Align date_range defaults with pandas, support tz (#15139)

Precursor to #15116

* Aligns `date_range` signature with pandas, _technically_ an API breakage with `closed` changing defaults even though it still isn't supported
* Copies pandas behavior of allowing `date_range` with just two of `start/end/periods`
* Supports `tz` arg now

Authors:
  - Matthew Roeschke (https://github.com/mroeschke)

Approvers:
  - GALI PREM SAGAR (https://github.com/galipremsagar)

URL: #15139

python/cudf/cudf/core/tools/datetimes.py

@@ -799,9 +799,11 @@ def date_range(
     periods=None,
     freq=None,
     tz=None,
-    normalize=False,
+    normalize: bool = False,
     name=None,
-    closed=None,
+    closed: Literal["left", "right", "both", "neither"] = "both",
+    *,
+    unit: Optional[str] = None,
 ):
     """Return a fixed frequency DatetimeIndex.
 
@@ -837,8 +839,13 @@ def date_range(
     name : str, default None
         Name of the resulting DatetimeIndex
 
-    closed : {None, 'left', 'right'}, optional
-        Not Supported
+    closed : {"left", "right", "both", "neither"}, default "both"
+        Whether to set each bound as closed or open.
+        Currently only "both" is supported
+
+    unit : str, default None
+        Specify the desired resolution of the result. Currently
+        not supported.
 
     Returns
     -------
@@ -875,11 +882,15 @@ def date_range(
                 '2026-04-23 08:00:00'],
                 dtype='datetime64[ns]')
     """
-    if tz is not None:
-        raise NotImplementedError("tz is currently unsupported.")
+    if closed != "both":
+        raise NotImplementedError(f"{closed=} is currently unsupported.")
+    if unit is not None:
+        raise NotImplementedError(f"{unit=} is currently unsupported.")
+    if normalize is not False:
+        raise NotImplementedError(f"{normalize=} is currently unsupported.")
 
-    if closed is not None:
-        raise NotImplementedError("closed is currently unsupported.")
+    if freq is None and any(arg is None for arg in (start, end, periods)):
+        freq = "D"
 
     if (start, end, periods, freq).count(None) > 1:
         raise ValueError(
@@ -894,7 +905,7 @@ def date_range(
             FutureWarning,
         )
 
-    dtype = np.dtype("<M8[ns]")
+    dtype = np.dtype("datetime64[ns]")
 
     if freq is None:
         # `start`, `end`, `periods` is specified, we treat the timestamps as
@@ -903,7 +914,7 @@ def date_range(
         end = cudf.Scalar(end, dtype=dtype).value.astype("int64")
         arr = cp.linspace(start=start, stop=end, num=periods)
         result = cudf.core.column.as_column(arr).astype("datetime64[ns]")
-        return cudf.DatetimeIndex._from_data({name: result})
+        return cudf.DatetimeIndex._from_data({name: result}).tz_localize(tz)
 
     # The code logic below assumes `freq` is defined. It is first normalized
     # into `DateOffset` for further computation with timestamps.
@@ -912,8 +923,8 @@ def date_range(
         offset = freq
     elif isinstance(freq, str):
         offset = pd.tseries.frequencies.to_offset(freq)
-        if not isinstance(offset, pd.tseries.offsets.Tick) and not isinstance(
-            offset, pd.tseries.offsets.Week
+        if not isinstance(
+            offset, (pd.tseries.offsets.Tick, pd.tseries.offsets.Week)
         ):
             raise ValueError(
                 f"Unrecognized frequency string {freq}. cuDF does "
@@ -923,7 +934,7 @@ def date_range(
     else:
         raise TypeError("`freq` must be a `str` or cudf.DateOffset object.")
 
-    if _has_mixed_freqeuency(offset):
+    if _has_fixed_frequency(offset) and _has_non_fixed_frequency(offset):
         raise NotImplementedError(
             "Mixing fixed and non-fixed frequency offset is unsupported."
         )
@@ -1001,7 +1012,9 @@ def date_range(
         arr = cp.arange(start=start, stop=stop, step=step, dtype="int64")
         res = cudf.core.column.as_column(arr).astype("datetime64[ns]")
 
-    return cudf.DatetimeIndex._from_data({name: res}, freq=freq)
+    return cudf.DatetimeIndex._from_data({name: res}, freq=freq).tz_localize(
+        tz
+    )
 
 
 def _has_fixed_frequency(freq: DateOffset) -> bool:
@@ -1026,14 +1039,6 @@ def _has_non_fixed_frequency(freq: DateOffset) -> bool:
     return len(freq.kwds.keys() & non_fixed_frequencies) > 0
 
 
-def _has_mixed_freqeuency(freq: DateOffset) -> bool:
-    """Utility to determine if `freq` contains mixed fixed and non-fixed
-    frequency offset. e.g. {months=1, days=5}
-    """
-
-    return _has_fixed_frequency(freq) and _has_non_fixed_frequency(freq)
-
-
 def _offset_to_nanoseconds_lower_bound(offset: DateOffset) -> int:
     """Given a DateOffset, which can consist of either fixed frequency or
     non-fixed frequency offset, convert to the smallest possible fixed

python/cudf/cudf/tests/test_datetime.py

@@ -2357,3 +2357,19 @@ def test_timezone_array_notimplemented():
 def test_to_datetime_errors_ignore_deprecated():
     with pytest.warns(FutureWarning):
         cudf.to_datetime("2001-01-01 00:04:45", errors="ignore")
+
+
+def test_date_range_freq_default():
+    result = pd.date_range("2020-01-01", periods=2, name="foo")
+    expected = cudf.date_range("2020-01-01", periods=2, name="foo")
+    assert_eq(result, expected)
+
+
+def test_date_range_tz():
+    result = pd.date_range("2020-01-01", periods=2, tz="UTC")
+    expected = cudf.date_range("2020-01-01", periods=2, tz="UTC")
+    assert_eq(result, expected)
+
+    result = pd.date_range("2020-01-01", "2020-01-02", periods=2, tz="UTC")
+    expected = cudf.date_range("2020-01-01", "2020-01-02", periods=2, tz="UTC")
+    assert_eq(result, expected)