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https://github.com/holoviz/datashader

Quickly and accurately render even the largest data.
https://github.com/holoviz/datashader

data-visualizations datashader holoviz rasterization

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Quickly and accurately render even the largest data.

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README 

        




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# Turn even the largest data into images, accurately



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| Downloads | ![https://pypistats.org/packages/datashader](https://img.shields.io/pypi/dm/datashader?label=pypi) ![https://anaconda.org/pyviz/datashader](https://pyviz.org/_static/cache/datashader_conda_downloads_badge.svg)

| Build Status | [![Build Status](https://github.com/holoviz/datashader/actions/workflows/test.yaml/badge.svg?branch=main)](https://github.com/holoviz/datashader/actions/workflows/test.yaml?query=branch%3Amain) |

| Coverage | [![codecov](https://codecov.io/gh/holoviz/datashader/branch/main/graph/badge.svg)](https://codecov.io/gh/holoviz/datashader) |

| Latest dev release | [![Github tag](https://img.shields.io/github/tag/holoviz/datashader.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/datashader/tags) [![dev-site](https://img.shields.io/website-up-down-green-red/https/holoviz-dev.github.io/datashader.svg?label=dev%20website)](https://holoviz-dev.github.io/datashader/) |

| Latest release | [![Github release](https://img.shields.io/github/release/holoviz/datashader.svg?label=tag&colorB=11ccbb)](https://github.com/holoviz/datashader/releases) [![PyPI version](https://img.shields.io/pypi/v/datashader.svg?colorB=cc77dd)](https://pypi.python.org/pypi/datashader) [![datashader version](https://img.shields.io/conda/v/pyviz/datashader.svg?colorB=4488ff&style=flat)](https://anaconda.org/pyviz/datashader) [![conda-forge version](https://img.shields.io/conda/v/conda-forge/datashader.svg?label=conda%7Cconda-forge&colorB=4488ff)](https://anaconda.org/conda-forge/datashader) [![defaults version](https://img.shields.io/conda/v/anaconda/datashader.svg?label=conda%7Cdefaults&style=flat&colorB=4488ff)](https://anaconda.org/anaconda/datashader) |

| Python | [![Python support](https://img.shields.io/pypi/pyversions/datashader.svg)](https://pypi.org/project/datashader/)

| Docs | [![DocBuildStatus](https://github.com/holoviz/datashader/workflows/docs/badge.svg?query=branch%3Amain)](https://github.com/holoviz/datashader/actions?query=workflow%3Adocs+branch%3Amain) [![site](https://img.shields.io/website-up-down-green-red/https/datashader.org.svg)](https://datashader.org) |

| Support | [![Discourse](https://img.shields.io/discourse/status?server=https%3A%2F%2Fdiscourse.holoviz.org)](https://discourse.holoviz.org/) |



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[![History of OS GIS Timeline](examples/assets/images/featured-badge-gh.svg)](https://makepath.com/history-of-open-source-gis/)



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## What is it?



Datashader is a data rasterization pipeline for automating the process of

creating meaningful representations of large amounts of data. Datashader

breaks the creation of images of data into 3 main steps:



1. Projection



   Each record is projected into zero or more bins of a nominal plotting grid

   shape, based on a specified glyph.



2. Aggregation



   Reductions are computed for each bin, compressing the potentially large

   dataset into a much smaller *aggregate* array.



3. Transformation



   These aggregates are then further processed, eventually creating an image.



Using this very general pipeline, many interesting data visualizations can be

created in a performant and scalable way. Datashader contains tools for easily

creating these pipelines in a composable manner, using only a few lines of code.

Datashader can be used on its own, but it is also designed to work as

a pre-processing stage in a plotting library, allowing that library

to work with much larger datasets than it would otherwise.



## Installation



Datashader supports Python 3.9, 3.10, 3.11, and 3.12 on Linux, Windows, or

Mac and can be installed with conda:



    conda install datashader



or with pip:



    pip install datashader



For the best performance, we recommend using conda so that you are sure

to get numerical libraries optimized for your platform. The latest

releases are avalailable on the pyviz channel `conda install -c pyviz

datashader` and the latest pre-release versions are avalailable on the

dev-labelled channel `conda install -c pyviz/label/dev datashader`.



## Fetching Examples



Once you've installed datashader as above you can fetch the examples:



    datashader examples

    cd datashader-examples



This will create a new directory called

datashader-examples with all the data

needed to run the examples.



To run all the examples you will need some extra dependencies. If you

installed datashader **within a conda environment**, with that

environment active run:



    conda env update --file environment.yml



Otherwise create a new environment:



    conda env create --name datashader --file environment.yml

    conda activate datashader



## Developer Instructions



1.  Install Python 3

    [miniconda](https://docs.conda.io/en/latest/miniconda.html) or

    [anaconda](https://www.anaconda.com/download/success), if you don't

    already have it on your system.



2.  Clone the datashader git repository if you do not already have it:



        git clone git://github.com/holoviz/datashader.git



3.  Set up a new conda environment with all of the dependencies needed

    to run the examples:



        cd datashader

        conda env create --name datashader --file ./examples/environment.yml

        conda activate datashader



4.  Put the datashader directory into the Python path in this

    environment:



        pip install --no-deps -e .



## Learning more



After working through the examples, you can find additional resources linked

from the [datashader documentation](https://datashader.org),

including API documentation and papers and talks about the approach.



## Some Examples



![USA census](examples/assets/images/usa_census.jpg)



![NYC races](examples/assets/images/nyc_races.jpg)



![NYC taxi](examples/assets/images/nyc_pickups_vs_dropoffs.jpg)