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

Resource	Link
User guide + recipes	jeffgallini.github.io/dash-mantine-datatable
Interactive demo app	python usage.py
API reference (local pdoc)	python scripts/build_docs.py

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

Area	dash-mantine-datatable	dash-ag-grid
UI fit	Best when the app is already Mantine/DMC	Best when the grid is its own major product surface
Authoring model	Compact Dash API with Python helpers	Richer but more verbose AG Grid configuration
Dash component slots	Strong support for DMC renderers, editors, filters, and states	Strong custom rendering with a broader grid API
Common app-table features	Sorting, search, selection, pagination, expansion, dragging, grouped headers	Same core set plus spreadsheet-style tooling
Large-data strategy	Client/server pagination, sorting, and search	Additional row models and enterprise features
Best fit	Mantine-native Dash apps that want polished tables without AG Grid complexity	Data-heavy apps that need spreadsheet-grade grid mechanics

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.