Port categorical plot docstrings to notebooks

mwaskom · Jun 15, 2022 · 9d8ce6a · 9d8ce6a
1 parent 057cdd8
commit 9d8ce6a
Show file tree

Hide file tree

Showing 29 changed files with 1,085 additions and 644 deletions.
diff --git a/doc/docstrings/FacetGrid.ipynb b/doc/docstrings/FacetGrid.ipynb
@@ -294,7 +294,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.9"
   }
  },
  "nbformat": 4,

diff --git a/doc/docstrings/JointGrid.ipynb b/doc/docstrings/JointGrid.ipynb
@@ -236,7 +236,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.9"
   }
  },
  "nbformat": 4,

diff --git a/doc/docstrings/PairGrid.ipynb b/doc/docstrings/PairGrid.ipynb
@@ -263,7 +263,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.9"
   }
  },
  "nbformat": 4,

diff --git a/doc/docstrings/axes_style.ipynb b/doc/docstrings/axes_style.ipynb
@@ -94,7 +94,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.5"
+   "version": "3.9.9"
   }
  },
  "nbformat": 4,

diff --git a/doc/docstrings/barplot.ipynb b/doc/docstrings/barplot.ipynb
@@ -0,0 +1,125 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6a6d582b-08c2-4fed-be56-afa1b986943a",
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.set_theme(style=\"whitegrid\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7ef20b6-3bd8-4992-a270-4c3ecc86a0fa",
+   "metadata": {},
+   "source": [
+    "Group by a categorical varaible and plot aggregated values, with confidence intervals:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0f5c3ece-6295-4933-8a87-e80cd604c089",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = sns.load_dataset(\"penguins\")\n",
+    "sns.barplot(data=df, x=\"island\", y=\"body_mass_g\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "38f7c39e-485d-4b50-ac21-f1b402f26aa4",
+   "metadata": {},
+   "source": [
+    "Add a second layer of grouping:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac1a28d1-b3bd-4158-86d0-3defc12f8566",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.barplot(data=df, x=\"island\", y=\"body_mass_g\", hue=\"sex\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f8fa070-a8f4-41fb-be74-c489acbdbcbe",
+   "metadata": {},
+   "source": [
+    "Use the error bars to show the standard deviation rather than a confidence interval:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "10445b78-a74a-4f14-a28b-a9164e592ae4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.barplot(data=df, x=\"island\", y=\"body_mass_g\", ci=\"sd\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7f579f70-39a2-4d0c-baa2-9adae11ce2ce",
+   "metadata": {},
+   "source": [
+    "Customize the appearance of the plot:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d6f9ac1c-a77d-4ee3-bc5e-fec2071b33df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.barplot(\n",
+    "    data=df, x=\"body_mass_g\", y=\"island\",\n",
+    "    ci=\"sd\", capsize=.4, errcolor=\".5\",\n",
+    "    linewidth=3, edgecolor=\".5\", facecolor=(0, 0, 0, 0),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "faedd6f9-a123-4927-9eff-a2046edf5c72",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "seaborn-py39-latest",
+   "language": "python",
+   "name": "seaborn-py39-latest"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/doc/docstrings/boxenplot.ipynb b/doc/docstrings/boxenplot.ipynb
@@ -0,0 +1,130 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "882d215b-88d8-4b5e-ae7a-0e3f6bb53bad",
+   "metadata": {
+    "tags": [
+     "hide"
+    ]
+   },
+   "outputs": [],
+   "source": [
+    "import seaborn as sns\n",
+    "sns.set_theme(style=\"whitegrid\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6809326c-14a9-4314-994d-b4e8e7414172",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = sns.load_dataset(\"diamonds\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9ccbc2d5-5a44-4e80-8b07-e12629729f4a",
+   "metadata": {},
+   "source": [
+    "Draw a single horizontal plot, assigning the data directly to the coordinate variable:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "391e1162-b438-4486-9a08-60686ee8e96a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.boxenplot(x=df[\"price\"])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a3b0e9b8-1673-494c-a27a-aa9c60457ba1",
+   "metadata": {},
+   "source": [
+    "Group by a categorical variable, referencing columns in a datafame"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e30fec18-f127-40a3-bfaf-f71324dd60ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.boxenplot(data=df, x=\"price\", y=\"clarity\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4f01a821-74d1-452d-a1f7-cf5b806169e8",
+   "metadata": {},
+   "source": [
+    "Use a different scaling rule to control the width of each box:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d0c1aa43-5e8a-486c-bd6d-3c29d6d23138",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.boxenplot(data=df, x=\"carat\", y=\"cut\", scale=\"linear\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "fd5d197c-8cbb-4be3-a14d-76447f06d3f1",
+   "metadata": {},
+   "source": [
+    "Use a different method to determine the number of boxes:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1aead6a3-6f12-47d3-b472-a39c61867963",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sns.boxenplot(data=df, x=\"carat\", y=\"cut\", k_depth=\"trustworthy\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "719fd61f-9795-47d6-96bd-4929d8647038",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "seaborn-py39-latest",
+   "language": "python",
+   "name": "seaborn-py39-latest"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}