[SPARK-52169][PYTHON][DOCS] User guide for native plotting

### What changes were proposed in this pull request?
User guide for native plotting

### Why are the changes needed?
Guide users to use PySpark native plotting

### Does this PR introduce _any_ user-facing change?
No.

### How was this patch tested?
Manual rendering.

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

Closes apache#50992 from xinrong-meng/usr_guide_plot.

Lead-authored-by: Xinrong Meng <xinrong@apache.org>
Co-authored-by: Xinrong Meng <xinrong.apache@gmail.com>
Signed-off-by: Xinrong Meng <xinrong@apache.org>

Author: xinrong-meng

python/docs/source/user_guide/dataframes.ipynb

@@ -751,6 +751,25 @@
             "source": [
                 "df.write().mode(\"overwrite\").saveAsTable(\"schemaName.tableName\")"
             ]
+        },
+        {
+          "cell_type": "markdown",
+          "metadata": {},
+          "source": [
+            "## Native DataFrame Plotting\n",
+            "\n",
+            "PySpark supports native plotting, allowing users to visualize data directly from PySpark DataFrames.\n",
+            "\n",
+            "The user interacts with PySpark Plotting by calling the `plot` property on a PySpark DataFrame and specifying the desired type of plot, either as a submethod or by setting the `kind` parameter. For instance:\n",
+            "\n",
+            "`df.plot.line(x=\"category\", y=\"int_val\")`\n",
+            "\n",
+            "or equivalently:\n",
+            "\n",
+            "`df.plot(kind=\"line\", x=\"category\", y=\"int_val\")`\n",
+            "\n",
+            "The feature is powered by [Plotly](https://plotly.com/python/) as the default visualization backend, offering rich, interactive plotting capabilities, while native [pandas](https://pandas.pydata.org/) is used internally to process data for most plots.\n"
+          ]
         }
     ],
     "metadata": {