polars_core/series/implementations/
boolean.rs

1use super::*;
2use crate::chunked_array::comparison::*;
3#[cfg(feature = "algorithm_group_by")]
4use crate::frame::group_by::*;
5use crate::prelude::*;
6
7impl private::PrivateSeries for SeriesWrap<BooleanChunked> {
8    fn compute_len(&mut self) {
9        self.0.compute_len()
10    }
11    fn _field(&self) -> Cow<'_, Field> {
12        Cow::Borrowed(self.0.ref_field())
13    }
14    fn _dtype(&self) -> &DataType {
15        self.0.ref_field().dtype()
16    }
17    fn _get_flags(&self) -> StatisticsFlags {
18        self.0.get_flags()
19    }
20    fn _set_flags(&mut self, flags: StatisticsFlags) {
21        self.0.set_flags(flags)
22    }
23
24    unsafe fn equal_element(&self, idx_self: usize, idx_other: usize, other: &Series) -> bool {
25        self.0.equal_element(idx_self, idx_other, other)
26    }
27
28    #[cfg(feature = "zip_with")]
29    fn zip_with_same_type(&self, mask: &BooleanChunked, other: &Series) -> PolarsResult<Series> {
30        ChunkZip::zip_with(&self.0, mask, other.as_ref().as_ref()).map(|ca| ca.into_series())
31    }
32    fn into_total_eq_inner<'a>(&'a self) -> Box<dyn TotalEqInner + 'a> {
33        (&self.0).into_total_eq_inner()
34    }
35    fn into_total_ord_inner<'a>(&'a self) -> Box<dyn TotalOrdInner + 'a> {
36        (&self.0).into_total_ord_inner()
37    }
38
39    fn vec_hash(
40        &self,
41        random_state: PlSeedableRandomStateQuality,
42        buf: &mut Vec<u64>,
43    ) -> PolarsResult<()> {
44        self.0.vec_hash(random_state, buf)?;
45        Ok(())
46    }
47
48    fn vec_hash_combine(
49        &self,
50        build_hasher: PlSeedableRandomStateQuality,
51        hashes: &mut [u64],
52    ) -> PolarsResult<()> {
53        self.0.vec_hash_combine(build_hasher, hashes)?;
54        Ok(())
55    }
56
57    #[cfg(feature = "algorithm_group_by")]
58    unsafe fn agg_min(&self, groups: &GroupsType) -> Series {
59        self.0.agg_min(groups)
60    }
61
62    #[cfg(feature = "algorithm_group_by")]
63    unsafe fn agg_max(&self, groups: &GroupsType) -> Series {
64        self.0.agg_max(groups)
65    }
66
67    #[cfg(feature = "algorithm_group_by")]
68    unsafe fn agg_sum(&self, groups: &GroupsType) -> Series {
69        self.0.agg_sum(groups)
70    }
71
72    #[cfg(feature = "algorithm_group_by")]
73    unsafe fn agg_list(&self, groups: &GroupsType) -> Series {
74        self.0.agg_list(groups)
75    }
76    #[cfg(feature = "algorithm_group_by")]
77    unsafe fn agg_std(&self, groups: &GroupsType, _ddof: u8) -> Series {
78        self.0
79            .cast_with_options(&DataType::Float64, CastOptions::Overflowing)
80            .unwrap()
81            .agg_std(groups, _ddof)
82    }
83    #[cfg(feature = "algorithm_group_by")]
84    unsafe fn agg_var(&self, groups: &GroupsType, _ddof: u8) -> Series {
85        self.0
86            .cast_with_options(&DataType::Float64, CastOptions::Overflowing)
87            .unwrap()
88            .agg_var(groups, _ddof)
89    }
90
91    #[cfg(feature = "bitwise")]
92    unsafe fn agg_and(&self, groups: &GroupsType) -> Series {
93        self.0.agg_and(groups).into_series()
94    }
95    #[cfg(feature = "bitwise")]
96    unsafe fn agg_or(&self, groups: &GroupsType) -> Series {
97        self.0.agg_or(groups).into_series()
98    }
99    #[cfg(feature = "bitwise")]
100    unsafe fn agg_xor(&self, groups: &GroupsType) -> Series {
101        self.0.agg_xor(groups).into_series()
102    }
103
104    #[cfg(feature = "algorithm_group_by")]
105    fn group_tuples(&self, multithreaded: bool, sorted: bool) -> PolarsResult<GroupsType> {
106        IntoGroupsType::group_tuples(&self.0, multithreaded, sorted)
107    }
108
109    fn arg_sort_multiple(
110        &self,
111        by: &[Column],
112        options: &SortMultipleOptions,
113    ) -> PolarsResult<IdxCa> {
114        self.0.arg_sort_multiple(by, options)
115    }
116    fn add_to(&self, rhs: &Series) -> PolarsResult<Series> {
117        NumOpsDispatch::add_to(&self.0, rhs)
118    }
119}
120
121impl SeriesTrait for SeriesWrap<BooleanChunked> {
122    fn rename(&mut self, name: PlSmallStr) {
123        self.0.rename(name);
124    }
125
126    fn chunk_lengths(&self) -> ChunkLenIter<'_> {
127        self.0.chunk_lengths()
128    }
129    fn name(&self) -> &PlSmallStr {
130        self.0.name()
131    }
132
133    fn chunks(&self) -> &Vec<ArrayRef> {
134        self.0.chunks()
135    }
136    unsafe fn chunks_mut(&mut self) -> &mut Vec<ArrayRef> {
137        self.0.chunks_mut()
138    }
139    fn shrink_to_fit(&mut self) {
140        self.0.shrink_to_fit()
141    }
142
143    fn slice(&self, offset: i64, length: usize) -> Series {
144        self.0.slice(offset, length).into_series()
145    }
146    fn split_at(&self, offset: i64) -> (Series, Series) {
147        let (a, b) = self.0.split_at(offset);
148        (a.into_series(), b.into_series())
149    }
150
151    fn append(&mut self, other: &Series) -> PolarsResult<()> {
152        polars_ensure!(self.0.dtype() == other.dtype(), append);
153        self.0.append(other.as_ref().as_ref())?;
154        Ok(())
155    }
156    fn append_owned(&mut self, other: Series) -> PolarsResult<()> {
157        polars_ensure!(self.0.dtype() == other.dtype(), append);
158        self.0.append_owned(other.take_inner())
159    }
160
161    fn extend(&mut self, other: &Series) -> PolarsResult<()> {
162        polars_ensure!(self.0.dtype() == other.dtype(), extend);
163        self.0.extend(other.as_ref().as_ref())?;
164        Ok(())
165    }
166
167    fn filter(&self, filter: &BooleanChunked) -> PolarsResult<Series> {
168        ChunkFilter::filter(&self.0, filter).map(|ca| ca.into_series())
169    }
170
171    fn _sum_as_f64(&self) -> f64 {
172        self.0.sum().unwrap() as f64
173    }
174
175    fn mean(&self) -> Option<f64> {
176        self.0.mean()
177    }
178
179    fn take(&self, indices: &IdxCa) -> PolarsResult<Series> {
180        Ok(self.0.take(indices)?.into_series())
181    }
182
183    unsafe fn take_unchecked(&self, indices: &IdxCa) -> Series {
184        self.0.take_unchecked(indices).into_series()
185    }
186
187    fn take_slice(&self, indices: &[IdxSize]) -> PolarsResult<Series> {
188        Ok(self.0.take(indices)?.into_series())
189    }
190
191    unsafe fn take_slice_unchecked(&self, indices: &[IdxSize]) -> Series {
192        self.0.take_unchecked(indices).into_series()
193    }
194
195    fn deposit(&self, validity: &Bitmap) -> Series {
196        self.0.deposit(validity).into_series()
197    }
198
199    fn len(&self) -> usize {
200        self.0.len()
201    }
202
203    fn rechunk(&self) -> Series {
204        self.0.rechunk().into_owned().into_series()
205    }
206
207    fn new_from_index(&self, index: usize, length: usize) -> Series {
208        ChunkExpandAtIndex::new_from_index(&self.0, index, length).into_series()
209    }
210
211    fn cast(&self, dtype: &DataType, options: CastOptions) -> PolarsResult<Series> {
212        self.0.cast_with_options(dtype, options)
213    }
214
215    #[inline]
216    unsafe fn get_unchecked(&self, index: usize) -> AnyValue<'_> {
217        self.0.get_any_value_unchecked(index)
218    }
219
220    fn sort_with(&self, options: SortOptions) -> PolarsResult<Series> {
221        Ok(ChunkSort::sort_with(&self.0, options).into_series())
222    }
223
224    fn arg_sort(&self, options: SortOptions) -> IdxCa {
225        ChunkSort::arg_sort(&self.0, options)
226    }
227
228    fn null_count(&self) -> usize {
229        self.0.null_count()
230    }
231
232    fn has_nulls(&self) -> bool {
233        self.0.has_nulls()
234    }
235
236    #[cfg(feature = "algorithm_group_by")]
237    fn unique(&self) -> PolarsResult<Series> {
238        ChunkUnique::unique(&self.0).map(|ca| ca.into_series())
239    }
240
241    #[cfg(feature = "algorithm_group_by")]
242    fn n_unique(&self) -> PolarsResult<usize> {
243        ChunkUnique::n_unique(&self.0)
244    }
245
246    #[cfg(feature = "algorithm_group_by")]
247    fn arg_unique(&self) -> PolarsResult<IdxCa> {
248        ChunkUnique::arg_unique(&self.0)
249    }
250
251    fn unique_id(&self) -> PolarsResult<(IdxSize, Vec<IdxSize>)> {
252        ChunkUnique::unique_id(&self.0)
253    }
254
255    fn is_null(&self) -> BooleanChunked {
256        self.0.is_null()
257    }
258
259    fn is_not_null(&self) -> BooleanChunked {
260        self.0.is_not_null()
261    }
262
263    fn reverse(&self) -> Series {
264        ChunkReverse::reverse(&self.0).into_series()
265    }
266
267    fn as_single_ptr(&mut self) -> PolarsResult<usize> {
268        self.0.as_single_ptr()
269    }
270
271    fn shift(&self, periods: i64) -> Series {
272        ChunkShift::shift(&self.0, periods).into_series()
273    }
274
275    fn sum_reduce(&self) -> PolarsResult<Scalar> {
276        Ok(ChunkAggSeries::sum_reduce(&self.0))
277    }
278    fn max_reduce(&self) -> PolarsResult<Scalar> {
279        Ok(ChunkAggSeries::max_reduce(&self.0))
280    }
281    fn min_reduce(&self) -> PolarsResult<Scalar> {
282        Ok(ChunkAggSeries::min_reduce(&self.0))
283    }
284    fn mean_reduce(&self) -> PolarsResult<Scalar> {
285        Ok(Scalar::new(DataType::Float64, self.mean().into()))
286    }
287    fn median_reduce(&self) -> PolarsResult<Scalar> {
288        let ca = self
289            .0
290            .cast_with_options(&DataType::Int8, CastOptions::Overflowing)
291            .unwrap();
292        let sc = ca.median_reduce()?;
293        let v = sc.value().cast(&DataType::Float64);
294        Ok(Scalar::new(DataType::Float64, v))
295    }
296    /// Get the variance of the Series as a new Series of length 1.
297    fn var_reduce(&self, _ddof: u8) -> PolarsResult<Scalar> {
298        let ca = self
299            .0
300            .cast_with_options(&DataType::Int8, CastOptions::Overflowing)
301            .unwrap();
302        let sc = ca.var_reduce(_ddof)?;
303        let v = sc.value().cast(&DataType::Float64);
304        Ok(Scalar::new(DataType::Float64, v))
305    }
306    /// Get the standard deviation of the Series as a new Series of length 1.
307    fn std_reduce(&self, _ddof: u8) -> PolarsResult<Scalar> {
308        let ca = self
309            .0
310            .cast_with_options(&DataType::Int8, CastOptions::Overflowing)
311            .unwrap();
312        let sc = ca.std_reduce(_ddof)?;
313        let v = sc.value().cast(&DataType::Float64);
314        Ok(Scalar::new(DataType::Float64, v))
315    }
316    fn and_reduce(&self) -> PolarsResult<Scalar> {
317        let dt = DataType::Boolean;
318
319        Ok(Scalar::new(
320            dt,
321            self.0
322                .downcast_iter()
323                .filter(|arr| !arr.is_empty())
324                .filter_map(polars_compute::bitwise::BitwiseKernel::reduce_and)
325                .reduce(|a, b| a & b)
326                .map_or(AnyValue::Null, Into::into),
327        ))
328    }
329    fn or_reduce(&self) -> PolarsResult<Scalar> {
330        let dt = DataType::Boolean;
331
332        Ok(Scalar::new(
333            dt,
334            self.0
335                .downcast_iter()
336                .filter(|arr| !arr.is_empty())
337                .filter_map(polars_compute::bitwise::BitwiseKernel::reduce_or)
338                .reduce(|a, b| a | b)
339                .map_or(AnyValue::Null, Into::into),
340        ))
341    }
342    fn xor_reduce(&self) -> PolarsResult<Scalar> {
343        let dt = DataType::Boolean;
344
345        Ok(Scalar::new(
346            dt,
347            self.0
348                .downcast_iter()
349                .filter(|arr| !arr.is_empty())
350                .filter_map(polars_compute::bitwise::BitwiseKernel::reduce_xor)
351                .reduce(|a, b| a ^ b)
352                .map_or(AnyValue::Null, Into::into),
353        ))
354    }
355
356    #[cfg(feature = "approx_unique")]
357    fn approx_n_unique(&self) -> PolarsResult<IdxSize> {
358        Ok(ChunkApproxNUnique::approx_n_unique(&self.0))
359    }
360
361    fn clone_inner(&self) -> Arc<dyn SeriesTrait> {
362        Arc::new(SeriesWrap(Clone::clone(&self.0)))
363    }
364
365    fn find_validity_mismatch(&self, other: &Series, idxs: &mut Vec<IdxSize>) {
366        self.0.find_validity_mismatch(other, idxs)
367    }
368
369    fn as_any(&self) -> &dyn Any {
370        &self.0
371    }
372
373    fn as_any_mut(&mut self) -> &mut dyn Any {
374        &mut self.0
375    }
376
377    fn as_phys_any(&self) -> &dyn Any {
378        &self.0
379    }
380
381    fn as_arc_any(self: Arc<Self>) -> Arc<dyn Any + Send + Sync> {
382        self as _
383    }
384}
polars_core/series/implementations/boolean.rs

polars_core/series/implementations/
boolean.rs
