Support coercing pandas datetime DataFrames

Author: jonmmease

Diff for: packages/python/plotly/_plotly_utils/basevalidators.py

@@ -103,6 +103,13 @@ def copy_to_readonly_numpy_array(v, kind=None, force_numeric=False):
             else:
                 # DatetimeIndex
                 v = v.to_pydatetime()
+    elif pd and isinstance(v, pd.DataFrame) and len(set(v.dtypes)) == 1:
+        dtype = v.dtypes[0]
+        if dtype.kind in numeric_kinds:
+            v = v.values
+        elif dtype.kind == "M":
+            v = [row.dt.to_pydatetime().tolist() for i, row in v.iterrows()]
+
     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):

Diff for: packages/python/plotly/_plotly_utils/tests/validators/test_pandas_series_input.py

@@ -173,7 +173,7 @@ def test_color_validator_categorical(color_validator, color_categorical_pandas):
     np.testing.assert_array_equal(res, np.array(color_categorical_pandas))
 
 
-def test_data_array_validator_dates(data_array_validator, datetime_pandas, dates_array):
+def test_data_array_validator_dates_series(data_array_validator, datetime_pandas, dates_array):
 
     res = data_array_validator.validate_coerce(datetime_pandas)
 
@@ -185,3 +185,18 @@ def test_data_array_validator_dates(data_array_validator, datetime_pandas, dates
 
     # Check values
     np.testing.assert_array_equal(res, dates_array)
+
+
+def test_data_array_validator_dates_dataframe(data_array_validator, datetime_pandas, dates_array):
+
+    df = pd.DataFrame({"d": datetime_pandas})
+    res = data_array_validator.validate_coerce(df)
+
+    # Check type
+    assert isinstance(res, np.ndarray)
+
+    # Check dtype
+    assert res.dtype == "object"
+
+    # Check values
+    np.testing.assert_array_equal(res, dates_array.reshape(len(dates_array), 1))

Diff for: packages/python/plotly/plotly/tests/test_optional/test_utils/test_utils.py

@@ -257,6 +257,61 @@ def test_pandas_json_encoding(self):
         j6 = _json.dumps(ts.index, cls=utils.PlotlyJSONEncoder)
         assert j6 == '["2011-01-01T00:00:00", "2011-01-01T01:00:00"]'
 
+    def test_encode_customdata_datetime_series(self):
+        df = pd.DataFrame(dict(t=pd.to_datetime(["2010-01-01", "2010-01-02"])))
+
+        # 1D customdata
+        fig = Figure(
+            Scatter(x=df["t"], customdata=df["t"]), layout=dict(template="none")
+        )
+        fig_json = _json.dumps(
+            fig, cls=utils.PlotlyJSONEncoder, separators=(",", ":"), sort_keys=True
+        )
+        self.assertTrue(
+            fig_json.startswith(
+                '{"data":[{"customdata":["2010-01-01T00:00:00","2010-01-02T00:00:00"]'
+            )
+        )
+
+    def test_encode_customdata_datetime_homogenous_dataframe(self):
+        df = pd.DataFrame(dict(
+            t1=pd.to_datetime(["2010-01-01", "2010-01-02"]),
+            t2=pd.to_datetime(["2011-01-01", "2011-01-02"]),
+        ))
+        # 2D customdata
+        fig = Figure(
+            Scatter(x=df["t1"], customdata=df[["t1", "t2"]]), layout=dict(template="none")
+        )
+        fig_json = _json.dumps(
+            fig, cls=utils.PlotlyJSONEncoder, separators=(",", ":"), sort_keys=True
+        )
+        self.assertTrue(
+            fig_json.startswith(
+                '{"data":[{"customdata":'
+                '[["2010-01-01T00:00:00","2011-01-01T00:00:00"],'
+                '["2010-01-02T00:00:00","2011-01-02T00:00:00"]'
+            )
+        )
+
+    def test_encode_customdata_datetime_inhomogenous_dataframe(self):
+        df = pd.DataFrame(dict(
+            t=pd.to_datetime(["2010-01-01", "2010-01-02"]),
+            v=np.arange(2),
+        ))
+        # 2D customdata
+        fig = Figure(
+            Scatter(x=df["t"], customdata=df[["t", "v"]]), layout=dict(template="none")
+        )
+        fig_json = _json.dumps(
+            fig, cls=utils.PlotlyJSONEncoder, separators=(",", ":"), sort_keys=True
+        )
+        self.assertTrue(
+            fig_json.startswith(
+                '{"data":[{"customdata":'
+                '[["2010-01-01T00:00:00",0],["2010-01-02T00:00:00",1]]'
+            )
+        )
+
     def test_numpy_masked_json_encoding(self):
         l = [1, 2, np.ma.core.masked]
         j1 = _json.dumps(l, cls=utils.PlotlyJSONEncoder)