Metadata-Version: 2.1
Name: datashader
Version: 0.6.8
Summary: Data visualization toolchain based on aggregating into a grid
Home-page: http://datashader.org
Maintainer: Datashader developers
Maintainer-email: dev@datashader.org
License: New BSD
Platform: UNKNOWN
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Datashader
----------

[![Travis build Status](https://travis-ci.org/pyviz/datashader.svg?branch=master)](https://travis-ci.org/pyviz/datashader)
[![Windows build status](https://ci.appveyor.com/api/projects/status/uc7atn5y35ay38eb/branch/master?svg=true)](https://ci.appveyor.com/project/pyviz/datashader/branch/master)
[![Task Status](https://badge.waffle.io/pyviz/datashader.png?label=ready&title=tasks)](https://waffle.io/pyviz/datashader)


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

The best way to get started with Datashader is install it together
with our extensive set of examples, following the instructions in the
[examples README](/examples/README.md).

If all you need is datashader itself, without any of the files used in
the examples, you can install it via 
[conda](https://conda.io/docs/install/quick.html) or 
[pip](https://pip.pypa.io/en/stable/installing/):


```bash
conda install datashader
```

or 

```
pip install datashader
```

For the best performance, we recommend using conda so that you are
sure to get numerical libraries optimized for your platform.

If you want the latest unreleased changes (e.g. to edit the source code
yourself), first install datashader as above, but then clone the source 
code and tell Python to use the clone instead:

```bash
conda remove --force datashader
git clone https://github.com/pyviz/datashader.git
cd datashader
pip install -e .
```

To run the test suite, first `conda install pytest` or
`pip install pytest`, then run `py.test datashader` in your
datashader source directory.

## Learning more

After working through the examples, you can find additional resources linked
from the [datashader documentation](http://datashader.org),
including API documentation and papers and talks about the approach.

## Screenshots

![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)


