dash-mantine-datatable

dash-mantine-datatable is a Dash wrapper around Mantine DataTable for apps that already use dash-mantine-components. It adds a Dash-friendly prop model, Mantine style props, component templates for renderers/editors/filters, and chainable Python helpers for columns, grouping, rows, selection, pagination, sorting, and search.

Read the full user guide and recipes on GitHub Pages.

Install

pip install dash-mantine-datatable

Optional demo dependencies for the live gallery in usage.py:

pip install "dash-mantine-datatable[demo]"

Quick start

from dash import Dash
import dash_mantine_components as dmc
import dash_mantine_datatable as dmdt

app = Dash()

app.layout = dmc.MantineProvider(
    dmdt.DataTable(
        id="employees",
        data=[
            {"id": 1, "name": "Avery Stone", "team": "Platform", "status": "On Track"},
            {"id": 2, "name": "Mina Patel", "team": "Growth", "status": "Planning"},
        ],
        columns=[
            {"accessor": "name", "sortable": True},
            {"accessor": "team", "sortable": True},
            {"accessor": "status", "presentation": "badge"},
        ],
    ).update_layout(radius="lg", withTableBorder=True, striped=True)
)

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

Features

• Mantine-native styling via radius, bg, classNames, styles, and related props
• Fluent helpers: update_layout(), update_table_properties(), update_columns(), group_columns(), update_rows(), update_selection(), update_pagination(), update_sorting(), and update_search()
• Dash component slots for renderers, editors, filters, empty states, loaders, row expansion, and sort icons
• Client and server modes for pagination, sorting, and search
• Column filtering with Dash Mantine controls in header popovers
• Grouped headers, inline row grouping, nested child rows, and row expansion
• Checkbox selection with shift-range support, row dragging, inline editing, and callback payloads for row/cell interactions
• Generated Python, R, and Julia component packages from the same source tree

Documentation

Build the public docs site locally:

python -m pip install -e ".[demo]"
python scripts/build_great_docs.py

Regenerate recipe pages from usage.py:

python scripts/generate_recipes.py

Capture README/recipe screenshots from the running demo:

python usage.py
python scripts/capture_docs_media.py

Helper example

table = (
    dmdt.DataTable(
        data=[{"id": 1, "name": "Avery", "salary": 128000, "status": "On Track"}],
        columns=[
            dmdt.Column("name"),
            dmdt.Column("salary", textAlign="right", presentation="currency", currency="USD"),
            dmdt.Column("status", presentation="badge"),
        ],
    )
    .update_columns(selector="name", title="Employee")
    .update_rows(selector={"status": "On Track"}, className="row-ok")
    .update_selection(selectionTrigger="checkbox")
    .update_pagination(recordsPerPage=10)
)

Compared with dash-ag-grid

Local development

npm install --legacy-peer-deps
python -m pip install -r requirements.txt -r tests/requirements.txt
npm run build
python -m pytest
python usage.py

Publishing

Preflight locally:

.\scripts\check-release.ps1

python scripts/check_release.py

Release flow:

1. Land changes on staging.
2. Open a staging -> main PR titled v1.0.0 Release - Stable feature set, docs site, and bug fixes.
3. Merge to publish to PyPI, create the GitHub release, and deploy docs to GitHub Pages.

Required one-time setup:

• Protect main and require the Release PR Guard workflow.
• Configure GitHub Pages to publish from GitHub Actions.
• Add PYPI_API_TOKEN to the pypi environment.

See CONTRIBUTING.md and CHANGELOG.md for details.