minimal_example_jupyterlab_myst.ipynb

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   "source": [
    "from datetime import datetime\n",
    "from scipy import constants"
   ]
  },
  {
   "cell_type": "markdown",
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      "expression": "datetime.now().strftime('%H:%M:%S')",
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       "data": {
        "text/plain": "'22:57:42'"
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      "expression": "f\"{constants.pi:.2f}\" ",
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        "text/plain": "'3.14'"
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   "source": [
    "# Some constants:\n",
    "\n",
    "* This cell was executed at {eval}`datetime.now().strftime('%H:%M:%S')`\n",
    "* The number of pi: $ \\pi = $ {eval}`f\"{constants.pi:.2f}\" `\n"
   ]
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   "metadata": {
    "tags": [],
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "x = pd.DataFrame({'A': [1, 2, 3]})"
   ]
  },
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   "cell_type": "markdown",
   "id": "8cb16029-3766-427c-afcf-4de6ac1bfb05",
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     {
      "expression": "x ",
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       "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>A</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
        "text/plain": "   A\n0  1\n1  2\n2  3"
       },
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      }
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      "expression": "x.describe()",
      "result": {
       "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>A</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>count</th>\n      <td>3.0</td>\n    </tr>\n    <tr>\n      <th>mean</th>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>std</th>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>min</th>\n      <td>1.0</td>\n    </tr>\n    <tr>\n      <th>25%</th>\n      <td>1.5</td>\n    </tr>\n    <tr>\n      <th>50%</th>\n      <td>2.0</td>\n    </tr>\n    <tr>\n      <th>75%</th>\n      <td>2.5</td>\n    </tr>\n    <tr>\n      <th>max</th>\n      <td>3.0</td>\n    </tr>\n  </tbody>\n</table>\n</div>",
        "text/plain": "         A\ncount  3.0\nmean   2.0\nstd    1.0\nmin    1.0\n25%    1.5\n50%    2.0\n75%    2.5\nmax    3.0"
       },
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   },
   "source": [
    "And now you can find a pandas dataframe here:  \n",
    "{eval}`x `  \n",
    "It also has some intersting stats:  \n",
    "{eval}`x.describe()`\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5077e626",
   "metadata": {},
   "outputs": [],
   "source": []
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