# Scatter ¶

Title
Scatter Element
Dependencies
Matplotlib
Backends
Matplotlib
Bokeh
In [1]:
import numpy as np
import holoviews as hv
hv.extension('matplotlib')


The  Scatter  element visualizes as markers placed in a space of one independent variable, traditionally denoted as x , against a dependent variable, traditonally denoted as y . In HoloViews, the name  'x'  is the default dimension name used in the  key_dimensions  and  'y'  is the default dimension name used in the  value_dimensions  . We can see this from the default axis labels when visualizing a simple  Scatter  element:

In [2]:
%%opts Scatter (color='k' marker='s' size=10)
np.random.seed(42)
coords = [(i, np.random.random()) for i in range(20)]
hv.Scatter(coords)

Out[2]:

Here the random y values are considered to be the 'data' whereas the x positions express where those values are located (compare this to how  Points  elements are defined). In this sense,  Scatter  can be thought of as a  Curve  without any lines connecting the samples and you can use slicing to view the y values corresponding to a chosen x range:

In [3]:
%%opts Scatter (color='k' marker='s' size=10)
hv.Scatter(coords)[0:12] + hv.Scatter(coords)[12:20]

Out[3]:

A  Scatter  element must always have at least one value dimension but that doesn't mean additional value dimensions aren't supported. Here is an example with two additional quantities for each point, declared as the  value_dimension  s z and α visualized as the color and size of the dots, respectively:

In [4]:
%%opts Scatter [color_index=2 size_index=3 scaling_factor=50]
np.random.seed(10)
data = np.random.rand(100,4)

scatter = hv.Scatter(data, vdims=['y', 'z', 'size'])
scatter + scatter[0.3:0.7, 0.3:0.7].hist()

Out[4]:

In the right subplot, the  hist  method is used to show the distribution of samples along our first value dimension, ( y ).

The marker shape specified above can be any supported by matplotlib , e.g.  s  ,  d  , or  o  ; the other options select the color and size of the marker. For convenience with the bokeh backend , the matplotlib marker options are supported using a compatibility function in HoloViews.

Note : Although the  Scatter  element is superficially similar to the  Points  element (they can generate plots that look identical), the two element types are semantically quite different:  Points  are used to visualize data where the y variable is dependent . This semantic difference also explains why the histogram generated by  hist  call above visualizes the distribution of a different dimension than it does for  Points  .

This difference means that  Scatter  naturally combine elements that express dependent variables in two-dimensional space such as the  Chart  types, such as  Curve  . Similarly,  Points  express a independent relationship in two-dimensions and combine naturally with  Raster  types such as  Image  .