chore(rust): move moment to ops (#11941)

crates/polars-core/Cargo.toml

@@ -91,7 +91,6 @@ take_opt_iter = []
 group_by_list = []
 # rolling window functions
 rolling_window = []
-moment = []
 diagonal_concat = []
 horizontal_concat = []
 abs = []
@@ -145,7 +144,6 @@ docs-selection = [
   "dot_product",
   "row_hash",
   "rolling_window",
-  "moment",
   "dtype-categorical",
   "dtype-decimal",
   "diagonal_concat",

crates/polars-core/src/series/ops/mod.rs

@@ -1,7 +1,5 @@
 mod downcast;
 mod extend;
-#[cfg(feature = "moment")]
-pub mod moment;
 mod null;
 mod to_list;
 mod unique;

crates/polars-ops/Cargo.toml

@@ -93,7 +93,7 @@ hash = []
 zip_with = ["polars-core/zip_with"]
 group_by_list = ["polars-core/group_by_list"]
 rolling_window = ["polars-core/rolling_window"]
-moment = ["polars-core/moment"]
+moment = []
 mode = []
 search_sorted = []
 merge_sorted = []

crates/polars-ops/src/series/ops/mod.rs

@@ -28,6 +28,8 @@ mod is_last_distinct;
 mod is_unique;
 #[cfg(feature = "log")]
 mod log;
+#[cfg(feature = "moment")]
+mod moment;
 #[cfg(feature = "pct_change")]
 mod pct_change;
 #[cfg(feature = "rank")]
@@ -74,6 +76,8 @@ pub use is_last_distinct::*;
 pub use is_unique::*;
 #[cfg(feature = "log")]
 pub use log::*;
+#[cfg(feature = "moment")]
+pub use moment::*;
 #[cfg(feature = "pct_change")]
 pub use pct_change::*;
 use polars_core::prelude::*;

crates/polars-core/src/series/ops/moment.rs → crates/polars-ops/src/series/ops/moment.rs

@@ -1,4 +1,6 @@
-use crate::prelude::*;
+use polars_core::prelude::*;
+
+use crate::prelude::SeriesSealed;
 
 fn moment_precomputed_mean(s: &Series, moment: usize, mean: f64) -> PolarsResult<Option<f64>> {
     // see: https://github.com/scipy/scipy/blob/47bb6febaa10658c72962b9615d5d5aa2513fa3a/scipy/stats/stats.py#L922
@@ -39,7 +41,7 @@ fn moment_precomputed_mean(s: &Series, moment: usize, mean: f64) -> PolarsResult
     Ok(out)
 }
 
-impl Series {
+pub trait MomentSeries: SeriesSealed {
     /// Compute the sample skewness of a data set.
     ///
     /// For normally distributed data, the skewness should be about zero. For
@@ -49,19 +51,21 @@ impl Series {
     /// is close enough to zero, statistically speaking.
     ///
     /// see: [scipy](https://github.com/scipy/scipy/blob/47bb6febaa10658c72962b9615d5d5aa2513fa3a/scipy/stats/stats.py#L1024)
-    pub fn skew(&self, bias: bool) -> PolarsResult<Option<f64>> {
-        let mean = match self.mean() {
+    fn skew(&self, bias: bool) -> PolarsResult<Option<f64>> {
+        let s = self.as_series();
+
+        let mean = match s.mean() {
             Some(mean) => mean,
             None => return Ok(None),
         };
         // we can unwrap because if it were None, we already return None above
-        let m2 = moment_precomputed_mean(self, 2, mean)?.unwrap();
-        let m3 = moment_precomputed_mean(self, 3, mean)?.unwrap();
+        let m2 = moment_precomputed_mean(s, 2, mean)?.unwrap();
+        let m3 = moment_precomputed_mean(s, 3, mean)?.unwrap();
 
         let out = m3 / m2.powf(1.5);
 
         if !bias {
-            let n = (self.len() - self.null_count()) as f64;
+            let n = (s.len() - s.null_count()) as f64;
             Ok(Some(((n - 1.0) * n).sqrt() / (n - 2.0) * out))
         } else {
             Ok(Some(out))
@@ -77,17 +81,19 @@ impl Series {
     /// eliminate bias coming from biased moment estimators
     ///
     /// see: [scipy](https://github.com/scipy/scipy/blob/47bb6febaa10658c72962b9615d5d5aa2513fa3a/scipy/stats/stats.py#L1027)
-    pub fn kurtosis(&self, fisher: bool, bias: bool) -> PolarsResult<Option<f64>> {
-        let mean = match self.mean() {
+    fn kurtosis(&self, fisher: bool, bias: bool) -> PolarsResult<Option<f64>> {
+        let s = self.as_series();
+
+        let mean = match s.mean() {
             Some(mean) => mean,
             None => return Ok(None),
         };
         // we can unwrap because if it were None, we already return None above
-        let m2 = moment_precomputed_mean(self, 2, mean)?.unwrap();
-        let m4 = moment_precomputed_mean(self, 4, mean)?.unwrap();
+        let m2 = moment_precomputed_mean(s, 2, mean)?.unwrap();
+        let m4 = moment_precomputed_mean(s, 4, mean)?.unwrap();
 
         let out = if !bias {
-            let n = (self.len() - self.null_count()) as f64;
+            let n = (s.len() - s.null_count()) as f64;
             3.0 + 1.0 / (n - 2.0) / (n - 3.0)
                 * ((n.powf(2.0) - 1.0) * m4 / m2.powf(2.0) - 3.0 * (n - 1.0).powf(2.0))
         } else {
@@ -101,27 +107,27 @@ impl Series {
     }
 }
 
+impl MomentSeries for Series {}
+
 #[cfg(test)]
 mod test {
     use super::*;
 
-    impl Series {
-        fn moment(&self, moment: usize) -> PolarsResult<Option<f64>> {
-            match self.mean() {
-                Some(mean) => moment_precomputed_mean(self, moment, mean),
-                None => Ok(None),
-            }
+    fn moment(s: &Series, moment: usize) -> PolarsResult<Option<f64>> {
+        match s.mean() {
+            Some(mean) => moment_precomputed_mean(s, moment, mean),
+            None => Ok(None),
         }
     }
 
     #[test]
     fn test_moment_compute() -> PolarsResult<()> {
         let s = Series::new("", &[1, 2, 3, 4, 5, 23]);
 
-        assert_eq!(s.moment(0)?, Some(1.0));
-        assert_eq!(s.moment(1)?, Some(0.0));
-        assert!((s.moment(2)?.unwrap() - 57.22222222222223).abs() < 0.00001);
-        assert!((s.moment(3)?.unwrap() - 724.0740740740742).abs() < 0.00001);
+        assert_eq!(moment(&s, 0)?, Some(1.0));
+        assert_eq!(moment(&s, 1)?, Some(0.0));
+        assert!((moment(&s, 2)?.unwrap() - 57.22222222222223).abs() < 0.00001);
+        assert!((moment(&s, 3)?.unwrap() - 724.0740740740742).abs() < 0.00001);
 
         Ok(())
     }

crates/polars-ops/src/series/ops/rolling.rs

@@ -1,6 +1,7 @@
 use polars_core::prelude::*;
 #[cfg(feature = "moment")]
 use {
+    crate::series::ops::moment::MomentSeries,
     polars_core::export::num::{self, Float, FromPrimitive},
     polars_core::utils::with_unstable_series,
     std::ops::SubAssign,

crates/polars-plan/Cargo.toml

@@ -109,7 +109,7 @@ rolling_window = [
 rank = ["polars-ops/rank"]
 diff = ["polars-ops/diff"]
 pct_change = ["polars-ops/pct_change"]
-moment = ["polars-core/moment", "polars-ops/moment"]
+moment = ["polars-ops/moment"]
 abs = ["polars-core/abs"]
 random = ["polars-core/random"]
 dynamic_group_by = ["polars-core/dynamic_group_by"]

crates/polars/Cargo.toml

@@ -145,7 +145,7 @@ interpolate = ["polars-ops/interpolate", "polars-lazy?/interpolate"]
 rank = ["polars-lazy?/rank", "polars-ops/rank"]
 diff = ["polars-ops/diff", "polars-lazy?/diff"]
 pct_change = ["polars-ops/pct_change", "polars-lazy?/pct_change"]
-moment = ["polars-core/moment", "polars-lazy?/moment", "polars-ops/moment"]
+moment = ["polars-ops/moment", "polars-lazy?/moment"]
 range = ["polars-lazy?/range"]
 true_div = ["polars-lazy?/true_div"]
 diagonal_concat = ["polars-core/diagonal_concat", "polars-lazy?/diagonal_concat", "polars-sql?/diagonal_concat"]