itables

ITables is compatible with a wide range of notebook and document rendering environments, including:

• Jupyter Notebook and Jupyter Lab
• Jupyter NB convert: Tables remain interactive when downloading notebooks as HTML or using jupyter nbconvert --to html.
• Jupyter Book: Supported in interactive books.
• Google Colab
• VS Code: Works in both Jupyter Notebooks and Python scripts.
• PyCharm: Requires init_notebook_mode(connected=True) for reliable reloading.
• Quarto: Works with html and revealjs formats.

When calling itables.show() in an environment that cannot render HTML (such as a plain Python script or a standard python interactive session without an IPython-based frontend), ITables automatically falls back to printing a static preview.

This preview is a Markdown table containing a subset of the data: by default, it shows the first 10 rows, or a number determined by your pageLength or lengthMenu configuration options. This allows you to see a representation of your data in text-only consoles.

The itables.to_html_datatable function accepts a connected argument that controls how the necessary JavaScript dependencies are loaded:

• connected=True (default): Generates an autonomous HTML fragment that loads dt_for_itables from the Internet. This is the easiest way to get a working table if you have an active internet connection.
• connected=False: Generates an HTML snippet that requires local dependencies. This snippet will only work if you have previously added the output of generate_init_offline_itables_html() to your HTML document. This is suitable for offline environments or air-gapped systems.

ITables can be configured to work either offline or by loading libraries dynamically from the internet (connected mode).

• Offline Mode (Default): When using init_notebook_mode(), the JavaScript code is embedded directly into the notebook. This works without an internet connection but increases the notebook file size.
• Connected Mode: Use init_notebook_mode(connected=True) to load libraries dynamically from the internet. This makes the notebook file significantly lighter (by approximately 900kB).

Important: In Google Colab, connected=True is the only working option.

import itables

# Use connected mode (required for Google Colab)
itables.init_notebook_mode(connected=True)

When customizing tables, note the following distinctions:

• Height: Table height is not controlled by the style argument. Instead, it is governed by either lengthMenu or scrollY.
• Content Appearance: To change how the actual data/content inside the table looks (colors, fonts, etc.), use CSS or Pandas Style rather than the itables style argument.

For ITables v2.7.0 and above, floats in Polars DataFrames are formatted according to the Polars configuration. You can control precision using pl.Config(float_precision=...).

import polars as pl
import itables

with pl.Config(float_precision=2):
    itables.show(pl.Series([i * math.pi for i in range(1, 6)]))

To develop itables_anywidget, install the package in editable mode with development dependencies, then install and run the JavaScript dependencies to enable hot-reloading for changes made in the js/ directory.

1. Create and activate a virtual environment:

python -m venv .venv
source .venv/bin/activate

2. Install the package in editable mode with [dev] extras:

pip install -e ".[dev]"

3. Install and run JavaScript dependencies:

npm install
npm run dev

4. Open example.ipynb in JupyterLab, VS Code, or another editor to begin testing. Changes in the js/ folder will be reflected in the notebook.

If you prefer not to use the dedicated ITable Widget, you can use DT as an alternative way to render ITables in a Shiny application.

To set up the demo application using the DT approach:

1. Install the required dependencies:

   pip install -r itables_DT/requirements.txt

2. Launch the application using Shiny (choose either the Express or Core version):

   shiny run itables_DT/app-express.py
   # OR
   shiny run itables_DT/app-core.py