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refactor: Remove more @scalar-opt (pola-rs#19666)
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@coastalwhite @tylerriccio33
coastalwhite authored and tylerriccio33 committed Nov 8, 2024
1 parent e2e3088 commit 1d2acb7
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182 changes: 102 additions & 80 deletions crates/polars-core/src/frame/column/mod.rs
View file
Original file line number Diff line number Diff line change
Expand Up @@ -681,14 +681,11 @@ impl Column {
}

pub fn full_null(name: PlSmallStr, size: usize, dtype: &DataType) -> Self {
Series::full_null(name, size, dtype).into()
// @TODO: This causes failures
// Self::new_scalar(name, Scalar::new(dtype.clone(), AnyValue::Null), size)
Self::new_scalar(name, Scalar::new(dtype.clone(), AnyValue::Null), size)
}

pub fn is_empty(&self) -> bool {
// @scalar-opt
self.as_materialized_series().is_empty()
self.len() == 0
}

pub fn reverse(&self) -> Column {
Expand All @@ -699,16 +696,16 @@ impl Column {
}
}

pub fn equals(&self, right: &Column) -> bool {
pub fn equals(&self, other: &Column) -> bool {
// @scalar-opt
self.as_materialized_series()
.equals(right.as_materialized_series())
.equals(other.as_materialized_series())
}

pub fn equals_missing(&self, right: &Column) -> bool {
pub fn equals_missing(&self, other: &Column) -> bool {
// @scalar-opt
self.as_materialized_series()
.equals_missing(right.as_materialized_series())
.equals_missing(other.as_materialized_series())
}

pub fn set_sorted_flag(&mut self, sorted: IsSorted) {
Expand Down Expand Up @@ -740,11 +737,6 @@ impl Column {
}
}

pub fn get_data_ptr(&self) -> usize {
// @scalar-opt
self.as_materialized_series().get_data_ptr()
}

pub fn vec_hash(&self, build_hasher: PlRandomState, buf: &mut Vec<u64>) -> PolarsResult<()> {
// @scalar-opt?
self.as_materialized_series().vec_hash(build_hasher, buf)
Expand Down Expand Up @@ -782,13 +774,6 @@ impl Column {
unsafe { DataFrame::new_no_checks(self.len(), vec![self]) }
}

pub fn unique_stable(&self) -> PolarsResult<Column> {
// @scalar-opt?
self.as_materialized_series()
.unique_stable()
.map(Column::from)
}

pub fn extend(&mut self, other: &Column) -> PolarsResult<&mut Self> {
// @scalar-opt
self.into_materialized_series()
Expand All @@ -805,9 +790,11 @@ impl Column {
}

pub fn explode(&self) -> PolarsResult<Column> {
// @scalar-opt
self.as_materialized_series().explode().map(Column::from)
}
pub fn implode(&self) -> PolarsResult<ListChunked> {
self.as_materialized_series().implode()
}

pub fn fill_null(&self, strategy: FillNullStrategy) -> PolarsResult<Self> {
// @scalar-opt
Expand Down Expand Up @@ -849,8 +836,12 @@ impl Column {
}

pub fn drop_nulls(&self) -> Column {
// @scalar-opt
self.as_materialized_series().drop_nulls().into()
match self {
Column::Series(s) => s.drop_nulls().into_column(),
// @partition-opt
Column::Partitioned(s) => s.as_materialized_series().drop_nulls().into_column(),
Column::Scalar(s) => s.drop_nulls().into_column(),
}
}

pub fn is_sorted_flag(&self) -> IsSorted {
Expand All @@ -859,8 +850,34 @@ impl Column {
}

pub fn unique(&self) -> PolarsResult<Column> {
// @scalar-opt
self.as_materialized_series().unique().map(Column::from)
match self {
Column::Series(s) => s.unique().map(Column::from),
// @partition-opt
Column::Partitioned(s) => s.as_materialized_series().unique().map(Column::from),
Column::Scalar(s) => {
_ = s.as_single_value_series().unique()?;
if s.is_empty() {
return Ok(s.clone().into_column());
}

Ok(s.resize(1).into_column())
},
}
}
pub fn unique_stable(&self) -> PolarsResult<Column> {
match self {
Column::Series(s) => s.unique_stable().map(Column::from),
// @partition-opt
Column::Partitioned(s) => s.as_materialized_series().unique_stable().map(Column::from),
Column::Scalar(s) => {
_ = s.as_single_value_series().unique_stable()?;
if s.is_empty() {
return Ok(s.clone().into_column());
}

Ok(s.resize(1).into_column())
},
}
}

pub fn reshape_list(&self, dimensions: &[ReshapeDimension]) -> PolarsResult<Self> {
Expand All @@ -885,9 +902,26 @@ impl Column {
.map(Self::from)
}

pub fn filter(&self, filter: &ChunkedArray<BooleanType>) -> PolarsResult<Self> {
// @scalar-opt
self.as_materialized_series().filter(filter).map(Self::from)
pub fn filter(&self, filter: &BooleanChunked) -> PolarsResult<Self> {
match self {
Column::Series(s) => s.filter(filter).map(Column::from),
Column::Partitioned(s) => s.as_materialized_series().filter(filter).map(Column::from),
Column::Scalar(s) => {
if s.is_empty() {
return Ok(s.clone().into_column());
}

// Broadcasting
if filter.len() == 1 {
return match filter.get(0) {
Some(true) => Ok(s.clone().into_column()),
_ => Ok(s.resize(0).into_column()),
};
}

Ok(s.resize(filter.sum().unwrap() as usize).into_column())
},
}
}

#[cfg(feature = "random")]
Expand Down Expand Up @@ -959,23 +993,16 @@ impl Column {
}

pub fn is_finite(&self) -> PolarsResult<BooleanChunked> {
// @scalar-opt
self.as_materialized_series().is_finite()
self.try_map_unary_elementwise_to_bool(|s| s.is_finite())
}

pub fn is_infinite(&self) -> PolarsResult<BooleanChunked> {
// @scalar-opt
self.as_materialized_series().is_infinite()
self.try_map_unary_elementwise_to_bool(|s| s.is_infinite())
}

pub fn is_nan(&self) -> PolarsResult<BooleanChunked> {
// @scalar-opt
self.as_materialized_series().is_nan()
self.try_map_unary_elementwise_to_bool(|s| s.is_nan())
}

pub fn is_not_nan(&self) -> PolarsResult<BooleanChunked> {
// @scalar-opt
self.as_materialized_series().is_not_nan()
self.try_map_unary_elementwise_to_bool(|s| s.is_not_nan())
}

pub fn wrapping_trunc_div_scalar<T>(&self, rhs: T) -> Self
Expand Down Expand Up @@ -1044,25 +1071,22 @@ impl Column {
}

pub fn try_add_owned(self, other: Self) -> PolarsResult<Self> {
// @partition-opt
// @scalar-opt
self.take_materialized_series()
.try_add_owned(other.take_materialized_series())
.map(Column::from)
match (self, other) {
(Column::Series(lhs), Column::Series(rhs)) => lhs.try_add_owned(rhs).map(Column::from),
(lhs, rhs) => lhs + rhs,
}
}
pub fn try_sub_owned(self, other: Self) -> PolarsResult<Self> {
// @partition-opt
// @scalar-opt
self.take_materialized_series()
.try_sub_owned(other.take_materialized_series())
.map(Column::from)
match (self, other) {
(Column::Series(lhs), Column::Series(rhs)) => lhs.try_sub_owned(rhs).map(Column::from),
(lhs, rhs) => lhs - rhs,
}
}
pub fn try_mul_owned(self, other: Self) -> PolarsResult<Self> {
// @partition-opt
// @scalar-opt
self.take_materialized_series()
.try_mul_owned(other.take_materialized_series())
.map(Column::from)
match (self, other) {
(Column::Series(lhs), Column::Series(rhs)) => lhs.try_mul_owned(rhs).map(Column::from),
(lhs, rhs) => lhs * rhs,
}
}

pub(crate) fn str_value(&self, index: usize) -> PolarsResult<Cow<str>> {
Expand Down Expand Up @@ -1187,12 +1211,6 @@ impl Column {
.quantile_reduce(quantile, method)
}

pub fn implode(&self) -> PolarsResult<ListChunked> {
// @partition-opt
// @scalar-opt
self.as_materialized_series().implode()
}

pub(crate) fn estimated_size(&self) -> usize {
// @scalar-opt
self.as_materialized_series().estimated_size()
Expand All @@ -1215,16 +1233,27 @@ impl Column {
}
}

pub fn apply_unary_elementwise(&self, f: impl Fn(&Series) -> Series) -> Column {
pub fn map_unary_elementwise_to_bool(
&self,
f: impl Fn(&Series) -> BooleanChunked,
) -> BooleanChunked {
self.try_map_unary_elementwise_to_bool(|s| Ok(f(s)))
.unwrap()
}
pub fn try_map_unary_elementwise_to_bool(
&self,
f: impl Fn(&Series) -> PolarsResult<BooleanChunked>,
) -> PolarsResult<BooleanChunked> {
match self {
Column::Series(s) => f(s).into(),
Column::Partitioned(s) => s.apply_unary_elementwise(f).into(),
Column::Scalar(s) => {
ScalarColumn::from_single_value_series(f(&s.as_single_value_series()), s.len())
.into()
},
Column::Series(s) => f(s),
Column::Partitioned(s) => f(s.as_materialized_series()),
Column::Scalar(s) => Ok(f(&s.as_single_value_series())?.new_from_index(0, s.len())),
}
}

pub fn apply_unary_elementwise(&self, f: impl Fn(&Series) -> Series) -> Column {
self.try_apply_unary_elementwise(|s| Ok(f(s))).unwrap()
}
pub fn try_apply_unary_elementwise(
&self,
f: impl Fn(&Series) -> PolarsResult<Series>,
Expand Down Expand Up @@ -1279,12 +1308,7 @@ impl Column {
let lhs = lhs.as_single_value_series();
let rhs = rhs.as_single_value_series();

let result = op(&lhs, &rhs)?;
if result.is_empty() {
Ok(result.into_column())
} else {
Ok(ScalarColumn::from_single_value_series(result, length).into_column())
}
Ok(ScalarColumn::from_single_value_series(op(&lhs, &rhs)?, length).into_column())
},
// @partition-opt
(lhs, rhs) => {
Expand Down Expand Up @@ -1325,12 +1349,10 @@ impl Column {
let lhs = lhs.as_single_value_series();
let rhs = rhs.as_single_value_series();

let result = f(&lhs, &rhs)?;
if result.is_empty() {
Ok(result.into_column())
} else {
Ok(ScalarColumn::from_single_value_series(result, self.len()).into_column())
}
Ok(
ScalarColumn::from_single_value_series(f(&lhs, &rhs)?, self.len())
.into_column(),
)
},
// @partition-opt
(lhs, rhs) => {
Expand Down
15 changes: 12 additions & 3 deletions crates/polars-core/src/frame/column/scalar.rs
View file
Original file line number Diff line number Diff line change
Expand Up @@ -137,9 +137,10 @@ impl ScalarColumn {
///
/// This will panic if the value cannot be made static or if the series has length `0`.
pub fn from_single_value_series(series: Series, length: usize) -> Self {
debug_assert_eq!(series.len(), 1);
let value = series.get(0).unwrap();
let value = value.into_static();
debug_assert!(series.len() <= 1);
debug_assert!(length > 0 || series.is_empty());

let value = series.get(0).map_or(AnyValue::Null, |av| av.into_static());
let value = Scalar::new(series.dtype().clone(), value);
ScalarColumn::new(series.name().clone(), value, length)
}
Expand Down Expand Up @@ -270,6 +271,14 @@ impl ScalarColumn {
pub fn has_nulls(&self) -> bool {
self.length != 0 && self.scalar.is_null()
}

pub fn drop_nulls(&self) -> Self {
if self.scalar.is_null() {
self.resize(0)
} else {
self.clone()
}
}
}

impl IntoColumn for ScalarColumn {
Expand Down
1 change: 1 addition & 0 deletions crates/polars-core/src/scalar/from.rs
View file
Original file line number Diff line number Diff line change
Expand Up @@ -14,6 +14,7 @@ macro_rules! impl_from {
}

impl_from! {
(bool, Boolean, Boolean)
(i8, Int8, Int8)
(i16, Int16, Int16)
(i32, Int32, Int32)
Expand Down
36 changes: 0 additions & 36 deletions crates/polars-core/src/testing.rs
View file
Original file line number Diff line number Diff line change
@@ -1,5 +1,4 @@
//! Testing utilities.
use std::ops::Deref;

use crate::prelude::*;

Expand Down Expand Up @@ -36,21 +35,6 @@ impl Series {
}
}
}

/// Get a pointer to the underlying data of this [`Series`].
/// Can be useful for fast comparisons.
pub fn get_data_ptr(&self) -> usize {
let object = self.0.deref();

// SAFETY:
// A fat pointer consists of a data ptr and a ptr to the vtable.
// we specifically check that we only transmute &dyn SeriesTrait e.g.
// a trait object, therefore this is sound.
#[allow(clippy::transmute_undefined_repr)]
let (data_ptr, _vtable_ptr) =
unsafe { std::mem::transmute::<&dyn SeriesTrait, (usize, usize)>(object) };
data_ptr
}
}

impl PartialEq for Series {
Expand Down Expand Up @@ -128,26 +112,6 @@ impl DataFrame {
}
true
}

/// Checks if the Arc ptrs of the [`Series`] are equal
///
/// # Example
///
/// ```rust
/// # use polars_core::prelude::*;
/// let df1: DataFrame = df!("Atomic number" => &[1, 51, 300],
/// "Element" => &[Some("Hydrogen"), Some("Antimony"), None])?;
/// let df2: &DataFrame = &df1;
///
/// assert!(df1.ptr_equal(df2));
/// # Ok::<(), PolarsError>(())
/// ```
pub fn ptr_equal(&self, other: &DataFrame) -> bool {
self.columns
.iter()
.zip(other.columns.iter())
.all(|(a, b)| a.get_data_ptr() == b.get_data_ptr())
}
}

impl PartialEq for DataFrame {
Expand Down
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