{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2b5164e2",
   "metadata": {},
   "source": [
    "# Saving a plotnine graph to png\n",
    "\n",
    "Once you're done with exploratory data analysis, it's time to show your graphic to the world! That's when you export."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ca64e2e5",
   "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>amount</th>\n",
       "      <th>category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>34</td>\n",
       "      <td>almonds</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>strawberries</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20</td>\n",
       "      <td>oats</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   amount      category\n",
       "0      34       almonds\n",
       "1      11  strawberries\n",
       "2      20          oats"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from plotnine import *\n",
    "\n",
    "df = pd.DataFrame([\n",
    "    { 'amount': 34, 'category': 'almonds'},\n",
    "    { 'amount': 11, 'category': 'strawberries'},\n",
    "    { 'amount': 20, 'category': 'oats'},\n",
    "])\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5fd853cb",
   "metadata": {},
   "source": [
    "### Saving as a png\n",
    "\n",
    "Unless you're going to be editing it in software like Adobe Illustrator, you usually want to save your graphic as a png! If you went with a jpeg file you're going to lose quality!\n",
    "\n",
    "To save your graph, just save the graph as a variable and use `.save('filename.png')`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8d6cad5f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/soma/Development/plotnine/plotnine/ggplot.py:731: PlotnineWarning: Saving 6.4 x 4.8 in image.\n",
      "/Users/soma/Development/plotnine/plotnine/ggplot.py:734: PlotnineWarning: Filename: filename.png\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (311459307)>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart = (\n",
    "    ggplot(df)\n",
    "    + aes(x='category', y='amount')\n",
    "    + geom_bar(stat='identity')\n",
    ")\n",
    "chart.save(\"filename.png\")\n",
    "chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3f47a3fa",
   "metadata": {},
   "source": [
    "### Changing the size of the output file\n",
    "\n",
    "Sometimes you want a bigger or smaller image! To do this, you'll need to adjust the `figure_size` with the `theme`. It's in *inches*, which is very weird, but that's life. Just make it until the size seems right."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "10c4f8d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/soma/Development/plotnine/plotnine/ggplot.py:731: PlotnineWarning: Saving 10 x 3 in image.\n",
      "/Users/soma/Development/plotnine/plotnine/ggplot.py:734: PlotnineWarning: Filename: filename.png\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (313950067)>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chart = (\n",
    "    ggplot(df)\n",
    "    + aes(x='category', y='amount')\n",
    "    + geom_bar(stat='identity')\n",
    "    + theme(figure_size=(10, 3))\n",
    ")\n",
    "chart.save(\"filename.png\")\n",
    "chart"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4576e641",
   "metadata": {},
   "source": [
    "In theory you can also change the resolution by passing `dpi=` but... it doesn't really matter (I think) unless you're doing something for print publications or getting fancy with multiple resolution images for retina screens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4a04a1b1",
   "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
}