tests/test_execute/test_custom_convert_auto.ipynb

{
 "cells": [
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "---\n",
    "title: \"Test chunk options in Rmd/Jupyter conversion\"\n",
    "author: \"Marc Wouts\"\n",
    "date: \"June 16, 2018\"\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Custom Formats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "echo": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "x = pd.Series({'A':1, 'B':3, 'C':2})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "fig.height": 5,
    "fig.width": 8,
    "name": "bar_plot",
    "tags": [
     "remove_input"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Sample plot'}>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "filenames": {
       "image/png": "/private/var/folders/t2/xbl15_3n4tsb1vr_ccmmtmbr0000gn/T/pytest-of-chrisjsewell/pytest-534/test_custom_convert_auto0/source/_build/jupyter_execute/custom-formats_3_1.png"
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x.plot(kind='bar', title='Sample plot')"
   ]
  }
 ],
 "metadata": {
  "jupytext": {
   "text_representation": {
    "extension": ".Rmd",
    "format_name": "rmarkdown"
   }
  },
  "kernelspec": {
   "display_name": "Python",
   "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": "3.7.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}