# Pipeline#

Datashader provides a flexible series of processing stages that map from raw data into viewable images. As shown in the Introduction, using datashader can be as simple as calling datashade(), but understanding each of these stages will help you get the most out of the library.

The stages in a datashader pipeline are similar to those in a 3D graphics shading pipeline:

Here the computational steps are listed across the top of the diagram, while the data structures or objects are listed along the bottom. Breaking up the computations in this way is what makes Datashader able to handle arbitrarily large datasets, because only one stage (Aggregation) requires access to the entire dataset. The remaining stages use a fixed-sized data structure regardless of the input dataset, allowing you to use any visualization or embedding methods you prefer without running into performance limitations.

In this notebook, we’ll first put together a simple, artificial example to get some data, and then show how to configure and customize each of the data-processing stages involved:

## Data#

For an example, we’ll construct a dataset made of five overlapping 2D Gaussian distributions with different σs (spatial scales). By default we’ll have 10,000 datapoints from each category, but you should see sub-second response times even for 1 million datapoints per category if you increase num.

import pandas as pd
import numpy as np
from collections import OrderedDict as odict

num=10000
np.random.seed(1)

dists = {cat: pd.DataFrame(odict([('x',np.random.normal(x,s,num)),
('y',np.random.normal(y,s,num)),
('val',val),
('cat',cat)]))
for x,  y,  s,  val, cat in
[(  2,  2, 0.03, 10, "d1"),
(  2, -2, 0.10, 20, "d2"),
( -2, -2, 0.50, 30, "d3"),
( -2,  2, 1.00, 40, "d4"),
(  0,  0, 3.00, 50, "d5")] }

df = pd.concat(dists,ignore_index=True)
df["cat"]=df["cat"].astype("category")


Datashader can work many different data objects provided by different data libraries depending on the type of data involved, such as columnar data in Pandas or Dask dataframes, gridded multidimensional array data using xarray, columnar data on GPUs using cuDF, multidimensional arrays on GPUs using CuPy, and ragged arrays using SpatialPandas (see the Performance User Guide for a guide to selecting an appropriate library). Here, we’re using a Pandas dataframe, with 50,000 rows by default:

df.tail()

x y val cat
49995 -1.397579 0.610189 50 d5
49996 -2.649610 3.080821 50 d5
49997 1.933360 0.243676 50 d5
49998 4.306374 1.032139 50 d5
49999 -0.493567 -2.242669 50 d5

To illustrate this dataset, we’ll make a quick-and-dirty Datashader plot that dumps these x,y coordinates into an image:

import datashader as ds


CPU times: user 457 ms, sys: 8.28 ms, total: 466 ms
Wall time: 466 ms


Without any special tweaking, datashader is able to reveal the overall shape of this distribution faithfully: four summed 2D normal distributions of different variances, arranged at the corners of a square, overlapping another very high-variance 2D normal distribution centered in the square. This immediately obvious structure makes a great starting point for exploring the data, and you can then customize each of the various stages involved as described below.

Of course, this is just a static plot, and you can’t see what the axes are, so we can instead embed this data into an interactive plot if we prefer:

import holoviews as hv