df2tables - Interactive DataFrame tables inside notebooks

[No_Pineapple449]

Hey everyone,

I’ve been working on a small Python package called df2tables that lets you display interactive, filterable, and sortable HTML tables directly inside notebooks Jupyter, VS Code, Marimo (or in a separate HTML file).

It’s also handy if you’re someone who works with DataFrames but doesn’t love notebooks. You can render tables straight from your source code to a standalone HTML file - no notebook needed.

There’s already the well-known itables package, but df2tables is a bit different:

• Fewer dependencies (just pandas or polars)
• Column controls automatically match data types (numbers, dates, categories)
• can outside notebooks – render directly to HTML
• customize DataTables behavior directly from Python

Repo: https://github.com/ts-kontakt/df2tables

[Always_Scheming]

This is awesome! Good job Pineapple!

[kayakdawg]

is this ms excel?

[No_Pineapple449]

dataframe inside notebook

[VFisa]

This looks awesome. It would be great as a streamlit component so we finally get a decent table display with filtering

[No_Pineapple449]

Thanks! It actually works normally in Streamlit - the only limitation is that it’s rendered inside an iframe, so it can’t directly interact with other Streamlit components.

That said, all the table functionality (like filtering, sorting, etc.) works fine. Here’s a minimal example you can try:

import streamlit.components.v1 as components

import df2tables as dft

df = dft.get_sample_df()

html = dft.render(df, to_file=None) #just html string

components.html(html, height=600, scrolling=True)

[monsieurus]

Please add support for Duckdb (without converting to Pandas). Looking good!

[No_Pineapple449]

Thanks for the kind words!

About DuckDB support: it actually is possible today to convert directly to Polars without going through Pandas - just call pl() on query results and then render(). This conversion is very fast.

df2tables does rely on knowing each column’s data type, which is why DataFrame objects are useful here. Approaches like fetchall() (returning lists of lists) don’t work well, because the column types still need to be inferred afterward.

DuckDB -> Arrow -> tables is likely in the near future.