DashUI — Databricks Library

Part of the Dashlibs suite — Databricks libraries built for business users.

Shared ipywidgets components so every dash-* library looks and behaves the same way inside a Databricks (or plain Jupyter) notebook: headers, the UC Table / DataFrame / SQL source picker, output panels, status lines, and schema introspection helpers — styled to match the team's datapal-access Databricks App design system (IBM Plex Sans, a teal primary, rounded-lg cards with a soft shadow) instead of default ipywidgets styling.

What it looks like

The top card is dashsynthetic's "Single Table" tab, built entirely out of dashui components.

Installation

%pip install dash-uis

Note: the PyPI distribution is named dash-uis (the name dash-ui was already taken by an unrelated package). The importable module is still dashui.

Quick Start

import dashui
from IPython.display import display

src = dashui.source_selector()
run_btn = dashui.action_button("Run", style="success", emoji="▶")
out = dashui.output_panel()

def on_run(b):
    with out:
        out.clear_output()
        kind, value = src.value()
        print(f"✅ Source: {kind} = {value!r}")

run_btn.on_click(on_run)

ui = dashui.card([
    dashui.header("My Library", library="dashsynthetic", emoji="🧬"),
    dashui.section("Step 1: Source data"),
    src.toggle, src.box,
    run_btn, out,
])
display(ui)

Components

header(title, library="default", emoji="", subtitle="")

The banner at the top of every launch() UI. Color comes from a per-library accent (dashsynthetic is purple, dashobserve is red, etc. — see dashui.theme.ACCENTS).

dashui.header("DashObserve — Data Observability", library="dashobserve", emoji="👁️")

section(title)

A small uppercase step divider with a bottom border, used to break a form into steps ("Step 1: Source data", "Step 2: Generation settings", ...).

dashui.section("Step 2: Generation settings")

source_selector(label="Source:")

The UC Table / DataFrame variable / SQL Query picker used by every Dashlibs UI that reads from Databricks. Returns a SourceSelector with .toggle, .box (swap-on-select widgets) and helpers to resolve the chosen source.

src = dashui.source_selector()
ui = dashui.card([src.toggle, src.box])

kind, value = src.value()   # ("table", "catalog.schema.customers")
df = src.resolve_df()       # resolves straight to a Spark DataFrame

action_button(text, style="primary", emoji="")

A styled button matching the datapal-access button variants. style is one of primary | success | warning | danger | info.

run_btn = dashui.action_button("Generate Synthetic Data", style="success", emoji="▶")

status_line(text, kind="info")

A one-line colored status message. kind is one of success | error | warning | info.

dashui.status_line("Row count 9,842 within expected bounds", kind="success")
dashui.status_line("Schema changed — added: discount_code", kind="warning")

output_panel()

The standard scrollable result/error area used under every action button — an ipywidgets.Output pre-styled with a muted background and rounded border.

out = dashui.output_panel()
with out:
    print("✅ Done")

running_list(formatter)

A live-updating numbered list for "added items" accumulators (tables, relationships, monitors, ...). Returns (output_widget, render_fn).

items = []
out, render = dashui.running_list(lambda i, item: f"{i}. {item['name']}")
items.append({"name": "Customer"})
render(items)

card(children, padding="20px")

The bordered, shadowed container every launch() UI is wrapped in. Injects the shared <style> block (fonts, button variants, card shell) once per notebook kernel.

ui = dashui.card([header, section_title, src.toggle, src.box, run_btn, out])
display(ui)

list_columns(table) / list_columns_safe(table)

UC table column introspection — list_columns_safe returns [] instead of raising, which is what you want when populating a dropdown in a UI callback.

cols = dashui.list_columns_safe("catalog.schema.customers")

Design tokens

The raw palette is also exported for building fully custom widgets that still match the system: dashui.PRIMARY, dashui.SUCCESS, dashui.DANGER, dashui.WARNING, dashui.BORDER, dashui.CARD, dashui.MUTED, dashui.FONT_SANS, dashui.FONT_MONO.

Part of Dashlibs

Library	Purpose
dash-dq	Data Quality
dash-synthetic	Synthetic Data Generation
dash-observe	Data Observability (freshness, volume, schema)
dash-ml	ML Model Monitoring
dash-ingest	Data Ingestion
dash-gov	Data Governance
dash-ontology	Ontology & Lineage for AI
dash-ui	Shared UI components (PyPI: dash-uis)

Quality & Contributing

• 10 unit tests, zero Spark dependency to run them — pytest tests/ -v
• Lint-clean (ruff check dashui/), PEP 561 typed (py.typed)
• Every change ships through a reviewed pull request; CI (lint → test on Python 3.9–3.12 → build) gates every PR and every release
• See CONTRIBUTING.md for dev setup, CHANGELOG.md for release history, SECURITY.md to report a vulnerability, and CODE_OF_CONDUCT.md

License

Apache 2.0