Issue 1:
With PANDAS 1.5.3 (and Seaborn 0.11.2, Matplotlib 3.7.1), the following code works fine:

import seaborn as sns
sns.jointplot(x=[1,3,2], y=[1,2,3])

But with PANDAS 2.0.0 the identical code raises an exception:

OptionError: No such keys(s): 'mode.use_inf_as_null'

Here's the full output:

File ~/.../venv/lib/python3.10/site-packages/seaborn/_decorators.py:46, in _deprecate_positional_args.<locals>.inner_f(*args, **kwargs)
36 warnings.warn(
37 "Pass the following variable{} as {}keyword arg{}: {}. "
38 "From version 0.12, the only valid positional argument "
(...)
43 FutureWarning
44 )
45 kwargs.update({k: arg for k, arg in zip(sig.parameters, args)})
---> 46 return f(**kwargs)

File ~/.../venv/lib/python3.10/site-packages/seaborn/axisgrid.py:2254, in jointplot(x, y, data, kind, color, height, ratio, space, dropna, xlim, ylim, marginal_ticks, joint_kws, marginal_kws, hue, palette, hue_order, hue_norm, **kwargs)
2251 marginal_kws.setdefault("fill", True)
2253 marginal_kws.setdefault("color", color)
-> 2254 grid.plot_marginals(marg_func, **marginal_kws)
2256 elif kind.startswith("hist"):
2257
2258 # TODO process pair parameters for bins, etc. and pass
2259 # to both jount and marginal plots
2261 joint_kws.setdefault("color", color)

File ~/.../venv/lib/python3.10/site-packages/seaborn/axisgrid.py:1857, in JointGrid.plot_marginals(self, func, **kwargs)
1854 orient_kw_y = {"vertical": True}
1856 if seaborn_func:
-> 1857 func(x=self.x, ax=self.ax_marg_x, **kwargs)
1858 else:
1859 plt.sca(self.ax_marg_x)

File ~/.../venv/lib/python3.10/site-packages/seaborn/distributions.py:1462, 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)
1451 estimate_kws = dict(
1452 stat=stat,
1453 bins=bins,
(...)
1457 cumulative=cumulative,
1458 )
1460 if p.univariate:
-> 1462 p.plot_univariate_histogram(
1463 multiple=multiple,
1464 element=element,
1465 fill=fill,
1466 shrink=shrink,
1467 common_norm=common_norm,
1468 common_bins=common_bins,
1469 kde=kde,
1470 kde_kws=kde_kws,
1471 color=color,
1472 legend=legend,
1473 estimate_kws=estimate_kws,
1474 line_kws=line_kws,
1475 **kwargs,
1476 )
1478 else:
1480 p.plot_bivariate_histogram(
1481 common_bins=common_bins,
1482 common_norm=common_norm,
(...)
1492 **kwargs,
1493 )

File ~/.../venv/lib/python3.10/site-packages/seaborn/distributions.py:428, in _DistributionPlotter.plot_univariate_histogram(self, multiple, element, fill, common_norm, common_bins, shrink, kde, kde_kws, color, legend, line_kws, estimate_kws, **plot_kws)
418 densities = self._compute_univariate_density(
419 self.data_variable,
420 common_norm,
(...)
424 warn_singular=False,
425 )
427 # First pass through the data to compute the histograms
--> 428 for sub_vars, sub_data in self.iter_data("hue", from_comp_data=True):
429
430 # Prepare the relevant data
431 key = tuple(sub_vars.items())
432 sub_data = sub_data.dropna()

File ~/.../venv/lib/python3.10/site-packages/seaborn/_core.py:983, in VectorPlotter.iter_data(self, grouping_vars, reverse, from_comp_data)
978 grouping_vars = [
979 var for var in grouping_vars if var in self.variables
980 ]
982 if from_comp_data:
--> 983 data = self.comp_data
984 else:
985 data = self.plot_data

File ~/.../venv/lib/python3.10/site-packages/seaborn/_core.py:1054, in VectorPlotter.comp_data(self)
1050 axis = getattr(ax, f"{var}axis")
1052 # Use the converter assigned to the axis to get a float representation
1053 # of the data, passing np.nan or pd.NA through (pd.NA becomes np.nan)
-> 1054 with pd.option_context('mode.use_inf_as_null', True):
1055 orig = self.plot_data[var].dropna()
1056 comp_col = pd.Series(index=orig.index, dtype=float, name=var)

File ~/.../venv/lib/python3.10/site-packages/pandas/_config/config.py:441, in option_context.__enter__(self)
440 def __enter__(self) -> None:
--> 441 self.undo = [(pat, _get_option(pat, silent=True)) for pat, val in self.ops]
443 for pat, val in self.ops:
444 _set_option(pat, val, silent=True)

File ~/.../venv/lib/python3.10/site-packages/pandas/_config/config.py:441, in <listcomp>(.0)
440 def __enter__(self) -> None:
--> 441 self.undo = [(pat, _get_option(pat, silent=True)) for pat, val in self.ops]
443 for pat, val in self.ops:
444 _set_option(pat, val, silent=True)

File ~/.../venv/lib/python3.10/site-packages/pandas/_config/config.py:135, in _get_option(pat, silent)
134 def _get_option(pat: str, silent: bool = False) -> Any:
--> 135 key = _get_single_key(pat, silent)
137 # walk the nested dict
138 root, k = _get_root(key)

File ~/.../venv/lib/python3.10/site-packages/pandas/_config/config.py:121, in _get_single_key(pat, silent)
119 if not silent:
120 _warn_if_deprecated(pat)
--> 121 raise OptionError(f"No such keys(s): {repr(pat)}")
122 if len(keys) > 1:
123 raise OptionError("Pattern matched multiple keys")

OptionError: No such keys(s): 'mode.use_inf_as_null'

Issue 2:
I am making a joint plot using command -

sns.jointplot(xdata,ydata,kind='reg', order=1,color='k',ylim=[-0.62,0.22],xlim=[-16,15],annot_kws={"loc": 4})

With the annot_kws, I can move around where the annotation appears, but cannot change the font size. Also, can you select what stats you want to appear?

Thanks!