Merge master and update version for release

.github/CONTRIBUTING.md

@@ -16,7 +16,7 @@ If you think you've encountered a bug in seaborn, please report it on the [Githu
 - A clear explanation of why you think something is wrong
 - The specific versions of seaborn and matplotlib that you are working with
 
-Bug reports are easiest to address if they can be demonstrated using one of the example datasets from the seaborn docs (i.e. with `seaborn.load_dataset`). Otherwise, it is preferable that your example generate synthetic data to reproduce the problem. If you can only demonstrate the issue with your actual dataset, you will need to share it, ideally as a csv. Note that you can upload a csv directly to a github issue thread, but it must have a `.txt` suffix.
+Bug reports are easiest to address if they can be demonstrated using one of the example datasets from the seaborn docs (i.e. with `seaborn.load_dataset`). Otherwise, it is preferable that your example generate synthetic data to reproduce the problem. If you can only demonstrate the issue with your actual dataset, you will need to share it, ideally as a csv (do not share data as a pickle file).
 
 If you've encountered an error, searching the specific text of the message before opening a new issue can often help you solve the problem quickly and avoid making a duplicate report.
 

.github/workflows/ci.yaml

@@ -16,15 +16,16 @@ env:
   NB_KERNEL: python
   MPLBACKEND: Agg
   SEABORN_DATA: ${{ github.workspace }}/seaborn-data
+  PYDEVD_DISABLE_FILE_VALIDATION: 1
 
 jobs:
   build-docs:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # v3.6.0
+      - uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
 
       - name: Setup Python 3.11
-        uses: actions/setup-python@61a6322f88396a6271a6ee3565807d608ecaddd1 # v4.7.0
+        uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0
         with:
           python-version: "3.11"
 
@@ -35,7 +36,8 @@ jobs:
 
       - name: Install pandoc
         run: |
-          sudo apt-get install pandoc
+          wget https://github.com/jgm/pandoc/releases/download/3.1.11/pandoc-3.1.11-1-amd64.deb
+          sudo dpkg -i pandoc-3.1.11-1-amd64.deb
 
       - name: Cache datasets
         run: |
@@ -56,7 +58,7 @@ jobs:
 
     strategy:
       matrix:
-        python: ["3.8", "3.9", "3.10", "3.11"]
+        python: ["3.8", "3.9", "3.10", "3.11", "3.12"]
         install: [full]
         deps: [latest]
 
@@ -69,12 +71,13 @@ jobs:
             deps: latest
 
     steps:
-      - uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # v3.6.0
+      - uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
 
       - name: Setup Python ${{ matrix.python }}
-        uses: actions/setup-python@61a6322f88396a6271a6ee3565807d608ecaddd1 # v4.7.0
+        uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0
         with:
           python-version: ${{ matrix.python }}
+          allow-prereleases: true
 
       - name: Install seaborn
         run: |
@@ -97,10 +100,10 @@ jobs:
     steps:
 
       - name: Checkout
-        uses: actions/checkout@f43a0e5ff2bd294095638e18286ca9a3d1956744 # v3.6.0
+        uses: actions/checkout@b4ffde65f46336ab88eb53be808477a3936bae11 # v4.1.1
 
       - name: Setup Python
-        uses: actions/setup-python@61a6322f88396a6271a6ee3565807d608ecaddd1 # v4.7.0
+        uses: actions/setup-python@0a5c61591373683505ea898e09a3ea4f39ef2b9c # v5.0.0
 
       - name: Install tools
         run: pip install mypy flake8

LICENSE.md

@@ -1,4 +1,4 @@
-Copyright (c) 2012-2021, Michael L. Waskom
+Copyright (c) 2012-2023, Michael L. Waskom
 All rights reserved.
 
 Redistribution and use in source and binary forms, with or without

README.md

@@ -6,7 +6,7 @@ seaborn: statistical data visualization
 =======================================
 
 [![PyPI Version](https://img.shields.io/pypi/v/seaborn.svg)](https://pypi.org/project/seaborn/)
-[![License](https://img.shields.io/pypi/l/seaborn.svg)](https://github.com/mwaskom/seaborn/blob/master/LICENSE)
+[![License](https://img.shields.io/pypi/l/seaborn.svg)](https://github.com/mwaskom/seaborn/blob/master/LICENSE.md)
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.03021/status.svg)](https://doi.org/10.21105/joss.03021)
 [![Tests](https://github.com/mwaskom/seaborn/workflows/CI/badge.svg)](https://github.com/mwaskom/seaborn/actions)
 [![Code Coverage](https://codecov.io/gh/mwaskom/seaborn/branch/master/graph/badge.svg)](https://codecov.io/gh/mwaskom/seaborn)

doc/_docstrings/barplot.ipynb

@@ -22,7 +22,7 @@
    "id": "b53b65b8-5670-4905-aa39-36db04f4b813",
    "metadata": {},
    "source": [
-    "With long data, assign `x` and `y` to group by a categorical varaible and plot aggregated values, with confidence intervals:"
+    "With long data, assign `x` and `y` to group by a categorical variable and plot aggregated values, with confidence intervals:"
    ]
   },
   {

doc/_docstrings/histplot.ipynb

@@ -312,7 +312,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Step functions, esepcially when unfilled, make it easy to compare cumulative histograms:"
+    "Step functions, especially when unfilled, make it easy to compare cumulative histograms:"
    ]
   },
   {

doc/_docstrings/objects.Est.ipynb

@@ -109,12 +109,30 @@
     "p.add(so.Range(), so.Est(seed=0))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "df807ef8-b5fb-4eac-b539-1bd4e797ddc2",
+   "metadata": {},
+   "source": [
+    "To compute a weighted estimate (and confidence interval), assign a `weight` variable in the layer where you use the stat:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "5e4a0594-e1ee-4f72-971e-3763dd626e8b",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "p.add(so.Range(), so.Est(), weight=\"price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d0c34d7-fb76-44cf-9079-3ec7f45741d0",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],

doc/_docstrings/objects.Plot.layout.ipynb

@@ -69,10 +69,28 @@
     "p.facet([\"A\", \"B\"], [\"X\", \"Y\"]).layout(engine=\"constrained\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "d61054d1-dcef-4e11-9802-394bcc633f9f",
+   "metadata": {},
+   "source": [
+    "With `extent`, you can control the size of the plot relative to the underlying figure. Because the notebook display adapts the figure background to the plot, this appears only to change the plot size in a notebook context. But it can be useful when saving or displaying through a `pyplot` GUI window:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1b5d5969-2925-474f-8e3c-99e4f90a7a2b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p.layout(extent=[0, 0, .8, 1]).show()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "781ff58c-b805-4e93-8cae-be0442e273ea",
+   "id": "e5c41b7d-a064-4406-8571-a544b194f3dc",
    "metadata": {},
    "outputs": [],
    "source": []

doc/_docstrings/pointplot.ipynb

@@ -22,7 +22,7 @@
    "id": "f25d3647-9fad-47b2-b49d-db6f5b5c3795",
    "metadata": {},
    "source": [
-    "Group by a categorical varaible and plot aggregated values, with confidence intervals:"
+    "Group by a categorical variable and plot aggregated values, with confidence intervals:"
    ]
   },
   {
@@ -138,7 +138,7 @@
    "id": "00273ada-cd12-410a-a268-38243d6514ae",
    "metadata": {},
    "source": [
-    "Dodge by a specific amount, relative to the width alloted for each level:"
+    "Dodge by a specific amount, relative to the width allotted for each level:"
    ]
   },
   {
@@ -164,7 +164,7 @@
    "id": "e205e7c8-6b11-44e6-b43f-7416c427215d",
    "metadata": {},
    "source": [
-    "When variables are not explicity assigned and the dataset is two-dimensional, the plot will aggregate over each column:"
+    "When variables are not explicitly assigned and the dataset is two-dimensional, the plot will aggregate over each column:"
    ]
   },
   {

doc/_docstrings/scatterplot.ipynb

@@ -167,7 +167,7 @@
    "cell_type": "raw",
    "metadata": {},
    "source": [
-    "Control the range of marker areas with ``sizes``, and set ``lengend=\"full\"`` to force every unique value to appear in the legend:"
+    "Control the range of marker areas with ``sizes``, and set ``legend=\"full\"`` to force every unique value to appear in the legend:"
    ]
   },
   {

doc/_tutorial/color_palettes.ipynb

@@ -975,7 +975,7 @@
    "source": [
     "As you can see, there are many options for using color in your visualizations. Seaborn tries both to use good defaults and to offer a lot of flexibility.\n",
     "\n",
-    "This discussion is only the beginning, and there are a number of good resources for learning more about techniques for using color in visualizations. One great example is this `series of blog posts <https://earthobservatory.nasa.gov/blogs/elegantfigures/2013/08/05/subtleties-of-color-part-1-of-6/>`_ from the NASA Earth Observatory. The matplotlib docs also have a `nice tutorial <https://matplotlib.org/users/colormaps.html>`_ that illustrates some of the perceptual properties of their colormaps."
+    "This discussion is only the beginning, and there are a number of good resources for learning more about techniques for using color in visualizations. One great example is this `series of blog posts <https://earthobservatory.nasa.gov/blogs/elegantfigures/2013/08/05/subtleties-of-color-part-1-of-6/>`_ from the NASA Earth Observatory. The matplotlib docs also have a `nice tutorial <https://matplotlib.org/stable/users/explain/colors/colormaps.html>`_ that illustrates some of the perceptual properties of their colormaps."
    ]
   }
  ],

doc/_tutorial/data_structure.ipynb

@@ -19,7 +19,7 @@
     "As a data visualization library, seaborn requires that you provide it with data. This chapter explains the various ways to accomplish that task. Seaborn supports several different dataset formats, and most functions accept data represented with objects from the `pandas <https://pandas.pydata.org/>`_ or `numpy <https://numpy.org/>`_ libraries as well as built-in Python types like lists and dictionaries. Understanding the usage patterns associated with these different options will help you quickly create useful visualizations for nearly any dataset.\n",
     "\n",
     ".. note::\n",
-    "   As of current writing (v0.13.0), the full breadth of options covered here are supported by most, but not all, of the functions in seaborn. Namely, a few older functinos (e.g., :func:`lmplot` and :func:`regplot`) anre more limited in what they accept."
+    "   As of current writing (v0.13.0), the full breadth of options covered here are supported by most, but not all, of the functions in seaborn. Namely, a few older functions (e.g., :func:`lmplot` and :func:`regplot`) are more limited in what they accept."
    ]
   },
   {

doc/_tutorial/properties.ipynb

@@ -742,7 +742,7 @@
     "\n",
     "The `pointsize` property is relevant to dot marks and to line marks that can show markers at individual data points. The units correspond to the diameter of the mark in points.\n",
     "\n",
-    "The `pointsize` scales with the square root of the data by default so that magnitude is represented by diameter rather than area:"
+    "Note that, while the parameterization corresponds to diameter, scales will be applied with a square root transform so that data values are linearly proportional to area:"
    ]
   },
   {

doc/conf.py

@@ -88,7 +88,7 @@
 """
 
 # Define replacements (used in whatsnew bullets)
-rst_epilog = """
+rst_epilog = r"""
 
 .. role:: raw-html(raw)
    :format: html

doc/installing.rst

@@ -129,7 +129,7 @@ if you try to reproduce the issue in an example that uses only matplotlib,
 so that you can report it in the right place. But it is alright to skip this
 step if it's not obvious how to do it.
 
-General support questions are more at home on either `stackoverflow
+General support questions are more at home on `stackoverflow
 <https://stackoverflow.com/questions/tagged/seaborn/>`_, where there is a
 larger audience of people who will see your post and may be able to offer
 assistance. Your chance of getting a quick answer will be higher if you include

doc/whatsnew/index.rst

@@ -8,6 +8,7 @@ v0.13
 .. toctree::
    :maxdepth: 2
 
+   v0.13.1
    v0.13.0
 
 v0.12

doc/whatsnew/v0.13.1.rst

@@ -0,0 +1,22 @@
+v0.13.1 (December 2023)
+-----------------------
+
+This is a minor release with some bug fixes and a couple new features. All users are encouraged to update.
+
+- |Feature| Added support for weighted mean estimation (with boostrap CIs) in :func:`lineplot`, :func:`barplot`, :func:`pointplot`, and :class:`objects.Est` (:pr:`3580`, :pr:`3586`).
+
+- |Feature| Added the `extent` option to :meth:`objects.Plot.layout` (:pr:`3552`).
+
+- |Fix| Fixed a regression in v0.13.0 that triggered an exception when working with non-numpy data types (:pr:`3516`).
+
+- |Fix| Fixed a bug in :class:`objects.Plot` so that tick labels are shown for wrapped axes that aren't in the bottom-most row (:pr:`3600`).
+
+- |Fix| Fixed a bug in :func:`catplot` where a blank legend would be added when `hue` was redundantly assigned (:pr:`3540`).
+
+- |Fix| Fixed a bug in :func:`catplot` where the `edgecolor` parameter was ignored with `kind="bar"` (:pr:`3547`).
+
+- |Fix| Fixed a bug in :func:`boxplot` where an exception was raised when using the matplotlib `bootstrap` option (:pr:`3562`).
+
+- |Fix| Fixed a bug in :func:`lineplot` where an exception was raised when `hue` was assigned with an empty dataframe (:pr:`3569`).
+
+- |Fix| Fixed a bug in multiple categorical plots that raised with `hue=None` and `dodge=True`; this is now has no effect (:pr:`3605`).

pyproject.toml

@@ -15,6 +15,7 @@ classifiers = [
     "Programming Language :: Python :: 3.9",
     "Programming Language :: Python :: 3.10",
     "Programming Language :: Python :: 3.11",
+    "Programming Language :: Python :: 3.12",
     "License :: OSI Approved :: BSD License",
     "Topic :: Scientific/Engineering :: Visualization",
     "Topic :: Multimedia :: Graphics",
@@ -25,7 +26,7 @@ requires-python = ">=3.8"
 dependencies = [
     "numpy>=1.20,!=1.24.0",
     "pandas>=1.2",
-    "matplotlib>=3.3,!=3.6.1",
+    "matplotlib>=3.4,!=3.6.1",
 ]
 
 [project.optional-dependencies]

seaborn/__init__.py

@@ -18,4 +18,4 @@
 _orig_rc_params = mpl.rcParams.copy()
 
 # Define the seaborn version
-__version__ = "0.13.0"
+__version__ = "0.13.1"

seaborn/_base.py

@@ -933,7 +933,8 @@ def iter_data(
 
             grouping_keys = []
             for var in grouping_vars:
-                grouping_keys.append(levels.get(var, []))
+                key = levels.get(var)
+                grouping_keys.append([] if key is None else key)
 
             iter_keys = itertools.product(*grouping_keys)
             if reverse:
@@ -1159,11 +1160,7 @@ def _attach(
         # For categorical y, we want the "first" level to be at the top of the axis
         if self.var_types.get("y", None) == "categorical":
             for ax in ax_list:
-                try:
-                    ax.yaxis.set_inverted(True)
-                except AttributeError:  # mpl < 3.1
-                    if not ax.yaxis_inverted():
-                        ax.invert_yaxis()
+                ax.yaxis.set_inverted(True)
 
         # TODO -- Add axes labels
 
@@ -1520,8 +1517,13 @@ def variable_type(vector, boolean_type="numeric"):
         warnings.simplefilter(
             action='ignore', category=(FutureWarning, DeprecationWarning)
         )
-        if np.isin(vector, [0, 1]).all():
-            return VariableType(boolean_type)
+        try:
+            if np.isin(vector, [0, 1]).all():
+                return VariableType(boolean_type)
+        except TypeError:
+            # .isin comparison is not guaranteed to be possible under NumPy
+            # casting rules, depending on the (unknown) dtype of 'vector'
+            pass
 
     # Defer to positive pandas tests
     if pd.api.types.is_numeric_dtype(vector):