JSON encoding refactor and orjson encoding

doc/requirements.txt

@@ -31,3 +31,4 @@ umap-learn==0.5.1
 pooch
 wget
 nbconvert==5.6.1
+orjson

packages/python/plotly/_plotly_utils/basevalidators.py

@@ -53,7 +53,7 @@ def to_scalar_or_list(v):
  return v
 
 
-def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
+def copy_to_readonly_numpy_array_or_list(v, kind=None, force_numeric=False):
  """
  Convert an array-like value into a read-only numpy array
 
@@ -89,7 +89,13 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
 
  # u: unsigned int, i: signed int, f: float
  numeric_kinds = {"u", "i", "f"}
- kind_default_dtypes = {"u": "uint32", "i": "int32", "f": "float64", "O": "object"}
+ kind_default_dtypes = {
+ "u": "uint32",
+ "i": "int32",
+ "f": "float64",
+ "O": "object",
+ "U": "U",
+ }
 
  # Handle pandas Series and Index objects
  if pd and isinstance(v, (pd.Series, pd.Index)):
@@ -113,18 +119,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if not isinstance(v, np.ndarray):
  # v has its own logic on how to convert itself into a numpy array
  if is_numpy_convertable(v):
- return copy_to_readonly_numpy_array(
+ return copy_to_readonly_numpy_array_or_list(
  np.array(v), kind=kind, force_numeric=force_numeric
  )
  else:
  # v is not homogenous array
- v_list = [to_scalar_or_list(e) for e in v]
-
- # Lookup dtype for requested kind, if any
- dtype = kind_default_dtypes.get(first_kind, None)
-
- # construct new array from list
- new_v = np.array(v_list, order="C", dtype=dtype)
+ return [to_scalar_or_list(e) for e in v]
  elif v.dtype.kind in numeric_kinds:
  # v is a homogenous numeric array
  if kind and v.dtype.kind not in kind:
@@ -135,6 +135,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  else:
  # Either no kind was requested or requested kind is satisfied
  new_v = np.ascontiguousarray(v.copy())
+ elif v.dtype.kind == "O":
+ if kind:
+ dtype = kind_default_dtypes.get(first_kind, None)
+ return np.array(v, dtype=dtype)
+ else:
+ return v.tolist()
  else:
  # v is a non-numeric homogenous array
  new_v = v.copy()
@@ -149,12 +155,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if "U" not in kind:
  # Force non-numeric arrays to have object type
  # --------------------------------------------
- # Here we make sure that non-numeric arrays have the object
- # datatype. This works around cases like np.array([1, 2, '3']) where
+ # Here we make sure that non-numeric arrays become lists
+ # This works around cases like np.array([1, 2, '3']) where
  # numpy converts the integers to strings and returns array of dtype
  # '<U21'
  if new_v.dtype.kind not in ["u", "i", "f", "O", "M"]:
-new_v = np.array(v, dtype="object")
+return v.tolist()
 
  # Set new array to be read-only
  # -----------------------------
@@ -191,7 +197,7 @@ def is_homogeneous_array(v):
  if v_numpy.shape == ():
  return False
  else:
- return True
+ return True # v_numpy.dtype.kind in ["u", "i", "f", "M", "U"]
  return False
 
 
@@ -393,7 +399,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  elif is_simple_array(v):
  v = to_scalar_or_list(v)
  else:
@@ -598,7 +604,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els[:10])
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  else:
  v = to_scalar_or_list(v)
  else:
@@ -754,7 +760,7 @@ def validate_coerce(self, v):
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
  try:
- v_array = copy_to_readonly_numpy_array(v, force_numeric=True)
+ v_array = copy_to_readonly_numpy_array_or_list(v, force_numeric=True)
  except (ValueError, TypeError, OverflowError):
  self.raise_invalid_val(v)
 
@@ -881,7 +887,7 @@ def validate_coerce(self, v):
  pass
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
- v_array = copy_to_readonly_numpy_array(
+ v_array = copy_to_readonly_numpy_array_or_list(
  v, kind=("i", "u"), force_numeric=True
  )
 
@@ -1042,26 +1048,7 @@ def validate_coerce(self, v):
  if invalid_els:
  self.raise_invalid_elements(invalid_els)
 
- if is_homogeneous_array(v):
- np = get_module("numpy")
-
- # If not strict, let numpy cast elements to strings
- v = copy_to_readonly_numpy_array(v, kind="U")
-
- # Check no_blank
- if self.no_blank:
- invalid_els = v[v == ""][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- # Check values
- if self.values:
- invalid_inds = np.logical_not(np.isin(v, self.values))
- invalid_els = v[invalid_inds][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- elif is_simple_array(v):
+ if is_simple_array(v) or is_homogeneous_array(v):
  if not self.strict:
  v = [StringValidator.to_str_or_unicode_or_none(e) for e in v]
 
@@ -1338,8 +1325,12 @@ def validate_coerce(self, v, should_raise=True):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
- if self.numbers_allowed() and v.dtype.kind in ["u", "i", "f"]:
+ v = copy_to_readonly_numpy_array_or_list(v)
+ if (
+ not isinstance(v, list)
+ and self.numbers_allowed()
+ and v.dtype.kind in ["u", "i", "f"]
+ ):
  # Numbers are allowed and we have an array of numbers.
  # All good
  pass
@@ -1353,9 +1344,9 @@ def validate_coerce(self, v, should_raise=True):
 
  # ### Check that elements have valid colors types ###
  elif self.numbers_allowed() or invalid_els:
- v = copy_to_readonly_numpy_array(validated_v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="O")
  else:
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  elif self.array_ok and is_simple_array(v):
  validated_v = [self.validate_coerce(e, should_raise=False) for e in v]
 
@@ -1870,7 +1861,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els)
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  else:
  v = to_scalar_or_list(v)
  else:
@@ -1918,7 +1909,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(v, kind="O")
  elif self.array_ok and is_simple_array(v):
  v = to_scalar_or_list(v)
  return v

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

@@ -32,15 +32,29 @@ def test_validator_acceptance_simple(val, validator):
 
 
 @pytest.mark.parametrize(
- "val",
- [np.array([2, 3, 4]), pd.Series(["a", "b", "c"]), np.array([[1, 2, 3], [4, 5, 6]])],
+ "val", [np.array([2, 3, 4]), np.array([[1, 2, 3], [4, 5, 6]])],
 )
 def test_validator_acceptance_homogeneous(val, validator):
  coerce_val = validator.validate_coerce(val)
  assert isinstance(coerce_val, np.ndarray)
  assert np.array_equal(validator.present(coerce_val), val)
 
 
+# Accept object array as list
+@pytest.mark.parametrize(
+ "val",
+ [
+ ["A", "B", "C"],
+ np.array(["A", "B", "C"], dtype="object"),
+ pd.Series(["a", "b", "c"]),
+ ],
+)
+def test_validator_accept_object_array_as_list(val, validator):
+ coerce_val = validator.validate_coerce(val)
+ assert isinstance(coerce_val, list)
+ assert coerce_val == list(val)
+
+
 # ### Rejection ###
 @pytest.mark.parametrize("val", ["Hello", 23, set(), {}])
 def test_rejection(val, validator):

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

@@ -126,7 +126,7 @@ def test_rejection_by_element_aok(val, validator_aok):
  [],
  ["bar12"],
  ("foo", "bar012", "baz"),
- np.array([]),
+ np.array([], dtype="object"),
  np.array(["bar12"]),
  np.array(["foo", "bar012", "baz"]),
  ],
@@ -135,7 +135,7 @@ def test_acceptance_aok(val, validator_aok_re):
  # Values should be accepted and returned unchanged
  coerce_val = validator_aok_re.validate_coerce(val)
  if isinstance(val, (np.ndarray, pd.Series)):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == list(np.array(val))
  elif isinstance(val, (list, tuple)):
  assert validator_aok_re.present(coerce_val) == tuple(val)
  else:

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

@@ -149,13 +149,10 @@ def test_color_validator_object(color_validator, color_object_pandas):
  res = color_validator.validate_coerce(color_object_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_pandas)
+assert res == color_object_pandas.tolist()
 
 
 def test_color_validator_categorical(color_validator, color_categorical_pandas):
@@ -164,13 +161,10 @@ def test_color_validator_categorical(color_validator, color_categorical_pandas):
 
  # Check type
  assert color_categorical_pandas.dtype == "category"
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, np.array(color_categorical_pandas))
+assert res == color_categorical_pandas.tolist()
 
 
 def test_data_array_validator_dates_series(
@@ -180,13 +174,10 @@ def test_data_array_validator_dates_series(
  res = data_array_validator.validate_coerce(datetime_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array)
+assert res == dates_array.tolist()
 
 
 def test_data_array_validator_dates_dataframe(
@@ -197,10 +188,7 @@ def test_data_array_validator_dates_dataframe(
  res = data_array_validator.validate_coerce(df)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array.reshape(len(dates_array), 1))
+assert res == dates_array.reshape(len(dates_array), 1).tolist()

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

@@ -138,8 +138,7 @@ def test_acceptance_aok_scalars(val, validator_aok):
 def test_acceptance_aok_list(val, validator_aok):
  coerce_val = validator_aok.validate_coerce(val)
  if isinstance(val, np.ndarray):
- assert isinstance(coerce_val, np.ndarray)
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == val.tolist()
  elif isinstance(val, list):
  assert validator_aok.present(val) == tuple(val)
  else:
@@ -178,9 +177,7 @@ def test_rejection_aok_values(val, validator_aok_values):
 )
 def test_acceptance_no_blanks_aok(val, validator_no_blanks_aok):
  coerce_val = validator_no_blanks_aok.validate_coerce(val)
- if isinstance(val, np.ndarray):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
- elif isinstance(val, list):
+ if isinstance(val, (list, np.ndarray)):
  assert validator_no_blanks_aok.present(coerce_val) == tuple(val)
  else:
  assert coerce_val == val

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

@@ -126,10 +126,7 @@ def test_color_validator_object(color_validator, color_object_xarray):
  res = color_validator.validate_coerce(color_object_xarray)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_xarray)
+assert res == list(color_object_xarray)

packages/python/plotly/_plotly_utils/utils.py

@@ -61,8 +61,10 @@ def encode(self, o):
  # We catch false positive cases (e.g. strings such as titles, labels etc.)
  # but this is ok since the intention is to skip the decoding / reencoding
  # step when it's completely safe
+
  if not ("NaN" in encoded_o or "Infinity" in encoded_o):
  return encoded_o
+
  # now:
  # 1. `loads` to switch Infinity, -Infinity, NaN to None
  # 2. `dumps` again so you get 'null' instead of extended JSON

packages/python/plotly/plotly/basedatatypes.py

@@ -3297,6 +3297,7 @@ def to_dict(self):
  # Frame key is only added if there are any frames
  res = {"data": data, "layout": layout}
  frames = deepcopy([frame._props for frame in self._frame_objs])
+
  if frames:
  res["frames"] = frames
 
@@ -3413,6 +3414,13 @@ def to_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  str
@@ -3469,6 +3477,13 @@ def write_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  None

doc/requirements.txt

@@ -31,3 +31,4 @@ umap-learn==0.5.1
 pooch
 wget
 nbconvert==5.6.1
+orjson

packages/python/plotly/_plotly_utils/basevalidators.py

@@ -53,7 +53,7 @@ def to_scalar_or_list(v):
  return v
 
 
-def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
+def copy_to_readonly_numpy_array_or_list(v, kind=None, force_numeric=False):
  """
  Convert an array-like value into a read-only numpy array
 
@@ -89,7 +89,13 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
 
  # u: unsigned int, i: signed int, f: float
  numeric_kinds = {"u", "i", "f"}
- kind_default_dtypes = {"u": "uint32", "i": "int32", "f": "float64", "O": "object"}
+ kind_default_dtypes = {
+ "u": "uint32",
+ "i": "int32",
+ "f": "float64",
+ "O": "object",
+ "U": "U",
+ }
 
  # Handle pandas Series and Index objects
  if pd and isinstance(v, (pd.Series, pd.Index)):
@@ -113,18 +119,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if not isinstance(v, np.ndarray):
  # v has its own logic on how to convert itself into a numpy array
  if is_numpy_convertable(v):
- return copy_to_readonly_numpy_array(
+ return copy_to_readonly_numpy_array_or_list(
  np.array(v), kind=kind, force_numeric=force_numeric
  )
  else:
  # v is not homogenous array
- v_list = [to_scalar_or_list(e) for e in v]
-
- # Lookup dtype for requested kind, if any
- dtype = kind_default_dtypes.get(first_kind, None)
-
- # construct new array from list
- new_v = np.array(v_list, order="C", dtype=dtype)
+ return [to_scalar_or_list(e) for e in v]
  elif v.dtype.kind in numeric_kinds:
  # v is a homogenous numeric array
  if kind and v.dtype.kind not in kind:
@@ -135,6 +135,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  else:
  # Either no kind was requested or requested kind is satisfied
  new_v = np.ascontiguousarray(v.copy())
+ elif v.dtype.kind == "O":
+ if kind:
+ dtype = kind_default_dtypes.get(first_kind, None)
+ return np.array(v, dtype=dtype)
+ else:
+ return v.tolist()
  else:
  # v is a non-numeric homogenous array
  new_v = v.copy()
@@ -149,12 +155,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if "U" not in kind:
  # Force non-numeric arrays to have object type
  # --------------------------------------------
- # Here we make sure that non-numeric arrays have the object
- # datatype. This works around cases like np.array([1, 2, '3']) where
+ # Here we make sure that non-numeric arrays become lists
+ # This works around cases like np.array([1, 2, '3']) where
  # numpy converts the integers to strings and returns array of dtype
  # '<U21'
  if new_v.dtype.kind not in ["u", "i", "f", "O", "M"]:
-new_v = np.array(v, dtype="object")
+return v.tolist()
 
  # Set new array to be read-only
  # -----------------------------
@@ -191,7 +197,7 @@ def is_homogeneous_array(v):
  if v_numpy.shape == ():
  return False
  else:
- return True
+ return True # v_numpy.dtype.kind in ["u", "i", "f", "M", "U"]
  return False
 
 
@@ -393,7 +399,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  elif is_simple_array(v):
  v = to_scalar_or_list(v)
  else:
@@ -598,7 +604,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els[:10])
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  else:
  v = to_scalar_or_list(v)
  else:
@@ -754,7 +760,7 @@ def validate_coerce(self, v):
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
  try:
- v_array = copy_to_readonly_numpy_array(v, force_numeric=True)
+ v_array = copy_to_readonly_numpy_array_or_list(v, force_numeric=True)
  except (ValueError, TypeError, OverflowError):
  self.raise_invalid_val(v)
 
@@ -881,7 +887,7 @@ def validate_coerce(self, v):
  pass
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
- v_array = copy_to_readonly_numpy_array(
+ v_array = copy_to_readonly_numpy_array_or_list(
  v, kind=("i", "u"), force_numeric=True
  )
 
@@ -1042,26 +1048,7 @@ def validate_coerce(self, v):
  if invalid_els:
  self.raise_invalid_elements(invalid_els)
 
- if is_homogeneous_array(v):
- np = get_module("numpy")
-
- # If not strict, let numpy cast elements to strings
- v = copy_to_readonly_numpy_array(v, kind="U")
-
- # Check no_blank
- if self.no_blank:
- invalid_els = v[v == ""][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- # Check values
- if self.values:
- invalid_inds = np.logical_not(np.isin(v, self.values))
- invalid_els = v[invalid_inds][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- elif is_simple_array(v):
+ if is_simple_array(v) or is_homogeneous_array(v):
  if not self.strict:
  v = [StringValidator.to_str_or_unicode_or_none(e) for e in v]
 
@@ -1338,8 +1325,12 @@ def validate_coerce(self, v, should_raise=True):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
- if self.numbers_allowed() and v.dtype.kind in ["u", "i", "f"]:
+ v = copy_to_readonly_numpy_array_or_list(v)
+ if (
+ not isinstance(v, list)
+ and self.numbers_allowed()
+ and v.dtype.kind in ["u", "i", "f"]
+ ):
  # Numbers are allowed and we have an array of numbers.
  # All good
  pass
@@ -1353,9 +1344,9 @@ def validate_coerce(self, v, should_raise=True):
 
  # ### Check that elements have valid colors types ###
  elif self.numbers_allowed() or invalid_els:
- v = copy_to_readonly_numpy_array(validated_v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="O")
  else:
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  elif self.array_ok and is_simple_array(v):
  validated_v = [self.validate_coerce(e, should_raise=False) for e in v]
 
@@ -1870,7 +1861,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els)
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  else:
  v = to_scalar_or_list(v)
  else:
@@ -1918,7 +1909,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(v, kind="O")
  elif self.array_ok and is_simple_array(v):
  v = to_scalar_or_list(v)
  return v

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

@@ -32,15 +32,29 @@ def test_validator_acceptance_simple(val, validator):
 
 
 @pytest.mark.parametrize(
- "val",
- [np.array([2, 3, 4]), pd.Series(["a", "b", "c"]), np.array([[1, 2, 3], [4, 5, 6]])],
+ "val", [np.array([2, 3, 4]), np.array([[1, 2, 3], [4, 5, 6]])],
 )
 def test_validator_acceptance_homogeneous(val, validator):
  coerce_val = validator.validate_coerce(val)
  assert isinstance(coerce_val, np.ndarray)
  assert np.array_equal(validator.present(coerce_val), val)
 
 
+# Accept object array as list
+@pytest.mark.parametrize(
+ "val",
+ [
+ ["A", "B", "C"],
+ np.array(["A", "B", "C"], dtype="object"),
+ pd.Series(["a", "b", "c"]),
+ ],
+)
+def test_validator_accept_object_array_as_list(val, validator):
+ coerce_val = validator.validate_coerce(val)
+ assert isinstance(coerce_val, list)
+ assert coerce_val == list(val)
+
+
 # ### Rejection ###
 @pytest.mark.parametrize("val", ["Hello", 23, set(), {}])
 def test_rejection(val, validator):

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

@@ -126,7 +126,7 @@ def test_rejection_by_element_aok(val, validator_aok):
  [],
  ["bar12"],
  ("foo", "bar012", "baz"),
- np.array([]),
+ np.array([], dtype="object"),
  np.array(["bar12"]),
  np.array(["foo", "bar012", "baz"]),
  ],
@@ -135,7 +135,7 @@ def test_acceptance_aok(val, validator_aok_re):
  # Values should be accepted and returned unchanged
  coerce_val = validator_aok_re.validate_coerce(val)
  if isinstance(val, (np.ndarray, pd.Series)):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == list(np.array(val))
  elif isinstance(val, (list, tuple)):
  assert validator_aok_re.present(coerce_val) == tuple(val)
  else:

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

@@ -149,13 +149,10 @@ def test_color_validator_object(color_validator, color_object_pandas):
  res = color_validator.validate_coerce(color_object_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_pandas)
+assert res == color_object_pandas.tolist()
 
 
 def test_color_validator_categorical(color_validator, color_categorical_pandas):
@@ -164,13 +161,10 @@ def test_color_validator_categorical(color_validator, color_categorical_pandas):
 
  # Check type
  assert color_categorical_pandas.dtype == "category"
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, np.array(color_categorical_pandas))
+assert res == color_categorical_pandas.tolist()
 
 
 def test_data_array_validator_dates_series(
@@ -180,13 +174,10 @@ def test_data_array_validator_dates_series(
  res = data_array_validator.validate_coerce(datetime_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array)
+assert res == dates_array.tolist()
 
 
 def test_data_array_validator_dates_dataframe(
@@ -197,10 +188,7 @@ def test_data_array_validator_dates_dataframe(
  res = data_array_validator.validate_coerce(df)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array.reshape(len(dates_array), 1))
+assert res == dates_array.reshape(len(dates_array), 1).tolist()

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

@@ -138,8 +138,7 @@ def test_acceptance_aok_scalars(val, validator_aok):
 def test_acceptance_aok_list(val, validator_aok):
  coerce_val = validator_aok.validate_coerce(val)
  if isinstance(val, np.ndarray):
- assert isinstance(coerce_val, np.ndarray)
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == val.tolist()
  elif isinstance(val, list):
  assert validator_aok.present(val) == tuple(val)
  else:
@@ -178,9 +177,7 @@ def test_rejection_aok_values(val, validator_aok_values):
 )
 def test_acceptance_no_blanks_aok(val, validator_no_blanks_aok):
  coerce_val = validator_no_blanks_aok.validate_coerce(val)
- if isinstance(val, np.ndarray):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
- elif isinstance(val, list):
+ if isinstance(val, (list, np.ndarray)):
  assert validator_no_blanks_aok.present(coerce_val) == tuple(val)
  else:
  assert coerce_val == val

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

@@ -126,10 +126,7 @@ def test_color_validator_object(color_validator, color_object_xarray):
  res = color_validator.validate_coerce(color_object_xarray)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_xarray)
+assert res == list(color_object_xarray)

packages/python/plotly/_plotly_utils/utils.py

@@ -61,8 +61,10 @@ def encode(self, o):
  # We catch false positive cases (e.g. strings such as titles, labels etc.)
  # but this is ok since the intention is to skip the decoding / reencoding
  # step when it's completely safe
+
  if not ("NaN" in encoded_o or "Infinity" in encoded_o):
  return encoded_o
+
  # now:
  # 1. `loads` to switch Infinity, -Infinity, NaN to None
  # 2. `dumps` again so you get 'null' instead of extended JSON

packages/python/plotly/plotly/basedatatypes.py

@@ -3297,6 +3297,7 @@ def to_dict(self):
  # Frame key is only added if there are any frames
  res = {"data": data, "layout": layout}
  frames = deepcopy([frame._props for frame in self._frame_objs])
+
  if frames:
  res["frames"] = frames
 
@@ -3413,6 +3414,13 @@ def to_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  str
@@ -3469,6 +3477,13 @@ def write_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  None

doc/requirements.txt

@@ -31,3 +31,4 @@ umap-learn==0.5.1
 pooch
 wget
 nbconvert==5.6.1
+orjson

packages/python/plotly/_plotly_utils/basevalidators.py

@@ -53,7 +53,7 @@ def to_scalar_or_list(v):
  return v
 
 
-def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
+def copy_to_readonly_numpy_array_or_list(v, kind=None, force_numeric=False):
  """
  Convert an array-like value into a read-only numpy array
 
@@ -89,7 +89,13 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
 
  # u: unsigned int, i: signed int, f: float
  numeric_kinds = {"u", "i", "f"}
- kind_default_dtypes = {"u": "uint32", "i": "int32", "f": "float64", "O": "object"}
+ kind_default_dtypes = {
+ "u": "uint32",
+ "i": "int32",
+ "f": "float64",
+ "O": "object",
+ "U": "U",
+ }
 
  # Handle pandas Series and Index objects
  if pd and isinstance(v, (pd.Series, pd.Index)):
@@ -113,18 +119,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if not isinstance(v, np.ndarray):
  # v has its own logic on how to convert itself into a numpy array
  if is_numpy_convertable(v):
- return copy_to_readonly_numpy_array(
+ return copy_to_readonly_numpy_array_or_list(
  np.array(v), kind=kind, force_numeric=force_numeric
  )
  else:
  # v is not homogenous array
- v_list = [to_scalar_or_list(e) for e in v]
-
- # Lookup dtype for requested kind, if any
- dtype = kind_default_dtypes.get(first_kind, None)
-
- # construct new array from list
- new_v = np.array(v_list, order="C", dtype=dtype)
+ return [to_scalar_or_list(e) for e in v]
  elif v.dtype.kind in numeric_kinds:
  # v is a homogenous numeric array
  if kind and v.dtype.kind not in kind:
@@ -135,6 +135,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  else:
  # Either no kind was requested or requested kind is satisfied
  new_v = np.ascontiguousarray(v.copy())
+ elif v.dtype.kind == "O":
+ if kind:
+ dtype = kind_default_dtypes.get(first_kind, None)
+ return np.array(v, dtype=dtype)
+ else:
+ return v.tolist()
  else:
  # v is a non-numeric homogenous array
  new_v = v.copy()
@@ -149,12 +155,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if "U" not in kind:
  # Force non-numeric arrays to have object type
  # --------------------------------------------
- # Here we make sure that non-numeric arrays have the object
- # datatype. This works around cases like np.array([1, 2, '3']) where
+ # Here we make sure that non-numeric arrays become lists
+ # This works around cases like np.array([1, 2, '3']) where
  # numpy converts the integers to strings and returns array of dtype
  # '<U21'
  if new_v.dtype.kind not in ["u", "i", "f", "O", "M"]:
-new_v = np.array(v, dtype="object")
+return v.tolist()
 
  # Set new array to be read-only
  # -----------------------------
@@ -191,7 +197,7 @@ def is_homogeneous_array(v):
  if v_numpy.shape == ():
  return False
  else:
- return True
+ return True # v_numpy.dtype.kind in ["u", "i", "f", "M", "U"]
  return False
 
 
@@ -393,7 +399,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  elif is_simple_array(v):
  v = to_scalar_or_list(v)
  else:
@@ -598,7 +604,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els[:10])
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  else:
  v = to_scalar_or_list(v)
  else:
@@ -754,7 +760,7 @@ def validate_coerce(self, v):
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
  try:
- v_array = copy_to_readonly_numpy_array(v, force_numeric=True)
+ v_array = copy_to_readonly_numpy_array_or_list(v, force_numeric=True)
  except (ValueError, TypeError, OverflowError):
  self.raise_invalid_val(v)
 
@@ -881,7 +887,7 @@ def validate_coerce(self, v):
  pass
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
- v_array = copy_to_readonly_numpy_array(
+ v_array = copy_to_readonly_numpy_array_or_list(
  v, kind=("i", "u"), force_numeric=True
  )
 
@@ -1042,26 +1048,7 @@ def validate_coerce(self, v):
  if invalid_els:
  self.raise_invalid_elements(invalid_els)
 
- if is_homogeneous_array(v):
- np = get_module("numpy")
-
- # If not strict, let numpy cast elements to strings
- v = copy_to_readonly_numpy_array(v, kind="U")
-
- # Check no_blank
- if self.no_blank:
- invalid_els = v[v == ""][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- # Check values
- if self.values:
- invalid_inds = np.logical_not(np.isin(v, self.values))
- invalid_els = v[invalid_inds][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- elif is_simple_array(v):
+ if is_simple_array(v) or is_homogeneous_array(v):
  if not self.strict:
  v = [StringValidator.to_str_or_unicode_or_none(e) for e in v]
 
@@ -1338,8 +1325,12 @@ def validate_coerce(self, v, should_raise=True):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
- if self.numbers_allowed() and v.dtype.kind in ["u", "i", "f"]:
+ v = copy_to_readonly_numpy_array_or_list(v)
+ if (
+ not isinstance(v, list)
+ and self.numbers_allowed()
+ and v.dtype.kind in ["u", "i", "f"]
+ ):
  # Numbers are allowed and we have an array of numbers.
  # All good
  pass
@@ -1353,9 +1344,9 @@ def validate_coerce(self, v, should_raise=True):
 
  # ### Check that elements have valid colors types ###
  elif self.numbers_allowed() or invalid_els:
- v = copy_to_readonly_numpy_array(validated_v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="O")
  else:
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  elif self.array_ok and is_simple_array(v):
  validated_v = [self.validate_coerce(e, should_raise=False) for e in v]
 
@@ -1870,7 +1861,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els)
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  else:
  v = to_scalar_or_list(v)
  else:
@@ -1918,7 +1909,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(v, kind="O")
  elif self.array_ok and is_simple_array(v):
  v = to_scalar_or_list(v)
  return v

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

@@ -32,15 +32,29 @@ def test_validator_acceptance_simple(val, validator):
 
 
 @pytest.mark.parametrize(
- "val",
- [np.array([2, 3, 4]), pd.Series(["a", "b", "c"]), np.array([[1, 2, 3], [4, 5, 6]])],
+ "val", [np.array([2, 3, 4]), np.array([[1, 2, 3], [4, 5, 6]])],
 )
 def test_validator_acceptance_homogeneous(val, validator):
  coerce_val = validator.validate_coerce(val)
  assert isinstance(coerce_val, np.ndarray)
  assert np.array_equal(validator.present(coerce_val), val)
 
 
+# Accept object array as list
+@pytest.mark.parametrize(
+ "val",
+ [
+ ["A", "B", "C"],
+ np.array(["A", "B", "C"], dtype="object"),
+ pd.Series(["a", "b", "c"]),
+ ],
+)
+def test_validator_accept_object_array_as_list(val, validator):
+ coerce_val = validator.validate_coerce(val)
+ assert isinstance(coerce_val, list)
+ assert coerce_val == list(val)
+
+
 # ### Rejection ###
 @pytest.mark.parametrize("val", ["Hello", 23, set(), {}])
 def test_rejection(val, validator):

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

@@ -126,7 +126,7 @@ def test_rejection_by_element_aok(val, validator_aok):
  [],
  ["bar12"],
  ("foo", "bar012", "baz"),
- np.array([]),
+ np.array([], dtype="object"),
  np.array(["bar12"]),
  np.array(["foo", "bar012", "baz"]),
  ],
@@ -135,7 +135,7 @@ def test_acceptance_aok(val, validator_aok_re):
  # Values should be accepted and returned unchanged
  coerce_val = validator_aok_re.validate_coerce(val)
  if isinstance(val, (np.ndarray, pd.Series)):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == list(np.array(val))
  elif isinstance(val, (list, tuple)):
  assert validator_aok_re.present(coerce_val) == tuple(val)
  else:

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

@@ -149,13 +149,10 @@ def test_color_validator_object(color_validator, color_object_pandas):
  res = color_validator.validate_coerce(color_object_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_pandas)
+assert res == color_object_pandas.tolist()
 
 
 def test_color_validator_categorical(color_validator, color_categorical_pandas):
@@ -164,13 +161,10 @@ def test_color_validator_categorical(color_validator, color_categorical_pandas):
 
  # Check type
  assert color_categorical_pandas.dtype == "category"
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, np.array(color_categorical_pandas))
+assert res == color_categorical_pandas.tolist()
 
 
 def test_data_array_validator_dates_series(
@@ -180,13 +174,10 @@ def test_data_array_validator_dates_series(
  res = data_array_validator.validate_coerce(datetime_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array)
+assert res == dates_array.tolist()
 
 
 def test_data_array_validator_dates_dataframe(
@@ -197,10 +188,7 @@ def test_data_array_validator_dates_dataframe(
  res = data_array_validator.validate_coerce(df)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array.reshape(len(dates_array), 1))
+assert res == dates_array.reshape(len(dates_array), 1).tolist()

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

@@ -138,8 +138,7 @@ def test_acceptance_aok_scalars(val, validator_aok):
 def test_acceptance_aok_list(val, validator_aok):
  coerce_val = validator_aok.validate_coerce(val)
  if isinstance(val, np.ndarray):
- assert isinstance(coerce_val, np.ndarray)
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == val.tolist()
  elif isinstance(val, list):
  assert validator_aok.present(val) == tuple(val)
  else:
@@ -178,9 +177,7 @@ def test_rejection_aok_values(val, validator_aok_values):
 )
 def test_acceptance_no_blanks_aok(val, validator_no_blanks_aok):
  coerce_val = validator_no_blanks_aok.validate_coerce(val)
- if isinstance(val, np.ndarray):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
- elif isinstance(val, list):
+ if isinstance(val, (list, np.ndarray)):
  assert validator_no_blanks_aok.present(coerce_val) == tuple(val)
  else:
  assert coerce_val == val

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

@@ -126,10 +126,7 @@ def test_color_validator_object(color_validator, color_object_xarray):
  res = color_validator.validate_coerce(color_object_xarray)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_xarray)
+assert res == list(color_object_xarray)

packages/python/plotly/_plotly_utils/utils.py

@@ -61,8 +61,10 @@ def encode(self, o):
  # We catch false positive cases (e.g. strings such as titles, labels etc.)
  # but this is ok since the intention is to skip the decoding / reencoding
  # step when it's completely safe
+
  if not ("NaN" in encoded_o or "Infinity" in encoded_o):
  return encoded_o
+
  # now:
  # 1. `loads` to switch Infinity, -Infinity, NaN to None
  # 2. `dumps` again so you get 'null' instead of extended JSON

packages/python/plotly/plotly/basedatatypes.py

@@ -3297,6 +3297,7 @@ def to_dict(self):
  # Frame key is only added if there are any frames
  res = {"data": data, "layout": layout}
  frames = deepcopy([frame._props for frame in self._frame_objs])
+
  if frames:
  res["frames"] = frames
 
@@ -3413,6 +3414,13 @@ def to_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  str
@@ -3469,6 +3477,13 @@ def write_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  None

doc/requirements.txt

@@ -31,3 +31,4 @@ umap-learn==0.5.1
 pooch
 wget
 nbconvert==5.6.1
+orjson

packages/python/plotly/_plotly_utils/basevalidators.py

@@ -53,7 +53,7 @@ def to_scalar_or_list(v):
  return v
 
 
-def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
+def copy_to_readonly_numpy_array_or_list(v, kind=None, force_numeric=False):
  """
  Convert an array-like value into a read-only numpy array
 
@@ -89,7 +89,13 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
 
  # u: unsigned int, i: signed int, f: float
  numeric_kinds = {"u", "i", "f"}
- kind_default_dtypes = {"u": "uint32", "i": "int32", "f": "float64", "O": "object"}
+ kind_default_dtypes = {
+ "u": "uint32",
+ "i": "int32",
+ "f": "float64",
+ "O": "object",
+ "U": "U",
+ }
 
  # Handle pandas Series and Index objects
  if pd and isinstance(v, (pd.Series, pd.Index)):
@@ -113,18 +119,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if not isinstance(v, np.ndarray):
  # v has its own logic on how to convert itself into a numpy array
  if is_numpy_convertable(v):
- return copy_to_readonly_numpy_array(
+ return copy_to_readonly_numpy_array_or_list(
  np.array(v), kind=kind, force_numeric=force_numeric
  )
  else:
  # v is not homogenous array
- v_list = [to_scalar_or_list(e) for e in v]
-
- # Lookup dtype for requested kind, if any
- dtype = kind_default_dtypes.get(first_kind, None)
-
- # construct new array from list
- new_v = np.array(v_list, order="C", dtype=dtype)
+ return [to_scalar_or_list(e) for e in v]
  elif v.dtype.kind in numeric_kinds:
  # v is a homogenous numeric array
  if kind and v.dtype.kind not in kind:
@@ -135,6 +135,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  else:
  # Either no kind was requested or requested kind is satisfied
  new_v = np.ascontiguousarray(v.copy())
+ elif v.dtype.kind == "O":
+ if kind:
+ dtype = kind_default_dtypes.get(first_kind, None)
+ return np.array(v, dtype=dtype)
+ else:
+ return v.tolist()
  else:
  # v is a non-numeric homogenous array
  new_v = v.copy()
@@ -149,12 +155,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if "U" not in kind:
  # Force non-numeric arrays to have object type
  # --------------------------------------------
- # Here we make sure that non-numeric arrays have the object
- # datatype. This works around cases like np.array([1, 2, '3']) where
+ # Here we make sure that non-numeric arrays become lists
+ # This works around cases like np.array([1, 2, '3']) where
  # numpy converts the integers to strings and returns array of dtype
  # '<U21'
  if new_v.dtype.kind not in ["u", "i", "f", "O", "M"]:
-new_v = np.array(v, dtype="object")
+return v.tolist()
 
  # Set new array to be read-only
  # -----------------------------
@@ -191,7 +197,7 @@ def is_homogeneous_array(v):
  if v_numpy.shape == ():
  return False
  else:
- return True
+ return True # v_numpy.dtype.kind in ["u", "i", "f", "M", "U"]
  return False
 
 
@@ -393,7 +399,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  elif is_simple_array(v):
  v = to_scalar_or_list(v)
  else:
@@ -598,7 +604,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els[:10])
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  else:
  v = to_scalar_or_list(v)
  else:
@@ -754,7 +760,7 @@ def validate_coerce(self, v):
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
  try:
- v_array = copy_to_readonly_numpy_array(v, force_numeric=True)
+ v_array = copy_to_readonly_numpy_array_or_list(v, force_numeric=True)
  except (ValueError, TypeError, OverflowError):
  self.raise_invalid_val(v)
 
@@ -881,7 +887,7 @@ def validate_coerce(self, v):
  pass
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
- v_array = copy_to_readonly_numpy_array(
+ v_array = copy_to_readonly_numpy_array_or_list(
  v, kind=("i", "u"), force_numeric=True
  )
 
@@ -1042,26 +1048,7 @@ def validate_coerce(self, v):
  if invalid_els:
  self.raise_invalid_elements(invalid_els)
 
- if is_homogeneous_array(v):
- np = get_module("numpy")
-
- # If not strict, let numpy cast elements to strings
- v = copy_to_readonly_numpy_array(v, kind="U")
-
- # Check no_blank
- if self.no_blank:
- invalid_els = v[v == ""][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- # Check values
- if self.values:
- invalid_inds = np.logical_not(np.isin(v, self.values))
- invalid_els = v[invalid_inds][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- elif is_simple_array(v):
+ if is_simple_array(v) or is_homogeneous_array(v):
  if not self.strict:
  v = [StringValidator.to_str_or_unicode_or_none(e) for e in v]
 
@@ -1338,8 +1325,12 @@ def validate_coerce(self, v, should_raise=True):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
- if self.numbers_allowed() and v.dtype.kind in ["u", "i", "f"]:
+ v = copy_to_readonly_numpy_array_or_list(v)
+ if (
+ not isinstance(v, list)
+ and self.numbers_allowed()
+ and v.dtype.kind in ["u", "i", "f"]
+ ):
  # Numbers are allowed and we have an array of numbers.
  # All good
  pass
@@ -1353,9 +1344,9 @@ def validate_coerce(self, v, should_raise=True):
 
  # ### Check that elements have valid colors types ###
  elif self.numbers_allowed() or invalid_els:
- v = copy_to_readonly_numpy_array(validated_v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="O")
  else:
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  elif self.array_ok and is_simple_array(v):
  validated_v = [self.validate_coerce(e, should_raise=False) for e in v]
 
@@ -1870,7 +1861,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els)
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  else:
  v = to_scalar_or_list(v)
  else:
@@ -1918,7 +1909,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(v, kind="O")
  elif self.array_ok and is_simple_array(v):
  v = to_scalar_or_list(v)
  return v

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

@@ -32,15 +32,29 @@ def test_validator_acceptance_simple(val, validator):
 
 
 @pytest.mark.parametrize(
- "val",
- [np.array([2, 3, 4]), pd.Series(["a", "b", "c"]), np.array([[1, 2, 3], [4, 5, 6]])],
+ "val", [np.array([2, 3, 4]), np.array([[1, 2, 3], [4, 5, 6]])],
 )
 def test_validator_acceptance_homogeneous(val, validator):
  coerce_val = validator.validate_coerce(val)
  assert isinstance(coerce_val, np.ndarray)
  assert np.array_equal(validator.present(coerce_val), val)
 
 
+# Accept object array as list
+@pytest.mark.parametrize(
+ "val",
+ [
+ ["A", "B", "C"],
+ np.array(["A", "B", "C"], dtype="object"),
+ pd.Series(["a", "b", "c"]),
+ ],
+)
+def test_validator_accept_object_array_as_list(val, validator):
+ coerce_val = validator.validate_coerce(val)
+ assert isinstance(coerce_val, list)
+ assert coerce_val == list(val)
+
+
 # ### Rejection ###
 @pytest.mark.parametrize("val", ["Hello", 23, set(), {}])
 def test_rejection(val, validator):

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

@@ -126,7 +126,7 @@ def test_rejection_by_element_aok(val, validator_aok):
  [],
  ["bar12"],
  ("foo", "bar012", "baz"),
- np.array([]),
+ np.array([], dtype="object"),
  np.array(["bar12"]),
  np.array(["foo", "bar012", "baz"]),
  ],
@@ -135,7 +135,7 @@ def test_acceptance_aok(val, validator_aok_re):
  # Values should be accepted and returned unchanged
  coerce_val = validator_aok_re.validate_coerce(val)
  if isinstance(val, (np.ndarray, pd.Series)):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == list(np.array(val))
  elif isinstance(val, (list, tuple)):
  assert validator_aok_re.present(coerce_val) == tuple(val)
  else:

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

@@ -149,13 +149,10 @@ def test_color_validator_object(color_validator, color_object_pandas):
  res = color_validator.validate_coerce(color_object_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_pandas)
+assert res == color_object_pandas.tolist()
 
 
 def test_color_validator_categorical(color_validator, color_categorical_pandas):
@@ -164,13 +161,10 @@ def test_color_validator_categorical(color_validator, color_categorical_pandas):
 
  # Check type
  assert color_categorical_pandas.dtype == "category"
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, np.array(color_categorical_pandas))
+assert res == color_categorical_pandas.tolist()
 
 
 def test_data_array_validator_dates_series(
@@ -180,13 +174,10 @@ def test_data_array_validator_dates_series(
  res = data_array_validator.validate_coerce(datetime_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array)
+assert res == dates_array.tolist()
 
 
 def test_data_array_validator_dates_dataframe(
@@ -197,10 +188,7 @@ def test_data_array_validator_dates_dataframe(
  res = data_array_validator.validate_coerce(df)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array.reshape(len(dates_array), 1))
+assert res == dates_array.reshape(len(dates_array), 1).tolist()

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

@@ -138,8 +138,7 @@ def test_acceptance_aok_scalars(val, validator_aok):
 def test_acceptance_aok_list(val, validator_aok):
  coerce_val = validator_aok.validate_coerce(val)
  if isinstance(val, np.ndarray):
- assert isinstance(coerce_val, np.ndarray)
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == val.tolist()
  elif isinstance(val, list):
  assert validator_aok.present(val) == tuple(val)
  else:
@@ -178,9 +177,7 @@ def test_rejection_aok_values(val, validator_aok_values):
 )
 def test_acceptance_no_blanks_aok(val, validator_no_blanks_aok):
  coerce_val = validator_no_blanks_aok.validate_coerce(val)
- if isinstance(val, np.ndarray):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
- elif isinstance(val, list):
+ if isinstance(val, (list, np.ndarray)):
  assert validator_no_blanks_aok.present(coerce_val) == tuple(val)
  else:
  assert coerce_val == val

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

@@ -126,10 +126,7 @@ def test_color_validator_object(color_validator, color_object_xarray):
  res = color_validator.validate_coerce(color_object_xarray)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_xarray)
+assert res == list(color_object_xarray)

packages/python/plotly/_plotly_utils/utils.py

@@ -61,8 +61,10 @@ def encode(self, o):
  # We catch false positive cases (e.g. strings such as titles, labels etc.)
  # but this is ok since the intention is to skip the decoding / reencoding
  # step when it's completely safe
+
  if not ("NaN" in encoded_o or "Infinity" in encoded_o):
  return encoded_o
+
  # now:
  # 1. `loads` to switch Infinity, -Infinity, NaN to None
  # 2. `dumps` again so you get 'null' instead of extended JSON

packages/python/plotly/plotly/basedatatypes.py

@@ -3297,6 +3297,7 @@ def to_dict(self):
  # Frame key is only added if there are any frames
  res = {"data": data, "layout": layout}
  frames = deepcopy([frame._props for frame in self._frame_objs])
+
  if frames:
  res["frames"] = frames
 
@@ -3413,6 +3414,13 @@ def to_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  str
@@ -3469,6 +3477,13 @@ def write_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  None

doc/requirements.txt

@@ -31,3 +31,4 @@ umap-learn==0.5.1
 pooch
 wget
 nbconvert==5.6.1
+orjson

packages/python/plotly/_plotly_utils/basevalidators.py

@@ -53,7 +53,7 @@ def to_scalar_or_list(v):
  return v
 
 
-def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
+def copy_to_readonly_numpy_array_or_list(v, kind=None, force_numeric=False):
  """
  Convert an array-like value into a read-only numpy array
 
@@ -89,7 +89,13 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
 
  # u: unsigned int, i: signed int, f: float
  numeric_kinds = {"u", "i", "f"}
- kind_default_dtypes = {"u": "uint32", "i": "int32", "f": "float64", "O": "object"}
+ kind_default_dtypes = {
+ "u": "uint32",
+ "i": "int32",
+ "f": "float64",
+ "O": "object",
+ "U": "U",
+ }
 
  # Handle pandas Series and Index objects
  if pd and isinstance(v, (pd.Series, pd.Index)):
@@ -113,18 +119,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if not isinstance(v, np.ndarray):
  # v has its own logic on how to convert itself into a numpy array
  if is_numpy_convertable(v):
- return copy_to_readonly_numpy_array(
+ return copy_to_readonly_numpy_array_or_list(
  np.array(v), kind=kind, force_numeric=force_numeric
  )
  else:
  # v is not homogenous array
- v_list = [to_scalar_or_list(e) for e in v]
-
- # Lookup dtype for requested kind, if any
- dtype = kind_default_dtypes.get(first_kind, None)
-
- # construct new array from list
- new_v = np.array(v_list, order="C", dtype=dtype)
+ return [to_scalar_or_list(e) for e in v]
  elif v.dtype.kind in numeric_kinds:
  # v is a homogenous numeric array
  if kind and v.dtype.kind not in kind:
@@ -135,6 +135,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  else:
  # Either no kind was requested or requested kind is satisfied
  new_v = np.ascontiguousarray(v.copy())
+ elif v.dtype.kind == "O":
+ if kind:
+ dtype = kind_default_dtypes.get(first_kind, None)
+ return np.array(v, dtype=dtype)
+ else:
+ return v.tolist()
  else:
  # v is a non-numeric homogenous array
  new_v = v.copy()
@@ -149,12 +155,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if "U" not in kind:
  # Force non-numeric arrays to have object type
  # --------------------------------------------
- # Here we make sure that non-numeric arrays have the object
- # datatype. This works around cases like np.array([1, 2, '3']) where
+ # Here we make sure that non-numeric arrays become lists
+ # This works around cases like np.array([1, 2, '3']) where
  # numpy converts the integers to strings and returns array of dtype
  # '<U21'
  if new_v.dtype.kind not in ["u", "i", "f", "O", "M"]:
-new_v = np.array(v, dtype="object")
+return v.tolist()
 
  # Set new array to be read-only
  # -----------------------------
@@ -191,7 +197,7 @@ def is_homogeneous_array(v):
  if v_numpy.shape == ():
  return False
  else:
- return True
+ return True # v_numpy.dtype.kind in ["u", "i", "f", "M", "U"]
  return False
 
 
@@ -393,7 +399,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  elif is_simple_array(v):
  v = to_scalar_or_list(v)
  else:
@@ -598,7 +604,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els[:10])
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  else:
  v = to_scalar_or_list(v)
  else:
@@ -754,7 +760,7 @@ def validate_coerce(self, v):
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
  try:
- v_array = copy_to_readonly_numpy_array(v, force_numeric=True)
+ v_array = copy_to_readonly_numpy_array_or_list(v, force_numeric=True)
  except (ValueError, TypeError, OverflowError):
  self.raise_invalid_val(v)
 
@@ -881,7 +887,7 @@ def validate_coerce(self, v):
  pass
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
- v_array = copy_to_readonly_numpy_array(
+ v_array = copy_to_readonly_numpy_array_or_list(
  v, kind=("i", "u"), force_numeric=True
  )
 
@@ -1042,26 +1048,7 @@ def validate_coerce(self, v):
  if invalid_els:
  self.raise_invalid_elements(invalid_els)
 
- if is_homogeneous_array(v):
- np = get_module("numpy")
-
- # If not strict, let numpy cast elements to strings
- v = copy_to_readonly_numpy_array(v, kind="U")
-
- # Check no_blank
- if self.no_blank:
- invalid_els = v[v == ""][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- # Check values
- if self.values:
- invalid_inds = np.logical_not(np.isin(v, self.values))
- invalid_els = v[invalid_inds][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- elif is_simple_array(v):
+ if is_simple_array(v) or is_homogeneous_array(v):
  if not self.strict:
  v = [StringValidator.to_str_or_unicode_or_none(e) for e in v]
 
@@ -1338,8 +1325,12 @@ def validate_coerce(self, v, should_raise=True):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
- if self.numbers_allowed() and v.dtype.kind in ["u", "i", "f"]:
+ v = copy_to_readonly_numpy_array_or_list(v)
+ if (
+ not isinstance(v, list)
+ and self.numbers_allowed()
+ and v.dtype.kind in ["u", "i", "f"]
+ ):
  # Numbers are allowed and we have an array of numbers.
  # All good
  pass
@@ -1353,9 +1344,9 @@ def validate_coerce(self, v, should_raise=True):
 
  # ### Check that elements have valid colors types ###
  elif self.numbers_allowed() or invalid_els:
- v = copy_to_readonly_numpy_array(validated_v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="O")
  else:
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  elif self.array_ok and is_simple_array(v):
  validated_v = [self.validate_coerce(e, should_raise=False) for e in v]
 
@@ -1870,7 +1861,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els)
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  else:
  v = to_scalar_or_list(v)
  else:
@@ -1918,7 +1909,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(v, kind="O")
  elif self.array_ok and is_simple_array(v):
  v = to_scalar_or_list(v)
  return v

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

@@ -32,15 +32,29 @@ def test_validator_acceptance_simple(val, validator):
 
 
 @pytest.mark.parametrize(
- "val",
- [np.array([2, 3, 4]), pd.Series(["a", "b", "c"]), np.array([[1, 2, 3], [4, 5, 6]])],
+ "val", [np.array([2, 3, 4]), np.array([[1, 2, 3], [4, 5, 6]])],
 )
 def test_validator_acceptance_homogeneous(val, validator):
  coerce_val = validator.validate_coerce(val)
  assert isinstance(coerce_val, np.ndarray)
  assert np.array_equal(validator.present(coerce_val), val)
 
 
+# Accept object array as list
+@pytest.mark.parametrize(
+ "val",
+ [
+ ["A", "B", "C"],
+ np.array(["A", "B", "C"], dtype="object"),
+ pd.Series(["a", "b", "c"]),
+ ],
+)
+def test_validator_accept_object_array_as_list(val, validator):
+ coerce_val = validator.validate_coerce(val)
+ assert isinstance(coerce_val, list)
+ assert coerce_val == list(val)
+
+
 # ### Rejection ###
 @pytest.mark.parametrize("val", ["Hello", 23, set(), {}])
 def test_rejection(val, validator):

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

@@ -126,7 +126,7 @@ def test_rejection_by_element_aok(val, validator_aok):
  [],
  ["bar12"],
  ("foo", "bar012", "baz"),
- np.array([]),
+ np.array([], dtype="object"),
  np.array(["bar12"]),
  np.array(["foo", "bar012", "baz"]),
  ],
@@ -135,7 +135,7 @@ def test_acceptance_aok(val, validator_aok_re):
  # Values should be accepted and returned unchanged
  coerce_val = validator_aok_re.validate_coerce(val)
  if isinstance(val, (np.ndarray, pd.Series)):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == list(np.array(val))
  elif isinstance(val, (list, tuple)):
  assert validator_aok_re.present(coerce_val) == tuple(val)
  else:

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

@@ -149,13 +149,10 @@ def test_color_validator_object(color_validator, color_object_pandas):
  res = color_validator.validate_coerce(color_object_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_pandas)
+assert res == color_object_pandas.tolist()
 
 
 def test_color_validator_categorical(color_validator, color_categorical_pandas):
@@ -164,13 +161,10 @@ def test_color_validator_categorical(color_validator, color_categorical_pandas):
 
  # Check type
  assert color_categorical_pandas.dtype == "category"
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, np.array(color_categorical_pandas))
+assert res == color_categorical_pandas.tolist()
 
 
 def test_data_array_validator_dates_series(
@@ -180,13 +174,10 @@ def test_data_array_validator_dates_series(
  res = data_array_validator.validate_coerce(datetime_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array)
+assert res == dates_array.tolist()
 
 
 def test_data_array_validator_dates_dataframe(
@@ -197,10 +188,7 @@ def test_data_array_validator_dates_dataframe(
  res = data_array_validator.validate_coerce(df)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array.reshape(len(dates_array), 1))
+assert res == dates_array.reshape(len(dates_array), 1).tolist()

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

@@ -138,8 +138,7 @@ def test_acceptance_aok_scalars(val, validator_aok):
 def test_acceptance_aok_list(val, validator_aok):
  coerce_val = validator_aok.validate_coerce(val)
  if isinstance(val, np.ndarray):
- assert isinstance(coerce_val, np.ndarray)
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == val.tolist()
  elif isinstance(val, list):
  assert validator_aok.present(val) == tuple(val)
  else:
@@ -178,9 +177,7 @@ def test_rejection_aok_values(val, validator_aok_values):
 )
 def test_acceptance_no_blanks_aok(val, validator_no_blanks_aok):
  coerce_val = validator_no_blanks_aok.validate_coerce(val)
- if isinstance(val, np.ndarray):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
- elif isinstance(val, list):
+ if isinstance(val, (list, np.ndarray)):
  assert validator_no_blanks_aok.present(coerce_val) == tuple(val)
  else:
  assert coerce_val == val

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

@@ -126,10 +126,7 @@ def test_color_validator_object(color_validator, color_object_xarray):
  res = color_validator.validate_coerce(color_object_xarray)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_xarray)
+assert res == list(color_object_xarray)

packages/python/plotly/_plotly_utils/utils.py

@@ -61,8 +61,10 @@ def encode(self, o):
  # We catch false positive cases (e.g. strings such as titles, labels etc.)
  # but this is ok since the intention is to skip the decoding / reencoding
  # step when it's completely safe
+
  if not ("NaN" in encoded_o or "Infinity" in encoded_o):
  return encoded_o
+
  # now:
  # 1. `loads` to switch Infinity, -Infinity, NaN to None
  # 2. `dumps` again so you get 'null' instead of extended JSON

packages/python/plotly/plotly/basedatatypes.py

@@ -3297,6 +3297,7 @@ def to_dict(self):
  # Frame key is only added if there are any frames
  res = {"data": data, "layout": layout}
  frames = deepcopy([frame._props for frame in self._frame_objs])
+
  if frames:
  res["frames"] = frames
 
@@ -3413,6 +3414,13 @@ def to_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  str
@@ -3469,6 +3477,13 @@ def write_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  None

doc/requirements.txt

@@ -31,3 +31,4 @@ umap-learn==0.5.1
 pooch
 wget
 nbconvert==5.6.1
+orjson

packages/python/plotly/_plotly_utils/basevalidators.py

@@ -53,7 +53,7 @@ def to_scalar_or_list(v):
  return v
 
 
-def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
+def copy_to_readonly_numpy_array_or_list(v, kind=None, force_numeric=False):
  """
  Convert an array-like value into a read-only numpy array
 
@@ -89,7 +89,13 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
 
  # u: unsigned int, i: signed int, f: float
  numeric_kinds = {"u", "i", "f"}
- kind_default_dtypes = {"u": "uint32", "i": "int32", "f": "float64", "O": "object"}
+ kind_default_dtypes = {
+ "u": "uint32",
+ "i": "int32",
+ "f": "float64",
+ "O": "object",
+ "U": "U",
+ }
 
  # Handle pandas Series and Index objects
  if pd and isinstance(v, (pd.Series, pd.Index)):
@@ -113,18 +119,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if not isinstance(v, np.ndarray):
  # v has its own logic on how to convert itself into a numpy array
  if is_numpy_convertable(v):
- return copy_to_readonly_numpy_array(
+ return copy_to_readonly_numpy_array_or_list(
  np.array(v), kind=kind, force_numeric=force_numeric
  )
  else:
  # v is not homogenous array
- v_list = [to_scalar_or_list(e) for e in v]
-
- # Lookup dtype for requested kind, if any
- dtype = kind_default_dtypes.get(first_kind, None)
-
- # construct new array from list
- new_v = np.array(v_list, order="C", dtype=dtype)
+ return [to_scalar_or_list(e) for e in v]
  elif v.dtype.kind in numeric_kinds:
  # v is a homogenous numeric array
  if kind and v.dtype.kind not in kind:
@@ -135,6 +135,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  else:
  # Either no kind was requested or requested kind is satisfied
  new_v = np.ascontiguousarray(v.copy())
+ elif v.dtype.kind == "O":
+ if kind:
+ dtype = kind_default_dtypes.get(first_kind, None)
+ return np.array(v, dtype=dtype)
+ else:
+ return v.tolist()
  else:
  # v is a non-numeric homogenous array
  new_v = v.copy()
@@ -149,12 +155,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if "U" not in kind:
  # Force non-numeric arrays to have object type
  # --------------------------------------------
- # Here we make sure that non-numeric arrays have the object
- # datatype. This works around cases like np.array([1, 2, '3']) where
+ # Here we make sure that non-numeric arrays become lists
+ # This works around cases like np.array([1, 2, '3']) where
  # numpy converts the integers to strings and returns array of dtype
  # '<U21'
  if new_v.dtype.kind not in ["u", "i", "f", "O", "M"]:
-new_v = np.array(v, dtype="object")
+return v.tolist()
 
  # Set new array to be read-only
  # -----------------------------
@@ -191,7 +197,7 @@ def is_homogeneous_array(v):
  if v_numpy.shape == ():
  return False
  else:
- return True
+ return True # v_numpy.dtype.kind in ["u", "i", "f", "M", "U"]
  return False
 
 
@@ -393,7 +399,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  elif is_simple_array(v):
  v = to_scalar_or_list(v)
  else:
@@ -598,7 +604,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els[:10])
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  else:
  v = to_scalar_or_list(v)
  else:
@@ -754,7 +760,7 @@ def validate_coerce(self, v):
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
  try:
- v_array = copy_to_readonly_numpy_array(v, force_numeric=True)
+ v_array = copy_to_readonly_numpy_array_or_list(v, force_numeric=True)
  except (ValueError, TypeError, OverflowError):
  self.raise_invalid_val(v)
 
@@ -881,7 +887,7 @@ def validate_coerce(self, v):
  pass
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
- v_array = copy_to_readonly_numpy_array(
+ v_array = copy_to_readonly_numpy_array_or_list(
  v, kind=("i", "u"), force_numeric=True
  )
 
@@ -1042,26 +1048,7 @@ def validate_coerce(self, v):
  if invalid_els:
  self.raise_invalid_elements(invalid_els)
 
- if is_homogeneous_array(v):
- np = get_module("numpy")
-
- # If not strict, let numpy cast elements to strings
- v = copy_to_readonly_numpy_array(v, kind="U")
-
- # Check no_blank
- if self.no_blank:
- invalid_els = v[v == ""][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- # Check values
- if self.values:
- invalid_inds = np.logical_not(np.isin(v, self.values))
- invalid_els = v[invalid_inds][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- elif is_simple_array(v):
+ if is_simple_array(v) or is_homogeneous_array(v):
  if not self.strict:
  v = [StringValidator.to_str_or_unicode_or_none(e) for e in v]
 
@@ -1338,8 +1325,12 @@ def validate_coerce(self, v, should_raise=True):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
- if self.numbers_allowed() and v.dtype.kind in ["u", "i", "f"]:
+ v = copy_to_readonly_numpy_array_or_list(v)
+ if (
+ not isinstance(v, list)
+ and self.numbers_allowed()
+ and v.dtype.kind in ["u", "i", "f"]
+ ):
  # Numbers are allowed and we have an array of numbers.
  # All good
  pass
@@ -1353,9 +1344,9 @@ def validate_coerce(self, v, should_raise=True):
 
  # ### Check that elements have valid colors types ###
  elif self.numbers_allowed() or invalid_els:
- v = copy_to_readonly_numpy_array(validated_v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="O")
  else:
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  elif self.array_ok and is_simple_array(v):
  validated_v = [self.validate_coerce(e, should_raise=False) for e in v]
 
@@ -1870,7 +1861,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els)
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  else:
  v = to_scalar_or_list(v)
  else:
@@ -1918,7 +1909,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(v, kind="O")
  elif self.array_ok and is_simple_array(v):
  v = to_scalar_or_list(v)
  return v

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

@@ -32,15 +32,29 @@ def test_validator_acceptance_simple(val, validator):
 
 
 @pytest.mark.parametrize(
- "val",
- [np.array([2, 3, 4]), pd.Series(["a", "b", "c"]), np.array([[1, 2, 3], [4, 5, 6]])],
+ "val", [np.array([2, 3, 4]), np.array([[1, 2, 3], [4, 5, 6]])],
 )
 def test_validator_acceptance_homogeneous(val, validator):
  coerce_val = validator.validate_coerce(val)
  assert isinstance(coerce_val, np.ndarray)
  assert np.array_equal(validator.present(coerce_val), val)
 
 
+# Accept object array as list
+@pytest.mark.parametrize(
+ "val",
+ [
+ ["A", "B", "C"],
+ np.array(["A", "B", "C"], dtype="object"),
+ pd.Series(["a", "b", "c"]),
+ ],
+)
+def test_validator_accept_object_array_as_list(val, validator):
+ coerce_val = validator.validate_coerce(val)
+ assert isinstance(coerce_val, list)
+ assert coerce_val == list(val)
+
+
 # ### Rejection ###
 @pytest.mark.parametrize("val", ["Hello", 23, set(), {}])
 def test_rejection(val, validator):

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

@@ -126,7 +126,7 @@ def test_rejection_by_element_aok(val, validator_aok):
  [],
  ["bar12"],
  ("foo", "bar012", "baz"),
- np.array([]),
+ np.array([], dtype="object"),
  np.array(["bar12"]),
  np.array(["foo", "bar012", "baz"]),
  ],
@@ -135,7 +135,7 @@ def test_acceptance_aok(val, validator_aok_re):
  # Values should be accepted and returned unchanged
  coerce_val = validator_aok_re.validate_coerce(val)
  if isinstance(val, (np.ndarray, pd.Series)):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == list(np.array(val))
  elif isinstance(val, (list, tuple)):
  assert validator_aok_re.present(coerce_val) == tuple(val)
  else:

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

@@ -149,13 +149,10 @@ def test_color_validator_object(color_validator, color_object_pandas):
  res = color_validator.validate_coerce(color_object_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_pandas)
+assert res == color_object_pandas.tolist()
 
 
 def test_color_validator_categorical(color_validator, color_categorical_pandas):
@@ -164,13 +161,10 @@ def test_color_validator_categorical(color_validator, color_categorical_pandas):
 
  # Check type
  assert color_categorical_pandas.dtype == "category"
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, np.array(color_categorical_pandas))
+assert res == color_categorical_pandas.tolist()
 
 
 def test_data_array_validator_dates_series(
@@ -180,13 +174,10 @@ def test_data_array_validator_dates_series(
  res = data_array_validator.validate_coerce(datetime_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array)
+assert res == dates_array.tolist()
 
 
 def test_data_array_validator_dates_dataframe(
@@ -197,10 +188,7 @@ def test_data_array_validator_dates_dataframe(
  res = data_array_validator.validate_coerce(df)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array.reshape(len(dates_array), 1))
+assert res == dates_array.reshape(len(dates_array), 1).tolist()

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

@@ -138,8 +138,7 @@ def test_acceptance_aok_scalars(val, validator_aok):
 def test_acceptance_aok_list(val, validator_aok):
  coerce_val = validator_aok.validate_coerce(val)
  if isinstance(val, np.ndarray):
- assert isinstance(coerce_val, np.ndarray)
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == val.tolist()
  elif isinstance(val, list):
  assert validator_aok.present(val) == tuple(val)
  else:
@@ -178,9 +177,7 @@ def test_rejection_aok_values(val, validator_aok_values):
 )
 def test_acceptance_no_blanks_aok(val, validator_no_blanks_aok):
  coerce_val = validator_no_blanks_aok.validate_coerce(val)
- if isinstance(val, np.ndarray):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
- elif isinstance(val, list):
+ if isinstance(val, (list, np.ndarray)):
  assert validator_no_blanks_aok.present(coerce_val) == tuple(val)
  else:
  assert coerce_val == val

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

@@ -126,10 +126,7 @@ def test_color_validator_object(color_validator, color_object_xarray):
  res = color_validator.validate_coerce(color_object_xarray)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_xarray)
+assert res == list(color_object_xarray)

packages/python/plotly/_plotly_utils/utils.py

@@ -61,8 +61,10 @@ def encode(self, o):
  # We catch false positive cases (e.g. strings such as titles, labels etc.)
  # but this is ok since the intention is to skip the decoding / reencoding
  # step when it's completely safe
+
  if not ("NaN" in encoded_o or "Infinity" in encoded_o):
  return encoded_o
+
  # now:
  # 1. `loads` to switch Infinity, -Infinity, NaN to None
  # 2. `dumps` again so you get 'null' instead of extended JSON

packages/python/plotly/plotly/basedatatypes.py

@@ -3297,6 +3297,7 @@ def to_dict(self):
  # Frame key is only added if there are any frames
  res = {"data": data, "layout": layout}
  frames = deepcopy([frame._props for frame in self._frame_objs])
+
  if frames:
  res["frames"] = frames
 
@@ -3413,6 +3414,13 @@ def to_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  str
@@ -3469,6 +3477,13 @@ def write_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  None

doc/requirements.txt

@@ -31,3 +31,4 @@ umap-learn==0.5.1
 pooch
 wget
 nbconvert==5.6.1
+orjson

packages/python/plotly/_plotly_utils/basevalidators.py

@@ -53,7 +53,7 @@ def to_scalar_or_list(v):
  return v
 
 
-def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
+def copy_to_readonly_numpy_array_or_list(v, kind=None, force_numeric=False):
  """
  Convert an array-like value into a read-only numpy array
 
@@ -89,7 +89,13 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
 
  # u: unsigned int, i: signed int, f: float
  numeric_kinds = {"u", "i", "f"}
- kind_default_dtypes = {"u": "uint32", "i": "int32", "f": "float64", "O": "object"}
+ kind_default_dtypes = {
+ "u": "uint32",
+ "i": "int32",
+ "f": "float64",
+ "O": "object",
+ "U": "U",
+ }
 
  # Handle pandas Series and Index objects
  if pd and isinstance(v, (pd.Series, pd.Index)):
@@ -113,18 +119,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if not isinstance(v, np.ndarray):
  # v has its own logic on how to convert itself into a numpy array
  if is_numpy_convertable(v):
- return copy_to_readonly_numpy_array(
+ return copy_to_readonly_numpy_array_or_list(
  np.array(v), kind=kind, force_numeric=force_numeric
  )
  else:
  # v is not homogenous array
- v_list = [to_scalar_or_list(e) for e in v]
-
- # Lookup dtype for requested kind, if any
- dtype = kind_default_dtypes.get(first_kind, None)
-
- # construct new array from list
- new_v = np.array(v_list, order="C", dtype=dtype)
+ return [to_scalar_or_list(e) for e in v]
  elif v.dtype.kind in numeric_kinds:
  # v is a homogenous numeric array
  if kind and v.dtype.kind not in kind:
@@ -135,6 +135,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  else:
  # Either no kind was requested or requested kind is satisfied
  new_v = np.ascontiguousarray(v.copy())
+ elif v.dtype.kind == "O":
+ if kind:
+ dtype = kind_default_dtypes.get(first_kind, None)
+ return np.array(v, dtype=dtype)
+ else:
+ return v.tolist()
  else:
  # v is a non-numeric homogenous array
  new_v = v.copy()
@@ -149,12 +155,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if "U" not in kind:
  # Force non-numeric arrays to have object type
  # --------------------------------------------
- # Here we make sure that non-numeric arrays have the object
- # datatype. This works around cases like np.array([1, 2, '3']) where
+ # Here we make sure that non-numeric arrays become lists
+ # This works around cases like np.array([1, 2, '3']) where
  # numpy converts the integers to strings and returns array of dtype
  # '<U21'
  if new_v.dtype.kind not in ["u", "i", "f", "O", "M"]:
-new_v = np.array(v, dtype="object")
+return v.tolist()
 
  # Set new array to be read-only
  # -----------------------------
@@ -191,7 +197,7 @@ def is_homogeneous_array(v):
  if v_numpy.shape == ():
  return False
  else:
- return True
+ return True # v_numpy.dtype.kind in ["u", "i", "f", "M", "U"]
  return False
 
 
@@ -393,7 +399,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  elif is_simple_array(v):
  v = to_scalar_or_list(v)
  else:
@@ -598,7 +604,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els[:10])
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  else:
  v = to_scalar_or_list(v)
  else:
@@ -754,7 +760,7 @@ def validate_coerce(self, v):
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
  try:
- v_array = copy_to_readonly_numpy_array(v, force_numeric=True)
+ v_array = copy_to_readonly_numpy_array_or_list(v, force_numeric=True)
  except (ValueError, TypeError, OverflowError):
  self.raise_invalid_val(v)
 
@@ -881,7 +887,7 @@ def validate_coerce(self, v):
  pass
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
- v_array = copy_to_readonly_numpy_array(
+ v_array = copy_to_readonly_numpy_array_or_list(
  v, kind=("i", "u"), force_numeric=True
  )
 
@@ -1042,26 +1048,7 @@ def validate_coerce(self, v):
  if invalid_els:
  self.raise_invalid_elements(invalid_els)
 
- if is_homogeneous_array(v):
- np = get_module("numpy")
-
- # If not strict, let numpy cast elements to strings
- v = copy_to_readonly_numpy_array(v, kind="U")
-
- # Check no_blank
- if self.no_blank:
- invalid_els = v[v == ""][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- # Check values
- if self.values:
- invalid_inds = np.logical_not(np.isin(v, self.values))
- invalid_els = v[invalid_inds][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- elif is_simple_array(v):
+ if is_simple_array(v) or is_homogeneous_array(v):
  if not self.strict:
  v = [StringValidator.to_str_or_unicode_or_none(e) for e in v]
 
@@ -1338,8 +1325,12 @@ def validate_coerce(self, v, should_raise=True):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
- if self.numbers_allowed() and v.dtype.kind in ["u", "i", "f"]:
+ v = copy_to_readonly_numpy_array_or_list(v)
+ if (
+ not isinstance(v, list)
+ and self.numbers_allowed()
+ and v.dtype.kind in ["u", "i", "f"]
+ ):
  # Numbers are allowed and we have an array of numbers.
  # All good
  pass
@@ -1353,9 +1344,9 @@ def validate_coerce(self, v, should_raise=True):
 
  # ### Check that elements have valid colors types ###
  elif self.numbers_allowed() or invalid_els:
- v = copy_to_readonly_numpy_array(validated_v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="O")
  else:
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  elif self.array_ok and is_simple_array(v):
  validated_v = [self.validate_coerce(e, should_raise=False) for e in v]
 
@@ -1870,7 +1861,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els)
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  else:
  v = to_scalar_or_list(v)
  else:
@@ -1918,7 +1909,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(v, kind="O")
  elif self.array_ok and is_simple_array(v):
  v = to_scalar_or_list(v)
  return v

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

@@ -32,15 +32,29 @@ def test_validator_acceptance_simple(val, validator):
 
 
 @pytest.mark.parametrize(
- "val",
- [np.array([2, 3, 4]), pd.Series(["a", "b", "c"]), np.array([[1, 2, 3], [4, 5, 6]])],
+ "val", [np.array([2, 3, 4]), np.array([[1, 2, 3], [4, 5, 6]])],
 )
 def test_validator_acceptance_homogeneous(val, validator):
  coerce_val = validator.validate_coerce(val)
  assert isinstance(coerce_val, np.ndarray)
  assert np.array_equal(validator.present(coerce_val), val)
 
 
+# Accept object array as list
+@pytest.mark.parametrize(
+ "val",
+ [
+ ["A", "B", "C"],
+ np.array(["A", "B", "C"], dtype="object"),
+ pd.Series(["a", "b", "c"]),
+ ],
+)
+def test_validator_accept_object_array_as_list(val, validator):
+ coerce_val = validator.validate_coerce(val)
+ assert isinstance(coerce_val, list)
+ assert coerce_val == list(val)
+
+
 # ### Rejection ###
 @pytest.mark.parametrize("val", ["Hello", 23, set(), {}])
 def test_rejection(val, validator):

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

@@ -126,7 +126,7 @@ def test_rejection_by_element_aok(val, validator_aok):
  [],
  ["bar12"],
  ("foo", "bar012", "baz"),
- np.array([]),
+ np.array([], dtype="object"),
  np.array(["bar12"]),
  np.array(["foo", "bar012", "baz"]),
  ],
@@ -135,7 +135,7 @@ def test_acceptance_aok(val, validator_aok_re):
  # Values should be accepted and returned unchanged
  coerce_val = validator_aok_re.validate_coerce(val)
  if isinstance(val, (np.ndarray, pd.Series)):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == list(np.array(val))
  elif isinstance(val, (list, tuple)):
  assert validator_aok_re.present(coerce_val) == tuple(val)
  else:

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

@@ -149,13 +149,10 @@ def test_color_validator_object(color_validator, color_object_pandas):
  res = color_validator.validate_coerce(color_object_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_pandas)
+assert res == color_object_pandas.tolist()
 
 
 def test_color_validator_categorical(color_validator, color_categorical_pandas):
@@ -164,13 +161,10 @@ def test_color_validator_categorical(color_validator, color_categorical_pandas):
 
  # Check type
  assert color_categorical_pandas.dtype == "category"
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, np.array(color_categorical_pandas))
+assert res == color_categorical_pandas.tolist()
 
 
 def test_data_array_validator_dates_series(
@@ -180,13 +174,10 @@ def test_data_array_validator_dates_series(
  res = data_array_validator.validate_coerce(datetime_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array)
+assert res == dates_array.tolist()
 
 
 def test_data_array_validator_dates_dataframe(
@@ -197,10 +188,7 @@ def test_data_array_validator_dates_dataframe(
  res = data_array_validator.validate_coerce(df)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array.reshape(len(dates_array), 1))
+assert res == dates_array.reshape(len(dates_array), 1).tolist()

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

@@ -138,8 +138,7 @@ def test_acceptance_aok_scalars(val, validator_aok):
 def test_acceptance_aok_list(val, validator_aok):
  coerce_val = validator_aok.validate_coerce(val)
  if isinstance(val, np.ndarray):
- assert isinstance(coerce_val, np.ndarray)
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == val.tolist()
  elif isinstance(val, list):
  assert validator_aok.present(val) == tuple(val)
  else:
@@ -178,9 +177,7 @@ def test_rejection_aok_values(val, validator_aok_values):
 )
 def test_acceptance_no_blanks_aok(val, validator_no_blanks_aok):
  coerce_val = validator_no_blanks_aok.validate_coerce(val)
- if isinstance(val, np.ndarray):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
- elif isinstance(val, list):
+ if isinstance(val, (list, np.ndarray)):
  assert validator_no_blanks_aok.present(coerce_val) == tuple(val)
  else:
  assert coerce_val == val

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

@@ -126,10 +126,7 @@ def test_color_validator_object(color_validator, color_object_xarray):
  res = color_validator.validate_coerce(color_object_xarray)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_xarray)
+assert res == list(color_object_xarray)

packages/python/plotly/_plotly_utils/utils.py

@@ -61,8 +61,10 @@ def encode(self, o):
  # We catch false positive cases (e.g. strings such as titles, labels etc.)
  # but this is ok since the intention is to skip the decoding / reencoding
  # step when it's completely safe
+
  if not ("NaN" in encoded_o or "Infinity" in encoded_o):
  return encoded_o
+
  # now:
  # 1. `loads` to switch Infinity, -Infinity, NaN to None
  # 2. `dumps` again so you get 'null' instead of extended JSON

packages/python/plotly/plotly/basedatatypes.py

@@ -3297,6 +3297,7 @@ def to_dict(self):
  # Frame key is only added if there are any frames
  res = {"data": data, "layout": layout}
  frames = deepcopy([frame._props for frame in self._frame_objs])
+
  if frames:
  res["frames"] = frames
 
@@ -3413,6 +3414,13 @@ def to_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  str
@@ -3469,6 +3477,13 @@ def write_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  None

doc/requirements.txt

@@ -31,3 +31,4 @@ umap-learn==0.5.1
 pooch
 wget
 nbconvert==5.6.1
+orjson

packages/python/plotly/_plotly_utils/basevalidators.py

@@ -53,7 +53,7 @@ def to_scalar_or_list(v):
  return v
 
 
-def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
+def copy_to_readonly_numpy_array_or_list(v, kind=None, force_numeric=False):
  """
  Convert an array-like value into a read-only numpy array
 
@@ -89,7 +89,13 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
 
  # u: unsigned int, i: signed int, f: float
  numeric_kinds = {"u", "i", "f"}
- kind_default_dtypes = {"u": "uint32", "i": "int32", "f": "float64", "O": "object"}
+ kind_default_dtypes = {
+ "u": "uint32",
+ "i": "int32",
+ "f": "float64",
+ "O": "object",
+ "U": "U",
+ }
 
  # Handle pandas Series and Index objects
  if pd and isinstance(v, (pd.Series, pd.Index)):
@@ -113,18 +119,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if not isinstance(v, np.ndarray):
  # v has its own logic on how to convert itself into a numpy array
  if is_numpy_convertable(v):
- return copy_to_readonly_numpy_array(
+ return copy_to_readonly_numpy_array_or_list(
  np.array(v), kind=kind, force_numeric=force_numeric
  )
  else:
  # v is not homogenous array
- v_list = [to_scalar_or_list(e) for e in v]
-
- # Lookup dtype for requested kind, if any
- dtype = kind_default_dtypes.get(first_kind, None)
-
- # construct new array from list
- new_v = np.array(v_list, order="C", dtype=dtype)
+ return [to_scalar_or_list(e) for e in v]
  elif v.dtype.kind in numeric_kinds:
  # v is a homogenous numeric array
  if kind and v.dtype.kind not in kind:
@@ -135,6 +135,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  else:
  # Either no kind was requested or requested kind is satisfied
  new_v = np.ascontiguousarray(v.copy())
+ elif v.dtype.kind == "O":
+ if kind:
+ dtype = kind_default_dtypes.get(first_kind, None)
+ return np.array(v, dtype=dtype)
+ else:
+ return v.tolist()
  else:
  # v is a non-numeric homogenous array
  new_v = v.copy()
@@ -149,12 +155,12 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
  if "U" not in kind:
  # Force non-numeric arrays to have object type
  # --------------------------------------------
- # Here we make sure that non-numeric arrays have the object
- # datatype. This works around cases like np.array([1, 2, '3']) where
+ # Here we make sure that non-numeric arrays become lists
+ # This works around cases like np.array([1, 2, '3']) where
  # numpy converts the integers to strings and returns array of dtype
  # '<U21'
  if new_v.dtype.kind not in ["u", "i", "f", "O", "M"]:
-new_v = np.array(v, dtype="object")
+return v.tolist()
 
  # Set new array to be read-only
  # -----------------------------
@@ -191,7 +197,7 @@ def is_homogeneous_array(v):
  if v_numpy.shape == ():
  return False
  else:
- return True
+ return True # v_numpy.dtype.kind in ["u", "i", "f", "M", "U"]
  return False
 
 
@@ -393,7 +399,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  elif is_simple_array(v):
  v = to_scalar_or_list(v)
  else:
@@ -598,7 +604,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els[:10])
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
+ v = copy_to_readonly_numpy_array_or_list(v)
  else:
  v = to_scalar_or_list(v)
  else:
@@ -754,7 +760,7 @@ def validate_coerce(self, v):
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
  try:
- v_array = copy_to_readonly_numpy_array(v, force_numeric=True)
+ v_array = copy_to_readonly_numpy_array_or_list(v, force_numeric=True)
  except (ValueError, TypeError, OverflowError):
  self.raise_invalid_val(v)
 
@@ -881,7 +887,7 @@ def validate_coerce(self, v):
  pass
  elif self.array_ok and is_homogeneous_array(v):
  np = get_module("numpy")
- v_array = copy_to_readonly_numpy_array(
+ v_array = copy_to_readonly_numpy_array_or_list(
  v, kind=("i", "u"), force_numeric=True
  )
 
@@ -1042,26 +1048,7 @@ def validate_coerce(self, v):
  if invalid_els:
  self.raise_invalid_elements(invalid_els)
 
- if is_homogeneous_array(v):
- np = get_module("numpy")
-
- # If not strict, let numpy cast elements to strings
- v = copy_to_readonly_numpy_array(v, kind="U")
-
- # Check no_blank
- if self.no_blank:
- invalid_els = v[v == ""][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- # Check values
- if self.values:
- invalid_inds = np.logical_not(np.isin(v, self.values))
- invalid_els = v[invalid_inds][:10].tolist()
- if invalid_els:
- self.raise_invalid_elements(invalid_els)
-
- elif is_simple_array(v):
+ if is_simple_array(v) or is_homogeneous_array(v):
  if not self.strict:
  v = [StringValidator.to_str_or_unicode_or_none(e) for e in v]
 
@@ -1338,8 +1325,12 @@ def validate_coerce(self, v, should_raise=True):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v)
- if self.numbers_allowed() and v.dtype.kind in ["u", "i", "f"]:
+ v = copy_to_readonly_numpy_array_or_list(v)
+ if (
+ not isinstance(v, list)
+ and self.numbers_allowed()
+ and v.dtype.kind in ["u", "i", "f"]
+ ):
  # Numbers are allowed and we have an array of numbers.
  # All good
  pass
@@ -1353,9 +1344,9 @@ def validate_coerce(self, v, should_raise=True):
 
  # ### Check that elements have valid colors types ###
  elif self.numbers_allowed() or invalid_els:
- v = copy_to_readonly_numpy_array(validated_v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="O")
  else:
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  elif self.array_ok and is_simple_array(v):
  validated_v = [self.validate_coerce(e, should_raise=False) for e in v]
 
@@ -1870,7 +1861,7 @@ def validate_coerce(self, v):
  self.raise_invalid_elements(invalid_els)
 
  if is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(validated_v, kind="U")
+ v = copy_to_readonly_numpy_array_or_list(validated_v, kind="U")
  else:
  v = to_scalar_or_list(v)
  else:
@@ -1918,7 +1909,7 @@ def validate_coerce(self, v):
  # Pass None through
  pass
  elif self.array_ok and is_homogeneous_array(v):
- v = copy_to_readonly_numpy_array(v, kind="O")
+ v = copy_to_readonly_numpy_array_or_list(v, kind="O")
  elif self.array_ok and is_simple_array(v):
  v = to_scalar_or_list(v)
  return v

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

@@ -32,15 +32,29 @@ def test_validator_acceptance_simple(val, validator):
 
 
 @pytest.mark.parametrize(
- "val",
- [np.array([2, 3, 4]), pd.Series(["a", "b", "c"]), np.array([[1, 2, 3], [4, 5, 6]])],
+ "val", [np.array([2, 3, 4]), np.array([[1, 2, 3], [4, 5, 6]])],
 )
 def test_validator_acceptance_homogeneous(val, validator):
  coerce_val = validator.validate_coerce(val)
  assert isinstance(coerce_val, np.ndarray)
  assert np.array_equal(validator.present(coerce_val), val)
 
 
+# Accept object array as list
+@pytest.mark.parametrize(
+ "val",
+ [
+ ["A", "B", "C"],
+ np.array(["A", "B", "C"], dtype="object"),
+ pd.Series(["a", "b", "c"]),
+ ],
+)
+def test_validator_accept_object_array_as_list(val, validator):
+ coerce_val = validator.validate_coerce(val)
+ assert isinstance(coerce_val, list)
+ assert coerce_val == list(val)
+
+
 # ### Rejection ###
 @pytest.mark.parametrize("val", ["Hello", 23, set(), {}])
 def test_rejection(val, validator):

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

@@ -126,7 +126,7 @@ def test_rejection_by_element_aok(val, validator_aok):
  [],
  ["bar12"],
  ("foo", "bar012", "baz"),
- np.array([]),
+ np.array([], dtype="object"),
  np.array(["bar12"]),
  np.array(["foo", "bar012", "baz"]),
  ],
@@ -135,7 +135,7 @@ def test_acceptance_aok(val, validator_aok_re):
  # Values should be accepted and returned unchanged
  coerce_val = validator_aok_re.validate_coerce(val)
  if isinstance(val, (np.ndarray, pd.Series)):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == list(np.array(val))
  elif isinstance(val, (list, tuple)):
  assert validator_aok_re.present(coerce_val) == tuple(val)
  else:

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

@@ -149,13 +149,10 @@ def test_color_validator_object(color_validator, color_object_pandas):
  res = color_validator.validate_coerce(color_object_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_pandas)
+assert res == color_object_pandas.tolist()
 
 
 def test_color_validator_categorical(color_validator, color_categorical_pandas):
@@ -164,13 +161,10 @@ def test_color_validator_categorical(color_validator, color_categorical_pandas):
 
  # Check type
  assert color_categorical_pandas.dtype == "category"
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, np.array(color_categorical_pandas))
+assert res == color_categorical_pandas.tolist()
 
 
 def test_data_array_validator_dates_series(
@@ -180,13 +174,10 @@ def test_data_array_validator_dates_series(
  res = data_array_validator.validate_coerce(datetime_pandas)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array)
+assert res == dates_array.tolist()
 
 
 def test_data_array_validator_dates_dataframe(
@@ -197,10 +188,7 @@ def test_data_array_validator_dates_dataframe(
  res = data_array_validator.validate_coerce(df)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, dates_array.reshape(len(dates_array), 1))
+assert res == dates_array.reshape(len(dates_array), 1).tolist()

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

@@ -138,8 +138,7 @@ def test_acceptance_aok_scalars(val, validator_aok):
 def test_acceptance_aok_list(val, validator_aok):
  coerce_val = validator_aok.validate_coerce(val)
  if isinstance(val, np.ndarray):
- assert isinstance(coerce_val, np.ndarray)
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
+ assert coerce_val == val.tolist()
  elif isinstance(val, list):
  assert validator_aok.present(val) == tuple(val)
  else:
@@ -178,9 +177,7 @@ def test_rejection_aok_values(val, validator_aok_values):
 )
 def test_acceptance_no_blanks_aok(val, validator_no_blanks_aok):
  coerce_val = validator_no_blanks_aok.validate_coerce(val)
- if isinstance(val, np.ndarray):
- assert np.array_equal(coerce_val, np.array(val, dtype=coerce_val.dtype))
- elif isinstance(val, list):
+ if isinstance(val, (list, np.ndarray)):
  assert validator_no_blanks_aok.present(coerce_val) == tuple(val)
  else:
  assert coerce_val == val

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

@@ -126,10 +126,7 @@ def test_color_validator_object(color_validator, color_object_xarray):
  res = color_validator.validate_coerce(color_object_xarray)
 
  # Check type
- assert isinstance(res, np.ndarray)
-
- # Check dtype
- assert res.dtype == "object"
+ assert isinstance(res, list)
 
  # Check values
-np.testing.assert_array_equal(res, color_object_xarray)
+assert res == list(color_object_xarray)

packages/python/plotly/_plotly_utils/utils.py

@@ -61,8 +61,10 @@ def encode(self, o):
  # We catch false positive cases (e.g. strings such as titles, labels etc.)
  # but this is ok since the intention is to skip the decoding / reencoding
  # step when it's completely safe
+
  if not ("NaN" in encoded_o or "Infinity" in encoded_o):
  return encoded_o
+
  # now:
  # 1. `loads` to switch Infinity, -Infinity, NaN to None
  # 2. `dumps` again so you get 'null' instead of extended JSON

packages/python/plotly/plotly/basedatatypes.py

@@ -3297,6 +3297,7 @@ def to_dict(self):
  # Frame key is only added if there are any frames
  res = {"data": data, "layout": layout}
  frames = deepcopy([frame._props for frame in self._frame_objs])
+
  if frames:
  res["frames"] = frames
 
@@ -3413,6 +3414,13 @@ def to_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  str
@@ -3469,6 +3477,13 @@ def write_json(self, *args, **kwargs):
  remove_uids: bool (default True)
  True if trace UIDs should be omitted from the JSON representation
 
+ engine: str (default None)
+ The JSON encoding engine to use. One of:
+ - "json" for an encoder based on the built-in Python json module
+ - "orjson" for a fast encoder the requires the orjson package
+ If not specified, the default encoder is set to the current value of
+ plotly.io.json.config.default_encoder.
+
  Returns
  -------
  None