Update version

.github/dependabot.yml

@@ -0,0 +1,8 @@
+version: 2
+updates:
+  # Maintain dependencies for GitHub Actions
+  - package-ecosystem: "github-actions"
+    directory: "/"
+    schedule:
+      # Check for updates to GitHub Actions every week
+      interval: "weekly"

.github/workflows/ci.yaml

@@ -21,7 +21,7 @@ jobs:
       - uses: actions/checkout@v3
 
       - name: Setup Python 3.10
-        uses: actions/setup-python@v3
+        uses: actions/setup-python@v4
         with:
           python-version: "3.10"
 
@@ -69,7 +69,7 @@ jobs:
       - uses: actions/checkout@v3
 
       - name: Setup Python ${{ matrix.python }}
-        uses: actions/setup-python@v3
+        uses: actions/setup-python@v4
         with:
           python-version: ${{ matrix.python }}
 
@@ -84,7 +84,7 @@ jobs:
         run: make test
 
       - name: Upload coverage
-        uses: codecov/codecov-action@v2
+        uses: codecov/codecov-action@v3
         if: ${{ success() }}
 
   lint:
@@ -97,7 +97,7 @@ jobs:
         uses: actions/checkout@v2
 
       - name: Setup Python
-        uses: actions/setup-python@v2
+        uses: actions/setup-python@v4
 
       - name: Install tools
         run: pip install mypy flake8

Makefile

@@ -1,7 +1,7 @@
 export SHELL := /bin/bash
 
 test:
-	pytest -n auto --cov=seaborn --cov=tests --cov-config=.coveragerc tests
+	pytest -n auto --cov=seaborn --cov=tests --cov-config=setup.cfg tests
 
 lint:
 	flake8 seaborn

README.md

@@ -19,12 +19,10 @@ Documentation
 
 Online documentation is available at [seaborn.pydata.org](https://seaborn.pydata.org).
 
-The docs include a [tutorial](https://seaborn.pydata.org/tutorial.html), [example gallery](https://seaborn.pydata.org/examples/index.html), [API reference](https://seaborn.pydata.org/api.html), and other useful information.
+The docs include a [tutorial](https://seaborn.pydata.org/tutorial.html), [example gallery](https://seaborn.pydata.org/examples/index.html), [API reference](https://seaborn.pydata.org/api.html), [FAQ](https://seaborn.pydata.org/faq), and other useful information.
 
 To build the documentation locally, please refer to [`doc/README.md`](doc/README.md).
 
-There is also a [FAQ](https://github.com/mwaskom/seaborn/wiki/Frequently-Asked-Questions-(FAQs)) page, currently hosted on GitHub.
-
 Dependencies
 ------------
 

doc/_docstrings/objects.KDE.ipynb

@@ -0,0 +1,270 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dcc1ae12-bba4-4de9-af8d-543b3d65b42b",
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import seaborn.objects as so\n",
+    "from seaborn import load_dataset\n",
+    "penguins = load_dataset(\"penguins\")"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "1042b991-1471-43bd-934c-43caae3cb2fa",
+   "metadata": {},
+   "source": [
+    "This stat estimates transforms observations into a smooth function representing the estimated density:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2406e2aa-7f0f-4a51-af59-4cef827d28d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p = so.Plot(penguins, x=\"flipper_length_mm\")\n",
+    "p.add(so.Area(), so.KDE())"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "44515f21-683b-420f-967b-4c7568c907d7",
+   "metadata": {},
+   "source": [
+    "Adjust the smoothing bandwidth to see more or fewer details:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d4e6ba5b-4dd2-4210-8cf0-de057dc71e2a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p.add(so.Area(), so.KDE(bw_adjust=0.25))"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "fd665fe1-a5e4-4742-adc9-e40615d57d08",
+   "metadata": {},
+   "source": [
+    "The curve will extend beyond observed values in the dataset:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4cda1cb8-f663-4f94-aa24-6f1727a41031",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p2 = p.add(so.Bars(alpha=.3), so.Hist(\"density\"))\n",
+    "p2.add(so.Line(), so.KDE())"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "75235825-d522-4562-aacc-9b7413eabf5d",
+   "metadata": {},
+   "source": [
+    "Control the range of the density curve relative to the observations using `cut`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a7a9275e-9889-437d-bdc5-18653d2c92ef",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p2.add(so.Line(), so.KDE(cut=0))"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "6a885eeb-81ba-47c6-8402-1bef40544fd1",
+   "metadata": {},
+   "source": [
+    "When observations are assigned to the `y` variable, the density will be shown for `x`:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "38b3a0fb-54ff-493a-bd64-f83a12365723",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "so.Plot(penguins, y=\"flipper_length_mm\").add(so.Area(), so.KDE())"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "59996340-168e-479f-a0c6-c7e1fcab0fb0",
+   "metadata": {},
+   "source": [
+    "Use `gridsize` to increase or decrease the resolution of the grid where the density is evaluated:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "23715820-7df9-40ba-9e74-f11564704dd0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p.add(so.Dots(), so.KDE(gridsize=100))"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "4c9b6492-98c8-45ab-9f53-681cde2f767a",
+   "metadata": {},
+   "source": [
+    "Or pass `None` to evaluate the density at the original datapoints:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e1b6810-5c28-43aa-aa61-652521299b51",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p.add(so.Dots(), so.KDE(gridsize=None))"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "0970a56b-0cba-4c40-bb1b-b8e71739df5c",
+   "metadata": {},
+   "source": [
+    "Other variables will define groups for the estimation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5f0ce0b6-5742-4bc0-9ac3-abedde923684",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p.add(so.Area(), so.KDE(), color=\"species\")"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "22204fcd-4b25-46e5-a170-02b1419c23d5",
+   "metadata": {},
+   "source": [
+    "By default, the density is normalized across all groups (i.e., the joint density is shown); pass `common_norm=False` to show conditional densities:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6ad56958-dc45-4632-94d1-23039ad3ec58",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p.add(so.Area(), so.KDE(common_norm=False), color=\"species\")"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "b1627197-85d1-4476-b4ae-3e93044ee988",
+   "metadata": {},
+   "source": [
+    "Or pass a list of variables to condition on:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58f63734-5afd-4d90-bbfb-fc39c8d1981f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "(\n",
+    "    p.facet(\"sex\")\n",
+    "    .add(so.Area(), so.KDE(common_norm=[\"col\"]), color=\"species\")\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "2b7e018e-1374-4939-909c-e95f5ffd086e",
+   "metadata": {},
+   "source": [
+    "This stat can be combined with other transforms, such as :class:`Stack` (when `common_grid=True`):"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "96e5b2d0-c7e2-47df-91f1-7f9ec0bb08a9",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p.add(so.Area(), so.KDE(), so.Stack(), color=\"sex\")"
+   ]
+  },
+  {
+   "cell_type": "raw",
+   "id": "8500ff86-0b1f-4831-954b-08b6df690387",
+   "metadata": {},
+   "source": [
+    "Set `cumulative=True` to integrate the density:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "26bb736e-7cfd-421e-b80d-42fa450e88c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p.add(so.Line(), so.KDE(cumulative=True))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e8bfd9d2-ad60-4971-aa7f-71a285f44a20",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "py310",
+   "language": "python",
+   "name": "py310"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

doc/_docstrings/objects.Norm.ipynb

@@ -4,7 +4,11 @@
    "cell_type": "code",
    "execution_count": null,
    "id": "0bfee8b6-1e3e-499d-96ae-735a5c230b32",
-   "metadata": {},
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
    "outputs": [],
    "source": [
     "import seaborn.objects as so\n",

doc/api.rst

@@ -88,6 +88,7 @@ Stat objects
     Est
     Count
     Hist
+    KDE
     Perc
     PolyFit
 

doc/installing.rst

@@ -19,7 +19,7 @@ and it can be installed with `conda`::
 
     conda install seaborn
 
-As the main Anaconda repository can be slow to add new releaes, you may prefer using the
+As the main Anaconda repository can be slow to add new releases, you may prefer using the
 `conda-forge <https://conda-forge.org/>`_ channel::
 
     conda install seaborn -c conda-forge

doc/whatsnew/v0.12.2.rst

@@ -0,0 +1,15 @@
+
+v0.12.2 (Unreleased)
+--------------------
+
+- |Feature| Added the :class:`objects.KDE` stat (:pr:`3111`).
+
+- |Enhancement| Automatic mark widths are now calculated separately for unshared facet axes (:pr:`3119`).
+
+- |Fix| Fixed a bug where legends for numeric variables with large values with be incorrectly shown (i.e. with a missing offset or exponent; :pr:`3187`).
+
+- |Fix| Fixed a regression in v0.12.0 where manually-added labels could have duplicate legend entries (:pr:`3116`).
+
+- |Fix| Fixed a bug in :func:`histplot` with `kde=True` and `log_scale=True` where the curve was not scaled properly (:pr:`3173`).
+
+- |Fix| Fixed a bug in :func:`relplot` where inner axis labels would be shown when axis sharing was disabled (:pr:`3180`).

pyproject.toml

@@ -42,6 +42,7 @@ dev = [
     "mypy",
     "pandas-stubs",
     "pre-commit",
+    "flit",
 ]
 docs = [
     "numpydoc",

seaborn/__init__.py

@@ -18,4 +18,4 @@
 _orig_rc_params = mpl.rcParams.copy()
 
 # Define the seaborn version
-__version__ = "0.12.1"
+__version__ = "0.12.2.dev0"