<jupyter_start><jupyter_text>**Using `nb.display()`, the kernel will choose automatically the better output format possible according to the type of data.**<jupyter_code>nb.display(range(10))
nb.display(dict(name="Adam", color="blue"))
table = [{"x": 0, "y": 0},
         {"x": 1, "y": 10},
         {"x": 2, "y": 20},
         {"x": 3, "y": 30},
         {"x": 4, "y": 40},
         {"x": 5, "y": 50},
         {"x": 6, "y": 60}]
nb.table(table)<jupyter_output><empty_output><jupyter_text>--- **Helper methods allow you to force the type of output you want.**<jupyter_code>nb.markdown("**This is bold** and *this is italic*")
nb.latex("$$\sum_{i=0}^n i^2 = \frac{(n^2+n)(2n+1)}{6}$$")
html_code = """
<html>
<head>
<style>
table {
    font-family: arial, sans-serif;
    border-collapse: collapse;
    width: 100%;
}

td, th {
    border: 1px solid #dddddd;
    text-align: left;
    padding: 8px;
}

tr:nth-child(even) {
    background-color: #dddddd;
}
</style>
</head>
<body>

<table>
  <tr>
    <th>Company</th>
    <th>Contact</th>
    <th>Country</th>
  </tr>
  <tr>
    <td><NAME></td>
    <td><NAME></td>
    <td>Germany</td>
  </tr>
  <tr>
    <td>Centro comercial Moctezuma</td>
    <td>Francisco Chang</td>
    <td>Mexico</td>
  </tr>
  <tr>
    <td><NAME></td>
    <td><NAME></td>
    <td>Austria</td>
  </tr>
</table>

</body>
</html>"""
nb.html(html_code)<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.23214285714285715, 'fraction_numerical': 0.023571428571428573, 'mean_word_length': 3.2713414634146343, 'pattern_counts': {'":': 14, '<': 55, '<?xml version=': 0, '>': 55, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '2', 'dir': 'jupyter-structured-clean-dedup', 'id': '276624', 'license': 'Apache-2.0', 'n_tokens_mistral': 617, 'n_tokens_neox': 577, 'n_words': 127, 'path': 'notebooks/Rich Output.ipynb', 'repo_name': 'jasminmartin/scijava-jupyter-kernel', 'size': '1432'}