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seismanweiji14yvonnefroehlichmichaelgrund
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clib.converison._to_numpy: Add tests for pandas.Series with pandas numeric dtypes (#3584)
Co-authored-by: Wei Ji <23487320+weiji14@users.noreply.github.com> Co-authored-by: Yvonne Fröhlich <94163266+yvonnefroehlich@users.noreply.github.com> Co-authored-by: Michael Grund <23025878+michaelgrund@users.noreply.github.com>
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‎pygmt/tests/test_clib_to_numpy.py‎

Lines changed: 88 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -34,6 +34,9 @@ def timestamp(unit: str, tz: str | None = None):
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_HAS_PYARROW = False
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# Mark tests that require pyarrow
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pa_marks = {"marks": skip_if_no(package="pyarrow")}
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def _check_result(result, expected_dtype):
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"""
@@ -173,22 +176,105 @@ def test_to_numpy_numpy_string(dtype):
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# - BooleanDtype
174177
# - ArrowDtype: a special dtype used to store data in the PyArrow format.
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#
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# In pandas, PyArrow types can be specified using the following formats:
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#
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# - Prefixed with the name of the dtype and "[pyarrow]" (e.g., "int8[pyarrow]")
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# - Specified using ``ArrowDType`` (e.g., "pd.ArrowDtype(pa.int8())")
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#
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# References:
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# 1. https://pandas.pydata.org/docs/reference/arrays.html
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# 2. https://pandas.pydata.org/docs/user_guide/basics.html#basics-dtypes
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# 3. https://pandas.pydata.org/docs/user_guide/pyarrow.html
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########################################################################################
181-
@pytest.mark.parametrize(("dtype", "expected_dtype"), np_dtype_params)
189+
@pytest.mark.parametrize(
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("dtype", "expected_dtype"),
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[
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*np_dtype_params,
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pytest.param(pd.Int8Dtype(), np.int8, id="Int8"),
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pytest.param(pd.Int16Dtype(), np.int16, id="Int16"),
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pytest.param(pd.Int32Dtype(), np.int32, id="Int32"),
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pytest.param(pd.Int64Dtype(), np.int64, id="Int64"),
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pytest.param(pd.UInt8Dtype(), np.uint8, id="UInt8"),
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pytest.param(pd.UInt16Dtype(), np.uint16, id="UInt16"),
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pytest.param(pd.UInt32Dtype(), np.uint32, id="UInt32"),
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pytest.param(pd.UInt64Dtype(), np.uint64, id="UInt64"),
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pytest.param(pd.Float32Dtype(), np.float32, id="Float32"),
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pytest.param(pd.Float64Dtype(), np.float64, id="Float64"),
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pytest.param("int8[pyarrow]", np.int8, id="int8[pyarrow]", **pa_marks),
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pytest.param("int16[pyarrow]", np.int16, id="int16[pyarrow]", **pa_marks),
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pytest.param("int32[pyarrow]", np.int32, id="int32[pyarrow]", **pa_marks),
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pytest.param("int64[pyarrow]", np.int64, id="int64[pyarrow]", **pa_marks),
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pytest.param("uint8[pyarrow]", np.uint8, id="uint8[pyarrow]", **pa_marks),
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pytest.param("uint16[pyarrow]", np.uint16, id="uint16[pyarrow]", **pa_marks),
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pytest.param("uint32[pyarrow]", np.uint32, id="uint32[pyarrow]", **pa_marks),
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pytest.param("uint64[pyarrow]", np.uint64, id="uint64[pyarrow]", **pa_marks),
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pytest.param("float16[pyarrow]", np.float16, id="float16[pyarrow]", **pa_marks),
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pytest.param("float32[pyarrow]", np.float32, id="float32[pyarrow]", **pa_marks),
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pytest.param("float64[pyarrow]", np.float64, id="float64[pyarrow]", **pa_marks),
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],
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)
182216
def test_to_numpy_pandas_numeric(dtype, expected_dtype):
183217
"""
184218
Test the _to_numpy function with pandas.Series of numeric dtypes.
185219
"""
186-
series = pd.Series([1, 2, 3, 4, 5, 6], dtype=dtype)[::2] # Not C-contiguous
220+
data = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
221+
if dtype == "float16[pyarrow]" and Version(pd.__version__) < Version("2.2"):
222+
# float16 needs special handling for pandas < 2.2.
223+
# Example from https://arrow.apache.org/docs/python/generated/pyarrow.float16.html
224+
data = np.array(data, dtype=np.float16)
225+
series = pd.Series(data, dtype=dtype)[::2] # Not C-contiguous
187226
result = _to_numpy(series)
188227
_check_result(result, expected_dtype)
189228
npt.assert_array_equal(result, series)
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231+
@pytest.mark.parametrize(
232+
("dtype", "expected_dtype"),
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[
234+
pytest.param(np.float16, np.float16, id="float16"),
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pytest.param(np.float32, np.float32, id="float32"),
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pytest.param(np.float64, np.float64, id="float64"),
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pytest.param(np.longdouble, np.longdouble, id="longdouble"),
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pytest.param(pd.Int8Dtype(), np.float64, id="Int8"),
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pytest.param(pd.Int16Dtype(), np.float64, id="Int16"),
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pytest.param(pd.Int32Dtype(), np.float64, id="Int32"),
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pytest.param(pd.Int64Dtype(), np.float64, id="Int64"),
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pytest.param(pd.UInt8Dtype(), np.float64, id="UInt8"),
243+
pytest.param(pd.UInt16Dtype(), np.float64, id="UInt16"),
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pytest.param(pd.UInt32Dtype(), np.float64, id="UInt32"),
245+
pytest.param(pd.UInt64Dtype(), np.float64, id="UInt64"),
246+
pytest.param(pd.Float32Dtype(), np.float32, id="Float32"),
247+
pytest.param(pd.Float64Dtype(), np.float64, id="Float64"),
248+
pytest.param("int8[pyarrow]", np.float64, id="int8[pyarrow]", **pa_marks),
249+
pytest.param("int16[pyarrow]", np.float64, id="int16[pyarrow]", **pa_marks),
250+
pytest.param("int32[pyarrow]", np.float64, id="int32[pyarrow]", **pa_marks),
251+
pytest.param("int64[pyarrow]", np.float64, id="int64[pyarrow]", **pa_marks),
252+
pytest.param("uint8[pyarrow]", np.float64, id="uint8[pyarrow]", **pa_marks),
253+
pytest.param("uint16[pyarrow]", np.float64, id="uint16[pyarrow]", **pa_marks),
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pytest.param("uint32[pyarrow]", np.float64, id="uint32[pyarrow]", **pa_marks),
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pytest.param("uint64[pyarrow]", np.float64, id="uint64[pyarrow]", **pa_marks),
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pytest.param("float16[pyarrow]", np.float16, id="float16[pyarrow]", **pa_marks),
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pytest.param("float32[pyarrow]", np.float32, id="float32[pyarrow]", **pa_marks),
258+
pytest.param("float64[pyarrow]", np.float64, id="float64[pyarrow]", **pa_marks),
259+
],
260+
)
261+
def test_to_numpy_pandas_numeric_with_na(dtype, expected_dtype):
262+
"""
263+
Test the _to_numpy function with pandas.Series of NumPy/pandas/PyArrow numeric
264+
dtypes and missing values (NA).
265+
"""
266+
data = [1.0, 2.0, None, 4.0, 5.0, 6.0]
267+
if dtype == "float16[pyarrow]" and Version(pd.__version__) < Version("2.2"):
268+
# float16 needs special handling for pandas < 2.2.
269+
# Example from https://arrow.apache.org/docs/python/generated/pyarrow.float16.html
270+
data = np.array(data, dtype=np.float16)
271+
series = pd.Series(data, dtype=dtype)[::2] # Not C-contiguous
272+
assert series.isna().any()
273+
result = _to_numpy(series)
274+
_check_result(result, expected_dtype)
275+
npt.assert_array_equal(result, np.array([1.0, np.nan, 5.0], dtype=expected_dtype))
276+
277+
192278
@pytest.mark.parametrize(
193279
"dtype",
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[

‎pygmt/tests/test_clib_vectors_to_arrays.py‎

Lines changed: 0 additions & 11 deletions
Original file line numberDiff line numberDiff line change
@@ -69,17 +69,6 @@ def test_vectors_to_arrays_not_c_contiguous():
6969
_check_arrays(arrays)
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7171

72-
def test_vectors_to_arrays_pandas_nan():
73-
"""
74-
Test the vectors_to_arrays function with pandas Series containing NaNs.
75-
"""
76-
vectors = [pd.Series(data=[0, 4, pd.NA, 8, 6], dtype=pd.Int32Dtype())]
77-
arrays = vectors_to_arrays(vectors)
78-
npt.assert_equal(arrays[0], np.array([0, 4, np.nan, 8, 6], dtype=np.float64))
79-
assert arrays[0].dtype == np.float64
80-
_check_arrays(arrays)
81-
82-
8372
@pytest.mark.skipif(not _HAS_PYARROW, reason="pyarrow is not installed.")
8473
def test_vectors_to_arrays_pyarrow_datetime():
8574
"""

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