src/notebooks/grouped-barplot.ipynb

{
 "cells": [
  {
   "source": [
    "## Grouped barplot"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's consider the total bills paid by a set of people at a restaurant. Those bills are split:\n",
    "- in groups: the day of the week\n",
    "- in subgroups: wether the clients were smokers or not\n",
    "\n",
    "This kind of data allows to build a grouped barplot. Each bar represents the mean bill price for each group and subgroups. The groups are provided the the `x` parameter of the `barplot()` function, the subgroups are passed to the `hue` parameter and will control the color."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# import libraries\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# set plot style: grey grid in the background:\n",
    "sns.set_theme(style=\"darkgrid\")\n",
    "\n",
    "# load dataset\n",
    "tips = sns.load_dataset(\"tips\")\n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(8, 8))\n",
    "\n",
    "# grouped barplot\n",
    "sns.barplot(x=\"day\", y=\"total_bill\", hue=\"smoker\", data=tips, ci=None);"
   ]
  },
  {
   "source": [
    "## More than 2 groups"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "With more than 2 groups you can use the `catplot()` function to split the plot window. There will be one distinct plot for each of the last subgroup levels."
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    " sns.catplot(x=\"sex\", y=\"total_bill\",\n",
    "                 hue=\"smoker\", col=\"day\",\n",
    "                 data=tips, kind=\"bar\",\n",
    "                 height=4, aspect=.7);"
   ]
  },
  {
   "source": [
    "## Color"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "source": [
    "You can create an array of colors to use on the chart, pass it to the `color_palette()` function to create a palette with it, and set it as the palette to use thanks to `set_palette()`"
   ],
   "cell_type": "markdown",
   "metadata": {}
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "<Figure size 720x720 with 1 Axes>",
      "image/svg+xml": "<?xml version=\"1.0\" encoding=\"utf-8\" standalone=\"no\"?>\n<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n  \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n<!-- Created with matplotlib (https://matplotlib.org/) -->\n<svg height=\"607.410469pt\" version=\"1.1\" viewBox=\"0 0 609.108437 607.410469\" width=\"609.108437pt\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n <metadata>\n  <rdf:RDF xmlns:cc=\"http://creativecommons.org/ns#\" xmlns:dc=\"http://purl.org/dc/elements/1.1/\" xmlns:rdf=\"http://www.w3.org/1999/02/22-rdf-syntax-ns#\">\n   <cc:Work>\n    <dc:type rdf:resource=\"http://purl.org/dc/dcmitype/StillImage\"/>\n    <dc:date>2021-02-10T18:34:18.926161</dc:date>\n    <dc:format>image/svg+xml</dc:format>\n    <dc:creator>\n     <cc:Agent>\n      <dc:title>Matplotlib v3.3.0, https://matplotlib.org/</dc:title>\n     </cc:Agent>\n    </dc:creator>\n   </cc:Work>\n  </rdf:RDF>\n </metadata>\n <defs>\n  <style 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "# Create an array with the colors you want to use\n",
    "colors = [\"#69b3a2\", \"#4374B3\"]\n",
    "sns.set_palette(sns.color_palette(colors))\n",
    "\n",
    "# Set the figure size\n",
    "plt.figure(figsize=(10, 10))\n",
    "\n",
    "# grouped barplot\n",
    "ax = sns.barplot(\n",
    "    x=\"day\", \n",
    "    y=\"total_bill\", \n",
    "    hue=\"smoker\", \n",
    "    data=tips, \n",
    "    ci=None\n",
    "    )\n",
    "\n",
    "# Customize the axes and title\n",
    "ax.set_title(\"Smokers have bigger bills\")\n",
    "ax.set_ylabel(\"Bill value\")\n",
    "\n",
    "# Remove top and right borders\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n"
   ]
  }
 ],
 "metadata": {
  "chartType": "barplot",
  "description": "This post explains how to draw a <b>grouped barplot</b> using <a href='http://python-graph-gallery.com/seaborn/'>seaborn</a>. Note that you can check <a href='https://python-graph-gallery.com/basic-barplot-with-seaborn/'>this post</a> to see how to make a basic barplot using seaborn.",
  "family": "ranking",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "keywords": "python, chart, plot, seaborn, matplotlib, barplot, grouped bar plot",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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