Optimize copy_to_readonly_numpy_array for numeric pandas series/index…

… objects (#1149)

* Removed redundant test

* Performance optimization for numeric coercion of pandas series/index

* Preserve integer type in homogenous array (don't case to double)

_plotly_utils/basevalidators.py

@@ -83,11 +83,20 @@ def copy_to_readonly_numpy_array(v, dtype=None, force_numeric=False):
     # u: unsigned int, i: signed int, f: float
     numeric_kinds = ['u', 'i', 'f']
 
+    # Unwrap data types that have a `values` property that might be a numpy
+    # array. If this values property is a numeric numpy array then we
+    # can take the fast path below
+    if pd and isinstance(v, (pd.Series, pd.Index)):
+        v = v.values
+
     if not isinstance(v, np.ndarray):
         v_list = [to_scalar_or_list(e) for e in v]
         new_v = np.array(v_list, order='C', dtype=dtype)
     elif v.dtype.kind in numeric_kinds:
-        new_v = np.ascontiguousarray(v.astype(dtype))
+        if dtype:
+            new_v = np.ascontiguousarray(v.astype(dtype))
+        else:
+            new_v = np.ascontiguousarray(v)
     else:
         new_v = v.copy()
 

_plotly_utils/tests/validators/test_color_validator.py

@@ -192,12 +192,6 @@ def test_description_aok(validator_aok):
     assert 'A list or array of any of the above' in desc
 
 
-def test_description_aok(validator_colorscale):
-    desc = validator_colorscale.description()
-    assert 'A number that will be interpreted as a color' in desc
-    assert 'A list or array of any of the above' not in desc
-
-
 def test_description_aok_colorscale(validator_aok_colorscale):
     desc = validator_aok_colorscale.description()
     assert 'A number that will be interpreted as a color' in desc

_plotly_utils/tests/validators/test_integer_validator.py

@@ -4,6 +4,7 @@
 from pytest import approx
 from _plotly_utils.basevalidators import IntegerValidator
 import numpy as np
+import pandas as pd
 
 
 # ### Fixtures ###
@@ -121,17 +122,20 @@ def test_acceptance_aok_list(val, validator_aok):
                          [([1, 0], (1, 0)),
                           ((1, -1), (1, -1)),
                           (np.array([-1, 0, 5.0], dtype='int16'), [-1, 0, 5]),
-                          (np.array([1, 0], dtype=np.int64), [1, 0])])
+                          (np.array([1, 0], dtype=np.int64), [1, 0]),
+                          (pd.Series([1, 0], dtype=np.int64), [1, 0]),
+                          (pd.Index([1, 0], dtype=np.int64), [1, 0])])
 def test_coercion_aok_list(val, expected, validator_aok):
     v = validator_aok.validate_coerce(val)
-    if isinstance(val, np.ndarray):
+    if isinstance(val, (np.ndarray, pd.Series, pd.Index)):
         assert v.dtype == np.int32
         assert np.array_equal(validator_aok.present(v),
                               np.array(expected, dtype=np.int32))
     else:
         assert isinstance(v, list)
         assert validator_aok.present(v) == expected
 
+
 # ### Rejection ###
 #
 @pytest.mark.parametrize('val',

_plotly_utils/tests/validators/test_number_validator.py

@@ -3,7 +3,7 @@
 
 from _plotly_utils.basevalidators import NumberValidator
 import numpy as np
-
+import pandas as pd
 
 # Fixtures
 # --------
@@ -118,12 +118,13 @@ def test_acceptance_aok_list(val, validator_aok):
 #     Coerced to general consistent numeric type
 @pytest.mark.parametrize('val,expected',
                          [([1.0, 0], (1.0, 0)),
-                          (np.array([1, -1]), np.array([1.0, -1.0])),
+                          (np.array([1, -1]), np.array([1, -1])),
+                          (pd.Series([1, -1]), np.array([1, -1])),
+                          (pd.Index([1, -1]), np.array([1, -1])),
                           ((-0.1234, 0, -1), (-0.1234, 0.0, -1.0))])
 def test_coercion_aok_list(val, expected, validator_aok):
     v = validator_aok.validate_coerce(val)
-    if isinstance(val, np.ndarray):
-        assert v.dtype == 'float'
+    if isinstance(val, (np.ndarray, pd.Series, pd.Index)):
         assert np.array_equal(v, expected)
     else:
         assert isinstance(v, list)