diff --git a/python/pyspark/sql/plot/core.py b/python/pyspark/sql/plot/core.py
index 392ef73b38845..ed22d02370ca6 100644
--- a/python/pyspark/sql/plot/core.py
+++ b/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)
diff --git a/python/pyspark/sql/tests/plot/test_frame_plot_plotly.py b/python/pyspark/sql/tests/plot/test_frame_plot_plotly.py
index 72a3ed267d192..1c52c93a23d3a 100644
--- a/python/pyspark/sql/tests/plot/test_frame_plot_plotly.py
+++ b/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):