ickc/pannb

{
 "cells": [
  {
   "cell_type": "raw",
   "id": "2d153021-ebd6-44b3-8fd4-f255233b7a83",
   "metadata": {},
   "source": [
    "---\n",
    "author: Kolen Cheung\n",
    "title: Pandoc filter for ipynb\n",
    "description: This will becomes pandoc's meta-block.\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc4e8ae7-9d4d-42a4-a880-d5e5d9e11d2f",
   "metadata": {},
   "source": [
    "# Example\n",
    "\n",
    "An example with some markdown texts.\n",
    "\n",
    "Compare `source </example>`{.interpreted-text role=\"doc\"} and `output </example-output>`{.interpreted-text role=\"doc\"}.\n",
    "\n",
    "Below we'll write some Python code that will becomes invisible after pannb filtering."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f02e5f1-d9a7-4645-9d3c-6d2914b2bc70",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "df = pd.DataFrame(np.arange(9).reshape(3, 3))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d68cd8b4-a9a1-4b2b-85a3-2fa0c3ad9f35",
   "metadata": {},
   "source": [
    "However, the HTML output of these cells should be visible, and converted to pandoc's internal format.\n",
    "\n",
    "For example, the below table will be valid in LaTeX output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2efe6488-91e7-48d0-86c5-791c594a9340",
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2\n",
       "0  0  1  2\n",
       "1  3  4  5\n",
       "2  6  7  8"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pannb",
   "language": "python",
   "name": "pannb"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}