src/notebooks/30-basic-boxplot-with-seaborn.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries\n",
    "\n",
    "First, we need to load a few libraries:\n",
    "\n",
    "- [matplotlib](https://python-graph-gallery.com/matplotlib/): for displaying the chart\n",
    "- [seaborn](https://python-graph-gallery.com/seaborn/): for creating the chart\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "Since [boxplot](https://python-graph-gallery.com/boxplot/) is used to display the **distribution of a numerical variable**, we need a dataset that contains at least one numerical variable.\n",
    "\n",
    "In this example, we will use the `iris` dataset that we can easily **load**:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset('iris')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One single numerical variable\n",
    "\n",
    "The simplest form of boxplot: analysis of the **overall distribution** of a single numerical variable with the `boxplot()` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1920x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"darkgrid\")\n",
    "sns.boxplot(y=df[\"sepal_length\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## One numerical variable and several groups\n",
    "\n",
    "Depending on your data, you may want to have a better understanding of the distribution of a given variable between **two or more groups**.\n",
    "\n",
    "You can do so by specifying the `x` parameter in the `boxplot()` function.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1920x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_theme(style=\"darkgrid\")\n",
    "sns.boxplot(x=df[\"species\"], y=df[\"sepal_length\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Going further\n",
    "\n",
    "This post explains how to create and customize a [boxplot](https://python-graph-gallery.com/boxplot/) with the [seaborn](https://python-graph-gallery.com/seaborn/) library.\n",
    "\n",
    "You might be interested in how adding [individual data points in boxplot](https://python-graph-gallery.com/36-add-jitter-over-boxplot-seaborn/) and how to create a [raincloud plot](https://python-graph-gallery.com/raincloud-plot-with-matplotlib-and-ptitprince/) with the `ptitprince` library.\n"
   ]
  }
 ],
 "metadata": {
  "chartType": "boxplot",
  "description": "This page aims at explaining how to plot a basic boxplot with seaborn thanks to the boxplot() function. In a similar way as violinplots, one can use boxplots to differentiate groups based on one numerical variable only, or to study a particular group through several numerical variables. We will go through three basic examples with the 'iris' dataset.",
  "family": "distribution",
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "keywords": "python, chart, plot, seaborn, matplotlib, boxplot",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "seoDescription": "How to build basic boxplots to analyze distributions",
  "slug": "30-basic-boxplot-with-seaborn",
  "title": "Basic boxplot with Seaborn"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}