Best Data Grid for Python Dash in 2026

Dash gives us a way to put Python data in a browser. There is a lot of stuff we can add there, but main focus of this article is data grids, its the most common request for us.

I work on RevoGrid, so I know that option best. But RevoGrid is not the only choice, and different grids suit different projects. Here is the short, practical version of how the main Dash data grids compare in 2026.

The quick answer

Here is the practical comparison I would use before building a prototype:

Grid	Rendering approach	Editing and spreadsheet tools	Large-data approach	Licensing and status	Best fit
RevoGrid for Dash	Virtualized DOM grid	Editing, range selection, keyboard navigation, copy and paste	Virtualizes both rows and columns	MIT core, with optional Pro modules	Fast, editable data applications
Dash AG Grid	Virtualized DOM grid	Mature editing, selection and clipboard features	Multiple row models, including enterprise options	Community edition plus commercial Enterprise features	Enterprise teams needing the broadest feature set
Dash Glide Grid	Canvas	Spreadsheet-style editing, ranges, fill handle, copy and paste, undo and redo	Efficient canvas rendering	Open-source wrapper	Apps that should feel more like a spreadsheet
Dash DataTable	HTML table with virtualization options	Editing, filtering, sorting and conditional formatting	Works for moderate data; larger datasets need more care	Deprecated; planned for removal from the core Dash API in Dash 5.0	Maintaining existing Dash applications
Dash Tabulator	Virtual DOM table	Common editing and table interactions	Virtual DOM rendering	Open-source wrapper; not every Tabulator feature is exposed	Smaller projects with straightforward table needs

The grids can look similar with ten rows. The differences become clearer when you add large datasets, editing, callbacks, custom cells, or commercial licensing.

RevoGrid for Dash

RevoGrid for Dash is built for applications where the grid becomes a real workspace.

Its open-source core includes virtual scrolling, inline editing, range selection, copy and paste, sorting, filtering, pinned rows and columns, and keyboard navigation. Both rows and columns are virtualized, so the browser only renders the cells currently in view.

One detail matters a lot in Dash: when a user edits a cell, RevoGrid can send a small edit event back to Python instead of returning the entire dataset.

That keeps callbacks easier to understand and avoids moving thousands of unchanged rows after every edit.

The trade-off is that JavaScript functions cannot be passed directly through a normal Python component property. Most standard grid behavior works naturally from Dash, but deeply custom JavaScript renderers or editors need a browser-side integration.

Dash AG Grid

Dash AG Grid is the strongest alternative and probably the most familiar choice for large enterprise teams.

It has mature documentation, a large feature set, several row models, and many examples. If your company already uses AG Grid, choosing its Dash wrapper may be the simplest decision.

The main thing to check early is licensing. AG Grid Community covers many normal requirements, while features such as server-side row grouping, master/detail, tree data, and advanced aggregation belong to AG Grid Enterprise.

This is not necessarily a problem. It just means you should compare your likely future requirements—not only the features in your first prototype.

Dash Glide Grid

Dash Glide Grid uses canvas rendering and offers a smooth spreadsheet-style experience with range selection, copy and paste, a fill handle, undo and redo, and several rich cell types.

Canvas is both its strength and its main trade-off. It can render a large visual surface very efficiently, but cells are not regular DOM elements. That changes how CSS customization, browser inspection, accessibility, and embedded interactive content work.

It is worth testing if the product should feel closer to a spreadsheet than a normal web table.

Dash DataTable

Dash DataTable was the default table for many Dash applications for years. It still supports useful features such as editing, filtering, sorting, conditional formatting, and callbacks.

However, it is deprecated and is expected to leave the core Dash API in Dash 5.0. That makes it difficult to recommend for a new application.

There is no need to rewrite a stable app overnight. But if you are starting something new—or adding major table functionality—it makes sense to choose a grid with a clearer path forward.

Dash Tabulator

Dash Tabulator wraps the open-source Tabulator table.

It can be a good fit for a smaller project, especially if you already know Tabulator. The important limitation is that the Dash wrapper does not expose every feature of the underlying JavaScript library, so check its actual Python API before committing to it.

A small RevoGrid example

Install the Dash package:

pip install dash-datagrid

Then create app.py:

from dash import Dash, Input, Output, callback, html
from dash_datagrid import RevoGrid

app = Dash(__name__)

rows = [
    {"order": "A-100", "customer": "Ada", "amount": 120},
    {"order": "A-101", "customer": "Grace", "amount": 85},
    {"order": "A-102", "customer": "Linus", "amount": 210},
]

columns = [
    {"prop": "order", "name": "Order", "readonly": True},
    {"prop": "customer", "name": "Customer", "sortable": True},
    {
        "prop": "amount",
        "name": "Amount",
        "sortable": True,
        "filter": "number",
    },
]

app.layout = html.Main(
    [
        RevoGrid(
            id="orders-grid",
            source=rows,
            columns=columns,
            range=True,
            filter=True,
            resize=True,
            style={"height": 360},
        ),
        html.Pre("Edit a cell", id="edit-output"),
    ]
)


@callback(
    Output("edit-output", "children"),
    Input("orders-grid", "afteredit"),
    prevent_initial_call=True,
)
def show_edit(event):
    change = event["detail"]
    return (
        f'Row {change["rowIndex"]}: '
        f'{change["prop"]} = {change["val"]!r}'
    )


if __name__ == "__main__":
    app.run(debug=True)

Run it with:

python app.py

One easy detail to miss: give the grid an explicit height. RevoGrid fills its container, so a missing height is a common reason a new grid appears blank.

If your data is already in Pandas, the conversion is simple:

rows = dataframe.to_dict("records")

For timestamps, NumPy values, or missing values, normalizing through DataFrame.to_json() first is safer.

What I would choose

For a new, editable Dash application, I would start with RevoGrid for Dash. It gives you a capable MIT core, good spreadsheet-style interaction, virtualized rendering, and focused events for Python callbacks.

I would choose Dash AG Grid when an organization already uses AG Grid or needs specific Enterprise features. I would test Dash Glide Grid for a canvas-heavy spreadsheet experience.

For an existing Dash DataTable, I would plan a migration rather than continue building new functionality around it. And for a smaller, simpler table, Dash Tabulator may already be enough.

The best test is not a ten-row demo. Load something close to your real dataset, edit several cells, paste a range from Excel, add a callback, and see how much data travels between the browser and Python. That usually makes the right choice much more obvious.

You can try RevoGrid with:

pip install dash-datagrid

The full setup guide and API examples are in the RevoGrid Dash documentation.