:orphan:
:html_theme.sidebar_secondary.remove:

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

.. _gallery_ridgeline_plot:


Ridgeline plot
--------------
A `Ridgeline plot <https://serialmentor.com/blog/2017/9/15/goodbye-joyplots>`_
lets you visualize distribution of a numeric value for different
subsets of data (what we call "facets" in Altair).

Such a chart can be created in Altair by first transforming the data into a
suitable representation.


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


    import altair as alt
    from vega_datasets import data

    source = data.seattle_weather.url

    step = 20
    overlap = 1

    alt.Chart(source, height=step).transform_timeunit(
        Month='month(date)'
    ).transform_joinaggregate(
        mean_temp='mean(temp_max)', groupby=['Month']
    ).transform_bin(
        ['bin_max', 'bin_min'], 'temp_max'
    ).transform_aggregate(
        value='count()', groupby=['Month', 'mean_temp', 'bin_min', 'bin_max']
    ).transform_impute(
        impute='value', groupby=['Month', 'mean_temp'], key='bin_min', value=0
    ).mark_area(
        interpolate='monotone',
        fillOpacity=0.8,
        stroke='lightgray',
        strokeWidth=0.5
    ).encode(
        alt.X('bin_min:Q', bin='binned', title='Maximum Daily Temperature (C)'),
        alt.Y(
            'value:Q',
            scale=alt.Scale(range=[step, -step * overlap]),
            axis=None
        ),
        alt.Fill(
            'mean_temp:Q',
            legend=None,
            scale=alt.Scale(domain=[30, 5], scheme='redyellowblue')
        )
    ).facet(
        row=alt.Row(
            'Month:T',
            title=None,
            header=alt.Header(labelAngle=0, labelAlign='left', format='%B')
        )
    ).properties(
        title='Seattle Weather',
        bounds='flush'
    ).configure_facet(
        spacing=0
    ).configure_view(
        stroke=None
    ).configure_title(
        anchor='end'
    )


.. tab-set::

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

        .. code:: python


            import altair as alt
            from vega_datasets import data

            source = data.seattle_weather.url

            step = 20
            overlap = 1

            alt.Chart(source, height=step).transform_timeunit(
                Month='month(date)'
            ).transform_joinaggregate(
                mean_temp='mean(temp_max)', groupby=['Month']
            ).transform_bin(
                ['bin_max', 'bin_min'], 'temp_max'
            ).transform_aggregate(
                value='count()', groupby=['Month', 'mean_temp', 'bin_min', 'bin_max']
            ).transform_impute(
                impute='value', groupby=['Month', 'mean_temp'], key='bin_min', value=0
            ).mark_area(
                interpolate='monotone',
                fillOpacity=0.8,
                stroke='lightgray',
                strokeWidth=0.5
            ).encode(
                alt.X('bin_min:Q')
                    .bin('binned')
                    .title('Maximum Daily Temperature (C)'),
                alt.Y('value:Q')
                    .axis(None)
                    .scale(range=[step, -step * overlap]),
                alt.Fill('mean_temp:Q')
                    .legend(None)
                    .scale(domain=[30, 5], scheme='redyellowblue')
            ).facet(
                row=alt.Row('Month:T')
                    .title(None)
                    .header(labelAngle=0, labelAlign='left', format='%B')
            ).properties(
                title='Seattle Weather',
                bounds='flush'
            ).configure_facet(
                spacing=0
            ).configure_view(
                stroke=None
            ).configure_title(
                anchor='end'
            )


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

        .. code:: python


            import altair as alt
            from vega_datasets import data

            source = data.seattle_weather.url

            step = 20
            overlap = 1

            alt.Chart(source, height=step).transform_timeunit(
                Month='month(date)'
            ).transform_joinaggregate(
                mean_temp='mean(temp_max)', groupby=['Month']
            ).transform_bin(
                ['bin_max', 'bin_min'], 'temp_max'
            ).transform_aggregate(
                value='count()', groupby=['Month', 'mean_temp', 'bin_min', 'bin_max']
            ).transform_impute(
                impute='value', groupby=['Month', 'mean_temp'], key='bin_min', value=0
            ).mark_area(
                interpolate='monotone',
                fillOpacity=0.8,
                stroke='lightgray',
                strokeWidth=0.5
            ).encode(
                alt.X('bin_min:Q', bin='binned', title='Maximum Daily Temperature (C)'),
                alt.Y(
                    'value:Q',
                    scale=alt.Scale(range=[step, -step * overlap]),
                    axis=None
                ),
                alt.Fill(
                    'mean_temp:Q',
                    legend=None,
                    scale=alt.Scale(domain=[30, 5], scheme='redyellowblue')
                )
            ).facet(
                row=alt.Row(
                    'Month:T',
                    title=None,
                    header=alt.Header(labelAngle=0, labelAlign='left', format='%B')
                )
            ).properties(
                title='Seattle Weather',
                bounds='flush'
            ).configure_facet(
                spacing=0
            ).configure_view(
                stroke=None
            ).configure_title(
                anchor='end'
            )