
:orphan:
:html_theme.sidebar_secondary.remove:

.. This document is auto-generated by the altair-gallery extension. Do not modify directly.

.. _gallery_isotype:


Isotype Visualization
=====================
Isotype Visualization shows the distribution of animals across UK and US.
Inspired by `Only An Ocean Between, 1943 <http://www.thomwhite.co.uk/?p=1303>`_. Population Live Stock, p.13.
This is adapted from Vega-Lite example https://vega.github.io/editor/#/examples/vega-lite/isotype_bar_chart


.. altair-plot::
    :remove-code:
    


    import altair as alt
    import pandas as pd

    source = pd.DataFrame([
          {'country': 'Great Britain', 'animal': 'cattle'},
          {'country': 'Great Britain', 'animal': 'cattle'},
          {'country': 'Great Britain', 'animal': 'cattle'},
          {'country': 'Great Britain', 'animal': 'pigs'},
          {'country': 'Great Britain', 'animal': 'pigs'},
          {'country': 'Great Britain', 'animal': 'sheep'},
          {'country': 'Great Britain', 'animal': 'sheep'},
          {'country': 'Great Britain', 'animal': 'sheep'},
          {'country': 'Great Britain', 'animal': 'sheep'},
          {'country': 'Great Britain', 'animal': 'sheep'},
          {'country': 'Great Britain', 'animal': 'sheep'},
          {'country': 'Great Britain', 'animal': 'sheep'},
          {'country': 'Great Britain', 'animal': 'sheep'},
          {'country': 'Great Britain', 'animal': 'sheep'},
          {'country': 'Great Britain', 'animal': 'sheep'},
          {'country': 'United States', 'animal': 'cattle'},
          {'country': 'United States', 'animal': 'cattle'},
          {'country': 'United States', 'animal': 'cattle'},
          {'country': 'United States', 'animal': 'cattle'},
          {'country': 'United States', 'animal': 'cattle'},
          {'country': 'United States', 'animal': 'cattle'},
          {'country': 'United States', 'animal': 'cattle'},
          {'country': 'United States', 'animal': 'cattle'},
          {'country': 'United States', 'animal': 'cattle'},
          {'country': 'United States', 'animal': 'pigs'},
          {'country': 'United States', 'animal': 'pigs'},
          {'country': 'United States', 'animal': 'pigs'},
          {'country': 'United States', 'animal': 'pigs'},
          {'country': 'United States', 'animal': 'pigs'},
          {'country': 'United States', 'animal': 'pigs'},
          {'country': 'United States', 'animal': 'sheep'},
          {'country': 'United States', 'animal': 'sheep'},
          {'country': 'United States', 'animal': 'sheep'},
          {'country': 'United States', 'animal': 'sheep'},
          {'country': 'United States', 'animal': 'sheep'},
          {'country': 'United States', 'animal': 'sheep'},
          {'country': 'United States', 'animal': 'sheep'}
        ])

    domains = ['person', 'cattle', 'pigs', 'sheep']

    shape_scale = alt.Scale(
        domain=domains,
        range=[
            'M1.7 -1.7h-0.8c0.3 -0.2 0.6 -0.5 0.6 -0.9c0 -0.6 -0.4 -1 -1 -1c-0.6 0 -1 0.4 -1 1c0 0.4 0.2 0.7 0.6 0.9h-0.8c-0.4 0 -0.7 0.3 -0.7 0.6v1.9c0 0.3 0.3 0.6 0.6 0.6h0.2c0 0 0 0.1 0 0.1v1.9c0 0.3 0.2 0.6 0.3 0.6h1.3c0.2 0 0.3 -0.3 0.3 -0.6v-1.8c0 0 0 -0.1 0 -0.1h0.2c0.3 0 0.6 -0.3 0.6 -0.6v-2c0.2 -0.3 -0.1 -0.6 -0.4 -0.6z',
            'M4 -2c0 0 0.9 -0.7 1.1 -0.8c0.1 -0.1 -0.1 0.5 -0.3 0.7c-0.2 0.2 1.1 1.1 1.1 1.2c0 0.2 -0.2 0.8 -0.4 0.7c-0.1 0 -0.8 -0.3 -1.3 -0.2c-0.5 0.1 -1.3 1.6 -1.5 2c-0.3 0.4 -0.6 0.4 -0.6 0.4c0 0.1 0.3 1.7 0.4 1.8c0.1 0.1 -0.4 0.1 -0.5 0c0 0 -0.6 -1.9 -0.6 -1.9c-0.1 0 -0.3 -0.1 -0.3 -0.1c0 0.1 -0.5 1.4 -0.4 1.6c0.1 0.2 0.1 0.3 0.1 0.3c0 0 -0.4 0 -0.4 0c0 0 -0.2 -0.1 -0.1 -0.3c0 -0.2 0.3 -1.7 0.3 -1.7c0 0 -2.8 -0.9 -2.9 -0.8c-0.2 0.1 -0.4 0.6 -0.4 1c0 0.4 0.5 1.9 0.5 1.9l-0.5 0l-0.6 -2l0 -0.6c0 0 -1 0.8 -1 1c0 0.2 -0.2 1.3 -0.2 1.3c0 0 0.3 0.3 0.2 0.3c0 0 -0.5 0 -0.5 0c0 0 -0.2 -0.2 -0.1 -0.4c0 -0.1 0.2 -1.6 0.2 -1.6c0 0 0.5 -0.4 0.5 -0.5c0 -0.1 0 -2.7 -0.2 -2.7c-0.1 0 -0.4 2 -0.4 2c0 0 0 0.2 -0.2 0.5c-0.1 0.4 -0.2 1.1 -0.2 1.1c0 0 -0.2 -0.1 -0.2 -0.2c0 -0.1 -0.1 -0.7 0 -0.7c0.1 -0.1 0.3 -0.8 0.4 -1.4c0 -0.6 0.2 -1.3 0.4 -1.5c0.1 -0.2 0.6 -0.4 0.6 -0.4z',
            'M1.2 -2c0 0 0.7 0 1.2 0.5c0.5 0.5 0.4 0.6 0.5 0.6c0.1 0 0.7 0 0.8 0.1c0.1 0 0.2 0.2 0.2 0.2c0 0 -0.6 0.2 -0.6 0.3c0 0.1 0.4 0.9 0.6 0.9c0.1 0 0.6 0 0.6 0.1c0 0.1 0 0.7 -0.1 0.7c-0.1 0 -1.2 0.4 -1.5 0.5c-0.3 0.1 -1.1 0.5 -1.1 0.7c-0.1 0.2 0.4 1.2 0.4 1.2l-0.4 0c0 0 -0.4 -0.8 -0.4 -0.9c0 -0.1 -0.1 -0.3 -0.1 -0.3l-0.2 0l-0.5 1.3l-0.4 0c0 0 -0.1 -0.4 0 -0.6c0.1 -0.1 0.3 -0.6 0.3 -0.7c0 0 -0.8 0 -1.5 -0.1c-0.7 -0.1 -1.2 -0.3 -1.2 -0.2c0 0.1 -0.4 0.6 -0.5 0.6c0 0 0.3 0.9 0.3 0.9l-0.4 0c0 0 -0.4 -0.5 -0.4 -0.6c0 -0.1 -0.2 -0.6 -0.2 -0.5c0 0 -0.4 0.4 -0.6 0.4c-0.2 0.1 -0.4 0.1 -0.4 0.1c0 0 -0.1 0.6 -0.1 0.6l-0.5 0l0 -1c0 0 0.5 -0.4 0.5 -0.5c0 -0.1 -0.7 -1.2 -0.6 -1.4c0.1 -0.1 0.1 -1.1 0.1 -1.1c0 0 -0.2 0.1 -0.2 0.1c0 0 0 0.9 0 1c0 0.1 -0.2 0.3 -0.3 0.3c-0.1 0 0 -0.5 0 -0.9c0 -0.4 0 -0.4 0.2 -0.6c0.2 -0.2 0.6 -0.3 0.8 -0.8c0.3 -0.5 1 -0.6 1 -0.6z',
            'M-4.1 -0.5c0.2 0 0.2 0.2 0.5 0.2c0.3 0 0.3 -0.2 0.5 -0.2c0.2 0 0.2 0.2 0.4 0.2c0.2 0 0.2 -0.2 0.5 -0.2c0.2 0 0.2 0.2 0.4 0.2c0.2 0 0.2 -0.2 0.4 -0.2c0.1 0 0.2 0.2 0.4 0.1c0.2 0 0.2 -0.2 0.4 -0.3c0.1 0 0.1 -0.1 0.4 0c0.3 0 0.3 -0.4 0.6 -0.4c0.3 0 0.6 -0.3 0.7 -0.2c0.1 0.1 1.4 1 1.3 1.4c-0.1 0.4 -0.3 0.3 -0.4 0.3c-0.1 0 -0.5 -0.4 -0.7 -0.2c-0.3 0.2 -0.1 0.4 -0.2 0.6c-0.1 0.1 -0.2 0.2 -0.3 0.4c0 0.2 0.1 0.3 0 0.5c-0.1 0.2 -0.3 0.2 -0.3 0.5c0 0.3 -0.2 0.3 -0.3 0.6c-0.1 0.2 0 0.3 -0.1 0.5c-0.1 0.2 -0.1 0.2 -0.2 0.3c-0.1 0.1 0.3 1.1 0.3 1.1l-0.3 0c0 0 -0.3 -0.9 -0.3 -1c0 -0.1 -0.1 -0.2 -0.3 -0.2c-0.2 0 -0.3 0.1 -0.4 0.4c0 0.3 -0.2 0.8 -0.2 0.8l-0.3 0l0.3 -1c0 0 0.1 -0.6 -0.2 -0.5c-0.3 0.1 -0.2 -0.1 -0.4 -0.1c-0.2 -0.1 -0.3 0.1 -0.4 0c-0.2 -0.1 -0.3 0.1 -0.5 0c-0.2 -0.1 -0.1 0 -0.3 0.3c-0.2 0.3 -0.4 0.3 -0.4 0.3l0.2 1.1l-0.3 0l-0.2 -1.1c0 0 -0.4 -0.6 -0.5 -0.4c-0.1 0.3 -0.1 0.4 -0.3 0.4c-0.1 -0.1 -0.2 1.1 -0.2 1.1l-0.3 0l0.2 -1.1c0 0 -0.3 -0.1 -0.3 -0.5c0 -0.3 0.1 -0.5 0.1 -0.7c0.1 -0.2 -0.1 -1 -0.2 -1.1c-0.1 -0.2 -0.2 -0.8 -0.2 -0.8c0 0 -0.1 -0.5 0.4 -0.8z'
        ]
    )

    color_scale = alt.Scale(
        domain=domains,
        range=['rgb(162,160,152)', 'rgb(194,81,64)', 'rgb(93,93,93)', 'rgb(91,131,149)']
    )

    alt.Chart(source).mark_point(filled=True, opacity=1, size=100).encode(
        alt.X('x:O', axis=None),
        alt.Y('animal:O', axis=None),
        alt.Row('country:N', header=alt.Header(title='')),
        alt.Shape('animal:N', legend=None, scale=shape_scale),
        alt.Color('animal:N', legend=None, scale=color_scale),
    ).transform_window(
        x='rank()',
        groupby=['country', 'animal']
    ).properties(width=550, height=140)


.. tab-set::

    .. tab-item:: Method syntax
        :sync: method

        .. code:: python


            import altair as alt
            import pandas as pd

            source = pd.DataFrame([
                  {'country': 'Great Britain', 'animal': 'cattle'},
                  {'country': 'Great Britain', 'animal': 'cattle'},
                  {'country': 'Great Britain', 'animal': 'cattle'},
                  {'country': 'Great Britain', 'animal': 'pigs'},
                  {'country': 'Great Britain', 'animal': 'pigs'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'pigs'},
                  {'country': 'United States', 'animal': 'pigs'},
                  {'country': 'United States', 'animal': 'pigs'},
                  {'country': 'United States', 'animal': 'pigs'},
                  {'country': 'United States', 'animal': 'pigs'},
                  {'country': 'United States', 'animal': 'pigs'},
                  {'country': 'United States', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'sheep'}
                ])

            domains = ['person', 'cattle', 'pigs', 'sheep']

            shape_scale = alt.Scale(
                domain=domains,
                range=[
                    'M1.7 -1.7h-0.8c0.3 -0.2 0.6 -0.5 0.6 -0.9c0 -0.6 -0.4 -1 -1 -1c-0.6 0 -1 0.4 -1 1c0 0.4 0.2 0.7 0.6 0.9h-0.8c-0.4 0 -0.7 0.3 -0.7 0.6v1.9c0 0.3 0.3 0.6 0.6 0.6h0.2c0 0 0 0.1 0 0.1v1.9c0 0.3 0.2 0.6 0.3 0.6h1.3c0.2 0 0.3 -0.3 0.3 -0.6v-1.8c0 0 0 -0.1 0 -0.1h0.2c0.3 0 0.6 -0.3 0.6 -0.6v-2c0.2 -0.3 -0.1 -0.6 -0.4 -0.6z',
                    'M4 -2c0 0 0.9 -0.7 1.1 -0.8c0.1 -0.1 -0.1 0.5 -0.3 0.7c-0.2 0.2 1.1 1.1 1.1 1.2c0 0.2 -0.2 0.8 -0.4 0.7c-0.1 0 -0.8 -0.3 -1.3 -0.2c-0.5 0.1 -1.3 1.6 -1.5 2c-0.3 0.4 -0.6 0.4 -0.6 0.4c0 0.1 0.3 1.7 0.4 1.8c0.1 0.1 -0.4 0.1 -0.5 0c0 0 -0.6 -1.9 -0.6 -1.9c-0.1 0 -0.3 -0.1 -0.3 -0.1c0 0.1 -0.5 1.4 -0.4 1.6c0.1 0.2 0.1 0.3 0.1 0.3c0 0 -0.4 0 -0.4 0c0 0 -0.2 -0.1 -0.1 -0.3c0 -0.2 0.3 -1.7 0.3 -1.7c0 0 -2.8 -0.9 -2.9 -0.8c-0.2 0.1 -0.4 0.6 -0.4 1c0 0.4 0.5 1.9 0.5 1.9l-0.5 0l-0.6 -2l0 -0.6c0 0 -1 0.8 -1 1c0 0.2 -0.2 1.3 -0.2 1.3c0 0 0.3 0.3 0.2 0.3c0 0 -0.5 0 -0.5 0c0 0 -0.2 -0.2 -0.1 -0.4c0 -0.1 0.2 -1.6 0.2 -1.6c0 0 0.5 -0.4 0.5 -0.5c0 -0.1 0 -2.7 -0.2 -2.7c-0.1 0 -0.4 2 -0.4 2c0 0 0 0.2 -0.2 0.5c-0.1 0.4 -0.2 1.1 -0.2 1.1c0 0 -0.2 -0.1 -0.2 -0.2c0 -0.1 -0.1 -0.7 0 -0.7c0.1 -0.1 0.3 -0.8 0.4 -1.4c0 -0.6 0.2 -1.3 0.4 -1.5c0.1 -0.2 0.6 -0.4 0.6 -0.4z',
                    'M1.2 -2c0 0 0.7 0 1.2 0.5c0.5 0.5 0.4 0.6 0.5 0.6c0.1 0 0.7 0 0.8 0.1c0.1 0 0.2 0.2 0.2 0.2c0 0 -0.6 0.2 -0.6 0.3c0 0.1 0.4 0.9 0.6 0.9c0.1 0 0.6 0 0.6 0.1c0 0.1 0 0.7 -0.1 0.7c-0.1 0 -1.2 0.4 -1.5 0.5c-0.3 0.1 -1.1 0.5 -1.1 0.7c-0.1 0.2 0.4 1.2 0.4 1.2l-0.4 0c0 0 -0.4 -0.8 -0.4 -0.9c0 -0.1 -0.1 -0.3 -0.1 -0.3l-0.2 0l-0.5 1.3l-0.4 0c0 0 -0.1 -0.4 0 -0.6c0.1 -0.1 0.3 -0.6 0.3 -0.7c0 0 -0.8 0 -1.5 -0.1c-0.7 -0.1 -1.2 -0.3 -1.2 -0.2c0 0.1 -0.4 0.6 -0.5 0.6c0 0 0.3 0.9 0.3 0.9l-0.4 0c0 0 -0.4 -0.5 -0.4 -0.6c0 -0.1 -0.2 -0.6 -0.2 -0.5c0 0 -0.4 0.4 -0.6 0.4c-0.2 0.1 -0.4 0.1 -0.4 0.1c0 0 -0.1 0.6 -0.1 0.6l-0.5 0l0 -1c0 0 0.5 -0.4 0.5 -0.5c0 -0.1 -0.7 -1.2 -0.6 -1.4c0.1 -0.1 0.1 -1.1 0.1 -1.1c0 0 -0.2 0.1 -0.2 0.1c0 0 0 0.9 0 1c0 0.1 -0.2 0.3 -0.3 0.3c-0.1 0 0 -0.5 0 -0.9c0 -0.4 0 -0.4 0.2 -0.6c0.2 -0.2 0.6 -0.3 0.8 -0.8c0.3 -0.5 1 -0.6 1 -0.6z',
                    'M-4.1 -0.5c0.2 0 0.2 0.2 0.5 0.2c0.3 0 0.3 -0.2 0.5 -0.2c0.2 0 0.2 0.2 0.4 0.2c0.2 0 0.2 -0.2 0.5 -0.2c0.2 0 0.2 0.2 0.4 0.2c0.2 0 0.2 -0.2 0.4 -0.2c0.1 0 0.2 0.2 0.4 0.1c0.2 0 0.2 -0.2 0.4 -0.3c0.1 0 0.1 -0.1 0.4 0c0.3 0 0.3 -0.4 0.6 -0.4c0.3 0 0.6 -0.3 0.7 -0.2c0.1 0.1 1.4 1 1.3 1.4c-0.1 0.4 -0.3 0.3 -0.4 0.3c-0.1 0 -0.5 -0.4 -0.7 -0.2c-0.3 0.2 -0.1 0.4 -0.2 0.6c-0.1 0.1 -0.2 0.2 -0.3 0.4c0 0.2 0.1 0.3 0 0.5c-0.1 0.2 -0.3 0.2 -0.3 0.5c0 0.3 -0.2 0.3 -0.3 0.6c-0.1 0.2 0 0.3 -0.1 0.5c-0.1 0.2 -0.1 0.2 -0.2 0.3c-0.1 0.1 0.3 1.1 0.3 1.1l-0.3 0c0 0 -0.3 -0.9 -0.3 -1c0 -0.1 -0.1 -0.2 -0.3 -0.2c-0.2 0 -0.3 0.1 -0.4 0.4c0 0.3 -0.2 0.8 -0.2 0.8l-0.3 0l0.3 -1c0 0 0.1 -0.6 -0.2 -0.5c-0.3 0.1 -0.2 -0.1 -0.4 -0.1c-0.2 -0.1 -0.3 0.1 -0.4 0c-0.2 -0.1 -0.3 0.1 -0.5 0c-0.2 -0.1 -0.1 0 -0.3 0.3c-0.2 0.3 -0.4 0.3 -0.4 0.3l0.2 1.1l-0.3 0l-0.2 -1.1c0 0 -0.4 -0.6 -0.5 -0.4c-0.1 0.3 -0.1 0.4 -0.3 0.4c-0.1 -0.1 -0.2 1.1 -0.2 1.1l-0.3 0l0.2 -1.1c0 0 -0.3 -0.1 -0.3 -0.5c0 -0.3 0.1 -0.5 0.1 -0.7c0.1 -0.2 -0.1 -1 -0.2 -1.1c-0.1 -0.2 -0.2 -0.8 -0.2 -0.8c0 0 -0.1 -0.5 0.4 -0.8z'
                ]
            )

            color_scale = alt.Scale(
                domain=domains,
                range=['rgb(162,160,152)', 'rgb(194,81,64)', 'rgb(93,93,93)', 'rgb(91,131,149)']
            )

            alt.Chart(source).mark_point(filled=True, opacity=1, size=100).encode(
                alt.X('x:O').axis(None),
                alt.Y('animal:O').axis(None),
                alt.Row('country:N').title(None),
                alt.Shape('animal:N').legend(None).scale(shape_scale),
                alt.Color('animal:N').legend(None).scale(color_scale),
            ).transform_window(
                x='rank()',
                groupby=['country', 'animal']
            ).properties(
                width=550,
                height=140
            )


    .. tab-item:: Attribute syntax
        :sync: attribute

        .. code:: python


            import altair as alt
            import pandas as pd

            source = pd.DataFrame([
                  {'country': 'Great Britain', 'animal': 'cattle'},
                  {'country': 'Great Britain', 'animal': 'cattle'},
                  {'country': 'Great Britain', 'animal': 'cattle'},
                  {'country': 'Great Britain', 'animal': 'pigs'},
                  {'country': 'Great Britain', 'animal': 'pigs'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'Great Britain', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'cattle'},
                  {'country': 'United States', 'animal': 'pigs'},
                  {'country': 'United States', 'animal': 'pigs'},
                  {'country': 'United States', 'animal': 'pigs'},
                  {'country': 'United States', 'animal': 'pigs'},
                  {'country': 'United States', 'animal': 'pigs'},
                  {'country': 'United States', 'animal': 'pigs'},
                  {'country': 'United States', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'sheep'},
                  {'country': 'United States', 'animal': 'sheep'}
                ])

            domains = ['person', 'cattle', 'pigs', 'sheep']

            shape_scale = alt.Scale(
                domain=domains,
                range=[
                    'M1.7 -1.7h-0.8c0.3 -0.2 0.6 -0.5 0.6 -0.9c0 -0.6 -0.4 -1 -1 -1c-0.6 0 -1 0.4 -1 1c0 0.4 0.2 0.7 0.6 0.9h-0.8c-0.4 0 -0.7 0.3 -0.7 0.6v1.9c0 0.3 0.3 0.6 0.6 0.6h0.2c0 0 0 0.1 0 0.1v1.9c0 0.3 0.2 0.6 0.3 0.6h1.3c0.2 0 0.3 -0.3 0.3 -0.6v-1.8c0 0 0 -0.1 0 -0.1h0.2c0.3 0 0.6 -0.3 0.6 -0.6v-2c0.2 -0.3 -0.1 -0.6 -0.4 -0.6z',
                    'M4 -2c0 0 0.9 -0.7 1.1 -0.8c0.1 -0.1 -0.1 0.5 -0.3 0.7c-0.2 0.2 1.1 1.1 1.1 1.2c0 0.2 -0.2 0.8 -0.4 0.7c-0.1 0 -0.8 -0.3 -1.3 -0.2c-0.5 0.1 -1.3 1.6 -1.5 2c-0.3 0.4 -0.6 0.4 -0.6 0.4c0 0.1 0.3 1.7 0.4 1.8c0.1 0.1 -0.4 0.1 -0.5 0c0 0 -0.6 -1.9 -0.6 -1.9c-0.1 0 -0.3 -0.1 -0.3 -0.1c0 0.1 -0.5 1.4 -0.4 1.6c0.1 0.2 0.1 0.3 0.1 0.3c0 0 -0.4 0 -0.4 0c0 0 -0.2 -0.1 -0.1 -0.3c0 -0.2 0.3 -1.7 0.3 -1.7c0 0 -2.8 -0.9 -2.9 -0.8c-0.2 0.1 -0.4 0.6 -0.4 1c0 0.4 0.5 1.9 0.5 1.9l-0.5 0l-0.6 -2l0 -0.6c0 0 -1 0.8 -1 1c0 0.2 -0.2 1.3 -0.2 1.3c0 0 0.3 0.3 0.2 0.3c0 0 -0.5 0 -0.5 0c0 0 -0.2 -0.2 -0.1 -0.4c0 -0.1 0.2 -1.6 0.2 -1.6c0 0 0.5 -0.4 0.5 -0.5c0 -0.1 0 -2.7 -0.2 -2.7c-0.1 0 -0.4 2 -0.4 2c0 0 0 0.2 -0.2 0.5c-0.1 0.4 -0.2 1.1 -0.2 1.1c0 0 -0.2 -0.1 -0.2 -0.2c0 -0.1 -0.1 -0.7 0 -0.7c0.1 -0.1 0.3 -0.8 0.4 -1.4c0 -0.6 0.2 -1.3 0.4 -1.5c0.1 -0.2 0.6 -0.4 0.6 -0.4z',
                    'M1.2 -2c0 0 0.7 0 1.2 0.5c0.5 0.5 0.4 0.6 0.5 0.6c0.1 0 0.7 0 0.8 0.1c0.1 0 0.2 0.2 0.2 0.2c0 0 -0.6 0.2 -0.6 0.3c0 0.1 0.4 0.9 0.6 0.9c0.1 0 0.6 0 0.6 0.1c0 0.1 0 0.7 -0.1 0.7c-0.1 0 -1.2 0.4 -1.5 0.5c-0.3 0.1 -1.1 0.5 -1.1 0.7c-0.1 0.2 0.4 1.2 0.4 1.2l-0.4 0c0 0 -0.4 -0.8 -0.4 -0.9c0 -0.1 -0.1 -0.3 -0.1 -0.3l-0.2 0l-0.5 1.3l-0.4 0c0 0 -0.1 -0.4 0 -0.6c0.1 -0.1 0.3 -0.6 0.3 -0.7c0 0 -0.8 0 -1.5 -0.1c-0.7 -0.1 -1.2 -0.3 -1.2 -0.2c0 0.1 -0.4 0.6 -0.5 0.6c0 0 0.3 0.9 0.3 0.9l-0.4 0c0 0 -0.4 -0.5 -0.4 -0.6c0 -0.1 -0.2 -0.6 -0.2 -0.5c0 0 -0.4 0.4 -0.6 0.4c-0.2 0.1 -0.4 0.1 -0.4 0.1c0 0 -0.1 0.6 -0.1 0.6l-0.5 0l0 -1c0 0 0.5 -0.4 0.5 -0.5c0 -0.1 -0.7 -1.2 -0.6 -1.4c0.1 -0.1 0.1 -1.1 0.1 -1.1c0 0 -0.2 0.1 -0.2 0.1c0 0 0 0.9 0 1c0 0.1 -0.2 0.3 -0.3 0.3c-0.1 0 0 -0.5 0 -0.9c0 -0.4 0 -0.4 0.2 -0.6c0.2 -0.2 0.6 -0.3 0.8 -0.8c0.3 -0.5 1 -0.6 1 -0.6z',
                    'M-4.1 -0.5c0.2 0 0.2 0.2 0.5 0.2c0.3 0 0.3 -0.2 0.5 -0.2c0.2 0 0.2 0.2 0.4 0.2c0.2 0 0.2 -0.2 0.5 -0.2c0.2 0 0.2 0.2 0.4 0.2c0.2 0 0.2 -0.2 0.4 -0.2c0.1 0 0.2 0.2 0.4 0.1c0.2 0 0.2 -0.2 0.4 -0.3c0.1 0 0.1 -0.1 0.4 0c0.3 0 0.3 -0.4 0.6 -0.4c0.3 0 0.6 -0.3 0.7 -0.2c0.1 0.1 1.4 1 1.3 1.4c-0.1 0.4 -0.3 0.3 -0.4 0.3c-0.1 0 -0.5 -0.4 -0.7 -0.2c-0.3 0.2 -0.1 0.4 -0.2 0.6c-0.1 0.1 -0.2 0.2 -0.3 0.4c0 0.2 0.1 0.3 0 0.5c-0.1 0.2 -0.3 0.2 -0.3 0.5c0 0.3 -0.2 0.3 -0.3 0.6c-0.1 0.2 0 0.3 -0.1 0.5c-0.1 0.2 -0.1 0.2 -0.2 0.3c-0.1 0.1 0.3 1.1 0.3 1.1l-0.3 0c0 0 -0.3 -0.9 -0.3 -1c0 -0.1 -0.1 -0.2 -0.3 -0.2c-0.2 0 -0.3 0.1 -0.4 0.4c0 0.3 -0.2 0.8 -0.2 0.8l-0.3 0l0.3 -1c0 0 0.1 -0.6 -0.2 -0.5c-0.3 0.1 -0.2 -0.1 -0.4 -0.1c-0.2 -0.1 -0.3 0.1 -0.4 0c-0.2 -0.1 -0.3 0.1 -0.5 0c-0.2 -0.1 -0.1 0 -0.3 0.3c-0.2 0.3 -0.4 0.3 -0.4 0.3l0.2 1.1l-0.3 0l-0.2 -1.1c0 0 -0.4 -0.6 -0.5 -0.4c-0.1 0.3 -0.1 0.4 -0.3 0.4c-0.1 -0.1 -0.2 1.1 -0.2 1.1l-0.3 0l0.2 -1.1c0 0 -0.3 -0.1 -0.3 -0.5c0 -0.3 0.1 -0.5 0.1 -0.7c0.1 -0.2 -0.1 -1 -0.2 -1.1c-0.1 -0.2 -0.2 -0.8 -0.2 -0.8c0 0 -0.1 -0.5 0.4 -0.8z'
                ]
            )

            color_scale = alt.Scale(
                domain=domains,
                range=['rgb(162,160,152)', 'rgb(194,81,64)', 'rgb(93,93,93)', 'rgb(91,131,149)']
            )

            alt.Chart(source).mark_point(filled=True, opacity=1, size=100).encode(
                alt.X('x:O', axis=None),
                alt.Y('animal:O', axis=None),
                alt.Row('country:N', header=alt.Header(title='')),
                alt.Shape('animal:N', legend=None, scale=shape_scale),
                alt.Color('animal:N', legend=None, scale=color_scale),
            ).transform_window(
                x='rank()',
                groupby=['country', 'animal']
            ).properties(width=550, height=140)
