We are currently solving the following issue within our repository. Here is the issue text:
--- BEGIN ISSUE ---
Bug with hvplot plotly violin plots using 'by' argument
Violin plots on hvplot with the plotly extension do not work properly when using the 'by' argument to make a violin for each category. Instead of one violin per category as expected, only the first letter of each category is used, and violins of categories with the same first letter are stacked. The legend shows only the first letter of each category name instead of the full names.

This can also be seen in the hvplot documentation. For example [the violin plot at the bottom of this page](https://hvplot.holoviz.org/user_guide/Plotting.html), using bokeh, in which each category has its own violin and its full name shown on the x axis and on the legend. This is the expected behaviour.

Compare with [the same plot using the plotly extension](https://hvplot.holoviz.org/user_guide/Plotting_with_Plotly.html), you can see that any categories that share the same first letter are stacked e.g. OO and OH are stacked and are both labelled as O.

Software versions, although I think it doesn't matter since the bug is visible in the documentation as well:
Python 3.12 running on mac os 13.3.1 and on a Linux server
Holoviews 1.18.3

Reproducible code:
```
import numpy as np
import hvplot.pandas
import hvplot.dask

hvplot.extension('plotly')
from hvplot.sample_data import us_crime, airline_flights
flights = airline_flights.to_dask().persist()
bugged_plot = flights.hvplot.violin(y='depdelay', by='carrier', ylim=(-20, 60), height=500)
hvplot.show(bugged_plot)

hvplot.extension('bokeh')
correct_plot = flights.hvplot.violin(y='depdelay', by='carrier', ylim=(-20, 60), height=500)
hvplot.show(correct_plot)
```


--- END ISSUE ---

Below are some code segments, each from a relevant file. One or more of these files may contain bugs.
--- BEGIN FILES ---
Path: `holoviews/plotting/plotly/stats.py`
Content:
```
1 import param
2 
3 from ..mixins import MultiDistributionMixin
4 from .chart import ChartPlot
5 from .element import ColorbarPlot, ElementPlot
6 from .selection import PlotlyOverlaySelectionDisplay
7 
8 
9 class BivariatePlot(ChartPlot, ColorbarPlot):
10 
11     filled = param.Boolean(default=False)
12 
13     ncontours = param.Integer(default=None)
14 
15     style_opts = ['visible', 'cmap', 'showlabels', 'labelfont', 'labelformat', 'showlines']
16 
17     _style_key = 'contours'
18 
19     selection_display = PlotlyOverlaySelectionDisplay()
20 
21     @classmethod
22     def trace_kwargs(cls, is_geo=False, **kwargs):
23         return {'type': 'histogram2dcontour'}
24 
25     def graph_options(self, element, ranges, style, **kwargs):
26         opts = super().graph_options(element, ranges, style, **kwargs)
27         copts = self.get_color_opts(element.vdims[0], element, ranges, style)
28 
29         if self.ncontours:
30             opts['autocontour'] = False
31             opts['ncontours'] = self.ncontours
32 
33         # Make line width a little wider (default is less than 1)
34         opts['line'] = {'width': 1}
35 
36         # Configure contours
37         opts['contours'] = {
38             'coloring': 'fill' if self.filled else 'lines',
39             'showlines': style.get('showlines', True)
40         }
41 
42         # Add colorscale
43         opts['colorscale'] = copts['colorscale']
44 
45         # Add colorbar
46         if 'colorbar' in copts:
47             opts['colorbar'] = copts['colorbar']
48 
49         opts['showscale'] = copts.get('showscale', False)
50 
51         # Add visible
52         opts['visible'] = style.get('visible', True)
53 
54         return opts
55 
56 
57 class DistributionPlot(ElementPlot):
58 
59     bandwidth = param.Number(default=None, doc="""
60         The bandwidth of the kernel for the density estimate.""")
61 
62     cut = param.Number(default=3, doc="""
63         Draw the estimate to cut * bw from the extreme data points.""")
64 
65     filled = param.Boolean(default=True, doc="""
66         Whether the bivariate contours should be filled.""")
67 
68     style_opts = ['visible', 'color', 'dash', 'line_width']
69 
70     _style_key = 'line'
71 
72     selection_display = PlotlyOverlaySelectionDisplay()
73 
74     @classmethod
75     def trace_kwargs(cls, is_geo=False, **kwargs):
76         return {'type': 'scatter', 'mode': 'lines'}
77 
78 
79 class MultiDistributionPlot(MultiDistributionMixin, ElementPlot):
80 
81     def get_data(self, element, ranges, style, **kwargs):
82         if element.kdims:
83             groups = element.groupby(element.kdims).items()
84         else:
85             groups = [(element.label, element)]
86         plots = []
87         axis = 'x' if self.invert_axes else 'y'
88         for key, group in groups:
89             if element.kdims:
90                 label = ','.join([d.pprint_value(v) for d, v in zip(element.kdims, key)])
91             else:
92                 label = key
93             data = {axis: group.dimension_values(group.vdims[0]), 'name': label}
94             plots.append(data)
95         return plots
96 
97 
98 
99 class BoxWhiskerPlot(MultiDistributionPlot):
100 
101     boxpoints = param.ObjectSelector(objects=["all", "outliers",
102                                               "suspectedoutliers", False],
103                                      default='outliers', doc="""
104         Which points to show, valid options are 'all', 'outliers',
105         'suspectedoutliers' and False""")
106 
107     jitter = param.Number(default=0, doc="""
108         Sets the amount of jitter in the sample points drawn. If "0",
109         the sample points align along the distribution axis. If "1",
110         the sample points are drawn in a random jitter of width equal
111         to the width of the box(es).""")
112 
113     mean = param.ObjectSelector(default=False, objects=[True, False, 'sd'],
114                                 doc="""
115         If "True", the mean of the box(es)' underlying distribution
116         is drawn as a dashed line inside the box(es). If "sd" the
117         standard deviation is also drawn.""")
118 
119     style_opts = ['visible', 'color', 'alpha', 'outliercolor', 'marker', 'size']
120 
121     _style_key = 'marker'
122 
123     selection_display = PlotlyOverlaySelectionDisplay()
124 
125     @classmethod
126     def trace_kwargs(cls, is_geo=False, **kwargs):
127         return {'type': 'box'}
128 
129     def graph_options(self, element, ranges, style, **kwargs):
130         options = super().graph_options(element, ranges, style, **kwargs)
131         options['boxmean'] = self.mean
132         options['jitter'] = self.jitter
133         return options
134 
135 
136 class ViolinPlot(MultiDistributionPlot):
137 
138     box = param.Boolean(default=True, doc="""
139         Whether to draw a boxplot inside the violin""")
140 
141     meanline = param.Boolean(default=False, doc="""
142         If "True", the mean of the box(es)' underlying distribution
143         is drawn as a dashed line inside the box(es). If "sd" the
144         standard deviation is also drawn.""")
145 
146     style_opts = ['visible', 'color', 'alpha', 'outliercolor', 'marker', 'size']
147 
148     _style_key = 'marker'
149 
150     @classmethod
151     def trace_kwargs(cls, is_geo=False, **kwargs):
152         return {'type': 'violin'}
153 
154     def graph_options(self, element, ranges, style, **kwargs):
155         options = super().graph_options(
156             element, ranges, style, **kwargs
157         )
158         options['meanline'] = {'visible': self.meanline}
159         options['box'] = {'visible': self.box}
160         return options
161
```
--- END FILES ---

Please first localize the bug based on the issue statement, and then generate a patch according to the `git diff` format fenced by three backticks.

Here is an example:

```diff
diff --git a/examples/server_async.py b/examples/server_async.py
--- a/examples/server_async.py
+++ b/examples/server_async.py
@@ -313,4 +313,4 @@
 
 
 if __name__ == "__main__":
-    asyncio.run(run_async_server("."), debug=True)
+    asyncio.run(run_async_server(), debug=True)
diff --git a/examples/server_sync.py b/examples/server_sync.py
--- a/examples/server_sync.py
+++ b/examples/server_sync.py
@@ -313,5 +313,5 @@
 
 
 if __name__ == "__main__":
-    server = run_sync_server(".")
+    server = run_sync_server()
     server.shutdown()

```

diff --git a/holoviews/plotting/plotly/stats.py b/holoviews/plotting/plotly/stats.py
--- a/holoviews/plotting/plotly/stats.py
+++ b/holoviews/plotting/plotly/stats.py
@@ -87,6 +87,8 @@
         axis = 'x' if self.invert_axes else 'y'
         for key, group in groups:
             if element.kdims:
+                if isinstance(key, str):
+                    key = (key,)
                 label = ','.join([d.pprint_value(v) for d, v in zip(element.kdims, key)])
             else:
                 label = key

{"golden_diff": "diff --git a/holoviews/plotting/plotly/stats.py b/holoviews/plotting/plotly/stats.py\n--- a/holoviews/plotting/plotly/stats.py\n+++ b/holoviews/plotting/plotly/stats.py\n@@ -87,6 +87,8 @@\n         axis = 'x' if self.invert_axes else 'y'\n         for key, group in groups:\n             if element.kdims:\n+                if isinstance(key, str):\n+                    key = (key,)\n                 label = ','.join([d.pprint_value(v) for d, v in zip(element.kdims, key)])\n             else:\n                 label = key\n", "issue": "Bug with hvplot plotly violin plots using 'by' argument\nViolin plots on hvplot with the plotly extension do not work properly when using the 'by' argument to make a violin for each category. Instead of one violin per category as expected, only the first letter of each category is used, and violins of categories with the same first letter are stacked. The legend shows only the first letter of each category name instead of the full names.\r\n\r\nThis can also be seen in the hvplot documentation. For example [the violin plot at the bottom of this page](https://hvplot.holoviz.org/user_guide/Plotting.html), using bokeh, in which each category has its own violin and its full name shown on the x axis and on the legend. This is the expected behaviour.\r\n\r\nCompare with [the same plot using the plotly extension](https://hvplot.holoviz.org/user_guide/Plotting_with_Plotly.html), you can see that any categories that share the same first letter are stacked e.g. OO and OH are stacked and are both labelled as O.\r\n\r\nSoftware versions, although I think it doesn't matter since the bug is visible in the documentation as well:\r\nPython 3.12 running on mac os 13.3.1 and on a Linux server\r\nHoloviews 1.18.3\r\n\r\nReproducible code:\r\n```\r\nimport numpy as np\r\nimport hvplot.pandas\r\nimport hvplot.dask\r\n\r\nhvplot.extension('plotly')\r\nfrom hvplot.sample_data import us_crime, airline_flights\r\nflights = airline_flights.to_dask().persist()\r\nbugged_plot = flights.hvplot.violin(y='depdelay', by='carrier', ylim=(-20, 60), height=500)\r\nhvplot.show(bugged_plot)\r\n\r\nhvplot.extension('bokeh')\r\ncorrect_plot = flights.hvplot.violin(y='depdelay', by='carrier', ylim=(-20, 60), height=500)\r\nhvplot.show(correct_plot)\r\n```\r\n\n", "before_files": [{"content": "import param\n\nfrom ..mixins import MultiDistributionMixin\nfrom .chart import ChartPlot\nfrom .element import ColorbarPlot, ElementPlot\nfrom .selection import PlotlyOverlaySelectionDisplay\n\n\nclass BivariatePlot(ChartPlot, ColorbarPlot):\n\n    filled = param.Boolean(default=False)\n\n    ncontours = param.Integer(default=None)\n\n    style_opts = ['visible', 'cmap', 'showlabels', 'labelfont', 'labelformat', 'showlines']\n\n    _style_key = 'contours'\n\n    selection_display = PlotlyOverlaySelectionDisplay()\n\n    @classmethod\n    def trace_kwargs(cls, is_geo=False, **kwargs):\n        return {'type': 'histogram2dcontour'}\n\n    def graph_options(self, element, ranges, style, **kwargs):\n        opts = super().graph_options(element, ranges, style, **kwargs)\n        copts = self.get_color_opts(element.vdims[0], element, ranges, style)\n\n        if self.ncontours:\n            opts['autocontour'] = False\n            opts['ncontours'] = self.ncontours\n\n        # Make line width a little wider (default is less than 1)\n        opts['line'] = {'width': 1}\n\n        # Configure contours\n        opts['contours'] = {\n            'coloring': 'fill' if self.filled else 'lines',\n            'showlines': style.get('showlines', True)\n        }\n\n        # Add colorscale\n        opts['colorscale'] = copts['colorscale']\n\n        # Add colorbar\n        if 'colorbar' in copts:\n            opts['colorbar'] = copts['colorbar']\n\n        opts['showscale'] = copts.get('showscale', False)\n\n        # Add visible\n        opts['visible'] = style.get('visible', True)\n\n        return opts\n\n\nclass DistributionPlot(ElementPlot):\n\n    bandwidth = param.Number(default=None, doc=\"\"\"\n        The bandwidth of the kernel for the density estimate.\"\"\")\n\n    cut = param.Number(default=3, doc=\"\"\"\n        Draw the estimate to cut * bw from the extreme data points.\"\"\")\n\n    filled = param.Boolean(default=True, doc=\"\"\"\n        Whether the bivariate contours should be filled.\"\"\")\n\n    style_opts = ['visible', 'color', 'dash', 'line_width']\n\n    _style_key = 'line'\n\n    selection_display = PlotlyOverlaySelectionDisplay()\n\n    @classmethod\n    def trace_kwargs(cls, is_geo=False, **kwargs):\n        return {'type': 'scatter', 'mode': 'lines'}\n\n\nclass MultiDistributionPlot(MultiDistributionMixin, ElementPlot):\n\n    def get_data(self, element, ranges, style, **kwargs):\n        if element.kdims:\n            groups = element.groupby(element.kdims).items()\n        else:\n            groups = [(element.label, element)]\n        plots = []\n        axis = 'x' if self.invert_axes else 'y'\n        for key, group in groups:\n            if element.kdims:\n                label = ','.join([d.pprint_value(v) for d, v in zip(element.kdims, key)])\n            else:\n                label = key\n            data = {axis: group.dimension_values(group.vdims[0]), 'name': label}\n            plots.append(data)\n        return plots\n\n\n\nclass BoxWhiskerPlot(MultiDistributionPlot):\n\n    boxpoints = param.ObjectSelector(objects=[\"all\", \"outliers\",\n                                              \"suspectedoutliers\", False],\n                                     default='outliers', doc=\"\"\"\n        Which points to show, valid options are 'all', 'outliers',\n        'suspectedoutliers' and False\"\"\")\n\n    jitter = param.Number(default=0, doc=\"\"\"\n        Sets the amount of jitter in the sample points drawn. If \"0\",\n        the sample points align along the distribution axis. If \"1\",\n        the sample points are drawn in a random jitter of width equal\n        to the width of the box(es).\"\"\")\n\n    mean = param.ObjectSelector(default=False, objects=[True, False, 'sd'],\n                                doc=\"\"\"\n        If \"True\", the mean of the box(es)' underlying distribution\n        is drawn as a dashed line inside the box(es). If \"sd\" the\n        standard deviation is also drawn.\"\"\")\n\n    style_opts = ['visible', 'color', 'alpha', 'outliercolor', 'marker', 'size']\n\n    _style_key = 'marker'\n\n    selection_display = PlotlyOverlaySelectionDisplay()\n\n    @classmethod\n    def trace_kwargs(cls, is_geo=False, **kwargs):\n        return {'type': 'box'}\n\n    def graph_options(self, element, ranges, style, **kwargs):\n        options = super().graph_options(element, ranges, style, **kwargs)\n        options['boxmean'] = self.mean\n        options['jitter'] = self.jitter\n        return options\n\n\nclass ViolinPlot(MultiDistributionPlot):\n\n    box = param.Boolean(default=True, doc=\"\"\"\n        Whether to draw a boxplot inside the violin\"\"\")\n\n    meanline = param.Boolean(default=False, doc=\"\"\"\n        If \"True\", the mean of the box(es)' underlying distribution\n        is drawn as a dashed line inside the box(es). If \"sd\" the\n        standard deviation is also drawn.\"\"\")\n\n    style_opts = ['visible', 'color', 'alpha', 'outliercolor', 'marker', 'size']\n\n    _style_key = 'marker'\n\n    @classmethod\n    def trace_kwargs(cls, is_geo=False, **kwargs):\n        return {'type': 'violin'}\n\n    def graph_options(self, element, ranges, style, **kwargs):\n        options = super().graph_options(\n            element, ranges, style, **kwargs\n        )\n        options['meanline'] = {'visible': self.meanline}\n        options['box'] = {'visible': self.box}\n        return options\n", "path": "holoviews/plotting/plotly/stats.py"}], "after_files": [{"content": "import param\n\nfrom ..mixins import MultiDistributionMixin\nfrom .chart import ChartPlot\nfrom .element import ColorbarPlot, ElementPlot\nfrom .selection import PlotlyOverlaySelectionDisplay\n\n\nclass BivariatePlot(ChartPlot, ColorbarPlot):\n\n    filled = param.Boolean(default=False)\n\n    ncontours = param.Integer(default=None)\n\n    style_opts = ['visible', 'cmap', 'showlabels', 'labelfont', 'labelformat', 'showlines']\n\n    _style_key = 'contours'\n\n    selection_display = PlotlyOverlaySelectionDisplay()\n\n    @classmethod\n    def trace_kwargs(cls, is_geo=False, **kwargs):\n        return {'type': 'histogram2dcontour'}\n\n    def graph_options(self, element, ranges, style, **kwargs):\n        opts = super().graph_options(element, ranges, style, **kwargs)\n        copts = self.get_color_opts(element.vdims[0], element, ranges, style)\n\n        if self.ncontours:\n            opts['autocontour'] = False\n            opts['ncontours'] = self.ncontours\n\n        # Make line width a little wider (default is less than 1)\n        opts['line'] = {'width': 1}\n\n        # Configure contours\n        opts['contours'] = {\n            'coloring': 'fill' if self.filled else 'lines',\n            'showlines': style.get('showlines', True)\n        }\n\n        # Add colorscale\n        opts['colorscale'] = copts['colorscale']\n\n        # Add colorbar\n        if 'colorbar' in copts:\n            opts['colorbar'] = copts['colorbar']\n\n        opts['showscale'] = copts.get('showscale', False)\n\n        # Add visible\n        opts['visible'] = style.get('visible', True)\n\n        return opts\n\n\nclass DistributionPlot(ElementPlot):\n\n    bandwidth = param.Number(default=None, doc=\"\"\"\n        The bandwidth of the kernel for the density estimate.\"\"\")\n\n    cut = param.Number(default=3, doc=\"\"\"\n        Draw the estimate to cut * bw from the extreme data points.\"\"\")\n\n    filled = param.Boolean(default=True, doc=\"\"\"\n        Whether the bivariate contours should be filled.\"\"\")\n\n    style_opts = ['visible', 'color', 'dash', 'line_width']\n\n    _style_key = 'line'\n\n    selection_display = PlotlyOverlaySelectionDisplay()\n\n    @classmethod\n    def trace_kwargs(cls, is_geo=False, **kwargs):\n        return {'type': 'scatter', 'mode': 'lines'}\n\n\nclass MultiDistributionPlot(MultiDistributionMixin, ElementPlot):\n\n    def get_data(self, element, ranges, style, **kwargs):\n        if element.kdims:\n            groups = element.groupby(element.kdims).items()\n        else:\n            groups = [(element.label, element)]\n        plots = []\n        axis = 'x' if self.invert_axes else 'y'\n        for key, group in groups:\n            if element.kdims:\n                if isinstance(key, str):\n                    key = (key,)\n                label = ','.join([d.pprint_value(v) for d, v in zip(element.kdims, key)])\n            else:\n                label = key\n            data = {axis: group.dimension_values(group.vdims[0]), 'name': label}\n            plots.append(data)\n        return plots\n\n\n\nclass BoxWhiskerPlot(MultiDistributionPlot):\n\n    boxpoints = param.ObjectSelector(objects=[\"all\", \"outliers\",\n                                              \"suspectedoutliers\", False],\n                                     default='outliers', doc=\"\"\"\n        Which points to show, valid options are 'all', 'outliers',\n        'suspectedoutliers' and False\"\"\")\n\n    jitter = param.Number(default=0, doc=\"\"\"\n        Sets the amount of jitter in the sample points drawn. If \"0\",\n        the sample points align along the distribution axis. If \"1\",\n        the sample points are drawn in a random jitter of width equal\n        to the width of the box(es).\"\"\")\n\n    mean = param.ObjectSelector(default=False, objects=[True, False, 'sd'],\n                                doc=\"\"\"\n        If \"True\", the mean of the box(es)' underlying distribution\n        is drawn as a dashed line inside the box(es). If \"sd\" the\n        standard deviation is also drawn.\"\"\")\n\n    style_opts = ['visible', 'color', 'alpha', 'outliercolor', 'marker', 'size']\n\n    _style_key = 'marker'\n\n    selection_display = PlotlyOverlaySelectionDisplay()\n\n    @classmethod\n    def trace_kwargs(cls, is_geo=False, **kwargs):\n        return {'type': 'box'}\n\n    def graph_options(self, element, ranges, style, **kwargs):\n        options = super().graph_options(element, ranges, style, **kwargs)\n        options['boxmean'] = self.mean\n        options['jitter'] = self.jitter\n        return options\n\n\nclass ViolinPlot(MultiDistributionPlot):\n\n    box = param.Boolean(default=True, doc=\"\"\"\n        Whether to draw a boxplot inside the violin\"\"\")\n\n    meanline = param.Boolean(default=False, doc=\"\"\"\n        If \"True\", the mean of the box(es)' underlying distribution\n        is drawn as a dashed line inside the box(es). If \"sd\" the\n        standard deviation is also drawn.\"\"\")\n\n    style_opts = ['visible', 'color', 'alpha', 'outliercolor', 'marker', 'size']\n\n    _style_key = 'marker'\n\n    @classmethod\n    def trace_kwargs(cls, is_geo=False, **kwargs):\n        return {'type': 'violin'}\n\n    def graph_options(self, element, ranges, style, **kwargs):\n        options = super().graph_options(\n            element, ranges, style, **kwargs\n        )\n        options['meanline'] = {'visible': self.meanline}\n        options['box'] = {'visible': self.box}\n        return options\n", "path": "holoviews/plotting/plotly/stats.py"}]}