deckgl-marimo 0.5.0

deck.gl visualization library for marimo notebooks — interactive maps with 33 layer types

deckgl-marimo

Interactive deck.gl visualization library for marimo notebooks. Render GPU-accelerated maps with 33 layer types, powered by MapLibre GL and anywidget.

Features

• 12 deck.gl layer types — scatter plots, hexagonal bins, heatmaps, arcs, paths, polygons, GeoJSON, 3D columns, lines, point clouds, and more
• Binary data transfer — bypass JSON serialization for large datasets (30x faster, 4x smaller payloads)
• Multi-layer maps — compose multiple layers on a single map
• Standalone layers — display any layer directly without explicit map setup
• Marimo-native reactivity — bind layer properties to sliders, dropdowns, and other widgets
• Performance metrics — built-in FPS counter and frame time tracking via perf_metrics traitlet
• Color scales — map numeric columns to color palettes (viridis, plasma, etc.) with linear or log scaling
• Callable accessors — pass Python functions to any get_* accessor for custom per-row logic
• Multiple data sources — pandas, polars, geopandas, DuckDB, GeoJSON dicts, and URLs
• Authenticated data loading — pass HTTP headers, API keys, or credentials for remote data sources
• Fully offline — all JavaScript bundled in the package, no CDN dependencies
• Viewport readback — read the current map center, zoom, pitch, and bearing from Python
• Click & hover events — inspect picked objects reactively in downstream cells

Installation

pip install deckgl-marimo
# or
uv add deckgl-marimo

Quickstart

Standalone layer

import marimo as mo
import deckgl_marimo as dgl

layer = dgl.ScatterplotLayer(
    data=df,
    get_position=["longitude", "latitude"],
    get_fill_color=[255, 140, 0],
    get_radius="population",
    radius_scale=10,
)

# Displays a map with one layer
widget = mo.ui.anywidget(layer)

Multi-layer map

m = dgl.Map(
    layers=[
        dgl.ScatterplotLayer(
            data=cities_df,
            get_position=["lon", "lat"],
            get_fill_color=[255, 140, 0],
            get_radius=5,
            radius_min_pixels=3,
        ),
        dgl.ArcLayer(
            data=flights_df,
            get_source_position=["src_lon", "src_lat"],
            get_target_position=["dst_lon", "dst_lat"],
            get_source_color=[0, 128, 255],
            get_target_color=[255, 0, 128],
        ),
    ],
    basemap="dark-matter",
    center=(-98.5, 39.8),
    zoom=4,
    pitch=45,
)

widget = mo.ui.anywidget(m)

Reactive controls

Important: Create the Map widget in a cell that does not depend on slider values. Update layers by assigning to map_widget.layer_specs in a separate cell. This keeps the map instance stable — sliders update layers via traitlet sync instead of recreating the entire map, which would cause tile reloads and a black screen flash.

# Cell 1 — sliders
radius = mo.ui.slider(200, 5000, value=1000, label="Radius")

# Cell 2 — create map widget (NO slider deps — stable, never re-executes)
map_widget = dgl.Map(basemap="dark-matter", center=(-1.4, 52.2), zoom=6, pitch=40)
widget = mo.ui.anywidget(map_widget)

# Cell 3 — display
widget

# Cell 4 — update layers reactively (re-executes when slider changes)
map_widget.layer_specs = [
    dgl.HexagonLayer(
        data=df,
        get_position=["lon", "lat"],
        radius=radius.value,
        extruded=True,
        elevation_scale=250,
    ).to_spec()
]

# Cell 5 — viewport readback
vp = widget.value.get("viewport", {})
mo.md(f"Zoom: {vp.get('zoom', 'N/A'):.1f}")

Color scales

Map a numeric column to interpolated colors using ColorScale. Supports named palettes, custom color ramps, and linear or logarithmic scaling.

# Named palette
layer = dgl.ScatterplotLayer(
    data=df,
    get_position=["lon", "lat"],
    get_fill_color=dgl.ColorScale("temperature", palette="viridis"),
)

# Two-color ramp with log scale
layer = dgl.ScatterplotLayer(
    data=df,
    get_position=["lon", "lat"],
    get_fill_color=dgl.ColorScale("population", colors=["blue", "red"], scale="log"),
)

Available palettes: viridis, plasma, inferno, magma, cividis, coolwarm, RdBu, spectral, turbo

ColorScale parameters:

Parameter	Default	Description
column	(required)	Numeric column name to map
palette	—	Named palette (mutually exclusive with colors)
colors	—	List of 2+ colors: names ("blue"), hex ("#FF0000"), or RGB ([255, 0, 0])
domain	auto	(min, max) value range; auto-detected from data if omitted
scale	"linear"	"linear" or "log"
alpha	255	Alpha channel (0–255) for all output colors

Callable accessors

Pass a Python function to any get_* accessor for full control over per-row values:

layer = dgl.ScatterplotLayer(
    data=df,
    get_position=["lon", "lat"],
    get_fill_color=lambda row: [
        int(row["temperature"] * 2.55),
        50,
        255 - int(row["temperature"] * 2.55),
        200,
    ],
)

Both ColorScale and callable accessors work with the binary data path (use_binary=True) — colors are resolved in Python and packed into the binary buffer automatically.

DuckDB integration

import duckdb

rel = duckdb.sql("SELECT lon, lat, value FROM 'data.parquet' WHERE value > 100")
layer = dgl.ScatterplotLayer(data=rel, get_position=["lon", "lat"])

Binary data for large datasets

For datasets with 100k+ rows, binary data transfer bypasses JSON serialization entirely, sending typed arrays directly to the GPU via deck.gl's native binary format.

Using use_binary=True (automatic packing from list-of-dicts):

layer = dgl.ScatterplotLayer(
    data=large_df.to_dict("records"),
    get_position=["lon", "lat"],
    get_fill_color="color",
    get_radius="radius",
    use_binary=True,
)

Using pre-built numpy arrays (fastest — zero dict iteration):

import numpy as np
from deckgl_marimo._binary import pack_binary

# Prepare arrays
positions = np.column_stack([lons, lats]).astype(np.float32)
colors = np.array(color_data, dtype=np.uint8)  # (n, 4)

# Create layer spec (no data — binary provides it)
layer = dgl.ScatterplotLayer(
    get_fill_color=[255, 140, 0],  # constant props still go in spec
    radius_min_pixels=2,
    use_binary=True,
)
spec = layer.to_spec()

# Pack binary buffer
meta, buf = pack_binary(
    n=len(positions),
    attributes={
        "getPosition": (positions, "float32", 2),
        "getFillColor": (colors, "uint8", 4),
    },
)
meta["id"] = spec["id"]

# Send to map
map_widget.binary_metadata = {"layers": [meta]}
map_widget.binary_data = buf
map_widget.layer_specs = [spec]

Performance at 200k polygons:

Mode	Serialization	Payload	Speedup
JSON	1,167 ms	62 MB	—
Binary	39 ms	14.5 MB	30x faster, 4.3x smaller

Binary data is supported on: ScatterplotLayer, PolygonLayer, PathLayer, ArcLayer, LineLayer, ColumnLayer, and PointCloudLayer.

Performance metrics

The Map widget includes a built-in FPS counter that reports metrics back to Python:

map_widget = dgl.Map(basemap="dark-matter", center=(0, 0), zoom=1)
widget = mo.ui.anywidget(map_widget)

# Read performance metrics (updated every 500ms)
perf = widget.value.get("perf_metrics", {})
fps = perf.get("fps")           # frames per second
frame_time = perf.get("frameTimeAvg")  # ms per frame

Authenticated remote data

Any layer that loads data from a URL supports custom HTTP headers via fetch_headers, or full control over the fetch request via load_options.

# Bearer token
layer = dgl.GeoJsonLayer(
    data="https://secure-api.example.com/data.geojson",
    fetch_headers={"Authorization": "Bearer my-token"},
    get_fill_color=[0, 180, 230, 160],
)

# API key
layer = dgl.GeoJsonLayer(
    data="https://api.example.com/features",
    fetch_headers={"X-API-Key": "abc123"},
)

# Full fetch control (mTLS / CORS / custom options)
layer = dgl.GeoJsonLayer(
    data="https://internal.example.com/data.geojson",
    load_options={
        "fetch": {
            "credentials": "include",
            "mode": "cors",
            "headers": {"Authorization": "Bearer token"},
        }
    },
)

Both parameters are available on all layer types via BaseLayer. When fetch_headers and load_options both specify headers, the load_options headers take precedence.

Available layers

Fully tested (12)

Layer	Use case	Binary support
ScatterplotLayer	Point data	Yes
GeoJsonLayer	Polygons, lines, points from GeoJSON/GeoDataFrame	—
ArcLayer	Origin-destination flows	Yes
PathLayer	Routes, trajectories	Yes
PolygonLayer	Filled regions	Yes
IconLayer	Marker icons	—
TextLayer	Labels	—
ColumnLayer	3D bars on map	Yes
HexagonLayer	Hexagonal binning	—
HeatmapLayer	Density visualization	—
LineLayer	Straight lines between point pairs	Yes
PointCloudLayer	3D point clouds (LiDAR, etc.)	Yes

LineLayer and PointCloudLayer are newly exported experimental layers.

Experimental (21+)

All additional deck.gl layers are available as experimental stubs via deckgl_marimo.layers:

from deckgl_marimo.layers import TripsLayer, MVTLayer, H3HexagonLayer, ContourLayer
# ... and more

Basemaps

dgl.Basemaps.list_available()
# ['bright', 'dark', 'dark-matter', 'embedded', 'liberty', 'light', 'none', 'osm', 'positron', 'voyager']

# Use any MapLibre-compatible style URL
dgl.Map(basemap="https://my-tileserver.example.com/style.json")

Troubleshooting

Content-Length errors in the marimo console

This is fixed in Marimo >= 0.22.0 please update to that.

License

MIT