Adapt to deprecations in pandas 2.2.0

pyproject.toml

@@ -66,4 +66,6 @@ exclude = ["doc/_static/*.svg"]
 [tool.pytest.ini_options]
 filterwarnings = [
    "ignore:The --rsyncdir command line argument and rsyncdirs config variable are deprecated.:DeprecationWarning",
+   "ignore:\\s*Pyarrow will become a required dependency of pandas:DeprecationWarning",
+   "ignore:datetime.datetime.utcfromtimestamp\\(\\) is deprecated:DeprecationWarning",
  ]

seaborn/_base.py

@@ -942,9 +942,9 @@ def iter_data(
 
             for key in iter_keys:
 
-                # Pandas fails with singleton tuple inputs
-                pd_key = key[0] if len(key) == 1 else key
-
+                pd_key = (
+                    key[0] if len(key) == 1 and _version_predates(pd, "2.2.0") else key
+                )
                 try:
                     data_subset = grouped_data.get_group(pd_key)
                 except KeyError:

seaborn/_compat.py

@@ -2,6 +2,7 @@
 from typing import Literal
 
 import numpy as np
+import pandas as pd
 import matplotlib as mpl
 from matplotlib.figure import Figure
 from seaborn.utils import _version_predates
@@ -114,3 +115,9 @@ def get_legend_handles(legend):
         return legend.legendHandles
     else:
         return legend.legend_handles
+
+
+def groupby_apply_include_groups(val):
+    if _version_predates(pd, "2.2.0"):
+        return {}
+    return {"include_groups": val}

seaborn/_core/plot.py

@@ -41,6 +41,7 @@
 from seaborn._core.exceptions import PlotSpecError
 from seaborn._core.rules import categorical_order
 from seaborn._compat import get_layout_engine, set_layout_engine
+from seaborn.utils import _version_predates
 from seaborn.rcmod import axes_style, plotting_context
 from seaborn.palettes import color_palette
 
@@ -1637,9 +1638,10 @@ def split_generator(keep_na=False) -> Generator:
 
                 for key in itertools.product(*grouping_keys):
 
-                    # Pandas fails with singleton tuple inputs
-                    pd_key = key[0] if len(key) == 1 else key
-
+                    pd_key = (
+                        key[0] if len(key) == 1 and _version_predates(pd, "2.2.0")
+                        else key
+                    )
                     try:
                         df_subset = grouped_df.get_group(pd_key)
                     except KeyError:

seaborn/categorical.py

@@ -28,6 +28,7 @@
     _scatter_legend_artist,
     _version_predates,
 )
+from seaborn._compat import groupby_apply_include_groups
 from seaborn._statistics import (
     EstimateAggregator,
     LetterValues,
@@ -634,10 +635,10 @@ def get_props(element, artist=mpl.lines.Line2D):
             ax = self._get_axes(sub_vars)
 
             grouped = sub_data.groupby(self.orient)[value_var]
+            positions = sorted(sub_data[self.orient].unique().astype(float))
             value_data = [x.to_numpy() for _, x in grouped]
             stats = pd.DataFrame(mpl.cbook.boxplot_stats(value_data, whis=whis,
                                                          bootstrap=bootstrap))
-            positions = grouped.grouper.result_index.to_numpy(dtype=float)
 
             orig_width = width * self._native_width
             data = pd.DataFrame({self.orient: positions, "width": orig_width})
@@ -1207,7 +1208,7 @@ def plot_points(
             agg_data = sub_data if sub_data.empty else (
                 sub_data
                 .groupby(self.orient)
-                .apply(aggregator, agg_var)
+                .apply(aggregator, agg_var, **groupby_apply_include_groups(False))
                 .reindex(pd.Index(positions, name=self.orient))
                 .reset_index()
             )
@@ -1278,7 +1279,7 @@ def plot_bars(
             agg_data = sub_data if sub_data.empty else (
                 sub_data
                 .groupby(self.orient)
-                .apply(aggregator, agg_var)
+                .apply(aggregator, agg_var, **groupby_apply_include_groups(False))
                 .reset_index()
             )
 

seaborn/relational.py

@@ -17,6 +17,7 @@
     _get_transform_functions,
     _scatter_legend_artist,
 )
+from ._compat import groupby_apply_include_groups
 from ._statistics import EstimateAggregator, WeightedAggregator
 from .axisgrid import FacetGrid, _facet_docs
 from ._docstrings import DocstringComponents, _core_docs
@@ -290,7 +291,11 @@ def plot(self, ax, kws):
                 grouped = sub_data.groupby(orient, sort=self.sort)
                 # Could pass as_index=False instead of reset_index,
                 # but that fails on a corner case with older pandas.
-                sub_data = grouped.apply(agg, other).reset_index()
+                sub_data = (
+                    grouped
+                    .apply(agg, other, **groupby_apply_include_groups(False))
+                    .reset_index()
+                )
             else:
                 sub_data[f"{other}min"] = np.nan
                 sub_data[f"{other}max"] = np.nan

tests/_stats/test_density.py

@@ -6,6 +6,7 @@
 
 from seaborn._core.groupby import GroupBy
 from seaborn._stats.density import KDE, _no_scipy
+from seaborn._compat import groupby_apply_include_groups
 
 
 class TestKDE:
@@ -93,7 +94,10 @@ def test_common_norm(self, df, common_norm):
 
         areas = (
             res.groupby("alpha")
-            .apply(lambda x: self.integrate(x["density"], x[ori]))
+            .apply(
+                lambda x: self.integrate(x["density"], x[ori]),
+                **groupby_apply_include_groups(False),
+            )
         )
 
         if common_norm:
@@ -111,11 +115,18 @@ def test_common_norm_variables(self, df):
         def integrate_by_color_and_sum(x):
             return (
                 x.groupby("color")
-                .apply(lambda y: self.integrate(y["density"], y[ori]))
+                .apply(
+                    lambda y: self.integrate(y["density"], y[ori]),
+                    **groupby_apply_include_groups(False)
+                )
                 .sum()
             )
 
-        areas = res.groupby("alpha").apply(integrate_by_color_and_sum)
+        areas = (
+            res
+            .groupby("alpha")
+            .apply(integrate_by_color_and_sum, **groupby_apply_include_groups(False))
+        )
         assert_array_almost_equal(areas, [1, 1], decimal=3)
 
     @pytest.mark.parametrize("param", ["norm", "grid"])

tests/test_categorical.py

@@ -2078,7 +2078,7 @@ def test_xy_native_scale_log_transform(self):
 
     def test_datetime_native_scale_axis(self):
 
-        x = pd.date_range("2010-01-01", periods=20, freq="m")
+        x = pd.date_range("2010-01-01", periods=20, freq="MS")
         y = np.arange(20)
         ax = barplot(x=x, y=y, native_scale=True)
         assert "Date" in ax.xaxis.get_major_locator().__class__.__name__