[SPARK-49776][PYTHON][CONNECT] Support pie 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(kind="pie", x="date", y="sales")  # df.plot(kind="pie", x="date", y="sales")
>>> fig.show()
```
![newplot (8)](https://github.com/user-attachments/assets/c4078bb7-4d84-4607-bcd7-bdd6fbbf8e28)

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

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

Closes apache#48256 from xinrong-meng/plot_pie.

Authored-by: Xinrong Meng <xinrong@apache.org>
Signed-off-by: Xinrong Meng <xinrong@apache.org>

python/pyspark/errors/error-conditions.json

@@ -812,6 +812,11 @@
       "Pipe function `<func_name>` exited with error code <error_code>."
     ]
   },
+  "PLOT_NOT_NUMERIC_COLUMN": {
+    "message": [
+      "Argument <arg_name> must be a numerical column for plotting, got <arg_type>."
+    ]
+  },
   "PYTHON_HASH_SEED_NOT_SET": {
     "message": [
       "Randomness of hash of string should be disabled via PYTHONHASHSEED."

python/pyspark/sql/plot/core.py

@@ -17,7 +17,8 @@
 
 from typing import Any, TYPE_CHECKING, Optional, Union
 from types import ModuleType
-from pyspark.errors import PySparkRuntimeError, PySparkValueError
+from pyspark.errors import PySparkRuntimeError, PySparkTypeError, PySparkValueError
+from pyspark.sql.types import NumericType
 from pyspark.sql.utils import require_minimum_plotly_version
 
 
@@ -97,6 +98,7 @@ class PySparkPlotAccessor:
         "bar": PySparkTopNPlotBase().get_top_n,
         "barh": PySparkTopNPlotBase().get_top_n,
         "line": PySparkSampledPlotBase().get_sampled,
+        "pie": PySparkTopNPlotBase().get_top_n,
         "scatter": PySparkSampledPlotBase().get_sampled,
     }
     _backends = {}  # type: ignore[var-annotated]
@@ -299,3 +301,40 @@ def area(self, x: str, y: str, **kwargs: Any) -> "Figure":
         >>> df.plot.area(x='date', y=['sales', 'signups', 'visits'])  # doctest: +SKIP
         """
         return self(kind="area", x=x, y=y, **kwargs)
+
+    def pie(self, x: str, y: str, **kwargs: Any) -> "Figure":
+        """
+        Generate a pie plot.
+
+        A pie plot is a proportional representation of the numerical data in a
+        column.
+
+        Parameters
+        ----------
+        x : str
+            Name of column to be used as the category labels for the pie plot.
+        y : str
+            Name of the column to plot.
+        **kwargs
+            Additional keyword arguments.
+
+        Returns
+        -------
+        :class:`plotly.graph_objs.Figure`
+
+        Examples
+        --------
+        """
+        schema = self.data.schema
+
+        # Check if 'y' is a numerical column
+        y_field = schema[y] if y in schema.names else None
+        if y_field is None or not isinstance(y_field.dataType, NumericType):
+            raise PySparkTypeError(
+                errorClass="PLOT_NOT_NUMERIC_COLUMN",
+                messageParameters={
+                    "arg_name": "y",
+                    "arg_type": str(y_field.dataType) if y_field else "None",
+                },
+            )
+        return self(kind="pie", x=x, y=y, **kwargs)

python/pyspark/sql/plot/plotly.py

@@ -27,4 +27,19 @@
 def plot_pyspark(data: "DataFrame", kind: str, **kwargs: Any) -> "Figure":
     import plotly
 
+    if kind == "pie":
+        return plot_pie(data, **kwargs)
+
     return plotly.plot(PySparkPlotAccessor.plot_data_map[kind](data), kind, **kwargs)
+
+
+def plot_pie(data: "DataFrame", **kwargs: Any) -> "Figure":
+    # TODO(SPARK-49530): Support pie subplots with plotly backend
+    from plotly import express
+
+    pdf = PySparkPlotAccessor.plot_data_map["pie"](data)
+    x = kwargs.pop("x", None)
+    y = kwargs.pop("y", None)
+    fig = express.pie(pdf, values=y, names=x, **kwargs)
+
+    return fig

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

@@ -19,6 +19,7 @@
 from datetime import datetime
 
 import pyspark.sql.plot  # noqa: F401
+from pyspark.errors import PySparkTypeError
 from pyspark.testing.sqlutils import ReusedSQLTestCase, have_plotly, plotly_requirement_message
 
 
@@ -64,6 +65,11 @@ def _check_fig_data(self, kind, fig_data, expected_x, expected_y, expected_name=
             self.assertEqual(fig_data["type"], "scatter")
             self.assertEqual(fig_data["orientation"], "v")
             self.assertEqual(fig_data["mode"], "lines")
+        elif kind == "pie":
+            self.assertEqual(fig_data["type"], "pie")
+            self.assertEqual(list(fig_data["labels"]), expected_x)
+            self.assertEqual(list(fig_data["values"]), expected_y)
+            return
 
         self.assertEqual(fig_data["xaxis"], "x")
         self.assertEqual(list(fig_data["x"]), expected_x)
@@ -133,6 +139,25 @@ def test_area_plot(self):
         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")
 
+    def test_pie_plot(self):
+        fig = self.sdf3.plot(kind="pie", 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("pie", fig["data"][0], expected_x, [3, 2, 3, 9])
+
+        # y is not a numerical column
+        with self.assertRaises(PySparkTypeError) as pe:
+            self.sdf.plot.pie(x="int_val", y="category")
+        self.check_error(
+            exception=pe.exception,
+            errorClass="PLOT_NOT_NUMERIC_COLUMN",
+            messageParameters={"arg_name": "y", "arg_type": "StringType()"},
+        )
+
 
 class DataFramePlotPlotlyTests(DataFramePlotPlotlyTestsMixin, ReusedSQLTestCase):
     pass