Support JSON serialization of numpy datetime64 arrays

packages/python/plotly/_plotly_utils/basevalidators.py

@@ -146,7 +146,7 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
         # datatype. 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"]:
+        if new_v.dtype.kind not in ["u", "i", "f", "O", "M"]:
             new_v = np.array(v, dtype="object")
 
     # Set new array to be read-only

packages/python/plotly/_plotly_utils/utils.py

@@ -184,8 +184,13 @@ def encode_as_numpy(obj):
 
         if obj is numpy.ma.core.masked:
             return float("nan")
-        else:
-            raise NotEncodable
+        elif isinstance(obj, numpy.ndarray):
+            try:
+                return numpy.datetime_as_string(obj).tolist()
+            except TypeError:
+                pass
+
+        raise NotEncodable
 
     @staticmethod
     def encode_as_datetime(obj):

packages/python/plotly/plotly/tests/test_optional/test_utils/test_utils.py

@@ -277,6 +277,15 @@ def test_datetime_dot_date(self):
         j1 = _json.dumps(a, cls=utils.PlotlyJSONEncoder)
         assert j1 == '["2014-01-01", "2014-01-02"]'
 
+    def test_numpy_datetime64(self):
+        a = pd.date_range("2011-07-11", "2011-07-13", freq="D").values
+        j1 = _json.dumps(a, cls=utils.PlotlyJSONEncoder)
+        assert (
+            j1 == '["2011-07-11T00:00:00.000000000", '
+                  '"2011-07-12T00:00:00.000000000", '
+                  '"2011-07-13T00:00:00.000000000"]'
+        )
+
     def test_pil_image_encoding(self):
         import _plotly_utils