merge master and rm FIXME comment

Author: FBruzzesi

.circleci/config.yml

@@ -249,6 +249,14 @@ jobs:
       - test_optional:
           py: "312_no_numpy"
 
+  python_312_np2:
+    docker:
+      - image: cimg/python:3.12-browsers
+    steps:
+      - test_optional:
+          py: "312_np2"
+
+
   # Percy
   python_39_percy:
     docker:
@@ -607,4 +615,5 @@ workflows:
       - python_39_pandas_2_optional
       - python_39_percy
       - python_312_no_numpy
+      - python_312_np2
       - build-doc

packages/python/plotly/_plotly_utils/basevalidators.py

@@ -92,7 +92,7 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
         "O": "object",
     }
 
-    # With `pass_through=True``, the original object will be returned if unable to convert
+    # With `pass_through=True`, the original object will be returned if unable to convert
     # to a Narwhals DataFrame or Series.
     v = nw.from_native(v, allow_series=True, pass_through=True)
 

packages/python/plotly/_plotly_utils/tests/validators/test_any_validator.py

@@ -1,6 +1,7 @@
 import pytest
 from _plotly_utils.basevalidators import AnyValidator
 import numpy as np
+from plotly.tests.test_optional.test_utils.test_utils import np_nan, np_inf
 
 
 # Fixtures
@@ -18,7 +19,7 @@ def validator_aok():
 # Tests
 # -----
 # ### Acceptance ###
-@pytest.mark.parametrize("val", [set(), "Hello", 123, np.inf, np.nan, {}])
+@pytest.mark.parametrize("val", [set(), "Hello", 123, np_inf(), np_nan(), {}])
 def test_acceptance(val, validator):
     assert validator.validate_coerce(val) is val
 

packages/python/plotly/_plotly_utils/tests/validators/test_boolean_validator.py

@@ -1,7 +1,6 @@
 import pytest
 from _plotly_utils.basevalidators import BooleanValidator
-import numpy as np
-
+from plotly.tests.test_optional.test_utils.test_utils import np_nan
 
 # Boolean Validator
 # =================
@@ -18,7 +17,7 @@ def test_acceptance(val, validator):
 
 
 # ### Rejection ###
-@pytest.mark.parametrize("val", [1.0, 0.0, "True", "False", [], 0, np.nan])
+@pytest.mark.parametrize("val", [1.0, 0.0, "True", "False", [], 0, np_nan()])
 def test_rejection(val, validator):
     with pytest.raises(ValueError) as validation_failure:
         validator.validate_coerce(val)

packages/python/plotly/_plotly_utils/tests/validators/test_enumerated_validator.py

@@ -2,7 +2,7 @@
 import numpy as np
 import pandas as pd
 from _plotly_utils.basevalidators import EnumeratedValidator
-
+from plotly.tests.test_optional.test_utils.test_utils import np_inf
 
 # Fixtures
 # --------
@@ -42,7 +42,7 @@ def test_acceptance(val, validator):
 # ### Value Rejection ###
 @pytest.mark.parametrize(
     "val",
-    [True, 0, 1, 23, np.inf, set(), ["first", "second"], [True], ["third", 4], [4]],
+    [True, 0, 1, 23, np_inf(), set(), ["first", "second"], [True], ["third", 4], [4]],
 )
 def test_rejection_by_value(val, validator):
     with pytest.raises(ValueError) as validation_failure:
@@ -95,7 +95,7 @@ def test_acceptance_aok(val, validator_aok):
 
 
 # ### Rejection by value ###
-@pytest.mark.parametrize("val", [True, 0, 1, 23, np.inf, set()])
+@pytest.mark.parametrize("val", [True, 0, 1, 23, np_inf(), set()])
 def test_rejection_by_value_aok(val, validator_aok):
     with pytest.raises(ValueError) as validation_failure:
         validator_aok.validate_coerce(val)
@@ -105,7 +105,7 @@ def test_rejection_by_value_aok(val, validator_aok):
 
 # ### Reject by elements ###
 @pytest.mark.parametrize(
-    "val", [[True], [0], [1, 23], [np.inf, set()], ["ffirstt", "second", "third"]]
+    "val", [[True], [0], [1, 23], [np_inf(), set()], ["ffirstt", "second", "third"]]
 )
 def test_rejection_by_element_aok(val, validator_aok):
     with pytest.raises(ValueError) as validation_failure:

packages/python/plotly/_plotly_utils/tests/validators/test_integer_validator.py

@@ -5,7 +5,7 @@
 from _plotly_utils.basevalidators import IntegerValidator
 import numpy as np
 import pandas as pd
-
+from plotly.tests.test_optional.test_utils.test_utils import np_nan, np_inf
 
 # ### Fixtures ###
 @pytest.fixture()
@@ -53,7 +53,7 @@ def test_acceptance(val, validator):
 
 # ### Rejection by value ###
 @pytest.mark.parametrize(
-    "val", ["hello", (), [], [1, 2, 3], set(), "34", np.nan, np.inf, -np.inf]
+    "val", ["hello", (), [], [1, 2, 3], set(), "34", np_nan(), np_inf(), -np_inf()]
 )
 def test_rejection_by_value(val, validator):
     with pytest.raises(ValueError) as validation_failure:

packages/python/plotly/_plotly_utils/tests/validators/test_number_validator.py

@@ -4,6 +4,7 @@
 from _plotly_utils.basevalidators import NumberValidator
 import numpy as np
 import pandas as pd
+from plotly.tests.test_optional.test_utils.test_utils import np_nan, np_inf
 
 # Fixtures
 # --------
@@ -36,7 +37,7 @@ def validator_aok():
 # ------------
 # ### Acceptance ###
 @pytest.mark.parametrize(
-    "val", [1.0, 0.0, 1, -1234.5678, 54321, np.pi, np.nan, np.inf, -np.inf]
+    "val", [1.0, 0.0, 1, -1234.5678, 54321, np.pi, np_nan(), np_inf(), -np_inf()]
 )
 def test_acceptance(val, validator):
     assert validator.validate_coerce(val) == approx(val, nan_ok=True)
@@ -57,7 +58,7 @@ def test_acceptance_min_max(val, validator_min_max):
     assert validator_min_max.validate_coerce(val) == approx(val)
 
 
-@pytest.mark.parametrize("val", [-1.01, -10, 2.1, 234, -np.inf, np.nan, np.inf])
+@pytest.mark.parametrize("val", [-1.01, -10, 2.1, 234, -np_inf(), np_nan(), np_inf()])
 def test_rejection_min_max(val, validator_min_max):
     with pytest.raises(ValueError) as validation_failure:
         validator_min_max.validate_coerce(val)
@@ -66,12 +67,12 @@ def test_rejection_min_max(val, validator_min_max):
 
 
 # ### With min only ###
-@pytest.mark.parametrize("val", [0, 0.0, -0.5, 99999, np.inf])
+@pytest.mark.parametrize("val", [0, 0.0, -0.5, 99999, np_inf()])
 def test_acceptance_min(val, validator_min):
     assert validator_min.validate_coerce(val) == approx(val)
 
 
-@pytest.mark.parametrize("val", [-1.01, -np.inf, np.nan])
+@pytest.mark.parametrize("val", [-1.01, -np_inf(), np_nan()])
 def test_rejection_min(val, validator_min):
     with pytest.raises(ValueError) as validation_failure:
         validator_min.validate_coerce(val)
@@ -80,12 +81,12 @@ def test_rejection_min(val, validator_min):
 
 
 # ### With max only ###
-@pytest.mark.parametrize("val", [0, 0.0, -np.inf, -123456, np.pi / 2])
+@pytest.mark.parametrize("val", [0, 0.0, -np_inf(), -123456, np.pi / 2])
 def test_acceptance_max(val, validator_max):
     assert validator_max.validate_coerce(val) == approx(val)
 
 
-@pytest.mark.parametrize("val", [2.01, np.inf, np.nan])
+@pytest.mark.parametrize("val", [2.01, np_inf(), np_nan()])
 def test_rejection_max(val, validator_max):
     with pytest.raises(ValueError) as validation_failure:
         validator_max.validate_coerce(val)
@@ -142,7 +143,13 @@ def test_rejection_aok(val, validator_aok):
 # ### Rejection by element ###
 @pytest.mark.parametrize(
     "val",
-    [[-1.6, 0.0], [1, 1.5, 2], [-0.1234, 0.41, np.nan], [0, np.inf], [0, -np.inf]],
+    [
+        [-1.6, 0.0],
+        [1, 1.5, 2],
+        [-0.1234, 0.41, np_nan()],
+        [0, np_inf()],
+        [0, -np_inf()],
+    ],
 )
 def test_rejection_aok_min_max(val, validator_aok):
     with pytest.raises(ValueError) as validation_failure:

packages/python/plotly/_plotly_utils/tests/validators/test_string_validator.py

@@ -2,6 +2,7 @@
 
 from _plotly_utils.basevalidators import StringValidator
 import numpy as np
+from plotly.tests.test_optional.test_utils.test_utils import np_nan
 
 
 # Fixtures
@@ -53,7 +54,7 @@ def validator_no_blanks_aok():
 # Not strict
 # ### Acceptance ###
 @pytest.mark.parametrize(
-    "val", ["bar", 234, np.nan, "HELLO!!!", "world!@#$%^&*()", "", "\u03BC"]
+    "val", ["bar", 234, np_nan(), "HELLO!!!", "world!@#$%^&*()", "", "\u03BC"]
 )
 def test_acceptance(val, validator):
     expected = str(val) if not isinstance(val, str) else val
@@ -108,7 +109,7 @@ def test_acceptance_strict(val, validator_strict):
 
 
 # ### Rejection by value ###
-@pytest.mark.parametrize("val", [(), [], [1, 2, 3], set(), np.nan, np.pi, 23])
+@pytest.mark.parametrize("val", [(), [], [1, 2, 3], set(), np_nan(), np.pi, 23])
 def test_rejection_strict(val, validator_strict):
     with pytest.raises(ValueError) as validation_failure:
         validator_strict.validate_coerce(val)

packages/python/plotly/_plotly_utils/tests/validators/test_subplotid_validator.py

@@ -1,6 +1,6 @@
 import pytest
 from _plotly_utils.basevalidators import SubplotidValidator
-import numpy as np
+from plotly.tests.test_optional.test_utils.test_utils import np_nan, np_inf
 
 
 # Fixtures
@@ -19,7 +19,7 @@ def test_acceptance(val, validator):
 
 
 # ### Rejection by type ###
-@pytest.mark.parametrize("val", [23, [], {}, set(), np.inf, np.nan])
+@pytest.mark.parametrize("val", [23, [], {}, set(), np_inf(), np_nan()])
 def test_rejection_type(val, validator):
     with pytest.raises(ValueError) as validation_failure:
         validator.validate_coerce(val)

packages/python/plotly/plotly/express/_core.py

@@ -412,10 +412,8 @@ def make_trace_kwargs(args, trace_spec, trace_data, mapping_labels, sizeref):
                     # NB this means trendline functions must output one-to-one with the input series
                     # i.e. we can't do resampling, because then the X values might not line up!
                     non_missing = ~(x.is_null() | y.is_null())
-                    trace_patch["x"] = (
-                        sorted_trace_data.filter(non_missing).get_column(args["x"])
-                        # FIXME: Converting to numpy is needed to pass `test_trendline_on_timeseries`
-                        # test, but I wonder if it is the right way to do it in the first place.
+                    trace_patch["x"] = sorted_trace_data.filter(non_missing).get_column(
+                        args["x"]
                     )
                     if (
                         trace_patch["x"].dtype == nw.Datetime
@@ -1178,6 +1176,8 @@ def to_unindexed_series(x, name=None, native_namespace=None):
     its index reset if pandas-like). Stripping the index from existing pd.Series is
     required to get things to match up right in the new DataFrame we're building.
     """
+    # With `pass_through=True`, the original object will be returned if unable to convert
+    # to a Narwhals Series.
     x = nw.from_native(x, series_only=True, pass_through=True)
     if isinstance(x, nw.Series):
         return nw.maybe_reset_index(x).rename(name)
@@ -1383,6 +1383,8 @@ def process_args_into_dataframe(
                         % (field, len_arg, str(list(df_output.keys())), length)
                     )
 
+                # With `pass_through=True`, the original object will be returned if unable to convert
+                # to a Narwhals Series.
                 df_output[str(col_name)] = to_unindexed_series(
                     x=nw.from_native(argument, series_only=True, pass_through=True),
                     name=str(col_name),
@@ -1512,7 +1514,7 @@ def build_dataframe(args, constructor):
             is_pd_like = True
 
         # data_frame is any other DataFrame object natively supported via Narwhals.
-        # With pass_through=True, the original object will be returned if unable to convert
+        # With `pass_through=True`, the original object will be returned if unable to convert
         # to a Narwhals DataFrame, making this condition False.
         elif isinstance(
             data_frame := nw.from_native(
@@ -1525,7 +1527,7 @@ def build_dataframe(args, constructor):
             columns = args["data_frame"].columns
 
         # data_frame is any other Series object natively supported via Narwhals.
-        # With pass_through=True, the original object will be returned if unable to convert
+        # With `pass_through=True`, the original object will be returned if unable to convert
         # to a Narwhals Series, making this condition False.
         elif isinstance(
             series := nw.from_native(