clib.conversion._to_numpy: Add tests for pandas.Series and pyarrow.array with pyarrow numeric dtypes

Author: seisman

pygmt/tests/test_clib_to_numpy.py

@@ -8,6 +8,13 @@
 import pytest
 from pygmt.clib.conversion import _to_numpy
 
+try:
+    import pyarrow as pa
+
+    _HAS_PYARROW = True
+except ImportError:
+    _HAS_PYARROW = False
+
 
 def _check_result(result, expected_dtype):
     """
@@ -122,6 +129,11 @@ def test_to_numpy_ndarray_numpy_dtypes_numeric(dtype, expected_dtype):
 # - BooleanDtype
 # - ArrowDtype: a special dtype used to store data in the PyArrow format.
 #
+# PyArrow dtypes can be specified using the following formats:
+#
+# - Prefixed with the name of the dtype and "[pyarrow]" (e.g., "int8[pyarrow]")
+# - Specified using ``ArrowDType`` (e.g., "pd.ArrowDtype(pa.int8())")
+#
 # References:
 # 1. https://pandas.pydata.org/docs/reference/arrays.html
 # 2. https://pandas.pydata.org/docs/user_guide/basics.html#basics-dtypes
@@ -207,3 +219,142 @@ def test_to_numpy_pandas_series_pandas_dtypes_numeric_with_na(dtype, expected_dt
     result = _to_numpy(series)
     _check_result(result, expected_dtype)
     npt.assert_array_equal(result, np.array([1.0, np.nan, 3.0], dtype=expected_dtype))
+
+
+@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
+@pytest.mark.parametrize(
+    ("dtype", "expected_dtype"),
+    [
+        pytest.param("int8[pyarrow]", np.int8, id="int8[pyarrow]"),
+        pytest.param("int16[pyarrow]", np.int16, id="int16[pyarrow]"),
+        pytest.param("int32[pyarrow]", np.int32, id="int32[pyarrow]"),
+        pytest.param("int64[pyarrow]", np.int64, id="int64[pyarrow]"),
+        pytest.param("uint8[pyarrow]", np.uint8, id="uint8[pyarrow]"),
+        pytest.param("uint16[pyarrow]", np.uint16, id="uint16[pyarrow]"),
+        pytest.param("uint32[pyarrow]", np.uint32, id="uint32[pyarrow]"),
+        pytest.param("uint64[pyarrow]", np.uint64, id="uint64[pyarrow]"),
+        pytest.param("float16[pyarrow]", np.float16, id="float16[pyarrow]"),
+        pytest.param("float32[pyarrow]", np.float32, id="float32[pyarrow]"),
+        pytest.param("float64[pyarrow]", np.float64, id="float64[pyarrow]"),
+    ],
+)
+def test_to_numpy_pandas_series_pyarrow_dtypes_numeric(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with pandas.Series of pandas numeric dtypes.
+    """
+    series = pd.Series([1, 2, 3], dtype=dtype)
+    result = _to_numpy(series)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, series)
+
+
+@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
+@pytest.mark.parametrize(
+    ("dtype", "expected_dtype"),
+    [
+        pytest.param("int8[pyarrow]", np.float64, id="int8[pyarrow]"),
+        pytest.param("int16[pyarrow]", np.float64, id="int16[pyarrow]"),
+        pytest.param("int32[pyarrow]", np.float64, id="int32[pyarrow]"),
+        pytest.param("int64[pyarrow]", np.float64, id="int64[pyarrow]"),
+        pytest.param("uint8[pyarrow]", np.float64, id="uint8[pyarrow]"),
+        pytest.param("uint16[pyarrow]", np.float64, id="uint16[pyarrow]"),
+        pytest.param("uint32[pyarrow]", np.float64, id="uint32[pyarrow]"),
+        pytest.param("uint64[pyarrow]", np.float64, id="uint64[pyarrow]"),
+        pytest.param("float16[pyarrow]", np.float16, id="float16[pyarrow]"),
+        pytest.param("float32[pyarrow]", np.float32, id="float32[pyarrow]"),
+        pytest.param("float64[pyarrow]", np.float64, id="float64[pyarrow]"),
+    ],
+)
+def test_to_numpy_pandas_series_pyarrow_dtypes_numeric_with_na(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with pandas.Series of pandas numeric dtypes and NA.
+    """
+    series = pd.Series([1, pd.NA, 3], dtype=dtype)
+    result = _to_numpy(series)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, np.array([1.0, np.nan, 3.0], dtype=expected_dtype))
+
+
+########################################################################################
+# Test the _to_numpy function with PyArrow arrays.
+#
+# PyArrow provides the following dtypes:
+#
+# - Numeric dtypes:
+#   - int8, int16, int32, int64
+#   - uint8, uint16, uint32, uint64
+#   - float16, float32, float64
+#
+# In PyArrow, array types can be specified in two ways:
+#
+# - Using string aliases (e.g., "int8")
+# - Using pyarrow.DataType (e.g., ``pa.int8()``)
+#
+# Reference: https://arrow.apache.org/docs/python/api/datatypes.html
+########################################################################################
+@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
+@pytest.mark.parametrize(
+    ("dtype", "expected_dtype"),
+    [
+        pytest.param("int8", np.int8, id="int8"),
+        pytest.param("int16", np.int16, id="int16"),
+        pytest.param("int32", np.int32, id="int32"),
+        pytest.param("int64", np.int64, id="int64"),
+        pytest.param("uint8", np.uint8, id="uint8"),
+        pytest.param("uint16", np.uint16, id="uint16"),
+        pytest.param("uint32", np.uint32, id="uint32"),
+        pytest.param("uint64", np.uint64, id="uint64"),
+        pytest.param("float16", np.float16, id="float16"),
+        pytest.param("float32", np.float32, id="float32"),
+        pytest.param("float64", np.float64, id="float64"),
+    ],
+)
+def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with PyArrow arrays of PyArrow numeric dtypes.
+    """
+    if dtype == "float16":
+        # float16 needs special handling
+        # Example from https://arrow.apache.org/docs/python/generated/pyarrow.float16.html
+        array = pa.array(np.array([1.0, 2.0, 3.0], dtype=np.float16), type=pa.float16())
+    else:
+        array = pa.array([1, 2, 3], type=dtype)
+    assert array.type == dtype
+    result = _to_numpy(array)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, array)
+
+
+@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed")
+@pytest.mark.parametrize(
+    ("dtype", "expected_dtype"),
+    [
+        pytest.param("int8", np.float64, id="int8"),
+        pytest.param("int16", np.float64, id="int16"),
+        pytest.param("int32", np.float64, id="int32"),
+        pytest.param("int64", np.float64, id="int64"),
+        pytest.param("uint8", np.float64, id="uint8"),
+        pytest.param("uint16", np.float64, id="uint16"),
+        pytest.param("uint32", np.float64, id="uint32"),
+        pytest.param("uint64", np.float64, id="uint64"),
+        pytest.param("float16", np.float16, id="float16"),
+        pytest.param("float32", np.float32, id="float32"),
+        pytest.param("float64", np.float64, id="float64"),
+    ],
+)
+def test_to_numpy_pyarrow_array_pyarrow_dtypes_numeric_with_na(dtype, expected_dtype):
+    """
+    Test the _to_numpy function with PyArrow arrays of PyArrow numeric dtypes and NA.
+    """
+    if dtype == "float16":
+        # float16 needs special handling
+        # Example from https://arrow.apache.org/docs/python/generated/pyarrow.float16.html
+        array = pa.array(
+            np.array([1.0, None, 3.0], dtype=np.float16), type=pa.float16()
+        )
+    else:
+        array = pa.array([1, None, 3], type=dtype)
+    assert array.type == dtype
+    result = _to_numpy(array)
+    _check_result(result, expected_dtype)
+    npt.assert_array_equal(result, array)