[BUG] Importing Lux breaks Seaborn

[tomaz-suller]

Describe the bug
Importing Lux breaks Seaborn.

To Reproduce
Generate histograms normally using Seaborn without importing Lux:

import pandas as pd
import seaborn as sns

BEERS = pd.read_csv('https://raw.githubusercontent.com/camillasancricca/DATADIQ/master/BEERS.csv')
sns.histplot(BEERS)
# Plot generated with no errors

importing Lux raises IndexError: Requested axis not found in manager:

import lux
import pandas as pd
import seaborn as sns

BEERS = pd.read_csv('https://raw.githubusercontent.com/camillasancricca/DATADIQ/master/BEERS.csv')
sns.histplot(BEERS)
# `IndexError` thrown

Expected behavior
Lux should not impact Seaborn plot generation.

Debugging information
Seaborn version 0.13.2

Package Versions
----------------
               Version      
        python        3.12.6
           lux         0.5.1
        pandas         2.2.3
     luxwidget        0.1.11
    matplotlib         3.9.2
        altair         5.4.1
       IPython        8.28.0
     ipykernel        6.29.5
    ipywidgets         8.1.5
jupyter_client         8.6.3
  jupyter_core         5.7.2
jupyter_server        2.14.2
    jupyterlab         4.2.5
      nbclient        0.10.0
     nbconvert        7.16.4
      nbformat        5.10.4
      notebook         7.2.2
     qtconsole not installed
     traitlets        5.14.3

Widget Setup
-------------
✅ Jupyter Notebook Running
❌ Failed to run 'jupyter nbextension list'
❌ WARNING: luxwidget is not enabled in Jupyter Notebook.
You may need to run the following code in your command line:
  jupyter nbextension install --py luxwidget
  jupyter nbextension enable --py luxwidget

Additional context
Filing as a bug here rather than on Seaborn since the traceback shows the error being thrown by Pandas, which I assume would be due to some modification Lux makes to it.

Traceback:

---------------------------------------------------------------------------
IndexError                                Traceback (most recent call last)
Cell In[1], line 6
      3 import seaborn as sns
      5 BEERS = pd.read_csv('https://raw.githubusercontent.com/camillasancricca/DATADIQ/master/BEERS.csv')
----> 6 sns.histplot(BEERS)

File ~/Documents/Classes/3rd Semester/Data and Information Quality/Notebooks/.venv/lib/python3.12/site-packages/seaborn/distributions.py:1389, in histplot(data, x, y, hue, weights, stat, bins, binwidth, binrange, discrete, cumulative, common_bins, common_norm, multiple, element, fill, shrink, kde, kde_kws, line_kws, thresh, pthresh, pmax, cbar, cbar_ax, cbar_kws, palette, hue_order, hue_norm, color, log_scale, legend, ax, **kwargs)
   1386 if ax is None:
   1387     ax = plt.gca()
-> 1389 p._attach(ax, log_scale=log_scale)
   1391 if p.univariate:  # Note, bivariate plots won't cycle
   1392     if fill:

File ~/Documents/Classes/3rd Semester/Data and Information Quality/Notebooks/.venv/lib/python3.12/site-packages/seaborn/_base.py:1128, in VectorPlotter._attach(self, obj, allowed_types, log_scale)
   1126 # Now actually update the matplotlib objects to do the conversion we want
   1127 grouped = self.plot_data[var].groupby(self.converters[var], sort=False)
-> 1128 for converter, seed_data in grouped:
   1129     if self.var_types[var] == "categorical":
   1130         if self._var_ordered[var]:

File ~/Documents/Classes/3rd Semester/Data and Information Quality/Notebooks/.venv/lib/python3.12/site-packages/pandas/core/groupby/ops.py:620, in BaseGrouper.get_iterator(self, data, axis)
    618 splitter = self._get_splitter(data, axis=axis)
    619 keys = self.group_keys_seq
--> 620 yield from zip(keys, splitter)

File ~/Documents/Classes/3rd Semester/Data and Information Quality/Notebooks/.venv/lib/python3.12/site-packages/pandas/core/groupby/ops.py:1160, in DataSplitter.__iter__(self)
   1157 starts, ends = lib.generate_slices(self._slabels, self.ngroups)
   1159 for start, end in zip(starts, ends):
-> 1160     yield self._chop(sdata, slice(start, end))

File ~/Documents/Classes/3rd Semester/Data and Information Quality/Notebooks/.venv/lib/python3.12/site-packages/pandas/core/groupby/ops.py:1186, in FrameSplitter._chop(self, sdata, slice_obj)
   1180 def _chop(self, sdata: DataFrame, slice_obj: slice) -> DataFrame:
   1181     # Fastpath equivalent to:
   1182     # if self.axis == 0:
   1183     #     return sdata.iloc[slice_obj]
   1184     # else:
   1185     #     return sdata.iloc[:, slice_obj]
-> 1186     mgr = sdata._mgr.get_slice(slice_obj, axis=1 - self.axis)
   1187     df = sdata._constructor_from_mgr(mgr, axes=mgr.axes)
   1188     return df.__finalize__(sdata, method="groupby")

File ~/Documents/Classes/3rd Semester/Data and Information Quality/Notebooks/.venv/lib/python3.12/site-packages/pandas/core/internals/managers.py:1978, in SingleBlockManager.get_slice(self, slobj, axis)
   1974 def get_slice(self, slobj: slice, axis: AxisInt = 0) -> SingleBlockManager:
   1975     # Assertion disabled for performance
   1976     # assert isinstance(slobj, slice), type(slobj)
   1977     if axis >= self.ndim:
-> 1978         raise IndexError("Requested axis not found in manager")
   1980     blk = self._block
   1981     array = blk.values[slobj]

IndexError: Requested axis not found in manager