polars_core/series/implementations/
decimal.rs1use polars_compute::rolling::QuantileMethod;
2
3use super::*;
4use crate::prelude::*;
5
6unsafe impl IntoSeries for DecimalChunked {
7 fn into_series(self) -> Series {
8 Series(Arc::new(SeriesWrap(self)))
9 }
10}
11
12impl private::PrivateSeriesNumeric for SeriesWrap<DecimalChunked> {
13 fn bit_repr(&self) -> Option<BitRepr> {
14 Some(self.0.physical().to_bit_repr())
15 }
16}
17
18impl SeriesWrap<DecimalChunked> {
19 fn apply_physical_to_s<F: Fn(&Int128Chunked) -> Int128Chunked>(&self, f: F) -> Series {
20 f(self.0.physical())
21 .into_decimal_unchecked(self.0.precision(), self.0.scale())
22 .into_series()
23 }
24
25 fn apply_physical<T, F: Fn(&Int128Chunked) -> T>(&self, f: F) -> T {
26 f(self.0.physical())
27 }
28
29 fn scale_factor(&self) -> u128 {
30 10u128.pow(self.0.scale() as u32)
31 }
32
33 fn apply_scale(&self, mut scalar: Scalar) -> Scalar {
34 if scalar.is_null() {
35 return scalar;
36 }
37
38 debug_assert_eq!(scalar.dtype(), &DataType::Float64);
39 let v = scalar
40 .value()
41 .try_extract::<f64>()
42 .expect("should be f64 scalar");
43 scalar.update((v / self.scale_factor() as f64).into());
44 scalar
45 }
46
47 fn agg_helper<F: Fn(&Int128Chunked) -> Series>(&self, f: F) -> Series {
48 let agg_s = f(self.0.physical());
49 match agg_s.dtype() {
50 DataType::Int128 => {
51 let ca = agg_s.i128().unwrap();
52 let ca = ca.as_ref().clone();
53 let precision = self.0.precision();
54 let scale = self.0.scale();
55 ca.into_decimal_unchecked(precision, scale).into_series()
56 },
57 DataType::List(dtype) if matches!(dtype.as_ref(), DataType::Int128) => {
58 let dtype = self.0.dtype();
59 let ca = agg_s.list().unwrap();
60 let arr = ca.downcast_iter().next().unwrap();
61 let precision = self.0.precision();
63 let scale = self.0.scale();
64 let s = unsafe {
65 Series::from_chunks_and_dtype_unchecked(
66 PlSmallStr::EMPTY,
67 vec![arr.values().clone()],
68 dtype,
69 )
70 }
71 .into_decimal(precision, scale)
72 .unwrap();
73 let new_values = s.array_ref(0).clone();
74 let dtype = DataType::Int128;
75 let arrow_dtype =
76 ListArray::<i64>::default_datatype(dtype.to_arrow(CompatLevel::newest()));
77 let new_arr = ListArray::<i64>::new(
78 arrow_dtype,
79 arr.offsets().clone(),
80 new_values,
81 arr.validity().cloned(),
82 );
83 unsafe {
84 ListChunked::from_chunks_and_dtype_unchecked(
85 agg_s.name().clone(),
86 vec![Box::new(new_arr)],
87 DataType::List(Box::new(DataType::Decimal(precision, scale))),
88 )
89 .into_series()
90 }
91 },
92 _ => unreachable!(),
93 }
94 }
95}
96
97impl private::PrivateSeries for SeriesWrap<DecimalChunked> {
98 fn compute_len(&mut self) {
99 self.0.physical_mut().compute_len()
100 }
101
102 fn _field(&self) -> Cow<'_, Field> {
103 Cow::Owned(self.0.field())
104 }
105
106 fn _dtype(&self) -> &DataType {
107 self.0.dtype()
108 }
109 fn _get_flags(&self) -> StatisticsFlags {
110 self.0.physical().get_flags()
111 }
112 fn _set_flags(&mut self, flags: StatisticsFlags) {
113 self.0.physical_mut().set_flags(flags)
114 }
115
116 #[cfg(feature = "zip_with")]
117 fn zip_with_same_type(&self, mask: &BooleanChunked, other: &Series) -> PolarsResult<Series> {
118 let other = other.decimal()?;
119
120 Ok(self
121 .0
122 .physical()
123 .zip_with(mask, other.physical())?
124 .into_decimal_unchecked(self.0.precision(), self.0.scale())
125 .into_series())
126 }
127 fn into_total_eq_inner<'a>(&'a self) -> Box<dyn TotalEqInner + 'a> {
128 self.0.physical().into_total_eq_inner()
129 }
130 fn into_total_ord_inner<'a>(&'a self) -> Box<dyn TotalOrdInner + 'a> {
131 self.0.physical().into_total_ord_inner()
132 }
133
134 fn vec_hash(
135 &self,
136 random_state: PlSeedableRandomStateQuality,
137 buf: &mut Vec<u64>,
138 ) -> PolarsResult<()> {
139 self.0.physical().vec_hash(random_state, buf)?;
140 Ok(())
141 }
142
143 fn vec_hash_combine(
144 &self,
145 build_hasher: PlSeedableRandomStateQuality,
146 hashes: &mut [u64],
147 ) -> PolarsResult<()> {
148 self.0.physical().vec_hash_combine(build_hasher, hashes)?;
149 Ok(())
150 }
151
152 #[cfg(feature = "algorithm_group_by")]
153 unsafe fn agg_sum(&self, groups: &GroupsType) -> Series {
154 self.agg_helper(|ca| ca.agg_sum(groups))
155 }
156
157 #[cfg(feature = "algorithm_group_by")]
158 unsafe fn agg_min(&self, groups: &GroupsType) -> Series {
159 self.agg_helper(|ca| ca.agg_min(groups))
160 }
161
162 #[cfg(feature = "algorithm_group_by")]
163 unsafe fn agg_max(&self, groups: &GroupsType) -> Series {
164 self.agg_helper(|ca| ca.agg_max(groups))
165 }
166
167 #[cfg(feature = "algorithm_group_by")]
168 unsafe fn agg_list(&self, groups: &GroupsType) -> Series {
169 self.agg_helper(|ca| ca.agg_list(groups))
170 }
171
172 #[cfg(feature = "algorithm_group_by")]
173 unsafe fn agg_var(&self, groups: &GroupsType, ddof: u8) -> Series {
174 self.0
175 .cast(&DataType::Float64)
176 .unwrap()
177 .agg_var(groups, ddof)
178 }
179
180 #[cfg(feature = "algorithm_group_by")]
181 unsafe fn agg_std(&self, groups: &GroupsType, ddof: u8) -> Series {
182 self.0
183 .cast(&DataType::Float64)
184 .unwrap()
185 .agg_std(groups, ddof)
186 }
187
188 fn subtract(&self, rhs: &Series) -> PolarsResult<Series> {
189 let rhs = rhs.decimal()?;
190 ((&self.0) - rhs).map(|ca| ca.into_series())
191 }
192 fn add_to(&self, rhs: &Series) -> PolarsResult<Series> {
193 let rhs = rhs.decimal()?;
194 ((&self.0) + rhs).map(|ca| ca.into_series())
195 }
196 fn multiply(&self, rhs: &Series) -> PolarsResult<Series> {
197 let rhs = rhs.decimal()?;
198 ((&self.0) * rhs).map(|ca| ca.into_series())
199 }
200 fn divide(&self, rhs: &Series) -> PolarsResult<Series> {
201 let rhs = rhs.decimal()?;
202 ((&self.0) / rhs).map(|ca| ca.into_series())
203 }
204 #[cfg(feature = "algorithm_group_by")]
205 fn group_tuples(&self, multithreaded: bool, sorted: bool) -> PolarsResult<GroupsType> {
206 self.0.physical().group_tuples(multithreaded, sorted)
207 }
208 fn arg_sort_multiple(
209 &self,
210 by: &[Column],
211 options: &SortMultipleOptions,
212 ) -> PolarsResult<IdxCa> {
213 self.0.physical().arg_sort_multiple(by, options)
214 }
215}
216
217impl SeriesTrait for SeriesWrap<DecimalChunked> {
218 fn rename(&mut self, name: PlSmallStr) {
219 self.0.rename(name)
220 }
221
222 fn chunk_lengths(&self) -> ChunkLenIter<'_> {
223 self.0.physical().chunk_lengths()
224 }
225
226 fn name(&self) -> &PlSmallStr {
227 self.0.name()
228 }
229
230 fn chunks(&self) -> &Vec<ArrayRef> {
231 self.0.physical().chunks()
232 }
233 unsafe fn chunks_mut(&mut self) -> &mut Vec<ArrayRef> {
234 self.0.physical_mut().chunks_mut()
235 }
236
237 fn slice(&self, offset: i64, length: usize) -> Series {
238 self.apply_physical_to_s(|ca| ca.slice(offset, length))
239 }
240
241 fn split_at(&self, offset: i64) -> (Series, Series) {
242 let (a, b) = self.0.split_at(offset);
243 (a.into_series(), b.into_series())
244 }
245
246 fn append(&mut self, other: &Series) -> PolarsResult<()> {
247 polars_ensure!(self.0.dtype() == other.dtype(), append);
248 let mut other = other.to_physical_repr().into_owned();
249 self.0
250 .physical_mut()
251 .append_owned(std::mem::take(other._get_inner_mut().as_mut()))
252 }
253 fn append_owned(&mut self, mut other: Series) -> PolarsResult<()> {
254 polars_ensure!(self.0.dtype() == other.dtype(), append);
255 self.0.physical_mut().append_owned(std::mem::take(
256 &mut other
257 ._get_inner_mut()
258 .as_any_mut()
259 .downcast_mut::<DecimalChunked>()
260 .unwrap()
261 .phys,
262 ))
263 }
264
265 fn extend(&mut self, other: &Series) -> PolarsResult<()> {
266 polars_ensure!(self.0.dtype() == other.dtype(), extend);
267 let other = other.to_physical_repr();
272 self.0
273 .physical_mut()
274 .extend(other.as_ref().as_ref().as_ref())?;
275 Ok(())
276 }
277
278 fn filter(&self, filter: &BooleanChunked) -> PolarsResult<Series> {
279 Ok(self
280 .0
281 .physical()
282 .filter(filter)?
283 .into_decimal_unchecked(self.0.precision(), self.0.scale())
284 .into_series())
285 }
286
287 fn take(&self, indices: &IdxCa) -> PolarsResult<Series> {
288 Ok(self
289 .0
290 .physical()
291 .take(indices)?
292 .into_decimal_unchecked(self.0.precision(), self.0.scale())
293 .into_series())
294 }
295
296 unsafe fn take_unchecked(&self, indices: &IdxCa) -> Series {
297 self.0
298 .physical()
299 .take_unchecked(indices)
300 .into_decimal_unchecked(self.0.precision(), self.0.scale())
301 .into_series()
302 }
303
304 fn take_slice(&self, indices: &[IdxSize]) -> PolarsResult<Series> {
305 Ok(self
306 .0
307 .physical()
308 .take(indices)?
309 .into_decimal_unchecked(self.0.precision(), self.0.scale())
310 .into_series())
311 }
312
313 unsafe fn take_slice_unchecked(&self, indices: &[IdxSize]) -> Series {
314 self.0
315 .physical()
316 .take_unchecked(indices)
317 .into_decimal_unchecked(self.0.precision(), self.0.scale())
318 .into_series()
319 }
320
321 fn deposit(&self, validity: &Bitmap) -> Series {
322 self.0
323 .physical()
324 .deposit(validity)
325 .into_decimal_unchecked(self.0.precision(), self.0.scale())
326 .into_series()
327 }
328
329 fn len(&self) -> usize {
330 self.0.len()
331 }
332
333 fn rechunk(&self) -> Series {
334 let ca = self.0.physical().rechunk().into_owned();
335 ca.into_decimal_unchecked(self.0.precision(), self.0.scale())
336 .into_series()
337 }
338
339 fn new_from_index(&self, index: usize, length: usize) -> Series {
340 self.0
341 .physical()
342 .new_from_index(index, length)
343 .into_decimal_unchecked(self.0.precision(), self.0.scale())
344 .into_series()
345 }
346
347 fn cast(&self, dtype: &DataType, cast_options: CastOptions) -> PolarsResult<Series> {
348 self.0.cast_with_options(dtype, cast_options)
349 }
350
351 #[inline]
352 unsafe fn get_unchecked(&self, index: usize) -> AnyValue<'_> {
353 self.0.get_any_value_unchecked(index)
354 }
355
356 fn sort_with(&self, options: SortOptions) -> PolarsResult<Series> {
357 Ok(self
358 .0
359 .physical()
360 .sort_with(options)
361 .into_decimal_unchecked(self.0.precision(), self.0.scale())
362 .into_series())
363 }
364
365 fn arg_sort(&self, options: SortOptions) -> IdxCa {
366 self.0.physical().arg_sort(options)
367 }
368
369 fn null_count(&self) -> usize {
370 self.0.null_count()
371 }
372
373 fn has_nulls(&self) -> bool {
374 self.0.has_nulls()
375 }
376
377 #[cfg(feature = "algorithm_group_by")]
378 fn unique(&self) -> PolarsResult<Series> {
379 Ok(self.apply_physical_to_s(|ca| ca.unique().unwrap()))
380 }
381
382 #[cfg(feature = "algorithm_group_by")]
383 fn n_unique(&self) -> PolarsResult<usize> {
384 self.0.physical().n_unique()
385 }
386
387 #[cfg(feature = "algorithm_group_by")]
388 fn arg_unique(&self) -> PolarsResult<IdxCa> {
389 self.0.physical().arg_unique()
390 }
391
392 fn is_null(&self) -> BooleanChunked {
393 self.0.is_null()
394 }
395
396 fn is_not_null(&self) -> BooleanChunked {
397 self.0.is_not_null()
398 }
399
400 fn reverse(&self) -> Series {
401 self.apply_physical_to_s(|ca| ca.reverse())
402 }
403
404 fn shift(&self, periods: i64) -> Series {
405 self.apply_physical_to_s(|ca| ca.shift(periods))
406 }
407
408 #[cfg(feature = "approx_unique")]
409 fn approx_n_unique(&self) -> PolarsResult<IdxSize> {
410 Ok(ChunkApproxNUnique::approx_n_unique(self.0.physical()))
411 }
412
413 fn clone_inner(&self) -> Arc<dyn SeriesTrait> {
414 Arc::new(SeriesWrap(Clone::clone(&self.0)))
415 }
416
417 fn sum_reduce(&self) -> PolarsResult<Scalar> {
418 Ok(self.apply_physical(|ca| {
419 let sum = ca.sum();
420 let DataType::Decimal(prec, scale) = self.dtype() else {
421 unreachable!()
422 };
423 let av = AnyValue::Decimal(sum.unwrap(), *prec, *scale);
424 Scalar::new(self.dtype().clone(), av)
425 }))
426 }
427
428 fn min_reduce(&self) -> PolarsResult<Scalar> {
429 Ok(self.apply_physical(|ca| {
430 let min = ca.min();
431 let DataType::Decimal(prec, scale) = self.dtype() else {
432 unreachable!()
433 };
434 let av = if let Some(min) = min {
435 AnyValue::Decimal(min, *prec, *scale)
436 } else {
437 AnyValue::Null
438 };
439 Scalar::new(self.dtype().clone(), av)
440 }))
441 }
442
443 fn max_reduce(&self) -> PolarsResult<Scalar> {
444 Ok(self.apply_physical(|ca| {
445 let max = ca.max();
446 let DataType::Decimal(prec, scale) = self.dtype() else {
447 unreachable!()
448 };
449 let av = if let Some(m) = max {
450 AnyValue::Decimal(m, *prec, *scale)
451 } else {
452 AnyValue::Null
453 };
454 Scalar::new(self.dtype().clone(), av)
455 }))
456 }
457
458 fn _sum_as_f64(&self) -> f64 {
459 self.0.physical()._sum_as_f64() / self.scale_factor() as f64
460 }
461
462 fn mean(&self) -> Option<f64> {
463 self.0
464 .physical()
465 .mean()
466 .map(|v| v / self.scale_factor() as f64)
467 }
468 fn mean_reduce(&self) -> PolarsResult<Scalar> {
469 Ok(Scalar::new(DataType::Float64, self.mean().into()))
470 }
471
472 fn median(&self) -> Option<f64> {
473 self.0
474 .physical()
475 .median()
476 .map(|v| v / self.scale_factor() as f64)
477 }
478
479 fn median_reduce(&self) -> PolarsResult<Scalar> {
480 Ok(self.apply_scale(self.0.physical().median_reduce()))
481 }
482
483 fn std(&self, ddof: u8) -> Option<f64> {
484 self.0.cast(&DataType::Float64).ok()?.std(ddof)
485 }
486
487 fn std_reduce(&self, ddof: u8) -> PolarsResult<Scalar> {
488 self.0.cast(&DataType::Float64)?.std_reduce(ddof)
489 }
490
491 fn var(&self, ddof: u8) -> Option<f64> {
492 self.0.cast(&DataType::Float64).ok()?.var(ddof)
493 }
494
495 fn var_reduce(&self, ddof: u8) -> PolarsResult<Scalar> {
496 self.0.cast(&DataType::Float64)?.var_reduce(ddof)
497 }
498
499 fn quantile_reduce(&self, quantile: f64, method: QuantileMethod) -> PolarsResult<Scalar> {
500 self.0
501 .physical()
502 .quantile_reduce(quantile, method)
503 .map(|v| self.apply_scale(v))
504 }
505
506 fn find_validity_mismatch(&self, other: &Series, idxs: &mut Vec<IdxSize>) {
507 self.0.physical().find_validity_mismatch(other, idxs)
508 }
509
510 fn as_any(&self) -> &dyn Any {
511 &self.0
512 }
513
514 fn as_any_mut(&mut self) -> &mut dyn Any {
515 &mut self.0
516 }
517
518 fn as_phys_any(&self) -> &dyn Any {
519 self.0.physical()
520 }
521
522 fn as_arc_any(self: Arc<Self>) -> Arc<dyn Any + Send + Sync> {
523 self as _
524 }
525}