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‎.github/dependabot.yml‎

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

‎.github/workflows/ci.yaml‎

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

‎Makefile‎

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

‎README.md‎

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2020
Online documentation is available at [seaborn.pydata.org](https://seaborn.pydata.org).
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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.
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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.
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To build the documentation locally, please refer to [`doc/README.md`](doc/README.md).
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There is also a [FAQ](https://github.com/mwaskom/seaborn/wiki/Frequently-Asked-Questions-(FAQs)) page, currently hosted on GitHub.
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Dependencies
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------------
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‎doc/_docstrings/objects.KDE.ipynb‎

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

‎doc/_docstrings/objects.Norm.ipynb‎

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

‎doc/api.rst‎

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Est
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Count
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KDE
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‎doc/installing.rst‎

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conda install seaborn
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As the main Anaconda repository can be slow to add new releaes, you may prefer using the
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As the main Anaconda repository can be slow to add new releases, you may prefer using the
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`conda-forge <https://conda-forge.org/>`_ channel::
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‎doc/whatsnew/v0.12.2.rst‎

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v0.12.2 (Unreleased)
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--------------------
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- |Feature| Added the :class:`objects.KDE` stat (:pr:`3111`).
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- |Enhancement| Automatic mark widths are now calculated separately for unshared facet axes (:pr:`3119`).
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- |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`).
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- |Fix| Fixed a regression in v0.12.0 where manually-added labels could have duplicate legend entries (:pr:`3116`).
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- |Fix| Fixed a bug in :func:`histplot` with `kde=True` and `log_scale=True` where the curve was not scaled properly (:pr:`3173`).
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- |Fix| Fixed a bug in :func:`relplot` where inner axis labels would be shown when axis sharing was disabled (:pr:`3180`).

‎pyproject.toml‎

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"pandas-stubs",
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"flit",
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docs = [
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"numpydoc",

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