Turn even the largest data into images, accurately


Build Status
Coverage
Latest dev release
Latest release
Python
Docs
Support

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:

Projection

Each record is projected into zero or more bins of a nominal plotting grid shape, based on a specified glyph.
Aggregation

Reductions are computed for each bin, compressing the potentially large dataset into a much smaller aggregate array.
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 3.8, 3.9 and 3.10 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

Install Python 3 miniconda or anaconda, if you don't already have it on your system.
Clone the datashader git repository if you do not already have it:
```
git clone git://github.com/holoviz/datashader.git
```

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

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, including API documentation and papers and talks about the approach.

Some Examples

USA census

NYC races

NYC taxi

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

datashader-0.15.0.tar.gz (35.6 MB view details)

Uploaded May 30, 2023 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

datashader-0.15.0-py2.py3-none-any.whl (18.3 MB view details)

Uploaded May 30, 2023 Python 2Python 3

File details

Details for the file datashader-0.15.0.tar.gz.

File metadata

Download URL: datashader-0.15.0.tar.gz
Upload date: May 30, 2023
Size: 35.6 MB
Tags: Source
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.8.16

File hashes

Hashes for datashader-0.15.0.tar.gz
Algorithm	Hash digest
SHA256	`81b0481a75530e713cfacf421da5e062ca5791832466987f70b59724cc03872f`
MD5	`d8a8f323427726fea285418bb7f09d20`
BLAKE2b-256	`6c21ccf2b120ae97715338a724d5345510e19ba91222fa8a6654e3b198cfb5b3`

See more details on using hashes here.

File details

Details for the file datashader-0.15.0-py2.py3-none-any.whl.

File metadata

Download URL: datashader-0.15.0-py2.py3-none-any.whl
Upload date: May 30, 2023
Size: 18.3 MB
Tags: Python 2, Python 3
Uploaded using Trusted Publishing? No
Uploaded via: twine/4.0.2 CPython/3.8.16

File hashes

Hashes for datashader-0.15.0-py2.py3-none-any.whl
Algorithm	Hash digest
SHA256	`25832aec902b73d485a21d526fc518ae659e5004aad64db97b6f39d87d066453`
MD5	`2c4cf874d5ce4b7490ac9b509ca1b7c7`
BLAKE2b-256	`94f9ebf384b2f9bb9787812a2fe91373700994d06e5777ea541e403024f41ad5`