Implemented bindings for ceil timestamp operation

python/cudf/cudf/_lib/cpp/datetime.pxd

@@ -12,6 +12,17 @@ cdef extern from "cudf/datetime.hpp" namespace "cudf::datetime" nogil:
     cdef unique_ptr[column] extract_hour(const column_view& column) except +
     cdef unique_ptr[column] extract_minute(const column_view& column) except +
     cdef unique_ptr[column] extract_second(const column_view& column) except +
+    cdef unique_ptr[column] ceil_day(const column_view& column) except +
+    cdef unique_ptr[column] ceil_hour(const column_view& column) except +
+    cdef unique_ptr[column] ceil_minute(const column_view& column) except +
+    cdef unique_ptr[column] ceil_second(const column_view& column) except +
+    cdef unique_ptr[column] ceil_millisecond(
+        const column_view& column
+    ) except +
+    cdef unique_ptr[column] ceil_microsecond(
+        const column_view& column
+    ) except +
+    cdef unique_ptr[column] ceil_nanosecond(const column_view& column) except +
     cdef unique_ptr[column] add_calendrical_months(
         const column_view& timestamps,
         const column_view& months

python/cudf/cudf/_lib/datetime.pyx

@@ -59,6 +59,33 @@ def extract_datetime_component(Column col, object field):
     return result
 
 
+def ceil_datetime(Column col, object field):
+    cdef unique_ptr[column] c_result
+    cdef column_view col_view = col.view()
+
+    with nogil:
+        # https://pandas.pydata.org/pandas-docs/version/0.25.0/reference/api/pandas.Timedelta.resolution.html
+        if field == "D":
+            c_result = move(libcudf_datetime.ceil_day(col_view))
+        elif field == "H":
+            c_result = move(libcudf_datetime.ceil_hour(col_view))
+        elif field == "T":
+            c_result = move(libcudf_datetime.ceil_minute(col_view))
+        elif field == "S":
+            c_result = move(libcudf_datetime.ceil_second(col_view))
+        elif field == "L":
+            c_result = move(libcudf_datetime.ceil_millisecond(col_view))
+        elif field == "U":
+            c_result = move(libcudf_datetime.ceil_microsecond(col_view))
+        elif field == "N":
+            c_result = move(libcudf_datetime.ceil_nanosecond(col_view))
+        else:
+            raise ValueError(f"Invalid resolution: '{field}'")
+
+    result = Column.from_unique_ptr(move(c_result))
+    return result
+
+
 def is_leap_year(Column col):
     """Returns a boolean indicator whether the year of the date is a leap year
     """

python/cudf/cudf/core/column/datetime.py

@@ -170,6 +170,9 @@ def values(self):
     def get_dt_field(self, field: str) -> ColumnBase:
         return libcudf.datetime.extract_datetime_component(self, field)
 
+    def ceil(self, field: str) -> ColumnBase:
+        return libcudf.datetime.ceil_datetime(self, field)
+
     def normalize_binop_value(self, other: DatetimeLikeScalar) -> ScalarLike:
         if isinstance(other, cudf.Scalar):
             return other

python/cudf/cudf/core/series.py

@@ -5923,6 +5923,13 @@ def _get_dt_field(self, field):
             data=out_column, index=self.series._index, name=self.series.name
         )
 
+    def ceil(self, field):
+        out_column = self.series._column.ceil(field)
+
+        return Series(
+            data=out_column, index=self.series._index, name=self.series.name
+        )
+
     def strftime(self, date_format, *args, **kwargs):
         """
         Convert to Series using specified ``date_format``.

python/cudf/cudf/tests/test_datetime.py

@@ -1551,3 +1551,32 @@ def test_error_values():
         match="DateTime Arrays is not yet implemented in cudf",
     ):
         s.values
+
+
+@pytest.mark.parametrize(
+    "data",
+    [
+        (
+            [
+                "2020-05-31 08:00:00",
+                "1999-12-31 18:40:10",
+                "2000-12-31 04:00:05",
+                "1900-02-28 07:00:06",
+                "1800-03-14 07:30:20",
+                "2100-03-14 07:30:20",
+                "1970-01-01 00:00:09",
+                "1969-12-31 12:59:10",
+            ]
+        )
+    ],
+)
+@pytest.mark.parametrize("time_type", DATETIME_TYPES)
+@pytest.mark.parametrize("resolution", ["D", "H", "T", "S", "L", "U", "N"])
+def test_ceil(data, time_type, resolution):
+
+    ps = pd.Series(data, dtype=time_type)
+    gs = cudf.from_pandas(ps)
+
+    expect = ps.dt.ceil(resolution)
+    got = gs.dt.ceil(resolution)
+    assert_eq(expect, got)