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Lonboard\n\n[![PyPI][pypi_badge]][pypi_link]\n[![Conda Version][conda_version_badge]][conda_version]\n[![Binder][binder_badge]][binder_jupyterlab_url]\n[![open_in_colab][colab_badge]][colab_notebook_link]\n\n[pypi_badge]: https://badge.fury.io/py/lonboard.svg\n[pypi_link]: https://pypi.org/project/lonboard/\n[binder_badge]: https://mybinder.org/badge_logo.svg\n[binder_jupyterlab_url]: https://mybinder.org/v2/gh/developmentseed/lonboard/HEAD?urlpath=lab/tree/examples/\n[colab_badge]: https://colab.research.google.com/assets/colab-badge.svg\n[colab_notebook_link]: https://colab.research.google.com/github/developmentseed/lonboard/blob/main\n[conda_version_badge]: https://img.shields.io/conda/vn/conda-forge/lonboard.svg\n[conda_version]: https://anaconda.org/conda-forge/lonboard\n\nA Python library for fast, interactive geospatial vector data visualization in Jupyter.\n\nBuilding on cutting-edge technologies like [GeoArrow](https://github.com/geoarrow/geoarrow) and [GeoParquet](https://github.com/opengeospatial/geoparquet) in conjunction with [GPU-based map rendering](https://deck.gl/), Lonboard aims to enable visualizing large geospatial datasets interactively through a simple interface.\n\n![](assets/hero-animated.gif)\n\n\u003cp align=\"center\"\u003e3 million points rendered from a GeoPandas \u003ccode\u003eGeoDataFrame\u003c/code\u003e in JupyterLab. \u003ca href=\"https://developmentseed.org/lonboard/latest/examples/internet-speeds/\" target=\"_blank\"\u003e Example notebook\u003c/a\u003e.\u003c/p\u003e\n\n## Install\n\nTo install Lonboard using pip:\n\n```\npip install lonboard\n```\n\nLonboard is on [conda-forge](https://anaconda.org/conda-forge/lonboard) and can be installed using [conda](https://docs.conda.io), [mamba](https://mamba.readthedocs.io/), or [pixi](https://pixi.sh/). To install Lonboard using conda:\n\n```\nconda install -c conda-forge lonboard\n```\n\nTo install from source, refer to the [developer documentation](https://github.com/developmentseed/lonboard/blob/main/DEVELOP.md).\n\n## Get Started\n\nFor the simplest rendering, pass geospatial data into the top-level [`viz` function](https://developmentseed.org/lonboard/latest/api/viz/#lonboard.viz.viz).\n\n```py\nimport geopandas as gpd\nfrom lonboard import viz\n\ngdf = gpd.GeoDataFrame(...)\nviz(gdf)\n```\n\nUnder the hood, this delegates to a [`ScatterplotLayer`](https://developmentseed.org/lonboard/latest/api/layers/scatterplot-layer/), [`PathLayer`](https://developmentseed.org/lonboard/latest/api/layers/path-layer/), or [`PolygonLayer`](https://developmentseed.org/lonboard/latest/api/layers/polygon-layer/). Refer to the [documentation](https://developmentseed.org/lonboard/) and [examples](https://developmentseed.org/lonboard/latest/examples/internet-speeds/) for more control over rendering.\n\n## Documentation\n\nRefer to the documentation at [developmentseed.org/lonboard](https://developmentseed.org/lonboard/).\n\n## Why the name?\n\nThis is a new binding to the [deck.gl](https://deck.gl) geospatial data visualization library. A \"deck\" is the part of a skateboard you ride on. What's a fast, geospatial skateboard? A \u003cem\u003elon\u003c/em\u003eboard.\n\n![](assets/dalle-lonboard.jpg)\n","funding_links":[],"categories":["Python"],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevelopmentseed%2Flonboard","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdevelopmentseed%2Flonboard","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdevelopmentseed%2Flonboard/lists"}