fix panic in dynamic groupby of single groups

polars/polars-time/src/bounds.rs

@@ -17,21 +17,10 @@ impl Display for Bounds {
     }
 }
 
-// Get a wrapping boundary form a slice of nanoseconds.
-// E.g. the first and last value are the start and stop of the boundary
-impl<S: AsRef<[i64]>> From<S> for Bounds {
-    fn from(s: S) -> Self {
-        let slice = s.as_ref();
-        let start = slice[0];
-        let stop = slice[slice.len() - 1];
-        Self::new(start, stop)
-    }
-}
-
 impl Bounds {
     pub fn new(start: TimeNanoseconds, stop: TimeNanoseconds) -> Self {
         assert!(
-            start < stop,
+            start <= stop,
             "boundary start must be smaller than stop; is your time column sorted in ascending order?"
         );
         Bounds { start, stop }

polars/polars-time/src/groupby.rs

@@ -18,7 +18,15 @@ pub fn groupby(
     include_boundaries: bool,
     closed_window: ClosedWindow,
 ) -> (GroupTuples, Vec<TimeNanoseconds>, Vec<TimeNanoseconds>) {
-    let boundary = Bounds::from(time);
+    let start = time[0];
+    let boundary = if time.len() > 1 {
+        let stop = time[time.len() - 1];
+        Bounds::new(start, stop)
+    } else {
+        let stop = start + 1;
+        Bounds::new(start, stop)
+    };
+
     let size = if include_boundaries {
         window.estimate_overlapping_bounds(boundary)
     } else {

py-polars/tests/test_datelike.py

@@ -272,3 +272,28 @@ def test_truncate_negative_offset() -> None:
         datetime(2021, 4, 1),
         datetime(2021, 5, 1),
     ]
+    df = pl.DataFrame(
+        {
+            "event_date": [
+                datetime(2021, 4, 11),
+                datetime(2021, 4, 29),
+                datetime(2021, 5, 29),
+            ],
+            "adm1_code": [1, 2, 1],
+            "five_type": ["a", "b", "a"],
+            "actor": ["a", "a", "a"],
+            "admin": ["a", "a", "a"],
+            "fatalities": [10, 20, 30],
+        }
+    )
+
+    out = df.groupby_dynamic(
+        time_column="event_date",
+        every="1mo",
+        by=["admin", "five_type", "actor"],
+    ).agg([pl.col("adm1_code").unique(), (pl.col("fatalities") > 0).sum()])
+    assert out["event_date"].to_list() == [
+        datetime(2021, 4, 1),
+        datetime(2021, 5, 1),
+        datetime(2021, 4, 1),
+    ]