coffee_championship_recipes_aeropress.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d06fb190-6da4-45a7-b6b3-258e839c9f4b",
   "metadata": {},
   "source": [
    "# World AeroPress Championship Recipes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e3be0479-2ba4-42b0-8560-51e1eac00aff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import FixedLocator, FixedFormatter\n",
    "import pandas as pd\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "56107c07-49d9-4957-8815-cfc9ffd2ea71",
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext blackcellmagic\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d38c9ecb-a028-4a9a-bd91-43ce9ed3efb9",
   "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>Year</th>\n",
       "      <th>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Country</th>\n",
       "      <th>Coffee [gr]</th>\n",
       "      <th>Water for brewing [gr]</th>\n",
       "      <th>Water after brew [gr]</th>\n",
       "      <th>Total Water [gr]</th>\n",
       "      <th>Temperature [°C]</th>\n",
       "      <th>Time [min]</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2024</td>\n",
       "      <td>1</td>\n",
       "      <td>George Stanica</td>\n",
       "      <td>Romania</td>\n",
       "      <td>18.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>150</td>\n",
       "      <td>96.0</td>\n",
       "      <td>2.17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2024</td>\n",
       "      <td>2</td>\n",
       "      <td>Sophan Nugraha</td>\n",
       "      <td>Indonesia</td>\n",
       "      <td>18.0</td>\n",
       "      <td>190.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>200</td>\n",
       "      <td>95.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2024</td>\n",
       "      <td>3</td>\n",
       "      <td>Mahmoud Jamika</td>\n",
       "      <td>Egypt</td>\n",
       "      <td>18.0</td>\n",
       "      <td>150.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>180</td>\n",
       "      <td>80.0</td>\n",
       "      <td>2.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2023</td>\n",
       "      <td>1</td>\n",
       "      <td>Tay Wipvasutt</td>\n",
       "      <td>Thailand</td>\n",
       "      <td>18.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>55.0</td>\n",
       "      <td>155</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2023</td>\n",
       "      <td>2</td>\n",
       "      <td>Carlo Graf Bülow</td>\n",
       "      <td>Germany</td>\n",
       "      <td>18.0</td>\n",
       "      <td>160.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>160</td>\n",
       "      <td>100.0</td>\n",
       "      <td>1.83</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Year  Rank              Name    Country  Coffee [gr]  \\\n",
       "0  2024     1    George Stanica    Romania         18.0   \n",
       "1  2024     2    Sophan Nugraha  Indonesia         18.0   \n",
       "2  2024     3    Mahmoud Jamika      Egypt         18.0   \n",
       "3  2023     1     Tay Wipvasutt   Thailand         18.0   \n",
       "4  2023     2  Carlo Graf Bülow    Germany         18.0   \n",
       "\n",
       "   Water for brewing [gr]  Water after brew [gr]  Total Water [gr]  \\\n",
       "0                   100.0                   50.0               150   \n",
       "1                   190.0                   10.0               200   \n",
       "2                   150.0                   30.0               180   \n",
       "3                   100.0                   55.0               155   \n",
       "4                   160.0                    NaN               160   \n",
       "\n",
       "   Temperature [°C]  Time [min]  \n",
       "0              96.0        2.17  \n",
       "1              95.0        2.00  \n",
       "2              80.0        2.00  \n",
       "3              89.0        2.08  \n",
       "4             100.0        1.83  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes = pd.read_csv(\"data/aeropress.csv\")\n",
    "recipes.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a672b423-6239-4210-acb0-13d98cc34a46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(43, 28)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes[\"Name\"].nunique(), recipes[\"Country\"].nunique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "005ba9b2-9444-43d3-b787-40575fec7d43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Jeff Verellen          4\n",
       "George Stanica         1\n",
       "Charlene de Buysere    1\n",
       "Name: Name, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes[\"Name\"].value_counts()[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "a30393e0-0faa-4ce9-b99e-600012a0153c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Belgium        6\n",
       "USA            3\n",
       "Norway         3\n",
       "Denmark        2\n",
       "Poland         2\n",
       "Japan          2\n",
       "Slovakia       2\n",
       "France         2\n",
       "Netherlands    2\n",
       "Finland        2\n",
       "Name: Country, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recipes[\"Country\"].value_counts()[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "01eddca4-cbb2-43c6-b24e-be765a1b3894",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 727.2x410.4 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_rank = lambda recipe: recipe[\"Year\"] + (7 - 2 * recipe[\"Rank\"]) / 6\n",
    "\n",
    "fig, ax = plt.subplots(\n",
    "    3, 1, figsize=[10.1, 5.7], sharex=True, gridspec_kw={\"height_ratios\": [5, 1, 1]}\n",
    ")\n",
    "plt.suptitle(\"World AeroPress Championship Recipes\")\n",
    "\n",
    "alpha = 0.75\n",
    "\n",
    "ax[0].set_ylabel(\"Weight [gr]\")\n",
    "ax[0].bar(\n",
    "    x=year_rank(recipes),\n",
    "    height=-recipes[\"Coffee [gr]\"],\n",
    "    width=0.25,\n",
    "    color=\"C5\",\n",
    "    alpha=alpha,\n",
    "    label=\"Coffee\",\n",
    ")\n",
    "ax[0].bar(\n",
    "    x=year_rank(recipes),\n",
    "    height=recipes[\"Water for brewing [gr]\"],\n",
    "    width=0.25,\n",
    "    hatch=\"..\",\n",
    "    color=\"C0\",\n",
    "    alpha=alpha,\n",
    "    label=\"Brew water\",\n",
    ")\n",
    "ax[0].bar(\n",
    "    x=year_rank(recipes),\n",
    "    bottom=recipes[\"Water for brewing [gr]\"],\n",
    "    height=recipes[\"Water after brew [gr]\"],\n",
    "    width=0.25,\n",
    "    color=\"C0\",\n",
    "    alpha=alpha,\n",
    "    label=\"Dilution\",\n",
    ")\n",
    "\n",
    "# Oldest reference I could find on 200ml rule was 2021:\n",
    "# https://manlycoffee.tumblr.com/post/20242539158/rules-japan-aeropress-championships-2012\n",
    "ax[0].hlines(200, 2012.1, 2020.1, color=\"C3\", linestyle=\":\")\n",
    "ax[0].hlines(150, 2020.9, 2025.1, color=\"C3\", linestyle=\":\")\n",
    "ax[0].hlines(-18, 2020.9, 2025.1, color=\"C3\", linestyle=\":\", label=\"Rules\")\n",
    "ax[0].annotate(\n",
    "    \"?\",\n",
    "    (2012.08, 200),\n",
    "    horizontalalignment=\"right\",\n",
    "    verticalalignment=\"center\",\n",
    "    color=\"C3\",\n",
    "    fontsize=18,\n",
    ")\n",
    "\n",
    "handles, labels = ax[0].get_legend_handles_labels()\n",
    "ax[0].legend(\n",
    "    handles[1:4] + [handles[0]],\n",
    "    labels[1:4] + [labels[0]],\n",
    "    frameon=False,\n",
    ")\n",
    "\n",
    "ax[1].set_ylabel(\"Temp. [°C]\")\n",
    "ax[1].bar(\n",
    "    x=year_rank(recipes),\n",
    "    height=recipes[\"Temperature [°C]\"],\n",
    "    width=0.25,\n",
    "    color=\"C3\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "ax[1].set_ylim(70)\n",
    "\n",
    "ax[2].set_ylabel(\"Time [min]\")\n",
    "ax[2].bar(\n",
    "    x=year_rank(recipes),\n",
    "    height=recipes[\"Time [min]\"],\n",
    "    width=0.25,\n",
    "    color=\"C5\",\n",
    "    alpha=alpha,\n",
    ")\n",
    "\n",
    "ax[2].set_xlim(2008.9, 2025.1)\n",
    "ax[2].set_xticks(range(2009, recipes[\"Year\"].max() + 1))\n",
    "ax[2].set_xticklabels(\n",
    "    range(2009, recipes[\"Year\"].max() + 1),\n",
    "    rotation=60,\n",
    "    rotation_mode=\"anchor\",\n",
    "    ha=\"right\",\n",
    ")\n",
    "ax[2].xaxis.set_minor_locator(FixedLocator(recipes.apply(year_rank, axis=1)))\n",
    "ax[2].xaxis.set_minor_formatter(FixedFormatter([\"1\", \"2\", \"3\"] * 15))\n",
    "\n",
    "ax[2].set_yticks([0, 2, 4])\n",
    "\n",
    "plt.tight_layout()\n",
    "sns.despine()\n",
    "plt.savefig(\"wac-recipes.png\", transparent=False, facecolor=\"white\", dpi=150)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b7d0a70-5ae9-452b-9395-84acfd7dbbf9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}