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//! Traits for miscellaneous operations on ChunkedArray
use arrow::offset::OffsetsBuffer;
use crate::prelude::*;
pub(crate) mod aggregate;
pub(crate) mod any_value;
pub(crate) mod append;
mod apply;
pub mod arity;
mod bit_repr;
pub(crate) mod chunkops;
pub(crate) mod compare_inner;
#[cfg(feature = "dtype-decimal")]
mod decimal;
pub(crate) mod downcast;
pub(crate) mod explode;
mod explode_and_offsets;
mod extend;
pub mod fill_null;
mod filter;
pub mod float_sorted_arg_max;
mod for_each;
pub mod full;
pub mod gather;
#[cfg(feature = "zip_with")]
pub(crate) mod min_max_binary;
pub(crate) mod nulls;
mod reverse;
#[cfg(feature = "rolling_window")]
pub(crate) mod rolling_window;
pub mod search_sorted;
mod set;
mod shift;
pub mod sort;
#[cfg(feature = "algorithm_group_by")]
pub(crate) mod unique;
#[cfg(feature = "zip_with")]
pub mod zip;
#[cfg(feature = "serde-lazy")]
use serde::{Deserialize, Serialize};
pub use sort::options::*;
use crate::chunked_array::cast::CastOptions;
use crate::series::IsSorted;
#[cfg(feature = "reinterpret")]
pub trait Reinterpret {
fn reinterpret_signed(&self) -> Series {
unimplemented!()
}
fn reinterpret_unsigned(&self) -> Series {
unimplemented!()
}
}
/// Transmute [`ChunkedArray`] to bit representation.
/// This is useful in hashing context and reduces no.
/// of compiled code paths.
pub(crate) trait ToBitRepr {
fn bit_repr_is_large() -> bool;
fn bit_repr_large(&self) -> UInt64Chunked;
fn bit_repr_small(&self) -> UInt32Chunked;
}
pub trait ChunkAnyValue {
/// Get a single value. Beware this is slow.
/// If you need to use this slightly performant, cast Categorical to UInt32
///
/// # Safety
/// Does not do any bounds checking.
unsafe fn get_any_value_unchecked(&self, index: usize) -> AnyValue;
/// Get a single value. Beware this is slow.
fn get_any_value(&self, index: usize) -> PolarsResult<AnyValue>;
}
/// Explode/flatten a List or String Series
pub trait ChunkExplode {
fn explode(&self) -> PolarsResult<Series> {
self.explode_and_offsets().map(|t| t.0)
}
fn offsets(&self) -> PolarsResult<OffsetsBuffer<i64>>;
fn explode_and_offsets(&self) -> PolarsResult<(Series, OffsetsBuffer<i64>)>;
}
pub trait ChunkBytes {
fn to_byte_slices(&self) -> Vec<&[u8]>;
}
/// This differs from ChunkWindowCustom and ChunkWindow
/// by not using a fold aggregator, but reusing a `Series` wrapper and calling `Series` aggregators.
/// This likely is a bit slower than ChunkWindow
#[cfg(feature = "rolling_window")]
pub trait ChunkRollApply: AsRefDataType {
fn rolling_map(
&self,
_f: &dyn Fn(&Series) -> Series,
_options: RollingOptionsFixedWindow,
) -> PolarsResult<Series>
where
Self: Sized,
{
polars_bail!(opq = rolling_map, self.as_ref_dtype());
}
}
pub trait ChunkTake<Idx: ?Sized>: ChunkTakeUnchecked<Idx> {
/// Gather values from ChunkedArray by index.
fn take(&self, indices: &Idx) -> PolarsResult<Self>
where
Self: Sized;
}
pub trait ChunkTakeUnchecked<Idx: ?Sized> {
/// Gather values from ChunkedArray by index.
///
/// # Safety
/// The non-null indices must be valid.
unsafe fn take_unchecked(&self, indices: &Idx) -> Self;
}
/// Create a `ChunkedArray` with new values by index or by boolean mask.
/// Note that these operations clone data. This is however the only way we can modify at mask or
/// index level as the underlying Arrow arrays are immutable.
pub trait ChunkSet<'a, A, B> {
/// Set the values at indexes `idx` to some optional value `Option<T>`.
///
/// # Example
///
/// ```rust
/// # use polars_core::prelude::*;
/// let ca = UInt32Chunked::new("a", &[1, 2, 3]);
/// let new = ca.scatter_single(vec![0, 1], Some(10)).unwrap();
///
/// assert_eq!(Vec::from(&new), &[Some(10), Some(10), Some(3)]);
/// ```
fn scatter_single<I: IntoIterator<Item = IdxSize>>(
&'a self,
idx: I,
opt_value: Option<A>,
) -> PolarsResult<Self>
where
Self: Sized;
/// Set the values at indexes `idx` by applying a closure to these values.
///
/// # Example
///
/// ```rust
/// # use polars_core::prelude::*;
/// let ca = Int32Chunked::new("a", &[1, 2, 3]);
/// let new = ca.scatter_with(vec![0, 1], |opt_v| opt_v.map(|v| v - 5)).unwrap();
///
/// assert_eq!(Vec::from(&new), &[Some(-4), Some(-3), Some(3)]);
/// ```
fn scatter_with<I: IntoIterator<Item = IdxSize>, F>(
&'a self,
idx: I,
f: F,
) -> PolarsResult<Self>
where
Self: Sized,
F: Fn(Option<A>) -> Option<B>;
/// Set the values where the mask evaluates to `true` to some optional value `Option<T>`.
///
/// # Example
///
/// ```rust
/// # use polars_core::prelude::*;
/// let ca = Int32Chunked::new("a", &[1, 2, 3]);
/// let mask = BooleanChunked::new("mask", &[false, true, false]);
/// let new = ca.set(&mask, Some(5)).unwrap();
/// assert_eq!(Vec::from(&new), &[Some(1), Some(5), Some(3)]);
/// ```
fn set(&'a self, mask: &BooleanChunked, opt_value: Option<A>) -> PolarsResult<Self>
where
Self: Sized;
}
/// Cast `ChunkedArray<T>` to `ChunkedArray<N>`
pub trait ChunkCast {
/// Cast a [`ChunkedArray`] to [`DataType`]
fn cast(&self, data_type: &DataType) -> PolarsResult<Series> {
self.cast_with_options(data_type, CastOptions::NonStrict)
}
/// Cast a [`ChunkedArray`] to [`DataType`]
fn cast_with_options(&self, data_type: &DataType, options: CastOptions)
-> PolarsResult<Series>;
/// Does not check if the cast is a valid one and may over/underflow
///
/// # Safety
/// - This doesn't do utf8 validation checking when casting from binary
/// - This doesn't do categorical bound checking when casting from UInt32
unsafe fn cast_unchecked(&self, data_type: &DataType) -> PolarsResult<Series>;
}
/// Fastest way to do elementwise operations on a [`ChunkedArray<T>`] when the operation is cheaper than
/// branching due to null checking.
pub trait ChunkApply<'a, T> {
type FuncRet;
/// Apply a closure elementwise. This is fastest when the null check branching is more expensive
/// than the closure application. Often it is.
///
/// Null values remain null.
///
/// # Example
///
/// ```
/// use polars_core::prelude::*;
/// fn double(ca: &UInt32Chunked) -> UInt32Chunked {
/// ca.apply_values(|v| v * 2)
/// }
/// ```
#[must_use]
fn apply_values<F>(&'a self, f: F) -> Self
where
F: Fn(T) -> Self::FuncRet + Copy;
/// Apply a closure elementwise including null values.
#[must_use]
fn apply<F>(&'a self, f: F) -> Self
where
F: Fn(Option<T>) -> Option<Self::FuncRet> + Copy;
/// Apply a closure elementwise and write results to a mutable slice.
fn apply_to_slice<F, S>(&'a self, f: F, slice: &mut [S])
// (value of chunkedarray, value of slice) -> value of slice
where
F: Fn(Option<T>, &S) -> S;
}
/// Aggregation operations.
pub trait ChunkAgg<T> {
/// Aggregate the sum of the ChunkedArray.
/// Returns `None` if not implemented for `T`.
/// If the array is empty, `0` is returned
fn sum(&self) -> Option<T> {
None
}
fn min(&self) -> Option<T> {
None
}
/// Returns the maximum value in the array, according to the natural order.
/// Returns `None` if the array is empty or only contains null values.
fn max(&self) -> Option<T> {
None
}
fn min_max(&self) -> Option<(T, T)> {
Some((self.min()?, self.max()?))
}
/// Returns the mean value in the array.
/// Returns `None` if the array is empty or only contains null values.
fn mean(&self) -> Option<f64> {
None
}
}
/// Quantile and median aggregation.
pub trait ChunkQuantile<T> {
/// Returns the mean value in the array.
/// Returns `None` if the array is empty or only contains null values.
fn median(&self) -> Option<T> {
None
}
/// Aggregate a given quantile of the ChunkedArray.
/// Returns `None` if the array is empty or only contains null values.
fn quantile(
&self,
_quantile: f64,
_interpol: QuantileInterpolOptions,
) -> PolarsResult<Option<T>> {
Ok(None)
}
}
/// Variance and standard deviation aggregation.
pub trait ChunkVar {
/// Compute the variance of this ChunkedArray/Series.
fn var(&self, _ddof: u8) -> Option<f64> {
None
}
/// Compute the standard deviation of this ChunkedArray/Series.
fn std(&self, _ddof: u8) -> Option<f64> {
None
}
}
/// Compare [`Series`] and [`ChunkedArray`]'s and get a `boolean` mask that
/// can be used to filter rows.
///
/// # Example
///
/// ```
/// use polars_core::prelude::*;
/// fn filter_all_ones(df: &DataFrame) -> PolarsResult<DataFrame> {
/// let mask = df
/// .column("column_a")?
/// .equal(1)?;
///
/// df.filter(&mask)
/// }
/// ```
pub trait ChunkCompare<Rhs> {
type Item;
/// Check for equality.
fn equal(&self, rhs: Rhs) -> Self::Item;
/// Check for equality where `None == None`.
fn equal_missing(&self, rhs: Rhs) -> Self::Item;
/// Check for inequality.
fn not_equal(&self, rhs: Rhs) -> Self::Item;
/// Check for inequality where `None == None`.
fn not_equal_missing(&self, rhs: Rhs) -> Self::Item;
/// Greater than comparison.
fn gt(&self, rhs: Rhs) -> Self::Item;
/// Greater than or equal comparison.
fn gt_eq(&self, rhs: Rhs) -> Self::Item;
/// Less than comparison.
fn lt(&self, rhs: Rhs) -> Self::Item;
/// Less than or equal comparison
fn lt_eq(&self, rhs: Rhs) -> Self::Item;
}
/// Get unique values in a `ChunkedArray`
pub trait ChunkUnique {
// We don't return Self to be able to use AutoRef specialization
/// Get unique values of a ChunkedArray
fn unique(&self) -> PolarsResult<Self>
where
Self: Sized;
/// Get first index of the unique values in a `ChunkedArray`.
/// This Vec is sorted.
fn arg_unique(&self) -> PolarsResult<IdxCa>;
/// Number of unique values in the `ChunkedArray`
fn n_unique(&self) -> PolarsResult<usize> {
self.arg_unique().map(|v| v.len())
}
}
/// Sort operations on `ChunkedArray`.
pub trait ChunkSort<T: PolarsDataType> {
#[allow(unused_variables)]
fn sort_with(&self, options: SortOptions) -> ChunkedArray<T>;
/// Returned a sorted `ChunkedArray`.
fn sort(&self, descending: bool) -> ChunkedArray<T>;
/// Retrieve the indexes needed to sort this array.
fn arg_sort(&self, options: SortOptions) -> IdxCa;
/// Retrieve the indexes need to sort this and the other arrays.
#[allow(unused_variables)]
fn arg_sort_multiple(
&self,
by: &[Series],
_options: &SortMultipleOptions,
) -> PolarsResult<IdxCa> {
polars_bail!(opq = arg_sort_multiple, T::get_dtype());
}
}
pub type FillNullLimit = Option<IdxSize>;
#[derive(Copy, Clone, Debug, PartialEq, Hash)]
#[cfg_attr(feature = "serde-lazy", derive(Serialize, Deserialize))]
pub enum FillNullStrategy {
/// previous value in array
Backward(FillNullLimit),
/// next value in array
Forward(FillNullLimit),
/// mean value of array
Mean,
/// minimal value in array
Min,
/// maximum value in array
Max,
/// replace with the value zero
Zero,
/// replace with the value one
One,
/// replace with the maximum value of that data type
MaxBound,
/// replace with the minimal value of that data type
MinBound,
}
/// Replace None values with a value
pub trait ChunkFillNullValue<T> {
/// Replace None values with a give value `T`.
fn fill_null_with_values(&self, value: T) -> PolarsResult<Self>
where
Self: Sized;
}
/// Fill a ChunkedArray with one value.
pub trait ChunkFull<T> {
/// Create a ChunkedArray with a single value.
fn full(name: &str, value: T, length: usize) -> Self
where
Self: Sized;
}
pub trait ChunkFullNull {
fn full_null(_name: &str, _length: usize) -> Self
where
Self: Sized;
}
/// Reverse a [`ChunkedArray<T>`]
pub trait ChunkReverse {
/// Return a reversed version of this array.
fn reverse(&self) -> Self;
}
/// Filter values by a boolean mask.
pub trait ChunkFilter<T: PolarsDataType> {
/// Filter values in the ChunkedArray with a boolean mask.
///
/// ```rust
/// # use polars_core::prelude::*;
/// let array = Int32Chunked::new("array", &[1, 2, 3]);
/// let mask = BooleanChunked::new("mask", &[true, false, true]);
///
/// let filtered = array.filter(&mask).unwrap();
/// assert_eq!(Vec::from(&filtered), [Some(1), Some(3)])
/// ```
fn filter(&self, filter: &BooleanChunked) -> PolarsResult<ChunkedArray<T>>
where
Self: Sized;
}
/// Create a new ChunkedArray filled with values at that index.
pub trait ChunkExpandAtIndex<T: PolarsDataType> {
/// Create a new ChunkedArray filled with values at that index.
fn new_from_index(&self, length: usize, index: usize) -> ChunkedArray<T>;
}
macro_rules! impl_chunk_expand {
($self:ident, $length:ident, $index:ident) => {{
if $self.is_empty() {
return $self.clone();
}
let opt_val = $self.get($index);
match opt_val {
Some(val) => ChunkedArray::full($self.name(), val, $length),
None => ChunkedArray::full_null($self.name(), $length),
}
}};
}
impl<T: PolarsNumericType> ChunkExpandAtIndex<T> for ChunkedArray<T>
where
ChunkedArray<T>: ChunkFull<T::Native>,
{
fn new_from_index(&self, index: usize, length: usize) -> ChunkedArray<T> {
let mut out = impl_chunk_expand!(self, length, index);
out.set_sorted_flag(IsSorted::Ascending);
out
}
}
impl ChunkExpandAtIndex<BooleanType> for BooleanChunked {
fn new_from_index(&self, index: usize, length: usize) -> BooleanChunked {
let mut out = impl_chunk_expand!(self, length, index);
out.set_sorted_flag(IsSorted::Ascending);
out
}
}
impl ChunkExpandAtIndex<StringType> for StringChunked {
fn new_from_index(&self, index: usize, length: usize) -> StringChunked {
let mut out = impl_chunk_expand!(self, length, index);
out.set_sorted_flag(IsSorted::Ascending);
out
}
}
impl ChunkExpandAtIndex<BinaryType> for BinaryChunked {
fn new_from_index(&self, index: usize, length: usize) -> BinaryChunked {
let mut out = impl_chunk_expand!(self, length, index);
out.set_sorted_flag(IsSorted::Ascending);
out
}
}
impl ChunkExpandAtIndex<BinaryOffsetType> for BinaryOffsetChunked {
fn new_from_index(&self, index: usize, length: usize) -> BinaryOffsetChunked {
let mut out = impl_chunk_expand!(self, length, index);
out.set_sorted_flag(IsSorted::Ascending);
out
}
}
impl ChunkExpandAtIndex<ListType> for ListChunked {
fn new_from_index(&self, index: usize, length: usize) -> ListChunked {
let opt_val = self.get_as_series(index);
match opt_val {
Some(val) => {
let mut ca = ListChunked::full(self.name(), &val, length);
unsafe { ca.to_logical(self.inner_dtype().clone()) };
ca
},
None => ListChunked::full_null_with_dtype(self.name(), length, self.inner_dtype()),
}
}
}
#[cfg(feature = "dtype-array")]
impl ChunkExpandAtIndex<FixedSizeListType> for ArrayChunked {
fn new_from_index(&self, index: usize, length: usize) -> ArrayChunked {
let opt_val = self.get_as_series(index);
match opt_val {
Some(val) => {
let mut ca = ArrayChunked::full(self.name(), &val, length);
unsafe { ca.to_logical(self.inner_dtype().clone()) };
ca
},
None => ArrayChunked::full_null_with_dtype(
self.name(),
length,
self.inner_dtype(),
self.width(),
),
}
}
}
#[cfg(feature = "object")]
impl<T: PolarsObject> ChunkExpandAtIndex<ObjectType<T>> for ObjectChunked<T> {
fn new_from_index(&self, index: usize, length: usize) -> ObjectChunked<T> {
let opt_val = self.get(index);
match opt_val {
Some(val) => ObjectChunked::<T>::full(self.name(), val.clone(), length),
None => ObjectChunked::<T>::full_null(self.name(), length),
}
}
}
/// Shift the values of a [`ChunkedArray`] by a number of periods.
pub trait ChunkShiftFill<T: PolarsDataType, V> {
/// Shift the values by a given period and fill the parts that will be empty due to this operation
/// with `fill_value`.
fn shift_and_fill(&self, periods: i64, fill_value: V) -> ChunkedArray<T>;
}
pub trait ChunkShift<T: PolarsDataType> {
fn shift(&self, periods: i64) -> ChunkedArray<T>;
}
/// Combine two [`ChunkedArray`] based on some predicate.
pub trait ChunkZip<T: PolarsDataType> {
/// Create a new ChunkedArray with values from self where the mask evaluates `true` and values
/// from `other` where the mask evaluates `false`
fn zip_with(
&self,
mask: &BooleanChunked,
other: &ChunkedArray<T>,
) -> PolarsResult<ChunkedArray<T>>;
}
/// Apply kernels on the arrow array chunks in a ChunkedArray.
pub trait ChunkApplyKernel<A: Array> {
/// Apply kernel and return result as a new ChunkedArray.
#[must_use]
fn apply_kernel(&self, f: &dyn Fn(&A) -> ArrayRef) -> Self;
/// Apply a kernel that outputs an array of different type.
fn apply_kernel_cast<S>(&self, f: &dyn Fn(&A) -> ArrayRef) -> ChunkedArray<S>
where
S: PolarsDataType;
}
#[cfg(feature = "is_first_distinct")]
/// Mask the first unique values as `true`
pub trait IsFirstDistinct<T: PolarsDataType> {
fn is_first_distinct(&self) -> PolarsResult<BooleanChunked> {
polars_bail!(opq = is_first_distinct, T::get_dtype());
}
}
#[cfg(feature = "is_last_distinct")]
/// Mask the last unique values as `true`
pub trait IsLastDistinct<T: PolarsDataType> {
fn is_last_distinct(&self) -> PolarsResult<BooleanChunked> {
polars_bail!(opq = is_last_distinct, T::get_dtype());
}
}