snakedown 0.3.0

This is a snakedown. Hand over your docs, nice and clean, and nobody gets confused.
Documentation
{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots()             # Create a figure containing a single Axes.\n",
    "ax.plot([1, 2, 3, 4], [1, 4, 2, 3])  # Plot some data on the Axes.\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## SVG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/svg+xml": [
       "<svg height=\"100\" width=\"100\" xmlns=\"http://www.w3.org/2000/svg\"><circle cx=\"50\" cy=\"50\" r=\"40\" stroke=\"black\"></circle></svg>\n"
      ],
      "text/plain": [
       "<__main__.SVG at 0x13529c24d70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "svg = \"\"\"<svg height=\"100\" width=\"100\" xmlns=\"http://www.w3.org/2000/svg\"><circle cx=\"50\" cy=\"50\" r=\"40\" stroke=\"black\"></circle></svg>\"\"\"\n",
    "\n",
    "class SVG:\n",
    "    def _repr_svg_(self):\n",
    "        return svg\n",
    "\n",
    "display(SVG())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n",
      "6\n",
      "7\n",
      "8\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "for i in range(10):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Latex\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$$e^{i\\pi}+1=0$$"
      ],
      "text/plain": [
       "<__main__.Latex at 0x13529c24ec0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "latex = \"$$e^{i\\\\pi}+1=0$$\"\n",
    "\n",
    "class Latex:\n",
    "    def _repr_latex_(self):\n",
    "        return latex\n",
    "\n",
    "display(Latex())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### HTML"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2\n",
       "0     1     3\n",
       "1     2     4"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas\n",
    "pandas.DataFrame(data={\"col1\": [1, 2], \"col2\": [3, 4]})"
   ]
  }
 ],
 "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": "3.14.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}