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\n\n-----------------\n\n# Turn even the largest data into images, accurately\n\n| | |\n| --- | --- |\n| Build Status | [![Linux/MacOS Build Status](https://travis-ci.org/pyviz/datashader.svg?branch=master)](https://travis-ci.org/pyviz/datashader) [![Windows Build status](https://img.shields.io/appveyor/ci/pyviz/datashader/master.svg?logo=appveyor)](https://ci.appveyor.com/project/pyviz/datashader/branch/master) |\n| Coverage | [![codecov](https://codecov.io/gh/pyviz/datashader/branch/master/graph/badge.svg)](https://codecov.io/gh/pyviz/datashader) |\n| Latest dev release | [![Github tag](https://img.shields.io/github/tag/pyviz/datashader.svg?label=tag&colorB=11ccbb)](https://github.com/pyviz/datashader/tags) |\n| Latest release | [![Github release](https://img.shields.io/github/release/pyviz/datashader.svg?label=tag&colorB=11ccbb)](https://github.com/pyviz/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) |\n| Docs | [![site](https://img.shields.io/website-up-down-green-red/http/datashader.org.svg)](http://datashader.org) |\n\n\n## What is it?\n\nDatashader is a data rasterization pipeline for automating the process of\ncreating meaningful representations of large amounts of data. Datashader\nbreaks the creation of images of data into 3 main steps:\n\n1. Projection\n\n Each record is projected into zero or more bins of a nominal plotting grid\n shape, based on a specified glyph.\n\n2. Aggregation\n\n Reductions are computed for each bin, compressing the potentially large\n dataset into a much smaller *aggregate* array.\n\n3. Transformation\n\n These aggregates are then further processed, eventually creating an image.\n\nUsing this very general pipeline, many interesting data visualizations can be\ncreated in a performant and scalable way. Datashader contains tools for easily\ncreating these pipelines in a composable manner, using only a few lines of code.\nDatashader can be used on its own, but it is also designed to work as\na pre-processing stage in a plotting library, allowing that library\nto work with much larger datasets than it would otherwise.\n\n## Installation\n\nDatashader supports Python 2.7, 3.6 and 3.7 on Linux, Windows, or\nMac and can be installed with conda:\n\n conda install datashader\n\nor with pip:\n\n pip install datashader\n\nFor the best performance, we recommend using conda so that you are sure\nto get numerical libraries optimized for your platform. The latest\nreleases are avalailable on the pyviz channel `conda install -c pyviz\ndatashader` and the latest pre-release versions are avalailable on the\ndev-labelled channel `conda install -c pyviz/label/dev datashader`.\n\n## Fetching Examples\n\nOnce you've installed datashader as above you can fetch the examples:\n\n datashader examples\n cd datashader-examples\n\nThis will create a new directory called\ndatashader-examples with all the data\nneeded to run the examples.\n\nTo run all the examples you will need some extra dependencies. If you\ninstalled datashader **within a conda environment**, with that\nenvironment active run:\n\n conda env update --file environment.yml\n\nOtherwise create a new environment:\n\n conda env create --name datashader --file environment.yml\n conda activate datashader\n\n## Developer Instructions\n\n1. Install Python 3\n [miniconda](https://docs.conda.io/en/latest/miniconda.html) or\n [anaconda](https://www.anaconda.com/distribution/), if you don't\n already have it on your system.\n\n2. Clone the datashader git repository if you do not already have it:\n\n git clone git://github.com/pyviz/datashader.git\n\n3. Set up a new conda environment with all of the dependencies needed\n to run the examples:\n\n cd datashader\n conda env create --name datashader --file ./examples/environment.yml\n conda activate datashader\n\n4. Put the datashader directory into the Python path in this\n environment:\n\n pip install -e .\n\n## Learning more\n\nAfter working through the examples, you can find additional resources linked\nfrom the [datashader documentation](http://datashader.org),\nincluding API documentation and papers and talks about the approach.\n\n## Some Examples\n\n![USA census](examples/assets/images/usa_census.jpg)\n\n![NYC races](examples/assets/images/nyc_races.jpg)\n\n![NYC taxi](examples/assets/images/nyc_pickups_vs_dropoffs.jpg)\n\n\n## About PyViz\n\nDatashader is part of the PyViz initiative for making Python-based visualization tools work well together.\nSee [pyviz.org](http://pyviz.org) for related packages that you can use with Datashader and\n[status.pyviz.org](http://status.pyviz.org) for the current status of each PyViz project.\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, 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