fig_1A.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 barplot(fig):\n",
    "    ax = fig.subplots()\n",
    "\n",
    "    sns.barplot(\n",
    "        data=penguins, x=\"island\", y=\"body_mass_g\", hue=\"sex\", errorbar=\"sd\", errwidth=1.5, ax=ax\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",
    "barplot(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
}