# Gridspace¶

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- Title
- GridSpace Container
- Dependencies
- Matplotlib
- Backends
- Matplotlib
- Bokeh
- Plotly

```
import numpy as np
import holoviews as hv
hv.extension('matplotlib')
```

A `GridSpace`

is a two-dimensional dictionary of HoloViews objects presented onscreen as a grid. In one sense, due to the restriction on it's dimensionality, a `GridSpace`

may be considered a special-case of `HoloMap`

. In another sense, `GridSpace`

may be seen as more general as a `GridSpace`

can hold a `HoloMap`

but the converse is not permitted; see the Building Composite Objects user guide for details on how to compose containers.

`GridSpace`

holds two-dimensional dictionaries¶

Using the `sine_curve`

function below, we can declare a two-dimensional dictionary of `Curve`

elements, where the keys are 2-tuples corresponding to (phase, frequency) values:

```
def sine_curve(phase, freq):
xvals = [0.1* i for i in range(100)]
return hv.Curve((xvals, [np.sin(phase+freq*x) for x in xvals]))
phases = [0, np.pi/2, np.pi, 3*np.pi/2]
frequencies = [0.5, 0.75, 1.0, 1.25]
curve_dict_2D = {(p,f):sine_curve(p,f) for p in phases for f in frequencies}
```

We can now pass this dictionary of curves to `GridSpace`

:

```
gridspace = hv.GridSpace(curve_dict_2D, kdims=['phase', 'frequency'])
gridspace
```

`GridSpace`

is similar to `HoloMap`

¶

Other than the difference in the visual semantics, whereby `GridSpaces`

display their contents together in a two-dimensional grid, `GridSpaces`

are very similar to `HoloMap`

s (see the `HoloMap`

notebook for more information).

One way to demonstrate the similarity of these two containers is to cast our `gridspace`

object to `HoloMap`

and back to a `GridSpace`

:

```
hmap = hv.HoloMap(gridspace)
hmap + hv.GridSpace(hmap)
```

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