Add temporal horizontal mean

pola-rs · Dec 24, 2024 · e26138a · e26138a
1 parent 7fe70da
commit e26138a
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Showing 3 changed files with 127 additions and 18 deletions.
diff --git a/.github/workflows/release-python.yml b/.github/workflows/release-python.yml
@@ -385,7 +385,7 @@ jobs:
           merge-multiple: true
 
       - name: Remove Emscripten wheel
-        run: rm -f dist/*emscripten-wasm32*.whl
+        run: rm -f dist/*emscripten*.whl
 
       - name: Publish to PyPI
         if: inputs.dry-run == false

diff --git a/crates/polars-ops/src/series/ops/horizontal.rs b/crates/polars-ops/src/series/ops/horizontal.rs
@@ -1,5 +1,6 @@
 use std::borrow::Cow;
 
+use arrow::temporal_conversions::MILLISECONDS_IN_DAY;
 use polars_core::chunked_array::cast::CastOptions;
 use polars_core::prelude::*;
 use polars_core::series::arithmetic::coerce_lhs_rhs;
@@ -267,24 +268,67 @@ pub fn mean_horizontal(
 ) -> PolarsResult<Option<Column>> {
     validate_column_lengths(columns)?;
 
-    let (numeric_columns, non_numeric_columns): (Vec<_>, Vec<_>) = columns.iter().partition(|s| {
-        let dtype = s.dtype();
-        dtype.is_numeric() || dtype.is_decimal() || dtype.is_bool() || dtype.is_null()
-    });
+    let first_dtype = columns[0].dtype();
+    let is_temporal = first_dtype.is_temporal();
+    let columns = if is_temporal {
+        // All remaining must be the same temporal dtype.
+        for col in &columns[1..] {
+            if col.dtype() != first_dtype {
+                polars_bail!(
+                    InvalidOperation: "'horizontal_mean' expects all numeric or all temporal expressions, found {:?} (dtype={}) and {:?} (dtype={})",
+                    columns[0].name(),
+                    first_dtype,
+                    col.name(),
+                    col.dtype(),
+                );
+            };
+        }
 
-    if !non_numeric_columns.is_empty() {
-        let col = non_numeric_columns.first().cloned();
+        // Convert to physical
+        columns
+            .into_iter()
+            .map(|c| c.cast(&DataType::Int64).unwrap())
+            .collect::<Vec<_>>()
+    } else if first_dtype.is_numeric()
+        || first_dtype.is_decimal()
+        || first_dtype.is_bool()
+        || first_dtype.is_null()
+        || first_dtype.is_temporal()
+    {
+        // All remaining must be numeric.
+        for col in &columns[1..] {
+            let dtype = col.dtype();
+            if !(dtype.is_numeric()
+                || dtype.is_decimal()
+                || dtype.is_bool()
+                || dtype.is_null()
+                || dtype.is_temporal())
+            {
+                polars_bail!(
+                    InvalidOperation: "'horizontal_mean' expects all numeric or all temporal expressions, found {:?} (dtype={}) and {:?} (dtype={})",
+                    columns[0].name(),
+                    first_dtype,
+                    col.name(),
+                    dtype,
+                );
+            }
+        }
+        columns
+            .into_iter()
+            .map(|s| s.cast(&DataType::Int64).unwrap().into_column())
+            .collect::<Vec<_>>()
+    } else {
         polars_bail!(
-            InvalidOperation: "'horizontal_mean' expects numeric expressions, found {:?} (dtype={})",
-            col.unwrap().name(),
-            col.unwrap().dtype(),
+            InvalidOperation: "'horizontal_mean' expects all numeric or all temporal expressions, found {:?} (dtype={})",
+            columns[0].name(),
+            first_dtype,
         );
-    }
-    let columns = numeric_columns.into_iter().cloned().collect::<Vec<_>>();
+    };
+
     match columns.len() {
         0 => Ok(None),
         1 => Ok(Some(match columns[0].dtype() {
-            dt if dt != &DataType::Float32 && !dt.is_decimal() => {
+            dt if dt != &DataType::Float32 && !dt.is_temporal() && !dt.is_decimal() => {
                 columns[0].cast(&DataType::Float64)?
             },
             _ => columns[0].clone(),
@@ -331,8 +375,26 @@ pub fn mean_horizontal(
                 .into_column()
                 .cast(&DataType::Float64)?;
 
-            sum.map(|sum| std::ops::Div::div(&sum, &value_length))
-                .transpose()
+            let out = sum.map(|sum| std::ops::Div::div(&sum, &value_length));
+
+            let x = out.map(|opt| {
+                opt.and_then(|value| {
+                    if is_temporal {
+                        if first_dtype == &DataType::Date {
+                            // Cast to DateTime(us)
+                            (value * MILLISECONDS_IN_DAY)
+                                .cast(&DataType::Datetime(TimeUnit::Milliseconds, None))
+                        } else {
+                            // Cast to original
+                            value.cast(&first_dtype)
+                        }
+                    } else {
+                        Ok(value)
+                    }
+                })
+            });
+
+            x.transpose()
         },
     }
 }

diff --git a/py-polars/tests/unit/operations/aggregation/test_horizontal.py b/py-polars/tests/unit/operations/aggregation/test_horizontal.py
@@ -1,7 +1,7 @@
 from __future__ import annotations
 
-import datetime
 from collections import OrderedDict
+from datetime import date, datetime, time, timedelta
 from typing import TYPE_CHECKING, Any
 
 import pytest
@@ -12,7 +12,7 @@
 from polars.testing import assert_frame_equal, assert_series_equal
 
 if TYPE_CHECKING:
-    from polars._typing import PolarsDataType
+    from polars._typing import PolarsDataType, TimeUnit
 
 
 def test_any_expr(fruits_cars: pl.DataFrame) -> None:
@@ -340,7 +340,7 @@ def test_sum_dtype_12028() -> None:
         [
             pl.Series(
                 "sum_duration",
-                [datetime.timedelta(seconds=10)],
+                [timedelta(seconds=10)],
                 dtype=pl.Duration(time_unit="us"),
             ),
         ]
@@ -442,6 +442,53 @@ def test_mean_horizontal_all_null() -> None:
     assert_frame_equal(result, expected)
 
 
+@pytest.mark.parametrize("tz", [None, "UTC", "Asia/Kathmandu"])
+@pytest.mark.parametrize("tu", ["ms", "us", "ns"])
+def test_mean_horizontal_temporal(tu: TimeUnit, tz: str) -> None:
+    dt1 = [date(2024, 1, 1), date(2024, 1, 3)]
+    dt2 = [date(2024, 1, 2), date(2024, 1, 4)]
+    dur1 = [timedelta(hours=1), timedelta(hours=3)]
+    dur2 = [timedelta(hours=2), timedelta(hours=4)]
+    lf = pl.LazyFrame(
+        {
+            "date1": pl.Series(dt1, dtype=pl.Date),
+            "date2": pl.Series(dt2, dtype=pl.Date),
+            "datetime1": pl.Series(dt1, dtype=pl.Datetime(time_unit=tu, time_zone=tz)),
+            "datetime2": pl.Series(dt2, dtype=pl.Datetime(time_unit=tu, time_zone=tz)),
+            "time1": [time(1), time(3)],
+            "time2": [time(2), time(4)],
+            "duration1": pl.Series(dur1, dtype=pl.Duration(time_unit=tu)),
+            "duration2": pl.Series(dur2, dtype=pl.Duration(time_unit=tu)),
+        }
+    )
+    out = lf.select(
+        pl.mean_horizontal("date1", "date2").alias("date"),
+        pl.mean_horizontal("datetime1", "datetime2").alias("datetime"),
+        pl.mean_horizontal("time1", "time2").alias("time"),
+        pl.mean_horizontal("duration1", "duration2").alias("duration"),
+    ).collect()
+
+    expected = pl.DataFrame(
+        {
+            "date": pl.Series(
+                [datetime(2024, 1, 1, 12), datetime(2024, 1, 3, 12)],
+                dtype=pl.Datetime("ms"),
+            ),
+            "datetime": pl.Series(
+                [datetime(2024, 1, 1, 12), datetime(2024, 1, 3, 12)],
+                dtype=pl.Datetime(tu, tz),
+            ),
+            "time": [time(hour=1, minute=30), time(hour=3, minute=30)],
+            "duration": pl.Series(
+                [timedelta(hours=1, minutes=30), timedelta(hours=3, minutes=30)],
+                dtype=pl.Duration(time_unit=tu),
+            ),
+        }
+    )
+
+    assert_frame_equal(out, expected)
+
+
 @pytest.mark.parametrize(
     ("in_dtype", "out_dtype"),
     [