clib.converison._to_numpy: Add tests for pandas.Series with datetime …

…dtypes (#3670)

pygmt/clib/conversion.py

@@ -192,6 +192,17 @@ def _to_numpy(data: Any) -> np.ndarray:
                 numpy_dtype = np.float64
             data = data.to_numpy(na_value=np.nan)
 
+    # Deal with timezone-aware datetime dtypes.
+    if isinstance(dtype, pd.DatetimeTZDtype):  # pandas.DatetimeTZDtype
+        numpy_dtype = getattr(dtype, "base", None)
+    elif isinstance(dtype, pd.ArrowDtype) and hasattr(dtype.pyarrow_dtype, "tz"):
+        # pd.ArrowDtype[pa.Timestamp]
+        numpy_dtype = getattr(dtype, "numpy_dtype", None)
+        # TODO(pandas>=2.1): Remove the workaround for pandas<2.1.
+        if Version(pd.__version__) < Version("2.1"):
+            # In pandas 2.0, dtype.numpy_type is dtype("O").
+            numpy_dtype = np.dtype(f"M8[{dtype.pyarrow_dtype.unit}]")  # type: ignore[assignment, attr-defined]
+
     array = np.ascontiguousarray(data, dtype=numpy_dtype)
 
     # Check if a np.object_ array can be converted to np.str_.

pygmt/tests/test_clib_to_numpy.py

@@ -365,6 +365,113 @@ def test_to_numpy_pandas_date(dtype, expected_dtype):
     )
 
 
+pandas_old_version = pytest.mark.xfail(
+    condition=Version(pd.__version__) < Version("2.1"),
+    reason="pandas 2.0 bug reported in https://github.com/pandas-dev/pandas/issues/52705",
+)
+
+
+@pytest.mark.parametrize(
+    ("dtype", "expected_dtype"),
+    [
+        # NumPy datetime64 types. Only unit 's'/'ms'/'us'/'ns' are supported.
+        pytest.param("datetime64[s]", "datetime64[s]", id="datetime64[s]"),
+        pytest.param("datetime64[ms]", "datetime64[ms]", id="datetime64[ms]"),
+        pytest.param("datetime64[us]", "datetime64[us]", id="datetime64[us]"),
+        pytest.param("datetime64[ns]", "datetime64[ns]", id="datetime64[ns]"),
+        # pandas.DatetimeTZDtype can be given in two ways [tz is required]:
+        # 1. pandas.DatetimeTZDtype(unit, tz)
+        # 2. String aliases: "datetime64[unit, tz]"
+        pytest.param(
+            "datetime64[s, UTC]",
+            "datetime64[s]",
+            id="datetime64[s, tz=UTC]",
+            marks=pandas_old_version,
+        ),
+        pytest.param(
+            "datetime64[s, America/New_York]",
+            "datetime64[s]",
+            id="datetime64[s, tz=America/New_York]",
+            marks=pandas_old_version,
+        ),
+        pytest.param(
+            "datetime64[s, +07:30]",
+            "datetime64[s]",
+            id="datetime64[s, +07:30]",
+            marks=pandas_old_version,
+        ),
+        # PyArrow timestamp types can be given in two ways [tz is optional]:
+        # 1. pd.ArrowDtype(pyarrow.Timestamp(unit, tz=tz))
+        # 2. String aliases: "timestamp[unit, tz][pyarrow]"
+        pytest.param(
+            "timestamp[s][pyarrow]",
+            "datetime64[s]",
+            id="timestamp[s][pyarrow]",
+            marks=skip_if_no(package="pyarrow"),
+        ),
+        pytest.param(
+            "timestamp[ms][pyarrow]",
+            "datetime64[ms]",
+            id="timestamp[ms][pyarrow]",
+            marks=[skip_if_no(package="pyarrow"), pandas_old_version],
+        ),
+        pytest.param(
+            "timestamp[us][pyarrow]",
+            "datetime64[us]",
+            id="timestamp[us][pyarrow]",
+            marks=[skip_if_no(package="pyarrow"), pandas_old_version],
+        ),
+        pytest.param(
+            "timestamp[ns][pyarrow]",
+            "datetime64[ns]",
+            id="timestamp[ns][pyarrow]",
+            marks=skip_if_no(package="pyarrow"),
+        ),
+        pytest.param(
+            "timestamp[s, UTC][pyarrow]",
+            "datetime64[s]",
+            id="timestamp[s, UTC][pyarrow]",
+            marks=skip_if_no(package="pyarrow"),
+        ),
+        pytest.param(
+            "timestamp[s, America/New_York][pyarrow]",
+            "datetime64[s]",
+            id="timestamp[s, America/New_York][pyarrow]",
+            marks=skip_if_no(package="pyarrow"),
+        ),
+        pytest.param(
+            "timestamp[s, +08:00][pyarrow]",
+            "datetime64[s]",
+            id="timestamp[s, +08:00][pyarrow]",
+            marks=skip_if_no(package="pyarrow"),
+        ),
+    ],
+)
+def test_to_numpy_pandas_datetime(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with pandas.Series of datetime types.
+    """
+    series = pd.Series(
+        [pd.Timestamp("2024-01-02T03:04:05"), pd.Timestamp("2024-01-02T03:04:06")],
+        dtype=dtype,
+    )
+    result = _to_numpy(series)
+    _check_result(result, np.datetime64)
+    assert result.dtype == expected_dtype
+
+    # Convert to UTC if the dtype is timezone-aware
+    if "," in str(dtype):  # A hacky way to decide if the dtype is timezone-aware.
+        # TODO(pandas>=2.1): Simplify the if-else statement.
+        if Version(pd.__version__) < Version("2.1") and dtype.startswith("timestamp"):
+            # pandas 2.0 doesn't have the dt.tz_convert method for pyarrow.Timestamp.
+            series = pd.to_datetime(series, utc=True)
+        else:
+            series = series.dt.tz_convert("UTC")
+    # Remove time zone information and preserve local time.
+    expected_series = series.dt.tz_localize(tz=None)
+    npt.assert_array_equal(result, np.array(expected_series, dtype=expected_dtype))
+
+
 ########################################################################################
 # Test the _to_numpy function with PyArrow arrays.
 #