Add cudf::calendrical_month_sequence to pylibcudf

python/cudf/cudf/_lib/datetime.pyx

@@ -10,13 +10,12 @@ from libcpp.utility cimport move
 cimport pylibcudf.libcudf.datetime as libcudf_datetime
 from pylibcudf.libcudf.column.column cimport column
 from pylibcudf.libcudf.column.column_view cimport column_view
-from pylibcudf.libcudf.filling cimport calendrical_month_sequence
-from pylibcudf.libcudf.scalar.scalar cimport scalar
 from pylibcudf.libcudf.types cimport size_type
 from pylibcudf.datetime import DatetimeComponent
 
 from cudf._lib.column cimport Column
 from cudf._lib.scalar cimport DeviceScalar
+import pylibcudf as plc
 
 
 @acquire_spill_lock()
@@ -177,20 +176,17 @@ def is_leap_year(Column col):
 
 @acquire_spill_lock()
 def date_range(DeviceScalar start, size_type n, offset):
-    cdef unique_ptr[column] c_result
     cdef size_type months = (
         offset.kwds.get("years", 0) * 12
         + offset.kwds.get("months", 0)
     )
-
-    cdef const scalar* c_start = start.get_raw_ptr()
-    with nogil:
-        c_result = move(calendrical_month_sequence(
+    return Column.from_pylibcudf(
+        plc.filling.calendrical_month_sequence(
             n,
-            c_start[0],
-            months
-        ))
-    return Column.from_unique_ptr(move(c_result))
+            start.c_value,
+            months,
+        )
+    )
 
 
 @acquire_spill_lock()

python/pylibcudf/pylibcudf/filling.pxd

@@ -33,3 +33,9 @@ cpdef Table repeat(
     Table input_table,
     ColumnOrSize count
 )
+
+cpdef Column calendrical_month_sequence(
+    size_type n,
+    Scalar init,
+    size_type months,
+)

python/pylibcudf/pylibcudf/filling.pyx

@@ -9,6 +9,7 @@ from pylibcudf.libcudf.filling cimport (
     fill_in_place as cpp_fill_in_place,
     repeat as cpp_repeat,
     sequence as cpp_sequence,
+    calendrical_month_sequence as cpp_calendrical_month_sequence
 )
 from pylibcudf.libcudf.table.table cimport table
 from pylibcudf.libcudf.types cimport size_type
@@ -164,3 +165,39 @@ cpdef Table repeat(
                 count
             )
     return Table.from_libcudf(move(result))
+
+
+cpdef Column calendrical_month_sequence(
+    size_type n,
+    Scalar init,
+    size_type months,
+):
+
+    """Fill destination column from begin to end with value.
+
+    For details, see :cpp:func:`calendrical_month_sequence`.
+
+    Parameters
+    ----------
+    n : size_type
+        Number of timestamps to generate
+    init : Scalar
+        The initial timestamp
+    months : size_type
+        Months to increment
+
+    Returns
+    -------
+    pylibcudf.Column
+        Timestamps column with sequences of months
+    """
+
+    cdef unique_ptr[column] c_result
+
+    with nogil:
+        c_result = cpp_calendrical_month_sequence(
+            n,
+            dereference(init.c_obj),
+            months
+        )
+    return Column.from_libcudf(move(c_result))

python/pylibcudf/pylibcudf/tests/test_filling.py

@@ -0,0 +1,85 @@
+# Copyright (c) 2024, NVIDIA CORPORATION.
+
+import pandas as pd
+import pyarrow as pa
+import pytest
+from utils import assert_column_eq, assert_table_eq
+
+import pylibcudf as plc
+
+
+@pytest.fixture
+def pa_col():
+    return pa.array([2, 3, 5, 7, 11])
+
+
+@pytest.fixture
+def pa_table():
+    pa_col = pa.array([1, 2, 3])
+    return pa.table([pa_col], names=["a"])
+
+
+def test_fill(pa_col):
+    result = plc.filling.fill(
+        plc.interop.from_arrow(pa_col),
+        1,
+        3,
+        plc.interop.from_arrow(pa.scalar(5)),
+    )
+    expect = pa.array([2, 5, 5, 7, 11])
+    assert_column_eq(result, expect)
+
+
+def test_fill_in_place(pa_col):
+    result = plc.interop.from_arrow(pa_col)
+    plc.filling.fill_in_place(
+        result,
+        1,
+        3,
+        plc.interop.from_arrow(pa.scalar(5)),
+    )
+    expect = pa.array([2, 5, 5, 7, 11])
+    assert_column_eq(result, expect)
+
+
+def test_sequence():
+    size = 5
+    init_scalar = plc.interop.from_arrow(pa.scalar(10))
+    step_scalar = plc.interop.from_arrow(pa.scalar(2))
+    result = plc.filling.sequence(
+        size,
+        init_scalar,
+        step_scalar,
+    )
+    expect = pa.array([10, 12, 14, 16, 18])
+    assert_column_eq(result, expect)
+
+
+def test_repeat_with_count_int(pa_table):
+    input_table = plc.interop.from_arrow(pa_table)
+    count = 2
+    result = plc.filling.repeat(input_table, count)
+    expect = pa.table([[1, 1, 2, 2, 3, 3]], names=["a"])
+    assert_table_eq(expect, result)
+
+
+def test_repeat_with_count_column(pa_table):
+    input_table = plc.interop.from_arrow(pa_table)
+    count = plc.interop.from_arrow(pa.array([1, 2, 3]))
+    result = plc.filling.repeat(input_table, count)
+    expect = pa.table([[1] + [2] * 2 + [3] * 3], names=["a"])
+    assert_table_eq(expect, result)
+
+
+def test_calendrical_month_sequence():
+    n = 5
+    init = plc.interop.from_arrow(
+        pa.scalar(pd.Timestamp("2020-01-31"), type=pa.timestamp("ms"))
+    )
+    months = 1
+    result = plc.filling.calendrical_month_sequence(n, init, months)
+    expected_dates = pd.to_datetime(
+        ["2020-01-31", "2020-02-29", "2020-03-31", "2020-04-30", "2020-05-31"]
+    )
+    expect = pa.array(expected_dates, type=pa.timestamp("ms"))
+    assert_column_eq(result, expect)