Merge branch 'master' into v0.13

doc/whatsnew/index.rst

@@ -8,6 +8,7 @@ v0.13
 .. toctree::
    :maxdepth: 2
 
+   v0.13.2
    v0.13.1
    v0.13.0
 

doc/whatsnew/v0.13.2.rst

@@ -0,0 +1,4 @@
+v0.13.2 (January 2024)
+----------------------
+
+This is a minor release containing only internal changes that adapt to upcoming deprecations in pandas. All users are encouraged to update.

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()
             )
 
@@ -2281,7 +2282,7 @@ def swarmplot(
     {order_vars}
     dodge : bool
         When a `hue` variable is assigned, setting this to `True` will
-        separate the swaarms for different hue levels along the categorical
+        separate the swarms for different hue levels along the categorical
         axis and narrow the amount of space allotedto each strip. Otherwise,
         the points for each level will be plotted in the same swarm.
     {orient}

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__