[SPARK-49626][PYTHON][CONNECT] Support horizontal and vertical bar plots

### What changes were proposed in this pull request?
Support horizontal and vertical bar 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.

```python
>>> data = [("A", 10, 1.5), ("B", 30, 2.5), ("C", 20, 3.5)]
>>> columns = ["category", "int_val", "float_val"]
>>> sdf = spark.createDataFrame(data, columns)
>>> sdf.show()
+--------+-------+---------+
|category|int_val|float_val|
+--------+-------+---------+
|       A|     10|      1.5|
|       B|     30|      2.5|
|       C|     20|      3.5|
+--------+-------+---------+

>>> f = sdf.plot(kind="bar", x="category", y=["int_val", "float_val"])
>>> f.show()  # see below
>>> g = sdf.plot.barh(x=["int_val", "float_val"], y="category")
>>> g.show()  # see below
```
`f.show()`:
![newplot (4)](https://github.com/user-attachments/assets/0df9ee86-fb48-4796-b6c3-aaf2879217aa)

`g.show()`:
![newplot (3)](https://github.com/user-attachments/assets/f39b01c3-66e6-464b-b2e8-badebb39bc67)

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

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

Closes #48100 from xinrong-meng/plot_bar.

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

python/pyspark/sql/plot/core.py

@@ -75,6 +75,8 @@ def get_sampled(self, sdf: "DataFrame") -> "pd.DataFrame":
 
 class PySparkPlotAccessor:
     plot_data_map = {
+        "bar": PySparkTopNPlotBase().get_top_n,
+        "barh": PySparkTopNPlotBase().get_top_n,
         "line": PySparkSampledPlotBase().get_sampled,
     }
     _backends = {}  # type: ignore[var-annotated]
@@ -133,3 +135,80 @@ def line(self, x: str, y: Union[str, list[str]], **kwargs: Any) -> "Figure":
         >>> df.plot.line(x="category", y=["int_val", "float_val"])  # doctest: +SKIP
         """
         return self(kind="line", x=x, y=y, **kwargs)
+
+    def bar(self, x: str, y: Union[str, list[str]], **kwargs: Any) -> "Figure":
+        """
+        Vertical bar plot.
+
+        A bar plot is a plot that presents categorical data with rectangular bars with lengths
+        proportional to the values that they represent. A bar plot shows comparisons among
+        discrete categories. One axis of the plot shows the specific categories being compared,
+        and the other axis represents a measured value.
+
+        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 use for the vertical axis.
+            Multiple columns can be plotted.
+        **kwargs : optional
+            Additional keyword arguments.
+
+        Returns
+        -------
+        :class:`plotly.graph_objs.Figure`
+
+        Examples
+        --------
+        >>> data = [("A", 10, 1.5), ("B", 30, 2.5), ("C", 20, 3.5)]
+        >>> columns = ["category", "int_val", "float_val"]
+        >>> df = spark.createDataFrame(data, columns)
+        >>> df.plot.bar(x="category", y="int_val")  # doctest: +SKIP
+        >>> df.plot.bar(x="category", y=["int_val", "float_val"])  # doctest: +SKIP
+        """
+        return self(kind="bar", x=x, y=y, **kwargs)
+
+    def barh(self, x: str, y: Union[str, list[str]], **kwargs: Any) -> "Figure":
+        """
+        Make a horizontal bar plot.
+
+        A horizontal bar plot is a plot that presents quantitative data with
+        rectangular bars with lengths proportional to the values that they
+        represent. A bar plot shows comparisons among discrete categories. One
+        axis of the plot shows the specific categories being compared, and the
+        other axis represents a measured value.
+
+        Parameters
+        ----------
+        x : str or list of str
+            Name(s) of the column(s) to use for the horizontal axis.
+            Multiple columns can be plotted.
+        y : str or list of str
+            Name(s) of the column(s) to use for the vertical axis.
+            Multiple columns can be plotted.
+        **kwargs : optional
+            Additional keyword arguments.
+
+        Returns
+        -------
+        :class:`plotly.graph_objs.Figure`
+
+        Notes
+        -----
+        In Plotly and Matplotlib, the interpretation of `x` and `y` for `barh` plots differs.
+        In Plotly, `x` refers to the values and `y` refers to the categories.
+        In Matplotlib, `x` refers to the categories and `y` refers to the values.
+        Ensure correct axis labeling based on the backend used.
+
+        Examples
+        --------
+        >>> data = [("A", 10, 1.5), ("B", 30, 2.5), ("C", 20, 3.5)]
+        >>> columns = ["category", "int_val", "float_val"]
+        >>> df = spark.createDataFrame(data, columns)
+        >>> df.plot.barh(x="int_val", y="category")  # doctest: +SKIP
+        >>> df.plot.barh(
+        ...     x=["int_val", "float_val"], y="category"
+        ... )  # doctest: +SKIP
+        """
+        return self(kind="barh", x=x, y=y, **kwargs)

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

@@ -28,9 +28,16 @@ def sdf(self):
         columns = ["category", "int_val", "float_val"]
         return self.spark.createDataFrame(data, columns)
 
-    def _check_fig_data(self, fig_data, expected_x, expected_y, expected_name=""):
-        self.assertEqual(fig_data["mode"], "lines")
-        self.assertEqual(fig_data["type"], "scatter")
+    def _check_fig_data(self, kind, fig_data, expected_x, expected_y, expected_name=""):
+        if kind == "line":
+            self.assertEqual(fig_data["mode"], "lines")
+            self.assertEqual(fig_data["type"], "scatter")
+        elif kind == "bar":
+            self.assertEqual(fig_data["type"], "bar")
+        elif kind == "barh":
+            self.assertEqual(fig_data["type"], "bar")
+            self.assertEqual(fig_data["orientation"], "h")
+
         self.assertEqual(fig_data["xaxis"], "x")
         self.assertEqual(list(fig_data["x"]), expected_x)
         self.assertEqual(fig_data["yaxis"], "y")
@@ -40,12 +47,37 @@ def _check_fig_data(self, fig_data, expected_x, expected_y, expected_name=""):
     def test_line_plot(self):
         # single column as vertical axis
         fig = self.sdf.plot(kind="line", x="category", y="int_val")
-        self._check_fig_data(fig["data"][0], ["A", "B", "C"], [10, 30, 20])
+        self._check_fig_data("line", fig["data"][0], ["A", "B", "C"], [10, 30, 20])
 
         # multiple columns as vertical axis
         fig = self.sdf.plot.line(x="category", y=["int_val", "float_val"])
-        self._check_fig_data(fig["data"][0], ["A", "B", "C"], [10, 30, 20], "int_val")
-        self._check_fig_data(fig["data"][1], ["A", "B", "C"], [1.5, 2.5, 3.5], "float_val")
+        self._check_fig_data("line", fig["data"][0], ["A", "B", "C"], [10, 30, 20], "int_val")
+        self._check_fig_data("line", fig["data"][1], ["A", "B", "C"], [1.5, 2.5, 3.5], "float_val")
+
+    def test_bar_plot(self):
+        # single column as vertical axis
+        fig = self.sdf.plot(kind="bar", x="category", y="int_val")
+        self._check_fig_data("bar", fig["data"][0], ["A", "B", "C"], [10, 30, 20])
+
+        # multiple columns as vertical axis
+        fig = self.sdf.plot.bar(x="category", y=["int_val", "float_val"])
+        self._check_fig_data("bar", fig["data"][0], ["A", "B", "C"], [10, 30, 20], "int_val")
+        self._check_fig_data("bar", fig["data"][1], ["A", "B", "C"], [1.5, 2.5, 3.5], "float_val")
+
+    def test_barh_plot(self):
+        # single column as vertical axis
+        fig = self.sdf.plot(kind="barh", x="category", y="int_val")
+        self._check_fig_data("barh", fig["data"][0], ["A", "B", "C"], [10, 30, 20])
+
+        # multiple columns as vertical axis
+        fig = self.sdf.plot.barh(x="category", y=["int_val", "float_val"])
+        self._check_fig_data("barh", fig["data"][0], ["A", "B", "C"], [10, 30, 20], "int_val")
+        self._check_fig_data("barh", fig["data"][1], ["A", "B", "C"], [1.5, 2.5, 3.5], "float_val")
+
+        # multiple columns as horizontal axis
+        fig = self.sdf.plot.barh(x=["int_val", "float_val"], y="category")
+        self._check_fig_data("barh", fig["data"][0], [10, 30, 20], ["A", "B", "C"], "int_val")
+        self._check_fig_data("barh", fig["data"][1], [1.5, 2.5, 3.5], ["A", "B", "C"], "float_val")
 
 
 class DataFramePlotPlotlyTests(DataFramePlotPlotlyTestsMixin, ReusedSQLTestCase):