# Points¶

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- Title
- Points Element
- Dependencies
- Matplotlib
- Backends
- Matplotlib
- Bokeh

```
import numpy as np
import holoviews as hv
from holoviews import dim, opts
hv.extension('matplotlib')
```

The `Points`

element visualizes as markers placed in a space of two independent variables, traditionally denoted `x`

and `y`

. In HoloViews, the names `'x'`

and `'y'`

are used as the default key dimensions (`kdims`

) of the element. We can see this from the default axis labels when visualizing a simple `Points`

element:

```
np.random.seed(12)
coords = np.random.rand(50,2)
points = hv.Points(coords)
points.opts(color='k', marker='+', s=50)
```

Here both the random `x`

values and random `y`

values are *both* considered to be the coordinates with no dependency between them (compare this to how `Scatter`

elements are defined). You can think of `Points`

as simply marking positions in some two-dimensional space that can be sliced by specifying a 2D region-of-interest:

```
points + points[0.6:0.8,0.2:0.5]
```

Although the simplest `Points`

element simply mark positions in a two-dimensional space without any associated value this doesn't mean value dimensions aren't supported. Here is an example with two additional quantities for each point, declared as the `vdims`

s `z`

and `size`

visualized as the color and size of the dots, respectively:

```
np.random.seed(10)
data = np.random.rand(100,4)
points = hv.Points(data, vdims=['z', 'size'])
(points + points[0.3:0.7, 0.3:0.7].hist()).opts(
opts.Points(color='z', s=dim('size')*50))
```

In the right subplot, the `hist`

method is used to show the distribution of samples along the first value dimension we added (*z*).

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. The fundamental difference is that `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 `Scatter`

.

This difference means that `Points`

naturally combine elements that express independent variables in two-dimensional space, for instance `Raster`

types such as `Image`

. Similarly, `Scatter`

expresses a dependent relationship in two-dimensions and combine naturally with `Chart`

types such as `Curve`

.

For full documentation and the available style and plot options, use `hv.help(hv.Points).`

Download this notebook from GitHub (right-click to download).