[SPARK-49764][PYTHON][CONNECT] Support area plots

### What changes were proposed in this pull request?
Support area plots with plotly backend on both Spark Connect and Spark classic.

### Why are the changes needed?
While Pandas on Spark supports plotting, PySpark currently lacks this feature. The proposed API will enable users to generate visualizations. This will provide users with an intuitive, interactive way to explore and understand large datasets directly from PySpark DataFrames, streamlining the data analysis workflow in distributed environments.

See more at [PySpark Plotting API Specification](https://docs.google.com/document/d/1IjOEzC8zcetG86WDvqkereQPj_NGLNW7Bdu910g30Dg/edit?usp=sharing) in progress.

Part of https://issues.apache.org/jira/browse/SPARK-49530.

### Does this PR introduce _any_ user-facing change?
Yes. Area plots are supported as shown below.

```py
>>> from datetime import datetime
>>> data = [
...     (3, 5, 20, datetime(2018, 1, 31)),
...     (2, 5, 42, datetime(2018, 2, 28)),
...     (3, 6, 28, datetime(2018, 3, 31)),
...     (9, 12, 62, datetime(2018, 4, 30))]
>>> columns = ["sales", "signups", "visits", "date"]
>>> df = spark.createDataFrame(data, columns)
>>> fig = df.plot.area(x="date", y=["sales", "signups", "visits"])  # df.plot(kind="area", x="date", y=["sales", "signups", "visits"])
>>> fig.show()
```
![newplot (7)](https://github.com/user-attachments/assets/e603cd99-ce8b-4448-8e1f-cbc093097c45)

### How was this patch tested?
Unit tests.

### Was this patch authored or co-authored using generative AI tooling?
No.

Closes apache#48236 from xinrong-meng/plot_area.

Authored-by: Xinrong Meng <[email protected]>
Signed-off-by: Hyukjin Kwon <[email protected]>

python/pyspark/sql/plot/core.py

@@ -93,6 +93,7 @@ def get_sampled(self, sdf: "DataFrame") -> "pd.DataFrame":
 
 class PySparkPlotAccessor:
     plot_data_map = {
+        "area": PySparkSampledPlotBase().get_sampled,
         "bar": PySparkTopNPlotBase().get_top_n,
         "barh": PySparkTopNPlotBase().get_top_n,
         "line": PySparkSampledPlotBase().get_sampled,
@@ -264,3 +265,37 @@ def scatter(self, x: str, y: str, **kwargs: Any) -> "Figure":
         >>> df.plot.scatter(x='length', y='width')  # doctest: +SKIP
         """
         return self(kind="scatter", x=x, y=y, **kwargs)
+
+    def area(self, x: str, y: str, **kwargs: Any) -> "Figure":
+        """
+        Draw a stacked area plot.
+
+        An area plot displays quantitative data visually.
+
+        Parameters
+        ----------
+        x : str
+            Name of column to use for the horizontal axis.
+        y : str or list of str
+            Name(s) of the column(s) to plot.
+        **kwargs: Optional
+            Additional keyword arguments.
+
+        Returns
+        -------
+        :class:`plotly.graph_objs.Figure`
+
+        Examples
+        --------
+        >>> from datetime import datetime
+        >>> data = [
+        ...     (3, 5, 20, datetime(2018, 1, 31)),
+        ...     (2, 5, 42, datetime(2018, 2, 28)),
+        ...     (3, 6, 28, datetime(2018, 3, 31)),
+        ...     (9, 12, 62, datetime(2018, 4, 30))
+        ... ]
+        >>> columns = ["sales", "signups", "visits", "date"]
+        >>> df = spark.createDataFrame(data, columns)
+        >>> df.plot.area(x='date', y=['sales', 'signups', 'visits'])  # doctest: +SKIP
+        """
+        return self(kind="area", x=x, y=y, **kwargs)

python/pyspark/sql/tests/plot/test_frame_plot_plotly.py

@@ -16,6 +16,8 @@
 #
 
 import unittest
+from datetime import datetime
+
 import pyspark.sql.plot  # noqa: F401
 from pyspark.testing.sqlutils import ReusedSQLTestCase, have_plotly, plotly_requirement_message
 
@@ -34,6 +36,17 @@ def sdf2(self):
         columns = ["length", "width", "species"]
         return self.spark.createDataFrame(data, columns)
 
+    @property
+    def sdf3(self):
+        data = [
+            (3, 5, 20, datetime(2018, 1, 31)),
+            (2, 5, 42, datetime(2018, 2, 28)),
+            (3, 6, 28, datetime(2018, 3, 31)),
+            (9, 12, 62, datetime(2018, 4, 30)),
+        ]
+        columns = ["sales", "signups", "visits", "date"]
+        return self.spark.createDataFrame(data, columns)
+
     def _check_fig_data(self, kind, fig_data, expected_x, expected_y, expected_name=""):
         if kind == "line":
             self.assertEqual(fig_data["mode"], "lines")
@@ -46,6 +59,11 @@ def _check_fig_data(self, kind, fig_data, expected_x, expected_y, expected_name=
         elif kind == "scatter":
             self.assertEqual(fig_data["type"], "scatter")
             self.assertEqual(fig_data["orientation"], "v")
+            self.assertEqual(fig_data["mode"], "markers")
+        elif kind == "area":
+            self.assertEqual(fig_data["type"], "scatter")
+            self.assertEqual(fig_data["orientation"], "v")
+            self.assertEqual(fig_data["mode"], "lines")
 
         self.assertEqual(fig_data["xaxis"], "x")
         self.assertEqual(list(fig_data["x"]), expected_x)
@@ -98,6 +116,23 @@ def test_scatter_plot(self):
             "scatter", fig["data"][0], [3.5, 3.0, 3.2, 3.2, 3.0], [5.1, 4.9, 7.0, 6.4, 5.9]
         )
 
+    def test_area_plot(self):
+        # single column as vertical axis
+        fig = self.sdf3.plot(kind="area", x="date", y="sales")
+        expected_x = [
+            datetime(2018, 1, 31, 0, 0),
+            datetime(2018, 2, 28, 0, 0),
+            datetime(2018, 3, 31, 0, 0),
+            datetime(2018, 4, 30, 0, 0),
+        ]
+        self._check_fig_data("area", fig["data"][0], expected_x, [3, 2, 3, 9])
+
+        # multiple columns as vertical axis
+        fig = self.sdf3.plot.area(x="date", y=["sales", "signups", "visits"])
+        self._check_fig_data("area", fig["data"][0], expected_x, [3, 2, 3, 9], "sales")
+        self._check_fig_data("area", fig["data"][1], expected_x, [5, 5, 6, 12], "signups")
+        self._check_fig_data("area", fig["data"][2], expected_x, [20, 42, 28, 62], "visits")
+
 
 class DataFramePlotPlotlyTests(DataFramePlotPlotlyTestsMixin, ReusedSQLTestCase):
     pass