max")
... ).to_native()
We can then pass any supported library such as Pandas, Polars, or
PyArrow to `agnostic_arg_max`:
>>> agnostic_arg_max(df_pd)
a_arg_max b_arg_max
0 1 0
>>> agnostic_arg_max(df_pl)
shape: (1, 2)
┌───────────┬───────────┐
│ a_arg_max ┆ b_arg_max │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═══════════╪═══════════╡
│ 1 ┆ 0 │
└───────────┴───────────┘
>>> agnostic_arg_max(df_pa)
pyarrow.Table
a_arg_max: int64
b_arg_max: int64
----
a_arg_max: [[1]]
b_arg_max: [[0]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).arg_max())
def count(self) -> Self:
"""Returns the number of non-null elements in the column.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3], "b": [None, 4, 4]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_count(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.all().count()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_count`:
>>> agnostic_count(df_pd)
a b
0 3 2
>>> agnostic_count(df_pl)
shape: (1, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 3 ┆ 2 │
└─────┴─────┘
>>> agnostic_count(df_pa)
pyarrow.Table
a: int64
b: int64
----
a: [[3]]
b: [[2]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).count())
def n_unique(self) -> Self:
"""Returns count of unique values.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3, 4, 5], "b": [1, 1, 3, 3, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_n_unique(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a", "b").n_unique()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_n_unique`:
>>> agnostic_n_unique(df_pd)
a b
0 5 3
>>> agnostic_n_unique(df_pl)
shape: (1, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 5 ┆ 3 │
└─────┴─────┘
>>> agnostic_n_unique(df_pa)
pyarrow.Table
a: int64
b: int64
----
a: [[5]]
b: [[3]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).n_unique())
def unique(self, *, maintain_order: bool = False) -> Self:
"""Return unique values of this expression.
Arguments:
maintain_order: Keep the same order as the original expression. This may be more
expensive to compute. Settings this to `True` blocks the possibility
to run on the streaming engine for Polars.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 1, 3, 5, 5], "b": [2, 4, 4, 6, 6]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_unique(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a", "b").unique(maintain_order=True)).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_unique`:
>>> agnostic_unique(df_pd)
a b
0 1 2
1 3 4
2 5 6
>>> agnostic_unique(df_pl)
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1 ┆ 2 │
│ 3 ┆ 4 │
│ 5 ┆ 6 │
└─────┴─────┘
>>> agnostic_unique(df_pa)
pyarrow.Table
a: int64
b: int64
----
a: [[1,3,5]]
b: [[2,4,6]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).unique(maintain_order=maintain_order)
)
def abs(self) -> Self:
"""Return absolute value of each element.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, -2], "b": [-3, 4]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_abs(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a", "b").abs()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_abs`:
>>> agnostic_abs(df_pd)
a b
0 1 3
1 2 4
>>> agnostic_abs(df_pl)
shape: (2, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1 ┆ 3 │
│ 2 ┆ 4 │
└─────┴─────┘
>>> agnostic_abs(df_pa)
pyarrow.Table
a: int64
b: int64
----
a: [[1,2]]
b: [[3,4]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).abs())
def cum_sum(self: Self, *, reverse: bool = False) -> Self:
"""Return cumulative sum.
Arguments:
reverse: reverse the operation
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 1, 3, 5, 5], "b": [2, 4, 4, 6, 6]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_cum_sum(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a", "b").cum_sum()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_cum_sum`:
>>> agnostic_cum_sum(df_pd)
a b
0 1 2
1 2 6
2 5 10
3 10 16
4 15 22
>>> agnostic_cum_sum(df_pl)
shape: (5, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 1 ┆ 2 │
│ 2 ┆ 6 │
│ 5 ┆ 10 │
│ 10 ┆ 16 │
│ 15 ┆ 22 │
└─────┴─────┘
>>> agnostic_cum_sum(df_pa)
pyarrow.Table
a: int64
b: int64
----
a: [[1,2,5,10,15]]
b: [[2,6,10,16,22]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).cum_sum(reverse=reverse)
)
def diff(self) -> Self:
"""Returns the difference between each element and the previous one.
Returns:
A new expression.
Notes:
pandas may change the dtype here, for example when introducing missing
values in an integer column. To ensure, that the dtype doesn't change,
you may want to use `fill_null` and `cast`. For example, to calculate
the diff and fill missing values with `0` in a Int64 column, you could
do:
nw.col("a").diff().fill_null(0).cast(nw.Int64)
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 1, 3, 5, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_diff(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(a_diff=nw.col("a").diff()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_diff`:
>>> agnostic_diff(df_pd)
a_diff
0 NaN
1 0.0
2 2.0
3 2.0
4 0.0
>>> agnostic_diff(df_pl)
shape: (5, 1)
┌────────┐
│ a_diff │
│ --- │
│ i64 │
╞════════╡
│ null │
│ 0 │
│ 2 │
│ 2 │
│ 0 │
└────────┘
>>> agnostic_diff(df_pa)
pyarrow.Table
a_diff: int64
----
a_diff: [[null,0,2,2,0]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).diff())
def shift(self, n: int) -> Self:
"""Shift values by `n` positions.
Arguments:
n: Number of positions to shift values by.
Returns:
A new expression.
Notes:
pandas may change the dtype here, for example when introducing missing
values in an integer column. To ensure, that the dtype doesn't change,
you may want to use `fill_null` and `cast`. For example, to shift
and fill missing values with `0` in a Int64 column, you could
do:
nw.col("a").shift(1).fill_null(0).cast(nw.Int64)
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 1, 3, 5, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_shift(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(a_shift=nw.col("a").shift(n=1)).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_shift`:
>>> agnostic_shift(df_pd)
a_shift
0 NaN
1 1.0
2 1.0
3 3.0
4 5.0
>>> agnostic_shift(df_pl)
shape: (5, 1)
┌─────────┐
│ a_shift │
│ --- │
│ i64 │
╞═════════╡
│ null │
│ 1 │
│ 1 │
│ 3 │
│ 5 │
└─────────┘
>>> agnostic_shift(df_pa)
pyarrow.Table
a_shift: int64
----
a_shift: [[null,1,1,3,5]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).shift(n))
def replace_strict(
self,
old: Sequence[Any] | Mapping[Any, Any],
new: Sequence[Any] | None = None,
*,
return_dtype: DType | type[DType] | None = None,
) -> Self:
"""Replace all values by different values.
This function must replace all non-null input values (else it raises an error).
Arguments:
old: Sequence of values to replace. It also accepts a mapping of values to
their replacement as syntactic sugar for
`replace_all(old=list(mapping.keys()), new=list(mapping.values()))`.
new: Sequence of values to replace by. Length must match the length of `old`.
return_dtype: The data type of the resulting expression. If set to `None`
(default), the data type is determined automatically based on the other
inputs.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [3, 0, 1, 2]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define dataframe-agnostic functions:
>>> def agnostic_replace_strict(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... b=nw.col("a").replace_strict(
... [0, 1, 2, 3],
... ["zero", "one", "two", "three"],
... return_dtype=nw.String,
... )
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_replace_strict`:
>>> agnostic_replace_strict(df_pd)
a b
0 3 three
1 0 zero
2 1 one
3 2 two
>>> agnostic_replace_strict(df_pl)
shape: (4, 2)
┌─────┬───────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ str │
╞═════╪═══════╡
│ 3 ┆ three │
│ 0 ┆ zero │
│ 1 ┆ one │
│ 2 ┆ two │
└─────┴───────┘
>>> agnostic_replace_strict(df_pa)
pyarrow.Table
a: int64
b: string
----
a: [[3,0,1,2]]
b: [["three","zero","one","two"]]
"""
if new is None:
if not isinstance(old, Mapping):
msg = "`new` argument is required if `old` argument is not a Mapping type"
raise TypeError(msg)
new = list(old.values())
old = list(old.keys())
return self.__class__(
lambda plx: self._to_compliant_expr(plx).replace_strict(
old, new, return_dtype=return_dtype
)
)
def sort(self, *, descending: bool = False, nulls_last: bool = False) -> Self:
"""Sort this column. Place null values first.
Arguments:
descending: Sort in descending order.
nulls_last: Place null values last instead of first.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [5, None, 1, 2]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define dataframe-agnostic functions:
>>> def agnostic_sort(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").sort()).to_native()
>>> def agnostic_sort_descending(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").sort(descending=True)).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_sort` and `agnostic_sort_descending`:
>>> agnostic_sort(df_pd)
a
1 NaN
2 1.0
3 2.0
0 5.0
>>> agnostic_sort(df_pl)
shape: (4, 1)
┌──────┐
│ a │
│ --- │
│ i64 │
╞══════╡
│ null │
│ 1 │
│ 2 │
│ 5 │
└──────┘
>>> agnostic_sort(df_pa)
pyarrow.Table
a: int64
----
a: [[null,1,2,5]]
>>> agnostic_sort_descending(df_pd)
a
1 NaN
0 5.0
3 2.0
2 1.0
>>> agnostic_sort_descending(df_pl)
shape: (4, 1)
┌──────┐
│ a │
│ --- │
│ i64 │
╞══════╡
│ null │
│ 5 │
│ 2 │
│ 1 │
└──────┘
>>> agnostic_sort_descending(df_pa)
pyarrow.Table
a: int64
----
a: [[null,5,2,1]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).sort(
descending=descending, nulls_last=nulls_last
)
)
# --- transform ---
def is_between(
self,
lower_bound: Any | IntoExpr,
upper_bound: Any | IntoExpr,
closed: str = "both",
) -> Self:
"""Check if this expression is between the given lower and upper bounds.
Arguments:
lower_bound: Lower bound value.
upper_bound: Upper bound value.
closed: Define which sides of the interval are closed (inclusive).
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3, 4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_is_between(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").is_between(2, 4, "right")).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_is_between`:
>>> agnostic_is_between(df_pd)
a
0 False
1 False
2 True
3 True
4 False
>>> agnostic_is_between(df_pl)
shape: (5, 1)
┌───────┐
│ a │
│ --- │
│ bool │
╞═══════╡
│ false │
│ false │
│ true │
│ true │
│ false │
└───────┘
>>> agnostic_is_between(df_pa)
pyarrow.Table
a: bool
----
a: [[false,false,true,true,false]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).is_between(
extract_compliant(plx, lower_bound),
extract_compliant(plx, upper_bound),
closed,
)
)
def is_in(self, other: Any) -> Self:
"""Check if elements of this expression are present in the other iterable.
Arguments:
other: iterable
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 9, 10]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_is_in(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(b=nw.col("a").is_in([1, 2])).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_is_in`:
>>> agnostic_is_in(df_pd)
a b
0 1 True
1 2 True
2 9 False
3 10 False
>>> agnostic_is_in(df_pl)
shape: (4, 2)
┌─────┬───────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ bool │
╞═════╪═══════╡
│ 1 ┆ true │
│ 2 ┆ true │
│ 9 ┆ false │
│ 10 ┆ false │
└─────┴───────┘
>>> agnostic_is_in(df_pa)
pyarrow.Table
a: int64
b: bool
----
a: [[1,2,9,10]]
b: [[true,true,false,false]]
"""
if isinstance(other, Iterable) and not isinstance(other, (str, bytes)):
return self.__class__(
lambda plx: self._to_compliant_expr(plx).is_in(
extract_compliant(plx, other)
)
)
else:
msg = "Narwhals `is_in` doesn't accept expressions as an argument, as opposed to Polars. You should provide an iterable instead."
raise NotImplementedError(msg)
def filter(self, *predicates: Any) -> Self:
"""Filters elements based on a condition, returning a new expression.
Arguments:
predicates: Conditions to filter by (which get ANDed together).
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [2, 3, 4, 5, 6, 7], "b": [10, 11, 12, 13, 14, 15]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_filter(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("a").filter(nw.col("a") > 4),
... nw.col("b").filter(nw.col("b") < 13),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_filter`:
>>> agnostic_filter(df_pd)
a b
3 5 10
4 6 11
5 7 12
>>> agnostic_filter(df_pl)
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 5 ┆ 10 │
│ 6 ┆ 11 │
│ 7 ┆ 12 │
└─────┴─────┘
>>> agnostic_filter(df_pa)
pyarrow.Table
a: int64
b: int64
----
a: [[5,6,7]]
b: [[10,11,12]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).filter(
*[extract_compliant(plx, pred) for pred in flatten(predicates)],
)
)
def is_null(self) -> Self:
"""Returns a boolean Series indicating which values are null.
Returns:
A new expression.
Notes:
pandas handles null values differently from Polars and PyArrow.
See [null_handling](../pandas_like_concepts/null_handling.md/)
for reference.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> df_pd = pd.DataFrame(
... {
... "a": [2, 4, None, 3, 5],
... "b": [2.0, 4.0, float("nan"), 3.0, 5.0],
... }
... )
>>> data = {
... "a": [2, 4, None, 3, 5],
... "b": [2.0, 4.0, None, 3.0, 5.0],
... }
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_is_null(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... a_is_null=nw.col("a").is_null(), b_is_null=nw.col("b").is_null()
... ).to_native()
We can then pass any supported library such as Pandas, Polars, or
PyArrow to `agnostic_is_null`:
>>> agnostic_is_null(df_pd)
a b a_is_null b_is_null
0 2.0 2.0 False False
1 4.0 4.0 False False
2 NaN NaN True True
3 3.0 3.0 False False
4 5.0 5.0 False False
>>> agnostic_is_null(df_pl)
shape: (5, 4)
┌──────┬──────┬───────────┬───────────┐
│ a ┆ b ┆ a_is_null ┆ b_is_null │
│ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ bool ┆ bool │
╞══════╪══════╪═══════════╪═══════════╡
│ 2 ┆ 2.0 ┆ false ┆ false │
│ 4 ┆ 4.0 ┆ false ┆ false │
│ null ┆ null ┆ true ┆ true │
│ 3 ┆ 3.0 ┆ false ┆ false │
│ 5 ┆ 5.0 ┆ false ┆ false │
└──────┴──────┴───────────┴───────────┘
>>> agnostic_is_null(df_pa)
pyarrow.Table
a: int64
b: double
a_is_null: bool
b_is_null: bool
----
a: [[2,4,null,3,5]]
b: [[2,4,null,3,5]]
a_is_null: [[false,false,true,false,false]]
b_is_null: [[false,false,true,false,false]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).is_null())
def is_nan(self) -> Self:
"""Indicate which values are NaN.
Returns:
A new expression.
Notes:
pandas handles null values differently from Polars and PyArrow.
See [null_handling](../pandas_like_concepts/null_handling.md/)
for reference.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"orig": [0.0, None, 2.0]}
>>> df_pd = pd.DataFrame(data).astype({"orig": "Float64"})
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_self_div_is_nan(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... divided=nw.col("orig") / nw.col("orig"),
... divided_is_nan=(nw.col("orig") / nw.col("orig")).is_nan(),
... ).to_native()
We can then pass any supported library such as Pandas, Polars, or
PyArrow to `agnostic_self_div_is_nan`:
>>> print(agnostic_self_div_is_nan(df_pd))
orig divided divided_is_nan
0 0.0 NaN True
1
2 2.0 1.0 False
>>> print(agnostic_self_div_is_nan(df_pl))
shape: (3, 3)
┌──────┬─────────┬────────────────┐
│ orig ┆ divided ┆ divided_is_nan │
│ --- ┆ --- ┆ --- │
│ f64 ┆ f64 ┆ bool │
╞══════╪═════════╪════════════════╡
│ 0.0 ┆ NaN ┆ true │
│ null ┆ null ┆ null │
│ 2.0 ┆ 1.0 ┆ false │
└──────┴─────────┴────────────────┘
>>> print(agnostic_self_div_is_nan(df_pa))
pyarrow.Table
orig: double
divided: double
divided_is_nan: bool
----
orig: [[0,null,2]]
divided: [[nan,null,1]]
divided_is_nan: [[true,null,false]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).is_nan())
def arg_true(self) -> Self:
"""Find elements where boolean expression is True.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, None, None, 2]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_arg_true(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").is_null().arg_true()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_arg_true`:
>>> agnostic_arg_true(df_pd)
a
1 1
2 2
>>> agnostic_arg_true(df_pl)
shape: (2, 1)
┌─────┐
│ a │
│ --- │
│ u32 │
╞═════╡
│ 1 │
│ 2 │
└─────┘
>>> agnostic_arg_true(df_pa)
pyarrow.Table
a: int64
----
a: [[1,2]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).arg_true())
def fill_null(
self,
value: Any | None = None,
strategy: Literal["forward", "backward"] | None = None,
limit: int | None = None,
) -> Self:
"""Fill null values with given value.
Arguments:
value: Value used to fill null values.
strategy: Strategy used to fill null values.
limit: Number of consecutive null values to fill when using the 'forward' or 'backward' strategy.
Returns:
A new expression.
Notes:
pandas handles null values differently from Polars and PyArrow.
See [null_handling](../pandas_like_concepts/null_handling.md/)
for reference.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> df_pd = pd.DataFrame(
... {
... "a": [2, 4, None, None, 3, 5],
... "b": [2.0, 4.0, float("nan"), float("nan"), 3.0, 5.0],
... }
... )
>>> data = {
... "a": [2, 4, None, None, 3, 5],
... "b": [2.0, 4.0, None, None, 3.0, 5.0],
... }
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_fill_null(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(nw.col("a", "b").fill_null(0)).to_native()
We can then pass any supported library such as Pandas, Polars, or
PyArrow to `agnostic_fill_null`:
>>> agnostic_fill_null(df_pd)
a b
0 2.0 2.0
1 4.0 4.0
2 0.0 0.0
3 0.0 0.0
4 3.0 3.0
5 5.0 5.0
>>> agnostic_fill_null(df_pl)
shape: (6, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ f64 │
╞═════╪═════╡
│ 2 ┆ 2.0 │
│ 4 ┆ 4.0 │
│ 0 ┆ 0.0 │
│ 0 ┆ 0.0 │
│ 3 ┆ 3.0 │
│ 5 ┆ 5.0 │
└─────┴─────┘
>>> agnostic_fill_null(df_pa)
pyarrow.Table
a: int64
b: double
----
a: [[2,4,0,0,3,5]]
b: [[2,4,0,0,3,5]]
Using a strategy:
>>> def agnostic_fill_null_with_strategy(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("a", "b")
... .fill_null(strategy="forward", limit=1)
... .name.suffix("_filled")
... ).to_native()
>>> agnostic_fill_null_with_strategy(df_pd)
a b a_filled b_filled
0 2.0 2.0 2.0 2.0
1 4.0 4.0 4.0 4.0
2 NaN NaN 4.0 4.0
3 NaN NaN NaN NaN
4 3.0 3.0 3.0 3.0
5 5.0 5.0 5.0 5.0
>>> agnostic_fill_null_with_strategy(df_pl)
shape: (6, 4)
┌──────┬──────┬──────────┬──────────┐
│ a ┆ b ┆ a_filled ┆ b_filled │
│ --- ┆ --- ┆ --- ┆ --- │
│ i64 ┆ f64 ┆ i64 ┆ f64 │
╞══════╪══════╪══════════╪══════════╡
│ 2 ┆ 2.0 ┆ 2 ┆ 2.0 │
│ 4 ┆ 4.0 ┆ 4 ┆ 4.0 │
│ null ┆ null ┆ 4 ┆ 4.0 │
│ null ┆ null ┆ null ┆ null │
│ 3 ┆ 3.0 ┆ 3 ┆ 3.0 │
│ 5 ┆ 5.0 ┆ 5 ┆ 5.0 │
└──────┴──────┴──────────┴──────────┘
>>> agnostic_fill_null_with_strategy(df_pa)
pyarrow.Table
a: int64
b: double
a_filled: int64
b_filled: double
----
a: [[2,4,null,null,3,5]]
b: [[2,4,null,null,3,5]]
a_filled: [[2,4,4,null,3,5]]
b_filled: [[2,4,4,null,3,5]]
"""
if value is not None and strategy is not None:
msg = "cannot specify both `value` and `strategy`"
raise ValueError(msg)
if value is None and strategy is None:
msg = "must specify either a fill `value` or `strategy`"
raise ValueError(msg)
if strategy is not None and strategy not in {"forward", "backward"}:
msg = f"strategy not supported: {strategy}"
raise ValueError(msg)
return self.__class__(
lambda plx: self._to_compliant_expr(plx).fill_null(
value=value, strategy=strategy, limit=limit
)
)
# --- partial reduction ---
def drop_nulls(self) -> Self:
"""Drop null values.
Returns:
A new expression.
Notes:
pandas handles null values differently from Polars and PyArrow.
See [null_handling](../pandas_like_concepts/null_handling.md/)
for reference.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> df_pd = pd.DataFrame({"a": [2.0, 4.0, float("nan"), 3.0, None, 5.0]})
>>> df_pl = pl.DataFrame({"a": [2.0, 4.0, None, 3.0, None, 5.0]})
>>> df_pa = pa.table({"a": [2.0, 4.0, None, 3.0, None, 5.0]})
Let's define a dataframe-agnostic function:
>>> def agnostic_drop_nulls(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").drop_nulls()).to_native()
We can then pass any supported library such as Pandas, Polars, or
PyArrow to `agnostic_drop_nulls`:
>>> agnostic_drop_nulls(df_pd)
a
0 2.0
1 4.0
3 3.0
5 5.0
>>> agnostic_drop_nulls(df_pl)
shape: (4, 1)
┌─────┐
│ a │
│ --- │
│ f64 │
╞═════╡
│ 2.0 │
│ 4.0 │
│ 3.0 │
│ 5.0 │
└─────┘
>>> agnostic_drop_nulls(df_pa)
pyarrow.Table
a: double
----
a: [[2,4,3,5]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).drop_nulls())
def sample(
self: Self,
n: int | None = None,
*,
fraction: float | None = None,
with_replacement: bool = False,
seed: int | None = None,
) -> Self:
"""Sample randomly from this expression.
Arguments:
n: Number of items to return. Cannot be used with fraction.
fraction: Fraction of items to return. Cannot be used with n.
with_replacement: Allow values to be sampled more than once.
seed: Seed for the random number generator. If set to None (default), a random
seed is generated for each sample operation.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_sample(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("a").sample(fraction=1.0, with_replacement=True)
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_sample`:
>>> agnostic_sample(df_pd) # doctest: +SKIP
a
2 3
0 1
2 3
>>> agnostic_sample(df_pl) # doctest: +SKIP
shape: (3, 1)
┌─────┐
│ a │
│ --- │
│ f64 │
╞═════╡
│ 2 │
│ 3 │
│ 3 │
└─────┘
>>> agnostic_sample(df_pa) # doctest: +SKIP
pyarrow.Table
a: int64
----
a: [[1,3,3]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).sample(
n, fraction=fraction, with_replacement=with_replacement, seed=seed
)
)
def over(self, *keys: str | Iterable[str]) -> Self:
"""Compute expressions over the given groups.
Arguments:
keys: Names of columns to compute window expression over.
Must be names of columns, as opposed to expressions -
so, this is a bit less flexible than Polars' `Expr.over`.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3], "b": [1, 1, 2]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_min_over_b(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... a_min_per_group=nw.col("a").min().over("b")
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_min_over_b`:
>>> agnostic_min_over_b(df_pd)
a b a_min_per_group
0 1 1 1
1 2 1 1
2 3 2 3
>>> agnostic_min_over_b(df_pl)
shape: (3, 3)
┌─────┬─────┬─────────────────┐
│ a ┆ b ┆ a_min_per_group │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════════════════╡
│ 1 ┆ 1 ┆ 1 │
│ 2 ┆ 1 ┆ 1 │
│ 3 ┆ 2 ┆ 3 │
└─────┴─────┴─────────────────┘
>>> agnostic_min_over_b(df_pa)
pyarrow.Table
a: int64
b: int64
a_min_per_group: int64
----
a: [[1,2,3]]
b: [[1,1,2]]
a_min_per_group: [[1,1,3]]
Cumulative operations are also supported, but (currently) only for
pandas and Polars:
>>> def agnostic_cum_sum(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(c=nw.col("a").cum_sum().over("b")).to_native()
>>> agnostic_cum_sum(df_pd)
a b c
0 1 1 1
1 2 1 3
2 3 2 3
>>> agnostic_cum_sum(df_pl)
shape: (3, 3)
┌─────┬─────┬─────┐
│ a ┆ b ┆ c │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞═════╪═════╪═════╡
│ 1 ┆ 1 ┆ 1 │
│ 2 ┆ 1 ┆ 3 │
│ 3 ┆ 2 ┆ 3 │
└─────┴─────┴─────┘
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).over(flatten(keys))
)
def is_duplicated(self) -> Self:
r"""Return a boolean mask indicating duplicated values.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3, 1], "b": ["a", "a", "b", "c"]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_is_duplicated(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.all().is_duplicated()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_is_duplicated`:
>>> agnostic_is_duplicated(df_pd)
a b
0 True True
1 False True
2 False False
3 True False
>>> agnostic_is_duplicated(df_pl)
shape: (4, 2)
┌───────┬───────┐
│ a ┆ b │
│ --- ┆ --- │
│ bool ┆ bool │
╞═══════╪═══════╡
│ true ┆ true │
│ false ┆ true │
│ false ┆ false │
│ true ┆ false │
└───────┴───────┘
>>> agnostic_is_duplicated(df_pa)
pyarrow.Table
a: bool
b: bool
----
a: [[true,false,false,true]]
b: [[true,true,false,false]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).is_duplicated())
def is_unique(self) -> Self:
r"""Return a boolean mask indicating unique values.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3, 1], "b": ["a", "a", "b", "c"]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_is_unique(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.all().is_unique()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_is_unique`:
>>> agnostic_is_unique(df_pd)
a b
0 False False
1 True False
2 True True
3 False True
>>> agnostic_is_unique(df_pl)
shape: (4, 2)
┌───────┬───────┐
│ a ┆ b │
│ --- ┆ --- │
│ bool ┆ bool │
╞═══════╪═══════╡
│ false ┆ false │
│ true ┆ false │
│ true ┆ true │
│ false ┆ true │
└───────┴───────┘
>>> agnostic_is_unique(df_pa)
pyarrow.Table
a: bool
b: bool
----
a: [[false,true,true,false]]
b: [[false,false,true,true]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).is_unique())
def null_count(self) -> Self:
r"""Count null values.
Returns:
A new expression.
Notes:
pandas handles null values differently from Polars and PyArrow.
See [null_handling](../pandas_like_concepts/null_handling.md/)
for reference.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, None, 1], "b": ["a", None, "b", None]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_null_count(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.all().null_count()).to_native()
We can then pass any supported library such as Pandas, Polars, or
PyArrow to `agnostic_null_count`:
>>> agnostic_null_count(df_pd)
a b
0 1 2
>>> agnostic_null_count(df_pl)
shape: (1, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 1 ┆ 2 │
└─────┴─────┘
>>> agnostic_null_count(df_pa)
pyarrow.Table
a: int64
b: int64
----
a: [[1]]
b: [[2]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).null_count())
def is_first_distinct(self) -> Self:
r"""Return a boolean mask indicating the first occurrence of each distinct value.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3, 1], "b": ["a", "a", "b", "c"]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_is_first_distinct(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.all().is_first_distinct()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_is_first_distinct`:
>>> agnostic_is_first_distinct(df_pd)
a b
0 True True
1 True False
2 True True
3 False True
>>> agnostic_is_first_distinct(df_pl)
shape: (4, 2)
┌───────┬───────┐
│ a ┆ b │
│ --- ┆ --- │
│ bool ┆ bool │
╞═══════╪═══════╡
│ true ┆ true │
│ true ┆ false │
│ true ┆ true │
│ false ┆ true │
└───────┴───────┘
>>> agnostic_is_first_distinct(df_pa)
pyarrow.Table
a: bool
b: bool
----
a: [[true,true,true,false]]
b: [[true,false,true,true]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).is_first_distinct()
)
def is_last_distinct(self) -> Self:
r"""Return a boolean mask indicating the last occurrence of each distinct value.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3, 1], "b": ["a", "a", "b", "c"]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_is_last_distinct(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.all().is_last_distinct()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_is_last_distinct`:
>>> agnostic_is_last_distinct(df_pd)
a b
0 False False
1 True True
2 True True
3 True True
>>> agnostic_is_last_distinct(df_pl)
shape: (4, 2)
┌───────┬───────┐
│ a ┆ b │
│ --- ┆ --- │
│ bool ┆ bool │
╞═══════╪═══════╡
│ false ┆ false │
│ true ┆ true │
│ true ┆ true │
│ true ┆ true │
└───────┴───────┘
>>> agnostic_is_last_distinct(df_pa)
pyarrow.Table
a: bool
b: bool
----
a: [[false,true,true,true]]
b: [[false,true,true,true]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).is_last_distinct())
def quantile(
self,
quantile: float,
interpolation: Literal["nearest", "higher", "lower", "midpoint", "linear"],
) -> Self:
r"""Get quantile value.
Arguments:
quantile: Quantile between 0.0 and 1.0.
interpolation: Interpolation method.
Returns:
A new expression.
Note:
- pandas and Polars may have implementation differences for a given interpolation method.
- [dask](https://docs.dask.org/en/stable/generated/dask.dataframe.Series.quantile.html) has
its own method to approximate quantile and it doesn't implement 'nearest', 'higher',
'lower', 'midpoint' as interpolation method - use 'linear' which is closest to the
native 'dask' - method.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": list(range(50)), "b": list(range(50, 100))}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_quantile(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("a", "b").quantile(0.5, interpolation="linear")
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_quantile`:
>>> agnostic_quantile(df_pd)
a b
0 24.5 74.5
>>> agnostic_quantile(df_pl)
shape: (1, 2)
┌──────┬──────┐
│ a ┆ b │
│ --- ┆ --- │
│ f64 ┆ f64 │
╞══════╪══════╡
│ 24.5 ┆ 74.5 │
└──────┴──────┘
>>> agnostic_quantile(df_pa)
pyarrow.Table
a: double
b: double
----
a: [[24.5]]
b: [[74.5]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).quantile(quantile, interpolation)
)
def head(self, n: int = 10) -> Self:
r"""Get the first `n` rows.
Arguments:
n: Number of rows to return.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": list(range(10))}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function that returns the first 3 rows:
>>> def agnostic_head(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").head(3)).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_head`:
>>> agnostic_head(df_pd)
a
0 0
1 1
2 2
>>> agnostic_head(df_pl)
shape: (3, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 0 │
│ 1 │
│ 2 │
└─────┘
>>> agnostic_head(df_pa)
pyarrow.Table
a: int64
----
a: [[0,1,2]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).head(n))
def tail(self, n: int = 10) -> Self:
r"""Get the last `n` rows.
Arguments:
n: Number of rows to return.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": list(range(10))}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function that returns the last 3 rows:
>>> def agnostic_tail(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").tail(3)).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_tail`:
>>> agnostic_tail(df_pd)
a
7 7
8 8
9 9
>>> agnostic_tail(df_pl)
shape: (3, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 7 │
│ 8 │
│ 9 │
└─────┘
>>> agnostic_tail(df_pa)
pyarrow.Table
a: int64
----
a: [[7,8,9]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).tail(n))
def round(self, decimals: int = 0) -> Self:
r"""Round underlying floating point data by `decimals` digits.
Arguments:
decimals: Number of decimals to round by.
Returns:
A new expression.
Notes:
For values exactly halfway between rounded decimal values pandas behaves differently than Polars and Arrow.
pandas rounds to the nearest even value (e.g. -0.5 and 0.5 round to 0.0, 1.5 and 2.5 round to 2.0, 3.5 and
4.5 to 4.0, etc..).
Polars and Arrow round away from 0 (e.g. -0.5 to -1.0, 0.5 to 1.0, 1.5 to 2.0, 2.5 to 3.0, etc..).
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1.12345, 2.56789, 3.901234]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function that rounds to the first decimal:
>>> def agnostic_round(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").round(1)).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_round`:
>>> agnostic_round(df_pd)
a
0 1.1
1 2.6
2 3.9
>>> agnostic_round(df_pl)
shape: (3, 1)
┌─────┐
│ a │
│ --- │
│ f64 │
╞═════╡
│ 1.1 │
│ 2.6 │
│ 3.9 │
└─────┘
>>> agnostic_round(df_pa)
pyarrow.Table
a: double
----
a: [[1.1,2.6,3.9]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).round(decimals))
def len(self) -> Self:
r"""Return the number of elements in the column.
Null values count towards the total.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": ["x", "y", "z"], "b": [1, 2, 1]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function that computes the len over
different values of "b" column:
>>> def agnostic_len(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("a").filter(nw.col("b") == 1).len().alias("a1"),
... nw.col("a").filter(nw.col("b") == 2).len().alias("a2"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_len`:
>>> agnostic_len(df_pd)
a1 a2
0 2 1
>>> agnostic_len(df_pl)
shape: (1, 2)
┌─────┬─────┐
│ a1 ┆ a2 │
│ --- ┆ --- │
│ u32 ┆ u32 │
╞═════╪═════╡
│ 2 ┆ 1 │
└─────┴─────┘
>>> agnostic_len(df_pa)
pyarrow.Table
a1: int64
a2: int64
----
a1: [[2]]
a2: [[1]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).len())
def gather_every(self: Self, n: int, offset: int = 0) -> Self:
r"""Take every nth value in the Series and return as new Series.
Arguments:
n: Gather every *n*-th row.
offset: Starting index.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3, 4], "b": [5, 6, 7, 8]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function in which gather every 2 rows,
starting from a offset of 1:
>>> def agnostic_gather_every(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").gather_every(n=2, offset=1)).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_gather_every`:
>>> agnostic_gather_every(df_pd)
a
1 2
3 4
>>> agnostic_gather_every(df_pl)
shape: (2, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 2 │
│ 4 │
└─────┘
>>> agnostic_gather_every(df_pa)
pyarrow.Table
a: int64
----
a: [[2,4]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).gather_every(n=n, offset=offset)
)
# need to allow numeric typing
# TODO @aivanoved: make type alias for numeric type
def clip(
self,
lower_bound: IntoExpr | Any | None = None,
upper_bound: IntoExpr | Any | None = None,
) -> Self:
r"""Clip values in the Series.
Arguments:
lower_bound: Lower bound value.
upper_bound: Upper bound value.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_clip_lower(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").clip(2)).to_native()
We can then pass any supported library such as Pandas, Polars, or
PyArrow to `agnostic_clip_lower`:
>>> agnostic_clip_lower(df_pd)
a
0 2
1 2
2 3
>>> agnostic_clip_lower(df_pl)
shape: (3, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 2 │
│ 2 │
│ 3 │
└─────┘
>>> agnostic_clip_lower(df_pa)
pyarrow.Table
a: int64
----
a: [[2,2,3]]
We define another library agnostic function:
>>> def agnostic_clip_upper(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").clip(upper_bound=2)).to_native()
We can then pass any supported library such as Pandas, Polars, or
PyArrow to `agnostic_clip_upper`:
>>> agnostic_clip_upper(df_pd)
a
0 1
1 2
2 2
>>> agnostic_clip_upper(df_pl)
shape: (3, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 1 │
│ 2 │
│ 2 │
└─────┘
>>> agnostic_clip_upper(df_pa)
pyarrow.Table
a: int64
----
a: [[1,2,2]]
We can have both at the same time
>>> data = {"a": [-1, 1, -3, 3, -5, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_clip(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").clip(-1, 3)).to_native()
We can pass any supported library such as Pandas, Polars, or
PyArrow to `agnostic_clip`:
>>> agnostic_clip(df_pd)
a
0 -1
1 1
2 -1
3 3
4 -1
5 3
>>> agnostic_clip(df_pl)
shape: (6, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ -1 │
│ 1 │
│ -1 │
│ 3 │
│ -1 │
│ 3 │
└─────┘
>>> agnostic_clip(df_pa)
pyarrow.Table
a: int64
----
a: [[-1,1,-1,3,-1,3]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).clip(
extract_compliant(plx, lower_bound),
extract_compliant(plx, upper_bound),
)
)
def mode(self: Self) -> Self:
r"""Compute the most occurring value(s).
Can return multiple values.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "a": [1, 1, 2, 3],
... "b": [1, 1, 2, 2],
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_mode(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").mode()).sort("a").to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_mode`:
>>> agnostic_mode(df_pd)
a
0 1
>>> agnostic_mode(df_pl)
shape: (1, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 1 │
└─────┘
>>> agnostic_mode(df_pa)
pyarrow.Table
a: int64
----
a: [[1]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).mode())
def is_finite(self: Self) -> Self:
"""Returns boolean values indicating which original values are finite.
Warning:
Different backend handle null values differently. `is_finite` will return
False for NaN and Null's in the Dask and pandas non-nullable backend, while
for Polars, PyArrow and pandas nullable backends null values are kept as such.
Returns:
Expression of `Boolean` data type.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [float("nan"), float("inf"), 2.0, None]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_is_finite(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").is_finite()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_is_finite`:
>>> agnostic_is_finite(df_pd)
a
0 False
1 False
2 True
3 False
>>> agnostic_is_finite(df_pl)
shape: (4, 1)
┌───────┐
│ a │
│ --- │
│ bool │
╞═══════╡
│ false │
│ false │
│ true │
│ null │
└───────┘
>>> agnostic_is_finite(df_pa)
pyarrow.Table
a: bool
----
a: [[false,false,true,null]]
"""
return self.__class__(lambda plx: self._to_compliant_expr(plx).is_finite())
def cum_count(self: Self, *, reverse: bool = False) -> Self:
r"""Return the cumulative count of the non-null values in the column.
Arguments:
reverse: reverse the operation
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": ["x", "k", None, "d"]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_cum_count(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("a").cum_count().alias("cum_count"),
... nw.col("a").cum_count(reverse=True).alias("cum_count_reverse"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_cum_count`:
>>> agnostic_cum_count(df_pd)
a cum_count cum_count_reverse
0 x 1 3
1 k 2 2
2 None 2 1
3 d 3 1
>>> agnostic_cum_count(df_pl)
shape: (4, 3)
┌──────┬───────────┬───────────────────┐
│ a ┆ cum_count ┆ cum_count_reverse │
│ --- ┆ --- ┆ --- │
│ str ┆ u32 ┆ u32 │
╞══════╪═══════════╪═══════════════════╡
│ x ┆ 1 ┆ 3 │
│ k ┆ 2 ┆ 2 │
│ null ┆ 2 ┆ 1 │
│ d ┆ 3 ┆ 1 │
└──────┴───────────┴───────────────────┘
>>> agnostic_cum_count(df_pa)
pyarrow.Table
a: string
cum_count: uint32
cum_count_reverse: uint32
----
a: [["x","k",null,"d"]]
cum_count: [[1,2,2,3]]
cum_count_reverse: [[3,2,1,1]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).cum_count(reverse=reverse)
)
def cum_min(self: Self, *, reverse: bool = False) -> Self:
r"""Return the cumulative min of the non-null values in the column.
Arguments:
reverse: reverse the operation
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [3, 1, None, 2]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_cum_min(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("a").cum_min().alias("cum_min"),
... nw.col("a").cum_min(reverse=True).alias("cum_min_reverse"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_cum_min`:
>>> agnostic_cum_min(df_pd)
a cum_min cum_min_reverse
0 3.0 3.0 1.0
1 1.0 1.0 1.0
2 NaN NaN NaN
3 2.0 1.0 2.0
>>> agnostic_cum_min(df_pl)
shape: (4, 3)
┌──────┬─────────┬─────────────────┐
│ a ┆ cum_min ┆ cum_min_reverse │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞══════╪═════════╪═════════════════╡
│ 3 ┆ 3 ┆ 1 │
│ 1 ┆ 1 ┆ 1 │
│ null ┆ null ┆ null │
│ 2 ┆ 1 ┆ 2 │
└──────┴─────────┴─────────────────┘
>>> agnostic_cum_min(df_pa)
pyarrow.Table
a: int64
cum_min: int64
cum_min_reverse: int64
----
a: [[3,1,null,2]]
cum_min: [[3,1,null,1]]
cum_min_reverse: [[1,1,null,2]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).cum_min(reverse=reverse)
)
def cum_max(self: Self, *, reverse: bool = False) -> Self:
r"""Return the cumulative max of the non-null values in the column.
Arguments:
reverse: reverse the operation
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 3, None, 2]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_cum_max(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("a").cum_max().alias("cum_max"),
... nw.col("a").cum_max(reverse=True).alias("cum_max_reverse"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_`:
>>> agnostic_cum_max(df_pd)
a cum_max cum_max_reverse
0 1.0 1.0 3.0
1 3.0 3.0 3.0
2 NaN NaN NaN
3 2.0 3.0 2.0
>>> agnostic_cum_max(df_pl)
shape: (4, 3)
┌──────┬─────────┬─────────────────┐
│ a ┆ cum_max ┆ cum_max_reverse │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞══════╪═════════╪═════════════════╡
│ 1 ┆ 1 ┆ 3 │
│ 3 ┆ 3 ┆ 3 │
│ null ┆ null ┆ null │
│ 2 ┆ 3 ┆ 2 │
└──────┴─────────┴─────────────────┘
>>> agnostic_cum_max(df_pa)
pyarrow.Table
a: int64
cum_max: int64
cum_max_reverse: int64
----
a: [[1,3,null,2]]
cum_max: [[1,3,null,3]]
cum_max_reverse: [[3,3,null,2]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).cum_max(reverse=reverse)
)
def cum_prod(self: Self, *, reverse: bool = False) -> Self:
r"""Return the cumulative product of the non-null values in the column.
Arguments:
reverse: reverse the operation
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 3, None, 2]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_cum_prod(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("a").cum_prod().alias("cum_prod"),
... nw.col("a").cum_prod(reverse=True).alias("cum_prod_reverse"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_cum_prod`:
>>> agnostic_cum_prod(df_pd)
a cum_prod cum_prod_reverse
0 1.0 1.0 6.0
1 3.0 3.0 6.0
2 NaN NaN NaN
3 2.0 6.0 2.0
>>> agnostic_cum_prod(df_pl)
shape: (4, 3)
┌──────┬──────────┬──────────────────┐
│ a ┆ cum_prod ┆ cum_prod_reverse │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i64 │
╞══════╪══════════╪══════════════════╡
│ 1 ┆ 1 ┆ 6 │
│ 3 ┆ 3 ┆ 6 │
│ null ┆ null ┆ null │
│ 2 ┆ 6 ┆ 2 │
└──────┴──────────┴──────────────────┘
>>> agnostic_cum_prod(df_pa)
pyarrow.Table
a: int64
cum_prod: int64
cum_prod_reverse: int64
----
a: [[1,3,null,2]]
cum_prod: [[1,3,null,6]]
cum_prod_reverse: [[6,6,null,2]]
"""
return self.__class__(
lambda plx: self._to_compliant_expr(plx).cum_prod(reverse=reverse)
)
def rolling_sum(
self: Self,
window_size: int,
*,
min_periods: int | None = None,
center: bool = False,
) -> Self:
"""Apply a rolling sum (moving sum) over the values.
!!! warning
This functionality is considered **unstable**. It may be changed at any point
without it being considered a breaking change.
A window of length `window_size` will traverse the values. The resulting values
will be aggregated to their sum.
The window at a given row will include the row itself and the `window_size - 1`
elements before it.
Arguments:
window_size: The length of the window in number of elements. It must be a
strictly positive integer.
min_periods: The number of values in the window that should be non-null before
computing a result. If set to `None` (default), it will be set equal to
`window_size`. If provided, it must be a strictly positive integer, and
less than or equal to `window_size`
center: Set the labels at the center of the window.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1.0, 2.0, None, 4.0]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_rolling_sum(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... b=nw.col("a").rolling_sum(window_size=3, min_periods=1)
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_rolling_sum`:
>>> agnostic_rolling_sum(df_pd)
a b
0 1.0 1.0
1 2.0 3.0
2 NaN 3.0
3 4.0 6.0
>>> agnostic_rolling_sum(df_pl)
shape: (4, 2)
┌──────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ f64 ┆ f64 │
╞══════╪═════╡
│ 1.0 ┆ 1.0 │
│ 2.0 ┆ 3.0 │
│ null ┆ 3.0 │
│ 4.0 ┆ 6.0 │
└──────┴─────┘
>>> agnostic_rolling_sum(df_pa)
pyarrow.Table
a: double
b: double
----
a: [[1,2,null,4]]
b: [[1,3,3,6]]
"""
window_size, min_periods = _validate_rolling_arguments(
window_size=window_size, min_periods=min_periods
)
return self.__class__(
lambda plx: self._to_compliant_expr(plx).rolling_sum(
window_size=window_size,
min_periods=min_periods,
center=center,
)
)
def rolling_mean(
self: Self,
window_size: int,
*,
min_periods: int | None = None,
center: bool = False,
) -> Self:
"""Apply a rolling mean (moving mean) over the values.
!!! warning
This functionality is considered **unstable**. It may be changed at any point
without it being considered a breaking change.
A window of length `window_size` will traverse the values. The resulting values
will be aggregated to their mean.
The window at a given row will include the row itself and the `window_size - 1`
elements before it.
Arguments:
window_size: The length of the window in number of elements. It must be a
strictly positive integer.
min_periods: The number of values in the window that should be non-null before
computing a result. If set to `None` (default), it will be set equal to
`window_size`. If provided, it must be a strictly positive integer, and
less than or equal to `window_size`
center: Set the labels at the center of the window.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1.0, 2.0, None, 4.0]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_rolling_mean(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... b=nw.col("a").rolling_mean(window_size=3, min_periods=1)
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_rolling_mean`:
>>> agnostic_rolling_mean(df_pd)
a b
0 1.0 1.0
1 2.0 1.5
2 NaN 1.5
3 4.0 3.0
>>> agnostic_rolling_mean(df_pl)
shape: (4, 2)
┌──────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ f64 ┆ f64 │
╞══════╪═════╡
│ 1.0 ┆ 1.0 │
│ 2.0 ┆ 1.5 │
│ null ┆ 1.5 │
│ 4.0 ┆ 3.0 │
└──────┴─────┘
>>> agnostic_rolling_mean(df_pa)
pyarrow.Table
a: double
b: double
----
a: [[1,2,null,4]]
b: [[1,1.5,1.5,3]]
"""
window_size, min_periods = _validate_rolling_arguments(
window_size=window_size, min_periods=min_periods
)
return self.__class__(
lambda plx: self._to_compliant_expr(plx).rolling_mean(
window_size=window_size,
min_periods=min_periods,
center=center,
)
)
def rolling_var(
self: Self,
window_size: int,
*,
min_periods: int | None = None,
center: bool = False,
ddof: int = 1,
) -> Self:
"""Apply a rolling variance (moving variance) over the values.
!!! warning
This functionality is considered **unstable**. It may be changed at any point
without it being considered a breaking change.
A window of length `window_size` will traverse the values. The resulting values
will be aggregated to their variance.
The window at a given row will include the row itself and the `window_size - 1`
elements before it.
Arguments:
window_size: The length of the window in number of elements. It must be a
strictly positive integer.
min_periods: The number of values in the window that should be non-null before
computing a result. If set to `None` (default), it will be set equal to
`window_size`. If provided, it must be a strictly positive integer, and
less than or equal to `window_size`.
center: Set the labels at the center of the window.
ddof: Delta Degrees of Freedom; the divisor for a length N window is N - ddof.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1.0, 2.0, None, 4.0]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_rolling_var(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... b=nw.col("a").rolling_var(window_size=3, min_periods=1)
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_rolling_var`:
>>> agnostic_rolling_var(df_pd)
a b
0 1.0 NaN
1 2.0 0.5
2 NaN 0.5
3 4.0 2.0
>>> agnostic_rolling_var(df_pl) # doctest:+SKIP
shape: (4, 2)
┌──────┬──────┐
│ a ┆ b │
│ --- ┆ --- │
│ f64 ┆ f64 │
╞══════╪══════╡
│ 1.0 ┆ null │
│ 2.0 ┆ 0.5 │
│ null ┆ 0.5 │
│ 4.0 ┆ 2.0 │
└──────┴──────┘
>>> agnostic_rolling_var(df_pa)
pyarrow.Table
a: double
b: double
----
a: [[1,2,null,4]]
b: [[nan,0.5,0.5,2]]
"""
window_size, min_periods = _validate_rolling_arguments(
window_size=window_size, min_periods=min_periods
)
return self.__class__(
lambda plx: self._to_compliant_expr(plx).rolling_var(
window_size=window_size, min_periods=min_periods, center=center, ddof=ddof
)
)
def rolling_std(
self: Self,
window_size: int,
*,
min_periods: int | None = None,
center: bool = False,
ddof: int = 1,
) -> Self:
"""Apply a rolling standard deviation (moving standard deviation) over the values.
!!! warning
This functionality is considered **unstable**. It may be changed at any point
without it being considered a breaking change.
A window of length `window_size` will traverse the values. The resulting values
will be aggregated to their standard deviation.
The window at a given row will include the row itself and the `window_size - 1`
elements before it.
Arguments:
window_size: The length of the window in number of elements. It must be a
strictly positive integer.
min_periods: The number of values in the window that should be non-null before
computing a result. If set to `None` (default), it will be set equal to
`window_size`. If provided, it must be a strictly positive integer, and
less than or equal to `window_size`.
center: Set the labels at the center of the window.
ddof: Delta Degrees of Freedom; the divisor for a length N window is N - ddof.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1.0, 2.0, None, 4.0]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_rolling_std(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... b=nw.col("a").rolling_std(window_size=3, min_periods=1)
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_rolling_std`:
>>> agnostic_rolling_std(df_pd)
a b
0 1.0 NaN
1 2.0 0.707107
2 NaN 0.707107
3 4.0 1.414214
>>> agnostic_rolling_std(df_pl) # doctest:+SKIP
shape: (4, 2)
┌──────┬──────────┐
│ a ┆ b │
│ --- ┆ --- │
│ f64 ┆ f64 │
╞══════╪══════════╡
│ 1.0 ┆ null │
│ 2.0 ┆ 0.707107 │
│ null ┆ 0.707107 │
│ 4.0 ┆ 1.414214 │
└──────┴──────────┘
>>> agnostic_rolling_std(df_pa)
pyarrow.Table
a: double
b: double
----
a: [[1,2,null,4]]
b: [[nan,0.7071067811865476,0.7071067811865476,1.4142135623730951]]
"""
window_size, min_periods = _validate_rolling_arguments(
window_size=window_size, min_periods=min_periods
)
return self.__class__(
lambda plx: self._to_compliant_expr(plx).rolling_std(
window_size=window_size,
min_periods=min_periods,
center=center,
ddof=ddof,
)
)
def rank(
self: Self,
method: Literal["average", "min", "max", "dense", "ordinal"] = "average",
*,
descending: bool = False,
) -> Self:
"""Assign ranks to data, dealing with ties appropriately.
Notes:
The resulting dtype may differ between backends.
Arguments:
method: The method used to assign ranks to tied elements.
The following methods are available (default is 'average'):
- 'average' : The average of the ranks that would have been assigned to
all the tied values is assigned to each value.
- 'min' : The minimum of the ranks that would have been assigned to all
the tied values is assigned to each value. (This is also referred to
as "competition" ranking.)
- 'max' : The maximum of the ranks that would have been assigned to all
the tied values is assigned to each value.
- 'dense' : Like 'min', but the rank of the next highest element is
assigned the rank immediately after those assigned to the tied
elements.
- 'ordinal' : All values are given a distinct rank, corresponding to the
order that the values occur in the Series.
descending: Rank in descending order.
Returns:
A new expression with rank data.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [3, 6, 1, 1, 6]}
We define a dataframe-agnostic function that computes the dense rank for
the data:
>>> def agnostic_dense_rank(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... result = df.with_columns(rnk=nw.col("a").rank(method="dense"))
... return result.to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dense_rank`:
>>> agnostic_dense_rank(pd.DataFrame(data))
a rnk
0 3 2.0
1 6 3.0
2 1 1.0
3 1 1.0
4 6 3.0
>>> agnostic_dense_rank(pl.DataFrame(data))
shape: (5, 2)
┌─────┬─────┐
│ a ┆ rnk │
│ --- ┆ --- │
│ i64 ┆ u32 │
╞═════╪═════╡
│ 3 ┆ 2 │
│ 6 ┆ 3 │
│ 1 ┆ 1 │
│ 1 ┆ 1 │
│ 6 ┆ 3 │
└─────┴─────┘
>>> agnostic_dense_rank(pa.table(data))
pyarrow.Table
a: int64
rnk: uint64
----
a: [[3,6,1,1,6]]
rnk: [[2,3,1,1,3]]
"""
supported_rank_methods = {"average", "min", "max", "dense", "ordinal"}
if method not in supported_rank_methods:
msg = (
"Ranking method must be one of {'average', 'min', 'max', 'dense', 'ordinal'}. "
f"Found '{method}'"
)
raise ValueError(msg)
return self.__class__(
lambda plx: self._to_compliant_expr(plx).rank(
method=method, descending=descending
)
)
@property
def str(self: Self) -> ExprStringNamespace[Self]:
return ExprStringNamespace(self)
@property
def dt(self: Self) -> ExprDateTimeNamespace[Self]:
return ExprDateTimeNamespace(self)
@property
def cat(self: Self) -> ExprCatNamespace[Self]:
return ExprCatNamespace(self)
@property
def name(self: Self) -> ExprNameNamespace[Self]:
return ExprNameNamespace(self)
@property
def list(self: Self) -> ExprListNamespace[Self]:
return ExprListNamespace(self)
ExprT = TypeVar("ExprT", bound=Expr)
class ExprCatNamespace(Generic[ExprT]):
def __init__(self: Self, expr: ExprT) -> None:
self._expr = expr
def get_categories(self: Self) -> ExprT:
"""Get unique categories from column.
Returns:
A new expression.
Examples:
Let's create some dataframes:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"fruits": ["apple", "mango", "mango"]}
>>> df_pd = pd.DataFrame(data, dtype="category")
>>> df_pl = pl.DataFrame(data, schema={"fruits": pl.Categorical})
We define a dataframe-agnostic function to get unique categories
from column 'fruits':
>>> def agnostic_cat_get_categories(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("fruits").cat.get_categories()).to_native()
We can then pass any supported library such as pandas or Polars to
`agnostic_cat_get_categories`:
>>> agnostic_cat_get_categories(df_pd)
fruits
0 apple
1 mango
>>> agnostic_cat_get_categories(df_pl)
shape: (2, 1)
┌────────┐
│ fruits │
│ --- │
│ str │
╞════════╡
│ apple │
│ mango │
└────────┘
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).cat.get_categories()
)
class ExprStringNamespace(Generic[ExprT]):
def __init__(self: Self, expr: ExprT) -> None:
self._expr = expr
def len_chars(self: Self) -> ExprT:
r"""Return the length of each string as the number of characters.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"words": ["foo", "Café", "345", "東京", None]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_str_len_chars(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... words_len=nw.col("words").str.len_chars()
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_len_chars`:
>>> agnostic_str_len_chars(df_pd)
words words_len
0 foo 3.0
1 Café 4.0
2 345 3.0
3 東京 2.0
4 None NaN
>>> agnostic_str_len_chars(df_pl)
shape: (5, 2)
┌───────┬───────────┐
│ words ┆ words_len │
│ --- ┆ --- │
│ str ┆ u32 │
╞═══════╪═══════════╡
│ foo ┆ 3 │
│ Café ┆ 4 │
│ 345 ┆ 3 │
│ 東京 ┆ 2 │
│ null ┆ null │
└───────┴───────────┘
>>> agnostic_str_len_chars(df_pa)
pyarrow.Table
words: string
words_len: int32
----
words: [["foo","Café","345","東京",null]]
words_len: [[3,4,3,2,null]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.len_chars()
)
def replace(
self, pattern: str, value: str, *, literal: bool = False, n: int = 1
) -> ExprT:
r"""Replace first matching regex/literal substring with a new string value.
Arguments:
pattern: A valid regular expression pattern.
value: String that will replace the matched substring.
literal: Treat `pattern` as a literal string.
n: Number of matches to replace.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"foo": ["123abc", "abc abc123"]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_str_replace(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... df = df.with_columns(replaced=nw.col("foo").str.replace("abc", ""))
... return df.to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_replace`:
>>> agnostic_str_replace(df_pd)
foo replaced
0 123abc 123
1 abc abc123 abc123
>>> agnostic_str_replace(df_pl)
shape: (2, 2)
┌────────────┬──────────┐
│ foo ┆ replaced │
│ --- ┆ --- │
│ str ┆ str │
╞════════════╪══════════╡
│ 123abc ┆ 123 │
│ abc abc123 ┆ abc123 │
└────────────┴──────────┘
>>> agnostic_str_replace(df_pa)
pyarrow.Table
foo: string
replaced: string
----
foo: [["123abc","abc abc123"]]
replaced: [["123"," abc123"]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.replace(
pattern, value, literal=literal, n=n
)
)
def replace_all(
self: Self, pattern: str, value: str, *, literal: bool = False
) -> ExprT:
r"""Replace all matching regex/literal substring with a new string value.
Arguments:
pattern: A valid regular expression pattern.
value: String that will replace the matched substring.
literal: Treat `pattern` as a literal string.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"foo": ["123abc", "abc abc123"]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_str_replace_all(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... df = df.with_columns(replaced=nw.col("foo").str.replace_all("abc", ""))
... return df.to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_replace_all`:
>>> agnostic_str_replace_all(df_pd)
foo replaced
0 123abc 123
1 abc abc123 123
>>> agnostic_str_replace_all(df_pl)
shape: (2, 2)
┌────────────┬──────────┐
│ foo ┆ replaced │
│ --- ┆ --- │
│ str ┆ str │
╞════════════╪══════════╡
│ 123abc ┆ 123 │
│ abc abc123 ┆ 123 │
└────────────┴──────────┘
>>> agnostic_str_replace_all(df_pa)
pyarrow.Table
foo: string
replaced: string
----
foo: [["123abc","abc abc123"]]
replaced: [["123"," 123"]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.replace_all(
pattern, value, literal=literal
)
)
def strip_chars(self: Self, characters: str | None = None) -> ExprT:
r"""Remove leading and trailing characters.
Arguments:
characters: The set of characters to be removed. All combinations of this
set of characters will be stripped from the start and end of the string.
If set to None (default), all leading and trailing whitespace is removed
instead.
Returns:
A new expression.
Examples:
>>> from typing import Any
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrame
>>>
>>> data = {"fruits": ["apple", "\nmango"]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_str_strip_chars(df_native: IntoFrame) -> dict[str, Any]:
... df = nw.from_native(df_native)
... df = df.with_columns(stripped=nw.col("fruits").str.strip_chars())
... return df.to_dict(as_series=False)
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_strip_chars`:
>>> agnostic_str_strip_chars(df_pd)
{'fruits': ['apple', '\nmango'], 'stripped': ['apple', 'mango']}
>>> agnostic_str_strip_chars(df_pl)
{'fruits': ['apple', '\nmango'], 'stripped': ['apple', 'mango']}
>>> agnostic_str_strip_chars(df_pa)
{'fruits': ['apple', '\nmango'], 'stripped': ['apple', 'mango']}
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.strip_chars(characters)
)
def starts_with(self: Self, prefix: str) -> ExprT:
r"""Check if string values start with a substring.
Arguments:
prefix: prefix substring
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"fruits": ["apple", "mango", None]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_str_starts_with(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... has_prefix=nw.col("fruits").str.starts_with("app")
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_starts_with`:
>>> agnostic_str_starts_with(df_pd)
fruits has_prefix
0 apple True
1 mango False
2 None None
>>> agnostic_str_starts_with(df_pl)
shape: (3, 2)
┌────────┬────────────┐
│ fruits ┆ has_prefix │
│ --- ┆ --- │
│ str ┆ bool │
╞════════╪════════════╡
│ apple ┆ true │
│ mango ┆ false │
│ null ┆ null │
└────────┴────────────┘
>>> agnostic_str_starts_with(df_pa)
pyarrow.Table
fruits: string
has_prefix: bool
----
fruits: [["apple","mango",null]]
has_prefix: [[true,false,null]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.starts_with(prefix)
)
def ends_with(self: Self, suffix: str) -> ExprT:
r"""Check if string values end with a substring.
Arguments:
suffix: suffix substring
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"fruits": ["apple", "mango", None]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_str_ends_with(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... has_suffix=nw.col("fruits").str.ends_with("ngo")
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_ends_with`:
>>> agnostic_str_ends_with(df_pd)
fruits has_suffix
0 apple False
1 mango True
2 None None
>>> agnostic_str_ends_with(df_pl)
shape: (3, 2)
┌────────┬────────────┐
│ fruits ┆ has_suffix │
│ --- ┆ --- │
│ str ┆ bool │
╞════════╪════════════╡
│ apple ┆ false │
│ mango ┆ true │
│ null ┆ null │
└────────┴────────────┘
>>> agnostic_str_ends_with(df_pa)
pyarrow.Table
fruits: string
has_suffix: bool
----
fruits: [["apple","mango",null]]
has_suffix: [[false,true,null]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.ends_with(suffix)
)
def contains(self: Self, pattern: str, *, literal: bool = False) -> ExprT:
r"""Check if string contains a substring that matches a pattern.
Arguments:
pattern: A Character sequence or valid regular expression pattern.
literal: If True, treats the pattern as a literal string.
If False, assumes the pattern is a regular expression.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"pets": ["cat", "dog", "rabbit and parrot", "dove", None]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_str_contains(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... default_match=nw.col("pets").str.contains("parrot|Dove"),
... case_insensitive_match=nw.col("pets").str.contains("(?i)parrot|Dove"),
... literal_match=nw.col("pets").str.contains(
... "parrot|Dove", literal=True
... ),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_contains`:
>>> agnostic_str_contains(df_pd)
pets default_match case_insensitive_match literal_match
0 cat False False False
1 dog False False False
2 rabbit and parrot True True False
3 dove False True False
4 None None None None
>>> agnostic_str_contains(df_pl)
shape: (5, 4)
┌───────────────────┬───────────────┬────────────────────────┬───────────────┐
│ pets ┆ default_match ┆ case_insensitive_match ┆ literal_match │
│ --- ┆ --- ┆ --- ┆ --- │
│ str ┆ bool ┆ bool ┆ bool │
╞═══════════════════╪═══════════════╪════════════════════════╪═══════════════╡
│ cat ┆ false ┆ false ┆ false │
│ dog ┆ false ┆ false ┆ false │
│ rabbit and parrot ┆ true ┆ true ┆ false │
│ dove ┆ false ┆ true ┆ false │
│ null ┆ null ┆ null ┆ null │
└───────────────────┴───────────────┴────────────────────────┴───────────────┘
>>> agnostic_str_contains(df_pa)
pyarrow.Table
pets: string
default_match: bool
case_insensitive_match: bool
literal_match: bool
----
pets: [["cat","dog","rabbit and parrot","dove",null]]
default_match: [[false,false,true,false,null]]
case_insensitive_match: [[false,false,true,true,null]]
literal_match: [[false,false,false,false,null]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.contains(
pattern, literal=literal
)
)
def slice(self: Self, offset: int, length: int | None = None) -> ExprT:
r"""Create subslices of the string values of an expression.
Arguments:
offset: Start index. Negative indexing is supported.
length: Length of the slice. If set to `None` (default), the slice is taken to the
end of the string.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"s": ["pear", None, "papaya", "dragonfruit"]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_str_slice(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... s_sliced=nw.col("s").str.slice(4, length=3)
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_slice`:
>>> agnostic_str_slice(df_pd) # doctest: +NORMALIZE_WHITESPACE
s s_sliced
0 pear
1 None None
2 papaya ya
3 dragonfruit onf
>>> agnostic_str_slice(df_pl)
shape: (4, 2)
┌─────────────┬──────────┐
│ s ┆ s_sliced │
│ --- ┆ --- │
│ str ┆ str │
╞═════════════╪══════════╡
│ pear ┆ │
│ null ┆ null │
│ papaya ┆ ya │
│ dragonfruit ┆ onf │
└─────────────┴──────────┘
>>> agnostic_str_slice(df_pa)
pyarrow.Table
s: string
s_sliced: string
----
s: [["pear",null,"papaya","dragonfruit"]]
s_sliced: [["",null,"ya","onf"]]
Using negative indexes:
>>> def agnostic_str_slice_negative(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(s_sliced=nw.col("s").str.slice(-3)).to_native()
>>> agnostic_str_slice_negative(df_pd)
s s_sliced
0 pear ear
1 None None
2 papaya aya
3 dragonfruit uit
>>> agnostic_str_slice_negative(df_pl)
shape: (4, 2)
┌─────────────┬──────────┐
│ s ┆ s_sliced │
│ --- ┆ --- │
│ str ┆ str │
╞═════════════╪══════════╡
│ pear ┆ ear │
│ null ┆ null │
│ papaya ┆ aya │
│ dragonfruit ┆ uit │
└─────────────┴──────────┘
>>> agnostic_str_slice_negative(df_pa)
pyarrow.Table
s: string
s_sliced: string
----
s: [["pear",null,"papaya","dragonfruit"]]
s_sliced: [["ear",null,"aya","uit"]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.slice(
offset=offset, length=length
)
)
def head(self: Self, n: int = 5) -> ExprT:
r"""Take the first n elements of each string.
Arguments:
n: Number of elements to take. Negative indexing is **not** supported.
Returns:
A new expression.
Notes:
If the length of the string has fewer than `n` characters, the full string is returned.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"lyrics": ["Atatata", "taata", "taatatata", "zukkyun"]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_str_head(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... lyrics_head=nw.col("lyrics").str.head()
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_head`:
>>> agnostic_str_head(df_pd)
lyrics lyrics_head
0 Atatata Atata
1 taata taata
2 taatatata taata
3 zukkyun zukky
>>> agnostic_str_head(df_pl)
shape: (4, 2)
┌───────────┬─────────────┐
│ lyrics ┆ lyrics_head │
│ --- ┆ --- │
│ str ┆ str │
╞═══════════╪═════════════╡
│ Atatata ┆ Atata │
│ taata ┆ taata │
│ taatatata ┆ taata │
│ zukkyun ┆ zukky │
└───────────┴─────────────┘
>>> agnostic_str_head(df_pa)
pyarrow.Table
lyrics: string
lyrics_head: string
----
lyrics: [["Atatata","taata","taatatata","zukkyun"]]
lyrics_head: [["Atata","taata","taata","zukky"]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.slice(0, n)
)
def tail(self: Self, n: int = 5) -> ExprT:
r"""Take the last n elements of each string.
Arguments:
n: Number of elements to take. Negative indexing is **not** supported.
Returns:
A new expression.
Notes:
If the length of the string has fewer than `n` characters, the full string is returned.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"lyrics": ["Atatata", "taata", "taatatata", "zukkyun"]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_str_tail(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... lyrics_tail=nw.col("lyrics").str.tail()
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_tail`:
>>> agnostic_str_tail(df_pd)
lyrics lyrics_tail
0 Atatata atata
1 taata taata
2 taatatata atata
3 zukkyun kkyun
>>> agnostic_str_tail(df_pl)
shape: (4, 2)
┌───────────┬─────────────┐
│ lyrics ┆ lyrics_tail │
│ --- ┆ --- │
│ str ┆ str │
╞═══════════╪═════════════╡
│ Atatata ┆ atata │
│ taata ┆ taata │
│ taatatata ┆ atata │
│ zukkyun ┆ kkyun │
└───────────┴─────────────┘
>>> agnostic_str_tail(df_pa)
pyarrow.Table
lyrics: string
lyrics_tail: string
----
lyrics: [["Atatata","taata","taatatata","zukkyun"]]
lyrics_tail: [["atata","taata","atata","kkyun"]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.slice(
offset=-n, length=None
)
)
def to_datetime(self: Self, format: str | None = None) -> ExprT: # noqa: A002
"""Convert to Datetime dtype.
Warning:
As different backends auto-infer format in different ways, if `format=None`
there is no guarantee that the result will be equal.
Arguments:
format: Format to use for conversion. If set to None (default), the format is
inferred from the data.
Returns:
A new expression.
Notes:
pandas defaults to nanosecond time unit, Polars to microsecond.
Prior to pandas 2.0, nanoseconds were the only time unit supported
in pandas, with no ability to set any other one. The ability to
set the time unit in pandas, if the version permits, will arrive.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = ["2020-01-01", "2020-01-02"]
>>> df_pd = pd.DataFrame({"a": data})
>>> df_pl = pl.DataFrame({"a": data})
>>> df_pa = pa.table({"a": data})
We define a dataframe-agnostic function:
>>> def agnostic_str_to_datetime(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("a").str.to_datetime(format="%Y-%m-%d")
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_to_datetime`:
>>> agnostic_str_to_datetime(df_pd)
a
0 2020-01-01
1 2020-01-02
>>> agnostic_str_to_datetime(df_pl)
shape: (2, 1)
┌─────────────────────┐
│ a │
│ --- │
│ datetime[μs] │
╞═════════════════════╡
│ 2020-01-01 00:00:00 │
│ 2020-01-02 00:00:00 │
└─────────────────────┘
>>> agnostic_str_to_datetime(df_pa)
pyarrow.Table
a: timestamp[us]
----
a: [[2020-01-01 00:00:00.000000,2020-01-02 00:00:00.000000]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.to_datetime(format=format)
)
def to_uppercase(self: Self) -> ExprT:
r"""Transform string to uppercase variant.
Returns:
A new expression.
Notes:
The PyArrow backend will convert 'ß' to 'ẞ' instead of 'SS'.
For more info see [the related issue](https://github.com/apache/arrow/issues/34599).
There may be other unicode-edge-case-related variations across implementations.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"fruits": ["apple", "mango", None]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_str_to_uppercase(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... upper_col=nw.col("fruits").str.to_uppercase()
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_to_uppercase`:
>>> agnostic_str_to_uppercase(df_pd)
fruits upper_col
0 apple APPLE
1 mango MANGO
2 None None
>>> agnostic_str_to_uppercase(df_pl)
shape: (3, 2)
┌────────┬───────────┐
│ fruits ┆ upper_col │
│ --- ┆ --- │
│ str ┆ str │
╞════════╪═══════════╡
│ apple ┆ APPLE │
│ mango ┆ MANGO │
│ null ┆ null │
└────────┴───────────┘
>>> agnostic_str_to_uppercase(df_pa)
pyarrow.Table
fruits: string
upper_col: string
----
fruits: [["apple","mango",null]]
upper_col: [["APPLE","MANGO",null]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.to_uppercase()
)
def to_lowercase(self: Self) -> ExprT:
r"""Transform string to lowercase variant.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"fruits": ["APPLE", "MANGO", None]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_str_to_lowercase(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... lower_col=nw.col("fruits").str.to_lowercase()
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_str_to_lowercase`:
>>> agnostic_str_to_lowercase(df_pd)
fruits lower_col
0 APPLE apple
1 MANGO mango
2 None None
>>> agnostic_str_to_lowercase(df_pl)
shape: (3, 2)
┌────────┬───────────┐
│ fruits ┆ lower_col │
│ --- ┆ --- │
│ str ┆ str │
╞════════╪═══════════╡
│ APPLE ┆ apple │
│ MANGO ┆ mango │
│ null ┆ null │
└────────┴───────────┘
>>> agnostic_str_to_lowercase(df_pa)
pyarrow.Table
fruits: string
lower_col: string
----
fruits: [["APPLE","MANGO",null]]
lower_col: [["apple","mango",null]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).str.to_lowercase()
)
class ExprDateTimeNamespace(Generic[ExprT]):
def __init__(self: Self, expr: ExprT) -> None:
self._expr = expr
def date(self: Self) -> ExprT:
"""Extract the date from underlying DateTime representation.
Returns:
A new expression.
Raises:
NotImplementedError: If pandas default backend is being used.
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [datetime(2012, 1, 7, 10, 20), datetime(2023, 3, 10, 11, 32)]}
>>> df_pd = pd.DataFrame(data).convert_dtypes(dtype_backend="pyarrow")
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a library agnostic function:
>>> def agnostic_dt_date(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a").dt.date()).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_date`:
>>> agnostic_dt_date(df_pd)
a
0 2012-01-07
1 2023-03-10
>>> agnostic_dt_date(df_pl)
shape: (2, 1)
┌────────────┐
│ a │
│ --- │
│ date │
╞════════════╡
│ 2012-01-07 │
│ 2023-03-10 │
└────────────┘
>>> agnostic_dt_date(df_pa)
pyarrow.Table
a: date32[day]
----
a: [[2012-01-07,2023-03-10]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.date()
)
def year(self: Self) -> ExprT:
"""Extract year from underlying DateTime representation.
Returns the year number in the calendar date.
Returns:
A new expression.
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "datetime": [
... datetime(1978, 6, 1),
... datetime(2024, 12, 13),
... datetime(2065, 1, 1),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_year(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("datetime").dt.year().alias("year")
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_year`:
>>> agnostic_dt_year(df_pd)
datetime year
0 1978-06-01 1978
1 2024-12-13 2024
2 2065-01-01 2065
>>> agnostic_dt_year(df_pl)
shape: (3, 2)
┌─────────────────────┬──────┐
│ datetime ┆ year │
│ --- ┆ --- │
│ datetime[μs] ┆ i32 │
╞═════════════════════╪══════╡
│ 1978-06-01 00:00:00 ┆ 1978 │
│ 2024-12-13 00:00:00 ┆ 2024 │
│ 2065-01-01 00:00:00 ┆ 2065 │
└─────────────────────┴──────┘
>>> agnostic_dt_year(df_pa)
pyarrow.Table
datetime: timestamp[us]
year: int64
----
datetime: [[1978-06-01 00:00:00.000000,2024-12-13 00:00:00.000000,2065-01-01 00:00:00.000000]]
year: [[1978,2024,2065]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.year()
)
def month(self: Self) -> ExprT:
"""Extract month from underlying DateTime representation.
Returns the month number starting from 1. The return value ranges from 1 to 12.
Returns:
A new expression.
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "datetime": [
... datetime(1978, 6, 1),
... datetime(2024, 12, 13),
... datetime(2065, 1, 1),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_month(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("datetime").dt.month().alias("month"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_month`:
>>> agnostic_dt_month(df_pd)
datetime month
0 1978-06-01 6
1 2024-12-13 12
2 2065-01-01 1
>>> agnostic_dt_month(df_pl)
shape: (3, 2)
┌─────────────────────┬───────┐
│ datetime ┆ month │
│ --- ┆ --- │
│ datetime[μs] ┆ i8 │
╞═════════════════════╪═══════╡
│ 1978-06-01 00:00:00 ┆ 6 │
│ 2024-12-13 00:00:00 ┆ 12 │
│ 2065-01-01 00:00:00 ┆ 1 │
└─────────────────────┴───────┘
>>> agnostic_dt_month(df_pa)
pyarrow.Table
datetime: timestamp[us]
month: int64
----
datetime: [[1978-06-01 00:00:00.000000,2024-12-13 00:00:00.000000,2065-01-01 00:00:00.000000]]
month: [[6,12,1]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.month()
)
def day(self: Self) -> ExprT:
"""Extract day from underlying DateTime representation.
Returns the day of month starting from 1. The return value ranges from 1 to 31. (The last day of month differs by months.)
Returns:
A new expression.
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "datetime": [
... datetime(1978, 6, 1),
... datetime(2024, 12, 13),
... datetime(2065, 1, 1),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_day(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("datetime").dt.day().alias("day"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_day`:
>>> agnostic_dt_day(df_pd)
datetime day
0 1978-06-01 1
1 2024-12-13 13
2 2065-01-01 1
>>> agnostic_dt_day(df_pl)
shape: (3, 2)
┌─────────────────────┬─────┐
│ datetime ┆ day │
│ --- ┆ --- │
│ datetime[μs] ┆ i8 │
╞═════════════════════╪═════╡
│ 1978-06-01 00:00:00 ┆ 1 │
│ 2024-12-13 00:00:00 ┆ 13 │
│ 2065-01-01 00:00:00 ┆ 1 │
└─────────────────────┴─────┘
>>> agnostic_dt_day(df_pa)
pyarrow.Table
datetime: timestamp[us]
day: int64
----
datetime: [[1978-06-01 00:00:00.000000,2024-12-13 00:00:00.000000,2065-01-01 00:00:00.000000]]
day: [[1,13,1]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.day()
)
def hour(self: Self) -> ExprT:
"""Extract hour from underlying DateTime representation.
Returns the hour number from 0 to 23.
Returns:
A new expression.
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "datetime": [
... datetime(1978, 1, 1, 1),
... datetime(2024, 10, 13, 5),
... datetime(2065, 1, 1, 10),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_hour(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("datetime").dt.hour().alias("hour")
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_hour`:
>>> agnostic_dt_hour(df_pd)
datetime hour
0 1978-01-01 01:00:00 1
1 2024-10-13 05:00:00 5
2 2065-01-01 10:00:00 10
>>> agnostic_dt_hour(df_pl)
shape: (3, 2)
┌─────────────────────┬──────┐
│ datetime ┆ hour │
│ --- ┆ --- │
│ datetime[μs] ┆ i8 │
╞═════════════════════╪══════╡
│ 1978-01-01 01:00:00 ┆ 1 │
│ 2024-10-13 05:00:00 ┆ 5 │
│ 2065-01-01 10:00:00 ┆ 10 │
└─────────────────────┴──────┘
>>> agnostic_dt_hour(df_pa)
pyarrow.Table
datetime: timestamp[us]
hour: int64
----
datetime: [[1978-01-01 01:00:00.000000,2024-10-13 05:00:00.000000,2065-01-01 10:00:00.000000]]
hour: [[1,5,10]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.hour()
)
def minute(self: Self) -> ExprT:
"""Extract minutes from underlying DateTime representation.
Returns the minute number from 0 to 59.
Returns:
A new expression.
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "datetime": [
... datetime(1978, 1, 1, 1, 1),
... datetime(2024, 10, 13, 5, 30),
... datetime(2065, 1, 1, 10, 20),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_minute(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("datetime").dt.minute().alias("minute"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_minute`:
>>> agnostic_dt_minute(df_pd)
datetime minute
0 1978-01-01 01:01:00 1
1 2024-10-13 05:30:00 30
2 2065-01-01 10:20:00 20
>>> agnostic_dt_minute(df_pl)
shape: (3, 2)
┌─────────────────────┬────────┐
│ datetime ┆ minute │
│ --- ┆ --- │
│ datetime[μs] ┆ i8 │
╞═════════════════════╪════════╡
│ 1978-01-01 01:01:00 ┆ 1 │
│ 2024-10-13 05:30:00 ┆ 30 │
│ 2065-01-01 10:20:00 ┆ 20 │
└─────────────────────┴────────┘
>>> agnostic_dt_minute(df_pa)
pyarrow.Table
datetime: timestamp[us]
minute: int64
----
datetime: [[1978-01-01 01:01:00.000000,2024-10-13 05:30:00.000000,2065-01-01 10:20:00.000000]]
minute: [[1,30,20]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.minute()
)
def second(self: Self) -> ExprT:
"""Extract seconds from underlying DateTime representation.
Returns:
A new expression.
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "datetime": [
... datetime(1978, 1, 1, 1, 1, 1),
... datetime(2024, 10, 13, 5, 30, 14),
... datetime(2065, 1, 1, 10, 20, 30),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_second(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("datetime").dt.second().alias("second"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_second`:
>>> agnostic_dt_second(df_pd)
datetime second
0 1978-01-01 01:01:01 1
1 2024-10-13 05:30:14 14
2 2065-01-01 10:20:30 30
>>> agnostic_dt_second(df_pl)
shape: (3, 2)
┌─────────────────────┬────────┐
│ datetime ┆ second │
│ --- ┆ --- │
│ datetime[μs] ┆ i8 │
╞═════════════════════╪════════╡
│ 1978-01-01 01:01:01 ┆ 1 │
│ 2024-10-13 05:30:14 ┆ 14 │
│ 2065-01-01 10:20:30 ┆ 30 │
└─────────────────────┴────────┘
>>> agnostic_dt_second(df_pa)
pyarrow.Table
datetime: timestamp[us]
second: int64
----
datetime: [[1978-01-01 01:01:01.000000,2024-10-13 05:30:14.000000,2065-01-01 10:20:30.000000]]
second: [[1,14,30]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.second()
)
def millisecond(self: Self) -> ExprT:
"""Extract milliseconds from underlying DateTime representation.
Returns:
A new expression.
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "datetime": [
... datetime(1978, 1, 1, 1, 1, 1, 0),
... datetime(2024, 10, 13, 5, 30, 14, 505000),
... datetime(2065, 1, 1, 10, 20, 30, 67000),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_millisecond(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("datetime").dt.millisecond().alias("millisecond"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_millisecond`:
>>> agnostic_dt_millisecond(df_pd)
datetime millisecond
0 1978-01-01 01:01:01.000 0
1 2024-10-13 05:30:14.505 505
2 2065-01-01 10:20:30.067 67
>>> agnostic_dt_millisecond(df_pl)
shape: (3, 2)
┌─────────────────────────┬─────────────┐
│ datetime ┆ millisecond │
│ --- ┆ --- │
│ datetime[μs] ┆ i32 │
╞═════════════════════════╪═════════════╡
│ 1978-01-01 01:01:01 ┆ 0 │
│ 2024-10-13 05:30:14.505 ┆ 505 │
│ 2065-01-01 10:20:30.067 ┆ 67 │
└─────────────────────────┴─────────────┘
>>> agnostic_dt_millisecond(df_pa)
pyarrow.Table
datetime: timestamp[us]
millisecond: int64
----
datetime: [[1978-01-01 01:01:01.000000,2024-10-13 05:30:14.505000,2065-01-01 10:20:30.067000]]
millisecond: [[0,505,67]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.millisecond()
)
def microsecond(self: Self) -> ExprT:
"""Extract microseconds from underlying DateTime representation.
Returns:
A new expression.
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "datetime": [
... datetime(1978, 1, 1, 1, 1, 1, 0),
... datetime(2024, 10, 13, 5, 30, 14, 505000),
... datetime(2065, 1, 1, 10, 20, 30, 67000),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_microsecond(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("datetime").dt.microsecond().alias("microsecond"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_microsecond`:
>>> agnostic_dt_microsecond(df_pd)
datetime microsecond
0 1978-01-01 01:01:01.000 0
1 2024-10-13 05:30:14.505 505000
2 2065-01-01 10:20:30.067 67000
>>> agnostic_dt_microsecond(df_pl)
shape: (3, 2)
┌─────────────────────────┬─────────────┐
│ datetime ┆ microsecond │
│ --- ┆ --- │
│ datetime[μs] ┆ i32 │
╞═════════════════════════╪═════════════╡
│ 1978-01-01 01:01:01 ┆ 0 │
│ 2024-10-13 05:30:14.505 ┆ 505000 │
│ 2065-01-01 10:20:30.067 ┆ 67000 │
└─────────────────────────┴─────────────┘
>>> agnostic_dt_microsecond(df_pa)
pyarrow.Table
datetime: timestamp[us]
microsecond: int64
----
datetime: [[1978-01-01 01:01:01.000000,2024-10-13 05:30:14.505000,2065-01-01 10:20:30.067000]]
microsecond: [[0,505000,67000]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.microsecond()
)
def nanosecond(self: Self) -> ExprT:
"""Extract Nanoseconds from underlying DateTime representation.
Returns:
A new expression.
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "datetime": [
... datetime(1978, 1, 1, 1, 1, 1, 0),
... datetime(2024, 10, 13, 5, 30, 14, 500000),
... datetime(2065, 1, 1, 10, 20, 30, 60000),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_nanosecond(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("datetime").dt.nanosecond().alias("nanosecond"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_nanosecond`:
>>> agnostic_dt_nanosecond(df_pd)
datetime nanosecond
0 1978-01-01 01:01:01.000 0
1 2024-10-13 05:30:14.500 500000000
2 2065-01-01 10:20:30.060 60000000
>>> agnostic_dt_nanosecond(df_pl)
shape: (3, 2)
┌─────────────────────────┬────────────┐
│ datetime ┆ nanosecond │
│ --- ┆ --- │
│ datetime[μs] ┆ i32 │
╞═════════════════════════╪════════════╡
│ 1978-01-01 01:01:01 ┆ 0 │
│ 2024-10-13 05:30:14.500 ┆ 500000000 │
│ 2065-01-01 10:20:30.060 ┆ 60000000 │
└─────────────────────────┴────────────┘
>>> agnostic_dt_nanosecond(df_pa)
pyarrow.Table
datetime: timestamp[us]
nanosecond: int64
----
datetime: [[1978-01-01 01:01:01.000000,2024-10-13 05:30:14.500000,2065-01-01 10:20:30.060000]]
nanosecond: [[0,500000000,60000000]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.nanosecond()
)
def ordinal_day(self: Self) -> ExprT:
"""Get ordinal day.
Returns:
A new expression.
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [datetime(2020, 1, 1), datetime(2020, 8, 3)]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_ordinal_day(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... a_ordinal_day=nw.col("a").dt.ordinal_day()
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_ordinal_day`:
>>> agnostic_dt_ordinal_day(df_pd)
a a_ordinal_day
0 2020-01-01 1
1 2020-08-03 216
>>> agnostic_dt_ordinal_day(df_pl)
shape: (2, 2)
┌─────────────────────┬───────────────┐
│ a ┆ a_ordinal_day │
│ --- ┆ --- │
│ datetime[μs] ┆ i16 │
╞═════════════════════╪═══════════════╡
│ 2020-01-01 00:00:00 ┆ 1 │
│ 2020-08-03 00:00:00 ┆ 216 │
└─────────────────────┴───────────────┘
>>> agnostic_dt_ordinal_day(df_pa)
pyarrow.Table
a: timestamp[us]
a_ordinal_day: int64
----
a: [[2020-01-01 00:00:00.000000,2020-08-03 00:00:00.000000]]
a_ordinal_day: [[1,216]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.ordinal_day()
)
def weekday(self: Self) -> ExprT:
"""Extract the week day from the underlying Date representation.
Returns:
Returns the ISO weekday number where monday = 1 and sunday = 7
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [datetime(2020, 1, 1), datetime(2020, 8, 3)]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_weekday(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(a_weekday=nw.col("a").dt.weekday()).to_native()
We can then pass either pandas, Polars, PyArrow, and other supported libraries to
`agnostic_dt_weekday`:
>>> agnostic_dt_weekday(df_pd)
a a_weekday
0 2020-01-01 3
1 2020-08-03 1
>>> agnostic_dt_weekday(df_pl)
shape: (2, 2)
┌─────────────────────┬───────────┐
│ a ┆ a_weekday │
│ --- ┆ --- │
│ datetime[μs] ┆ i8 │
╞═════════════════════╪═══════════╡
│ 2020-01-01 00:00:00 ┆ 3 │
│ 2020-08-03 00:00:00 ┆ 1 │
└─────────────────────┴───────────┘
>>> agnostic_dt_weekday(df_pa)
pyarrow.Table
a: timestamp[us]
a_weekday: int64
----
a: [[2020-01-01 00:00:00.000000,2020-08-03 00:00:00.000000]]
a_weekday: [[3,1]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.weekday()
)
def total_minutes(self: Self) -> ExprT:
"""Get total minutes.
Returns:
A new expression.
Notes:
The function outputs the total minutes in the int dtype by default,
however, pandas may change the dtype to float when there are missing values,
consider using `fill_null()` and `cast` in this case.
Examples:
>>> from datetime import timedelta
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [timedelta(minutes=10), timedelta(minutes=20, seconds=40)]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_total_minutes(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... a_total_minutes=nw.col("a").dt.total_minutes()
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_total_minutes`:
>>> agnostic_dt_total_minutes(df_pd)
a a_total_minutes
0 0 days 00:10:00 10
1 0 days 00:20:40 20
>>> agnostic_dt_total_minutes(df_pl)
shape: (2, 2)
┌──────────────┬─────────────────┐
│ a ┆ a_total_minutes │
│ --- ┆ --- │
│ duration[μs] ┆ i64 │
╞══════════════╪═════════════════╡
│ 10m ┆ 10 │
│ 20m 40s ┆ 20 │
└──────────────┴─────────────────┘
>>> agnostic_dt_total_minutes(df_pa)
pyarrow.Table
a: duration[us]
a_total_minutes: int64
----
a: [[600000000,1240000000]]
a_total_minutes: [[10,20]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.total_minutes()
)
def total_seconds(self: Self) -> ExprT:
"""Get total seconds.
Returns:
A new expression.
Notes:
The function outputs the total seconds in the int dtype by default,
however, pandas may change the dtype to float when there are missing values,
consider using `fill_null()` and `cast` in this case.
Examples:
>>> from datetime import timedelta
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [timedelta(seconds=10), timedelta(seconds=20, milliseconds=40)]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_total_seconds(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... a_total_seconds=nw.col("a").dt.total_seconds()
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_total_seconds`:
>>> agnostic_dt_total_seconds(df_pd)
a a_total_seconds
0 0 days 00:00:10 10
1 0 days 00:00:20.040000 20
>>> agnostic_dt_total_seconds(df_pl)
shape: (2, 2)
┌──────────────┬─────────────────┐
│ a ┆ a_total_seconds │
│ --- ┆ --- │
│ duration[μs] ┆ i64 │
╞══════════════╪═════════════════╡
│ 10s ┆ 10 │
│ 20s 40ms ┆ 20 │
└──────────────┴─────────────────┘
>>> agnostic_dt_total_seconds(df_pa)
pyarrow.Table
a: duration[us]
a_total_seconds: int64
----
a: [[10000000,20040000]]
a_total_seconds: [[10,20]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.total_seconds()
)
def total_milliseconds(self: Self) -> ExprT:
"""Get total milliseconds.
Returns:
A new expression.
Notes:
The function outputs the total milliseconds in the int dtype by default,
however, pandas may change the dtype to float when there are missing values,
consider using `fill_null()` and `cast` in this case.
Examples:
>>> from datetime import timedelta
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "a": [
... timedelta(milliseconds=10),
... timedelta(milliseconds=20, microseconds=40),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_total_milliseconds(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... a_total_milliseconds=nw.col("a").dt.total_milliseconds()
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_total_milliseconds`:
>>> agnostic_dt_total_milliseconds(df_pd)
a a_total_milliseconds
0 0 days 00:00:00.010000 10
1 0 days 00:00:00.020040 20
>>> agnostic_dt_total_milliseconds(df_pl)
shape: (2, 2)
┌──────────────┬──────────────────────┐
│ a ┆ a_total_milliseconds │
│ --- ┆ --- │
│ duration[μs] ┆ i64 │
╞══════════════╪══════════════════════╡
│ 10ms ┆ 10 │
│ 20040µs ┆ 20 │
└──────────────┴──────────────────────┘
>>> agnostic_dt_total_milliseconds(df_pa)
pyarrow.Table
a: duration[us]
a_total_milliseconds: int64
----
a: [[10000,20040]]
a_total_milliseconds: [[10,20]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.total_milliseconds()
)
def total_microseconds(self: Self) -> ExprT:
"""Get total microseconds.
Returns:
A new expression.
Notes:
The function outputs the total microseconds in the int dtype by default,
however, pandas may change the dtype to float when there are missing values,
consider using `fill_null()` and `cast` in this case.
Examples:
>>> from datetime import timedelta
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "a": [
... timedelta(microseconds=10),
... timedelta(milliseconds=1, microseconds=200),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_total_microseconds(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... a_total_microseconds=nw.col("a").dt.total_microseconds()
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_total_microseconds`:
>>> agnostic_dt_total_microseconds(df_pd)
a a_total_microseconds
0 0 days 00:00:00.000010 10
1 0 days 00:00:00.001200 1200
>>> agnostic_dt_total_microseconds(df_pl)
shape: (2, 2)
┌──────────────┬──────────────────────┐
│ a ┆ a_total_microseconds │
│ --- ┆ --- │
│ duration[μs] ┆ i64 │
╞══════════════╪══════════════════════╡
│ 10µs ┆ 10 │
│ 1200µs ┆ 1200 │
└──────────────┴──────────────────────┘
>>> agnostic_dt_total_microseconds(df_pa)
pyarrow.Table
a: duration[us]
a_total_microseconds: int64
----
a: [[10,1200]]
a_total_microseconds: [[10,1200]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.total_microseconds()
)
def total_nanoseconds(self: Self) -> ExprT:
"""Get total nanoseconds.
Returns:
A new expression.
Notes:
The function outputs the total nanoseconds in the int dtype by default,
however, pandas may change the dtype to float when there are missing values,
consider using `fill_null()` and `cast` in this case.
Examples:
>>> from datetime import timedelta
>>> import pandas as pd
>>> import polars as pl
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = ["2024-01-01 00:00:00.000000001", "2024-01-01 00:00:00.000000002"]
>>> df_pd = pd.DataFrame({"a": pd.to_datetime(data)})
>>> df_pl = pl.DataFrame({"a": data}).with_columns(
... pl.col("a").str.to_datetime(time_unit="ns")
... )
We define a dataframe-agnostic function:
>>> def agnostic_dt_total_nanoseconds(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... a_diff_total_nanoseconds=nw.col("a").diff().dt.total_nanoseconds()
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_total_nanoseconds`:
>>> agnostic_dt_total_nanoseconds(df_pd)
a a_diff_total_nanoseconds
0 2024-01-01 00:00:00.000000001 NaN
1 2024-01-01 00:00:00.000000002 1.0
>>> agnostic_dt_total_nanoseconds(df_pl)
shape: (2, 2)
┌───────────────────────────────┬──────────────────────────┐
│ a ┆ a_diff_total_nanoseconds │
│ --- ┆ --- │
│ datetime[ns] ┆ i64 │
╞═══════════════════════════════╪══════════════════════════╡
│ 2024-01-01 00:00:00.000000001 ┆ null │
│ 2024-01-01 00:00:00.000000002 ┆ 1 │
└───────────────────────────────┴──────────────────────────┘
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.total_nanoseconds()
)
def to_string(self: Self, format: str) -> ExprT: # noqa: A002
"""Convert a Date/Time/Datetime column into a String column with the given format.
Arguments:
format: Format to format temporal column with.
Returns:
A new expression.
Notes:
Unfortunately, different libraries interpret format directives a bit
differently.
- Chrono, the library used by Polars, uses `"%.f"` for fractional seconds,
whereas pandas and Python stdlib use `".%f"`.
- PyArrow interprets `"%S"` as "seconds, including fractional seconds"
whereas most other tools interpret it as "just seconds, as 2 digits".
Therefore, we make the following adjustments:
- for pandas-like libraries, we replace `"%S.%f"` with `"%S%.f"`.
- for PyArrow, we replace `"%S.%f"` with `"%S"`.
Workarounds like these don't make us happy, and we try to avoid them as
much as possible, but here we feel like it's the best compromise.
If you just want to format a date/datetime Series as a local datetime
string, and have it work as consistently as possible across libraries,
we suggest using:
- `"%Y-%m-%dT%H:%M:%S%.f"` for datetimes
- `"%Y-%m-%d"` for dates
though note that, even then, different tools may return a different number
of trailing zeros. Nonetheless, this is probably consistent enough for
most applications.
If you have an application where this is not enough, please open an issue
and let us know.
Examples:
>>> from datetime import datetime
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "a": [
... datetime(2020, 3, 1),
... datetime(2020, 4, 1),
... datetime(2020, 5, 1),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_dt_to_string(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("a").dt.to_string("%Y/%m/%d %H:%M:%S")
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_to_string`:
>>> agnostic_dt_to_string(df_pd)
a
0 2020/03/01 00:00:00
1 2020/04/01 00:00:00
2 2020/05/01 00:00:00
>>> agnostic_dt_to_string(df_pl)
shape: (3, 1)
┌─────────────────────┐
│ a │
│ --- │
│ str │
╞═════════════════════╡
│ 2020/03/01 00:00:00 │
│ 2020/04/01 00:00:00 │
│ 2020/05/01 00:00:00 │
└─────────────────────┘
>>> agnostic_dt_to_string(df_pa)
pyarrow.Table
a: string
----
a: [["2020/03/01 00:00:00.000000","2020/04/01 00:00:00.000000","2020/05/01 00:00:00.000000"]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.to_string(format)
)
def replace_time_zone(self: Self, time_zone: str | None) -> ExprT:
"""Replace time zone.
Arguments:
time_zone: Target time zone.
Returns:
A new expression.
Examples:
>>> from datetime import datetime, timezone
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "a": [
... datetime(2024, 1, 1, tzinfo=timezone.utc),
... datetime(2024, 1, 2, tzinfo=timezone.utc),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_dt_replace_time_zone(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("a").dt.replace_time_zone("Asia/Kathmandu")
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_replace_time_zone`:
>>> agnostic_dt_replace_time_zone(df_pd)
a
0 2024-01-01 00:00:00+05:45
1 2024-01-02 00:00:00+05:45
>>> agnostic_dt_replace_time_zone(df_pl)
shape: (2, 1)
┌──────────────────────────────┐
│ a │
│ --- │
│ datetime[μs, Asia/Kathmandu] │
╞══════════════════════════════╡
│ 2024-01-01 00:00:00 +0545 │
│ 2024-01-02 00:00:00 +0545 │
└──────────────────────────────┘
>>> agnostic_dt_replace_time_zone(df_pa)
pyarrow.Table
a: timestamp[us, tz=Asia/Kathmandu]
----
a: [[2023-12-31 18:15:00.000000Z,2024-01-01 18:15:00.000000Z]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.replace_time_zone(time_zone)
)
def convert_time_zone(self: Self, time_zone: str) -> ExprT:
"""Convert to a new time zone.
If converting from a time-zone-naive column, then conversion happens
as if converting from UTC.
Arguments:
time_zone: Target time zone.
Returns:
A new expression.
Examples:
>>> from datetime import datetime, timezone
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "a": [
... datetime(2024, 1, 1, tzinfo=timezone.utc),
... datetime(2024, 1, 2, tzinfo=timezone.utc),
... ]
... }
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_dt_convert_time_zone(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.col("a").dt.convert_time_zone("Asia/Kathmandu")
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_convert_time_zone`:
>>> agnostic_dt_convert_time_zone(df_pd)
a
0 2024-01-01 05:45:00+05:45
1 2024-01-02 05:45:00+05:45
>>> agnostic_dt_convert_time_zone(df_pl)
shape: (2, 1)
┌──────────────────────────────┐
│ a │
│ --- │
│ datetime[μs, Asia/Kathmandu] │
╞══════════════════════════════╡
│ 2024-01-01 05:45:00 +0545 │
│ 2024-01-02 05:45:00 +0545 │
└──────────────────────────────┘
>>> agnostic_dt_convert_time_zone(df_pa)
pyarrow.Table
a: timestamp[us, tz=Asia/Kathmandu]
----
a: [[2024-01-01 00:00:00.000000Z,2024-01-02 00:00:00.000000Z]]
"""
if time_zone is None:
msg = "Target `time_zone` cannot be `None` in `convert_time_zone`. Please use `replace_time_zone(None)` if you want to remove the time zone."
raise TypeError(msg)
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.convert_time_zone(time_zone)
)
def timestamp(self: Self, time_unit: Literal["ns", "us", "ms"] = "us") -> ExprT:
"""Return a timestamp in the given time unit.
Arguments:
time_unit: {'ns', 'us', 'ms'}
Time unit.
Returns:
A new expression.
Examples:
>>> from datetime import date
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"date": [date(2001, 1, 1), None, date(2001, 1, 3)]}
>>> df_pd = pd.DataFrame(data, dtype="datetime64[ns]")
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_dt_timestamp(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.col("date").dt.timestamp().alias("timestamp_us"),
... nw.col("date").dt.timestamp("ms").alias("timestamp_ms"),
... ).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_dt_timestamp`:
>>> agnostic_dt_timestamp(df_pd)
date timestamp_us timestamp_ms
0 2001-01-01 9.783072e+14 9.783072e+11
1 NaT NaN NaN
2 2001-01-03 9.784800e+14 9.784800e+11
>>> agnostic_dt_timestamp(df_pl)
shape: (3, 3)
┌────────────┬─────────────────┬──────────────┐
│ date ┆ timestamp_us ┆ timestamp_ms │
│ --- ┆ --- ┆ --- │
│ date ┆ i64 ┆ i64 │
╞════════════╪═════════════════╪══════════════╡
│ 2001-01-01 ┆ 978307200000000 ┆ 978307200000 │
│ null ┆ null ┆ null │
│ 2001-01-03 ┆ 978480000000000 ┆ 978480000000 │
└────────────┴─────────────────┴──────────────┘
>>> agnostic_dt_timestamp(df_pa)
pyarrow.Table
date: date32[day]
timestamp_us: int64
timestamp_ms: int64
----
date: [[2001-01-01,null,2001-01-03]]
timestamp_us: [[978307200000000,null,978480000000000]]
timestamp_ms: [[978307200000,null,978480000000]]
"""
if time_unit not in {"ns", "us", "ms"}:
msg = (
"invalid `time_unit`"
f"\n\nExpected one of {{'ns', 'us', 'ms'}}, got {time_unit!r}."
)
raise ValueError(msg)
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).dt.timestamp(time_unit)
)
class ExprNameNamespace(Generic[ExprT]):
def __init__(self: Self, expr: ExprT) -> None:
self._expr = expr
def keep(self: Self) -> ExprT:
r"""Keep the original root name of the expression.
Returns:
A new expression.
Notes:
This will undo any previous renaming operations on the expression.
Due to implementation constraints, this method can only be called as the last
expression in a chain. Only one name operation per expression will work.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrame
>>>
>>> data = {"foo": [1, 2], "BAR": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_name_keep(df_native: IntoFrame) -> list[str]:
... df = nw.from_native(df_native)
... return df.select(nw.col("foo").alias("alias_for_foo").name.keep()).columns
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_name_keep`:
>>> agnostic_name_keep(df_pd)
['foo']
>>> agnostic_name_keep(df_pl)
['foo']
>>> agnostic_name_keep(df_pa)
['foo']
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).name.keep()
)
def map(self: Self, function: Callable[[str], str]) -> ExprT:
r"""Rename the output of an expression by mapping a function over the root name.
Arguments:
function: Function that maps a root name to a new name.
Returns:
A new expression.
Notes:
This will undo any previous renaming operations on the expression.
Due to implementation constraints, this method can only be called as the last
expression in a chain. Only one name operation per expression will work.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrame
>>>
>>> data = {"foo": [1, 2], "BAR": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> renaming_func = lambda s: s[::-1] # reverse column name
>>> def agnostic_name_map(df_native: IntoFrame) -> list[str]:
... df = nw.from_native(df_native)
... return df.select(nw.col("foo", "BAR").name.map(renaming_func)).columns
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_name_map`:
>>> agnostic_name_map(df_pd)
['oof', 'RAB']
>>> agnostic_name_map(df_pl)
['oof', 'RAB']
>>> agnostic_name_map(df_pa)
['oof', 'RAB']
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).name.map(function)
)
def prefix(self: Self, prefix: str) -> ExprT:
r"""Add a prefix to the root column name of the expression.
Arguments:
prefix: Prefix to add to the root column name.
Returns:
A new expression.
Notes:
This will undo any previous renaming operations on the expression.
Due to implementation constraints, this method can only be called as the last
expression in a chain. Only one name operation per expression will work.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrame
>>>
>>> data = {"foo": [1, 2], "BAR": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_name_prefix(df_native: IntoFrame, prefix: str) -> list[str]:
... df = nw.from_native(df_native)
... return df.select(nw.col("foo", "BAR").name.prefix(prefix)).columns
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_name_prefix`:
>>> agnostic_name_prefix(df_pd, "with_prefix_")
['with_prefix_foo', 'with_prefix_BAR']
>>> agnostic_name_prefix(df_pl, "with_prefix_")
['with_prefix_foo', 'with_prefix_BAR']
>>> agnostic_name_prefix(df_pa, "with_prefix_")
['with_prefix_foo', 'with_prefix_BAR']
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).name.prefix(prefix)
)
def suffix(self: Self, suffix: str) -> ExprT:
r"""Add a suffix to the root column name of the expression.
Arguments:
suffix: Suffix to add to the root column name.
Returns:
A new expression.
Notes:
This will undo any previous renaming operations on the expression.
Due to implementation constraints, this method can only be called as the last
expression in a chain. Only one name operation per expression will work.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrame
>>>
>>> data = {"foo": [1, 2], "BAR": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_name_suffix(df_native: IntoFrame, suffix: str) -> list[str]:
... df = nw.from_native(df_native)
... return df.select(nw.col("foo", "BAR").name.suffix(suffix)).columns
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_name_suffix`:
>>> agnostic_name_suffix(df_pd, "_with_suffix")
['foo_with_suffix', 'BAR_with_suffix']
>>> agnostic_name_suffix(df_pl, "_with_suffix")
['foo_with_suffix', 'BAR_with_suffix']
>>> agnostic_name_suffix(df_pa, "_with_suffix")
['foo_with_suffix', 'BAR_with_suffix']
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).name.suffix(suffix)
)
def to_lowercase(self: Self) -> ExprT:
r"""Make the root column name lowercase.
Returns:
A new expression.
Notes:
This will undo any previous renaming operations on the expression.
Due to implementation constraints, this method can only be called as the last
expression in a chain. Only one name operation per expression will work.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrame
>>>
>>> data = {"foo": [1, 2], "BAR": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_name_to_lowercase(df_native: IntoFrame) -> list[str]:
... df = nw.from_native(df_native)
... return df.select(nw.col("foo", "BAR").name.to_lowercase()).columns
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_name_to_lowercase`:
>>> agnostic_name_to_lowercase(df_pd)
['foo', 'bar']
>>> agnostic_name_to_lowercase(df_pl)
['foo', 'bar']
>>> agnostic_name_to_lowercase(df_pa)
['foo', 'bar']
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).name.to_lowercase()
)
def to_uppercase(self: Self) -> ExprT:
r"""Make the root column name uppercase.
Returns:
A new expression.
Notes:
This will undo any previous renaming operations on the expression.
Due to implementation constraints, this method can only be called as the last
expression in a chain. Only one name operation per expression will work.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrame
>>>
>>> data = {"foo": [1, 2], "BAR": [4, 5]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_name_to_uppercase(df_native: IntoFrame) -> list[str]:
... df = nw.from_native(df_native)
... return df.select(nw.col("foo", "BAR").name.to_uppercase()).columns
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_name_to_uppercase`:
>>> agnostic_name_to_uppercase(df_pd)
['FOO', 'BAR']
>>> agnostic_name_to_uppercase(df_pl)
['FOO', 'BAR']
>>> agnostic_name_to_uppercase(df_pa)
['FOO', 'BAR']
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).name.to_uppercase()
)
class ExprListNamespace(Generic[ExprT]):
def __init__(self: Self, expr: ExprT) -> None:
self._expr = expr
def len(self: Self) -> ExprT:
"""Return the number of elements in each list.
Null values count towards the total.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [[1, 2], [3, 4, None], None, []]}
Let's define a dataframe-agnostic function:
>>> def agnostic_list_len(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(a_len=nw.col("a").list.len()).to_native()
We can then pass pandas / PyArrow / Polars / any other supported library:
>>> agnostic_list_len(
... pd.DataFrame(data).astype({"a": pd.ArrowDtype(pa.list_(pa.int64()))})
... ) # doctest: +SKIP
a a_len
0 [1. 2.] 2
1 [ 3. 4. nan] 3
2
3 [] 0
>>> agnostic_list_len(pl.DataFrame(data))
shape: (4, 2)
┌──────────────┬───────┐
│ a ┆ a_len │
│ --- ┆ --- │
│ list[i64] ┆ u32 │
╞══════════════╪═══════╡
│ [1, 2] ┆ 2 │
│ [3, 4, null] ┆ 3 │
│ null ┆ null │
│ [] ┆ 0 │
└──────────────┴───────┘
>>> agnostic_list_len(pa.table(data))
pyarrow.Table
a: list
child 0, item: int64
a_len: uint32
----
a: [[[1,2],[3,4,null],null,[]]]
a_len: [[2,3,null,0]]
"""
return self._expr.__class__(
lambda plx: self._expr._to_compliant_expr(plx).list.len()
)
def col(*names: str | Iterable[str]) -> Expr:
"""Creates an expression that references one or more columns by their name(s).
Arguments:
names: Name(s) of the columns to use.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2], "b": [3, 4]}
>>> df_pl = pl.DataFrame(data)
>>> df_pd = pd.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_col(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.col("a") * nw.col("b")).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_col`:
>>> agnostic_col(df_pd)
a
0 3
1 8
>>> agnostic_col(df_pl)
shape: (2, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 3 │
│ 8 │
└─────┘
>>> agnostic_col(df_pa)
pyarrow.Table
a: int64
----
a: [[3,8]]
"""
def func(plx: Any) -> Any:
return plx.col(*flatten(names))
return Expr(func)
def nth(*indices: int | Sequence[int]) -> Expr:
"""Creates an expression that references one or more columns by their index(es).
Notes:
`nth` is not supported for Polars version<1.0.0. Please use
[`narwhals.col`][] instead.
Arguments:
indices: One or more indices representing the columns to retrieve.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2], "b": [3, 4]}
>>> df_pl = pl.DataFrame(data)
>>> df_pd = pd.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_nth(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.nth(0) * 2).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to `agnostic_nth`:
>>> agnostic_nth(df_pd)
a
0 2
1 4
>>> agnostic_nth(df_pl)
shape: (2, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 2 │
│ 4 │
└─────┘
>>> agnostic_nth(df_pa)
pyarrow.Table
a: int64
----
a: [[2,4]]
"""
def func(plx: Any) -> Any:
return plx.nth(*flatten(indices))
return Expr(func)
# Add underscore so it doesn't conflict with builtin `all`
def all_() -> Expr:
"""Instantiate an expression representing all columns.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3], "b": [4, 5, 6]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_all(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.all() * 2).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_all`:
>>> agnostic_all(df_pd)
a b
0 2 8
1 4 10
2 6 12
>>> agnostic_all(df_pl)
shape: (3, 2)
┌─────┬─────┐
│ a ┆ b │
│ --- ┆ --- │
│ i64 ┆ i64 │
╞═════╪═════╡
│ 2 ┆ 8 │
│ 4 ┆ 10 │
│ 6 ┆ 12 │
└─────┴─────┘
>>> agnostic_all(df_pa)
pyarrow.Table
a: int64
b: int64
----
a: [[2,4,6]]
b: [[8,10,12]]
"""
return Expr(lambda plx: plx.all())
# Add underscore so it doesn't conflict with builtin `len`
def len_() -> Expr:
"""Return the number of rows.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2], "b": [5, 10]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_len(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.len()).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_len`:
>>> agnostic_len(df_pd)
len
0 2
>>> agnostic_len(df_pl)
shape: (1, 1)
┌─────┐
│ len │
│ --- │
│ u32 │
╞═════╡
│ 2 │
└─────┘
>>> agnostic_len(df_pa)
pyarrow.Table
len: int64
----
len: [[2]]
"""
def func(plx: Any) -> Any:
return plx.len()
return Expr(func)
def sum(*columns: str) -> Expr:
"""Sum all values.
Note:
Syntactic sugar for ``nw.col(columns).sum()``
Arguments:
columns: Name(s) of the columns to use in the aggregation function
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2]}
>>> df_pl = pl.DataFrame(data)
>>> df_pd = pd.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_sum(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.sum("a")).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_sum`:
>>> agnostic_sum(df_pd)
a
0 3
>>> agnostic_sum(df_pl)
shape: (1, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 3 │
└─────┘
>>> agnostic_sum(df_pa)
pyarrow.Table
a: int64
----
a: [[3]]
"""
return Expr(lambda plx: plx.col(*columns).sum())
def mean(*columns: str) -> Expr:
"""Get the mean value.
Note:
Syntactic sugar for ``nw.col(columns).mean()``
Arguments:
columns: Name(s) of the columns to use in the aggregation function
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 8, 3]}
>>> df_pl = pl.DataFrame(data)
>>> df_pd = pd.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe agnostic function:
>>> def agnostic_mean(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.mean("a")).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_mean`:
>>> agnostic_mean(df_pd)
a
0 4.0
>>> agnostic_mean(df_pl)
shape: (1, 1)
┌─────┐
│ a │
│ --- │
│ f64 │
╞═════╡
│ 4.0 │
└─────┘
>>> agnostic_mean(df_pa)
pyarrow.Table
a: double
----
a: [[4]]
"""
return Expr(lambda plx: plx.col(*columns).mean())
def median(*columns: str) -> Expr:
"""Get the median value.
Notes:
- Syntactic sugar for ``nw.col(columns).median()``
- Results might slightly differ across backends due to differences in the
underlying algorithms used to compute the median.
Arguments:
columns: Name(s) of the columns to use in the aggregation function
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [4, 5, 2]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe agnostic function:
>>> def agnostic_median(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.median("a")).to_native()
We can then pass any supported library such as pandas, Polars, or
PyArrow to `agnostic_median`:
>>> agnostic_median(df_pd)
a
0 4.0
>>> agnostic_median(df_pl)
shape: (1, 1)
┌─────┐
│ a │
│ --- │
│ f64 │
╞═════╡
│ 4.0 │
└─────┘
>>> agnostic_median(df_pa)
pyarrow.Table
a: double
----
a: [[4]]
"""
return Expr(lambda plx: plx.col(*columns).median())
def min(*columns: str) -> Expr:
"""Return the minimum value.
Note:
Syntactic sugar for ``nw.col(columns).min()``.
Arguments:
columns: Name(s) of the columns to use in the aggregation function.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2], "b": [5, 10]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_min(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.min("b")).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_min`:
>>> agnostic_min(df_pd)
b
0 5
>>> agnostic_min(df_pl)
shape: (1, 1)
┌─────┐
│ b │
│ --- │
│ i64 │
╞═════╡
│ 5 │
└─────┘
>>> agnostic_min(df_pa)
pyarrow.Table
b: int64
----
b: [[5]]
"""
return Expr(lambda plx: plx.col(*columns).min())
def max(*columns: str) -> Expr:
"""Return the maximum value.
Note:
Syntactic sugar for ``nw.col(columns).max()``.
Arguments:
columns: Name(s) of the columns to use in the aggregation function.
Returns:
A new expression.
Examples:
>>> import polars as pl
>>> import pandas as pd
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2], "b": [5, 10]}
>>> df_pd = pd.DataFrame(data)
>>> df_pl = pl.DataFrame(data)
>>> df_pa = pa.table(data)
Let's define a dataframe-agnostic function:
>>> def agnostic_max(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.max("a")).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_max`:
>>> agnostic_max(df_pd)
a
0 2
>>> agnostic_max(df_pl)
shape: (1, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 2 │
└─────┘
>>> agnostic_max(df_pa)
pyarrow.Table
a: int64
----
a: [[2]]
"""
return Expr(lambda plx: plx.col(*columns).max())
def sum_horizontal(*exprs: IntoExpr | Iterable[IntoExpr]) -> Expr:
"""Sum all values horizontally across columns.
Warning:
Unlike Polars, we support horizontal sum over numeric columns only.
Arguments:
exprs: Name(s) of the columns to use in the aggregation function. Accepts
expression input.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3], "b": [5, 10, None]}
>>> df_pl = pl.DataFrame(data)
>>> df_pd = pd.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_sum_horizontal(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.sum_horizontal("a", "b")).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to `agnostic_sum_horizontal`:
>>> agnostic_sum_horizontal(df_pd)
a
0 6.0
1 12.0
2 3.0
>>> agnostic_sum_horizontal(df_pl)
shape: (3, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 6 │
│ 12 │
│ 3 │
└─────┘
>>> agnostic_sum_horizontal(df_pa)
pyarrow.Table
a: int64
----
a: [[6,12,3]]
"""
if not exprs:
msg = "At least one expression must be passed to `sum_horizontal`"
raise ValueError(msg)
return Expr(
lambda plx: plx.sum_horizontal(
*[extract_compliant(plx, v) for v in flatten(exprs)]
)
)
def min_horizontal(*exprs: IntoExpr | Iterable[IntoExpr]) -> Expr:
"""Get the minimum value horizontally across columns.
Notes:
We support `min_horizontal` over numeric columns only.
Arguments:
exprs: Name(s) of the columns to use in the aggregation function. Accepts
expression input.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "a": [1, 8, 3],
... "b": [4, 5, None],
... "c": ["x", "y", "z"],
... }
We define a dataframe-agnostic function that computes the horizontal min of "a"
and "b" columns:
>>> def agnostic_min_horizontal(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.min_horizontal("a", "b")).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_min_horizontal`:
>>> agnostic_min_horizontal(pd.DataFrame(data))
a
0 1.0
1 5.0
2 3.0
>>> agnostic_min_horizontal(pl.DataFrame(data))
shape: (3, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 1 │
│ 5 │
│ 3 │
└─────┘
>>> agnostic_min_horizontal(pa.table(data))
pyarrow.Table
a: int64
----
a: [[1,5,3]]
"""
if not exprs:
msg = "At least one expression must be passed to `min_horizontal`"
raise ValueError(msg)
return Expr(
lambda plx: plx.min_horizontal(
*[extract_compliant(plx, v) for v in flatten(exprs)]
)
)
def max_horizontal(*exprs: IntoExpr | Iterable[IntoExpr]) -> Expr:
"""Get the maximum value horizontally across columns.
Notes:
We support `max_horizontal` over numeric columns only.
Arguments:
exprs: Name(s) of the columns to use in the aggregation function. Accepts
expression input.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "a": [1, 8, 3],
... "b": [4, 5, None],
... "c": ["x", "y", "z"],
... }
We define a dataframe-agnostic function that computes the horizontal max of "a"
and "b" columns:
>>> def agnostic_max_horizontal(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.max_horizontal("a", "b")).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_max_horizontal`:
>>> agnostic_max_horizontal(pd.DataFrame(data))
a
0 4.0
1 8.0
2 3.0
>>> agnostic_max_horizontal(pl.DataFrame(data))
shape: (3, 1)
┌─────┐
│ a │
│ --- │
│ i64 │
╞═════╡
│ 4 │
│ 8 │
│ 3 │
└─────┘
>>> agnostic_max_horizontal(pa.table(data))
pyarrow.Table
a: int64
----
a: [[4,8,3]]
"""
if not exprs:
msg = "At least one expression must be passed to `max_horizontal`"
raise ValueError(msg)
return Expr(
lambda plx: plx.max_horizontal(
*[extract_compliant(plx, v) for v in flatten(exprs)]
)
)
class When:
def __init__(self, *predicates: IntoExpr | Iterable[IntoExpr]) -> None:
self._predicates = flatten([predicates])
def _extract_predicates(self, plx: Any) -> Any:
return [extract_compliant(plx, v) for v in self._predicates]
def then(self, value: Any) -> Then:
return Then(
lambda plx: plx.when(*self._extract_predicates(plx)).then(
extract_compliant(plx, value)
)
)
class Then(Expr):
def otherwise(self, value: Any) -> Expr:
return Expr(
lambda plx: self._to_compliant_expr(plx).otherwise(
extract_compliant(plx, value)
)
)
def when(*predicates: IntoExpr | Iterable[IntoExpr]) -> When:
"""Start a `when-then-otherwise` expression.
Expression similar to an `if-else` statement in Python. Always initiated by a
`pl.when().then()`, and optionally followed by
chaining one or more `.when().then()` statements.
Chained when-then operations should be read as Python `if, elif, ... elif`
blocks, not as `if, if, ... if`, i.e. the first condition that evaluates to
`True` will be picked.
If none of the conditions are `True`, an optional
`.otherwise()` can be appended at the end.
If not appended, and none of the conditions are `True`, `None` will be returned.
Arguments:
predicates: Condition(s) that must be met in order to apply the subsequent
statement. Accepts one or more boolean expressions, which are implicitly
combined with `&`. String input is parsed as a column name.
Returns:
A "when" object, which `.then` can be called on.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2, 3], "b": [5, 10, 15]}
>>> df_pl = pl.DataFrame(data)
>>> df_pd = pd.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_when_then_otherwise(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(
... nw.when(nw.col("a") < 3).then(5).otherwise(6).alias("a_when")
... ).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_when_then_otherwise`:
>>> agnostic_when_then_otherwise(df_pd)
a b a_when
0 1 5 5
1 2 10 5
2 3 15 6
>>> agnostic_when_then_otherwise(df_pl)
shape: (3, 3)
┌─────┬─────┬────────┐
│ a ┆ b ┆ a_when │
│ --- ┆ --- ┆ --- │
│ i64 ┆ i64 ┆ i32 │
╞═════╪═════╪════════╡
│ 1 ┆ 5 ┆ 5 │
│ 2 ┆ 10 ┆ 5 │
│ 3 ┆ 15 ┆ 6 │
└─────┴─────┴────────┘
>>> agnostic_when_then_otherwise(df_pa)
pyarrow.Table
a: int64
b: int64
a_when: int64
----
a: [[1,2,3]]
b: [[5,10,15]]
a_when: [[5,5,6]]
"""
return When(*predicates)
def all_horizontal(*exprs: IntoExpr | Iterable[IntoExpr]) -> Expr:
r"""Compute the bitwise AND horizontally across columns.
Arguments:
exprs: Name(s) of the columns to use in the aggregation function. Accepts
expression input.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "a": [False, False, True, True, False, None],
... "b": [False, True, True, None, None, None],
... }
>>> df_pl = pl.DataFrame(data)
>>> df_pd = pd.DataFrame(data).convert_dtypes(dtype_backend="pyarrow")
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_all_horizontal(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select("a", "b", all=nw.all_horizontal("a", "b")).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_all_horizontal`:
>>> agnostic_all_horizontal(df_pd)
a b all
0 False False False
1 False True False
2 True True True
3 True
4 False False
5
>>> agnostic_all_horizontal(df_pl)
shape: (6, 3)
┌───────┬───────┬───────┐
│ a ┆ b ┆ all │
│ --- ┆ --- ┆ --- │
│ bool ┆ bool ┆ bool │
╞═══════╪═══════╪═══════╡
│ false ┆ false ┆ false │
│ false ┆ true ┆ false │
│ true ┆ true ┆ true │
│ true ┆ null ┆ null │
│ false ┆ null ┆ false │
│ null ┆ null ┆ null │
└───────┴───────┴───────┘
>>> agnostic_all_horizontal(df_pa)
pyarrow.Table
a: bool
b: bool
all: bool
----
a: [[false,false,true,true,false,null]]
b: [[false,true,true,null,null,null]]
all: [[false,false,true,null,false,null]]
"""
if not exprs:
msg = "At least one expression must be passed to `all_horizontal`"
raise ValueError(msg)
return Expr(
lambda plx: plx.all_horizontal(
*[extract_compliant(plx, v) for v in flatten(exprs)]
)
)
def lit(value: Any, dtype: DType | type[DType] | None = None) -> Expr:
"""Return an expression representing a literal value.
Arguments:
value: The value to use as literal.
dtype: The data type of the literal value. If not provided, the data type will
be inferred.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {"a": [1, 2]}
>>> df_pl = pl.DataFrame(data)
>>> df_pd = pd.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_lit(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.with_columns(nw.lit(3)).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_lit`:
>>> agnostic_lit(df_pd)
a literal
0 1 3
1 2 3
>>> agnostic_lit(df_pl)
shape: (2, 2)
┌─────┬─────────┐
│ a ┆ literal │
│ --- ┆ --- │
│ i64 ┆ i32 │
╞═════╪═════════╡
│ 1 ┆ 3 │
│ 2 ┆ 3 │
└─────┴─────────┘
>>> agnostic_lit(df_pa)
pyarrow.Table
a: int64
literal: int64
----
a: [[1,2]]
literal: [[3,3]]
"""
if is_numpy_array(value):
msg = (
"numpy arrays are not supported as literal values. "
"Consider using `with_columns` to create a new column from the array."
)
raise ValueError(msg)
if isinstance(value, (list, tuple)):
msg = f"Nested datatypes are not supported yet. Got {value}"
raise NotImplementedError(msg)
return Expr(lambda plx: plx.lit(value, dtype))
def any_horizontal(*exprs: IntoExpr | Iterable[IntoExpr]) -> Expr:
r"""Compute the bitwise OR horizontally across columns.
Arguments:
exprs: Name(s) of the columns to use in the aggregation function. Accepts
expression input.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "a": [False, False, True, True, False, None],
... "b": [False, True, True, None, None, None],
... }
>>> df_pl = pl.DataFrame(data)
>>> df_pd = pd.DataFrame(data).convert_dtypes(dtype_backend="pyarrow")
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function:
>>> def agnostic_any_horizontal(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select("a", "b", any=nw.any_horizontal("a", "b")).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_any_horizontal`:
>>> agnostic_any_horizontal(df_pd)
a b any
0 False False False
1 False True True
2 True True True
3 True True
4 False
5
>>> agnostic_any_horizontal(df_pl)
shape: (6, 3)
┌───────┬───────┬───────┐
│ a ┆ b ┆ any │
│ --- ┆ --- ┆ --- │
│ bool ┆ bool ┆ bool │
╞═══════╪═══════╪═══════╡
│ false ┆ false ┆ false │
│ false ┆ true ┆ true │
│ true ┆ true ┆ true │
│ true ┆ null ┆ true │
│ false ┆ null ┆ null │
│ null ┆ null ┆ null │
└───────┴───────┴───────┘
>>> agnostic_any_horizontal(df_pa)
pyarrow.Table
a: bool
b: bool
any: bool
----
a: [[false,false,true,true,false,null]]
b: [[false,true,true,null,null,null]]
any: [[false,true,true,true,null,null]]
"""
if not exprs:
msg = "At least one expression must be passed to `any_horizontal`"
raise ValueError(msg)
return Expr(
lambda plx: plx.any_horizontal(
*[extract_compliant(plx, v) for v in flatten(exprs)]
)
)
def mean_horizontal(*exprs: IntoExpr | Iterable[IntoExpr]) -> Expr:
"""Compute the mean of all values horizontally across columns.
Arguments:
exprs: Name(s) of the columns to use in the aggregation function. Accepts
expression input.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "a": [1, 8, 3],
... "b": [4, 5, None],
... "c": ["x", "y", "z"],
... }
>>> df_pl = pl.DataFrame(data)
>>> df_pd = pd.DataFrame(data)
>>> df_pa = pa.table(data)
We define a dataframe-agnostic function that computes the horizontal mean of "a"
and "b" columns:
>>> def agnostic_mean_horizontal(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(nw.mean_horizontal("a", "b")).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow to
`agnostic_mean_horizontal`:
>>> agnostic_mean_horizontal(df_pd)
a
0 2.5
1 6.5
2 3.0
>>> agnostic_mean_horizontal(df_pl)
shape: (3, 1)
┌─────┐
│ a │
│ --- │
│ f64 │
╞═════╡
│ 2.5 │
│ 6.5 │
│ 3.0 │
└─────┘
>>> agnostic_mean_horizontal(df_pa)
pyarrow.Table
a: double
----
a: [[2.5,6.5,3]]
"""
if not exprs:
msg = "At least one expression must be passed to `mean_horizontal`"
raise ValueError(msg)
return Expr(
lambda plx: plx.mean_horizontal(
*[extract_compliant(plx, v) for v in flatten(exprs)]
)
)
def concat_str(
exprs: IntoExpr | Iterable[IntoExpr],
*more_exprs: IntoExpr,
separator: str = "",
ignore_nulls: bool = False,
) -> Expr:
r"""Horizontally concatenate columns into a single string column.
Arguments:
exprs: Columns to concatenate into a single string column. Accepts expression
input. Strings are parsed as column names, other non-expression inputs are
parsed as literals. Non-`String` columns are cast to `String`.
*more_exprs: Additional columns to concatenate into a single string column,
specified as positional arguments.
separator: String that will be used to separate the values of each column.
ignore_nulls: Ignore null values (default is `False`).
If set to `False`, null values will be propagated and if the row contains any
null values, the output is null.
Returns:
A new expression.
Examples:
>>> import pandas as pd
>>> import polars as pl
>>> import pyarrow as pa
>>> import narwhals as nw
>>> from narwhals.typing import IntoFrameT
>>>
>>> data = {
... "a": [1, 2, 3],
... "b": ["dogs", "cats", None],
... "c": ["play", "swim", "walk"],
... }
We define a dataframe-agnostic function that computes the horizontal string
concatenation of different columns
>>> def agnostic_concat_str(df_native: IntoFrameT) -> IntoFrameT:
... df = nw.from_native(df_native)
... return df.select(
... nw.concat_str(
... [
... nw.col("a") * 2,
... nw.col("b"),
... nw.col("c"),
... ],
... separator=" ",
... ).alias("full_sentence")
... ).to_native()
We can pass any supported library such as Pandas, Polars, or PyArrow
to `agnostic_concat_str`:
>>> agnostic_concat_str(pd.DataFrame(data))
full_sentence
0 2 dogs play
1 4 cats swim
2 None
>>> agnostic_concat_str(pl.DataFrame(data))
shape: (3, 1)
┌───────────────┐
│ full_sentence │
│ --- │
│ str │
╞═══════════════╡
│ 2 dogs play │
│ 4 cats swim │
│ null │
└───────────────┘
>>> agnostic_concat_str(pa.table(data))
pyarrow.Table
full_sentence: string
----
full_sentence: [["2 dogs play","4 cats swim",null]]
"""
return Expr(
lambda plx: plx.concat_str(
[extract_compliant(plx, v) for v in flatten([exprs])],
*[extract_compliant(plx, v) for v in more_exprs],
separator=separator,
ignore_nulls=ignore_nulls,
)
)
__all__ = [
"Expr",
]