fig_1B.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "042d8782-a6a6-40f0-960c-66c014a6cbc1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: OUTDATED_IGNORE=1\n"
     ]
    }
   ],
   "source": [
    "%env OUTDATED_IGNORE=1\n",
    "from matplotlib import pyplot as plt\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_theme()\n",
    "sns.set_style('ticks')\n",
    "\n",
    "plt.rc('figure', dpi=300)\n",
    "plt.rc('font', size=8, family='Times New Roman')\n",
    "plt.rc('axes', labelsize=8, linewidth=1)\n",
    "plt.rc('xtick', labelsize=8)\n",
    "plt.rc('xtick.major', size=3, width=1, pad=1.5)\n",
    "plt.rc('ytick', labelsize=8)\n",
    "plt.rc('ytick.major', size=3, width=1, pad=1.5)\n",
    "plt.rc('legend', fontsize=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "3415f27f-8242-4135-b8ee-1abfd314db82",
   "metadata": {},
   "outputs": [],
   "source": [
    "penguins = sns.load_dataset(\"penguins\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ba18fb53-cfbc-4a60-9d77-3226619e18c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "      <th>island</th>\n",
       "      <th>bill_length_mm</th>\n",
       "      <th>bill_depth_mm</th>\n",
       "      <th>flipper_length_mm</th>\n",
       "      <th>body_mass_g</th>\n",
       "      <th>sex</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.1</td>\n",
       "      <td>18.7</td>\n",
       "      <td>181.0</td>\n",
       "      <td>3750.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>39.5</td>\n",
       "      <td>17.4</td>\n",
       "      <td>186.0</td>\n",
       "      <td>3800.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>40.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>195.0</td>\n",
       "      <td>3250.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adelie</td>\n",
       "      <td>Torgersen</td>\n",
       "      <td>36.7</td>\n",
       "      <td>19.3</td>\n",
       "      <td>193.0</td>\n",
       "      <td>3450.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  species     island  bill_length_mm  bill_depth_mm  flipper_length_mm  \\\n",
       "0  Adelie  Torgersen            39.1           18.7              181.0   \n",
       "1  Adelie  Torgersen            39.5           17.4              186.0   \n",
       "2  Adelie  Torgersen            40.3           18.0              195.0   \n",
       "3  Adelie  Torgersen             NaN            NaN                NaN   \n",
       "4  Adelie  Torgersen            36.7           19.3              193.0   \n",
       "\n",
       "   body_mass_g     sex  \n",
       "0       3750.0    Male  \n",
       "1       3800.0  Female  \n",
       "2       3250.0  Female  \n",
       "3          NaN     NaN  \n",
       "4       3450.0  Female  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "penguins.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "29ef1453-ddd8-4d74-b44e-379cc03f4f92",
   "metadata": {},
   "outputs": [],
   "source": [
    "def boxplot(fig):\n",
    "    ax = fig.subplots()\n",
    "\n",
    "    sns.boxplot(\n",
    "        data=penguins, x=\"island\", y=\"body_mass_g\", hue=\"sex\", showfliers=False, linewidth=1, width=0.618\n",
    "    )\n",
    "    ax.set(xlabel=\"island\", ylabel=\"body mass (g)\")\n",
    "    ax.legend(ncol=2, loc=\"lower center\", bbox_to_anchor=(0.5, 1), frameon=False)\n",
    "    sns.despine(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "2eb3403d-d7a3-442a-879d-a0be780b0168",
   "metadata": {
    "tags": [
     "active-ipynb"
    ]
   },
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1080x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(7.2 / 2, 7.2 / 2))\n",
    "\n",
    "boxplot(fig)\n",
    "\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dcece93f-b9d4-41ce-93ae-594647a8533f",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "jupytext": {
   "formats": "ipynb,auto:light"
  },
  "kernelspec": {
   "display_name": "ipython",
   "language": "python",
   "name": "ipython"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}