use super::*;
use crate::chunked_array::comparison::*;
use crate::prelude::*;
unsafe impl<T: PolarsCategoricalType> IntoSeries for CategoricalChunked<T> {
fn into_series(self) -> Series {
with_match_categorical_physical_type!(T::physical(), |$C| {
unsafe {
Series(Arc::new(SeriesWrap(core::mem::transmute::<Self, CategoricalChunked<$C>>(self))))
}
})
}
}
impl<T: PolarsCategoricalType> SeriesWrap<CategoricalChunked<T>> {
unsafe fn apply_on_phys<F>(&self, apply: F) -> CategoricalChunked<T>
where
F: Fn(&ChunkedArray<T::PolarsPhysical>) -> ChunkedArray<T::PolarsPhysical>,
{
let cats = apply(self.0.physical());
unsafe { CategoricalChunked::from_cats_and_dtype_unchecked(cats, self.0.dtype().clone()) }
}
unsafe fn try_apply_on_phys<F>(&self, apply: F) -> PolarsResult<CategoricalChunked<T>>
where
F: Fn(&ChunkedArray<T::PolarsPhysical>) -> PolarsResult<ChunkedArray<T::PolarsPhysical>>,
{
let cats = apply(self.0.physical())?;
unsafe {
Ok(CategoricalChunked::from_cats_and_dtype_unchecked(
cats,
self.0.dtype().clone(),
))
}
}
}
macro_rules! impl_cat_series {
($ca: ident, $pdt:ty, $ca_fn:ident) => {
impl private::PrivateSeries for SeriesWrap<$ca> {
fn compute_len(&mut self) {
self.0.physical_mut().compute_len()
}
fn _field(&self) -> Cow<'_, Field> {
Cow::Owned(self.0.field())
}
fn _dtype(&self) -> &DataType {
self.0.dtype()
}
fn _get_flags(&self) -> StatisticsFlags {
self.0.get_flags()
}
fn _set_flags(&mut self, flags: StatisticsFlags) {
self.0.set_flags(flags)
}
unsafe fn equal_element(&self, idx_self: usize, idx_other: usize, other: &Series) -> bool {
self.0.physical().equal_element(idx_self, idx_other, other)
}
#[cfg(feature = "zip_with")]
fn zip_with_same_type(&self, mask: &BooleanChunked, other: &Series) -> PolarsResult<Series> {
polars_ensure!(self.dtype() == other.dtype(), SchemaMismatch: "expected '{}' found '{}'", self.dtype(), other.dtype());
let other = other.to_physical_repr().into_owned();
unsafe {
Ok(self.try_apply_on_phys(|ca| {
ca.zip_with(mask, other.as_ref().as_ref())
})?.into_series())
}
}
fn into_total_ord_inner<'a>(&'a self) -> Box<dyn TotalOrdInner + 'a> {
if self.0.uses_lexical_ordering() {
(&self.0).into_total_ord_inner()
} else {
self.0.physical().into_total_ord_inner()
}
}
fn into_total_eq_inner<'a>(&'a self) -> Box<dyn TotalEqInner + 'a> {
invalid_operation_panic!(into_total_eq_inner, self)
}
fn vec_hash(
&self,
random_state: PlSeedableRandomStateQuality,
buf: &mut Vec<u64>,
) -> PolarsResult<()> {
self.0.vec_hash(random_state, buf)
}
fn vec_hash_combine(
&self,
build_hasher: PlSeedableRandomStateQuality,
hashes: &mut [u64],
) -> PolarsResult<()> {
self.0.vec_hash_combine(build_hasher, hashes)
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_min(&self, groups: &GroupsType) -> Series {
if self.0.uses_lexical_ordering() {
unimplemented!()
} else {
self.apply_on_phys(|phys| phys.agg_min(groups).$ca_fn().unwrap().clone())
.into_series()
}
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_max(&self, groups: &GroupsType) -> Series {
if self.0.uses_lexical_ordering() {
unimplemented!()
} else {
self.apply_on_phys(|phys| phys.agg_max(groups).$ca_fn().unwrap().clone())
.into_series()
}
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_arg_min(&self, groups: &GroupsType) -> Series {
if self.0.uses_lexical_ordering() {
unimplemented!()
} else {
self.0.physical().agg_arg_min(groups)
}
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_arg_max(&self, groups: &GroupsType) -> Series {
if self.0.uses_lexical_ordering() {
unimplemented!()
} else {
self.0.physical().agg_arg_max(groups)
}
}
#[cfg(feature = "algorithm_group_by")]
unsafe fn agg_list(&self, groups: &GroupsType) -> Series {
let list = self.0.physical().agg_list(groups);
let mut list = list.list().unwrap().clone();
unsafe { list.to_logical(self.dtype().clone()) };
list.into_series()
}
#[cfg(feature = "algorithm_group_by")]
fn group_tuples(&self, multithreaded: bool, sorted: bool) -> PolarsResult<GroupsType> {
self.0.physical().group_tuples(multithreaded, sorted)
}
fn arg_sort_multiple(
&self,
by: &[Column],
options: &SortMultipleOptions,
) -> PolarsResult<IdxCa> {
self.0.arg_sort_multiple(by, options)
}
}
impl SeriesTrait for SeriesWrap<$ca> {
fn rename(&mut self, name: PlSmallStr) {
self.0.physical_mut().rename(name);
}
fn chunk_lengths(&self) -> ChunkLenIter<'_> {
self.0.physical().chunk_lengths()
}
fn name(&self) -> &PlSmallStr {
self.0.physical().name()
}
fn chunks(&self) -> &Vec<ArrayRef> {
self.0.physical().chunks()
}
unsafe fn chunks_mut(&mut self) -> &mut Vec<ArrayRef> {
self.0.physical_mut().chunks_mut()
}
fn shrink_to_fit(&mut self) {
self.0.physical_mut().shrink_to_fit()
}
fn slice(&self, offset: i64, length: usize) -> Series {
unsafe { self.apply_on_phys(|cats| cats.slice(offset, length)).into_series() }
}
fn split_at(&self, offset: i64) -> (Series, Series) {
unsafe {
let (a, b) = self.0.physical().split_at(offset);
let a = <$ca>::from_cats_and_dtype_unchecked(a, self.0.dtype().clone()).into_series();
let b = <$ca>::from_cats_and_dtype_unchecked(b, self.0.dtype().clone()).into_series();
(a, b)
}
}
fn append(&mut self, other: &Series) -> PolarsResult<()> {
polars_ensure!(self.0.dtype() == other.dtype(), append);
self.0.append(other.cat::<$pdt>().unwrap())
}
fn append_owned(&mut self, mut other: Series) -> PolarsResult<()> {
polars_ensure!(self.0.dtype() == other.dtype(), append);
self.0.physical_mut().append_owned(std::mem::take(
other
._get_inner_mut()
.as_any_mut()
.downcast_mut::<$ca>()
.unwrap()
.physical_mut(),
))
}
fn extend(&mut self, other: &Series) -> PolarsResult<()> {
polars_ensure!(self.0.dtype() == other.dtype(), extend);
self.0.extend(other.cat::<$pdt>().unwrap())
}
fn filter(&self, filter: &BooleanChunked) -> PolarsResult<Series> {
unsafe { Ok(self.try_apply_on_phys(|cats| cats.filter(filter))?.into_series()) }
}
fn take(&self, indices: &IdxCa) -> PolarsResult<Series> {
unsafe { Ok(self.try_apply_on_phys(|cats| cats.take(indices))?.into_series() ) }
}
unsafe fn take_unchecked(&self, indices: &IdxCa) -> Series {
unsafe { self.apply_on_phys(|cats| cats.take_unchecked(indices)).into_series() }
}
fn take_slice(&self, indices: &[IdxSize]) -> PolarsResult<Series> {
unsafe { Ok(self.try_apply_on_phys(|cats| cats.take(indices))?.into_series()) }
}
unsafe fn take_slice_unchecked(&self, indices: &[IdxSize]) -> Series {
unsafe { self.apply_on_phys(|cats| cats.take_unchecked(indices)).into_series() }
}
fn deposit(&self, validity: &Bitmap) -> Series {
unsafe { self.apply_on_phys(|cats| cats.deposit(validity)) }
.into_series()
}
fn len(&self) -> usize {
self.0.len()
}
fn rechunk(&self) -> Series {
unsafe { self.apply_on_phys(|cats| cats.rechunk().into_owned()).into_series() }
}
fn new_from_index(&self, index: usize, length: usize) -> Series {
unsafe { self.apply_on_phys(|cats| cats.new_from_index(index, length)).into_series() }
}
fn cast(&self, dtype: &DataType, options: CastOptions) -> PolarsResult<Series> {
self.0.cast_with_options(dtype, options)
}
#[inline]
unsafe fn get_unchecked(&self, index: usize) -> AnyValue<'_> {
self.0.get_any_value_unchecked(index)
}
fn sort_with(&self, options: SortOptions) -> PolarsResult<Series> {
Ok(self.0.sort_with(options).into_series())
}
fn arg_sort(&self, options: SortOptions) -> IdxCa {
self.0.arg_sort(options)
}
fn null_count(&self) -> usize {
self.0.physical().null_count()
}
fn has_nulls(&self) -> bool {
self.0.physical().has_nulls()
}
#[cfg(feature = "algorithm_group_by")]
fn unique(&self) -> PolarsResult<Series> {
unsafe { Ok(self.try_apply_on_phys(|cats| cats.unique())?.into_series()) }
}
#[cfg(feature = "algorithm_group_by")]
fn n_unique(&self) -> PolarsResult<usize> {
self.0.physical().n_unique()
}
#[cfg(feature = "approx_unique")]
fn approx_n_unique(&self) -> PolarsResult<IdxSize> {
Ok(self.0.physical().approx_n_unique())
}
#[cfg(feature = "algorithm_group_by")]
fn arg_unique(&self) -> PolarsResult<IdxCa> {
self.0.physical().arg_unique()
}
fn unique_id(&self) -> PolarsResult<(IdxSize, Vec<IdxSize>)> {
ChunkUnique::unique_id(self.0.physical())
}
fn is_null(&self) -> BooleanChunked {
self.0.physical().is_null()
}
fn is_not_null(&self) -> BooleanChunked {
self.0.physical().is_not_null()
}
fn reverse(&self) -> Series {
unsafe { self.apply_on_phys(|cats| cats.reverse()).into_series() }
}
fn as_single_ptr(&mut self) -> PolarsResult<usize> {
self.0.physical_mut().as_single_ptr()
}
fn shift(&self, periods: i64) -> Series {
unsafe { self.apply_on_phys(|ca| ca.shift(periods)).into_series() }
}
fn clone_inner(&self) -> Arc<dyn SeriesTrait> {
Arc::new(SeriesWrap(Clone::clone(&self.0)))
}
fn min_reduce(&self) -> PolarsResult<Scalar> {
Ok(ChunkAggSeries::min_reduce(&self.0))
}
fn max_reduce(&self) -> PolarsResult<Scalar> {
Ok(ChunkAggSeries::max_reduce(&self.0))
}
fn find_validity_mismatch(&self, other: &Series, idxs: &mut Vec<IdxSize>) {
self.0.physical().find_validity_mismatch(other, idxs)
}
fn as_any(&self) -> &dyn Any {
&self.0
}
fn as_any_mut(&mut self) -> &mut dyn Any {
&mut self.0
}
fn as_phys_any(&self) -> &dyn Any {
self.0.physical()
}
fn as_arc_any(self: Arc<Self>) -> Arc<dyn Any + Send + Sync> {
self as _
}
}
impl private::PrivateSeriesNumeric for SeriesWrap<$ca> {
fn bit_repr(&self) -> Option<BitRepr> {
Some(self.0.physical().to_bit_repr())
}
}
}
}
impl_cat_series!(Categorical8Chunked, Categorical8Type, u8);
impl_cat_series!(Categorical16Chunked, Categorical16Type, u16);
impl_cat_series!(Categorical32Chunked, Categorical32Type, u32);